Intelligent water management method, device and terminal equipment

Abstract

The application provides a kind of intelligent water management method, device and terminal equipment, applicable to data processing technical field, the method comprises: to water affair pipe network operation monitoring information is classified and processed, obtains pipe network area water affair operation demand information;Intelligent sensing device state information is analyzed and processed to obtain intelligent sensing device communication state analysis information;According to pipe network area water affair operation demand information, intelligent sensing device communication state analysis information, user water consumption behavior information and preset water affair management scheduling resource information, obtain initial water affair management information;Initial water affair management information is optimized and processed to obtain target water affair management information, to carry out intelligent water scheduling management according to target water affair management information by water affair management equipment.The application realizes the intelligentization, precision control of water scheduling management, effectively improves the operation efficiency of water system, reduces pipe network fault risk, and protects the reasonable allocation and efficient use of water resources.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to intelligent water management methods, devices and terminal equipment. Background Technology

[0002] Driven by both the construction of Digital China and the high-quality development of the water industry, smart water management has become the core direction of industry transformation, with the maturity of technologies such as the Internet of Things, big data, and artificial intelligence providing solid support.

[0003] In existing technologies, intelligent sensing devices are deployed at key nodes of the water supply network to collect monitoring information related to network operation, such as flow and pressure. At the same time, user water usage data is obtained through terminals such as smart water meters. Based on this, water supply scheduling suggestions are generated through basic algorithms to achieve water management functions.

[0004] However, existing technologies lack sufficient matching with pipeline network requirements and fail to consider the communication status of intelligent sensing devices, making it difficult to achieve precise and intelligent water dispatching and management. Summary of the Invention

[0005] In view of this, embodiments of this application provide an intelligent water management method, apparatus, and terminal equipment, aiming to solve the problem in the prior art that it is difficult to achieve precise and intelligent water scheduling and management.

[0006] The first aspect of this application provides an intelligent water management method, including:

[0007] Acquire multiple water supply network operation monitoring information, multiple water supply smart sensor device status information, and multiple user water usage behavior information;

[0008] The monitoring information of the multiple water supply networks is classified and processed to obtain water supply operation demand information for multiple network areas;

[0009] The status information of the multiple intelligent water sensing devices is parsed and processed to obtain the communication status parsing information of the multiple intelligent sensing devices;

[0010] Based on the preset water management information generation model, multiple initial water management information is obtained according to the water operation demand information of multiple pipe network areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information.

[0011] The initial water management information is optimized to obtain target water management information, which is then used by water management equipment for intelligent water scheduling and management.

[0012] A second aspect of this application provides an intelligent water management device, comprising:

[0013] The information acquisition module is used to acquire multiple water supply network operation monitoring information, multiple water supply intelligent sensing device status information, and multiple user water use behavior information.

[0014] The pipeline area water operation demand information generation module is used to classify and process the multiple water pipeline operation monitoring information to obtain multiple pipeline area water operation demand information;

[0015] The intelligent sensing device communication status parsing information generation module is used to parse and process the status information of the multiple intelligent water sensing devices to obtain the communication status parsing information of the multiple intelligent sensing devices.

[0016] The initial water management information generation module is used to generate multiple initial water management information based on a preset water management information generation model, according to the water operation demand information of multiple pipe network areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information.

[0017] The target water management information generation module is used to optimize the multiple initial water management information to obtain target water management information, so as to carry out intelligent water scheduling and management through water management equipment based on the target water management information.

[0018] A third aspect of this application provides a terminal device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the intelligent water management method described in the first aspect above.

[0019] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, which, when executed by a processor, implements the steps of the intelligent water management method described in the first aspect above.

[0020] Compared with the prior art, the beneficial effects of this application are as follows: This application is used to achieve comprehensive integration and in-depth processing of multi-source information in water systems. By classifying the operation monitoring information of multiple water pipe networks, it clarifies the water operation needs of different areas. By analyzing the status information of multiple intelligent water sensing devices, it grasps the communication operation status of the devices. Then, based on a preset water management information generation model, it generates and optimizes water management information by combining multi-dimensional information. This enables intelligent water scheduling and management to accurately match the operation needs of the pipe network, efficiently utilize scheduling resources, and conform to users' water use behavior patterns, thereby improving the scientific, accurate, and timely nature of water management. This ensures the stable operation of the water supply system, reduces the leakage rate and failure rate of the pipe network, improves the utilization efficiency of water resources, promotes the development of the water industry towards intelligence and refinement, and contributes to the construction and development of smart cities. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 1 of this application;

[0023] Figure 2 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 2 of this application;

[0024] Figure 3 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 3 of this application;

[0025] Figure 4 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 4 of this application;

[0026] Figure 5 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 5 of this application;

[0027] Figure 6 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment Six of this application;

[0028] Figure 7 This is a schematic diagram illustrating the implementation process of the intelligent water management method provided in Embodiment 7 of this application;

[0029] Figure 8 This is a schematic diagram of the intelligent water management device provided in the embodiments of this application;

[0030] Figure 9 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0031] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0032] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0033] Figure 1 A flowchart illustrating the implementation of the intelligent water management method provided in Embodiment 1 of this application is shown, and is described in detail below:

[0034] Step S101: Obtain multiple water supply network operation monitoring information, multiple water supply smart sensor device status information, and multiple user water usage behavior information.

