阅读说明：本技术 一种基于智能水表的漏水监测方法、系统 (Water leakage monitoring method and system based on intelligent water meter ) 是由 陈强 金建勇 于 2021-08-31 设计创作，主要内容包括：本发明公开一种基于智能水表的漏水监测方法、系统,所述智能水表采用超声波对出水的流量进行监测计算,所述漏水监测方法具体包括如下步骤：实时获取水表数值并记录；根据记录的水表数值进行时间点划分监测；根据预设阀值进行动态流量监测。通过建立分类模型配合智能水表的输出值进行漏水监测,先对输出值进行时间点分类,再根据时间点分类结果进行针对性再分类输出监测结果,达到了实时监测漏水的目的,有效防止漏水导致的安全问题和财产流失问题。(The invention discloses a water leakage monitoring method and system based on an intelligent water meter, wherein the intelligent water meter monitors and calculates the flow of outlet water by adopting ultrasonic waves, and the water leakage monitoring method specifically comprises the following steps: acquiring and recording the numerical value of the water meter in real time; carrying out time point division monitoring according to the recorded water meter numerical value; and carrying out dynamic flow monitoring according to a preset threshold value. The water leakage monitoring is carried out by establishing a classification model and matching with the output value of the intelligent water meter, the output value is classified at time points, and then the monitoring result is output in a targeted reclassification mode according to the time point classification result, so that the purpose of monitoring water leakage in real time is achieved, and the safety problem and the property loss problem caused by water leakage are effectively prevented.)

1. A water leakage monitoring method based on an intelligent water meter is characterized by comprising the following steps: the intelligent water meter monitors and calculates the flow of the outlet water by adopting ultrasonic waves, and the water leakage monitoring method specifically comprises the following steps:

acquiring and recording the numerical value of the water meter in real time;

carrying out time point division monitoring according to the recorded water meter numerical value;

and carrying out dynamic flow monitoring according to a preset threshold value.

2. The water leakage monitoring method based on the intelligent water meter according to claim 1, characterized in that: the time point division monitoring specifically comprises the following steps:

dividing a single day into 24 time points, wherein the interval between every two adjacent time points is 1 h;

monitoring and counting the water outlet flow between every two groups of time points to obtain daily time point records;

carrying out multi-dimensional classification on the daily time point records;

and monitoring and judging according to the multi-dimensional classification result.

3. The water leakage monitoring method based on the intelligent water meter according to claim 2, characterized in that: the multidimensional classification of the time point of each day records specifically comprises the following steps:

seasonal dimension classification: dividing a single day into spring days, summer days, autumn days and winter days according to the season of the single day;

and (3) classifying the dimensions of work and rest: classifying the single day after the seasonal dimension classification according to the statutory holidays, dividing the single day into working days and rest days of each season, analyzing the water outlet flow between every two groups of time points of the working days, determining an unmanned time point if no water outlet flow or the water outlet flow is less than a preset unmanned threshold value, and determining a manned time point if the water outlet flow is more than the preset unmanned threshold value; and analyzing the water outlet flow of the manned time points and all time points of the rest day, wherein the water outlet flow is the water consumption peak time point if the water outlet quantity is greater than a preset water consumption peak threshold value, and the water outlet flow is the water consumption conventional time point if the water outlet quantity is less than the preset water consumption peak threshold value.

4. The water leakage monitoring method based on the intelligent water meter according to claim 3, characterized in that: the preset unmanned threshold value and the preset water peak threshold value are classified in a pertinence mode corresponding to seasonal dimensions, namely the preset unmanned threshold value and the preset water peak threshold value in different seasons are different.

5. The water leakage monitoring method based on the intelligent water meter according to claim 4, characterized in that: the monitoring and judging aiming at the multi-dimensional classification result specifically comprises the following steps:

obtaining a multi-dimensional classification result and the daily time point record as a training set;

establishing a plurality of groups of corresponding classification models for water leakage alarm based on unmanned time points, water consumption peak time points and water consumption conventional time points of all seasons in the multi-dimensional classification result;

and performing water leakage monitoring alarm on the water outlet flow of each time point by using the classification model.

