Method and system for detecting abnormal water consumption of intelligent water meter and medium

Abstract

The invention provides an abnormal water consumption detection method and system for an intelligent water meter and a medium, and the method comprises the steps: obtaining water consumption data based on the intelligent water meter, and preprocessing the water consumption data to obtain preprocessed data; constructing a prediction model, inputting the preprocessed data into the prediction model, and outputting water consumption trend information; acquiring user habit information, and generating a water consumption rule curve based on the user habit information; generating a water consumption real-time curve based on the water consumption trend information, comparing the water consumption real-time curve with a water consumption rule curve, and generating a water consumption abnormal node and a water consumption abnormal difference value; generating abnormal water consumption condition information based on the abnormal water consumption node and the abnormal water consumption difference value, and transmitting the abnormal water consumption condition to the terminal in real time; the water consumption data is acquired in real time, and the water consumption rule is analyzed according to the habits of the user, so that the abnormal water consumption information is analyzed according to the water consumption rule, the abnormal water consumption condition of the user is accurately analyzed, and the monitoring precision of the intelligent water meter is improved.

Description

Technical Field

[0001] This application relates to the technical field of water meter anomaly detection. Specifically, it relates to a method, system and medium for detecting abnormal water use of intelligent water meters. Background Technique

[0002] The abnormal water use situation of residents usually refers to the sudden increase or abnormal fluctuation of residents' water consumption, which may be caused by various reasons. The following is a detailed analysis of the abnormal water use situation of residents: Water pipe leakage: Aging, damage or improper connection of water pipes in residents' homes may lead to water leakage; Water leakage may occur in exposed pipes or concealed pipes, and water leakage in concealed pipes is more difficult to detect. Malfunctions of water-using equipment: Malfunctions of water-using equipment such as washing machines, water heaters, and flush toilets, such as repeated water inlet, backwater or malfunction of the float ball valve in the water tank, may lead to abnormal increase in water consumption. Human factors: Forgetting to turn off the faucet or water-using equipment, resulting in waste of water resources. In summary, the abnormal water use situation of residents may be caused by various reasons, and it requires the joint efforts of residents, water supply departments and relevant departments to conduct investigations and treatments. A large amount of manpower is needed for supervision to effectively reduce the occurrence of abnormal water use and ensure the urban water supply safety. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide a method, system and medium for detecting abnormal water use of intelligent water meters. By obtaining water use data in real time and analyzing the water use pattern according to user habits, the abnormal water use information is analyzed according to the water use pattern, and the abnormal water use situation of users is accurately analyzed to improve the monitoring accuracy of intelligent water meters.

[0004] The embodiments of this application also provide a method for detecting abnormal water use of intelligent water meters, including: Obtaining water use data based on an intelligent water meter, preprocessing the water use data to obtain preprocessed data; Constructing a prediction model, inputting the preprocessed data into the prediction model, and outputting water use trend information; Obtaining user habit information, and generating a water use pattern curve based on the user habit information; Generating a real-time water use curve based on the water use trend information, comparing the real-time water use curve with the water use pattern curve, and generating water use abnormal nodes and water use abnormal differences; Generating abnormal water use situation information based on the water use abnormal nodes and water use abnormal differences, and transmitting the abnormal water use situation to the terminal in real time.

[0005] Optionally, in the method for detecting abnormal water use of intelligent water meters described in the embodiments of this application, obtaining water use data based on an intelligent water meter, preprocessing the water use data to obtain preprocessed data, specifically includes: Obtain water usage data, where the water usage data includes water consumption and water usage time information; Analyze the noise data in the water usage data, clean the noise data, remove the noise data, obtain optimized data, and analyze whether there is missing data in the optimized data; If there is missing data, calculate the missing value according to the amount of missing data of the missing data, and determine whether the missing value is greater than or equal to the set missing threshold; If it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data. If it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; If there is no missing data, obtain preprocessed data.

