阅读说明：本技术 一种基于遥感影像反演的水质污染溯源系统和方法 (Water pollution tracing system and method based on remote sensing image inversion ) 是由 邓贵源 阳春花 邓平 邹景强 陈杰 刘泽飞 于 2021-09-18 设计创作，主要内容包括：本发明提供一种基于遥感影像反演的水质污染溯源系统和方法,其中,系统包括：遥感反演层、数据统合层和分析溯源层；所述遥感反演层与所述数据统合层连接,所述数据统合层与所述分析溯源层连接；所述遥感反演层,用于获取遥感影像数据,根据所述遥感影像数据反演地面数据,所述地面数据包括有常规水质指标和水质重金属指标；所述数据统合层,用于对所述遥感影像数据和地面数据进行哈希表映射,获取显像结果；所述分析溯源层,用于根据所述显像结果进行分项分析溯源和综合分析溯源。本发明能够保证溯源结果的可靠性和准确性,并能够整合所有水质的污染情况,实现全面溯源同时提升了适应性。(The invention provides a water pollution tracing system and method based on remote sensing image inversion, wherein the system comprises: the remote sensing inversion layer, the data integration layer and the analysis traceability layer; the remote sensing inversion layer is connected with the data integration layer, and the data integration layer is connected with the analysis traceability layer; the remote sensing inversion layer is used for acquiring remote sensing image data and inverting ground data according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index; the data unification layer is used for carrying out hash table mapping on the remote sensing image data and the ground data to obtain a display result; and the analysis traceability layer is used for performing itemized analysis traceability and comprehensive analysis traceability according to the visualization result. The invention can ensure the reliability and accuracy of the tracing result, can integrate the pollution conditions of all water quality, realizes comprehensive tracing and simultaneously improves the adaptability.)

1. A water pollution traceability system based on remote sensing image inversion is characterized by comprising:

the remote sensing inversion layer, the data integration layer and the analysis traceability layer; the remote sensing inversion layer is connected with the data integration layer, and the data integration layer is connected with the analysis traceability layer;

the remote sensing inversion layer is used for acquiring remote sensing image data and inverting ground data according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index;

the data unification layer is used for carrying out hash table mapping on the remote sensing image data and the ground data to obtain a display result;

and the analysis traceability layer is used for performing itemized analysis traceability and comprehensive analysis traceability according to the visualization result.

2. The remote sensing image inversion-based water pollution traceability system is characterized in that the remote sensing inversion layer comprises a remote sensing image acquisition module, a conventional water quality index inversion module and a water quality heavy metal index inversion module, and the conventional water quality index inversion module and the water quality heavy metal index inversion module are parallel.

3. The remote sensing image inversion-based water pollution traceability system of claim 2, wherein the conventional water quality index inversion module comprises a remote sensing image acquisition unit, a remote sensing image degradation unit, an index inversion unit and a first imaging unit; the remote sensing image acquisition unit is used for acquiring remote sensing image data through a wired or wireless protocol; the remote sensing image difference reducing unit is used for reducing the aberration of the remote sensing image data in a remote sensing preprocessing and man-machine interaction mode, and the first remote sensing image data; the index inversion unit is used for inverting the conventional water quality index according to the first remote sensing image data; the first display unit is used for carrying out format conversion and displaying an inversion result;

the water quality heavy metal index inversion module comprises a cloud picture reexamination unit, a formula generation unit, a data accounting unit and a second imaging unit; the cloud picture re-inspection unit is used for reducing the aberration of the remote sensing image data and acquiring second remote sensing image data; the formula generation unit is used for generating and rendering an inversion formula; the data accounting unit is used for inverting the water quality heavy metal indexes according to the second remote sensing image data and an inversion formula and verifying the confidence coefficient of an inversion result; the second visualization unit is used for displaying the inversion result and the confidence coefficient.

4. The remote sensing image inversion-based water pollution traceability system of claim 1, wherein the data integration layer comprises a remote sensing image integration module, a ground data integration module and a data mapping module; the remote sensing image integration module and the ground data integration module are parallel and are respectively used for integrating the remote sensing image data and the ground data; and the data mapping module is used for mapping the integrated remote sensing image data and the ground data according to the hash table.

5. The remote-sensing-image-inversion-based water quality pollution traceability system according to claim 4, wherein the remote-sensing image integration module and the ground data integration module are provided with circuit models with the same structure, namely a remote-sensing circuit model and a ground circuit model, respectively, the input line width of the remote-sensing circuit model is the number of conventional water quality monitoring indexes, and the input line width of the ground circuit model is the number of water quality heavy metal indexes.

6. The remote sensing image inversion-based water quality pollution traceability system of claim 5, wherein the circuit model comprises an A/D digital-to-analog converter, a first delayer, a high-speed discrete sequence reader-writer, a high-speed format converter, a second delayer, a register, a D/A converter and a visualization device;

the A/D digital-to-analog converter receives the time sequence pulse and samples a continuous signal into a discrete number array, wherein the continuous signal is remote sensing image data or ground data;

the first time delay is used for delaying the time of the discrete data entering the high-speed discrete sequence reader-writer;

the high-speed discrete sequence reader-writer is used for integrating the discrete data to obtain integrated data;

the high-speed format converter is used for converting the integrated data into a binary format;

the second delayer is used for delaying the time for sending the integrated data to the register;

the register is used for registering the integrated data in the binary format;

the D/A converter is used for converting the digital quantity of the integrated data into an analog quantity;

the display device is used for displaying the integrated data converted into the analog quantity.