6. The water leakage monitoring method based on the intelligent water meter according to claim 5, characterized in that: the establishment of the classification model for water leakage alarm specifically comprises the following steps:

acquiring daily time point records and multi-dimensional classification results corresponding to all time points;

labeling the water outlet flow corresponding to each time point, wherein the label labeling comprises normal flow and water leakage flow;

taking the time point records and the multi-dimensional classification results of all time points as input, and taking the label labels as output training neural networks;

the neural network comprises a multi-dimensional classification network and an effluent flow monitoring classification network which are sequentially connected;

the multidimensional classification network is used for pre-classifying multidimensional classification results of all time points, and outputting the time point classification of each time point: an unmanned time point, a water peak time point or a water conventional time point;

the water outlet flow monitoring and classifying network is used for performing targeted reclassification on the time points after the time point classification, and outputting normal flow labels or water leakage flow labels according to the combination of the time point classification and the water outlet flow;

and obtaining the classification model.

7. The water leakage monitoring method based on the intelligent water meter according to claim 6, characterized in that: the step of performing water leakage monitoring alarm on the water outlet flow of each time point by using the classification model specifically comprises the following steps: when the label output by the classification model is a water leakage flow label, the intelligent water meter sends confirmation information to a portable terminal of an intelligent water meter user to perform interactive confirmation on whether the water consumption information of the large flow is detected: if the positive answer is obtained within the preset feedback time threshold, ending the alarm of the current stage; if a negative answer is obtained within a preset feedback time threshold value, controlling an external electric control water valve to be closed; if the current time is not obtained within the preset feedback time threshold, controlling an external electric control water valve to be closed; if the feedback time threshold value is not obtained within the preset feedback time threshold value, the step of controlling the external electric control water valve to be closed further comprises the following steps: and continuously waiting for the feedback of the portable terminal of the intelligent water meter owner, if no feedback is given or a negative response is obtained, keeping the external electric control water valve closed, and if a positive response is obtained outside a preset feedback time threshold value, controlling the closed external electric control water valve to be opened for use.

8. The water leakage monitoring method based on the intelligent water meter according to claim 7, characterized in that: also comprises the following steps:

storing the current time point data record of which the output label is a water leakage flow label and the positive interactive response of the user is obtained as data to be learned;

and retraining the classification model by utilizing the stored data to be learned, thereby further improving the judgment accuracy.

9. The water leakage monitoring method based on the intelligent water meter according to claim 1, characterized in that: also comprises the following steps:

the method comprises the steps of establishing a water utilization curve by utilizing water meter values acquired in real time, establishing an intelligent learning module to perform tracking learning of water outlet flow according to the water utilization curve, monitoring the water outlet flow and feeding back results to the Internet of things, a user mobile phone or central monitoring alarm equipment.

10. The utility model provides a monitoring system that leaks based on intelligent water gauge which characterized in that includes:

the intelligent water meter monitors and calculates the flow of the discharged water by adopting ultrasonic waves;

the external electric control water valve is used for receiving a signal to control the switch of water flow output;

the monitoring and judging module is used for acquiring the output value of the intelligent water meter, putting the output value into the established classification model of the water leakage alarm, outputting a normal flow label or a water leakage flow label, and sending a control signal to control the on-off of the external electric control water valve according to the label;

the portable interactive terminal is used for receiving the output label of the monitoring and judging module;

the water quality monitoring module is used for monitoring the water quality in real time and forming a water quality big data network by the acquired monitoring data;

and the whole-house purification module is used for detecting the input water quality and feeding back the detection result to the portable interactive terminal.

Technical Field

The invention is applied to the field of water leakage monitoring, and particularly relates to a water leakage monitoring method and system based on an intelligent water meter.

Background

The water leakage in the house becomes an important factor influencing the living happiness, and the water leakage phenomenon inevitably occurs in the joints, fittings and pipelines of the house decoration. At present, water leakage can be monitored only through manual patrol of a householder, and when the water leakage is large and cannot be found in time, the safety of household facilities is seriously influenced and a large amount of property loss is caused.