[0006] Optionally, in the intelligent water meter abnormal water usage detection method described in the embodiments of the present application, construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information, specifically including: Select an initial model framework, and establish a training set and a test set based on historical water usage data; Iteratively train the initial model based on the training set, and analyze the loss value between the model prediction value and the actual value; Generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters, and obtain a prediction model; Evaluate the prediction model based on the test set to obtain an evaluation result, and dynamically adjust the model hyperparameters based on the evaluation result; Output water usage trend information based on the prediction model.

[0007] Optionally, in the intelligent water meter abnormal water usage detection method described in the embodiments of the present application, obtain user habit information, and generate a water usage regular curve based on the user habit information, specifically including: Obtain user habit information, and analyze the user's historical water usage pattern based on the user habit information; Analyze the annual water usage mean data, monthly water usage mean data, daily water usage mean data, and the mean water usage volume data in the past 48 hours based on the user's historical water usage pattern; Generate a water usage regular curve based on the annual water usage volume mean data, monthly water usage volume mean data, daily water usage volume mean data, and the mean water usage volume data in the past 48 hours.

[0008] Optionally, in the intelligent water meter abnormal water usage detection method described in the embodiments of the present application, generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the water usage regular curve, and generate water usage abnormal nodes and water usage abnormal differences, specifically including: Obtain water usage trend information, and analyze the time when the recorded water volume changes by every 0.25L based on the water usage trend information; View the 0.25L water volume change information recorded in the past 48 hours, and generate a real-time water usage curve; Overlay and compare the real-time water usage curve with the regular water usage curve to generate water usage abnormal nodes and water usage abnormal differences.

[0009] Optionally, in the abnormal water usage detection method of the intelligent water meter described in the embodiments of the present application, generate abnormal water usage situation information based on the water usage abnormal nodes and water usage abnormal differences, and transmit the abnormal water usage situation to the terminal in real time, specifically including: Obtain the real-time water usage curve and the regular water usage curve, and establish multiple analysis points; Calculate the slope of each analysis point on the real-time water usage curve to obtain multiple first slopes; Calculate the slope of each analysis point on the regular water usage curve to obtain multiple second slopes; Compare the first slope and the second slope of the same analysis point, and analyze whether the first slope and the second slope are the same; If they are the same, determine that the current analysis point is a normal node; If they are not the same, determine that the current analysis point is an abnormal node, obtain the water usage situation of the abnormal node, calculate the abnormal water usage data to obtain the abnormal water usage situation, and transmit the abnormal water usage situation to the terminal in real time.

[0010] In a second aspect, the embodiments of the present application provide an abnormal water usage detection system for an intelligent water meter. The system includes: a memory and a processor. The memory includes a program for the abnormal water usage detection method of the intelligent water meter. When the program for the abnormal water usage detection method of the intelligent water meter is executed by the processor, the following steps are implemented: Obtain water usage data based on the intelligent water meter, preprocess the water usage data to obtain preprocessed data; Construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information; Obtain user habit information, and generate a regular water usage curve based on the user habit information; Generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the regular water usage curve, and generate water usage abnormal nodes and water usage abnormal differences; Generate abnormal water usage situation information based on the water usage abnormal nodes and water usage abnormal differences, and transmit the abnormal water usage situation to the terminal in real time.

[0011] Optionally, in the abnormal water usage detection system of the intelligent water meter described in the embodiments of the present application, obtain water usage data based on the intelligent water meter, preprocess the water usage data to obtain preprocessed data, specifically including: Obtain water usage data, where the water usage data includes water consumption and water usage time information; Analyze the noise data in the water usage data, clean the noise data, remove the noise data, obtain optimized data, and analyze whether there is missing data in the optimized data; If there is missing data, calculate the missing value according to the amount of missing data of the missing data, and judge whether the missing value is greater than or equal to the set missing threshold; If it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data. If it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; If there is no missing data, obtain preprocessed data.

[0012] Optionally, in the intelligent water meter abnormal water usage detection system described in the embodiments of the present application, a prediction model is constructed, and the preprocessed data is input into the prediction model to output water usage trend information, specifically including: Select an initial model framework, and establish a training set and a test set based on historical water usage data; Iteratively train the initial model based on the training set, and analyze the loss value between the model prediction value and the actual value; Generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters to obtain a prediction model; Evaluate the prediction model based on the test set to obtain an evaluation result, and dynamically adjust the model hyperparameters based on the evaluation result; Output water usage trend information based on the prediction model.

