Urban pipe network monitoring optimization method and system based on agent map and medium

Abstract

The invention provides a city pipe network monitoring optimization method and system based on an intelligent agent map and a medium. The method comprises the following steps: extracting pipe network structure layout data according to an agent map, processing to obtain a pipe network structure layout evaluation index, constructing an urban pipe network agent system model according to the agent map, performing simulation operation on an urban pipe network according to the urban pipe network agent system model, obtaining pipe network resource allocation data, and performing resource allocation according to the pipe network resource allocation data. Processing according to the pipe network resource allocation data to obtain a pipe network resource allocation evaluation index, obtaining supply data of the urban pipe network in a preset time period, and processing according to the supply data to obtain a pipe network supply capability evaluation index; according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capability evaluation index, determining an urban pipe network operation evaluation grade, and obtaining a corresponding urban pipe network optimization scheme; therefore, the purpose of monitoring and optimizing the urban pipe network is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of urban pipe network monitoring and optimization, and specifically, to an urban pipe network monitoring and optimization method, system and medium based on intelligent agent graph. Background Art

[0002] Urban pipe networks are like the "veins" of a city and are the core components of urban infrastructure. They cover various types of pipe network systems such as water supply, drainage, gas, and heat. These pipe network systems each have indispensable functions and provide basic guarantees for the normal operation of the city. Therefore, intelligent monitoring and optimization of urban pipe networks are of great practical significance. Most of the existing monitoring systems only stay at the level of data collection and simple display, lack in-depth data analysis and decision support functions, and cannot provide managers with scientific and reasonable optimization solutions for complex issues such as pipe network planning and upgrading. Therefore, there is an urgent need for an urban pipe network monitoring and optimization method that can effectively integrate various types of information in the pipe network and make intelligent analysis and decisions, improve the comprehensiveness, accuracy and intelligence level of pipe network monitoring, and ensure the safe and stable operation of the urban pipe network. Summary of the invention

[0003] The purpose of this application is to provide an urban pipeline network monitoring optimization method, system and medium based on the intelligent agent map, to quantitatively evaluate the pipeline network structure layout and resource allocation balance through the intelligent agent map, and to determine the urban pipeline network operation evaluation level in combination with the pipeline network supply capacity, so as to obtain the corresponding urban pipeline network optimization plan and achieve the purpose of comprehensive, accurate and intelligent monitoring of the urban pipeline network.

[0004] This application also provides an urban pipe network monitoring optimization method based on an intelligent agent graph, comprising the following steps: Extract the pipe network structure layout data according to the intelligent agent map, and process it to obtain the pipe network structure layout evaluation index; Build an intelligent agent system model for the urban pipe network based on the intelligent agent map, simulate the operation of the urban pipe network based on the intelligent agent system model, and obtain the pipe network resource allocation data; Obtaining a pipeline network resource allocation evaluation index according to the pipeline network resource allocation data processing; Obtain the supply data of the urban pipeline network within a preset time period, and obtain the pipeline network supply capacity evaluation index based on the supply data processing; The urban pipeline network operation evaluation level is determined based on the pipeline network structure layout evaluation index, the pipeline network resource allocation evaluation index and the pipeline network supply capacity evaluation index, and the corresponding urban pipeline network optimization plan is obtained.

[0005] Optionally, in the urban pipe network monitoring optimization method based on the agent map described in the present application, extracting the pipe network structure layout data according to the agent map and processing to obtain the pipe network structure layout evaluation index includes: Extract the pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; The pipeline network structure layout evaluation index is obtained according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

[0006] Optionally, in the urban pipe network monitoring optimization method based on the agent map described in the present application, the urban pipe network agent system model is constructed according to the agent map, and the urban pipe network is simulated according to the urban pipe network agent system model to obtain the pipe network resource allocation data, including: Obtain the preset agent model of each agent in the urban pipe network, and extract the connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; Connect and integrate the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; According to the urban pipe network intelligent system model, simulate the operation of the urban pipe network and obtain the pipe network resource allocation data; The pipeline network resource allocation data includes regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation.