Disclosure of Invention

The invention aims to solve the technical problem of the prior art and provides a water leakage monitoring method and system based on an intelligent water meter.

In order to solve the technical problem, the invention provides a water leakage monitoring method based on an intelligent water meter, wherein the intelligent water meter monitors and calculates the flow of outlet water by adopting ultrasonic waves, and the water leakage monitoring method specifically comprises the following steps:

acquiring and recording the numerical value of the water meter in real time;

carrying out time point division monitoring according to the recorded water meter numerical value;

and carrying out dynamic flow monitoring according to a preset threshold value.

As a possible implementation manner, further, the time-point division monitoring specifically includes the following steps:

dividing a single day into 24 time points, wherein the interval between every two adjacent time points is 1 h;

monitoring and counting the water outlet flow between every two groups of time points to obtain daily time point records;

carrying out multi-dimensional classification on the daily time point records;

and monitoring and judging according to the multi-dimensional classification result.

As a possible implementation manner, further, the multidimensional classification of the daily time point records specifically includes the following steps:

seasonal dimension classification: dividing a single day into spring days, summer days, autumn days and winter days according to the season of the single day;

and (3) classifying the dimensions of work and rest: classifying the single day after the seasonal dimension classification according to the statutory holidays, dividing the single day into working days and rest days of each season, analyzing the water outlet flow between every two groups of time points of the working days, determining an unmanned time point if no water outlet flow or the water outlet flow is less than a preset unmanned threshold value, and determining a manned time point if the water outlet flow is more than the preset unmanned threshold value; analyzing the water outlet flow of the manned time points and all time points of the rest day, wherein the water outlet flow is the water consumption peak time point if the water outlet amount is greater than a preset water consumption peak threshold value, and the water outlet flow is the water consumption conventional time point if the water outlet amount is less than the preset water consumption peak threshold value;

as a possible implementation manner, further, the preset unattended threshold and the preset peak water threshold are both classified in a targeted manner corresponding to seasonal dimensions, that is, the preset unattended threshold and the preset peak water threshold in different seasons are different.

As a possible implementation manner, further, the monitoring and determining performed on the multi-dimensional classification result specifically includes:

obtaining a multi-dimensional classification result and the daily time point record as a training set;

establishing a plurality of groups of corresponding classification models for water leakage alarm based on unmanned time points, water consumption peak time points and water consumption conventional time points of all seasons in the multi-dimensional classification result;

and performing water leakage monitoring alarm on the water outlet flow of each time point by using the classification model.

As a possible implementation manner, further, the establishing of the classification model for water leakage alarm specifically includes:

acquiring daily time point records and multi-dimensional classification results corresponding to all time points;

labeling the water outlet flow corresponding to each time point, wherein the label labeling comprises normal flow and water leakage flow;

taking the time point records and the multi-dimensional classification results of all time points as input, and taking the label labels as output training neural networks;

the neural network comprises a multi-dimensional classification network and an effluent flow monitoring classification network which are sequentially connected;

the multidimensional classification network is used for pre-classifying multidimensional classification results of all time points, and outputting the time point classification of each time point: an unmanned time point, a water peak time point or a water conventional time point;

the water outlet flow monitoring and classifying network is used for performing targeted reclassification on the time points after the time point classification, and outputting normal flow labels or water leakage flow labels according to the combination of the time point classification and the water outlet flow;

and obtaining the classification model.

As a possible implementation manner, further, the step of performing water leakage monitoring alarm on the water outlet flow at each time point by using the classification model specifically includes: when the label output by the classification model is a water leakage flow label, the intelligent water meter sends confirmation information to a portable terminal of an intelligent water meter user to perform interactive confirmation on whether the water consumption information of the large flow is detected: if the positive answer is obtained within the preset feedback time threshold, ending the alarm of the current stage; if a negative answer is obtained within a preset feedback time threshold value, controlling an external electric control water valve to be closed; and if the current time is not obtained within the preset feedback time threshold, controlling the external electric control water valve to be closed.