[0013] In a third aspect, the embodiments of the present application also provide a computer-readable storage medium, which includes a program for the intelligent water meter abnormal water usage detection method. When the program for the intelligent water meter abnormal water usage detection method is executed by a processor, the steps of the intelligent water meter abnormal water usage detection method described in any one of the above are implemented.

[0014] As described above, an intelligent water meter abnormal water use detection method, system and medium provided by the embodiments of the present application obtain water use data based on an intelligent water meter, preprocess the water use data to obtain preprocessed data; construct a prediction model, input the preprocessed data into the prediction model, and output water use trend information; obtain user habit information, generate a water use regular curve based on the user habit information; generate a real-time water use curve based on the water use trend information, compare the real-time water use curve with the water use regular curve, and generate water use abnormal nodes and water use abnormal differences; generate abnormal water use situation information based on the water use abnormal nodes and water use abnormal differences, and transmit the abnormal water use situation to the terminal in real time; by obtaining water use data in real time and analyzing the water use law according to the user habits, the abnormal water use information is analyzed according to the water use law, and the abnormal water use situation of the user is accurately analyzed, so as to improve the monitoring accuracy of the intelligent water meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained according to these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the intelligent water meter abnormal water use detection method provided by the embodiments of the present application; Figure 2 It is a flowchart of the water use data preprocessing method of the intelligent water meter abnormal water use detection method provided by the embodiments of the present application; Figure 3 It is a flowchart of the prediction model training method of the intelligent water meter abnormal water use detection method provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the following drawings is not intended to limit the scope of the claimed present application, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0018] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0019] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for detecting abnormal water use of an intelligent water meter in some embodiments of the present application. This method for detecting abnormal water use of an intelligent water meter is used in a terminal device and includes the following steps: S101, obtaining water use data based on the intelligent water meter, preprocessing the water use data to obtain preprocessed data; S101, constructing a prediction model, inputting the preprocessed data into the prediction model, and outputting water use trend information; S103, obtaining user habit information, and generating a water use regular curve based on the user habit information; S104, generating a real-time water use curve based on the water use trend information, comparing the real-time water use curve with the water use regular curve, and generating abnormal water use nodes and abnormal water use differences; S105, generating information on abnormal water use situations based on the abnormal water use nodes and abnormal water use differences, and transmitting the abnormal water use situations to the terminal in real time.

[0020] It should be noted that by obtaining water use data and analyzing the water use trend according to the prediction model, the abnormal water use nodes and abnormal water use differences can be analyzed, and the abnormal water use situations can be accurately analyzed to improve the monitoring effect.

[0021] Please refer to Figure 2 , Figure 2 which is a flowchart of a method for preprocessing water use data of a method for detecting abnormal water use of an intelligent water meter in some embodiments of the present application. According to the embodiments of the present invention, water use data is obtained based on the intelligent water meter, and the water use data is preprocessed to obtain preprocessed data, which specifically includes: S201, obtaining water use data, where the water use data includes water consumption and water use time information; S202, analyzing the noise data in the water use data, cleaning the noise data, removing the noise data to obtain optimized data, and analyzing whether there is missing data in the optimized data; S203, if there is missing data, calculating the missing value according to the amount of missing data of the missing data, and determining whether the missing value is greater than or equal to a set missing threshold; S204, if it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data; if it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; S205, if there is no missing data, obtain preprocessed data.

[0022] It should be noted that by cleaning the noise in the water consumption data and filling the missing data, the integrity of the water consumption data is ensured, and at the same time, the analysis error of the water consumption data is reduced.