7. The remote sensing image inversion-based water quality pollution traceability system of claim 4, wherein the data mapping module comprises a hash table mapper and a mapping checker, the hash table mapper is connected with the mapping checker;

the Hash table mapper is used for mapping the input signal by taking the remote sensing image and the ground data as the input signal according to a Hash table to obtain a display result as an output signal;

said mapping checker for determining whether there is an input signal mapping a plurality of output signals; when there is one input signal mapping multiple output signals, all output signals except the first output signal are fed back to the hash table for remapping.

8. The remote sensing image inversion-based water pollution traceability system of claim 1, wherein the analysis traceability layer comprises an active itemized traceability analysis module and a comprehensive traceability analysis module;

the subentry analysis module comprises a coordinate conversion unit, a substance distribution marking unit and a correlation analysis unit; the coordinate conversion unit is used for converting the longitude and latitude information into a two-dimensional space coordinate system according to the Euclidean distance; the substance concentration marking unit is used for marking substance concentration distribution corresponding to the water quality index in a two-dimensional space coordinate system according to the ground data; the correlation analysis unit is used for acquiring the correlation among the water quality indexes according to the ground data;

and the comprehensive traceability analysis module is used for acquiring traceability results of all water quality indexes according to the correlation and the substance concentration distribution.

9. A water pollution tracing method based on remote sensing image inversion is characterized by comprising the following steps:

acquiring remote sensing image data by adopting a remote sensing inversion layer, and acquiring ground data by inversion according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index;

integrating the remote sensing image data and the ground data according to a data integration layer, and mapping the integrated remote sensing image and ground data through a hash table to obtain a display result;

and performing itemized analysis traceability and comprehensive analysis traceability on the imaging result according to the analysis traceability layer to obtain the traceability result of each water quality index.

10. The remote-sensing-image-inversion-based water pollution tracing method according to claim 9, wherein the remote-sensing image data and the ground data are integrated according to a data integration layer, and the integrated remote-sensing image and the ground data are mapped through a hash table to obtain a visualization result, further comprising:

inputting the integrated remote sensing image and ground data into a circuit model as an input signal f (x);

when the circuit model detects abnormal time sequence pulse or unstable driving level, an interrupt signal is sent to the first delayer, and a test sequence f is sent to impulse response h (x)_test(x) According to the returned result y of the test sequence after impulse response_test(x) Judging the abnormal type;

wherein if the result y is returned_test(x) If the x intervals are different, the time sequence is abnormal, the current time sequence pulse signal is cleared, a new pulse signal is requested from the first delayer, and the time sequence processed at present is automatically synchronized;

if the result y is returned_test(x) And the prestored result y_truth(x) If the data is not consistent with the preset data, the data is abnormal, the whole group of abnormal data is discarded, and the data is required to be acquired again from the input signal f (x);

if the received return result y_test(x) Incomplete or not accepted y_test(x) If the signal is a hardware exception, trace back f_test(x) The position where the occurrence is lost is marked, the system is halted after the mark is sent, and an exception with the marked information is displayed.

Technical Field

The invention relates to the technical field of environment remote sensing, in particular to a water pollution tracing system and method based on remote sensing image inversion.

Background

In recent years, water pollution accidents have been frequent. The existing water pollution source tracing method is mainly an all-directional tracing method, namely conventional monitoring equipment is arranged along the banks of rivers and lakes, actual measurement data are obtained through monitoring, meanwhile, pollution source visiting and investigation are manually carried out, a pollution sample database is established according to different pollution discharge enterprises and sources, and water pollution source tracing is carried out through comparison between field sampling and the database. In addition, in the prior art, the remote sensing data of the satellite is used for obtaining the content of conventional elements and the content of heavy metals through inversion, and tracing the water pollution condition.

However, the omnibearing tracing method has long arrangement time in the early stage, so that the progress is slow and the omnibearing tracing method is difficult to effectively put into use; the existing satellite tracing method completely depends on an empirical formula or depends on the empirical formula after simple linear combination, so that the reliability is low, the error is large, the pollution conditions of all water qualities cannot be integrally analyzed, and the adaptability is poor.

Disclosure of Invention

Therefore, it is necessary to provide a water pollution tracing system and method based on remote sensing image inversion for solving the above technical problems.

A water pollution traceability system based on remote sensing image inversion comprises: the remote sensing inversion layer, the data integration layer and the analysis traceability layer; the remote sensing inversion layer is connected with the data integration layer, and the data integration layer is connected with the analysis traceability layer; the remote sensing inversion layer is used for acquiring remote sensing image data and inverting ground data according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index; the data unification layer is used for carrying out hash table mapping on the remote sensing image data and the ground data to obtain a display result; and the analysis traceability layer is used for performing itemized analysis traceability and comprehensive analysis traceability according to the visualization result.

In one embodiment, the remote sensing inversion layer comprises a remote sensing image acquisition module, a conventional water quality index inversion module and a water quality heavy metal index inversion module, wherein the conventional water quality index inversion module and the water quality heavy metal index inversion module are parallel.

In one embodiment, the conventional water quality index inversion module comprises a remote sensing image acquisition unit, a remote sensing image reduction unit, an index inversion unit and a first display unit; the remote sensing image acquisition unit is used for acquiring remote sensing image data through a wired or wireless protocol; the remote sensing image difference reducing unit is used for reducing the aberration of the remote sensing image data in a remote sensing preprocessing and man-machine interaction mode, and the first remote sensing image data; the index inversion unit is used for inverting the conventional water quality index according to the first remote sensing image data; the first display unit is used for carrying out format conversion and displaying an inversion result; the water quality heavy metal index inversion module comprises a cloud picture reexamination unit, a formula generation unit, a data accounting unit and a second imaging unit; the cloud picture re-inspection unit is used for reducing the aberration of the remote sensing image data and acquiring second remote sensing image data; the formula generation unit is used for generating and rendering an inversion formula; the data accounting unit is used for inverting the water quality heavy metal indexes according to the second remote sensing image data and an inversion formula and verifying the confidence coefficient of an inversion result; the second visualization unit is used for displaying the inversion result and the confidence coefficient.

