Water leakage detection method, storage medium and equipment

Abstract

The invention discloses a water leakage detection method, a storage medium and equipment, and relates to the technical field of smart home / smart home, and the water leakage detection method comprises the steps: obtaining the actual flow information of each branch in a first time period through a flow sensor, and obtaining the predicted flow information of the first time period determined based on the historical flow information; determining a first branch with abnormal water consumption based on the actual flow information and the predicted flow information; controlling the flow sensor of the first branch to move, detecting the flow of multiple positions of the first branch in the moving process, and determining whether the first branch leaks water or not and the position of a water leakage point according to the flow of the multiple positions of the first branch. According to the method, high-precision water flow measurement is realized on the premise that a pipeline is not damaged, accurate positioning of a water leakage point is realized, the accuracy of water leakage detection is improved, a firm and reliable guarantee is provided for household water safety, and the use experience of a user is improved.

Description

Technical Field

[0001] This application relates to the field of smart home / smart family, and more specifically, to a method for detecting water leakage, a storage medium, and a device. Background Art

[0002] The function of the household water system is to transport municipal water supply or other water sources to the interior of the home through pipelines to meet the daily water usage needs, such as drinking, washing, bathing, and cleaning.

[0003] As the usage time of the household water system increases, water leakage in the pipeline system frequently occurs. Among them, pipeline water leakage refers to the abnormal loss of water in the pipeline system, usually caused by physical damage, poor connection, or material aging of the pipeline. However, since the pipelines of the household water system are usually hidden in walls, floors, or underground, it is difficult to locate the leakage point. Summary of the Invention

[0004] This application provides a method for detecting water leakage, a storage medium, and a device to solve the technical problem of difficult leakage point location in the common water leakage phenomenon in the household water system.

[0005] In a first aspect, this application provides a method for detecting water leakage, including:

[0006] Obtain the actual flow rate information of each branch in the first time period through the flow rate sensor, and obtain the predicted flow rate information of the first time period determined based on historical flow rate information;

[0007] Based on the actual flow rate information and the predicted flow rate information, determine the first branch with abnormal water usage;

[0008] Control the flow rate sensor of the first branch to move, and detect the flow rate at multiple positions of the first branch during the movement;

[0009] Determine whether the first branch leaks and the location of the leakage point according to the flow rates at multiple positions of the first branch.

[0010] Optionally, the determining whether the first branch leaks and the location of the leakage point according to the flow rates at multiple positions of the first branch includes:

[0011] Compare the flow rates at adjacent positions among the multiple positions, and determine whether the first branch leaks and the location of the leakage point according to the comparison results of the flow rates at the adjacent positions.

[0012] Optionally, the determining whether the first branch leaks and the location of the leakage point according to the comparison results of the flow rates at the adjacent positions includes:

[0013] When the difference between the flow rate at the first position and the flow rate at the second position exceeds the first threshold, and the difference between the flow rate at another position adjacent to the first position and the flow rate at the first position is less than the first threshold, and the difference between the flow rate at another position adjacent to the second position and the flow rate at the second position is less than the first threshold, it is determined that there is a leak in the first branch, and the location of the leak point is between the first position and the second position, where the first position and the second position are adjacent positions among the multiple positions.

[0014] Optionally, determining whether there is a leak in the first branch and the location of the leak point according to the comparison result of the flow rates at the adjacent positions includes:

[0015] When the flow rates at N positions adjacent in sequence along the water flow direction of the first branch decrease in sequence, it is determined that there is a leak in the first branch, and the location of the leak point is between the N positions, where N is an integer greater than or equal to 2.

[0016] Optionally, the actual flow rate information includes one or more of the following: water consumption at different time granularities, water peak time, water consumption volatility at different time granularities, and the change trend of water consumption at different time granularities compared with the corresponding historical water consumption;

[0017] The predicted flow rate information includes one or more corresponding to the actual flow rate information: predicted values of water consumption at different time granularities, predicted water peak time, predicted values of water consumption volatility at different time granularities, and predicted change trends of water consumption at different time granularities compared with the corresponding historical water consumption.

[0018] Optionally, determining the first branch with abnormal water use based on the actual flow rate information and the predicted flow rate information includes:

[0019] When the change trend at any time granularity of the first branch is inconsistent with the predicted change trend value, and the difference between the water consumption and the predicted water consumption value at any time granularity exceeds the second threshold, it is determined that there is abnormal water use in the first branch.

[0020] Optionally, determining the first branch with abnormal water use based on the actual flow rate information and the predicted flow rate information includes:

[0021] When the water peak time of the first branch is inconsistent with the predicted water peak time, and the difference between the water consumption at the water peak time and the predicted water consumption value exceeds the third threshold, it is determined that there is abnormal water use in the first branch.

[0022] Optionally, determining the first branch with abnormal water use based on the actual flow rate information and the predicted flow rate information includes:

[0023] In the case where the difference between the water consumption volatility at any time granularity of the first branch and the predicted value of the water consumption volatility exceeds the fourth threshold, and the difference between the water consumption at any time granularity and the predicted value of the water consumption exceeds the fifth threshold, it is determined that there is an abnormal water use in the first branch.

[0024] In a second aspect, the present application provides a water leakage detection device, including:

[0025] An acquisition module, configured to respectively acquire the actual flow information of each branch in the first time period through the flow sensor, and acquire the predicted flow information of the first time period determined based on the historical flow information.

[0026] A determination module, configured to determine the first branch with abnormal water use based on the actual flow information and the predicted flow information.

[0027] A processing module, configured to control the movement of the flow sensor of the first branch.

[0028] The acquisition module is further configured to detect the flow rates at multiple positions of the first branch during the movement.

[0029] The determination module is further configured to determine whether the first branch leaks and the location of the leakage point according to the flow rates at multiple positions of the first branch.

[0030] Optionally, the processing module is further configured to compare the flow rates at adjacent positions among the multiple positions.