[0023] Please refer to Figure 3 , Figure 3 is a flowchart of a method for training a prediction model of an abnormal water consumption detection method for an intelligent water meter in some embodiments of the present application. According to the embodiments of the present invention, a prediction model is constructed, and the preprocessed data is input into the prediction model to output water consumption trend information, specifically including: S301, select an initial model framework, and establish a training set and a test set based on historical water consumption data; S302, perform iterative training on the initial model based on the training set, and analyze the loss value between the predicted value and the actual value of the model; S303, generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters, and obtain a prediction model; S304, evaluate the prediction model based on the test set to obtain an evaluation result, and dynamically adjust the model hyperparameters based on the evaluation result; S305, output water consumption trend information based on the prediction model.

[0024] It should be noted that by continuously training and iterating the model, the prediction accuracy of the model is improved. At the same time, the difference between the predicted value and the actual value is analyzed, the loss function is dynamically adjusted, and after the training is complete, the performance of the model is evaluated through the test set, and then the parameters are adjusted again to improve the output accuracy of the prediction model.

[0025] According to the embodiments of the present invention, user habit information is obtained, and a water consumption pattern curve is generated based on the user habit information, specifically including: Obtain user habit information, and analyze the user's historical water consumption pattern based on the user habit information; Analyze the annual water consumption mean data, monthly water consumption mean data, daily water consumption mean data, and the mean water consumption volume data in the past 48 hours based on the user's historical water consumption pattern; Generate a water consumption pattern curve based on the annual water consumption volume mean data, monthly water consumption volume mean data, daily water consumption volume mean data, and the mean water consumption volume data in the past 48 hours.

[0026] It should be noted that by analyzing the user's water consumption for the annual, monthly, daily, and average water consumption in the past 48 hours, the water consumption pattern curve can be obtained.

[0027] According to an embodiment of the present invention, a real-time water consumption curve is generated based on water consumption trend information, and the real-time water consumption curve is compared with the water consumption pattern curve to generate water consumption abnormal nodes and water consumption abnormal differences, specifically including: Obtain water consumption trend information, and analyze the time when the recorded water volume changes by every 0.25L based on the water consumption trend information; View the 0.25L water volume change information recorded in the past 48 hours to generate a real-time water consumption curve; Compare the real-time water consumption curve with the water consumption pattern curve to generate water consumption abnormal nodes and water consumption abnormal differences.

[0028] It should be noted that by analyzing the time when the measured water volume changes by every 0.25L and the water volume change information, a real-time user water consumption curve between time and water volume is established, and a coincidence comparison is made according to the water consumption pattern to analyze the abnormal water consumption situation.

[0029] According to an embodiment of the present invention, information on abnormal water consumption situations is generated based on water consumption abnormal nodes and water consumption abnormal differences, and the abnormal water consumption situations are transmitted to the terminal in real time, specifically including: Obtain the real-time water consumption curve and the water consumption pattern curve, and establish multiple analysis points; Calculate the slope of each analysis point on the real-time water consumption curve to obtain multiple first slopes; Calculate the slope of each analysis point on the water consumption pattern curve to obtain multiple second slopes; Compare the first slope and the second slope of the same analysis point to analyze whether the first slope and the second slope are the same; If they are the same, it is determined that the current analysis point is a normal node; If they are not the same, it is determined that the current analysis point is an abnormal node, obtain the water consumption situation of the abnormal node, calculate the abnormal water consumption data, obtain the abnormal water consumption situation, and transmit the abnormal water consumption situation to the terminal in real time.

[0030] It should be noted that by calculating the first slope and the second slope of multiple analysis points on the real-time water consumption curve and the water consumption pattern curve, the water consumption situation at the abnormal node is further analyzed, and the abnormal water consumption situation is transmitted to the terminal in real time for convenient subsequent maintenance judgment.

[0031] In a second aspect, an embodiment of the present application provides an intelligent water meter abnormal water consumption detection system, which includes: a memory and a processor. The memory includes a program for the intelligent water meter abnormal water consumption detection method. When the program for the intelligent water meter abnormal water consumption detection method is executed by the processor, the following steps are implemented: Obtain water consumption data based on an intelligent water meter, preprocess the water consumption data to obtain preprocessed data; Construct a prediction model, input the preprocessed data into the prediction model, and output water consumption trend information; Obtain user habit information, and generate a water consumption regular curve based on the user habit information; Generate a real-time water consumption curve based on the water consumption trend information, compare the real-time water consumption curve with the water consumption regular curve, and generate water consumption abnormal nodes and water consumption abnormal differences; Generate abnormal water consumption situation information based on the water consumption abnormal nodes and water consumption abnormal differences, and transmit the abnormal water consumption situation to the terminal in real time.