As a possible implementation manner, further, if the external electrically-controlled water valve is not obtained within the preset feedback time threshold, the step of controlling the external electrically-controlled water valve to close further includes: and continuously waiting for the feedback of the portable terminal of the intelligent water meter owner, if no feedback is given or a negative response is obtained, keeping the external electric control water valve closed, and if a positive response is obtained outside a preset feedback time threshold value, controlling the closed external electric control water valve to be opened for use.

A water leakage monitoring system based on an intelligent water meter comprises:

the intelligent water meter monitors and calculates the flow of the discharged water by adopting ultrasonic waves;

the external electric control water valve is used for receiving a signal to control the switch of water flow output;

the monitoring and judging module is used for acquiring the output value of the intelligent water meter, putting the output value into the established classification model of the water leakage alarm, outputting a normal flow label or a water leakage flow label, and sending a control signal to control the on-off of the external electric control water valve according to the label;

the portable interactive terminal is used for receiving the output label of the monitoring and judging module;

and the whole-house purification module is used for detecting the input water quality and feeding back the detection result to the portable interactive terminal.

By adopting the technical scheme, the invention has the following beneficial effects: according to the intelligent water meter water leakage monitoring method and device, the classification model is established to be matched with the output value of the intelligent water meter to monitor water leakage, the output value is classified at time points, and then the monitoring result is output in a targeted reclassification mode according to the time point classification result, so that the purpose of monitoring water leakage in real time is achieved, and the safety problem and the property loss problem caused by water leakage are effectively prevented.

Drawings

The invention is described in further detail below with reference to the following figures and embodiments:

FIG. 1 is a schematic diagram of a portion of the principle of the method of the present invention;

fig. 2 is a partial schematic diagram of the system of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described in detail and completely with reference to the accompanying drawings.

As shown in fig. 1-2, the present invention provides a water leakage monitoring method based on an intelligent water meter, wherein the intelligent water meter monitors and calculates the flow of outlet water by using ultrasonic waves, and the water leakage monitoring method specifically includes the following steps:

acquiring and recording the numerical value of the water meter in real time;

carrying out time point division monitoring according to the recorded water meter numerical value; the time point division monitoring specifically comprises the following steps:

dividing a single day into 24 time points, wherein the interval between every two adjacent time points is 1 h;

monitoring and counting the water outlet flow between every two groups of time points to obtain daily time point records;

carrying out multi-dimensional classification on the daily time point records; the method specifically comprises the following steps:

seasonal dimension classification: dividing a single day into spring days, summer days, autumn days and winter days according to the season of the single day;

and (3) classifying the dimensions of work and rest: classifying the single day after the seasonal dimension classification according to the statutory holidays, dividing the single day into working days and rest days of each season, analyzing the water outlet flow between every two groups of time points of the working days, determining an unmanned time point if no water outlet flow or the water outlet flow is less than a preset unmanned threshold value, and determining a manned time point if the water outlet flow is more than the preset unmanned threshold value; analyzing the water outlet flow of the manned time points and all time points of the rest day, wherein the water outlet flow is the water consumption peak time point if the water outlet amount is greater than a preset water consumption peak threshold value, and the water outlet flow is the water consumption conventional time point if the water outlet amount is less than the preset water consumption peak threshold value; the preset unmanned threshold value and the preset water peak threshold value are classified in a pertinence mode corresponding to seasonal dimensions, namely the preset unmanned threshold value and the preset water peak threshold value in different seasons are different.

And monitoring and judging according to the multi-dimensional classification result. The method specifically comprises the following steps:

obtaining a multi-dimensional classification result and the daily time point record as a training set;

establishing a plurality of groups of corresponding classification models for water leakage alarm based on unmanned time points, water consumption peak time points and water consumption conventional time points of all seasons in the multi-dimensional classification result;

and performing water leakage monitoring alarm on the water outlet flow of each time point by using the classification model.