[0031] The determination module is further configured to determine whether the first branch leaks and the location of the leakage point according to the comparison result of the flow rates at the adjacent positions.

[0032] Optionally, the determination module is further configured to determine that the first branch leaks and the leakage point is located between the first position and the second position when the difference between the flow rate at the first position and the flow rate at the second position exceeds the first threshold, and the difference between the flow rate at another position adjacent to the first position and the flow rate at the first position is less than the first threshold, and the difference between the flow rate at another position adjacent to the second position and the flow rate at the second position is less than the first threshold, where the first position and the second position are adjacent positions among the multiple positions.

[0033] Optionally, the determination module is further configured to determine that the first branch leaks and the leakage point is located between the N positions when the flow rates at N positions adjacent in sequence along the water flow direction of the first branch decrease in sequence, where N is an integer greater than or equal to 2.

[0034] Optionally, the actual flow information includes one or more of the following: water consumption at different time granularities, peak water consumption time, water consumption volatility at different time granularities, and the change trend of water consumption at different time granularities compared to the corresponding historical water consumption.

[0035] The predicted flow information includes one or more corresponding to the actual flow information: predicted values of water consumption at different time granularities, predicted peak water consumption time, predicted water consumption volatility at different time granularities, and predicted change trend of water consumption at different time granularities compared to the corresponding historical water consumption.

[0036] Optionally, the determining module is further configured to determine that there is an abnormal water use in the first branch when the change trend at any time granularity of the first branch is inconsistent with the predicted change trend value, and the difference between the water consumption and the predicted water consumption value at the any time granularity exceeds a second threshold.

[0037] Optionally, the determining module is further configured to determine that there is an abnormal water use in the first branch when the peak water consumption time of the first branch is inconsistent with the predicted peak water consumption time, and the difference between the water consumption at the peak water consumption time and the predicted water consumption value exceeds a third threshold.

[0038] Optionally, the determining module is further configured to determine that there is an abnormal water use in the first branch when the difference between the water consumption volatility at any time granularity of the first branch and the predicted water consumption volatility value exceeds a fourth threshold, and the difference between the water consumption and the predicted water consumption value at the any time granularity exceeds a fifth threshold.

[0039] In a third aspect, the present application provides a computer-readable storage medium, on which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the leakage detection method as described in the first aspect and various possible implementation manners of the first aspect.

[0040] In a fourth aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0041] The memory stores computer-executable instructions;

[0042] The processor executes the computer-executable instructions stored in the memory to implement the leakage detection method as described in the first aspect and various possible implementation manners of the first aspect.

[0043] In a fifth aspect, the present application provides a program product, including a computer program, and when the computer program is executed by a processor, it implements the leakage detection method as described in the first aspect and various possible implementation manners of the first aspect.

[0044] The leak detection method, storage medium, and device provided by this application respectively obtain the actual flow information of each branch in the first time period through the flow sensors of each branch, and obtain the predicted flow information of the first time period determined based on historical flow information; in the case where the change trend of any time granularity of the first branch is inconsistent with the predicted value of the change trend, and the difference between the water consumption of any time granularity and the predicted value of the water consumption exceeds the second threshold, it is determined that there is an abnormal water use in the first branch; control the flow sensor of the first branch to move, and detect the flow at multiple positions of the first branch during the movement; compare the flow at adjacent positions among the multiple positions, and determine whether the first branch leaks and the location of the leak point according to the comparison result of the flow at adjacent positions. This method ensures the measurement of high-precision water flow without damaging the pipeline, realizes the accurate positioning of the leak point, improves the accuracy of leak detection, and significantly improves the sensitivity of leak detection, providing a solid and reliable guarantee for household water safety, effectively preventing the damage caused by leaks, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 Schematic diagram of the hardware environment for the leak detection method provided by this application;

[0048] Figure 2 Schematic diagram of the partial structure of the water supply branch in the household water system provided by this application;

[0049] Figure 3 Flow chart of the leak detection method provided by this application Figure 1 ;

[0050] Figure 4 Flow chart of the leak detection method provided by this application Figure 2 ;

[0051] Figure 5 Schematic diagram of the structure of the leak detection device provided by this application;

[0052] Figure 6 Schematic diagram of the structure of the leak detection device provided by this application.

[0053] Reference numerals:

[0054] 1 - Flow sensor; 2 - Outer water supply pipe; 3 - Inner water supply pipe. Detailed implementation manners

[0055] In order to enable those skilled in the art of this technology to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0056] It should be noted that the terms "first", "second", etc. in the specification, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0058] According to one aspect of the embodiments of this application, an interaction method for a smart home device is provided. This interaction method for a smart home device is widely applied to whole-house intelligent digital control application scenarios such as Smart Home, smart home, smart home appliance ecosystem, Intelligence House ecosystem, etc. Optionally, in this embodiment, the above-mentioned interaction method for a smart home device can be applied to, for example, Figure 1 the hardware environment composed of a terminal device 102 and a server 104 as shown. As Figure 1As shown in the figure, the server 104 is connected to the terminal device 102 through a network, and can be used to provide services (such as application services, etc.) for the terminal or the client installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for the server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data operation services for the server 104.

[0059] The above network can include but is not limited to at least one of the following: wired network, wireless network. The above wired network can include but is not limited to at least one of the following: wide area network, metropolitan area network, local area network. The above wireless network can include but is not limited to at least one of the following: WIFI (Wireless Fidelity), Bluetooth. The terminal device 102 is not limited to a PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projection device, smart TV, smart clothes dryer, smart curtain, smart audio and video, smart socket, smart speaker, smart sound box, smart fresh air device, smart kitchen and bathroom equipment, smart bathroom equipment, smart floor sweeping robot, smart window cleaning robot, smart mopping robot, smart air purification device, smart steam box, smart microwave oven, smart kitchen water heater, smart purifier, smart water dispenser, smart door lock, etc.

[0060] First, the terms involved in this application are explained.

[0061] LSTM: Long Short-Term Memory, which is a special structure of Recurrent Neural Network (RNN for short), used to process and predict time series data.