In one embodiment, the data integration layer comprises a remote sensing image integration module, a ground data integration module and a data mapping module; the remote sensing image integration module and the ground data integration module are parallel and are respectively used for integrating the remote sensing image data and the ground data; and the data mapping module is used for mapping the integrated remote sensing image data and the ground data according to the hash table.

In one embodiment, the remote sensing image integration module and the ground data integration module are provided with circuit models with the same structure, namely a remote sensing circuit model and a ground circuit model, the input line width of the remote sensing circuit model is the number of conventional water quality monitoring indexes, and the input line width of the ground circuit model is the number of water quality heavy metal indexes.

In one embodiment, the circuit model comprises an A/D digital-to-analog converter, a first delayer, a high-speed discrete sequence reader-writer, a high-speed format converter, a second delayer, a register, a D/A converter and a visualization device; the A/D digital-to-analog converter receives the time sequence pulse and samples a continuous signal into a discrete number array, wherein the continuous signal is remote sensing image data or ground data; the first time delay is used for delaying the time of the discrete data entering the high-speed discrete sequence reader-writer; the high-speed discrete sequence reader-writer is used for integrating the discrete data to obtain integrated data; the high-speed format converter is used for converting the integrated data into a binary format; the second delayer is used for delaying the time for sending the integrated data to the register; the register is used for registering the integrated data in the binary format; the D/A converter is used for converting the digital quantity of the integrated data into an analog quantity; the display device is used for displaying the integrated data converted into the analog quantity.

In one embodiment, the data mapping module comprises a hash table mapper and a mapping checker, wherein the hash table mapper is connected with the mapping checker; the Hash table mapper is used for mapping the input signal by taking the remote sensing image and the ground data as the input signal according to a Hash table to obtain a display result as an output signal; said mapping checker for determining whether there is an input signal mapping a plurality of output signals; when there is one input signal mapping multiple output signals, all output signals except the first output signal are fed back to the hash table for remapping.

In one embodiment, the analysis traceability layer comprises a itemized traceability analysis module and a comprehensive traceability analysis module; the subentry analysis module comprises a coordinate conversion unit, a substance distribution marking unit and a correlation analysis unit; the coordinate conversion unit is used for converting the longitude and latitude information into a two-dimensional space coordinate system according to the Euclidean distance; the substance concentration marking unit is used for marking substance concentration distribution corresponding to the water quality index in a two-dimensional space coordinate system according to the ground data; the correlation analysis unit is used for acquiring the correlation among the water quality indexes according to the ground data; and the comprehensive traceability analysis module is used for acquiring traceability results of all water quality indexes according to the correlation and the substance concentration distribution.

A water pollution tracing method based on remote sensing image inversion comprises the following steps: acquiring remote sensing image data by adopting a remote sensing inversion layer, and acquiring ground data by inversion according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index; integrating the remote sensing image data and the ground data according to a data integration layer, and mapping the integrated remote sensing image and ground data through a hash table to obtain a display result; and performing itemized analysis traceability and comprehensive analysis traceability on the imaging result according to the analysis traceability layer to obtain the traceability result of each water quality index.

In one embodiment, the integrating the remote sensing image data and the ground data according to the data integration layer, and mapping the integrated remote sensing image and ground data through a hash table to obtain a visualization result further includes: inputting the integrated remote sensing image and ground data into a circuit model as an input signal f (x); when the circuit model detects abnormal time sequence pulse or unstable driving level, an interrupt signal is sent to the first delayer, and a test sequence f is sent to impulse response h (x)_test(x) According to the returned result y of the test sequence after impulse response_test(x) Judging the abnormal type; wherein if the result y is returned_test(x) If the x intervals are different, the time sequence is abnormal, the current time sequence pulse signal is cleared, a new pulse signal is requested from the first delayer, and the time sequence processed at present is automatically synchronized; if the result y is returned_test(x) And the prestored result y_truth(x) If the data is not consistent with the preset data, the data is abnormal, the whole group of abnormal data is discarded, and the data is required to be acquired again from the input signal f (x); if the received return result y_test(x) Incomplete or not accepted y_test(x) If the signal is a hardware exception, trace back f_test(x) Where the loss occurs and marking, in sending the markThe system is terminated after the recording, and the exception with the mark information is displayed.

Compared with the prior art, the invention has the advantages and beneficial effects that:

1. according to the invention, the reliability and the accuracy can be ensured, the ground data can be directly obtained through remote sensing image data inversion, the working efficiency is improved, the remote sensing data is mapped through a data integration layer, the corresponding imaging result is obtained, the data reliability is ensured, the itemized analysis traceability and the comprehensive analysis traceability can be carried out, the pollution conditions of all water qualities are integrally analyzed, and the comprehensive traceability is realized.

2. The invention can synchronously verify the remote sensing image data and the ground data for many times through the circuit model, thereby ensuring the accuracy of the mapping and developing result.

3. The invention can be widely applied to the aspects of environmental protection detection, city planning, emergency monitoring, agricultural supervision, industrial layout planning and the like.

