A water stoppage influence intelligent evaluation method and system based on water use behavior profiling

Abstract

The application relates to a water stop influence intelligent evaluation method and system based on a water behavior portrait, and relates to the technical field of a water supply network, which comprises the following steps: collecting water supply network specifications and water supply user parameters; generating user portrait labels through the water supply user parameters; constructing a water supply network space graph according to the water supply network specifications and the user portrait labels; calling detection flow from the water supply user parameters; obtaining a detection time point through the detection flow and the water supply network specifications; combining the water supply network space graph, the user portrait labels, the detection time point, the detection flow and a preset water stop period to obtain a target water stop vector; inputting preset abnormal coordinates into the water supply network space graph, and combining the user portrait labels and the target water stop vector to carry out valve closing simulation to obtain a target valve closing scheme; and generating and sending a water stop notification instruction based on the user portrait labels, the target valve closing scheme and the target water stop vector. The application has the effect of improving the accuracy of water stop influence evaluation.

Description

Technical Field

[0001] This invention relates to the technical field of water supply networks, and in particular to an intelligent assessment method and system for the impact of water outages based on water usage behavior profiling. Background Technology

[0002] A water supply network is a physical infrastructure network system used to transport and distribute domestic and industrial water.

[0003] During the water supply network's water flow, when a pipe bursts, the network's parameters and specifications are usually collected manually to create a distribution map. Engineers then combine the coordinates of the burst pipe with the distribution map to simulate water outages and assess their impact. This allows them to develop valve shut-off plans in advance for subsequent pipe repairs, ultimately finding the valve shut-off plan with the least impact on water flow.

[0004] During the simulation of water outage due to pipe bursts, manual data processing is inefficient and prone to errors. Furthermore, traditional methods only consider the physical topology of the pipe network and do not take into account the different water usage behaviors and emergency response capabilities of different users. As a result, the deduced valve shut-off schemes are crude and inaccurate, and the accuracy of the water outage impact assessment is seriously insufficient. Summary of the Invention

[0005] To improve the accuracy of water outage impact assessment, this invention provides an intelligent assessment method and system for water outage impact based on water usage behavior profiling.

[0006] In a first aspect, the present invention provides an intelligent assessment method for the impact of water outages based on water usage behavior profiles, employing the following technical solution: A smart assessment method for the impact of water outages based on water usage behavior profiles includes: S10: Collect water supply network specifications and water user parameters; S11: Generate user profile tags based on water supply user parameters; S12: Construct a spatial map of the water supply network based on the specifications of the water supply network and user profile tags; S13: Retrieve the detected flow rate from the water supply user parameters; S14: The detection time point is obtained by detecting the flow rate and the specifications of the water supply network; S15: Combine the water supply network spatial map, user profile tags, detection time points, detection flow rates, and preset water outage periods to obtain the target water outage vector; S16: Input the preset abnormal coordinates into the water supply network spatial map, and combine the user profile tags and the target water outage vector to perform valve shut-off simulation to obtain the target valve shut-off scheme. S17: Generate and send water outage notification instructions based on user profile tags, target valve shut-off schemes, and target water outage vectors.

[0007] By adopting the above technical solution, user profile tags, target valve shut-off schemes, and target water outage vectors are obtained by analyzing the specifications of the water supply network and the parameters of water supply users. Based on the user profile tags, target valve shut-off schemes, and target water outage vectors, water outage notification instructions are generated and sent to the corresponding users. This enables automatic data collection and processing of water supply pipelines and simulation of pipe bursts to generate the least impactful water outage schemes for different users, thereby improving the efficiency of valve shut-off scheme generation and the accuracy of water outage impact assessment.

[0008] Optionally, methods for constructing the spatial map of the water supply network include: S20: Construct a pipeline connection model based on water supply network specifications; S21: Based on the water supply user parameters, retrieve the marked pipe from the pipeline connection model and use the detected flow rate of the marked pipe as the marked flow rate; S22: Retrieve valve positions from the pipeline connection model; S23: Use the pipeline connection model to control the position of each valve to change according to a preset change threshold, and update the marked flow rate; S24: Calculate the difference in labeled flow before and after the update as the flow change value; S25: Combine flow rate changes, valve locations, user profile tags, water supply user parameters, and pipeline connection models to construct a spatial map of the water supply network.

[0009] By adopting the above technical solution, a spatial map of the water supply network is constructed by analyzing the specifications of the water supply network and user profile tags, thereby dynamically reflecting the impact of valve operation on the network flow and improving the accuracy of subsequent valve closure plan formulation.

[0010] Optionally, methods for obtaining the target water outage vector include: S30: Retrieve water supply equipment from water supply user parameters; S31: The pipeline to be inspected is determined based on the water supply network spatial diagram, water supply equipment, and water supply user parameters; S32: Collect and monitor the historical flow rate and historical usage time points of the pipeline; S33: Based on historical usage flow, historical usage time points, and water supply equipment, user habits are obtained; S34: Select water storage equipment based on water supply usage equipment and historical usage flow; S35: Obtain water storage usage parameters based on the water storage equipment and user habits; S36: Output a preset water outage pre-notification and combine it with user profile tags, water storage usage parameters and detected flow rate to obtain the target water outage vector.

[0011] By adopting the above technical solution, the target water outage vector is obtained by analyzing the water supply user parameters and the water supply network spatial map. This allows the user's water storage behavior to be incorporated into the evaluation of the impact of water outage, thereby improving the accuracy of the water outage impact assessment.