[0062] ARIMA: Autoregressive Integrated Moving Average, which is a statistical model used for time series data analysis and prediction, used to analyze and predict time series data.

[0063] The function of the household water system is to transport municipal water supply or other water sources to the interior of the household through pipelines to meet the daily water use needs, such as drinking, washing, bathing, and cleaning.

[0064] As the usage time of the household water system increases, the phenomenon of pipeline leakage frequently occurs in the system. Among them, pipeline leakage refers to the abnormal loss of water in the pipeline system, and the specific reasons for the leakage phenomenon include:

[0065] First, pipe materials and aging: Household water systems usually use materials such as cast iron, PVC, and copper pipes. After long-term use, these materials may develop cracks due to corrosion, aging, or wear, resulting in water leakage. In addition, pipe joints such as connectors and valves may also cause water leakage due to poor sealing or loosening.

[0066] Second, the impact of water pressure and water flow: Excessive water pressure will increase the burden on the water pipes and may cause the pipes to burst over time. The erosion of the water flow inside the pipes may also wear the pipe walls, thereby causing water leakage.

[0067] However, since the pipes of household water systems are usually hidden in walls, floors, or underground, it is difficult to locate the water leakage points. In addition, water leakage problems not only cause waste of water resources but may also lead to structural problems and economic losses in the house.

[0068] The leakage detection method provided in this application aims to solve the above technical problems of the prior art.

[0069] First, the implementation scenarios involved in this application are described.

[0070] Continue to refer to Figure 1 , Figure 1 , which is a schematic diagram of the hardware environment of the leakage detection method provided in this application. The intelligent device applying the leakage detection method provided in this application is in this hardware environment, and this intelligent device can interact with other intelligent devices. Figure 2 is a partial structural schematic diagram of the water supply branch in the household water system provided in this application. The household water system includes multiple water supply branches to ensure that stable and clean water sources can be obtained in every corner of the house. As Figure 2 shown, the pipes used in the household water system are water supply pipes with a sandwich structure, and this sandwich structure is composed of a water supply outer pipe 2 and a water supply inner pipe 3. A plurality of flow sensors 1 are also arranged in this sandwich structure. Among them, the water supply inner pipe 3 is used to transport household water. The water supply outer pipe 2 and the water supply inner pipe 3 together form the activity space of the flow sensor 1, and tracks for the movement of the flow sensor 1 can be arranged on both the water supply outer pipe 2 and the water supply inner pipe 3. The flow sensor 1 can move freely in the sandwich structure formed by the water supply outer pipe 2 and the water supply inner pipe 3.

[0071] In the embodiments of this application, for the changing geographical environment, the material selection of the water supply outer pipe 2 and the water supply inner pipe 3 has flexibility. Specifically, the water supply outer pipe 2 and the water supply inner pipe 3 can either choose the same material to meet specific environmental requirements or choose different materials according to the respective functional characteristics of the inner and outer pipes and the differences in environmental conditions.

[0072] Exemplarily, flow sensors 1 are installed in areas with frequent water use such as kitchens, bathrooms, and laundries, as well as at the branch nodes of the main pipeline, to ensure that all aspects of household water use can be comprehensively monitored. By controlling the movement of the flow sensors 1 at different positions, the water flow data at different positions can be collected in real time, so as to more comprehensively grasp the water use situation of the household water system, timely discover potential water leakage points or abnormal water use situations, and give early warnings in a timely manner, improving the comprehensiveness and accuracy of the system's detection of the entire pipeline system.

[0073] The present application provides a water leakage detection method. Based on a household water system provided with a sandwich structure, using a water use habit model and combining historical water flow data with different time granularities, the water flow data of the user at a future moment is predicted. By controlling the non-invasive flow sensors arranged in the sandwich to move according to a preset frequency, the water flow data of different regions and different branch pipelines are obtained in real time, and the real-time obtained water flow data is compared with the predicted values of the model in detail to timely discover abnormal changes in the water use situation and locate the water leakage points of different branch pipelines. This method ensures high-precision measurement of water flow rate without damaging the pipeline, realizes accurate positioning of water leakage points, improves the accuracy of water leakage detection, and significantly improves the sensitivity of water leakage detection, providing a solid and reliable guarantee for household water use safety, effectively preventing the damage caused by water leakage, and improving the user experience.

[0074] The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0075] Figure 3 Flow chart of the water leakage detection method provided by the embodiment of the present application Figure 1 The execution subject of this embodiment can be, for example, a household water system provided with a water leakage detection module. As Figure 3 shown, the water leakage detection method provided in this embodiment includes:

[0076] S301: Respectively obtain the actual flow information of each branch in the first time period through the flow sensors of each branch, and obtain the predicted flow information of the first time period determined based on the historical flow information.

[0077] Among them, the flow information is used to measure and detect the consumption situation of household water use, and the first time period can be, for example, 2h.

[0078] It can be understood that the actual flow information, historical flow information, and predicted flow information are essentially the same. The actual flow information refers to the water flow data obtained in real time by using a flow sensor during the current time period. The historical flow information refers to the user's historical water usage data during the same time period. The predicted flow information is the water flow data predicted by the household water system for the corresponding time period by combining the historical flow information during the same time period.

[0079] In the embodiments of the present application, the flow sensor is a non-contact sensor, and different numbers and different positions of sensors are provided in different branches. Specifically, at least two flow sensors are provided in the same branch. At this time, the corresponding flow sensors are arranged near the water inlet and outlet of the corresponding branch. The present application does not impose special restrictions on the number and installation position of the flow sensors in the same branch.

[0080] Exemplarily, the flow sensor is an ultrasonic sensor or an electromagnetic sensor. By using ultrasonic or electromagnetic induction technology, the effect of non-contact and accurate measurement of the water flow velocity is achieved. Then, combined with the geometric parameters of the pipeline, the water flow data of the corresponding branch is accurately calculated, avoiding potential damage to the pipeline caused by traditional invasive installation, and effectively ensuring the accuracy of the measurement data.