[0035] In this embodiment, the multiple water pipeline network operation monitoring information can refer to structured information such as flow rate, pressure, water quality, and leakage during the operation of the water pipeline network. This information can be obtained by monitoring terminals deployed at key nodes of the pipeline network to collect dynamic operation data of the pipeline network in real time, directly acquiring information such as flow rate changes, pressure fluctuation range, and water quality parameters. Alternatively, it can be achieved through real-time communication between the monitoring terminals and the management platform using Internet of Things (IoT) technology, combined with data analysis algorithms to infer potential operational risks of the pipeline network, such as automatically generating pipeline network fault warning information when pipeline network pressure is continuously abnormal. It can also include historical operation records of the pipeline network for subsequent analysis of periodic operation patterns, such as pipeline network load changes during seasonal peak water usage periods. Status information from multiple smart water sensors can refer to the operational status and communication capability parameters of various smart sensors deployed in the water system. This can include the power supply status, data acquisition frequency, signal transmission strength, etc. It can be achieved by the device's built-in status monitoring module collecting hardware operation data in real time and reporting it to the water management platform via wireless communication protocols, which then retrieves the data. Alternatively, it can be achieved by measuring parameters such as communication link stability and data transmission latency using signal detection technology to assess the device's communication quality. It can also be achieved by obtaining the device's installation location information using positioning technology and combining it with the pipeline network layout to assess the device's monitoring coverage. User water usage behavior information can refer to quantifiable characteristics such as the time of day, flow rate, and frequency of water usage. This can be achieved by recording users' daily water usage data through terminal devices such as smart water meters, forming raw data records of user water usage. Alternatively, it can be achieved by associating user water usage scenarios, such as residential water use, commercial water use, and industrial production water use, to analyze the water usage behavior patterns of different types of users, such as the concentrated water usage characteristics of industrial users during production periods.

[0036] Step S102: Classify and process the multiple water supply network operation monitoring information to obtain water supply operation demand information for multiple network areas.

[0037] In this embodiment, the first step is to determine the standards for dividing the pipeline network area and the dimensions for data classification. Multiple water pipeline network operation monitoring information are initially divided according to dimensions such as the administrative region, functional region, and pipe diameter. Then, the feature similarity of the pipeline network operation monitoring information in different regions is calculated. If the pipeline network operation monitoring information in a certain region has a high degree of consistency in core indicators such as flow rate and pressure, it is classified into the same category. The pipeline network operation monitoring information of the core category constitutes the basic operation data of the region, the pipeline network operation monitoring information of the edge category serves as supplementary reference data, and the pipeline network operation monitoring information of the noise category is separately marked and verified. By traversing the category affiliation relationship of all pipeline network operation monitoring information, pipeline network areas with the same operation characteristics are divided into the same group. Then, combined with the water demand characteristics of the region and the pipeline network carrying capacity, multiple pipeline network area water operation demand information is generated. Thus, the differentiated operation needs of different pipeline network areas are clarified through classification processing, effectively addressing the complex and diverse characteristics of pipeline network operation monitoring information.

[0038] Step S103: The status information of the multiple intelligent water sensing devices is parsed and processed to obtain the communication status parsing information of the multiple intelligent sensing devices.

[0039] In this embodiment, parameters such as power supply status, signal transmission strength, and data transmission delay are extracted from the status information of multiple smart water management sensors and mapped to a multi-dimensional set of device status features. Based on the similarity measurement between feature sets, a device status association is constructed. The distribution density of each device status feature set in the feature space is calculated. Regions with distribution density significantly higher than a threshold are classified as normal communication status clusters, while regions with abrupt changes in distribution density are identified as abnormal communication statuses. By traversing the feature distribution of all smart water management sensor status information, device statuses with similar communication characteristics are classified into the same category. Simultaneously, indicators such as communication stability and data transmission success rate for each category are calculated. This process completes the parsing and processing of the status information of multiple smart water management sensors, obtaining parsed communication status information for multiple smart sensors. This process effectively identifies normal and abnormal communication status patterns by analyzing the inherent patterns of device status through feature distribution analysis.

[0040] Step S104: Based on the preset water management information generation model, multiple initial water management information is obtained according to the water operation demand information of multiple pipeline areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information.

[0041] In this embodiment, the preset water management information generation model can be manually set, a reinforcement learning model, or an LSTM model. The preset water management scheduling resource information can also be manually preset. It can be achieved by fusing multiple water operation demand information from multiple pipe network areas, communication status analysis information from multiple intelligent sensing devices, water usage behavior information from multiple users, and the preset water management scheduling resource information from multiple dimensions, randomly generating multiple water management strategy combinations as initial schemes. These initial schemes simulate resource allocation modes under different scheduling scenarios, with each scheme corresponding to a specific content of water management information. Then, by evaluating the performance of the initial schemes in meeting pipe network operation needs, matching device communication status, aligning with user water usage behavior, and utilizing scheduling resources, a comprehensive score is calculated for each initial scheme, thereby generating multiple initial water management information sets.

[0042] Step S105: Optimize the multiple initial water management information to obtain target water management information, so as to carry out intelligent water scheduling and management through water management equipment based on the target water management information.

[0043] In this embodiment, the solution can be based on a comprehensive score of initial water management information, simulating the solution iteration and optimization mechanism in an intelligent decision-making system. Initial water management information with higher scores is further expanded to improve its resource allocation and demand matching. Initial water management information with lower scores undergoes parameter adjustments and strategy corrections to avoid falling into the decision-making trap of local optima. Then, in each optimization process, the optimization weights are dynamically adjusted based on changes in the solution's score and its adaptation to the actual application scenario, continuously updating the content of the water management information. After multiple rounds of optimization iterations, the solution with the optimal comprehensive score, i.e., the target water management information, is finally selected to guide water management equipment in efficient and accurate intelligent water scheduling and management.

[0044] The intelligent water management method provided in this application embodiment can achieve comprehensive integration and in-depth processing of multi-source information of the water system. By classifying the operation monitoring information of multiple water pipe networks, it clarifies the water operation needs of different areas. By analyzing the status information of multiple intelligent water sensor devices, it grasps the communication operation status of the devices. Then, based on the preset water management information generation model, it generates and optimizes water management information in combination with multi-dimensional information. This enables intelligent water scheduling and management to accurately match the operation needs of the pipe network, efficiently utilize scheduling resources, and conform to users' water use behavior patterns, thereby improving the scientific, accurate, and timely nature of water management. This ensures the stable operation of the water supply system, reduces the leakage rate and failure rate of the pipe network, improves the utilization efficiency of water resources, promotes the development of the water industry towards intelligence and refinement, and contributes to the construction and development of smart cities.

