阅读说明：本技术 一种高炉数据处理方法和装置 (Blast furnace data processing method and device ) 是由 何志娟 叶理德 欧燕 刘书文 于 2020-12-30 设计创作，主要内容包括：本发明公开了一种高炉数据处理方法和装置,所述方法包括：获取所需要处理的或图形显示的Json数据并分层,所述Json数据为用于监控和报警的高炉数据,其属性包括层高、目标点和值；对于垂直空间每一层的Json数据,执行以下操作：步骤S1：根据值属性,将每一层数据进行归集处理形成第一段流水池数据；步骤S2：根据值属性及报警的阈值范围,将第一段流水池数据进行归集、简化处理形成第二段流水池数据；步骤S3：对第二段流水池数据进行分类标识,形成第三段流水池数据；步骤S4：输出第三段流水池数据,进行图形或者表格的动态显示。本方法通过自定义的类Json结构和分池简化过程可以提高实时更新数据的处理效率。(The invention discloses a blast furnace data processing method and a blast furnace data processing device, wherein the method comprises the following steps: acquiring Json data to be processed or graphically displayed and layering the Json data, wherein the Json data is blast furnace data used for monitoring and alarming, and the attributes of the Json data comprise a layer height, a target point and a value; for the Json data of each layer in the vertical space, the following operations are performed: step S1: according to the value attribute, collecting and processing each layer of data to form first section of flow pool data; step S2: according to the value attribute and the alarm threshold range, the data of the first section of the flow water pool are collected and simplified to form data of a second section of the flow water pool; step S3: carrying out classification identification on the second section of flow pool data to form third section of flow pool data; step S4: and outputting the data of the third section of the pipeline pool, and dynamically displaying graphs or tables. The method can improve the processing efficiency of real-time updating data through a self-defined Json-like structure and a pool-dividing simplification process.)

1. A blast furnace data processing method is characterized by comprising the following steps:

acquiring Json data to be processed or graphically displayed, wherein the Json data are blast furnace data used for monitoring and alarming, and layering is performed according to distribution of acquisition points of the blast furnace data in a vertical space of a blast furnace; attributes of the Json data comprise layer height, a target point and a value;

for the Json data of each layer in the vertical space, the following operations are performed:

step S1: according to the value attribute, collecting and processing each layer of data to form first section of flow pool data;

step S2: according to the value attribute and the alarm threshold range, the data of the first section of the flow water pool are collected and simplified to form data of a second section of the flow water pool;

step S3: carrying out classification identification on the second section of flow pool data to form third section of flow pool data;

step S4: and outputting the data of the third section of the pipeline pool, and dynamically displaying graphs or tables.

2. The blast furnace data processing method according to claim 1, wherein the step S1 specifically includes: collecting target points of the Json data with the same value in a point collection of a Json-like structure, and taking the same value as a common value of the target points of the Json-like structure; the class Json structure includes attribute level height, class key value pair < point set: target point common value >; independently placing target points of the Json data which do not have the same value in a point set to form a Json-like structure independently, wherein the value of the Json-like structure is the common value of the target points of the Json-like structure; and defining a Json-like structure set consisting of a plurality of Json-like structures generated by the hierarchical data collection as first segment streaming pool data.

3. The blast furnace data processing method according to claim 2, wherein the step S2 specifically includes: for the first section of flow tank data, taking the Json-like structure data with the largest number of points in the point set as the reference Json-like structure data, taking the target point common value in the Json-like structure data as the reference target point common value, respectively comparing the target point common value in other Json-like structure data of the first section of flow tank data with the reference target point common value, and if the error range does not accord with the alarm rule, adding the point set corresponding to the target point common value into the point set attribute of the reference Json-like structure data; otherwise, keeping the previous Json structure data unchanged; and the set of the Json-like structure data after the first section of flow pool data are merged and simplified is defined as second section of flow pool data.

4. The blast furnace data processing method according to claim 3, wherein the alarm rule is specifically: comparing the target point common values in other Json structure data of the first section of flow tank data with the reference target point common values respectively, and if the error range is smaller than a set threshold value, the error range does not accord with the alarm rule; if the error range is larger than or equal to the set threshold, the alarm rule is met.

5. The blast furnace data processing method according to claim 3, wherein the step S3 specifically includes: for the second section of flow pool data, adding an attribute for representing the data state to each class Json structure data, and setting the state attribute of the class Json structure data with the maximum number of points in the point set as normal; and the other Json-like structure data sets the alarm degree according to the alarm rule.