[0081] Exemplarily, the first time period is from 7:00 to 8:00 in the morning. The currently obtained actual flow information includes: the water flow of branch A is 150 L, the water flow of branch B is 10 L, and the water flow of branch C is 50 L. The predicted flow information includes: the water flow of branch A is 155 L, the water flow of branch B is 12 L, and the water flow of branch C is 50 L.

[0082] In some embodiments, the predicted flow information is obtained by using the water usage habit model set in the current household water system, and the water usage habit model is obtained by training a machine learning model using the historical water usage data of the user corresponding to the current household water system. Specifically, a machine learning algorithm suitable for time series analysis (such as LSTM, ARIMA, etc.) can be selected to construct the user water usage habit model.

[0083] It can be understood that the historical water usage data used to construct the user water usage habit model not only includes key water flow data such as accurate flow values with timestamps, but also includes auxiliary data that affects water usage habits such as weather, solar terms, region, and the number of family members. For example, when the weather is hot, the household water consumption may increase; during holidays, when there are more family members gathered, the water usage pattern will also be different. By comprehensively collecting data information from various dimensions, a richer information dimension is provided for subsequent analysis, thereby improving the accuracy of the user water usage habit model in predicting the flow information in different branches.

[0084] It should be noted that LSTM is good at dealing with long-term dependencies in time series data and can effectively capture complex patterns and trends in water usage data; ARIMA is suitable for modeling and predicting stationary time series. In the actual construction process of the user water usage habit model, appropriate algorithms can be selected or multiple algorithms can be combined for model construction according to the characteristics of the data and actual needs to improve the accuracy and adaptability of the model.

[0085] Exemplarily, a large amount of historical water usage data is used as the training set to fully train the model. Specifically, the historical water usage data can be divided into a training set and a validation set according to a certain ratio. The training set is used for the learning and parameter adjustment of the model, and the validation set is used to evaluate the performance of the model on unseen data. By continuously iterating the training process, the parameters of the model are adjusted to enable the model to better fit the water usage patterns in the historical data. And the model parameters are optimized through methods such as cross-validation. Among them, cross-validation is a commonly used model evaluation and parameter optimization technique. The data set is divided into multiple subsets. One of the subsets is used as the validation set in turn, and the remaining subsets are used as the training set. The model is trained and validated multiple times to obtain multiple evaluation results. Then, these results are integrated to select the optimal model parameters, thereby improving the generalization ability and prediction accuracy of the model and ensuring that the model can accurately predict the water usage habits of users when facing new water usage data.

[0086] S302: Based on the actual flow information and the predicted flow information, determine the first branch with abnormal water usage.

[0087] Among them, the first branch is used to indicate the branch pipeline with abnormal water usage.

[0088] It can be understood that the household water system is divided into multiple water usage areas according to different living areas, and different branch pipelines and multiple flow sensors corresponding to the branch pipelines are provided in different water usage areas. The currently obtained flow information is about the actual and predicted water usage situations of different branches, and the water usage situations obtained from different branches vary according to different user water usage habits. Therefore, by comparing the actual flow information and the predicted flow information obtained from different branches, the branch with abnormal water usage currently can be determined. In addition, the number of the first branches can be one or multiple, which is determined by the comparison results of the actual flow information and the predicted flow information of different branches in the current household water system.

[0089] According to the actual flow information and predicted flow information of multiple branches currently obtained, the actual flow information and predicted flow information of the same branch are compared respectively; for any branch, if the actual flow information is inconsistent with the predicted flow information, it indicates that the actual water usage of the user does not conform to the predicted water usage, and there is abnormal water usage in the current branch. At this time, the corresponding branch is determined as the first branch; if the actual flow information is consistent with the predicted flow information, it indicates that the actual water usage of the user conforms to the predicted water usage, and there is no abnormal water usage in the current branch. At this time, continue to obtain the flow data detected by the flow sensor and determine whether there is abnormal water usage.

[0090] Exemplarily, the actual flow information of the first time period "7:00 - 8:00 in the morning" currently obtained includes: the water flow of branch A is 150L, the water flow of branch B is 10L, and the water flow of branch C is 50L. The corresponding predicted flow information includes: the water flow of branch A is 155L, the water flow of branch B is 12L, and the water flow of branch C is 50L; the actual flow information and predicted flow information of branch A, branch B, and branch C are compared respectively, and it can be obtained that: there are abnormalities in branch A and branch B, and there is no abnormality in branch C. At this time, branch A and branch B can be respectively determined as the first branch.

[0091] S303: Control the flow sensor of the first branch to move and detect the flow at multiple positions of the first branch during the movement.

[0092] S304: Determine whether the first branch leaks and the location of the leak point according to the flow at multiple positions of the first branch.

[0093] Control the flow sensor in the first branch where abnormal water usage is currently determined to move at a preset frequency, and detect the flow at different positions in the first branch in real time during the movement; based on the flow at multiple positions obtained this time, compare the flow at adjacent positions, and determine two adjacent positions where there is a flow change. At this time, it can be determined that the first branch leaks, and the leak point location is between the two adjacent positions determined this time.

[0094] In some embodiments, during the movement of the flow sensor, the distance between different positions is preset. If the distance between the two adjacent positions corresponding to the leak point location determined this time is relatively far, the flow sensor can be controlled to move again between the corresponding two positions, obtain the flow at multiple different positions, and accurately locate the leak point location again. This application does not make special restrictions on the movement frequency of the flow sensor and the distance of each movement.