Drawings

FIG. 1 is a schematic structural diagram of a water pollution traceability system based on remote sensing image inversion in one embodiment;

FIG. 2 is a schematic structural diagram of a conventional water quality index inversion module shown in FIG. 1;

FIG. 3 is a schematic structural diagram of a water heavy metal index inversion module in FIG. 1;

FIG. 4 is a schematic diagram of a circuit model according to an embodiment;

FIG. 5 is a block diagram of the data mapping module of FIG. 1;

FIG. 6 is a schematic structural diagram of the entry traceability analysis module shown in FIG. 1;

FIG. 7 is a schematic flow chart of a water pollution tracing method based on remote sensing image inversion in one embodiment;

fig. 8 is a schematic flowchart of step S102 in fig. 7.

In the figure, the remote sensing inversion layer 10, the remote sensing image acquisition module 11, the conventional water quality index inversion module 12, the remote sensing image acquisition unit 121, the remote sensing image degradation unit 122, the index inversion unit 123, the first visualization unit 124, the water quality heavy metal index inversion module 13, the cloud image re-inspection unit 131, the formula generation unit 132, the data accounting unit 133, the second visualization unit 134, the data integration layer 20, the remote sensing image integration module 21, the ground data integration module 22, the data mapping module 23, the hash table mapper 231, the mapping checker 232, the a/D digital-to-analog converter 241, the first delayer 242, the high-speed discrete sequence reader-writer 243, the high-speed format converter 244, the second delayer 245, the register 246, the D/a converter 247, the visualization device 248, the analysis traceability layer 30, the itemizationtraceability analysis module 31, the coordinate conversion unit 311, the material distribution marking unit 312, and the correlation analysis unit 313, the data analysis unit, A comprehensive traceability analysis module 32.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings by way of specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

In one embodiment, as shown in fig. 1, there is provided a water pollution tracing system based on remote sensing image inversion, including: the remote sensing inversion layer 10, the data integration layer 20 and the analysis traceability layer 30; the remote sensing inversion layer 10 is connected with the data integration layer 20, and the data integration layer 20 is connected with the analysis traceability layer 30; the remote sensing inversion layer 10 is used for acquiring remote sensing image data and inverting ground data according to the remote sensing image data, wherein the ground data comprises a conventional water quality index and a water quality heavy metal index; the data unification layer 20 is used for performing hash table mapping on the remote sensing image data and the ground data to obtain a display result; and the analysis traceability layer 30 is used for performing itemized analysis traceability and comprehensive analysis traceability according to the development result.

In the embodiment, remote sensing image data is obtained through the remote sensing inversion layer 10, and ground data including conventional water quality indexes and water quality heavy metal indexes are obtained through inversion; carry out hash table mapping to remote sensing image data and ground data through data integration layer 20, acquire the development result, and carry out the itemized analysis traceability and the comprehensive analysis traceability to the development result according to analysis traceability layer 30, can be when guaranteeing reliability and degree of accuracy, directly acquire ground data through remote sensing image data inversion, work efficiency has been promoted, and carry out the mapping processing to remote sensing data through data integration layer 20, acquire corresponding development result, ensure data reliability, can also carry out itemized analysis traceability and comprehensive analysis traceability, the pollution condition of all quality of water of integration analysis, realize comprehensive traceability.

The remote sensing inversion layer 10 comprises a remote sensing image acquisition module 11, a conventional water quality index inversion module 12 and a water quality heavy metal index inversion module 13, wherein the conventional water quality index inversion module 12 and the water quality heavy metal index inversion module 13 are parallel.

Specifically, the remote sensing image acquisition module 11 may acquire the remote sensing image data from an existing high-resolution satellite, a resource-based satellite, a sentry-based satellite, or an existing satellite whose detection band is located in a visible light band and an infrared band, or may download public remote sensing image data from the internet. The conventional water quality index inversion module 12 and the water quality heavy metal index inversion module 13 are parallel.

Wherein the conventional water quality indexes are dissolved oxygen, conductivity, turbidity, permanganate, ammonia nitrogen, total phosphorus and total nitrogen; the water quality heavy metal indexes comprise copper ions, zinc ions, hexavalent chromium ions and cyanide. The conventional water quality index and the water quality heavy metal index are water quality indexes.

As shown in fig. 2 and 3, the conventional water quality index inversion module 12 includes a remote sensing image obtaining unit 121, a remote sensing image difference reducing unit 122, an index inversion unit 123 and a first display unit 124; the remote sensing image obtaining unit 121 is configured to obtain remote sensing image data through a wired or wireless protocol; the remote sensing image difference reducing unit 122 is used for reducing the aberration of the remote sensing image data in a remote sensing preprocessing and man-machine interaction mode to obtain first remote sensing image data; the index inversion unit 123 is configured to invert the conventional water quality index according to the first remote sensing image data; the first visualization unit 124 is used for format conversion and displaying inversion results; the water quality heavy metal index inversion module 13 comprises a cloud picture reexamination unit 131, a formula generation unit 132, a data accounting unit 133 and a second imaging unit 134; the cloud image rechecking unit 131 is used for reducing the aberration of the remote sensing image data and acquiring second remote sensing image data; the formula generation unit 132 is used for generating and rendering an inversion formula; the data accounting unit 133 is configured to perform inversion on the water quality heavy metal index according to the second remote sensing image data and the inversion formula, and verify a confidence level of an inversion result; the second visualization unit 134 is used to display the inversion results and confidence.

Specifically, the conventional water quality index inversion module 12 acquires remote sensing image data according to a wired or wireless protocol of the remote sensing image acquisition unit 121, and the remote sensing image degradation unit 12 is configured to reduce aberration of the remote sensing image data through remote sensing preprocessing, such as image scaling, image enhancement, image correction, orthorectification, image mosaic, data fusion, image transformation, information extraction, content classification, and human-computer interaction, to acquire first remote sensing image data, thereby facilitating subsequent calculation; the index inversion unit 123 obtains the conventional water quality index content by inversion according to the first remote sensing image data, and performs format conversion and display through the first display unit 124.