[0007] Optionally, in the urban pipe network monitoring optimization method based on the agent map described in the present application, the obtaining of the pipe network resource allocation evaluation index according to the pipe network resource allocation data processing includes: The pipeline network resource allocation evaluation index is obtained according to the regional flow uniformity coefficient, the regional supply-demand matching standard deviation and the regional pipeline utilization standard deviation.

[0008] Optionally, in the urban pipe network monitoring and optimization method based on the agent graph described in the present application, the step of obtaining the supply data of the urban pipe network within a preset time period and obtaining the pipe network supply capacity evaluation index according to the supply data processing includes: The supply data includes supply-demand matching, number of outages, duration of outages and leakage rate; The pipeline network supply capacity evaluation index is obtained based on the supply-demand matching degree, the number of supply outages, the duration of supply outages and the leakage rate.

[0009] Optionally, in the urban pipe network monitoring and optimization method based on the agent graph described in the present application, determining the urban pipe network operation evaluation level according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index, and obtaining the corresponding urban pipe network optimization plan, includes: Obtaining an urban pipe network operation evaluation index according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index; Comparing the urban pipe network operation evaluation index with a preset urban pipe network operation evaluation index threshold, and determining the urban pipe network operation evaluation level according to the range level to which the threshold comparison result belongs; The urban pipe network operation assessment level is input into a preset urban pipe network monitoring and optimization platform database for matching and identification to obtain urban pipe network optimization plan data.

[0010] In a second aspect, the present application provides an urban pipe network monitoring and optimization system based on an intelligent agent graph, the system comprising: a memory and a processor, wherein the memory stores a program of an urban pipe network monitoring and optimization method based on an intelligent agent graph, and when the program of the urban pipe network monitoring and optimization method based on an intelligent agent graph is executed by the processor, the following steps are implemented: Extract the pipe network structure layout data according to the intelligent agent map, and process it to obtain the pipe network structure layout evaluation index; Build an intelligent agent system model for the urban pipe network based on the intelligent agent map, simulate the operation of the urban pipe network based on the intelligent agent system model, and obtain the pipe network resource allocation data; Obtaining a pipeline network resource allocation evaluation index according to the pipeline network resource allocation data processing; Obtain the supply data of the urban pipeline network within a preset time period, and obtain the pipeline network supply capacity evaluation index based on the supply data processing; The urban pipeline network operation evaluation level is determined based on the pipeline network structure layout evaluation index, the pipeline network resource allocation evaluation index and the pipeline network supply capacity evaluation index, and the corresponding urban pipeline network optimization plan is obtained.

[0011] Optionally, in the urban pipe network monitoring and optimization system based on the agent map described in the present application, extracting the pipe network structure layout data according to the agent map and processing to obtain the pipe network structure layout evaluation index includes: Extract the pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; The pipeline network structure layout evaluation index is obtained according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

[0012] Optionally, in the urban pipe network monitoring and optimization system based on the agent map described in the present application, the urban pipe network agent system model is constructed according to the agent map, and the urban pipe network is simulated and operated according to the urban pipe network agent system model to obtain the pipe network resource allocation data, including: Obtain the preset agent model of each agent in the urban pipe network, and extract the connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; Connect and integrate the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; According to the urban pipe network intelligent system model, simulate the operation of the urban pipe network and obtain the pipe network resource allocation data; The pipeline network resource allocation data includes regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation.

[0013] In the third aspect, the present application also provides a computer-readable storage medium, which stores a program for an urban pipe network monitoring optimization method based on an intelligent agent map. When the program for an urban pipe network monitoring optimization method based on an intelligent agent map is executed by a processor, the steps of the urban pipe network monitoring optimization method based on an intelligent agent map as described in any one of the above items are implemented.

[0014] From the above, it can be seen that the urban pipeline network monitoring optimization method, system and medium based on the intelligent agent map provided in this application quantitatively evaluate the pipeline network structure layout and resource allocation balance through the intelligent agent map, and determine the urban pipeline network operation evaluation level in combination with the pipeline network supply capacity, and then obtain the corresponding urban pipeline network optimization plan, so as to achieve the purpose of comprehensive, accurate and intelligent monitoring of the urban pipeline network.

