Intensive pipe network construction collaborative management system based on Internet of Things

Abstract

The invention relates to the technical field of pipe network collaborative management, in particular to an intensive pipe network construction collaborative management system based on the Internet of Things, which comprises a data acquisition module, a server processing module, a chlorine adding point optimization module, a chlorine adding equipment control module and sensors arranged at each node position of a water supply pipe network, monitoring the water quality data of each node of each water supply pipeline in the water supply network in real time, further processing and analyzing the data, and comprehensively performing chlorination judgment on each water supply pipeline in the water supply network by combining the node water age of each node of each water supply pipeline in the water supply network; therefore, the optimal midway chlorination point position in each target pipeline in the water supply pipe network and the chlorination amount of each midway chlorination point in each target pipeline in the water supply pipe network are calculated, the concentration of chlorine in the whole water supply system can be kept at an ideal level, pathogens can be effectively destroyed, the generation of chlorination by-products can be minimized, and the production cost is reduced. And the use of a chemical disinfectant can be optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipe network collaborative management, and relates to an intensive pipe network construction collaborative management system based on the Internet of Things. Background Art

[0002] The expansion of urbanization has led to an increase in the topological structure and scale complexity of the water supply network, which has also led to changes in the chemical and microbiological indicators of the water quality of the factory. If the retention time of drinking water is too long, the residual chlorine concentration of the water quality may decrease, thus threatening the water quality safety and the health of residents; on the contrary, if the water flow is too short, the residual chlorine concentration may increase, increasing the risk of disinfection by-products.

[0003] In the study of the mechanism of water quality change, it was found that the longer the water flow time from the water source to a certain node in the pipe network, the lower the residual chlorine concentration at that node; conversely, as the water flow time shortens, the residual chlorine concentration gradually increases.

[0004] Current technologies face multiple challenges when testing chlorine concentrations in water supply pipelines, including insufficient sensitivity and accuracy of detection equipment, inability to achieve real-time monitoring resulting in processing delays, cumbersome and costly maintenance and calibration processes, environmental interference factors affecting detection accuracy, expensive high-performance equipment, imperfect data management and analysis systems, and insufficient equipment stability and reliability. These issues limit the efficiency and accuracy of water supply safety monitoring and increase operational difficulty and cost. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides an intensive pipeline network construction collaborative management system based on the Internet of Things, which is used to solve the above technical problems.

[0006] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: The first aspect of the present invention provides an intensive pipe network construction collaborative management system based on the Internet of Things, comprising a data acquisition module, a server processing module, a chlorination point optimization module and a chlorination equipment control module; each module is connected by wired and / or wireless connection to achieve data transmission between each module; Data acquisition module: sensors deployed at various nodes of the water supply network to monitor water quality data at each node of each water supply pipeline in the water supply network in real time; Server processing module: used to receive and store water quality data from the data acquisition module, process and analyze the data, and make comprehensive chlorination judgments for each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Chlorination point optimization module: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Chlorination equipment control module: receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

[0007] Exemplarily, the water quality data of each node of each water supply pipeline in the water supply network includes residual chlorine concentration and flow rate.

[0008] For example, the node water age of each node of each water supply pipeline in the water supply network is obtained by the following logic: Obtaining a water supply network topology structure, and obtaining each upstream node adjacent to each node of each water supply pipeline; Based on the sensors deployed at each node of the water supply network, the flow rate of each node of each water supply pipeline in the water supply network and its adjacent upstream nodes is obtained. , pipe length and pipeline flow rate , i is the number of the adjacent i-th upstream node, j is the number of each node, j=1,2,...q, k is the number of each water supply pipeline, k=1,2,...u; Calculate the node water age of each node of each water supply pipeline in the water supply network , is the node water age of the adjacent i-th upstream node, It is the set of all upstream nodes adjacent to the jth node of the kth water supply pipeline in the water supply network.