6. The blast furnace data processing method according to claim 1, characterized by comprising: in step S4, the attribute of the node of the third section of pipeline pool data further includes: and displaying the identification color.

7. The blast furnace data processing method according to any one of claims 1 to 6, characterized by: the attributes of the Json data further include target data point types, the Json data of each layer in the vertical space are classified according to the target data point types, and the steps S1 to S4 are respectively executed for the target point of each target data point type.

8. The blast furnace data processing method according to claim 1, characterized by comprising: the blast furnace data is thermocouple-related data, wherein the layer number attribute corresponds to the insertion depth of the thermocouple in the blast furnace; the target point attribute corresponds to the setting angle of the thermocouple; the value attribute corresponds to a temperature value collected by the thermocouple.

9. A blast furnace data processing device is characterized by comprising an acquisition module, a layering module, a flow pool segmentation simplification module and an output module;

the acquisition module is used for acquiring Json data which needs to be processed or is displayed graphically, wherein the Json data is specifically blast furnace data used for monitoring and alarming;

the layering module is used for dividing the Json data into layers in a plurality of vertical spaces according to the data distribution situation of the acquisition points of the blast furnace data in the vertical spaces of the blast furnace to form layered data; the attributes of the hierarchical data comprise the number of layers, a target point and a value;

the flow water tank segmentation simplifying module is used for respectively carrying out collection, combination simplification and classification identification processing on the layered data of each layer according to an alarm rule to obtain third section flow water tank data, wherein the third section flow water tank data is provided with state attributes, and the state attributes are used for classification identification of alarm;

an output module: and the data processing module is used for outputting the data of the third section of the pipeline pool and dynamically displaying graphs or tables.

10. The blast furnace data processing device according to claim 9, wherein the flow basin segmentation simplification module comprises a first section flow basin module, a second section flow basin module and a third section flow basin module;

the first section of flow water tank module is used for performing data aggregation processing on the layered data to generate first section of flow water tank data: according to all data of each layer, target points with the same value are placed in the same group to form a point set, and the same value is defined as a common value of the target points; the point set and the target point common value are represented by a class key value pair of a class Json structure < point set: representing a common value of a target point, and defining a data set of a Json-like structure generated by collecting hierarchical data as first section of flow pool data;

the second section of flow water tank module is used for carrying out data combination and simplified processing on the first section of flow water tank data to generate second section of flow water tank data: selecting a reference Json structure and a corresponding reference target point common value according to the first section of flow tank data, merging point sets of difference values of target point common values of other Json structures and the reference target point common value within a set threshold range into a point set of the reference Json structure, merging and simplifying, and defining a data set of the Json structure after merging and simplifying the first section of flow tank data as second section of flow tank data;

the third section of flowing water pool module is used for carrying out classification identification on the second section of flowing water pool data to generate third section of flowing water pool data: and adding a state attribute for representing normal or alarm to each Json-like structure of the second section of flow line pool data according to an alarm judgment rule to form third section of flow line data.

Technical Field

The invention relates to a blast furnace rational data processing technology, in particular to a blast furnace data processing method and device based on vertical spatial data classification and conversion in different pools, which can be used for monitoring and alarming blast furnace thermocouple real-time temperature measurement data.

Background

At present, for monitoring and alarming blast furnace thermocouple real-time temperature measurement data, the main operation is to find out the data in a real-time database or a relational database and directly display the data on a graph and a table;

however, this approach has the following disadvantages: the temperature measured by each thermocouple in the blast furnace changes frequently along with the time, so the page also needs to update data in real time, and the method can cause the problems of slow page refreshing and data query blockage when acquiring the temperature measurement data which changes in real time.

For the problems of data monitoring and alarming, a data processing method exists in the prior art, namely, data searched through a background is transmitted to a front end in a Json format, and then traversal judgment and display are carried out on the front end. However, since the method adopts multiple traversal judgments, the time complexity and the space complexity are increased; in addition, in the method, data logic processing and data display are mixed in the same processing module, and data needing monitoring and alarming may have various conditions such as abnormal data of different degrees, if the data are directly transmitted to the front end, judgment is carried out at the code front end, the load pressure of the client is increased, and the problem of low data processing efficiency is caused.

Disclosure of Invention

In view of the above defects in the prior art, the present invention provides the following technical solutions for the problem of too slow monitoring and alarm query speed of furnace data such as thermocouple real-time temperature measurement data.