[0015] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of the urban pipe network monitoring optimization method based on the intelligent agent graph provided in an embodiment of the present application; Figure 2 A flow chart of obtaining a pipe network structure layout evaluation index for an urban pipe network monitoring optimization method based on an agent graph provided in an embodiment of the present application; Figure 3A flow chart of obtaining pipe network resource allocation data for an urban pipe network monitoring optimization method based on an intelligent agent graph provided in an embodiment of the present application; Figure 4 A flow chart of obtaining a pipeline supply capacity evaluation index for an urban pipeline network monitoring and optimization method based on an intelligent agent graph provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0019] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0020] Please refer to Figure 1 , Figure 1 Flow chart of the urban pipe network monitoring optimization method based on the intelligent agent map in some embodiments of the present application. The urban pipe network monitoring optimization method based on the intelligent agent map is used in terminal devices, such as computers, mobile terminals, etc. The urban pipe network monitoring optimization method based on the intelligent agent map includes the following steps: S11, extracting the pipe network structure layout data according to the intelligent agent map, and processing to obtain the pipe network structure layout evaluation index; S12. constructing an intelligent agent system model of the urban pipe network according to the intelligent agent map, simulating the operation of the urban pipe network according to the intelligent agent system model of the urban pipe network, and obtaining pipe network resource allocation data; S13, obtaining a pipeline resource allocation evaluation index according to the pipeline resource allocation data processing; S14, obtaining supply data of the urban pipe network within a preset time period, and obtaining a pipe network supply capacity evaluation index according to the supply data processing; S15. Determine the urban pipeline network operation evaluation level according to the pipeline network structure layout evaluation index, pipeline network resource allocation evaluation index and pipeline network supply capacity evaluation index, and obtain the corresponding urban pipeline network optimization plan.

[0021] It should be noted that the pipeline network structure layout is quantitatively evaluated through the intelligent agent map, and the urban pipeline network intelligent agent system model is constructed based on the intelligent agent map. Simulation operation is carried out based on the urban pipeline network intelligent agent system model. According to the simulation operation results, the resource allocation balance of the urban pipeline network is evaluated, and the urban pipeline network operation evaluation level is determined in combination with the pipeline network supply capacity, so as to obtain the corresponding urban pipeline network optimization plan and achieve the purpose of comprehensive, accurate and intelligent monitoring of the urban pipeline network.

[0022] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of obtaining a pipe network structure layout evaluation index of an urban pipe network monitoring optimization method based on an intelligent agent map in some embodiments of the present application. According to an embodiment of the present invention, extracting pipe network structure layout data according to the intelligent agent map and processing to obtain a pipe network structure layout evaluation index includes: S21. Extracting pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; S22. Obtaining a pipeline network structure layout evaluation index according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

[0023] It should be noted that the coverage ratio of the pipeline network is expressed by the ratio of the area covered by the pipeline network to the total area that should be covered; the path redundancy can be expressed by the number of different feasible paths between nodes; the equipment redundancy rate can be expressed by the ratio of the number of redundant equipment to the total number of equipment. Sufficient path redundancy (such as at least 2 independent paths between important nodes) and equipment redundancy rate can improve the emergency response capability of the pipeline network in the event of a fault and make the layout more reliable. The connectivity coefficient can be expressed by the ratio of the number of pipe sections to the number of nodes minus 1. The higher the connectivity coefficient, the better the connectivity of the pipeline network and the more reasonable the layout.

[0024] The calculation formula of the pipe network structure layout evaluation index is: ; in, is the evaluation index of the pipe network structure layout, , , and They are the pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient. , , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0025] Please refer to Figure 3 , Figure 3The present invention is a flowchart of obtaining pipe network resource allocation data of the urban pipe network monitoring optimization method based on the intelligent agent map in some embodiments of the present application. According to the embodiment of the present invention, the urban pipe network intelligent agent system model is constructed according to the intelligent agent map, and the urban pipe network is simulated and operated according to the urban pipe network intelligent agent system model to obtain pipe network resource allocation data, including: S31, obtaining a preset agent model of each agent in the urban pipe network, and extracting connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; S32, connecting and integrating the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; S33, simulating the operation of the urban pipe network according to the urban pipe network intelligent system model, and obtaining pipe network resource allocation data; S34. The pipeline network resource allocation data includes the regional flow uniformity coefficient, the regional supply and demand matching standard deviation and the regional pipeline utilization standard deviation.