As a possible implementation manner, further, the establishing of the classification model for water leakage alarm specifically includes:

acquiring daily time point records and multi-dimensional classification results corresponding to all time points;

labeling the water outlet flow corresponding to each time point, wherein the label labeling comprises normal flow and water leakage flow;

taking the time point records and the multi-dimensional classification results of all time points as input, and taking the label labels as output training neural networks;

the neural network comprises a multi-dimensional classification network and an effluent flow monitoring classification network which are sequentially connected;

the multidimensional classification network is used for pre-classifying multidimensional classification results of all time points, and outputting the time point classification of each time point: an unmanned time point, a water peak time point or a water conventional time point;

the water outlet flow monitoring and classifying network is used for performing targeted reclassification on the time points after the time point classification, and outputting normal flow labels or water leakage flow labels according to the combination of the time point classification and the water outlet flow;

and obtaining the classification model.

And carrying out dynamic flow monitoring according to a preset threshold value.

As a possible implementation manner, further, the step of performing water leakage monitoring alarm on the water outlet flow at each time point by using the classification model specifically includes: when the label output by the classification model is a water leakage flow label, the intelligent water meter sends confirmation information to a portable terminal of an intelligent water meter user to perform interactive confirmation on whether the water consumption information of the large flow is detected: if the positive answer is obtained within the preset feedback time threshold, ending the alarm of the current stage; if a negative answer is obtained within a preset feedback time threshold value, controlling an external electric control water valve to be closed; and if the current time is not obtained within the preset feedback time threshold, controlling the external electric control water valve to be closed. The method specifically comprises the following steps:

obtaining a multi-dimensional classification result and the daily time point record as a training set;

establishing a plurality of groups of corresponding classification models for water leakage alarm based on unmanned time points, water consumption peak time points and water consumption conventional time points of all seasons in the multi-dimensional classification result;

and performing water leakage monitoring alarm on the water outlet flow of each time point by using the classification model.

As a possible implementation manner, further, the establishing of the classification model for water leakage alarm specifically includes:

acquiring daily time point records and multi-dimensional classification results corresponding to all time points;

labeling the water outlet flow corresponding to each time point, wherein the label labeling comprises normal flow and water leakage flow;

taking the time point records and the multi-dimensional classification results of all time points as input, and taking the label labels as output training neural networks;

the neural network comprises a multi-dimensional classification network and an effluent flow monitoring classification network which are sequentially connected;

the multidimensional classification network is used for pre-classifying multidimensional classification results of all time points, and outputting the time point classification of each time point: an unmanned time point, a water peak time point or a water conventional time point;

the water outlet flow monitoring and classifying network is used for performing targeted reclassification on the time points after the time point classification, and outputting normal flow labels or water leakage flow labels according to the combination of the time point classification and the water outlet flow;

and obtaining the classification model. If the feedback time threshold value is not obtained within the preset feedback time threshold value, the step of controlling the external electric control water valve to be closed further comprises the following steps: and continuously waiting for the feedback of the portable terminal of the intelligent water meter owner, if no feedback is given or a negative response is obtained, keeping the external electric control water valve closed, and if a positive response is obtained outside a preset feedback time threshold value, controlling the closed external electric control water valve to be opened for use.

A water leakage monitoring system based on an intelligent water meter comprises:

the intelligent water meter monitors and calculates the flow of the discharged water by adopting ultrasonic waves;

the external electric control water valve is used for receiving a signal to control the switch of water flow output;

the monitoring and judging module is used for acquiring the output value of the intelligent water meter, putting the output value into the established classification model of the water leakage alarm, outputting a normal flow label or a water leakage flow label, and sending a control signal to control the on-off of the external electric control water valve according to the label;

the portable interactive terminal is used for receiving the output label of the monitoring and judging module;

and the whole-house purification module is used for detecting the input water quality and feeding back the detection result to the portable interactive terminal.

The foregoing is directed to embodiments of the present invention, and equivalents, modifications, substitutions and variations such as will occur to those skilled in the art, which fall within the scope and spirit of the appended claims.