[0012] Optionally, other methods for obtaining the target water outage vector include: S40: Obtain the user's water storage period based on the pre-outage notification and the outage period; S41: Update the detected flow rate and use the detected flow rate during the user's water storage period as the target flow rate; S42: Retrieve the baseline water storage flow rate from the water storage usage parameters; S43: Compare the consistency between the target flow rate and the baseline water storage flow rate, and combine them with the user's water storage period to obtain the target water storage flow rate, and take the time point of the target water storage flow rate as the target water storage time point; S44: Obtain the water storage usage period based on the target water storage time and the water outage period; S45: Update the target water storage flow rate based on water storage usage periods and user habits; S46: Combine the target water storage flow rate, water storage usage parameters, and user profile tags to obtain the target water outage vector.

[0013] By adopting the above technical solution, the target water outage vector is obtained by analyzing the water outage pre-notification, water outage period, and detected flow rate. This allows for the identification of water storage equipment for different users, and further water outage assessment is conducted based on the conditions of different water storage equipment, thereby improving the accuracy of water outage impact assessment.

[0014] Optionally, other methods for obtaining the target water outage vector include: S50: The baseline usage flow rate is obtained by using water storage usage parameters and water outage periods; S51: Compare the deviation of the target water storage flow rate from the baseline usage flow rate to calculate the water storage flow rate deviation. S52: Obtain the duration of the flow deviation based on the water storage flow deviation value and the water outage period; S53: Obtain the water storage impact vector based on the duration of flow deviation, the value of water storage flow deviation, and user profile tags; S54: Combine user profile tags with water storage impact vector to obtain target water outage vector.

[0015] By adopting the above technical solution, the water storage impact vector is obtained by analyzing the water storage usage parameters and the water outage period. The target water outage vector is obtained by combining the user profile tags and the water storage impact vector. This allows us to assess the impact of users consuming all the water in the water storage equipment during the water outage period, thereby improving the accuracy of the water outage impact assessment.

[0016] Optionally, methods for obtaining the water storage flow deviation value also include: S60: Retrieve other water storage devices from water supply user parameters; S61: To obtain a marked water storage device based on the water storage device and other water storage devices; S62: Obtain other water storage flow rates and total water storage flow rates by considering other water storage devices and user habits; S63: Compare the consistency of the target flow rate with other water storage flow rates to obtain the marked deviation time point based on the water storage flow rate deviation value and the marked water storage equipment; S64: Retrieve the frequency of use of the markings on the water storage equipment from user usage habits; S65: Update the water storage flow deviation value based on the total water storage flow, the time point of the marking deviation, the reference usage flow of the marked water storage equipment, and the marking usage frequency.

[0017] By adopting the above technical solution, and by analyzing other water storage devices and equipment to obtain updated water flow deviation values, it is possible to add water storage devices to more comprehensively assess users' water usage during water outage periods, thereby improving the accuracy of subsequent water outage impact assessments.

[0018] Optionally, methods for updating the water storage flow deviation value include: S70: Use the baseline usage flow rate of the marked water storage equipment as the marked usage flow rate; S71: Obtain the remaining water usage time based on the marked deviation time point and the water outage period; S72: Obtain the remaining water usage count based on the remaining water usage duration and the marked usage frequency; S73: Calculate the product of the remaining water usage times and the marked usage flow rate, and use it as the total remaining usage flow rate; S74: Compare the remaining total flow rate used with the total flow rate of the water storage to calculate the new water storage flow rate deviation value.

[0019] By adopting the above technical solution, and by analyzing the total water storage flow, the flow of other water storage devices and the marked water storage devices to calculate the new water storage flow deviation value, it is possible to estimate the water usage of other water storage devices on the marked water storage devices, thereby improving the accuracy of subsequent water outage impact assessment.

[0020] Optionally, the method for generating the water outage notification instruction also includes: S80: Determine the water usage type based on the marked water storage equipment and user habits; S81: To determine the maximum water consumption based on the water outage period, user habits, and water usage type; S82: Update the maximum water consumption based on the water storage flow deviation value; S83: Obtain the deviation time point based on the flow deviation duration and the water outage period; S84: Add the maximum water consumption, water type, and deviation time point to the water outage notification command.

[0021] By adopting the above technical solution, new water outage notification instructions can be obtained by analyzing the marked water storage equipment and user usage habits. This allows for the output of different water outage notification instructions for different users, and different strategies can be formulated based on the user's water usage during the outage period to notify the user, thereby improving the quality of notification to users.

[0022] Optionally, methods for obtaining the target valve-closing scheme include: S90: Generate a simulated valve shut-off scheme based on abnormal coordinates and water supply network spatial diagram; S91: Obtain the node influence weights by simulating valve-closing schemes; S92: Match the weight of water outage impact based on user profile tags; S93: Compare the impact weight of water outage with the preset baseline impact threshold to select the marked impact weight; S94: The buffer coefficient is obtained based on the water storage flow deviation, water storage equipment, and user habits. S95: Combine the node influence weight, buffer coefficient, and label influence weight to calculate the detection reward value, and take the simulated valve closing scheme corresponding to the largest detection reward value as the target valve closing scheme.

[0023] By adopting the above technical solution, and by introducing a buffer coefficient to quantify the buffering capacity of user water storage behavior, the target valve shut-off scheme can be obtained. This can improve the accuracy of valve shut-off direction determination while accurately analyzing the impact assessment of water outages.