[0032] It should be noted that by obtaining water consumption data and analyzing the water consumption trend according to the prediction model, the water consumption abnormal nodes and water consumption abnormal differences are analyzed, the abnormal water consumption situation is accurately analyzed, and the monitoring effect is improved.

[0033] According to an embodiment of the present invention, obtaining water consumption data based on an intelligent water meter, preprocessing the water consumption data to obtain preprocessed data specifically includes: Obtain water consumption data, where the water consumption data includes water consumption and water consumption time information; Analyze the noise data in the water consumption data, clean the noise data, remove the noise data to obtain optimized data, and analyze whether there is missing data in the optimized data; If there is missing data, calculate the missing value according to the amount of missing data of the missing data, and judge whether the missing value is greater than or equal to the set missing threshold; If it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data. If it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; If there is no missing data, obtain preprocessed data.

[0034] It should be noted that by cleaning the noise in the water consumption data and filling the missing data, the integrity of the water consumption data is ensured, and at the same time, the analysis error of the water consumption data is reduced.

[0035] According to an embodiment of the present invention, constructing a prediction model, inputting the preprocessed data into the prediction model, and outputting water consumption trend information specifically includes: Select an initial model framework, and establish a training set and a test set based on historical water consumption data; Iteratively train the initial model based on the training set, and analyze the loss value between the model prediction value and the actual value; Generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters to obtain a prediction model; Evaluate the prediction model based on the test set to obtain the evaluation results, and dynamically adjust the model hyperparameters based on the evaluation results; Output water usage trend information based on the prediction model.

[0036] It should be noted that by continuously training and iterating the model, the prediction accuracy of the model is improved. At the same time, the difference between the predicted value and the actual value is analyzed, and the loss function is dynamically adjusted. After the training is complete, the performance of the model is evaluated through the test set, and then the parameters are adjusted again to improve the output accuracy of the prediction model.

[0037] According to the embodiments of the present invention, obtain user habit information, and generate a water usage pattern curve based on the user habit information, specifically including: Obtain user habit information, and analyze the user's historical water usage pattern based on the user habit information; Analyze the annual water usage average data, monthly water usage average data, daily water usage average data, and the average water volume data for the past 48 hours based on the user's historical water usage pattern; Generate a water usage pattern curve based on the annual water volume average data, monthly water volume average data, daily water volume average data, and the average water volume data for the past 48 hours.

[0038] It should be noted that by analyzing the user's preferences, the average water volume of the user for the annual, monthly, daily, and the past 48 hours is analyzed to obtain the water usage pattern curve.

[0039] According to the embodiments of the present invention, generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the water usage pattern curve, and generate water usage abnormal nodes and water usage abnormal differences, specifically including: Obtain the water usage trend information, and analyze the time when the recorded water volume changes by every 0.25L based on the water usage trend information; View the 0.25L water volume change information recorded in the past 48 hours, and generate a real-time water usage curve; Compare the real-time water usage curve with the water usage pattern curve to generate water usage abnormal nodes and water usage abnormal differences.

[0040] It should be noted that by analyzing the time when the measured water volume changes by every 0.25L and the water volume change information, a real-time user water usage curve between time and water volume is established, and a coincidence comparison is made according to the water usage pattern to analyze the abnormal water usage situation.