The invention provides a blast furnace data processing method, which comprises the following steps:

acquiring Json data to be processed or graphically displayed, wherein the Json data are blast furnace data used for monitoring and alarming, and layering is performed according to distribution of acquisition points of the blast furnace data in a vertical space of a blast furnace; attributes of the Json data comprise layer height, a target point and a value;

for the Json data of each layer in the vertical space, the following operations are performed:

step S1: according to the value attribute, collecting and processing each layer of data to form first section of flow pool data;

step S2: according to the value attribute and the alarm threshold range, the data of the first section of the flow water pool are collected and simplified to form data of a second section of the flow water pool;

step S3: carrying out classification identification on the second section of flow pool data to form third section of flow pool data;

step S4: and outputting the data of the third section of the pipeline pool, and dynamically displaying graphs or tables.

By the method, monitored and alarming blast furnace data are collected, combined, simplified, classified and identified, and finally classified and output, so that the data volume of real-time updating data can be greatly reduced, state judgment is not needed at the front end, and the problem of low real-time updating data processing efficiency is solved.

Further, the step S1 specifically includes: collecting target points of the Json data with the same value in a point collection of a Json-like structure, and taking the same value as a common value of the target points of the Json-like structure; the class Json structure includes attribute level height, class key value pair < point set: target point common value >; independently placing target points of the Json data which do not have the same value in a point set to form a Json-like structure independently, wherein the value of the Json-like structure is the common value of the target points of the Json-like structure; and defining a Json-like structure set consisting of a plurality of Json-like structures generated by the hierarchical data collection as first segment streaming pool data.

Further, the step S2 specifically includes: for the first section of flow tank data, taking the Json-like structure data with the largest number of points in the point set as the reference Json-like structure data, taking the target point common value in the Json-like structure data as the reference target point common value, respectively comparing the target point common value in other Json-like structure data of the first section of flow tank data with the reference target point common value, and if the error range does not accord with the alarm rule, adding the point set corresponding to the target point common value into the point set attribute of the reference Json-like structure data; otherwise, keeping the previous Json structure data unchanged; and the set of the Json-like structure data after the first section of flow pool data are merged and simplified is defined as second section of flow pool data.

Further, the alarm rule is specifically as follows: comparing the target point common values in other Json structure data of the first section of flow tank data with the reference target point common values respectively, and if the error range is smaller than a set threshold value, the error range does not accord with the alarm rule; if the error range is larger than or equal to the set threshold, the alarm rule is met.

Further, the step S3 specifically includes: for the second section of flow pool data, adding an attribute for representing the data state to each class Json structure data, and setting the state attribute of the class Json structure data with the maximum number of points in the point set as normal; and the other Json-like structure data sets the alarm degree according to the alarm rule.

Further, in step S4, the attribute of the node of the third section of pipeline pool data further includes: and displaying the identification color. The status indications can be displayed more intuitively by means of color.

Furthermore, the attributes of the Json data further include data types of target points, and for the Json data of each layer in the vertical space, the Json data are classified according to the data types of the target points, and steps S1 to S4 are performed for the target point of each data type.

Further, the blast furnace data is thermocouple-related data, wherein the layer number attribute corresponds to the insertion depth of the thermocouple in the blast furnace; the target point attribute corresponds to the setting angle of the thermocouple; the value attribute corresponds to a temperature value collected by the thermocouple. The method is suitable for solving the technical problems of monitoring and alarming the real-time temperature measurement data of the thermocouple of the blast furnace.

The invention also provides a blast furnace data processing device based on the classification and conversion of the vertical spatial data in the sub-pools, which comprises an acquisition module, a layering module, a flow pool segmentation simplification module and an output module;

the acquisition module is used for acquiring Json data which needs to be processed or is displayed graphically, wherein the Json data is specifically blast furnace data used for monitoring and alarming;

the layering module is used for dividing the Json data into layers in a plurality of vertical spaces according to the data distribution situation of the acquisition points of the blast furnace data in the vertical spaces of the blast furnace to form layered data; the attributes of the hierarchical data comprise the number of layers, a target point and a value;

the flow water tank segmentation simplifying module is used for respectively carrying out collection, combination simplification and classification identification processing on the layered data of each layer according to an alarm rule to obtain third section flow water tank data, wherein the third section flow water tank data is provided with state attributes, and the state attributes are used for classification identification of alarm;

an output module: and the data processing module is used for outputting the data of the third section of the pipeline pool and dynamically displaying graphs or tables.