[0045] Figure 2The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 is that step S102 specifically includes:

[0046] Step S201: Analyze and process the multiple water network operation monitoring information to obtain multiple network operation monitoring text information, multiple network operation monitoring generation time information, and multiple network operation monitoring business type information.

[0047] In this embodiment, structured data parsing technology can be used to split and extract the recorded content from multiple water supply network operation monitoring information. After removing invalid and redundant data, multiple network operation monitoring text information is obtained, covering descriptions of network flow changes, pressure fluctuation records, water quality indicator test descriptions, etc. At the same time, the data collection timestamps are extracted from the monitoring data log files and converted into multiple network operation monitoring generation time information. Based on the network function attributes corresponding to the monitoring data, multiple network operation monitoring business type information is determined. For example, for pressure anomaly data collected from a certain section of the residential water supply main pipeline, after parsing, text information such as "pressure drop in the residential water supply main pipeline" is obtained, with the generation time being the specific monitoring collection time and the business type being "residential domestic water supply".

[0048] Step S202: Perform keyword matching processing based on the multiple pipeline operation monitoring text information and the preset pipeline operation demand information database to obtain multiple pipeline operation monitoring text keyword matching information.

[0049] In this embodiment, the preset pipeline operation demand information database can be manually preset. It can be achieved by comparing core keywords from multiple pipeline operation monitoring text messages with standard keywords in the preset database one by one, calculating the keyword matching similarity and corresponding weight percentage. For example, if the keyword "pressure anomaly" in the pipeline operation monitoring text message has a 95% matching similarity with the keyword "pipeline pressure fault early warning" in the preset database, then multiple corresponding pipeline operation monitoring text keyword matching information are generated, including the matched keyword content, matching similarity score, and corresponding pipeline operation demand type.

[0050] Step S203: Based on the keyword matching information of the multiple pipeline operation monitoring texts, the generation time information of the multiple pipeline operation monitoring, and the business type information of the multiple pipeline operation monitoring, the water operation demand information of multiple pipeline areas is obtained.

[0051] In this embodiment, multiple pipeline operation monitoring text keyword matching information can be associated and integrated with multiple pipeline operation monitoring generation time information and multiple pipeline operation monitoring business type information. For example, if the text keyword matching information corresponding to a certain pipeline operation monitoring data is "pressure anomaly", the generation time information shows that the time interval from the current is less than the preset emergency response time threshold, and the business type information is "industrial water supply dedicated pipeline", then pipeline area water affairs operation demand information containing the urgency of demand, pipeline business attributes, and keyword matching results can be generated, and finally multiple structured pipeline area water affairs operation demand information can be generated.

[0052] The intelligent water management method provided in this application embodiment achieves refined classification of pipeline network operation monitoring data by splitting and extracting multiple water pipeline network operation monitoring information, matching keywords, and integrating multi-dimensional information association. This makes the water operation demand information of multiple pipeline network areas more hierarchical and targeted, and can accurately match the urgency of operation demand, business attributes, and text content characteristics of different pipeline network areas. It provides more detailed demand dimensions for the generation and optimization of multiple initial water management information, improves the accuracy and effectiveness of intelligent water management, and strengthens the adaptability to different types of water pipeline network operation scenarios.

[0053] Figure 3 The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment 3 of this application is shown. Its difference from Embodiment 1 described above lies in:

[0054] The status information of the intelligent water sensing device includes the operating time information, data acquisition frequency information, channel status information, power status information, and communication resource occupancy status information.

[0055] Step S103 specifically includes:

[0056] Step S301: Based on the data acquisition frequency information of the multiple intelligent water sensing devices, the channel status information of the multiple intelligent water sensing devices, the communication resource occupancy status information of the multiple intelligent water sensing devices, and the multiple preset intelligent water sensing device status analysis weight information, calculate the multiple intelligent water sensing device status analysis variables.

[0057] In this embodiment, the preset weight information for the status analysis of intelligent water sensors can be manually preset, or it can be a parameter pre-set based on the operating characteristics of the intelligent water sensors and the monitoring needs of the water network, used to measure the importance of different device status indicators. For example, for network monitoring scenarios with high real-time data acquisition requirements, the weight of the data acquisition frequency information of multiple intelligent water sensors is set to a higher proportion; for monitoring scenarios with high wireless communication stability requirements, the weight of the channel status information of multiple intelligent water sensors is set to a higher proportion. The weighted sum can be calculated based on the preset weight information for the status analysis of multiple intelligent water sensors, including the data acquisition frequency information, channel status information, and communication resource occupancy status information of multiple intelligent water sensors, and the result of the weighted sum is used as the status analysis variable for multiple intelligent water sensors.

[0058] Step S302: Based on the running time information of the multiple intelligent water sensing devices, the power status information of the multiple intelligent water sensing devices, and the status parsing variables of the multiple intelligent water sensing devices, the communication status parsing information of the multiple intelligent sensing devices is obtained.

[0059] In this embodiment, the changing trends of the status resolution variables of multiple intelligent water management sensors over time are analyzed by combining the operating time information of multiple intelligent water management sensors. For example, if the operating time information of a certain intelligent water management sensor shows that it has been running at high load for a long time and the corresponding status resolution variables continue to deteriorate, such as a decrease in data acquisition frequency and a deterioration in channel status, then the device is determined to have entered a fatigue warning state. If the status resolution variables fluctuate drastically in a short period of time, such as a sudden surge in communication resource occupancy, the specific time period of the anomaly can be located by combining the operating time information, and intelligent sensor communication status resolution information including anomaly type, timestamp, and severity can be generated. At the same time, by comparing the status resolution variables at different time points, the operating modes of the intelligent water management sensors, such as normal operation, early warning reminder, and fault alarm, can be divided, and finally, multiple structured intelligent sensor communication status resolution information are output.