Specifically, the water quality heavy metal index inversion module 13 obtains remote sensing image data, reduces aberration of the remote sensing image by a human-computer interaction method according to the cloud image reexamination unit 131, obtains second remote sensing image data, generates a formula required for inversion through the formula generation unit 132, inverts the water quality heavy metal index content according to the generated optimal formula, checks the confidence level of the water quality heavy metal index content, and finally displays the water quality heavy metal index and the corresponding confidence level through the second imaging unit 134.

Specifically, the processes of remote sensing preprocessing of the remote sensing image data and remote sensing image subtraction mainly occur in the remote sensing image subtraction unit 122 and the cloud image reinspection unit 131; and converting ground data, namely conventional water quality indexes and water quality heavy metal indexes according to the processed remote sensing image data, mainly through an index inversion unit 123 and a data accounting unit 133.

The data integration layer 20 comprises a remote sensing image integration module 21, a ground data integration module 22 and a data mapping module 23; the remote sensing image integration module 21 and the ground data integration module 22 are parallel and are respectively used for integrating the remote sensing image data and the ground data; the data mapping module 23 is configured to map the integrated remote sensing image data and ground data according to the hash table.

Specifically, the remote sensing image integration module 21 and the ground data integration module 22 are parallel to integrate the remote sensing image data and the ground data, respectively, and map the integrated remote sensing image data and the ground data by using a hash table through the data mapping module 23, and send the obtained visualization result to the analysis traceability layer 30.

The remote sensing image integration module 21 and the ground data integration module 22 are provided with circuit models with the same structure, namely a remote sensing circuit model and a ground circuit model, the input line width of the remote sensing circuit model is the number of conventional water quality monitoring indexes, and the input line width of the ground circuit model is the number of water quality heavy metal indexes.

Specifically, the input line widths of the remote sensing circuit model and the ground circuit model are different, and the input line width of the remote sensing circuit model is 7, which is the number of the conventional water quality monitoring indexes; the input line width of the ground circuit model is 4 according to the number of water quality heavy metal indexes.

As shown in fig. 4, the circuit model includes an a/D digital-to-analog converter 241, a first delay 242, a high-speed discrete sequence reader 243, a high-speed format converter 244, a second delay 245, a register 246, a D/a converter 247, and a display device 248; the A/D converter 241 receives the time sequence pulse, samples the continuous signal into discrete number series, the continuous signal is remote sensing image data or ground data; the first delayer 242 is used to delay the time when the discrete data enters the high-speed discrete sequence reader 243; the high-speed discrete sequence reader 243 is configured to integrate the discrete data to obtain integrated data; the high-speed format converter 244 is used to convert the integrated data into binary format; the second delay 245 is used to delay the time for sending the integrated data to the register 246; the register 246 is used for registering the integrated data in binary format; a D/a converter 247 for converting digital quantity of the integrated data into analog quantity; the display device 248 is used for displaying the integrated data converted into analog quantity.

Specifically, the circuit model receives a timing pulse T (x), under the action of the timing pulse, the a/D data conversion module samples a continuous input signal f (T) as discrete data f (x) according to T, and under the action of the first delayer 242, the circuit model can have enough time to process the data of f (x- τ) without worrying about the situations of data loss and timing conflict; the discrete input signal is transmitted into the high-speed discrete sequence reader-writer 243, under the action of the first delayer 242, the high-speed discrete sequence reader-writer 243 can transmit the discrete input signal to the high-speed format converter 244 within the time of tau-a, and under the action of the second delayer 245, the high-speed format converter 246 converts the discrete input signal into the binary format which can be stored by the register 247 within the time of tau-b, in order to meet the real-time performance and avoid collision, there are:

τ-a+τ-b＝τ； (1)

τ＝a+b； (2)

where a is the relaxation time of the high-speed discrete sequence reader/writer 243, and b is the relaxation time of the high-speed format converter 244.

And (3) selecting two components with relaxation time less than tau according to the formula (2), and constructing a remote sensing image integration module 21 and a ground data integration module 22.

After the second delayer 245 receives a high level signal, the signal sequence within the time n is presented to the register 247, and then the automatic trigger signal is adjusted to a low level trigger model, generally, n is required to be less than or equal to τ, and the optimal working point is n is about τ; the digital signal is then converted into an analog signal by the D/a converter 247 and displayed by the development device 248.

As shown in fig. 5, the data mapping module 23 includes a hash table mapper 231 and a mapping checker 232, wherein the hash table mapper 231 is connected to the mapping checker 232; the hash table mapper 231 is configured to map the input signal according to the hash table by using the remote sensing image and the ground data as the input signal, and obtain a display result as an output signal; the mapping checker 232 is configured to determine whether there is an input signal mapping a plurality of output signals; when there is one input signal mapping multiple output signals, all output signals except the first output signal are fed back to the hash table for remapping.

In particular, the mapping checker 232 is set because the method of hash table mapping may cause remapping. After the input signal is mapped by the hash table mapper 231, the mapping checker 232 checks whether there is one input signal mapping a plurality of output signals, and if there is one input signal mapping a plurality of output signals, all output signals except the first output signal are fed back to the hash table mapper 231 for remapping. Because a feedback form is adopted, the situation that the mapping relation of an input query is deep can occur, the provided mapping function is reasonable, and the probability of the situation is smaller.