[0024] Secondly, this application provides an intelligent assessment system for the impact of water outages based on water usage behavior profiles, employing the following technical solution: A smart assessment system for the impact of water outages based on water usage behavior profiles includes: The acquisition module is used to obtain the specifications of the water supply network and the parameters of water supply users. The memory is used to store a program for an intelligent assessment method of the impact of water outages based on water use behavior profiles; The processor is used to load and execute programs stored in memory.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. By analyzing the specifications of the water supply network and the parameters of water supply users, user profile tags, target valve shut-off schemes, and target water outage vectors are obtained. Based on the user profile tags, target valve shut-off schemes, and target water outage vectors, water outage notification instructions are generated and sent to the corresponding users. This enables automatic data collection and processing of water supply pipelines and pipe burst simulation to generate the least impactful water outage scheme for different users, thereby improving the efficiency of valve shut-off scheme generation and the accuracy of water outage impact assessment. 2. By analyzing the pre-outage notice, outage period, and detected flow rate, the target outage vector can be obtained, thereby identifying the water storage equipment corresponding to different users. Then, based on the situation of different water storage equipment, further outage assessment can be carried out to improve the accuracy of outage impact assessment. 3. By introducing a buffer coefficient to quantify the buffering capacity of user water storage behavior, the target valve shut-off scheme can be obtained. This improves the accuracy of valve shut-off direction determination while accurately analyzing the impact of water outages. Attached Figure Description

[0026] Figure 1 This is a flowchart of a method for intelligent assessment of the impact of water outages based on water usage behavior profiling, according to an embodiment of the present invention. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0028] Reference Figure 1 This application discloses an intelligent assessment method for the impact of water outages based on water usage behavior profiles, including the following steps: S10: Collect water supply network specifications and water user parameters.

[0029] Water supply network specifications refer to a set of systematic parameters describing the physical structure, spatial topology, and operational status of the water supply network. These parameters include: connection diagrams between pipes, valves, water meters, and pumping stations; pipe material, diameter, length, and laying date; geographical location (GIS coordinates) of network components; and parameters such as pressure and flow distribution under normal conditions. The specifications of the pipelines that need to be shut down are retrieved from the databases of the water utility's existing geographic information system (GIS) and supervisory control and data acquisition (SCADA) system as the water supply network specifications.

[0030] Water supply user parameters refer to the data set of basic attributes, water usage patterns, and historical interaction records of users connected by water supply network pipes. These parameters include: user ID, address, account type (residential / commercial / key guaranteed unit), time-series flow curves collected by the user's smart water meter, peak water usage periods, average daily water consumption, historical water outage complaint records, response to water utility notifications, and parameters of the water-using equipment in the user's residence. These parameters can be obtained by retrieving data from the smart water meter data stream within the integrated Customer Information System (CIS), Advanced Metering Architecture (AMI), and customer service work order system database. In this embodiment, water supply user parameters can also be obtained through actual surveys conducted by staff.

[0031] S11: Generate user profile tags based on water supply user parameters.

[0032] User profile tags are structured feature vectors based on water supply user parameters to quantify user water usage characteristics and sensitivity to water outages. User profile tags include basic attribute tags (key service units, users with frequent complaints, multi-person households) and behavioral habit tags (concentrated nighttime water usage, regular weekday usage), etc. User profile tags are generated by inputting water supply user parameters into an unsupervised clustering model (such as K-means) for analysis.

[0033] S12: Construct a spatial map of the water supply network based on the specifications of the water supply network and user profile tags.

[0034] The water supply network spatial map is a spatiotemporal heterogeneous graph that integrates physical topology and user characteristics. Nodes in the spatial map include entities such as valves, water meters, users, and DMA partitions. Their attributes are derived from the spatial coordinates in the water supply network specifications and their corresponding user profile tags on the official website. Edges represent the physical connections, affiliations, and hydraulic influence relationships between entities. The construction of the water supply network spatial map is achieved by associating and mapping the topology and attribute data from the water supply network specifications with the user profile tags generated by S11.

[0035] S13: Retrieve the detected flow rate from the water supply user parameters.

[0036] Detected flow rate refers to the time-series data of water consumption monitored in real time by the user's smart water meter, which is obtained by retrieving the detected flow rate from the water supply user parameters.

[0037] S14: The detection time point is obtained by detecting the flow rate and water supply network specifications.

[0038] The detection time point refers to the point in time when a water use event occurs by analyzing the detected flow rate data. This is achieved by retrieving the baseline flow rate from the water supply network specifications and using the point in time when the detected flow rate is inconsistent with the baseline flow rate as the detection time point.

[0039] S15: Combine the water supply network spatial map, user profile tags, detection time points, detection flow rates, and preset water outage periods to obtain the target water outage vector.

[0040] The water outage period is the estimated time period set by technicians that requires the water supply network to be shut down.

[0041] The target outage vector is an outage sensitivity vector used to characterize the expected impact on each node (especially user nodes) in the pipeline network during a preset outage period. This vector is a multi-dimensional feature, with different dimensions representing factors such as basic flow dependence, user profile weight coefficient, and real-time water storage buffer coefficient. The target outage vector is calculated by inputting the dynamic features consisting of the water supply network spatial map, user profile labels, detection time points, detection flow rates, and outage periods into a pre-trained spatiotemporal graph attention network (ST-GAT) model.

[0042] S16: Input the preset abnormal coordinates into the water supply network spatial map, and combine the user profile tags and the target water outage vector to perform valve closure simulation to obtain the target valve closure scheme.

[0043] The abnormal coordinates are the coordinates of the location in the water supply pipeline where a water pipe burst, as set by the technicians.