[0095] Exemplarily, the preset frequency can be 50 cm / s. When it is determined that there is abnormal water use in branch A, the flow sensor in branch A is controlled to move, and the flow rates corresponding to multiple positions during the movement are obtained. The flow rates at multiple positions obtained this time include: position A - 5 L / min, position B - 5 L / min, position C - 7 L / min, position D - 7 L / min. By comparing the flow rates at multiple positions, it can be determined that along the water outlet direction of household water use, that is, during the process of moving from position D to position A in sequence, the flow rate changes between position B and position C. At this time, it can be determined that there is a leak in branch A, and the leak point is located between position B and position C; if the flow rates at multiple positions obtained this time include: position A - 7 L / min, position B - 7 L / min, position C - 7 L / min, position D - 7 L / min, by comparing the flow rates at multiple positions, it can be determined that there is no leak in the current branch A, and the abnormal water use may be caused by changes in user behavior.

[0096] The leak detection method provided in this embodiment obtains the actual flow rate information of each branch in the first time period through the flow sensors of each branch, and obtains the predicted flow rate information of the first time period determined based on historical flow rate information; based on the actual flow rate information and the predicted flow rate information, the first branch with abnormal water use is determined; the flow sensor of the first branch is controlled to move, and the flow rates at multiple positions of the first branch are detected during the movement, and then whether there is a leak in the first branch and the leak point position are determined according to the flow rates at multiple positions of the first branch. This method ensures the measurement of high-precision water flow rate without damaging the pipeline, realizes the accurate positioning of the leak point, improves the accuracy of leak detection, provides a solid and reliable guarantee for household water use safety, and improves the user experience.

[0097] Figure 4 It is a flow schematic of the leak detection method provided in the embodiment of the present application Figure 2 As Figure 4 shown, on the basis of the Figure 3 embodiment, the leak detection method is described in detail. The leak detection method shown in this embodiment includes:

[0098] S401: Obtain the actual flow rate information of each branch in the first time period through the flow sensors of each branch, and obtain the predicted flow rate information of the first time period determined based on historical flow rate information.

[0099] Step S401 is similar to the above step S301, and will not be elaborated here.

[0100] In some embodiments, the actual flow information includes one or more of the following: water consumption at different time granularities, peak water usage time, volatility of water consumption at different time granularities, and the change trend of water consumption at different time granularities compared to the corresponding historical water consumption; the predicted flow information includes one or more corresponding to the actual flow information: predicted values of water consumption at different time granularities, predicted peak water usage time, predicted volatility of water consumption at different time granularities, and predicted change trend of water consumption at different time granularities compared to the corresponding historical water consumption.

[0101] It can be understood that the flow information includes but is not limited to the water consumption of the corresponding branch and flow data in different dimensions; among them, the water consumption at different time granularities refers to the water consumption measured and recorded at different time intervals. For example, the water consumption can be recorded by minute, hour, day, week, or month, and different time granularities provide different perspectives to better understand the water usage pattern. For example, minute-level data may be used to detect instantaneous changes, while daily or monthly-level data is used to identify long-term trends; the peak water usage time refers to the time period when the water consumption reaches the highest. In the daily water usage pattern, the peak water usage time may occur in the morning (such as when washing and preparing breakfast) or in the evening (such as when cooking dinner and taking a bath). Identifying the peak water usage time helps optimize water resource allocation and management to ensure sufficient water supply capacity during the highest demand; the volatility of water consumption at different time granularities refers to the degree of change or instability of water consumption at different time intervals, and the volatility can be quantified by calculating the standard deviation or coefficient of variation of water consumption. A higher volatility may indicate an unstable water usage pattern that requires further analysis to determine the cause, such as equipment failure or abnormal water usage behavior; the change trend of water consumption at different time granularities compared to the corresponding historical water consumption is to compare the current water consumption with historical data to identify the change trend of user water usage. For example, by comparing the water consumption of the current month with the same month's data in the past few years, seasonal changes, growth trends, or decline trends can be identified, which helps predict future water demand.

[0102] Flow information in different dimensions can be used to construct a user water usage habit model. Since the water usage habits of each family are different, the water usage habits of different families can be distinguished through multi-dimensional flow information, enabling the user water usage habit model to better fit the actual situation of each family; in addition, flow information in different dimensions can also be used to determine abnormal water usage situations. When there is a large deviation between the flow information in the real-time water usage data and the flow information predicted by the model, it can prompt the possible existence of water leakage or other abnormal situations; for example, if a family's daily water consumption suddenly far exceeds the normal range predicted by the model, or there are abnormal water usage troughs or peaks during the peak water usage period, the alarm mechanism can be triggered in a timely manner.

[0103] In addition, data with different time granularities contain water usage patterns at different levels. The user water usage habit model can learn various patterns from short-term fluctuations to long-term trends, thereby improving its adaptability to different types of water usage data and prediction accuracy.

[0104] Exemplarily, a time granularity in hours can capture short-term water usage fluctuations, such as changes in the water usage activities of family members within a short period, like water usage behaviors within a short time range for washing, cooking, etc.; a time granularity in days can observe daily water usage patterns, such as the overall water usage distribution of a family in a day; a time granularity in weeks or months helps to study long-term water usage trends, for example, the water usage differences of a family in different seasons or months, and the water usage changes due to long-term changes in the living habits of family members; therefore, data with a short-term time granularity (such as hours) can enable the model to learn the immediate change rules of water usage. These data can help the model capture sudden water usage behaviors, such as changes in water consumption caused by suddenly turning on the faucet, brief water equipment failures, etc.; data with a medium-term time granularity (such as days) reflects the relatively stable water usage pattern of a family in a day. By learning these data, the model can determine daily water usage rules, such as peak and trough water usage hours per day, the approximate range of daily water consumption, etc.; data with a long-term time granularity (such as weeks or months) shows the macroscopic trends of family water usage. Using long-term time granularity data, the model can learn the water usage change rules caused by factors such as the passage of time, seasonal changes, and changes in the living habits of family members. For example, the model can learn from long-term data that the water consumption in summer is generally higher than that in winter, or the water consumption drops significantly during the long-term absence of family members. This helps the model predict water usage in a wider time range and provides strong support in analyzing whether the water usage trend is normal and whether it is affected by long-term factors (such as a gradual increase in water consumption due to pipeline aging).