As shown in fig. 6, the analysis traceability layer 30 includes a itemized traceability analysis module 31 and a comprehensive traceability analysis module 32; the item analysis module 31 comprises a coordinate conversion unit 311, a substance distribution marking unit 312 and a correlation analysis unit 313; the coordinate conversion unit 311 is configured to convert the longitude and latitude information into a two-dimensional spatial coordinate system according to the euclidean distance; the substance concentration marking unit 312 is configured to mark a substance concentration distribution corresponding to the water quality index in a two-dimensional space coordinate system according to the ground data; the correlation analysis unit 313 is used for acquiring correlation among the water quality indexes according to the ground data; the comprehensive traceability analysis module 32 is configured to obtain traceability results of each water quality index according to the correlation and the substance concentration distribution.

Specifically, the itemized traceability analysis module 31 is supported by a theoretical analysis method and an image combination method, and the integrated traceability analysis module 32 is supported by an integrated analysis method.

For example, after 2973 sets of remote sensing image data are selected, surface data inversion is performed on the remote sensing image data through the above features, longitude and latitude information is converted into a two-dimensional space coordinate system according to the coordinate conversion unit 311 by using the euclidean distance, substance concentration distribution of each water quality index is obtained on the remote sensing map, corresponding marking is performed through the substance concentration marking unit 312, the surface data is subjected to pearson correlation analysis by using the correlation analysis unit 313, and the correlation among the water quality indexes is obtained, as shown in table 1, the correlation table is a pearson correlation table of 11 water quality indexes (including 7 normal water quality indexes and 4 medium water quality heavy metal indexes).

TABLE 1 Pearson correlation Table for Water quality index

In this embodiment, the tracing analysis of the itemized tracing analysis module 31 specifically includes:

among them, regarding dissolved oxygen: the water temperature, oxygen content in air and photosynthesis of aquatic plants are related, and generally, the water quality is deteriorated when the amount of dissolved oxygen in water is less than 5mg/L and the amount of dissolved oxygen in pure water is about 9mg/L at 20 ℃ and 100Kpa atmospheric pressure. In table 1, there are nine indexes (in order of strength) having significant correlation with dissolved oxygen: cyanide, ammonia nitrogen, total phosphorus, copper ions, zinc ions, permanganate, turbidity, and hexavalent chromium ions. It is known that, in addition to the electrolysis rate, other water quality indicators have a large influence on dissolved oxygen, and cyanide has the greatest influence.

Therefore, the tracing direction of the decrease of the dissolved oxygen in the water quality is as follows: (1) the water temperature changes greatly; (2) the oxygen content of the air is low; (3) poor photosynthesis of aquatic plants; (4) factors associated with cyanide emission.

Among them, with respect to the electrolysis rate: the better the conductivity in water, the higher the electrolysis rate, the greater the relationship between temperature and doping concentration, generally, the conductivity of pure water is less than or equal to 2 mus/cm, the higher the electrolysis rate, the worse the quality of water. In table 1, ten indexes (ranked by strength) having significant correlation with the electrolysis rate are shown: turbidity, hexavalent chromium ions, copper ions, zinc ions, total phosphorus, permanganate, cyanide, total nitrogen, ammonia nitrogen. It is known that other water quality indicators have a large influence on dissolved oxygen, and the maximum influence is turbidity.

Therefore, the tracing direction for increasing the water electrolysis rate is as follows: (1) the water temperature changes greatly; (2) the doping concentration is high; (3) factors related to the turbidity degree of the water body.

Among them, with respect to turbidity: turbidity in water means the degree to which light passing through water is hindered by scattering or transmission of light due to a turbid state in which suspended matter and colloidal matter are contained in the water. Of waterTurbidity is mainly caused by adulteration and comprises silt, clay, aquatic organisms, bacteria, viruses, macromolecular organic matters and the like. Typically, with SiO₂As a result, the turbidity in the distilled water was not higher than 1mg/L, which resulted in high turbidity and poor quality of water. In table 1, there are nine indexes (in order of strength) having significant correlation with turbidity: hexavalent chromium ions, copper ions, zinc ions, total nitrogen, cyanide, electrolysis rate, ammonia nitrogen, permanganate and total phosphorus. It can be known that other water quality indexes have a great influence on dissolved oxygen, wherein hexavalent chromium ions have the greatest influence.

Therefore, the direction of origin causing the rise of the turbidity of the water: (1) the doping concentration is high; (2) a factor associated with hexavalent chromium ion emission.

Among them, regarding permanganate: the permanganate index in the water cannot be used as an index of the theoretical oxygen demand or the total organic matter content, and the permanganate content in the standard water is always smaller than the dissolved oxygen amount. Permanganate becomes high and the quality of the water quality becomes poor. In table 1, there are nine indexes (ranked by strength) that are significant in association with permanganate: copper ions, zinc ions, hexavalent chromium ions, electrolytic rate, turbidity, total phosphorus, total nitrogen, cyanide, dissolved oxygen. It is known that, except ammonia nitrogen, other water quality indexes have a great influence on dissolved oxygen, and the most significant of the influence is copper ions.

Thus, the direction of origin causing the rise of the water permanganate: (1) the doping concentration is high; (2) factors associated with copper ion emission.

Wherein, regarding ammonia nitrogen: the ammonia nitrogen in the water is nitrogen existing in the form of free ammonia and ammonium ions in the water, is a nutrient of the water, can cause the phenomenon of water eutrophication, is related to the content of algae, plankton and the like in the water, and has high ammonia nitrogen and poor water quality. In table 1, eight indexes (ranked by strength) with significant association with ammonia nitrogen are shown: total nitrogen, turbidity, dissolved oxygen, cyanide, zinc ions, copper ions, electrolysis rate, hexavalent chromium ions. It can be known that, in addition to permanganate and total phosphorus, other water quality indicators have a greater effect on dissolved oxygen, with the greatest effect being total nitrogen.