[0026] It should be noted that the urban pipe network contains many types of intelligent agents, including sensor intelligent agents, control intelligent agents (valve intelligent agents, pump station intelligent agents, flow regulator intelligent agents) and decision management intelligent agents (control center intelligent agents, maintenance management intelligent agents). The connection relationship data represents the connection relationship between the intelligent agents, and the interaction mode data represents the interaction rules between the intelligent agents, including data sharing interaction data, command interaction data, and event triggering interaction data. Among them, data sharing interaction means that the sensor intelligent agent will share the real-time monitored pipe network data with the control center intelligent agent, command interaction means that the control center intelligent agent can send control instructions and dispatch instructions to the valve intelligent agent, pump station intelligent agent and flow regulator intelligent agent, and event triggering interaction means that when the intelligent agent detects a pipe network fault event, it will trigger the sending of fault messages to the maintenance management intelligent agent and the control center intelligent agent, and when the intelligent agent's own state changes (such as the valve intelligent agent changes from the open state to the closed state), it will send a state change message to the control center intelligent agent.

[0027] According to the urban pipe network intelligent system model, the urban pipe network is simulated and operated to obtain pipe network resource allocation data, including regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation. Among them, the regional flow uniformity coefficient is used to measure the uniformity of flow distribution in each region of the urban pipe network, the regional supply and demand matching standard deviation is an indicator of the uniformity of supply and demand matching in each supply area, and the regional pipeline utilization standard deviation is an indicator of the uniformity of pipeline utilization in each supply area. These three indicators can reflect the uniformity of pipe network resource allocation.

[0028] According to an embodiment of the present invention, the step of obtaining a pipeline resource allocation evaluation index according to the pipeline resource allocation data processing includes: The pipeline network resource allocation evaluation index is obtained according to the regional flow uniformity coefficient, the regional supply-demand matching standard deviation and the regional pipeline utilization standard deviation.

[0029] It should be noted that the calculation formula of the pipeline resource allocation evaluation index is: ; in, Assign an evaluation index to the network resources. , and They are the regional flow uniformity coefficient, the regional supply-demand matching standard deviation, and the regional pipeline utilization standard deviation. , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0030] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a pipeline network supply capacity evaluation index of a method for optimizing urban pipeline network monitoring based on an intelligent agent graph in some embodiments of the present invention. According to an embodiment of the present invention, obtaining supply data of the urban pipeline network within a preset time period and obtaining a pipeline network supply capacity evaluation index according to supply data processing includes: S41, the supply data includes supply-demand matching, number of outages, outage duration and leakage rate; S42. Obtain a pipeline network supply capacity assessment index based on the supply-demand matching degree, the number of outages, the outage duration, and the leakage rate.

[0031] It should be noted that the supply-demand matching degree can be obtained by the ratio of user demand to supply, and the leakage rate can be obtained by subtracting the supply measured at the user end from the total resource supply volume and then dividing it by the total resource supply volume. The supply capacity of the urban pipeline network is evaluated based on the supply-demand matching degree, the number of outages, the duration of outages and the leakage rate. The calculation formula of the pipeline network supply capacity evaluation index is: ; in, is the pipeline supply capacity assessment index, , , and They are supply-demand matching, number of outages, duration of outages, and leakage rate. , , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0032] According to an embodiment of the present invention, determining the urban pipe network operation evaluation level according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index, and obtaining the corresponding urban pipe network optimization plan, includes: Obtaining an urban pipe network operation evaluation index according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index; Comparing the urban pipe network operation evaluation index with a preset urban pipe network operation evaluation index threshold, and determining the urban pipe network operation evaluation level according to the range level to which the threshold comparison result belongs; The urban pipe network operation assessment level is input into a preset urban pipe network monitoring and optimization platform database for matching and identification to obtain urban pipe network optimization plan data.