[0044] The target valve shut-off scheme refers to the recommended sequence of valves to be shut down and their operation timing to minimize the impact of water outage. The optimal valve shut-off scheme is obtained by inputting abnormal coordinates into the water supply network spatial map and combining user profile tags with the target water outage vector to perform reward function analysis.

[0045] S17: Generate and send water outage notification instructions based on user profile tags, target valve shut-off schemes, and target water outage vectors.

[0046] Water outage notification instructions refer to a set of personalized service instructions generated by users. These instructions include: the estimated duration of water outage, information on emergency water supply points, and personalized suggestions (such as enhanced reminders for users with "low water storage tendency"). Water outage notification instructions are generated by analyzing user profile tags, target valve shut-off schemes, and target water outage vectors, and then sent to the corresponding users via SMS / APP / voice robot.

[0047] Methods for constructing spatial maps of water supply networks include: S20: Construct a pipeline connection model based on the specifications of the water supply network.

[0048] A pipeline connection model is a diagram that describes the physical connections between all components (pipes, valves, water meters) in a water supply network. It is generated by analyzing data from the water supply network specifications using modeling software. In this embodiment, the pipeline connection model can be displayed in two or three dimensions.

[0049] S21: Based on the water supply user parameters, retrieve the marked pipe from the pipeline connection model and use the detected flow rate of the marked pipe as the marked flow rate.

[0050] Marked pipes refer to water supply pipes that are directly connected to the water supply user parameters. They are selected from the pipe network connection model and are directly connected to the water meter location corresponding to the water supply user parameters.

[0051] The labeled flow rate refers to the real-time water flow rate of a labeled pipeline, which is obtained by using the detected flow rate of the labeled pipeline as the labeled flow rate.

[0052] S22: Retrieve valve positions from the pipeline connection model.

[0053] Valve position refers to the coordinate position of each valve node in the pipeline connection model. The valve position is retrieved from the pipeline connection model.

[0054] S23: Use the pipeline connection model to control the position of each valve to change according to a preset change threshold, and update the marked flow rate.

[0055] The change threshold is the minimum operating step size set by the technician to simulate a change in the valve state (such as closing or partially closing the valve).

[0056] The change threshold is input into each valve position in the pipeline connection model to simulate the change one by one, and the marked flow rate is re-acquired.

[0057] S24: Calculate the difference between the marked flow before and after the update as the flow change value.

[0058] The flow change value refers to the impact of changing the position of each valve on the flow rate of the marked pipeline. The difference between the marked flow rates before and after the update is used as the flow change value.

[0059] S25: Combine flow rate changes, valve locations, user profile tags, water supply user parameters, and pipeline connection models to construct a spatial map of the water supply network.

[0060] Referring to S12, the flow rate change values ​​of the water supply user parameters corresponding to each valve position are added to the water supply network spatial diagram constructed in S12, thereby reflecting the control range of each valve and the change of control flow, etc., to form the final water supply network spatial diagram.

[0061] Methods for obtaining the target water outage vector include: S30: Retrieve water supply equipment from water supply user parameters.

[0062] Water supply equipment refers to the terminal water appliances in a user's residence, such as toilets, showers, washing machines, water heaters, and kettles. The water supply equipment is retrieved from the water supply user parameters.

[0063] S31: The pipeline to be tested is determined based on the water supply network spatial diagram, water supply equipment, and water supply user parameters.

[0064] The testing pipeline refers to the pipeline that supplies water to the water supply equipment. The pipeline directly connected to the water supply equipment is selected from the water supply network spatial diagram as the testing pipeline. If there are water supply equipment that is not directly connected to the pipeline, the water supply equipment corresponding to the outlet equipment connected to the pipeline is selected from the water supply user parameters as the water supply equipment (kettle).

[0065] S32: Collect and monitor the historical flow rate and historical usage time of the pipeline.

[0066] Historical usage flow refers to the record of past water flow on the detection pipeline, while historical usage time point refers to the time point at which the historical usage flow on the detection pipeline occurred. This is obtained by retrieving high-frequency data from the historical smart water meter corresponding to the detection pipeline.

[0067] S33: Based on historical usage flow, historical usage time points, and water supply equipment, user habits are obtained.

[0068] User usage habits refer to a quantitative description of a user's periodic water usage patterns, such as high water consumption during weekday morning peak hours and medium water consumption for extended periods on weekend nights. This is obtained by analyzing historical usage flow and time points using time-series pattern mining algorithms (such as periodic analysis and cluster analysis) and combining this with equipment characteristics (such as identifying the pipes connecting to the bathtub).

[0069] S34: Select water storage equipment based on water supply usage equipment and historical usage flow.

[0070] Water storage equipment refers to equipment that can store water for multiple uses, such as kettles and water heaters that can store water. The system selects equipment that meets the preset water storage type from the water supply equipment, retrieves the required water flow range for that type of equipment, and uses the equipment of the corresponding water storage type that falls within the water storage flow range in each historical usage flow as the water storage equipment.

[0071] Water storage type refers to the type of water-using equipment that is designed by technicians to store water for multiple uses by users.

[0072] S35: Obtain water storage usage parameters based on the water storage equipment and user habits.

[0073] Water storage usage parameters refer to a set of features used to quantify users' water storage behavior when using water storage equipment. Water storage usage parameters include water flow rate, water storage frequency, and preferred water storage time periods. By analyzing the flow range corresponding to the water storage equipment from the user's usage habits, the time points and intervals are generated, and then the flow range, time points, and intervals are combined to form water storage usage parameters.

[0074] S36: Output a preset water outage pre-notification and combine it with user profile tags, water storage usage parameters and detected flow rate to obtain the target water outage vector.