[0060] The intelligent water management method provided in this application embodiment decomposes, weights, integrates, and dynamically analyzes the status information of multiple intelligent water sensing devices from multiple dimensions. This enables a refined assessment of the operating status of the intelligent water sensing devices, providing more comprehensive and dynamic device status data support for generating accurate initial water management information. As a result, it optimizes the allocation strategy for water management scheduling resources, ensures the stability and efficiency of water network monitoring data transmission, and improves the overall reliability of the intelligent water management system.

[0061] Figure 4The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment 4 of this application is shown. Its difference from Embodiment 3 described above lies in:

[0062] Multiple preset weight information for the status analysis of intelligent water sensors include preset weight information for the data acquisition frequency of intelligent water sensors, preset weight information for the channel status of intelligent water sensors, preset weight information for the communication resource occupancy status of intelligent water sensors, and preset weight information for the status enhancement of intelligent water sensors.

[0063] Step S301 specifically includes:

[0064] Step S401: Based on the preset water intelligent sensor data acquisition frequency weight information, preset water intelligent sensor channel state weight information, and preset water intelligent sensor communication resource occupancy state weight information, perform weighted summation calculation on the multiple water intelligent sensor data acquisition frequency information, multiple water intelligent sensor channel state information, and multiple water intelligent sensor communication resource occupancy state information to obtain multiple water intelligent sensor state weighted information.

[0065] In this embodiment, the preset weight information for the data acquisition frequency of the intelligent water sensor can be preset manually, as can the preset weight information for the channel status of the intelligent water sensor and the preset weight information for the communication resource occupancy status of the intelligent water sensor. The specific values ​​of each preset weight information can be determined first. These values ​​are set according to the different needs of different water network monitoring scenarios. For example, in a real-time monitoring scenario of a city core area network, the preset weight information for the data acquisition frequency of the intelligent water sensor and the preset weight information for the channel status of the intelligent water sensor are set to a higher level, while the preset weight information for the communication resource occupancy status of the intelligent water sensor is set to a moderate level. Then, the data acquisition frequency information, channel status information, and communication resource occupancy status information corresponding to each intelligent water sensor are matched with the corresponding preset weight information. The matched information is then weighted and summed to obtain the weighted status information of multiple intelligent water sensors corresponding to each intelligent water sensor, which is used to initially integrate the influence of the status indicators of each core device.

[0066] Step S402: Multiply the weighted information of the multiple intelligent water sensor devices' states with the preset weighted information of the intelligent water sensor devices' states to calculate the multiple intelligent water sensor devices' state analysis variables.

[0067] In this embodiment, the preset weighted information for enhancing the status of intelligent water sensors can be manually preset. This weighted information is used to strengthen the influence of key features in the weighted information of multiple intelligent water sensor statuses. For example, for intelligent water sensors used for water quality monitoring, the preset weighted information for enhancing the status of intelligent water sensors can be set to a higher value to highlight the impact of the device status on the accuracy of water quality monitoring data. Alternatively, the weighted information of multiple intelligent water sensor statuses obtained in step S401 can be multiplied by the preset weighted information for enhancing the status of intelligent water sensors. This multiplication operation amplifies the representational strength of key status features, thereby generating multiple analytical variables for the status of intelligent water sensors to accurately reflect the core operating status of the intelligent water sensors.

[0068] The intelligent water management method provided in this application realizes hierarchical weighting and feature enhancement processing of the status information of intelligent water sensing devices, so that the generated status analysis variables of multiple intelligent water sensing devices are more in line with the core needs of different monitoring scenarios, improve the accuracy and pertinence of device status assessment, and provide high-quality data support for the subsequent generation of communication status analysis information of multiple intelligent sensing devices, thereby optimizing the overall decision-making efficiency and reliability of intelligent water management.

[0069] Figure 5 The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 1 is that step S104 specifically includes:

[0070] Step S501: Match the multiple water operation demand information of the multiple pipeline areas, the multiple user water use behavior information and the preset water management and scheduling resource information to generate multiple user water scheduling demand priority information and multiple user water scheduling resource quantity information; the user water use behavior information, user water scheduling demand priority information and user water scheduling resource quantity information correspond one-to-one.

[0071] In this embodiment, the preset water management and scheduling resource information can be manually preset. This can be achieved by first extracting core features such as the urgency of demand and the scale of regional water use from multiple pipeline area water operation demand information, and simultaneously extracting key features such as water use time period, water use frequency, and water flow from multiple user water use behavior information. These features are then matched with information such as resource type, total resource amount, and resource distribution from the preset water management and scheduling resource information. Based on the matching results and combined with preset priority determination rules, the water scheduling needs of different users are prioritized, generating multiple user water scheduling demand priority information. For example, the water use demand of industrial users during production hours has a higher priority than the water use demand of residents during off-peak hours. Simultaneously, based on the matched pipeline operation demand and user water use behavior characteristics, the amount of resources required to meet the water needs of each user is estimated, generating multiple user water scheduling resource quantity information, thereby achieving a one-to-one correspondence between multiple user water use behavior information and multiple user water scheduling demand priority information and multiple user water scheduling resource quantity information.

[0072] Step S502: Based on the preset water management information generation model, multiple initial water management information is obtained according to the priority information of multiple users' water scheduling needs, the information on the amount of water scheduling resources of multiple users, the communication status parsing information of multiple intelligent sensing devices, and the preset water management scheduling resource information.