Therefore, the tracing direction for causing the ammonia nitrogen in the water to rise is as follows: (1) the content of algae, plankton and the like in the water is increased; (2) factors related to total ammonia.

Wherein, with respect to total phosphorus: the total phosphorus in water is a measurement result after water samples are digested to convert phosphorus in various forms into orthophosphate, the main sources of the total phosphorus in water are domestic sewage, chemical fertilizers, organophosphorus pesticides and modern detergents, algae organisms in water grow depending on the total phosphorus, and excessive phosphorus can cause water eutrophication. Generally, the total phosphorus content in the water is not higher than 0.05mg/L, the total phosphorus becomes high, and the quality of the water becomes poor. In table 1, there are nine indexes (ranked by strength) that are significant in association with total phosphorus: copper ions, hexavalent chromium ions, zinc ions, electrolysis rate, turbidity, permanganate, dissolved oxygen, cyanide, total nitrogen. It is known that, except ammonia nitrogen, other water quality indexes have a great influence on dissolved oxygen, and the most significant of the influence is copper ions.

Therefore, the tracing direction for causing the total phosphorus in the water to rise is as follows: (1) discharging domestic sewage; (2) discharging agricultural chemical fertilizers; (3) agricultural pesticide emission; (4) factors associated with copper ion emission.

Wherein, with respect to total nitrogen: total phosphorus in water is the sum of various forms of inorganic and organic nitrogen in water, and is often used to indicate the degree of contamination of water by nutrients. Generally, the total nitrogen content in the water is not higher than 0.05mg/L, the total nitrogen becomes high, and the quality of the water becomes poor. In table 1, there are ten indexes (ranked by strength) that are significant in relation to total nitrogen: ammonia nitrogen, zinc ions, copper ions, hexavalent chromium ions, turbidity, dissolved oxygen, permanganate, electrolysis rate, total phosphorus and cyanide. It can be known that other water quality indexes have great influence on dissolved oxygen, wherein the most influence is ammonia nitrogen.

Therefore, the tracing direction of the total nitrogen rise of water quality is as follows: (1) the water body has more nutrient substances; (2) factors related to ammonia nitrogen discharge.

Among them, with respect to heavy metal ions in water: the indexes (in order of strength and weakness) with significant relevance to various ions are as follows:

copper ion: zinc ions, hexavalent chromium ions, turbidity, total nitrogen, permanganate, total phosphorus, electrolysis rate, cyanide, dissolved oxygen, ammonia nitrogen.

Zinc ion: copper ions, hexavalent chromium ions, turbidity, total nitrogen, permanganate, electrolysis rate, total phosphorus, cyanide, dissolved oxygen, ammonia nitrogen.

Hexavalent chromium ions: zinc ions, copper ions, turbidity, cyanide, total nitrogen, permanganate, electrolysis rate, total phosphorus, dissolved oxygen, ammonia nitrogen.

Cyanide compound: hexavalent chromium ions, turbidity, dissolved oxygen, copper ions, electrolysis rate, ammonia nitrogen, zinc ions, total phosphorus, permanganate and total nitrogen.

Therefore, the tracing direction of the heavy metal elements in the water quality is as follows: (1) chemical sewage discharge; (2) the soil pollution is serious; (3) turbidity-related factors; (4) factors associated with dissolved oxygen.

In this embodiment, the comprehensive traceability analysis module 32 obtains the traceability directions according to the traceability result obtained by the itemized traceability analysis module 31 as follows:

low dissolved oxygen: large water temperature variation, low oxygen content in air, poor photosynthesis by aquatic plants, factors related to cyanide emission.

The electrolysis rate is high: large water temperature change, high doping concentration and factors related to the turbidity degree of the water body.

High turbidity: high doping concentration and the factor related to the discharge of hexavalent chromium ions in water.

Permanganate high: high doping concentration, factors related to copper ion emission.

High ammonia nitrogen content: the content of algae, plankton and the like in water is increased, and factors related to total nitrogen are included.

The total phosphorus content is as follows: domestic sewage discharge, agricultural chemical fertilizer discharge, agricultural pesticide discharge and copper ion discharge related factors.

The total nitrogen is high: the water body has more nutrient substances and factors related to ammonia nitrogen discharge.

High heavy metal ion in water: chemical wastewater discharge, serious soil pollution, turbidity-related factors and dissolved oxygen-related factors.

The above "factors related to x" were replaced by comprehensive analysis, which was as follows:

tracing the source of the pollution reason of the cyanide and tracing the abnormal discharge of the heavy metal in the water; tracing the source of the reason for deepening the turbidity degree of the water body, and tracing the phenomena that the doping concentration is high and the heavy metal in the water is abnormally discharged; tracing the reason for the increase of the concentration of the copper ions to trace the abnormal discharge of the heavy metals in the water; tracing the reasons for the increase of the total ammonia concentration to water eutrophication, algae breeding and plankton content increase.

In one embodiment, as shown in fig. 7, a water pollution tracing method based on remote sensing image inversion is provided, which includes the following steps:

and S701, acquiring remote sensing image data by adopting a remote sensing inversion layer, and acquiring ground data according to inversion of the remote sensing image data, wherein the ground data comprises conventional water quality indexes and water quality heavy metal indexes.

And S702, integrating the remote sensing image data and the ground data according to the data integration layer, and mapping the integrated remote sensing image and ground data through a hash table to obtain a display result.

And step S703, performing item analysis traceability and comprehensive analysis traceability on the development result according to the analysis traceability layer, and obtaining the traceability result of each water quality index.