Furthermore, the flow pool subsection simplifying module comprises a first section of flow pool module, a second section of flow pool module and a third section of flow pool module;

the first section of flow water tank module is used for performing data aggregation processing on the layered data to generate first section of flow water tank data: according to all data of each layer, target points with the same value are placed in the same group to form a point set, and the same value is defined as a common value of the target points; the point set and the target point common value are represented by a class key value pair of a class Json structure < point set: representing a common value of a target point, and defining a data set of a Json-like structure generated by collecting hierarchical data as first section of flow pool data;

the second section of flow water tank module is used for carrying out data combination and simplified processing on the first section of flow water tank data to generate second section of flow water tank data: selecting a reference Json structure and a corresponding reference target point common value according to the first section of flow tank data, merging point sets of difference values of target point common values of other Json structures and the reference target point common value within a set threshold range into a point set of the reference Json structure, merging and simplifying, and defining a data set of the Json structure after merging and simplifying the first section of flow tank data as second section of flow tank data;

the third section of flowing water pool module is used for carrying out classification identification on the second section of flowing water pool data to generate third section of flowing water pool data: and adding a state attribute for representing normal or alarm to each Json-like structure of the second section of flow line pool data according to an alarm judgment rule to form third section of flow line data.

The technical effects are as follows:

the invention defines a class key value pair structure of Json data; previously the key-value pair structure was (key, value), and now is the < set of points: common value >. For multi-layered vertical spatial data, spatial complexity is reduced compared to conventional approaches.

The invention defines a Json-like structure: the class Json structure consists of three parts, namely a layer number, a class key value pair and a state. For multi-layer vertical spatial data, the efficiency of subsequent data display is improved compared with the traditional method.

The pool separation simplified process of the invention comprises the following steps: for one type of target data, the whole process of collection, combination simplification and classification identification can be completed only by one three-section flowing water pool, and for multilayer vertical space data, compared with the traditional method, the method has more obvious hierarchy and more visual data display.

Drawings

FIG. 1 is a flow chart of a blast furnace data processing method of the present invention;

FIG. 2 is a flow chart of a blast furnace data processing method of the present invention applied to thermocouple thermometry data;

FIG. 3 is a flow chart of a blast furnace processing method of the present invention applied to a plurality of target data point categories;

FIG. 4 is a block diagram of a blast furnace data processing apparatus according to the present invention.

Detailed Description

To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.

The invention will now be further described with reference to the accompanying drawings and detailed description.

As shown in fig. 1, the present invention provides a specific example of a blast furnace data processing method based on vertical spatial data hierarchical classification conversion, which includes the following steps:

step 101: json data required to be processed or displayed graphically is acquired and layered.

Acquiring Json data to be processed or graphically displayed, such as blast furnace data for monitoring and alarming, and layering according to distribution of acquisition points of the blast furnace data in a vertical space of the blast furnace; the blast furnace data are divided into a plurality of levels H1-Hn on the vertical space according to the data distribution condition on the vertical space in the blast furnace, then the data of each level of the vertical space are processed by hierarchical data processing, such as data collection, merging simplification and classification identification, and finally normal data and alarm data are output.

The specific data processing process is divided into three steps:

step 102: and converting certain layer of original data into first flowing water pool data.

All data in a certain layer are acquired, the traditional Json structure is (key, value), but now we collect the target point key with the same value into one data structure, namely, the point set dataset, and add the point set dataset into the new Json-like structure. The new Json-like structure has: layer height, class key value pair < point set dataset: the target point shares the value > two attributes. If a certain target point does not have the same value, the target point is placed in the point set individually to form a Json-like structure individually. Through data processing, data of each layer form a plurality of Json-like structures, and a Json-like structure data set formed by the Json-like structures is called as first-stage pipeline pool data.

Step 103: and converting the data of the first section of flow pool of a certain layer into the data of the second section of flow pool.

Merging and simplifying the first stage streaming pool data according to the following modes: regarding the first section of pipeline pool data, taking a target point common value A0 in one class Json structure data (C _ Jsondata) with the maximum number of points in the point set (namely, the maximum datasetnum) as a reference target point common value, comparing the target point common values A1 and A2 … An in other class Json structure data of the first section of pipeline pool data with A0 respectively, if An error range is smaller than a (which is An alarm rule of judgment data), adding the point set corresponding to the target point common value into the point set attribute of the C _ Jsondata, and otherwise, keeping the previous structure unchanged. The new set of Json-like structure data constitutes a second segment of the pipeline pool data.