[0041] According to the embodiments of the present invention, generate abnormal water usage situation information based on the water usage abnormal nodes and water usage abnormal differences, and transmit the abnormal water usage situation to the terminal in real time, specifically including: Obtain the real-time water usage curve and the water usage pattern curve, and establish multiple analysis points; Calculate the slopes of each analysis point on the real-time water consumption curve to obtain multiple first slopes; Calculate the slopes of each analysis point on the water consumption pattern curve to obtain multiple second slopes; Compare the first slope and the second slope of the same analysis point to analyze whether the first slope and the second slope are the same; If they are the same, determine that the current analysis point is a normal node; If they are not the same, determine that the current analysis point is an abnormal node, obtain the water consumption situation of the abnormal node, calculate the abnormal water consumption data to get the abnormal water consumption situation, and transmit the abnormal water consumption situation to the terminal in real time.

[0042] It should be noted that by calculating the first slopes and the second slopes of multiple analysis points on the real-time water consumption curve and the water consumption pattern curve, and then analyzing the water consumption situation of the abnormal node and transmitting the abnormal water consumption situation to the terminal in real time, it is convenient for subsequent maintenance judgment.

[0043] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the intelligent water meter abnormal water consumption detection method. When the program for the intelligent water meter abnormal water consumption detection method is executed by a processor, the steps of the intelligent water meter abnormal water consumption detection method as described in any one of the above are implemented.

[0044] An intelligent water meter abnormal water consumption detection method, system and medium disclosed by the present invention, by obtaining water consumption data based on an intelligent water meter, preprocessing the water consumption data to obtain preprocessed data; constructing a prediction model, inputting the preprocessed data into the prediction model to output water consumption trend information; obtaining user habit information, generating a water consumption pattern curve based on the user habit information; generating a real-time water consumption curve based on the water consumption trend information, comparing the real-time water consumption curve with the water consumption pattern curve to generate water consumption abnormal nodes and water consumption abnormal differences; generating abnormal water consumption situation information based on the water consumption abnormal nodes and the water consumption abnormal differences, and transmitting the abnormal water consumption situation to the terminal in real time; by obtaining water consumption data in real time and analyzing the water consumption pattern according to user habits, thereby analyzing water consumption abnormal information according to the water consumption pattern, accurately analyzing the abnormal water consumption situation of users, and improving the monitoring accuracy of intelligent water meters.

[0045] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the displayed or discussed components can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0046] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0047] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0048] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: mobile storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0049] Or, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: mobile storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

Claims

1. An abnormal water consumption detection method for an intelligent water meter, characterized in that, Including: Obtain water usage data based on an intelligent water meter, preprocess the water usage data to obtain preprocessed data; Construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information; Obtain user habit information, and generate a water usage pattern curve based on the user habit information; Generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the water usage pattern curve, and generate water usage abnormal nodes and water usage abnormal differences; Generate abnormal water usage situation information based on the water usage abnormal nodes and water usage abnormal differences, and transmit the abnormal water usage situation to the terminal in real time.

2. The abnormal water consumption detection method of the intelligent water meter according to claim 1, characterized in that Obtain water usage data based on an intelligent water meter, preprocess the water usage data to obtain preprocessed data, specifically including: Obtain water usage data, where the water usage data includes water consumption and water usage time information; Analyze the noise data in the water usage data, clean the noise data, remove the noise data to obtain optimized data, and analyze whether there is missing data in the optimized data; If there is missing data, calculate the missing value according to the amount of missing data of the missing data, and judge whether the missing value is greater than or equal to the set missing threshold; If it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data. If it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; If there is no missing data, obtain preprocessed data.

3. The abnormal water consumption detection method of the intelligent water meter according to claim 2, characterized in that Construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information, specifically including: Select an initial model framework, and establish a training set and a test set based on historical water usage data; Iteratively train the initial model based on the training set, and analyze the loss value between the model prediction value and the actual value; Generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters to obtain a prediction model; Evaluate the prediction model based on the test set to obtain an evaluation result, and dynamically adjust the model hyperparameters based on the evaluation result; Output water usage trend information based on the prediction model.

4. The abnormal water consumption detection method of the intelligent water meter according to claim 3, wherein Obtain user habit information, and generate a water usage pattern curve based on the user habit information, specifically including: Obtain user habit information, and analyze the user's historical water usage pattern based on the user habit information; Analyze the annual water usage mean data, monthly water usage mean data, daily water usage mean data, and the mean water usage volume data in the past 48 hours based on the user's historical water usage pattern; Generate a water usage pattern curve based on the annual water usage volume mean data, monthly water usage volume mean data, daily water usage volume mean data, and the mean water usage volume data in the past 48 hours.