[0009] Exemplarily, the chlorination of each water supply line in the water supply network is comprehensively judged, and the specific judgment logic is: Comprehensive calculation to obtain the required chlorine concentration of each water supply line in the water supply network , are the residual chlorine concentration and flow rate of the jth node of the kth water supply pipeline in the water supply network, is the target residual chlorine concentration, is the maximum water age allowed for a node, is the total flow in the water supply network, p is the adjustment coefficient, and q is the total number of nodes; The required increase in chlorine concentration of each water supply pipeline in the water supply network is compared with the predefined pipeline preset increase in chlorine concentration threshold. If the required increase in chlorine concentration of a water supply pipeline in the water supply network is greater than the predefined pipeline preset increase in chlorine concentration threshold, then the water supply pipeline in the water supply network is determined to require chlorine addition; otherwise, it is determined that the water supply pipeline in the water supply network does not require chlorine addition.

[0010] For example, the adjustment coefficient p is obtained by: The test is carried out in the water supply network. The specific test process is as follows: Randomly select y nodes from each water supply pipeline in the water supply network as sampling points, inject different initial chlorine addition amounts into the initial nodes of each water supply pipeline in the water supply network, and obtain the chlorine residual amount of each water supply pipeline in the water supply network corresponding to each sampling point based on the sensors arranged at each node position of the water supply network, and simultaneously obtain the flow rate of each water supply pipeline in the water supply network corresponding to each sampling point; Obtain the pipe length of each sampling point corresponding to each water supply pipeline in the water supply network from the initial node, thereby obtaining the flow rate of each sampling point corresponding to each water supply pipeline , Pipeline length , chlorine residual and the initial chlorine addition , f is the number of each sampling point, f=1,2,...y; Calculate the adjustment factor , y is the total number of sampling points, and u is the total number of water supply pipelines.

[0011] Exemplarily, the optimal midway chlorination point position in each target pipeline in the water supply network is calculated, and the specific acquisition logic is: Randomly group each node in each target pipeline in the water supply network to obtain a group set of each node corresponding to each target pipeline in the water supply network, and obtain the number of each node in the group set of each node; Utilizing the model: , is the residual chlorine concentration of each node in each node group set corresponding to each target pipeline in the water supply network, b is the number of each node, b=1,2,...s,n is the number of each node group set, and m is the number of each target pipeline; By iteratively solving the model, each node in the node group set with the smallest value for each target pipeline in the water supply network is taken as the optimal midway chlorination point position for each target pipeline in the water supply network.

[0012] For example, the chlorine addition amount of each chlorination point in each target pipeline in the water supply network is calculated, and the specific acquisition logic is as follows: Obtain the flow rate of each chlorination point in each target pipeline in the water supply network , c is the number of each intermediate chlorination point; Take a preset quantitative water sample from each target pipeline in the water supply network, add chlorine of known concentration to each water sample corresponding to each target pipeline in the water supply network, record the amount of chlorine added, mix each water sample corresponding to each target pipeline in the water supply network evenly and let it react for a set time, use a residual chlorine detection instrument to measure the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network, subtract the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network from the amount of chlorine added, and sum and average the results to obtain the chlorine demand of each target pipeline in the water supply network; Using the calculation formula , calculate the chlorine addition amount of each chlorination point in each target pipeline in the water supply network , is the chlorine demand of the mth target pipeline in the water supply network, It is the total number of chlorination points in the mth target pipeline in the water supply network.

[0013] The second aspect of the present invention provides an intensive pipe network construction collaborative management method based on the Internet of Things, comprising: Data collection: Sensors are deployed at various nodes in the water supply network to monitor the water quality data of each node of each water supply pipeline in the water supply network in real time; Server processing: receiving and storing water quality data from the data acquisition module, processing and analyzing the data, and comprehensively judging the chlorination of each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Chlorination point optimization: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Chlorination equipment control: Receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

[0014] A third aspect of the present invention provides an intensive pipe network construction collaborative management device based on the Internet of Things, including an electronic device and a computer-readable storage medium; The electronic device comprises a processor, a memory and a communication bus, wherein the memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, the module in the intensive pipe network construction collaborative management system based on the Internet of Things is implemented; The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the modules in the intensive pipeline network construction collaborative management system based on the Internet of Things as described above.