[0075] A pre-outage notification is a notification instruction set by technicians to inform users in advance that a water outage will occur. The sending method of the pre-outage notification is the same as that described in S17 above.

[0076] The target water outage vector is obtained by analyzing user profile tags, water storage usage parameters, detected flow rate, and water outage pre-notification.

[0077] Other methods for obtaining the target water outage vector include: S40: Obtain the user's water storage period based on the pre-outage notification and the outage period.

[0078] The user water storage period refers to the time between the pre-outage notice time and the initial time of the outage period. The user water storage period is obtained by analyzing the time points of the pre-outage notice and the outage period.

[0079] S41: Update the detected flow rate and use the detected flow rate during the user's water storage period as the target flow rate.

[0080] The target flow rate refers to the flow rate detected during the user's water storage period. The flow rate is re-collected and used as the target flow rate during the user's water storage period.

[0081] S42: Retrieve the baseline water storage flow rate from the water storage usage parameters.

[0082] The benchmark water flow rate refers to the average water flow rate in the pipeline when the water storage equipment is in use. The benchmark water flow rate is obtained by retrieving the water storage usage parameters.

[0083] S43: Compare the consistency between the target flow rate and the baseline water storage flow rate, and combine them with the user's water storage period to obtain the target water storage flow rate, and use the time point of the target water storage flow rate as the target water storage time point.

[0084] The target water storage flow rate refers to the flow rate used by the user when storing water in the water storage equipment. By analyzing the consistency between the target flow rate and the benchmark water storage flow rate, the target flow rate that is consistent with the benchmark water storage flow rate is marked, and then the target flow rate marked at the time point from the end of the user's water storage period is selected as the target water storage flow rate.

[0085] The target water storage time point refers to the last time a user stores water in the water storage equipment within the user's water storage period. The target water storage time point is defined as the time point of the target water storage flow rate.

[0086] S44: Obtain the water storage usage period based on the target water storage time and the water outage period.

[0087] The water storage usage period refers to the time period during which the user finally completes water storage and uses the water storage equipment before the water supply is cut off. The water storage usage period is defined as the time period formed by taking the target water storage time as the starting point and the start time of the water outage period as the ending point.

[0088] S45: Update the target water storage flow rate based on water storage usage periods and user habits.

[0089] By analyzing user habits, a linear consumption model for water storage equipment is established. Then, based on the water storage usage period and the linear consumption model, the consumption flow is calculated. The difference between the target water storage flow and the consumption flow is used as the new target water storage flow to further obtain the actual water storage volume in the water storage equipment during the water outage period.

[0090] S46: Combine the target water storage flow rate, water storage usage parameters, and user profile tags to obtain the target water outage vector.

[0091] The target water outage vector is obtained by analyzing the target water storage flow rate, water storage usage parameters, and user profile tags.

[0092] Other methods for obtaining the target water outage vector include: S50: The baseline usage flow rate is obtained by using water storage usage parameters and water outage periods.

[0093] The baseline usage flow rate refers to the total flow rate threshold required by the user to use the water storage equipment during the water outage period. Referring to S45, the historical water usage data for the corresponding water outage period is retrieved from the user's usage habits to re-establish the linear consumption model of the water storage equipment. Based on the linear consumption model and the water outage period, the total flow rate used by the water storage equipment is calculated as the baseline usage flow rate.

[0094] S51: Calculate the deviation value of the water storage flow rate by comparing the excess of the benchmark flow rate with the target water storage flow rate.

[0095] The water storage flow deviation value refers to the deviation between the baseline usage flow and the target water storage flow. By analyzing the situation where the baseline usage flow exceeds the target water storage flow, it is found that when the baseline usage flow does not exceed the target water storage flow, it means that the user will not consume all the water in the water storage equipment during the water outage period, and the water storage flow deviation value is set to 0.

[0096] When the baseline usage flow exceeds the target water storage flow, it means that the user will consume all the water in the water storage device during the water outage period. The difference between the baseline usage flow and the target water storage flow is then calculated as the water storage flow deviation value.

[0097] S52: Obtain the duration of flow deviation based on the water storage flow deviation value and the water outage period.

[0098] The duration of flow deviation refers to the duration of the water storage flow deviation that occurs during the water outage period. It is calculated by taking the time point when the water storage flow deviation occurs as the starting point and the end time of the water outage period as the ending point, and combining the duration of the starting point and the ending point as the flow deviation duration.

[0099] S53: Obtain the water storage impact vector based on the flow deviation duration, water storage flow deviation value, and user profile tags.

[0100] The water storage impact vector refers to the water outage sensitivity vector of users when the stored water runs out during the water outage period. The initial weight is matched from the preset water storage reference table by user profile tags, and the correction coefficient is matched from the water storage reference table according to the flow deviation duration and the water storage flow deviation value. The value obtained by calculating the product of the initial weight and the correction coefficient is used as the water storage impact vector.

[0101] The water storage reference table stores the initial weights corresponding to different user profile tags, and the correction coefficients corresponding to different flow deviation durations and water storage flow deviation values. The larger the flow deviation duration and water storage flow deviation value, the larger the correction coefficient. The parameters in the water storage reference table are set in advance by those skilled in the art based on actual conditions, and will not be elaborated here.

[0102] S54: Combine user profile tags with water storage impact vector to obtain target water outage vector.

[0103] Referring to S15, the initial target water outage vector is first obtained through user profile tags. Then, the water storage influence vector is added to the initial target water outage vector for weighted summation to form the final target water outage vector.

[0104] Methods for obtaining the water storage flow deviation value also include: S60: Retrieve other water storage devices from water supply user parameters.