Optionally, in step 103, if there are other rules for determining alarm, the following operations may be performed: aiming at the first section of streaming pool data, taking a target point common value V0 in one Json-like structure data (C _ Jsondata) with the largest datasetnum as a reference target point common value, respectively comparing the target point common values V1 and V2 … Vn in other Json-like structure data of the first section of streaming pool data with V0, if the target point common values do not accord with an alarm rule, adding a point set corresponding to the target point common values into the point set attribute of the C _ Jsondata, and if the target point common values do not accord with the alarm rule, keeping the previous structure unchanged. The new set of the Json-like structure data forms second section of flow pool data;

step 104: and converting the data of the second section of flow pool of a certain layer into data of a third section of flow pool.

The data of the second section of the flow pool are processed according to the following modes: for the second section of the pipeline pool data, adding a state attribute flag to the Json-like structure data (C _ Jsondata) with the largest datasetnum, wherein the flag is 0. The attribute flag is used for marking whether the data is normal, and the flag is 0 to indicate that the data is normal. Attribute flag is added to other Json structure data in the same way, the flag is set to be 1, data exception is represented, and the processed data is called third section pipeline pool data.

Optionally, if the alarm degree needs to be determined when determining whether the data is normal, the following method is adopted: and adding an attribute flag for marking whether the data is normal or not to the Json-like structure data (C _ Jsondata) with the largest datasetnum aiming at the second section of the pipeline pool data, wherein the flag is 0. And (3) adding a flag attribute to other Json structure data, and setting the alarm degree according to an alarm rule: 11-12-13-indicated data is called third-stage flow pool data;

step 105: and outputting the data of the third section of the pipeline pool, and performing graphic display or dynamic display of a table on the output data.

And (3) classifying and outputting the data of the third section of the flow pool according to the state attribute flag, wherein the data comprises two types of nodes, one type is normal data, and the attributes comprise the following: layer height, state, point set to be recorded, and display mark color, wherein the layer height, the state, the point set to be recorded, and the display mark color are 0; another category is alarm data, which contains the following data: the layer height < height >, the flag of whether the data is normal < flag ═ 1> (if there is alarm degree: flag ═ 11 indicates light data abnormity, flag ═ 12 indicates medium data abnormity, flag ═ 13 indicates heavy data abnormity), the point set required to be recorded < dataset >, and the flag color < color > is displayed.

The blast furnace data processing method comprises the steps of classifying monitoring and warning information into dynamic graphs after layering, processing results obtained by inquiry, placing the results in a prestored result pool, automatically reconfiguring and re-inquiring, reducing inquiry time and improving data inquiry efficiency under the same inquiry condition.

Example 2

In this embodiment, there is a target point data category, and the blast furnace data is specifically thermocouple temperature data. And inserting the thermocouples into different depths of the blast furnace, arranging a plurality of thermocouples at the same depth, and expressing by using parameter angles, thereby obtaining the temperature measurement data of the thermocouples inserted into different depths in the blast furnace.

The method comprises the following steps:

step 201: and acquiring all Json data needing processing or graphic display, namely thermocouple temperature measurement data of different insertion depths in the blast furnace, and layering.

For data of each layer of the vertical space, the following operations are performed:

step 202: and converting certain layer of original data into first flowing water pool data.

All data of a certain insertion depth are acquired, and Json data (angle and temperature) are converted into Json-like data (dataset ═ thermocouple angle data: temperature value). In the Json-like data, each node structure comprises two attributes of insertion depth and class key value pairs, wherein the class key value pair attributes are constructed according to the following method: thermocouple angles with the same temperature in the same layer are integrated into a set to serve as a dataset field in a class key value pair attribute in a class Json data node, and a corresponding common temperature value serves as a value field in the class Json data node; another attribute is thermocouple angle: indicating the current temperature displayed by the thermocouple at which angles of the layer is the common temperature value; at this point, the original data is processed into the first stage of pipeline pool data.

Step 203: and converting the data of the first section of flow pool of a certain layer into the data of the second section of flow pool.

And (3) selecting a node (C _ Jsondata) with the largest thermocouple angle according to the data of the first section of the flowing water pool with a certain insertion depth, wherein the temperature of a circle of thermocouples corresponding to the same insertion depth in the blast furnace is mostly V0, so that the C _ Jsondata are the thermoelectric even data with the temperature of V0. And comparing the temperature value (C _ value) in the C _ Jsondata with a standard value (S _ value), and if the temperature value (C _ value) meets the requirement, taking the layer temperature standard as C _ value, otherwise, taking the layer temperature standard as S _ value. Comparing the temperature standard of the layer with temperature values of other nodes, if the temperature difference meets the requirement and the temperatures of other nodes are also in the standard range, adding thermocouple angle data in the node into C _ Jsondata, and deleting the original node (only deleting the original node in the second layer and reserving the original node in the first layer); otherwise, the original node is kept unchanged. The data after processing is referred to as second stage pipeline pool data.