[0015] As described above, the Internet of Things-based intensive pipe network construction collaborative management system provided by the present invention has at least the following beneficial effects: The intensive pipe network construction collaborative management system based on the Internet of Things provided by the present invention is used for sensors arranged at each node position of the water supply network, real-time monitoring of water quality data of each node of each water supply pipeline in the water supply network, and then processing and analyzing the data, combining the node water age of each node of each water supply pipeline in the water supply network, and comprehensively judging the chlorination of each water supply pipeline in the water supply network; the water supply pipeline that is judged to need chlorination is recorded as the target pipeline, thereby calculating the best midway chlorination point position in each target pipeline in the water supply network and the chlorination amount of each midway chlorination point in each target pipeline in the water supply network, effectively solving the problem that the current technology still has certain limitations in the detection and treatment of water quality chlorine concentration in the water supply network, arranging sensors in the water supply network to monitor water quality in real time, and judging the chlorination demand in combination with the node water age, can significantly improve the efficiency and water quality safety of the water supply system. This method can ensure that the concentration of chlorine in the entire water supply system is maintained at an ideal level, which can effectively destroy pathogens and minimize the generation of chlorination byproducts. By accurately calculating the optimal mid-way chlorination point and chlorination amount, this strategy can optimize the use of chemical disinfectants, reduce costs and environmental impacts, while ensuring that regulatory requirements and consumer expectations for water quality are met. In addition, the analysis of real-time data helps to promptly detect and respond to potential water quality problems, thereby protecting public health and improving the emergency response capabilities and public trust of the water supply system. In short, this comprehensive water quality management approach is both beneficial and necessary for modern water supply systems. It ensures water quality safety through technical means, while improving operational efficiency, ensuring the safety of residents' water use, more effectively optimizing the water supply network, and improving the accuracy and effectiveness of water quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the connection of various modules of the system of the present invention.

[0018] Figure 2 It is a schematic diagram of the connection of each step of the method of the present invention.

[0019] Figure 3 It is a structural schematic diagram of the device shown in the present invention. DETAILED DESCRIPTION

[0020] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technical personnel in the relevant technical field may make various modifications or supplements to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0021] Example 1: Please refer to Figure 1 As shown, the intensive pipe network construction collaborative management system based on the Internet of Things includes a data acquisition module, a server processing module, a chlorination point optimization module and a chlorination equipment control module; each module is connected by wired and / or wireless connection to realize data transmission between each module; Among them, the server processing module is connected to the data acquisition module and the chlorination point optimization module respectively, and the chlorination equipment control module is connected to the chlorination point optimization module; Data acquisition module: sensors deployed at various nodes of the water supply network to monitor water quality data at each node of each water supply pipeline in the water supply network in real time; On the basis of the above-mentioned embodiment, the water quality data of each node of each water supply pipeline in the water supply network includes residual chlorine concentration and flow rate.

[0022] Server processing module: used to receive and store water quality data from the data acquisition module, process and analyze the data, and make comprehensive chlorination judgments for each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Based on the above embodiment, the node water age of each node of each water supply pipeline in the water supply network is specifically obtained by the following logic: Obtaining a water supply network topology structure, and obtaining each upstream node adjacent to each node of each water supply pipeline; Based on the sensors deployed at each node of the water supply network, the flow rate of each node of each water supply pipeline in the water supply network and its adjacent upstream nodes is obtained. , pipe length and pipeline flow rate , i is the number of the adjacent i-th upstream node, j is the number of each node, j=1,2,...q, k is the number of each water supply pipeline, k=1,2,...u; It should be explained that, according to the sensors arranged at each node of the water supply network, the flow rate of each node of each water supply line in the water supply network and the flow rate of each adjacent upstream node are obtained, and the two are added and divided by the number 2 to obtain the pipe section flow rate of each node of each water supply line in the water supply network and each adjacent upstream node; The flow rate of the section between each node of each water supply pipeline and each adjacent upstream node in the water supply network is calculated in the same way as the calculation method of the section flow between each node of each water supply pipeline and each adjacent upstream node in the water supply network.