[0033] It should be noted that the calculation formula of the urban pipe network operation evaluation index is: ; in, It is the urban pipe network operation evaluation index. , and It is the preset weight coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0034] According to an embodiment of the present invention, it also includes: Predicting the fault risk of the urban pipe network according to the urban pipe network intelligent system model to obtain fault risk monitoring data, including the fault location prediction accuracy, the fault type prediction accuracy and the warning level accuracy; Processing is performed according to the fault location prediction accuracy, fault type prediction accuracy and warning level accuracy to obtain a fault warning evaluation index; The fault warning evaluation index is compared with a preset fault warning evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, the urban pipe network intelligent system model is modified.

[0035] It should be noted that the urban pipe network intelligent system model is used to predict the fault risk of the urban pipe network, and the fault risk warning results are evaluated, and it is determined whether the urban pipe network intelligent system model needs to be revised according to the evaluation results; The calculation formula of the fault warning evaluation index is: ; in, is the fault warning evaluation index, , and They are respectively the fault location prediction accuracy, fault type prediction accuracy and warning level accuracy. , and It is the preset weight coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0036] The present invention also discloses an urban pipe network monitoring and optimization system based on an intelligent agent graph, comprising a memory and a processor, wherein the memory stores an urban pipe network monitoring and optimization method program based on an intelligent agent graph, and when the urban pipe network monitoring and optimization method program based on an intelligent agent graph is executed by the processor, the following steps are implemented: Extract the pipe network structure layout data according to the intelligent agent map, and process it to obtain the pipe network structure layout evaluation index; Build an intelligent agent system model for the urban pipe network based on the intelligent agent map, simulate the operation of the urban pipe network based on the intelligent agent system model, and obtain the pipe network resource allocation data; Obtaining a pipeline network resource allocation evaluation index according to the pipeline network resource allocation data processing; Obtain the supply data of the urban pipeline network within a preset time period, and obtain the pipeline network supply capacity evaluation index based on the supply data processing; The urban pipeline network operation evaluation level is determined based on the pipeline network structure layout evaluation index, the pipeline network resource allocation evaluation index and the pipeline network supply capacity evaluation index, and the corresponding urban pipeline network optimization plan is obtained.

[0037] It should be noted that the pipeline network structure layout is quantitatively evaluated through the intelligent agent map, and the urban pipeline network intelligent agent system model is constructed based on the intelligent agent map. Simulation operation is carried out based on the urban pipeline network intelligent agent system model. According to the simulation operation results, the resource allocation balance of the urban pipeline network is evaluated, and the urban pipeline network operation evaluation level is determined in combination with the pipeline network supply capacity, so as to obtain the corresponding urban pipeline network optimization plan and achieve the purpose of comprehensive, accurate and intelligent monitoring of the urban pipeline network.

[0038] According to an embodiment of the present invention, extracting the pipe network structure layout data according to the intelligent agent atlas and processing to obtain the pipe network structure layout evaluation index includes: Extract the pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; The pipeline network structure layout evaluation index is obtained according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

[0039] It should be noted that the coverage ratio of the pipeline network is expressed by the ratio of the area covered by the pipeline network to the total area that should be covered; the path redundancy can be expressed by the number of different feasible paths between nodes; the equipment redundancy rate can be expressed by the ratio of the number of redundant equipment to the total number of equipment. Sufficient path redundancy (such as at least 2 independent paths between important nodes) and equipment redundancy rate can improve the emergency response capability of the pipeline network in the event of a fault and make the layout more reliable. The connectivity coefficient can be expressed by the ratio of the number of pipe sections to the number of nodes minus 1. The higher the connectivity coefficient, the better the connectivity of the pipeline network and the more reasonable the layout.