Step 204: and converting the data of the second section of flow pool of a certain layer into data of a third section of flow pool.

Aiming at each node of the second section of flow pool data, adding a state attribute, wherein the state indicates whether the data is abnormal or not, if the data is abnormal, marking the abnormal degree of the data, and if the data conforms to the system standard and does not conform to the layer standard, displaying that the thermal electric even data is slightly abnormal, and only adjusting the temperature of the point; the non-compliance with the system criteria is a heavy adjustment, and the temperature of the insertion depth needs to be adjusted. After doing so, the third stage flowing water pool has the following data: including layer number, class key value pair data, status.

Step 205: and outputting the data of the third section of the pipeline pool, and performing graphic display or dynamic display of a table on the output data.

Example 3

In example 1 and example 2, a single target data point type, such as thermocouple thermometry data in example 2, is given, and in a particular application, multiple target data point types may be processed simultaneously. As shown in fig. 3, in the present embodiment, a processing procedure for multiple target data point types is provided, which includes the following steps:

step 301: acquiring and layering Json data needing to be processed or displayed graphically;

step 302: acquiring all data of a certain layer, establishing M flow water tanks according to the type total number M of target data points, wherein each flow water tank is provided with three sections, sequentially generating three sections of flow water tank data, generating first section of flow water tank data containing layer height and class key value pairs according to original Json data, simplifying the first section of flow water tank data to obtain second section of flow water tank data, and adding node state to the second section of flow water tank data to obtain third section of flow water tank data;

step 303: for each of the M flowing water pools, outputting normal data and alarm data by a method for generating three sections of flowing water pools;

step 304: and dynamically displaying the output data by a graph or a table.

Example 4

As shown in fig. 4, the present invention further provides a blast furnace data processing apparatus based on vertical spatial data classification and conversion by pools, which includes the following components:

the device comprises an acquisition module 401, a layering module 402, a flow pool segmentation simplification module 403 and an output module 404;

an obtaining module 401, configured to obtain Json data to be processed or graphically displayed, where the Json data is specifically blast furnace data used for monitoring and alarming;

the layering module 402 is used for dividing the Json data into layers in a plurality of vertical spaces according to the data distribution situation of the acquisition points of the blast furnace data in the vertical spaces of the blast furnace to form layered data; the attributes of the hierarchical data comprise the number of layers, a target point and a value;

the flow water tank segmentation simplifying module is used for respectively carrying out collection, combination simplification and classification identification processing on the layered data of each layer according to an alarm rule to obtain third section flow water tank data, wherein the third section flow water tank data is provided with state attributes, and the state attributes are used for classification identification of alarm;

the output module 404: outputting third section of flow pool data, wherein the third section of flow pool data comprises two types of nodes, one type of nodes is normal data, and the other type of nodes is alarm data; a dynamic display of a graph or table is performed.

Specifically, the flow pool segmentation simplification module 403 includes a first section flow pool module, a second section flow pool module and a third section flow pool module;

the first section of flow water tank module is used for performing data aggregation processing on the layered data to generate first section of flow water tank data: according to all data of each layer, target points with the same value are placed in the same group to form a point set, and the same value is defined as a common value of the target points; the point set and the target point common value are represented by a class key value pair of a class Json structure < point set: representing a common value of a target point, and defining a data set of a Json-like structure generated by collecting hierarchical data as first section of flow pool data;

the second section of flow water tank module is used for carrying out data combination and simplified processing on the first section of flow water tank data to generate second section of flow water tank data: selecting a reference Json structure and a corresponding reference target point common value according to the first section of flow tank data, merging point sets of difference values of target point common values of other Json structures and the reference target point common value within a set threshold range into a point set of the reference Json structure, merging and simplifying, and defining a data set of the Json structure after merging and simplifying the first section of flow tank data as second section of flow tank data;

the third section of flowing water pool module is used for carrying out classification identification on the second section of flowing water pool data to generate third section of flowing water pool data: and adding a state attribute for representing normal or alarm to each Json-like structure of the second section of flow line pool data according to an alarm judgment rule to form third section of flow line data.

While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.