5. The intelligent water meter abnormal water use detection method according to claim 4, wherein Generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the water usage pattern curve, and generate water usage abnormal nodes and water usage abnormal differences, specifically including: Obtain water usage trend information, and analyze the time when the recorded water volume changes by every 0.25L based on the water usage trend information; View the 0.25L water volume change information recorded in the past 48 hours, and generate a real-time water usage curve; Coincide and compare the real-time water usage curve with the water usage pattern curve to generate water usage abnormal nodes and water usage abnormal differences.

6. The abnormal water consumption detection method of the intelligent water meter according to claim 5, characterized in that, Generate abnormal water usage situation information based on abnormal water usage nodes and abnormal water usage differences, and transmit the abnormal water usage situation to the terminal in real time, specifically including: Obtain the real-time water usage curve and the regular water usage curve, and establish multiple analysis points; Calculate the slope of each analysis point on the real-time water usage curve to obtain multiple first slopes; Calculate the slope of each analysis point on the regular water usage curve to obtain multiple second slopes; Compare the first slope and the second slope of the same analysis point, and analyze whether the first slope and the second slope are the same; If they are the same, determine that the current analysis point is a normal node; If they are not the same, determine that the current analysis point is an abnormal node, obtain the water usage situation of the abnormal node, calculate the abnormal water usage data, obtain the abnormal water usage situation, and transmit the abnormal water usage situation to the terminal in real time.

7. An abnormal water consumption detection system for an intelligent water meter, characterized in that, The system includes: a memory and a processor. The memory includes a program for the intelligent water meter abnormal water usage detection method. When the program for the intelligent water meter abnormal water usage detection method is executed by the processor, the following steps are implemented: Obtain water usage data based on the intelligent water meter, preprocess the water usage data to obtain preprocessed data; Construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information; Obtain user habit information, and generate a regular water usage curve based on the user habit information; Generate a real-time water usage curve based on the water usage trend information, compare the real-time water usage curve with the regular water usage curve, and generate abnormal water usage nodes and abnormal water usage differences; Generate abnormal water usage situation information based on the abnormal water usage nodes and abnormal water usage differences, and transmit the abnormal water usage situation to the terminal in real time.

8. The intelligent water meter abnormal water use detection system according to claim 7, characterized in that, Obtain water usage data based on the intelligent water meter, preprocess the water usage data to obtain preprocessed data, specifically including: Obtain water usage data, where the water usage data includes water consumption and water usage time information; Analyze the noise data in the water usage data, clean the noise data, remove the noise data to obtain optimized data, and analyze whether there is missing data in the optimized data; If there is missing data, calculate the missing value according to the amount of missing data of the missing data, and judge whether the missing value is greater than or equal to the set missing threshold; If it is greater than or equal to the set missing threshold, fill the missing data based on the linear interpolation method to obtain preprocessed data. If it is less than the set missing value, fill the missing data based on the mean filling method to obtain preprocessed data; If there is no missing data, obtain preprocessed data.

9. The intelligent water meter abnormal water use detection system according to claim 8, characterized in that, Construct a prediction model, input the preprocessed data into the prediction model, and output water usage trend information, specifically including: Select an initial model framework, and establish a training set and a test set based on historical water usage data; Iteratively train the initial model based on the training set, and analyze the loss value between the model prediction value and the actual value; Generate optimization parameters based on the loss value, adjust the loss function of the model according to the optimization parameters to obtain a prediction model; Evaluate the prediction model based on the test set to obtain an evaluation result, and dynamically adjust the model hyperparameters based on the evaluation result; Output water usage trend information based on the prediction model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for the abnormal water consumption detection method of the intelligent water meter. When the program for the abnormal water consumption detection method of the intelligent water meter is executed by a processor, the steps of the abnormal water consumption detection method of the intelligent water meter as described in any one of claims 1 to 6 are implemented.

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