[0105] Other water storage devices refer to independent water storage containers owned by users that do not rely on the water supply network and can serve as an emergency water source during water outages, such as water storage tanks. Other water storage devices are retrieved from the water supply user parameters.

[0106] S61: A marked water storage device is obtained based on the water storage device and other water storage devices.

[0107] Marked water storage devices refer to water storage devices that can use other water storage devices. They are marked by identifying devices that correspond to a preset usage type from among the water storage devices. For example, a kettle can use water from a water storage tank.

[0108] The type of water storage equipment used is defined by technicians and uses external water sources directly without going through water supply pipes.

[0109] S62: Obtain other water storage flow rates and total water storage flow rates by considering other water storage devices and user habits.

[0110] Other water storage flow refers to the pipeline flow when the user draws water from other water storage devices. The water volume is calculated by analyzing the volume of other water storage devices and retrieving the flow and duration from the user's usage habits. The flow rate in the user's usage habits that corresponds to the water volume of the water storage device is taken as the other water storage flow.

[0111] Total water storage flow refers to the maximum volume of water that other water storage devices can draw. The total water storage flow is calculated by taking the volume of other water storage devices.

[0112] S63: Compare the consistency of the target flow rate with other water storage flow rates to obtain the marked deviation time point based on the water storage flow rate deviation value and the marked water storage equipment.

[0113] The mark deviation time point refers to the time point when the user finishes using the water stored in the mark water storage device. By analyzing the consistency between the target flow rate and other water storage flow rates, if the target flow rate is inconsistent with other water storage flow rates, it means that the user has not stored water, and no adjustment is made.

[0114] When the target flow rate is consistent with other water storage flow rates, it indicates that the user has started storing water. The time point corresponding to the deviation value of the water storage equipment is then marked as the deviation time point.

[0115] S64: Retrieve the frequency of use of the marked water storage device from the user's usage habits.

[0116] The frequency of use of the marked water storage equipment refers to the frequency of use of the marked water storage equipment. The frequency of flow of the marked water storage equipment is obtained by retrieving the frequency of flow from the user's usage habits.

[0117] S65: Update the water storage flow deviation value based on the total water storage flow, the time point of the marking deviation, the reference usage flow of the marked water storage equipment, and the marking usage frequency.

[0118] New water storage flow deviation values ​​are obtained by analyzing other water storage flow rates, marking deviation time points, the baseline usage flow rate of the marked water storage equipment, and the marking usage frequency.

[0119] Methods for updating the water storage flow deviation value include: S70: Use the baseline usage flow rate of the marked water storage equipment as the marked usage flow rate.

[0120] The labeled usage flow rate refers to the baseline usage flow rate of the labeled water storage equipment, which is used as the labeled usage flow rate.

[0121] S71: The remaining water usage time is obtained based on the marked deviation time point and the water outage period.

[0122] The remaining water usage time refers to the time from the marked deviation time point to the end of the water outage period. The remaining water usage time is obtained by calculating the time difference between the marked deviation time point and the end time of the water outage period.

[0123] S72: The number of remaining water usages is obtained based on the remaining water usage duration and the frequency of marked usage.

[0124] The remaining water usage count refers to the total number of times the marked water storage device is expected to be used to draw water within the remaining water usage time. It is calculated by multiplying the remaining water usage time by the marked usage frequency.

[0125] S73: Calculate the product of the remaining water usage times and the marked usage flow rate, and use it as the total remaining usage flow rate.

[0126] The remaining total usage flow rate refers to the total flow rate that the marked water storage equipment needs to use in the remaining time period. It is calculated by multiplying the remaining water usage times by the marked usage flow rate.

[0127] S74: Compare the remaining total flow rate used with the total flow rate of the water storage to calculate the new water storage flow rate deviation value.

[0128] By analyzing the excess of the remaining total usage flow and other water storage flow, when the remaining total usage flow does not exceed the total water storage flow, it indicates that the marked water storage equipment can be unaffected by water outages with the assistance of other water storage devices, and the new water storage flow deviation value is 0.

[0129] When the remaining total flow rate exceeds the total water storage flow rate, it indicates that the marked water storage equipment is still affected by the water outage even with the assistance of other water storage devices. In this case, the difference between the remaining total flow rate and the total water storage flow rate is calculated as the new water storage flow rate deviation value.

[0130] The methods for generating water outage notification instructions also include: S80: The water type is determined based on the marked water storage equipment and user habits.

[0131] Water usage type refers to the classification of users' main water needs during water outages, such as drinking, cooking, and washing. Water usage type is determined by combining the attributes of the water storage device (such as whether it is a kettle or water heater) with user usage habits.

[0132] S81: To obtain the maximum water consumption based on the water outage period, user habits, and water usage type.

[0133] Maximum water consumption refers to the maximum water consumption threshold corresponding to the type of water demanded by the user during the water outage period. It is calculated by retrieving the total flow rate of the equipment belonging to the water type during the water outage period from the user's usage habits.

[0134] S82: Update the maximum water consumption based on the water storage flow deviation value.

[0135] The sum of the water storage flow deviation and the maximum water consumption is calculated as the new maximum water consumption.

[0136] S83: Obtain the deviation time point based on the flow deviation duration and the water outage period.

[0137] The deviation time point refers to the time point at which a user experiences a deviation in flow usage. It is determined by taking the starting time of the flow deviation duration during the water outage period as the deviation time point (equivalent to the time point when the water storage flow deviation value appears).

[0138] S84: Add the maximum water consumption, water type, and deviation time point to the water outage notification command.