[0040] The calculation formula of the pipe network structure layout evaluation index is: ; in, is the evaluation index of the pipe network structure layout, , , and They are the pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient. , , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0041] According to an embodiment of the present invention, the step of constructing an urban pipe network agent system model according to an agent map, simulating the operation of the urban pipe network according to the urban pipe network agent system model, and obtaining pipe network resource allocation data includes: Obtain the preset agent model of each agent in the urban pipe network, and extract the connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; Connect and integrate the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; According to the urban pipe network intelligent system model, simulate the operation of the urban pipe network and obtain the pipe network resource allocation data; The pipeline network resource allocation data includes regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation.

[0042] It should be noted that the urban pipe network contains many types of intelligent agents, including sensor intelligent agents, control intelligent agents (valve intelligent agents, pump station intelligent agents, flow regulator intelligent agents) and decision management intelligent agents (control center intelligent agents, maintenance management intelligent agents). The connection relationship data represents the connection relationship between the intelligent agents, and the interaction mode data represents the interaction rules between the intelligent agents, including data sharing interaction data, command interaction data, and event triggering interaction data. Among them, data sharing interaction means that the sensor intelligent agent will share the real-time monitored pipe network data with the control center intelligent agent, command interaction means that the control center intelligent agent can send control instructions and dispatch instructions to the valve intelligent agent, pump station intelligent agent and flow regulator intelligent agent, and event triggering interaction means that when the intelligent agent detects a pipe network fault event, it will trigger the sending of fault messages to the maintenance management intelligent agent and the control center intelligent agent, and when the intelligent agent's own state changes (such as the valve intelligent agent changes from the open state to the closed state), it will send a state change message to the control center intelligent agent.

[0043] According to the urban pipe network intelligent system model, the urban pipe network is simulated and operated to obtain pipe network resource allocation data, including regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation. Among them, the regional flow uniformity coefficient is an indicator used to measure the uniformity of flow distribution in each region of the urban pipe network, the regional supply and demand matching standard deviation is an indicator that indicates the uniformity of supply and demand matching in each supply area, and the regional pipeline utilization standard deviation is an indicator that indicates the uniformity of pipeline utilization in each supply area. These three indicators can all reflect the uniformity of pipe network resource allocation.

[0044] According to an embodiment of the present invention, the step of obtaining a pipeline resource allocation evaluation index according to the pipeline resource allocation data processing includes: The pipeline network resource allocation evaluation index is obtained according to the regional flow uniformity coefficient, the regional supply-demand matching standard deviation and the regional pipeline utilization standard deviation.

[0045] It should be noted that the calculation formula of the pipeline resource allocation evaluation index is: ; in, Assign an evaluation index to the network resources. , and They are the regional flow uniformity coefficient, the regional supply-demand matching standard deviation, and the regional pipeline utilization standard deviation. , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0046] According to an embodiment of the present invention, the step of obtaining the supply data of the urban pipe network within a preset time period and obtaining the pipe network supply capacity evaluation index according to the supply data processing includes: The supply data includes supply-demand matching, number of outages, duration of outages and leakage rate; The pipeline network supply capacity evaluation index is obtained based on the supply-demand matching degree, the number of supply outages, the duration of supply outages and the leakage rate.

[0047] It should be noted that the supply-demand matching degree can be obtained by the ratio of user demand to supply, and the leakage rate can be obtained by subtracting the supply measured at the user end from the total resource supply volume and then dividing it by the total resource supply volume. The supply capacity of the urban pipeline network is evaluated based on the supply-demand matching degree, the number of outages, the duration of outages and the leakage rate. The calculation formula of the pipeline network supply capacity evaluation index is: ; in, is the pipeline supply capacity assessment index, , , and They are supply-demand matching, number of outages, duration of outages, and leakage rate. , , and It is the preset characteristic coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0048] According to an embodiment of the present invention, determining the urban pipe network operation evaluation level according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index, and obtaining the corresponding urban pipe network optimization plan, includes: Obtaining an urban pipe network operation evaluation index according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index; Comparing the urban pipe network operation evaluation index with a preset urban pipe network operation evaluation index threshold, and determining the urban pipe network operation evaluation level according to the range level to which the threshold comparison result belongs; The urban pipe network operation assessment level is input into a preset urban pipe network monitoring and optimization platform database for matching and identification to obtain urban pipe network optimization plan data.