[0073] In this embodiment, the preset water management information generation model can be manually preset, a reinforcement learning model, or an LSTM model. The preset water management scheduling resource information can also be manually preset. It can be achieved by inputting multiple user water scheduling request priority information, multiple user water scheduling resource quantity information, multiple intelligent sensor device communication status analysis information, and the preset water management scheduling resource information into the preset water management information generation model. The model first determines the order of resource allocation based on the user water scheduling request priority information, clarifies the resource allocation amount corresponding to each priority request based on the user water scheduling resource quantity information, and then judges the reliability of device data transmission by referring to the communication status analysis information of multiple intelligent sensor devices to ensure that scheduling instructions can be effectively conveyed. Simultaneously, it avoids exceeding the total resource allocation limit by combining the preset water management scheduling resource information. Through the model's computational processing, multiple initial water management information sets are generated, each containing a resource allocation scheme, scheduling execution sequence, and device coordination instructions. Each initial water management information set corresponds to a set of user demand and resource configuration matching schemes.

[0074] The intelligent water management method provided in this application embodiment achieves a precise correlation between water scheduling needs and resource allocation, making the generated initial water management information more aligned with the differentiated needs of different users and the actual resource supply, improving the pertinence and rationality of the initial water management information, providing high-quality basic data for subsequent optimization processing, thereby optimizing the accuracy and resource utilization efficiency of intelligent water scheduling management, and enhancing adaptability to complex water management scenarios.

[0075] Figure 6 The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment Six of this application is shown. Its difference from Embodiment One described above lies in:

[0076] The initial water management information includes initial water scheduling resource allocation priority information, initial water scheduling data transmission rate information, and initial water scheduling channel gain information;

[0077] Step S105 specifically includes:

[0078] Step S601: Based on the water operation demand information of the multiple pipeline areas and the user water scheduling resource quantity information corresponding to the multiple initial water scheduling resource allocation priority information, calculate the matching degree information of multiple initial water scheduling demands.

[0079] In this embodiment, for each initial water dispatch resource allocation priority information, the corresponding user water dispatch resource quantity information can be extracted. Then, this user water dispatch resource quantity information is compared and analyzed with the resource demand characteristics in the water operation demand information of multiple pipeline areas to determine the degree of matching between the two. For example, if the initial water dispatch resource allocation priority information corresponds to the user water dispatch resource quantity information of an industrial user, and the resource demand of the industrial pipeline area in the water operation demand information of multiple pipeline areas highly matches the resource quantity information, a higher value of initial water dispatch demand matching degree information is generated; if the matching degree is low, a lower value of initial water dispatch demand matching degree information is generated, ultimately obtaining multiple initial water dispatch demand matching degree information reflecting the degree of matching between resource quantity allocation and pipeline operation demand.

[0080] Step S602: Based on the multiple initial water scheduling resource allocation priority information, multiple initial water scheduling data transmission rate information, and multiple initial water scheduling channel gain information, multiple initial water scheduling resource utilization information are calculated.

[0081] In this embodiment, the resource allocation order for various water dispatching needs can be determined first based on multiple initial water dispatching resource allocation priority information. Then, the transmission efficiency of resources can be determined by combining multiple initial water dispatching data transmission rate information. At the same time, the stability and effectiveness of resource transmission can be evaluated by referring to multiple initial water dispatching channel gain information. Finally, the actual utilization of water management dispatching resources can be comprehensively calculated. For example, the resource usage of high-priority pipeline fault emergency dispatching needs can be combined with its corresponding initial water dispatching data transmission rate information and initial water dispatching channel gain information to calculate the actual utilization efficiency of this part of the resources. Then, the resource utilization of other priority needs can be superimposed to generate multiple initial water dispatching resource utilization rate information to measure the efficiency of resource allocation.

[0082] Step S603: Based on the multiple initial water dispatch demand matching degree information, multiple initial water dispatch resource utilization rate information, and preset water management dispatch efficiency measurement weight information, multiple initial water dispatch efficiency measurement information are calculated to obtain multiple initial water dispatch efficiency measurement information.

[0083] In this embodiment, the preset water management scheduling efficiency measurement weight information can be manually preset. This weight information is used to distinguish the importance of multiple initial water scheduling demand matching degree information and multiple initial water scheduling resource utilization rate information in evaluating water management scheduling efficiency. For example, in a water-scarce pipeline scheduling scenario, the weight of the initial water scheduling resource utilization rate information in the preset water management scheduling efficiency measurement weight information can be set to a higher level. Based on this preset weight information, the corresponding initial water scheduling demand matching degree information and initial water scheduling resource utilization rate information can be weighted and summed to generate multiple initial water scheduling efficiency measurement information. Each initial water scheduling efficiency measurement information corresponds to a set of initial water management information, which is used to quantitatively evaluate the overall quality of each set of initial water management information.

[0084] Step S604: Based on the multiple initial water dispatch efficiency measurement information, multiple initial water management information, preset water management equipment status parameter optimization step size information, and preset water management equipment status parameter optimization iteration number threshold, target water management information is obtained, so as to perform intelligent water dispatch management through the water management equipment based on the target water management information.

[0085] In this embodiment, the preset optimization step size information for the water management equipment state parameters can be preset manually, and the preset threshold for the number of optimization iterations of the water management equipment state parameters can also be preset manually. The process can begin by selecting initial water management information with higher values ​​from multiple initial water scheduling efficiency metrics as optimization candidates. Then, based on the preset optimization step size information for the water management equipment state parameters, the initial water scheduling resource allocation priority information, initial water scheduling data transmission rate information, and initial water scheduling channel gain information in the candidate initial water management information are gradually adjusted to generate multiple sets of adjusted intermediate water management information, while simultaneously recording the number of optimizations. During the optimization process, if the number of optimizations does not reach the preset threshold for the number of optimization iterations of the water management equipment state parameters, the optimization continues; if the threshold is reached, the optimization stops. Finally, the information corresponding to the maximum value of the initial water scheduling efficiency metrics is selected from all the adjusted intermediate water management information as the target water management information.