[0139] When generating the S17 water outage notification instruction, for users predicted to experience water shortages (water storage flow deviation > 0), their updated maximum water consumption, water type, and deviation time point are added as key fields to the extended information of the notification instruction. For example, the instruction sent to the emergency water supply dispatch center may include: "User A (hospital) is expected to experience water shortages after 15:00 and needs to ensure drinking water supply, with a minimum water requirement of 200 liters."

[0140] Methods for obtaining the target valve shut-off scheme include: S90: Generate a simulated valve shut-off scheme based on abnormal coordinates and a spatial map of the water supply network.

[0141] A simulated valve shut-off scheme refers to a preliminary sequence of one or more valve shut-off operations to isolate a burst pipe point located by abnormal coordinates. This is achieved by mapping the abnormal coordinates to the corresponding pipe segment node in the water supply network spatial diagram, using this node as the root to perform a breadth-first search (BFS) to find all controllable valves upstream of it, and combining them to form the minimum set of valves that can cut off the water flow. Based on this, multiple candidate operation sequences are generated as simulated valve shut-off schemes.

[0142] S91: Obtain the node influence weight by simulating the valve closing scheme.

[0143] The node impact weight refers to the quantified value of the expected water outage impact on each user node in the graph after executing a simulated valve closure scheme. The node impact weight is obtained by performing multiple rounds of inference on the graph using the Water-LLM large model, and then matched with the node impact weight from a preset weight lookup table according to the flow change.

[0144] The weight lookup table stores the node influence weights corresponding to different traffic changes. The greater the traffic change, the greater the node influence weight. The parameters in the weight lookup table are set in advance by those skilled in the art based on actual conditions and will not be elaborated here.

[0145] S92: Match the impact weight of water outages based on user profile tags.

[0146] The impact weight of water outage refers to the weight coefficient that is pre-assigned based on user profile tags and reflects the inherent importance of the user. For example, the "key guarantee unit" tag is matched with a high weight (e.g., 1.0), and the "ordinary resident" tag is matched with a basic weight (e.g., 0.1). The impact weight of water outage is matched from the weight lookup table through user profile tags.

[0147] The weighted comparison table stores the weights of water outage impact corresponding to different user profile tags, which will not be elaborated here.

[0148] S93: Compare the impact weight of water outage with the preset baseline impact threshold to select the marked impact weight.

[0149] The baseline impact threshold is a weighted threshold set by technical personnel to filter users with tags.

[0150] The labeled impact weight refers to the impact weight of water outages that exceeds the benchmark impact threshold. By analyzing the exceedance of the water outage impact weight with the benchmark impact threshold, the water outage impact weight that exceeds the benchmark impact threshold is used as the labeled impact weight.

[0151] S94: The buffer coefficient is obtained based on the water storage flow deviation, water storage equipment, and user habits.

[0152] The buffer coefficient is a coefficient value used to dynamically adjust the actual degree of impact on user nodes. The buffer coefficient is matched from the weighted lookup table by the water storage flow deviation value, water storage equipment, and user usage habits.

[0153] The weighted comparison table stores different water storage flow deviation values, water storage equipment, and buffer coefficients corresponding to user habits. When user habits and water storage equipment remain unchanged, the larger the water storage flow deviation value, the larger the correction coefficient, which means that the water storage will have less impact on the user's water supply during a water outage. This will not be elaborated on here.

[0154] S95: Combine the node influence weight, buffer coefficient, and label influence weight to calculate the detection reward value, and take the simulated valve closing scheme corresponding to the largest detection reward value as the target valve closing scheme.

[0155] The detection reward value is a quantitative indicator used to evaluate the merits of simulated valve-closing schemes. Using a large language model (LLM) as the inference engine, the impact of multiple simulated valve-closing schemes is extrapolated. The detection reward value of each simulated valve-closing scheme is calculated by using a reinforcement learning reward function with node influence weights, buffer coefficients, and label influence weights. Then, a new scheme is generated and evaluated iteratively through reinforcement learning algorithms (such as policy gradients). Finally, the simulated valve-closing scheme with the highest detection reward value is selected as the target valve-closing scheme.

[0156] Reinforcement learning reward function: Detection reward value = -α * ∑(node ​​influence weight * buffer coefficient) + β * ∑(label influence weight). α and β are adjustment coefficients pre-defined by those skilled in the art.

[0157] Based on the same inventive concept, embodiments of the present invention provide an intelligent assessment system for the impact of water outages based on water usage behavior profiling, comprising: The acquisition module is used to acquire water supply network specifications, water supply user parameters, historical usage flow, and historical usage time points; The memory is used to store a program for an intelligent assessment method of the impact of water outages based on water use behavior profiles; The processor is used to load and execute programs stored in memory.

[0158] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0159] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent assessment of the impact of water outages based on water usage behavior profiling, characterized in that, include: S10: Collect water supply network specifications and water user parameters; S11: Generate user profile tags based on water supply user parameters; S12: Construct a spatial map of the water supply network based on the specifications of the water supply network and user profile tags; S13: Retrieve the detected flow rate from the water supply user parameters; S14: The detection time point is obtained by detecting the flow rate and the specifications of the water supply network; S15: Combine the water supply network spatial map, user profile tags, detection time points, detection flow rates, and preset water outage periods to obtain the target water outage vector; S16: Input the preset abnormal coordinates into the water supply network spatial map, and combine the user profile tags and the target water outage vector to perform valve shut-off simulation to obtain the target valve shut-off scheme. S17: Generate and send water outage notification instructions based on user profile tags, target valve shut-off schemes, and target water outage vectors.

2. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 1, characterized in that, Methods for constructing spatial maps of water supply networks include: S20: Construct a pipeline connection model based on water supply network specifications; S21: Based on the water supply user parameters, retrieve the marked pipe from the pipeline connection model and use the detected flow rate of the marked pipe as the marked flow rate; S22: Retrieve valve positions from the pipeline connection model; S23: Use the pipeline connection model to control the position of each valve to change according to a preset change threshold, and update the marked flow rate; S24: Calculate the difference in labeled flow before and after the update as the flow change value; S25: Combine flow rate changes, valve locations, user profile tags, water supply user parameters, and pipeline connection models to construct a spatial map of the water supply network.

3. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 2, characterized in that, Methods for obtaining the target water outage vector include: S30: Retrieve water supply equipment from water supply user parameters; S31: The pipeline to be inspected is determined based on the water supply network spatial diagram, water supply equipment, and water supply user parameters; S32: Collect and monitor the historical flow rate and historical usage time of the pipeline; S33: Based on historical usage flow, historical usage time points, and water supply equipment, user habits are obtained; S34: Select water storage equipment based on water supply usage equipment and historical usage flow; S35: Obtain water storage usage parameters based on water storage equipment and user habits; S36: Output a preset water outage pre-notification and combine it with user profile tags, water storage usage parameters and detected flow rate to obtain the target water outage vector.

4. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 3, characterized in that, Other methods for obtaining the target water outage vector include: S40: Obtain the user's water storage period based on the pre-outage notification and the outage period; S41: Update the detected flow rate and use the detected flow rate during the user's water storage period as the target flow rate; S42: Retrieve the baseline water storage flow rate from the water storage usage parameters; S43: Compare the consistency between the target flow rate and the baseline water storage flow rate, and combine them with the user's water storage period to obtain the target water storage flow rate, and take the time point of the target water storage flow rate as the target water storage time point; S44: Obtain the water storage usage period based on the target water storage time and the water outage period; S45: Update the target water storage flow rate based on water storage usage periods and user habits; S46: Combine the target water storage flow rate, water storage usage parameters, and user profile tags to obtain the target water outage vector.

5. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 4, characterized in that, Other methods for obtaining the target water outage vector include: S50: The baseline usage flow rate is obtained by using water storage usage parameters and water outage periods; S51: Compare the deviation of the target water storage flow rate from the baseline usage flow rate to calculate the water storage flow rate deviation. S52: Obtain the duration of the flow deviation based on the water storage flow deviation value and the water outage period; S53: Obtain the water storage impact vector based on the duration of flow deviation, the value of water storage flow deviation, and user profile tags; S54: Combine user profile tags with water storage impact vector to obtain target water outage vector.

6. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 5, characterized in that, Methods for obtaining the water storage flow deviation value also include: S60: Retrieve other water storage devices from water supply user parameters; S61: To obtain a marked water storage device based on the water storage device and other water storage devices; S62: Obtain other water storage flow rates and total water storage flow rates by considering other water storage devices and user habits; S63: Compare the consistency of the target flow rate with other water storage flow rates to obtain the marked deviation time point based on the water storage flow rate deviation value and the marked water storage equipment; S64: Retrieve the frequency of use of the markings on the water storage equipment from user usage habits; S65: Update the water storage flow deviation value based on the total water storage flow, the time point of the marking deviation, the reference usage flow of the marked water storage equipment, and the marking usage frequency.

7. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 6, characterized in that, Methods for updating the water storage flow deviation value include: S70: Use the baseline usage flow rate of the marked water storage equipment as the marked usage flow rate; S71: Obtain the remaining water usage time based on the marked deviation time point and the water outage period; S72: Obtain the remaining water usage count based on the remaining water usage duration and the marked usage frequency; S73: Calculate the product of the remaining water usage times and the marked usage flow rate, and use it as the total remaining usage flow rate; S74: Compare the remaining total usage flow with the total storage flow to calculate the new storage flow deviation value.

8. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 6, characterized in that, The methods for generating water outage notification instructions also include: S80: Determine the water usage type based on the marked water storage equipment and user habits; S81: To determine the maximum water consumption based on the water outage period, user habits, and water usage type; S82: Update the maximum water consumption based on the water storage flow deviation value; S83: Obtain the deviation time point based on the duration of the flow deviation and the water outage period; S84: Add the maximum water consumption, water type, and deviation time point to the water outage notification command.

9. The intelligent assessment method for the impact of water outages based on water usage behavior profiling as described in claim 6, characterized in that, Methods for obtaining the target valve shut-off scheme include: S90: Generate a simulated valve shut-off scheme based on abnormal coordinates and water supply network spatial diagram; S91: Obtain the node influence weights by simulating valve-closing schemes; S92: Match the weight of water outage impact based on user profile tags; S93: Compare the impact weight of water outage with the preset baseline impact threshold to select the marked impact weight; S94: The buffer coefficient is obtained based on the water storage flow deviation, water storage equipment, and user habits. S95: Combine the node influence weight, buffer coefficient, and label influence weight to calculate the detection reward value, and take the simulated valve closing scheme corresponding to the largest detection reward value as the target valve closing scheme.

10. A smart assessment system for the impact of water outages based on water usage behavior profiling, characterized in that, include: The acquisition module is used to obtain the specifications of the water supply network and the parameters of water supply users. A memory for storing a program that implements a method for intelligent assessment of the impact of water outages based on water use behavior profiling as described in any one of claims 1 to 9; The processor is used to load and execute programs stored in memory.

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