[0023] Calculate the node water age of each node of each water supply pipeline in the water supply network , is the node water age of the adjacent i-th upstream node, It is the set of all upstream nodes adjacent to the jth node of the kth water supply pipeline in the water supply network.

[0024] On the basis of the above embodiment, the chlorination of each water supply pipeline in the water supply network is comprehensively judged, and the specific judgment logic is: Comprehensive calculation to obtain the required chlorine concentration of each water supply line in the water supply network , are the residual chlorine concentration and flow rate of the jth node of the kth water supply pipeline in the water supply network, is the target residual chlorine concentration, is the maximum water age allowed for a node, is the total flow in the water supply network, p is the adjustment coefficient, and q is the total number of nodes; The required increase in chlorine concentration of each water supply pipeline in the water supply network is compared with the predefined pipeline preset increase in chlorine concentration threshold. If the required increase in chlorine concentration of a water supply pipeline in the water supply network is greater than the predefined pipeline preset increase in chlorine concentration threshold, then the water supply pipeline in the water supply network is determined to require chlorine addition; otherwise, it is determined that the water supply pipeline in the water supply network does not require chlorine addition.

[0025] Based on the above embodiment, the coefficient p is adjusted, and the specific acquisition logic is: The test is carried out in the water supply network. The specific test process is as follows: Randomly select y nodes from each water supply pipeline in the water supply network as sampling points, inject different initial chlorine addition amounts into the initial nodes of each water supply pipeline in the water supply network, and obtain the chlorine residual amount of each water supply pipeline in the water supply network corresponding to each sampling point based on the sensors arranged at each node position of the water supply network, and simultaneously obtain the flow rate of each water supply pipeline in the water supply network corresponding to each sampling point; Obtain the pipe length of each sampling point corresponding to each water supply pipeline in the water supply network from the initial node, thereby obtaining the flow rate of each sampling point corresponding to each water supply pipeline , Pipeline length , chlorine residual and the initial chlorine addition , f is the number of each sampling point, f=1,2,...y; Calculate the adjustment factor , y is the total number of sampling points, and u is the total number of water supply pipelines.

[0026] Chlorination point optimization module: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Based on the above embodiment, the optimal midway chlorination point position in each target pipeline in the water supply network is calculated, and the specific acquisition logic is: Randomly group each node in each target pipeline in the water supply network to obtain a group set of each node corresponding to each target pipeline in the water supply network, and obtain the number of each node in the group set of each node; It should be added that the number of nodes in each node group is random, ranging from 1 to the total number of nodes q, but the only thing that is impossible is the duplication of nodes.

[0027] Utilizing the model: , is the residual chlorine concentration of each node in each node group set corresponding to each target pipeline in the water supply network, b is the number of each node, b=1,2,...s,n is the number of each node group set, and m is the number of each target pipeline; By iteratively solving the model, each node in the node group set with the smallest value for each target pipeline in the water supply network is taken as the optimal midway chlorination point position for each target pipeline in the water supply network.

[0028] On the basis of the above embodiment, the chlorine addition amount of each intermediate chlorination point in each target pipeline in the water supply network is calculated, and the specific acquisition logic is: Obtain the flow rate of each chlorination point in each target pipeline in the water supply network , c is the number of each intermediate chlorination point; Take a preset quantitative water sample from each target pipeline in the water supply network, add chlorine of known concentration to each water sample corresponding to each target pipeline in the water supply network, record the amount of chlorine added, mix each water sample corresponding to each target pipeline in the water supply network evenly and let it react for a set time, use a residual chlorine detection instrument to measure the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network, subtract the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network from the amount of chlorine added, and sum and average the results to obtain the chlorine demand of each target pipeline in the water supply network; Using the calculation formula , calculate the chlorine addition amount of each chlorination point in each target pipeline in the water supply network , is the chlorine demand of the mth target pipeline in the water supply network, It is the total number of chlorination points in the mth target pipeline in the water supply network.