[0086] The intelligent water management method provided in this application embodiment enables accurate evaluation and scientific optimization of initial water management information, thereby making the generated target water management information more in line with the needs of pipeline network operation and actual resource utilization, improving the efficiency and rationality of intelligent water scheduling management, and strengthening the adaptability to complex water scheduling scenarios.

[0087] Figure 7 The flowchart illustrating the implementation of the intelligent water management method provided in Embodiment Seven of this application is shown. The difference between this method and Embodiment Six is ​​that step S604 specifically includes:

[0088] Step S701: The initial water management information corresponding to the maximum value of the plurality of initial water scheduling efficiency measurement information is used as the benchmark information for optimizing the state parameters of the water management equipment.

[0089] In this embodiment, multiple initial water dispatch efficiency metrics can be compared and sorted first, and the initial water dispatch efficiency metric with the largest value can be selected. The initial water management information corresponding to this maximum value is the optimization benchmark information for the state parameters of the water management equipment. For example, if the initial water dispatch efficiency metric corresponding to a certain set of initial water management information has the highest value among all information, it indicates that this set of information performs optimally in terms of meeting the network operation requirements and utilizing dispatch resources. Therefore, it is determined as the optimization benchmark, providing the optimal initial starting point for subsequent iterative optimization.

[0090] Step S702: Based on the multiple initial water management information, water management equipment status parameter optimization benchmark information, and preset water management equipment status parameter optimization step size information, multiple intermediate water management equipment status parameter information and intermediate water management equipment status parameter optimization number information are obtained.

[0091] In this embodiment, the preset optimization step size information for the water management equipment status parameters can be manually preset. This step size information clarifies the adjustment range of the initial water scheduling resource allocation priority information, the initial water scheduling data transmission rate information, and the initial water scheduling channel gain information during each iteration of optimization. It can be based on the water management equipment status parameter optimization baseline information, combined with the distribution of multiple initial water management information, and gradually adjusts each parameter in the baseline information according to the preset optimization step size information, generating multiple sets of different intermediate water management equipment status parameter information. Simultaneously, this adjustment process is recorded as one optimization, generating corresponding intermediate water management equipment status parameter optimization count information.

[0092] Step S703: Determine whether the number of optimization iterations of the intermediate water management equipment status parameters is less than the preset threshold for the number of optimization iterations of the water management equipment status parameters; if yes, proceed to step S704; if no, proceed to step S705.

[0093] In this embodiment, the preset threshold for the number of iterations of water management equipment state parameter optimization can be manually set. This threshold is used to control the total number of iterations and avoid over-optimization leading to wasted computational resources or overfitting of optimization results. It can be achieved by comparing the current intermediate number of optimization iterations of water management equipment state parameters with the preset threshold. If the number of optimization iterations has not yet reached the threshold, it indicates that there is still room for optimization; if the number of optimization iterations has reached the threshold, it indicates that the optimization process has been completed.

[0094] Step S704: Use the multiple intermediate water scheduling resource allocation priority information as the initial water scheduling resource allocation priority information, use the intermediate water scheduling data transmission rate information as the initial water scheduling data transmission rate information, use the intermediate water scheduling channel gain information as the initial water scheduling channel gain information, and return to step S601.

[0095] In this embodiment, the intermediate water management resource allocation priority information, intermediate water management data transmission rate information, and intermediate water management channel gain information contained in the status parameter information of multiple intermediate water management devices can be used to replace the original initial water management resource allocation priority information, initial water management data transmission rate information, and initial water management channel gain information, respectively. New initial water management demand matching degree information, initial water management resource utilization rate information, and initial water management efficiency measurement information can be recalculated to achieve iterative optimization and gradually approach a better parameter configuration.

[0096] Step S705: Based on the water operation demand information of multiple pipeline areas and the user water scheduling resource quantity information corresponding to the intermediate water scheduling resource allocation priority information, calculate the intermediate water scheduling demand matching degree information.

[0097] In this embodiment, the intermediate water management equipment status parameter information may be extracted to obtain the intermediate water scheduling resource allocation priority information, find the corresponding user water scheduling resource quantity information, and then compare the user water scheduling resource quantity information with the resource demand characteristics in the water operation demand information of multiple pipeline areas to calculate the degree of fit between the two and generate intermediate water scheduling demand matching degree information. This information is used to evaluate the matching of the iteratively optimized parameter configuration with the pipeline operation demand.

[0098] Step S706: Calculate the intermediate water scheduling resource utilization information based on the multiple intermediate water scheduling resource allocation priority information, intermediate water scheduling data transmission rate information, and intermediate water scheduling channel gain information.

[0099] In this embodiment, the resource allocation order determined by the intermediate water affairs scheduling resource allocation priority information can be combined with the transmission efficiency corresponding to the intermediate water affairs scheduling data transmission rate information and the transmission stability corresponding to the intermediate water affairs scheduling channel gain information. The actual utilization efficiency of the water affairs management scheduling resources after iterative optimization can be comprehensively calculated to generate intermediate water affairs scheduling resource utilization rate information, thereby measuring the efficiency of the optimized resource allocation.

[0100] Step S707: Based on the multiple intermediate water dispatch demand matching degree information, intermediate water dispatch resource utilization rate information, and preset water management dispatch efficiency measurement weight information, multiple intermediate water dispatch efficiency measurement information are calculated.

[0101] In this embodiment, the preset water management scheduling efficiency measurement weight information can be manually preset. This weight information is consistent with the preset weight information in step S603 to ensure the uniformity of the evaluation standard. Based on this preset weight information, the intermediate water scheduling demand matching degree information and the intermediate water scheduling resource utilization rate information are weighted and summed to generate multiple intermediate water scheduling efficiency measurement information. Each piece of information corresponds to a set of intermediate water management equipment status parameter information, which is used to quantitatively evaluate the comprehensive performance of each set of optimized parameter configurations.