[0105] S402: When the change trend of any time granularity in the first branch is inconsistent with the predicted change trend value, and the difference between the water consumption and the predicted water consumption value of any time granularity exceeds the second threshold, it is determined that there is an abnormal water usage in the first branch.

[0106] Among them, the second threshold can be, for example, 5L.

[0107] It is understandable that there is a correlation between the time granularity, different branches, and the second threshold. Different time granularities and different branch pipelines correspond to different second thresholds. During the actual water usage process of users, there is an error between the actual water consumption and the predicted water consumption, and the existence of this error is reasonable. The reasons for this error include: changes in users' water usage behaviors, external factors (such as weather conditions, water price adjustments, water use restrictions, and changes in population or economic activities that may affect water consumption), seasonal changes, etc. (which may lead to periodic changes in water consumption, for example, increased irrigation demand in summer or decreased water use in winter).

[0108] Based on the historical water usage data with different time granularities, the predicted value of the change trend corresponding to the time granularity is obtained through the user water usage model. If the change trend of any time granularity of the first branch is inconsistent with the predicted value of the change trend based on the corresponding time granularity of the user water usage model, and the difference between the water consumption and the predicted water consumption value at the corresponding time granularity exceeds the second threshold, it indicates that the actual water consumption is higher than the predicted water consumption. At this time, it can be determined that there is a water usage anomaly in the first branch.

[0109] Exemplarily, for branch A, if the change trend corresponding to the time granularity "7:00 - 8:00 in the morning" is "rising", the predicted value of the change trend is "stable", and the water consumption corresponding to the time granularity "7:00 - 8:00 in the morning" is 150L, and the predicted water consumption value is 140L. At this time, the change trend corresponding to the time granularity "7:00 - 8:00 in the morning" is inconsistent with the predicted value of the change trend, and the difference between the water consumption and the predicted water consumption value is 10L, 10L > 5L. That is to say, the actual water consumption at the current time granularity is higher than the predicted water consumption, and there is a water usage situation beyond the predicted range. At this time, it can be determined that there is a water usage anomaly in branch A.

[0110] In some embodiments, the method for determining the first branch with a water usage anomaly specifically includes:

[0111] When the water usage peak time of the first branch is inconsistent with the predicted value of the water usage peak time, and the difference between the water consumption at the water usage peak time and the predicted water consumption value exceeds the third threshold, it is determined that there is a water usage anomaly in the first branch.

[0112] Among them, the third threshold can be, for example, 1L.

[0113] Exemplarily, for branch A, if the peak water consumption time of branch A currently obtained is "7:30 am", the corresponding predicted peak water consumption time is "7:20 am", and the water consumption at the currently obtained peak water consumption time is 10 L, and the corresponding predicted water consumption is 8 L. At this time, the difference between the water consumption corresponding to the peak water consumption time "7:30 am" and the predicted water consumption is 2 L, and 2 L > 1 L. That is to say, the actual water consumption at the current peak water consumption time is higher than the predicted water consumption, and there is a water consumption situation beyond the predicted range. At this time, it can be determined that there is a water use anomaly in branch A.

[0114] In some other embodiments, the method for determining the first branch with water use anomaly further includes:

[0115] When the difference between the water consumption volatility of any time granularity of the first branch and the predicted value of the water consumption volatility exceeds a fourth threshold, and the difference between the water consumption of any time granularity and the predicted water consumption value exceeds a fifth threshold, it is determined that there is a water use anomaly in the first branch.

[0116] Among them, the fourth threshold can be, for example, 2%, and the fifth threshold can be, for example, 5 L.

[0117] Exemplarily, for branch A, if the water consumption volatility corresponding to the time granularity "7:00 - 8:00 am" is 12%, the predicted value of the water consumption volatility is 9%, and the water consumption corresponding to the time granularity "7:00 - 8:00 am" is 150 L, and the predicted water consumption is 140 L. At this time, the difference between the water consumption volatility corresponding to the time granularity "7:00 - 8:00 am" and the predicted value of the water consumption volatility is 3%, and 3% > 2%, and the difference between the water consumption and the predicted water consumption value is 10 L, 10 L > 5 L. That is to say, the actual water consumption at the current time granularity is higher than the predicted water consumption, and there is a water consumption situation beyond the predicted range. At this time, it can be determined that there is a water use anomaly in branch A.

[0118] In the embodiments of the present application, the second threshold, the third threshold, and the fifth threshold are usually used to measure different levels of the difference between the actual water consumption and the predicted water consumption, and these thresholds can all be used to determine whether there is a water use anomaly in the branch. The difference between the second threshold, the third threshold, and the fifth threshold lies in that they are water consumption thresholds for different dimensions; water use anomalies include changes in water consumption caused by user water use behaviors and other external factors, and also include changes in water consumption caused by pipeline leakage. Therefore, after determining the first branch with water use anomaly, it is necessary to further determine whether there is leakage and the location of the leakage point in the case of leakage.

[0119] S403: Control the flow sensor of the first branch to move and detect the flow at multiple positions of the first branch during the movement.

[0120] S404: Compare the flow rates at adjacent positions among multiple positions, and determine whether the first branch leaks and the location of the leak point according to the comparison results of the flow rates at adjacent positions.

[0121] After determining that there is an abnormal water usage in the first branch, control multiple flow sensors in the first branch to move at a preset frequency; during the movement, detect and obtain the flow rates at different positions in the first branch in real time. By comparing the flow rate data at adjacent positions, identify two adjacent positions where the flow rate has changed. Thus, it can be determined that the first branch leaks, and the leak point is located between the two adjacent positions where the flow rate has changed.

[0122] In some embodiments, the method for determining whether the first branch leaks and the location of the leak point specifically includes:

[0123] When the difference between the flow rate at the first position and the flow rate at the second position exceeds the first threshold, and the difference between the flow rate at another position adjacent to the first position and the flow rate at the first position is less than the first threshold, and the difference between the flow rate at another position adjacent to the second position and the flow rate at the second position is less than the first threshold, it is determined that the first branch leaks, and the leak point is located between the first position and the second position.