[0049] It should be noted that the calculation formula of the urban pipe network operation evaluation index is: ; in, It is the urban pipe network operation evaluation index. , and It is the preset weight coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0050] According to an embodiment of the present invention, it also includes: Predicting the fault risk of the urban pipe network according to the urban pipe network intelligent system model to obtain fault risk monitoring data, including the fault location prediction accuracy, the fault type prediction accuracy and the warning level accuracy; Processing is performed according to the fault location prediction accuracy, fault type prediction accuracy and warning level accuracy to obtain a fault warning evaluation index; The fault warning evaluation index is compared with a preset fault warning evaluation index threshold. If the threshold comparison result does not meet the preset threshold comparison result requirement, the urban pipe network intelligent system model is modified.

[0051] It should be noted that the urban pipe network intelligent system model is used to predict the fault risk of the urban pipe network, and the fault risk warning results are evaluated, and it is determined whether the urban pipe network intelligent system model needs to be revised according to the evaluation results; The calculation formula of the fault warning evaluation index is: ; in, is the fault warning evaluation index, , and They are respectively the fault location prediction accuracy, fault type prediction accuracy and warning level accuracy. , and It is the preset weight coefficient (which can be obtained by querying the preset urban pipe network monitoring optimization platform database).

[0052] The third aspect of the present invention provides a readable storage medium, which stores a program for an urban pipe network monitoring optimization method based on an intelligent agent map. When the program for an urban pipe network monitoring optimization method based on an intelligent agent map is executed by a processor, the steps of the urban pipe network monitoring optimization method based on an intelligent agent map as described in any one of the above items are implemented.

[0053] The present invention discloses an urban pipe network monitoring optimization method, system and medium based on an intelligent agent graph, which quantitatively evaluates the pipe network structure layout and resource allocation balance through the intelligent agent graph, and determines the urban pipe network operation evaluation level in combination with the pipe network supply capacity, thereby obtaining the corresponding urban pipe network optimization plan, thereby achieving the purpose of comprehensive, accurate and intelligent monitoring of the urban pipe network.

[0054] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0055] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0056] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0057] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0058] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An urban pipe network monitoring optimization method based on agent graph, characterized in that: The following steps are involved: Extract the pipe network structure layout data according to the intelligent agent map, and process it to obtain the pipe network structure layout evaluation index; Build an intelligent agent system model for the urban pipe network based on the intelligent agent map, simulate the operation of the urban pipe network based on the intelligent agent system model, and obtain the pipe network resource allocation data; Obtaining a pipeline network resource allocation evaluation index according to the pipeline network resource allocation data processing; Obtain the supply data of the urban pipeline network within a preset time period, and obtain the pipeline network supply capacity evaluation index based on the supply data processing; The urban pipeline network operation evaluation level is determined based on the pipeline network structure layout evaluation index, the pipeline network resource allocation evaluation index and the pipeline network supply capacity evaluation index, and the corresponding urban pipeline network optimization plan is obtained.

2. The urban pipe network monitoring optimization method based on agent graph according to claim 1 is characterized in that: The extracting of the pipe network structure layout data according to the intelligent agent atlas and processing to obtain the pipe network structure layout evaluation index includes: Extract the pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; The pipeline network structure layout evaluation index is obtained according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

3. The method for optimizing urban pipe network monitoring based on agent graph according to claim 2 is characterized in that: The method of constructing a city pipe network intelligent agent system model according to the intelligent agent map, simulating the operation of the city pipe network according to the city pipe network intelligent agent system model, and obtaining pipe network resource allocation data includes: Obtain the preset agent model of each agent in the urban pipe network, and extract the connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; Connect and integrate the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; According to the urban pipe network intelligent system model, simulate the operation of the urban pipe network and obtain the pipe network resource allocation data; The pipeline network resource allocation data includes regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation.