[0102] Step S708: The intermediate water management equipment status parameter information corresponding to the maximum value of the multiple intermediate water scheduling efficiency measurement information is used as the target water management information.

[0103] In this embodiment, multiple intermediate water management efficiency metrics can be sorted and compared to select the one with the highest value. The intermediate water management equipment status parameter information corresponding to this maximum value is the target water management information. This target water management information is the optimal parameter configuration obtained after multiple rounds of iterative optimization, which can maximize the balance between the matching degree of pipeline network operation demand and the utilization rate of scheduling resources, providing accurate and efficient decision-making basis for water management equipment to carry out intelligent water management scheduling.

[0104] The intelligent water management method provided in this application improves the rationality and effectiveness of water dispatch parameter configuration by determining and optimizing benchmark information and performing multiple rounds of iterative optimization, combined with dynamic evaluation and parameter update mechanisms. This enables the final generated target water management information to accurately match the water operation needs of multiple pipe network areas, while achieving efficient utilization of water management and dispatch resources. It also improves the dispatch accuracy and operational efficiency of the intelligent water management system, providing more reliable technical support for the refined and intelligent development of the water industry.

[0105] Corresponding to the method in the above embodiments, Figure 8 A structural block diagram of the intelligent water management device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown. Figure 8 The example intelligent water management device can be the implementing entity of the intelligent water management method provided in the aforementioned embodiment one.

[0106] Reference Figure 8 The intelligent water management device includes:

[0107] The information acquisition module 810 is used to acquire multiple water supply network operation monitoring information, multiple water supply intelligent sensing device status information, and multiple user water use behavior information.

[0108] The pipeline area water operation demand information generation module 820 is used to classify and process the multiple water pipeline operation monitoring information to obtain multiple pipeline area water operation demand information;

[0109] The intelligent sensing device communication status parsing information generation module 830 is used to parse and process the status information of the multiple intelligent water sensing devices to obtain the communication status parsing information of the multiple intelligent sensing devices.

[0110] The initial water management information generation module 840 is used to generate multiple initial water management information based on a preset water management information generation model, according to the water operation demand information of multiple pipe network areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information.

[0111] The target water management information generation module 850 is used to optimize the multiple initial water management information to obtain target water management information, so as to carry out intelligent water scheduling and management through water management equipment based on the target water management information.

[0112] The process by which each module in the intelligent water management device provided in this application implements its respective function can be specifically referred to the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0114] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0115] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0116] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0117] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0118] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0119] The intelligent water management method provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality / virtual reality devices, laptops, super mobile personal computers, netbooks, and personal digital assistants. This application does not impose any restrictions on the specific type of terminal device.

[0120] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 (Only one is shown in the image) a memory 91, which stores a computer program 92 that can run on the processor 90. When the processor 90 executes the computer program 92, it implements the steps in the various embodiments of the intelligent water management method described above, for example... Figure 1 Steps S101 to S105 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 8 The functions of modules 810 to 850 are shown.

[0121] The terminal device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0122] The processor 90 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0123] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 91 can also be used to temporarily store data that has been sent or will be sent.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0126] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0127] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0128] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A smart water management method, characterized in that, include: Acquire multiple water supply network operation monitoring information, multiple water supply smart sensor device status information, and multiple user water usage behavior information; The monitoring information of the multiple water supply networks is classified and processed to obtain water supply operation demand information for multiple network areas; The status information of the multiple intelligent water sensing devices is parsed and processed to obtain the communication status parsing information of the multiple intelligent sensing devices; Based on the preset water management information generation model, multiple initial water management information is obtained according to the water operation demand information of multiple pipe network areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information. The initial water management information is optimized to obtain target water management information, which is then used by water management equipment for intelligent water scheduling and management.

2. The intelligent water management method as described in claim 1, characterized in that, The step of classifying and processing the multiple water supply network operation monitoring information to obtain water supply operation demand information for multiple network areas specifically includes: The multiple water supply network operation monitoring information is analyzed and processed to obtain multiple network operation monitoring text information, multiple network operation monitoring generation time information, and multiple network operation monitoring business type information. Based on the multiple pipeline operation monitoring text information and the preset pipeline operation demand information database, keyword matching processing is performed to obtain multiple pipeline operation monitoring text keyword matching information; Based on the keyword matching information of multiple pipeline operation monitoring texts, the generation time information of multiple pipeline operation monitoring, and the business type information of multiple pipeline operation monitoring, water supply operation demand information for multiple pipeline areas is obtained.

3. The intelligent water management method as described in claim 1, characterized in that, The status information of the intelligent water sensing device includes the operating time information, data acquisition frequency information, channel status information, power status information, and communication resource occupancy status information. The step of parsing and processing the status information of the multiple intelligent water sensing devices to obtain the communication status parsing information of the multiple intelligent sensing devices specifically includes: Based on the data acquisition frequency information of the multiple intelligent water sensing devices, the channel status information of the multiple intelligent water sensing devices, the communication resource occupancy status information of the multiple intelligent water sensing devices, and the multiple preset intelligent water sensing device status analysis weight information, the status analysis variables of the multiple intelligent water sensing devices are calculated. Based on the operating time information, power status information, and status analysis variables of the multiple intelligent water sensing devices, the communication status analysis information of the multiple intelligent sensing devices is obtained.