[0124] Wherein, the first position and the second position are adjacent positions among multiple positions, and the first threshold can be, for example, 0.5 L / min.

[0125] Exemplarily, when it is determined that there is an abnormal water usage in branch A, control the flow sensor in branch A to move at a speed of 50 cm / s, and obtain the flow rates at corresponding multiple positions during the movement. The flow rates at the multiple positions obtained this time include: position A - 5 L / min, position B - 5 L / min, position C - 7 L / min, position D - 7 L / min, and the direction from position D to position A is along the water outlet direction of domestic water use; calculate the flow rate differences between adjacent two positions respectively, and the calculation results are: the difference between position A and position B is 0 L / min, the difference between position B and position C is 2 L / min, and the difference between position C and position D is 0 L / min; use the first threshold to screen the obtained calculation results, and it can be obtained that the two adjacent positions where the flow rate difference exceeds 0.5 L / min are position B and position C; in addition, for position A adjacent to position B, the difference between the flow rate at position A and the flow rate at position B is less than the first threshold, and for position D adjacent to position C, the difference between the flow rate at position D and the flow rate at position C is also less than the first threshold. At this time, it can be determined that branch A leaks, and the leak point is located between position B and position C.

[0126] It can be understood that when comparing the flow rates at adjacent positions, the instantaneous flow rate at the corresponding position can be used, or the flow sensor can be controlled to stay at the corresponding position for a preset duration, and the average flow rate at the corresponding position within the preset duration can be calculated, and the comparison can be made using the average flow rate. This application does not impose special restrictions on the type of flow rate obtained during the movement of the flow sensor.

[0127] In some other embodiments, the method for determining whether the first branch leaks and the location of the leak point further includes:

[0128] When the flow rates at N sequentially adjacent positions along the water flow direction of the first branch decrease in sequence, it is determined that the first branch leaks, and the leak point location is between the N positions.

[0129] Wherein, N is an integer greater than or equal to 2.

[0130] Exemplarily, when it is determined that there is an abnormal water use in branch A, four sequentially adjacent positions along the water flow direction of branch A, specifically position D, position C, position B, and position A, the corresponding flow rates are 8 L / min, 7 L / min, 6 L / min, and 5 L / min respectively; if the flow rates at position D, position C, position B, and position A decrease in sequence along the water flow direction, it indicates that there is a leakage phenomenon in the pipeline from position D to position A. At this time, it can be determined that branch A leaks, and the leak point location is between position D, position C, position B, and position A.

[0131] The leakage detection method provided in this embodiment respectively obtains the actual flow rate information of each branch in the first time period through the flow sensors of each branch, and obtains the predicted flow rate information of the first time period determined based on the historical flow rate information; when the change trend of any time granularity in the first branch is inconsistent with the predicted value of the change trend, and the difference between the water consumption of any time granularity and the predicted value of the water consumption exceeds the second threshold, it is determined that there is an abnormal water use in the first branch; control the flow sensor of the first branch to move, and detect the flow rates at multiple positions of the first branch during the movement; compare the flow rates at adjacent positions among the multiple positions, and determine whether the first branch leaks and the location of the leak point according to the comparison result of the flow rates at adjacent positions. This method ensures high-precision measurement of the water flow rate without damaging the pipeline, realizes accurate positioning of the leak point, improves the accuracy of leakage detection, and significantly improves the sensitivity of leakage detection, providing a solid and reliable guarantee for household water safety, effectively preventing the damage caused by leakage, and improving the user experience.

[0132] Figure 5 It is a schematic structural diagram of the leakage detection device provided in this application. As Figure 5 shown, this application provides a leakage detection device, and the leakage detection device 500 includes:

[0133] An acquisition module 501 is configured to respectively acquire the actual flow information of each branch in the first time period through the flow sensor, and acquire the predicted flow information of the first time period determined based on historical flow information.

[0134] A determination module 502 is configured to determine the first branch with abnormal water use based on the actual flow information and the predicted flow information.

[0135] A processing module 503 is configured to control the movement of the flow sensor of the first branch.

[0136] The acquisition module 501 is further configured to detect the flow rates at multiple positions of the first branch during the movement.

[0137] The determination module 502 is further configured to determine whether the first branch leaks and the location of the leakage point according to the flow rates at multiple positions of the first branch.

[0138] Optionally, the processing module 503 is further configured to compare the flow rates at adjacent positions among the multiple positions.

[0139] The determination module 502 is further configured to determine whether the first branch leaks and the location of the leakage point according to the comparison result of the flow rates at the adjacent positions.

[0140] Optionally, the determination module 502 is further configured to determine that the first branch leaks and the leakage point is located between the first position and the second position when the difference between the flow rate at the first position and the flow rate at the second position exceeds the first threshold, and the difference between the flow rate at another position adjacent to the first position and the flow rate at the first position is less than the first threshold, and the difference between the flow rate at another position adjacent to the second position and the flow rate at the second position is less than the first threshold, where the first position and the second position are adjacent positions among the multiple positions.

[0141] Optionally, the determination module 502 is further configured to determine that the first branch leaks and the leakage point is located between the N positions when the flow rates at N positions adjacent in sequence along the water flow direction of the first branch decrease in sequence, where N is an integer greater than or equal to 2.

[0142] Optionally, the actual flow information includes one or more of the following: water consumption at different time granularities, water use peak time, water consumption volatility at different time granularities, and the change trend of water consumption at different time granularities compared with the corresponding historical water consumption.

[0143] The predicted flow information includes one or more items corresponding to the actual flow information: predicted water consumption values at different time granularities, predicted water peak time, predicted water consumption volatility values at different time granularities, and predicted change trends of water consumption at different time granularities compared to the corresponding historical water consumption.

[0144] Optionally, the determining module 502 is further configured to determine that there is an abnormal water use in the first branch when the change trend at any time granularity of the first branch is inconsistent with the predicted change trend value, and the difference between the water consumption and the predicted water consumption value at the any time granularity exceeds a second threshold.