[0029] Chlorination equipment control module: receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

[0030] Example 2: Please refer to Figure 2 As shown in the figure, the collaborative management method of intensive pipe network construction based on the Internet of Things includes: Data collection: Sensors are deployed at various nodes in the water supply network to monitor the water quality data of each node of each water supply pipeline in the water supply network in real time; Server processing: receiving and storing water quality data from the data acquisition module, processing and analyzing the data, and comprehensively judging the chlorination of each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Chlorination point optimization: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Chlorination equipment control: Receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

[0031] Embodiment 3: According to an exemplary embodiment, an intensive pipe network construction collaborative management device based on the Internet of Things includes: an electronic device and a computer-readable storage medium; Figure 3 It is a structural diagram of an intensive pipeline network construction collaborative management device based on the Internet of Things provided in an embodiment of the present application. The device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to realize the above-mentioned intensive pipeline network construction collaborative management system based on the Internet of Things.

[0032] The electronic device comprises a processor, a memory and a communication bus, wherein the memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; The processor implements the above-mentioned Internet of Things-based intensive pipe network construction collaborative management system by calling the computer program stored in the memory and executing the computer readable program; The computer-readable storage medium stores one or more programs. When executed on an electronic device, the one or more programs can be executed by one or more processors, providing a user input interface to implement the above-mentioned Internet of Things-based intensive pipeline network construction collaborative management system.

[0033] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0034] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0035] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0036] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The collaborative management system for intensive pipe network construction based on the Internet of Things is characterized by: It includes a data acquisition module, a server processing module, a chlorination point optimization module and a chlorination equipment control module; each module is connected by wired and / or wireless connection to achieve data transmission between the modules; Data acquisition module: sensors deployed at various nodes of the water supply network to monitor water quality data at each node of each water supply pipeline in the water supply network in real time; Server processing module: used to receive and store water quality data from the data acquisition module, process and analyze the data, and make comprehensive chlorination judgments for each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Chlorination point optimization module: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Chlorination equipment control module: receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

2. According to the Internet of Things-based intensive pipe network construction collaborative management system of claim 1, it is characterized in that: The water quality data of each node of each water supply pipeline in the water supply network includes residual chlorine concentration and flow rate.

3. The intensive pipe network construction collaborative management system based on the Internet of Things according to claim 2 is characterized in that: The node water age of each node of each water supply pipeline in the water supply network, the specific acquisition logic is: Obtaining a water supply network topology structure, and obtaining each upstream node adjacent to each node of each water supply pipeline; Based on the sensors deployed at each node of the water supply network, the flow rate of each node of each water supply pipeline in the water supply network and its adjacent upstream nodes is obtained. , pipe length and pipeline flow rate , i is the number of the adjacent i-th upstream node, j is the number of each node, j=1,2,...q, k is the number of each water supply pipeline, k=1,2,...u; Calculate the node water age of each node of each water supply pipeline in the water supply network , is the node water age of the adjacent i-th upstream node, It is the set of all upstream nodes adjacent to the jth node of the kth water supply pipeline in the water supply network.

4. The intensive pipe network construction collaborative management system based on the Internet of Things according to claim 3 is characterized in that: Comprehensively judge the chlorination of each water supply pipeline in the water supply network. The specific judgment logic is: Comprehensive calculation to obtain the required chlorine concentration of each water supply line in the water supply network , are the residual chlorine concentration and flow rate of the jth node of the kth water supply pipeline in the water supply network, is the target residual chlorine concentration, is the maximum water age allowed for a node, is the total flow in the water supply network, p is the adjustment coefficient, and q is the total number of nodes; The required increase in chlorine concentration of each water supply pipeline in the water supply network is compared with the predefined pipeline preset increase in chlorine concentration threshold. If the required increase in chlorine concentration of a water supply pipeline in the water supply network is greater than the predefined pipeline preset increase in chlorine concentration threshold, then the water supply pipeline in the water supply network is determined to require chlorine addition; otherwise, it is determined that the water supply pipeline in the water supply network does not require chlorine addition.