4. The intelligent water management method as described in claim 3, characterized in that, Multiple preset weight information for the status analysis of intelligent water sensors include preset weight information for the data acquisition frequency of intelligent water sensors, preset weight information for the channel status of intelligent water sensors, preset weight information for the communication resource occupancy status of intelligent water sensors, and preset weight information for the status enhancement of intelligent water sensors. The step of calculating multiple water affairs intelligent sensor state resolution variables based on the data acquisition frequency information of the multiple water affairs intelligent sensor devices, the channel status information of the multiple water affairs intelligent sensor devices, the communication resource occupancy status information of the multiple water affairs intelligent sensor devices, and the multiple preset water affairs intelligent sensor device state resolution weight information specifically includes: Based on the preset water intelligent sensor data acquisition frequency weight information, preset water intelligent sensor channel state weight information, and preset water intelligent sensor communication resource occupancy state weight information, a weighted summation calculation is performed on the multiple water intelligent sensor data acquisition frequency information, multiple water intelligent sensor channel state information, and multiple water intelligent sensor communication resource occupancy state information to obtain multiple water intelligent sensor state weighted information. The state analysis variables of multiple intelligent water sensing devices are calculated by multiplying the state weighted information of the multiple intelligent water sensing devices with the preset state enhancement weight information of the intelligent water sensing devices.

5. The intelligent water management method as described in claim 1, characterized in that, The steps of generating multiple initial water management information based on the preset water management information generation model, according to the water operation demand information of multiple pipeline areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information, specifically include: The system performs matching and processing based on the water operation demand information of multiple pipeline areas, the water use behavior information of multiple users, and the preset water management and scheduling resource information to generate multiple user water scheduling demand priority information and multiple user water scheduling resource quantity information; the user water use behavior information, user water scheduling demand priority information, and user water scheduling resource quantity information correspond one-to-one. Based on a preset water management information generation model, multiple initial water management information is obtained according to the priority information of multiple users' water scheduling needs, the water scheduling resource quantity information of multiple users, the communication status parsing information of multiple intelligent sensing devices, and the preset water management scheduling resource information.

6. The intelligent water management method as described in claim 1, characterized in that, The initial water management information includes initial water scheduling resource allocation priority information, initial water scheduling data transmission rate information, and initial water scheduling channel gain information; The step of optimizing the multiple initial water management information to obtain target water management information, and then using water management equipment to perform intelligent water scheduling and management based on the target water management information, specifically includes: Based on the water operation demand information of the multiple pipeline areas and the user water scheduling resource quantity information corresponding to the priority information of multiple initial water scheduling resource allocation, the matching degree information of multiple initial water scheduling demands is calculated. Based on the multiple initial water resources allocation priority information, multiple initial water resources data transmission rate information, and multiple initial water resources channel gain information, multiple initial water resources utilization information are calculated. Based on the matching degree information of multiple initial water dispatching demands, the resource utilization rate information of multiple initial water dispatching, and the preset water management dispatching efficiency measurement weight information, multiple initial water dispatching efficiency measurement information are calculated. Based on the multiple initial water dispatch efficiency metrics, multiple initial water management information, preset water management equipment status parameter optimization step size information, and preset water management equipment status parameter optimization iteration number threshold, target water management information is obtained, so as to perform intelligent water dispatch management through water management equipment based on the target water management information.

7. The intelligent water management method as described in claim 6, characterized in that, The step of obtaining target water management information based on the multiple initial water dispatch efficiency metrics, multiple initial water management information, preset water management equipment state parameter optimization step size information, and preset water management equipment state parameter optimization iteration number threshold, and then using water management equipment to perform intelligent water dispatch management based on the target water management information, specifically includes: The initial water management information corresponding to the maximum value of the plurality of initial water scheduling efficiency measurement information is used as the benchmark information for optimizing the state parameters of water management equipment. Based on multiple initial water management information, water management equipment status parameter optimization benchmark information, and preset water management equipment status parameter optimization step size information, multiple intermediate water management equipment status parameter information and intermediate water management equipment status parameter optimization number information are obtained; Determine whether the number of optimization iterations of the intermediate water management equipment status parameters is less than the preset threshold for the number of optimization iterations of the water management equipment status parameters; If so, then the intermediate water scheduling resource allocation priority information is used as the initial water scheduling resource allocation priority information, the intermediate water scheduling data transmission rate information is used as the initial water scheduling data transmission rate information, and the intermediate water scheduling channel gain information is used as the initial water scheduling channel gain information. Then, the process returns to the step of calculating the matching degree information of multiple initial water scheduling needs based on the multiple pipeline area water operation demand information and the user water scheduling resource quantity information corresponding to the multiple initial water scheduling resource allocation priority information. If not, then based on the water operation demand information of multiple pipeline areas and the user water scheduling resource quantity information corresponding to the intermediate water scheduling resource allocation priority information, the intermediate water scheduling demand matching degree information is calculated. Based on the priority information of intermediate water dispatch resources allocation, the data transmission rate information of intermediate water dispatch, and the channel gain information of intermediate water dispatch, calculate the resource utilization information of intermediate water dispatch. Based on the matching degree information of multiple intermediate water dispatching needs, the resource utilization rate information of intermediate water dispatching, and the preset water management dispatching efficiency measurement weight information, multiple intermediate water dispatching efficiency measurement information are calculated. The intermediate water management equipment status parameter information corresponding to the maximum value of multiple intermediate water scheduling efficiency measurement information is used as the target water management information.

8. An intelligent water management device, characterized in that, include: The information acquisition module is used to acquire multiple water supply network operation monitoring information, multiple water supply intelligent sensing device status information, and multiple user water use behavior information. The pipeline area water operation demand information generation module is used to classify and process the multiple water pipeline operation monitoring information to obtain multiple pipeline area water operation demand information; The intelligent sensing device communication status parsing information generation module is used to parse and process the status information of the multiple intelligent water sensing devices to obtain the communication status parsing information of the multiple intelligent sensing devices. The initial water management information generation module is used to generate multiple initial water management information based on a preset water management information generation model, according to the water operation demand information of multiple pipe network areas, the communication status parsing information of multiple intelligent sensing devices, the water use behavior information of multiple users, and the preset water management scheduling resource information. The target water management information generation module is used to optimize the multiple initial water management information to obtain target water management information, so as to carry out intelligent water scheduling and management through water management equipment based on the target water management information.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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