In the embodiment, the remote sensing image inversion layer is adopted to obtain remote sensing image data, the conventional water quality index and the water quality heavy metal index of ground data are obtained through inversion according to the remote sensing image data, the remote sensing image data and the ground data are integrated according to the data integration layer, the integrated remote sensing image and the ground data are mapped through the hash table to obtain a display result, the analysis traceability layer is adopted to carry out the itemized analysis traceability and the comprehensive analysis traceability on the display result to obtain the traceability result of each water quality index, the ground data can be directly obtained through the remote sensing image data inversion while the reliability and the accuracy are ensured, the working efficiency is improved, the remote sensing data are mapped to obtain the corresponding display result, the data reliability is ensured, the itemized analysis traceability and the comprehensive analysis traceability can be carried out to integrate and analyze the pollution conditions of all water qualities, and comprehensive tracing is realized.

As shown in fig. 8Step S102 further includes: the integrated remote sensing image and the ground data are used as input signals f (x) and input into a circuit model; when the circuit model detects abnormal time sequence pulse or unstable driving level, an interrupt signal is sent to the first delayer, and a test sequence f is sent to impulse response h (x)_test(x) According to the return result y of the test sequence after impulse response_test(x) Judging the abnormal type; wherein if the result y is returned_test(x) If the x intervals are different, the time sequence is abnormal, the current time sequence pulse signal is cleared, a new pulse signal is requested from the first delayer, and the time sequence processed at present is automatically synchronized; if the result y is returned_test(x) And the prestored result y_truth(x) If the data is not consistent with the preset data, the data is abnormal, the whole group of abnormal data is discarded, and the data is required to be acquired again from the input signal f (x); if the received return result y_test(x) Incomplete or not accepted y_test(x) If the signal is a hardware exception, trace back f_test(x) The position where the occurrence is lost is marked, the system is halted after the mark is sent, and an exception with the marked information is displayed.

Specifically, the stability of a circuit model is enhanced through the time sequence synchronization algorithm; and can ensure the complete recovery of two kinds of abnormalities: the time sequence abnormity and the data abnormity ensure the accurate positioning of the abnormity: hardware exceptions, when encountered, will prompt and stop the integration.

In one embodiment, a pollution source of 7 conventional water quality indicators and 4 water quality heavy metal indicators is analyzed by taking remote sensing section data (hereinafter referred to as experimental data) of a certain region of a certain city on 7, 27 and 2021 as an example.

The specific implementation steps are as follows:

step S701, obtaining remote sensing wave band data of the sentinel II as follows:

TABLE 2 downloaded section data (parts)

Band1	Band2	Band3	Band4	Band5	Band6	Band7	Band8	Band8a	Band9	Band11	Band12
												1167	1300	1684	1874	1747	777	806	604	469	214	240	190
1222	1374	1742	1936	1786	795	800	590	450	227	227	181
												1159	1346	1706	1920	1744	751	759	555	412	130	219	173
1308	1440	1754	1926	1815	884	909	720	538	428	255	182
												1373	1480	1830	1984	1867	940	1002	731	638	570	301	207
1388	1566	1868	2038	1899	1022	1114	872	738	631	358	263
												1128	1366	1758	1982	1870	889	923	681	557	263	315	255
1085	1294	1678	1886	1812	1001	1071	866	747	321	455	371
												1027	1186	1296	1186	1281	1167	1252	1212	1070	2460	954	733

In step S701, the acquired inversion data are shown in table 3 below:

11 water quality indexes (unit mg/L) (part) obtained by inversion of Table 3

O2	ELE	NTU	Mn	N	TP	TN	Cu	Zn	Cr6	CN
											5.41	960.78	388.18	5.72	0.32	0.03	6.02	0.00	0.06	0.00	0.01
6.01	1211.83	370.48	5.98	0.33	0.02	6.06	0.00	0.06	0.00	0.01
											6.53	1078.04	403.04	6.11	0.41	0.02	8.56	0.00	0.06	0.00	0.01
3.19	1458.18	225.07	5.49	0.01	0.05	6.07	0.00	0.07	0.00	0.01
											2.27	800.02	141.33	3.69	0.02	0.02	5.88	0.00	0.08	0.00	0.01
0.71	4598.28	99.59	3.23	0.10	0.02	4.89	0.00	0.08	0.00	0.01
											0.03	201.78	376.12	5.04	0.32	0.01	6.76	0.00	0.07	0.00	0.01
4.39	263.43	321.96	4.31	0.03	0.09	4.67	0.00	0.09	0.00	0.01
											12.53	381.69	84.42	3.71	0.11	0.12	3.58	0.00	0.11	0.00	0.00

Note: the decimal point number of the inverted 11 water quality index data is not only 2, and the decimal point number is used for convenience of display.

And S702, integrating the remote sensing image data and the ground data, mapping the integrated remote sensing image and the ground data through a hash table to obtain a display result, and displaying the substance concentration distribution corresponding to the water quality index on a remote sensing map.

Step S703, through the analysis of the two steps, the heavy metal and conventional water quality indexes (mainly TP and NTU) of a certain district of a certain city are found to be abnormal, and the conclusion is that:

(1) the turbidity increase in the water quality may be caused by the inflow of silt, the increase of water level and the influx of other rivers.

(2) The increase in total phosphorus in the water may be an increase in algae and plankton in the water.

(3) It is necessary to investigate whether abnormal drainage, bank lowering, and the like exist near the watershed.

Further, through field investigation by engineering personnel, the water areas of the brook are in the ebb tide period, and the silt on the bank is covered on the water surface, so that the turbidity of the water surface is increased and the eutrophication of the water body is caused.

It will be understood by those skilled in the art that all or part of the processes of the methods described in the above embodiments may be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disks, optical disks) and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.