5. According to the Internet of Things-based intensive pipe network construction collaborative management system of claim 4, it is characterized in that: Adjust the coefficient p, the specific acquisition logic is: The test is carried out in the water supply network. The specific test process is as follows: Randomly select y nodes from each water supply pipeline in the water supply network as sampling points, inject different initial chlorine addition amounts into the initial nodes of each water supply pipeline in the water supply network, and obtain the chlorine residual amount of each water supply pipeline in the water supply network corresponding to each sampling point based on the sensors arranged at each node position of the water supply network, and simultaneously obtain the flow rate of each water supply pipeline in the water supply network corresponding to each sampling point; Obtain the pipe length of each sampling point corresponding to each water supply pipeline in the water supply network from the initial node, thereby obtaining the flow rate of each sampling point corresponding to each water supply pipeline , Pipeline length , chlorine residual and the initial chlorine addition , f is the number of each sampling point, f=1,2,...y; Calculate the adjustment factor , y is the total number of sampling points, and u is the total number of water supply pipelines.

6. The intensive pipe network construction collaborative management system based on the Internet of Things according to claim 1 is characterized in that: Calculate the optimal midway chlorination point position in each target pipeline in the water supply network. The specific acquisition logic is: Randomly group each node in each target pipeline in the water supply network to obtain a group set of each node corresponding to each target pipeline in the water supply network, and obtain the number of each node in the group set of each node; Utilizing the model: , is the residual chlorine concentration of each node in each node group set corresponding to each target pipeline in the water supply network, b is the number of each node, b=1,2,...s,n is the number of each node group set, and m is the number of each target pipeline; By iteratively solving the model, each node in the node group set with the smallest value for each target pipeline in the water supply network is taken as the optimal midway chlorination point position for each target pipeline in the water supply network.

7. The intensive pipe network construction collaborative management system based on the Internet of Things according to claim 1 is characterized in that: Calculate the chlorine addition amount at each chlorination point in each target pipeline in the water supply network. The specific acquisition logic is: Obtain the flow rate of each chlorination point in each target pipeline in the water supply network , c is the number of each intermediate chlorination point; Take a preset quantitative water sample from each target pipeline in the water supply network, add chlorine of known concentration to each water sample corresponding to each target pipeline in the water supply network, record the amount of chlorine added, mix each water sample corresponding to each target pipeline in the water supply network evenly and let it react for a set time, use a residual chlorine detection instrument to measure the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network, subtract the residual chlorine concentration in each water sample corresponding to each target pipeline in the water supply network from the amount of chlorine added, and sum and average the results to obtain the chlorine demand of each target pipeline in the water supply network; Using the calculation formula , calculate the chlorine addition amount of each chlorination point in each target pipeline in the water supply network , is the chlorine demand of the mth target pipeline in the water supply network, It is the total number of chlorination points in the mth target pipeline in the water supply network.

8. The collaborative management method for intensive pipe network construction based on the Internet of Things is characterized by: The method is implemented based on the Internet of Things-based intensive pipe network construction collaborative management system according to any one of claims 1 to 7, and includes: Data collection: Sensors are deployed at various nodes in the water supply network to monitor the water quality data of each node of each water supply pipeline in the water supply network in real time; Server processing: receiving and storing water quality data from the data acquisition module, processing and analyzing the data, and comprehensively judging the chlorination of each water supply line in the water supply network based on the node water age of each node of each water supply line in the water supply network; Chlorination point optimization: the water supply pipeline that needs chlorination is recorded as the target pipeline, and the optimal mid-way chlorination point position in each target pipeline in the water supply network and the chlorination amount of each mid-way chlorination point in each target pipeline in the water supply network are calculated; Chlorination equipment control: Receives chlorination amount instructions from the cloud server, and adjusts the chlorination amount of the chlorination equipment in real time according to the analysis results of the chlorination point optimization module.

9. Intensive pipe network construction collaborative management equipment based on the Internet of Things, characterized by: It is implemented based on the Internet of Things-based intensive pipe network construction collaborative management system described in any one of claims 1 to 7, and includes an electronic device and a computer-readable storage medium; The electronic device comprises a processor, a memory and a communication bus, wherein the memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, a module in the collaborative management system for intensive pipe network construction based on the Internet of Things is implemented; The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement modules in the intensive pipeline network construction collaborative management system based on the Internet of Things.

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