[0145] Optionally, the determining module 502 is further configured to determine that there is an abnormal water use in the first branch when the water peak time of the first branch is inconsistent with the predicted water peak time value, and the difference between the water consumption at the water peak time and the predicted water consumption value exceeds a third threshold.

[0146] Optionally, the determining module 502 is further configured to determine that there is an abnormal water use in the first branch when the difference between the water consumption volatility at any time granularity of the first branch and the predicted water consumption volatility value exceeds a fourth threshold, and the difference between the water consumption and the predicted water consumption value at the any time granularity exceeds a fifth threshold.

[0147] Figure 6 It is a schematic structural diagram of the water leakage detection device provided by the present application. As Figure 6 shown, the present application provides a water leakage detection device 600, which includes: a receiver 601, a transmitter 602, a processor 603, and a memory 604.

[0148] The receiver 601 is configured to receive instructions and data;

[0149] The transmitter 602 is configured to transmit instructions and data;

[0150] The memory 604 is configured to store computer execution instructions;

[0151] The processor 603 is configured to execute the computer execution instructions stored in the memory 604 to implement each step performed by the water leakage detection method in the above embodiments. Specifically, reference can be made to the relevant descriptions in the foregoing embodiments of the water leakage detection method.

[0152] Optionally, the above memory 604 can be either independent or integrated with the processor 603.

[0153] When the memory 604 is independently provided, the electronic device further includes a bus for connecting the memory 604 and the processor 603.

[0154] The implementation principle and technical effects of the electronic device provided in this embodiment can be referred to the foregoing embodiments, and will not be elaborated herein.

[0155] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the processor executes the computer-executable instructions, the method described in any of the foregoing embodiments is implemented.

[0156] An embodiment of the present application further provides a computer program product, including a computer program that implements the method described in any of the foregoing embodiments when executed by a processor.

[0157] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0158] The integrated modules implemented in the form of software function modules as described above can be stored in a computer-readable storage medium. The software function modules are stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in various embodiments of the present application.

[0159] It should be understood that the above-mentioned processor may be a central processing unit (CPU for short), and may also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the application can be directly implemented by the execution of the hardware processor, or can be implemented by the combination of the hardware and software modules in the processor. The memory may include high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disc, etc.

[0160] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0161] An exemplary storage medium is coupled to the processor, enabling the processor to read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a master device.

[0162] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the presence of additional identical elements in the process, method, article or device including such element.

[0163] The serial numbers of the above embodiments of the present application are only for description and do not represent the superiority or inferiority of the embodiments.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0165] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A leak detection method, characterized in that, Each branch is respectively provided with a flow sensor, and the method includes: Obtaining the actual flow information of each branch in the first time period through the flow sensor respectively, and obtaining the predicted flow information of the first time period determined based on historical flow information; Determining a first branch with abnormal water use based on the actual flow information and the predicted flow information; Controlling the flow sensor of the first branch to move, and detecting the flow at multiple positions of the first branch during the movement; Determining whether the first branch leaks and the position of the leak point according to the flow at multiple positions of the first branch.

2. The method according to claim 1, characterized in that The determining whether the first branch leaks and the position of the leak point according to the flow at multiple positions of the first branch includes: Comparing the flow at adjacent positions among the multiple positions, and determining whether the first branch leaks and the position of the leak point according to the comparison result of the flow at the adjacent positions.

3. The method according to claim 2, characterized in that, The determining whether the first branch leaks and the position of the leak point according to the comparison result of the flow at the adjacent positions includes: When the difference between the flow at the first position and the flow at the second position exceeds a first threshold, and the difference between the flow at another position adjacent to the first position and the flow at the first position is less than the first threshold, and the difference between the flow at another position adjacent to the second position and the flow at the second position is less than the first threshold, it is determined that the first branch leaks, and the leak point position is between the first position and the second position, where the first position and the second position are adjacent positions among the multiple positions.

4. The method according to claim 2, characterized in that, The determining whether the first branch leaks and the position of the leak point according to the comparison result of the flow at the adjacent positions includes: When the flow at N positions adjacent in sequence along the water flow direction of the first branch decreases in sequence, it is determined that the first branch leaks, and the leak point position is between the N positions, where N is an integer greater than or equal to 2.

5. The method according to any one of claims 1 to 4, characterized in that The actual flow information includes one or more of the following: water consumption at different time granularities, peak water use time, water consumption volatility at different time granularities, and the change trend of water consumption at different time granularities compared with the corresponding historical water consumption; The predicted flow information includes one or more corresponding to the actual flow information: predicted value of water consumption at different time granularities, predicted value of peak water use time, predicted value of water consumption volatility at different time granularities, and predicted change trend of water consumption at different time granularities compared with the corresponding historical water consumption.

6. The method according to claim 5, characterized in that, The determining a first branch with abnormal water use based on the actual flow information and the predicted flow information includes: When the change trend at any time granularity of the first branch is inconsistent with the predicted change trend value, and the difference between the water consumption and the predicted water consumption value at the any time granularity exceeds a second threshold, it is determined that the first branch has abnormal water use.

7. The method according to claim 5, wherein The determining a first branch with abnormal water use based on the actual flow information and the predicted flow information includes: In the case where the peak water consumption time of the first branch is inconsistent with the predicted value of the peak water consumption time, and the difference between the water consumption at the peak water consumption time and the predicted value of the water consumption exceeds the third threshold, it is determined that there is an abnormal water use in the first branch.

8. The method according to claim 5, characterized in that The determining of the first branch with abnormal water use based on the actual flow information and the predicted flow information includes: In the case where the difference between the water consumption volatility of any time granularity of the first branch and the predicted value of the water consumption volatility exceeds the fourth threshold, and the difference between the water consumption of any time granularity and the predicted value of the water consumption exceeds the fifth threshold, it is determined that there is an abnormal water use in the first branch.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when running, executes the method according to any one of claims 1 to 8.

10. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 8 through the computer program.

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