4. The method for optimizing urban pipe network monitoring based on agent graph according to claim 3 is characterized in that: The step of obtaining a pipeline resource allocation evaluation index according to the pipeline resource allocation data processing includes: The pipeline network resource allocation evaluation index is obtained according to the regional flow uniformity coefficient, the regional supply-demand matching standard deviation and the regional pipeline utilization standard deviation.

5. The method for optimizing urban pipe network monitoring based on agent graph according to claim 4 is characterized in that: The obtaining of the supply data of the urban pipe network within a preset time period and obtaining the pipe network supply capacity evaluation index according to the supply data processing include: The supply data includes supply-demand matching, number of outages, duration of outages and leakage rate; The pipeline network supply capacity evaluation index is obtained based on the supply-demand matching degree, the number of supply outages, the duration of supply outages and the leakage rate.

6. The method for optimizing urban pipe network monitoring based on agent graph according to claim 5 is characterized in that: The determining of the urban pipe network operation evaluation level according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index, and obtaining the corresponding urban pipe network optimization plan, includes: Obtaining an urban pipe network operation evaluation index according to the pipe network structure layout evaluation index, the pipe network resource allocation evaluation index and the pipe network supply capacity evaluation index; Comparing the urban pipe network operation evaluation index with a preset urban pipe network operation evaluation index threshold, and determining the urban pipe network operation evaluation level according to the range level to which the threshold comparison result belongs; The urban pipe network operation assessment level is input into a preset urban pipe network monitoring and optimization platform database for matching and identification to obtain urban pipe network optimization plan data.

7. The urban pipe network monitoring and optimization system based on intelligent agent graph is characterized by: The invention comprises a memory and a processor, wherein the memory stores a program of an urban pipe network monitoring optimization method based on an intelligent agent map, and when the program of the urban pipe network monitoring optimization method based on an intelligent agent map is executed by the processor, the following steps are implemented: Extract the pipe network structure layout data according to the intelligent agent map, and process it to obtain the pipe network structure layout evaluation index; Build an intelligent agent system model for the urban pipe network based on the intelligent agent map, simulate the operation of the urban pipe network based on the intelligent agent system model, and obtain the pipe network resource allocation data; Obtaining a pipeline network resource allocation evaluation index according to the pipeline network resource allocation data processing; Obtain the supply data of the urban pipeline network within a preset time period, and obtain the pipeline network supply capacity evaluation index based on the supply data processing; The urban pipeline network operation evaluation level is determined based on the pipeline network structure layout evaluation index, the pipeline network resource allocation evaluation index and the pipeline network supply capacity evaluation index, and the corresponding urban pipeline network optimization plan is obtained.

8. The urban pipe network monitoring and optimization system based on agent graph according to claim 7 is characterized in that: The extracting of the pipe network structure layout data according to the intelligent agent atlas and processing to obtain the pipe network structure layout evaluation index includes: Extract the pipe network structure layout data according to the intelligent agent graph, including pipe network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient; The pipeline network structure layout evaluation index is obtained according to the pipeline network coverage ratio, path redundancy, equipment redundancy rate and connectivity coefficient.

9. The urban pipe network monitoring and optimization system based on agent graph according to claim 8 is characterized in that: The method of constructing a city pipe network intelligent agent system model according to the intelligent agent map, simulating the operation of the city pipe network according to the city pipe network intelligent agent system model, and obtaining pipe network resource allocation data includes: Obtain the preset agent model of each agent in the urban pipe network, and extract the connection relationship data and interaction mode data of each agent in the urban pipe network from the agent map; Connect and integrate the preset agent models of each agent according to the connection relationship data and the interaction mode data to generate an urban pipe network agent system model; According to the urban pipe network intelligent system model, simulate the operation of the urban pipe network and obtain the pipe network resource allocation data; The pipeline network resource allocation data includes regional flow uniformity coefficient, regional supply and demand matching standard deviation and regional pipeline utilization standard deviation.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an urban pipe network monitoring optimization program based on an intelligent agent map. When the urban pipe network monitoring optimization program based on an intelligent agent map is executed by a processor, the steps of the urban pipe network monitoring optimization method based on an intelligent agent map as described in any one of claims 1 to 6 are implemented.

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