Intelligent office auxiliary method and system based on water affair large model

Abstract

The application relates to the technical field of intelligent water affairs, and relates to an intelligent office assisting method and system based on a water affair large model, which comprises the following steps: acquiring pipeline data, a water supply network topology graph and a plurality of historical water quality data groups, confirming water sample monitoring mechanisms and pipeline monitoring mechanisms, calculating a water quality influence index according to a carbon coefficient, a reduction coefficient, a total organic carbon value and an oxidation-reduction potential value, acquiring a plurality of pipeline key nodes based on the water supply network topology graph, monitoring the pipeline key nodes by using the pipeline monitoring mechanisms, inputting node flow values, node pressure values, the water quality influence index, a pressure loss index, a temperature influence index and a stress influence index into a pre-constructed water affair analysis model, obtaining a water affair state comprehensive score, sending an office assisting report set to a pre-constructed intelligent office end, and completing intelligent office assistance. The application can improve the efficiency and the degree of automation of monitoring the water supply network and improve the accuracy of water affair state evaluation.

Description

Technical Field

[0001] This invention relates to the field of intelligent water management technology, and in particular to an intelligent office assistance method and system based on a large water management model. Background Technology

[0002] With the continuous advancement of urbanization, the scale and complexity of urban water supply systems are increasing day by day. How to achieve efficient monitoring and intelligent analysis of water supply networks has become a key issue in the construction of smart water management.

[0003] Currently, conventional methods for monitoring water supply networks mostly rely on manual inspections, water quality sampling at fixed locations, and simple flow and pressure measurement points.

[0004] While this method can monitor water supply pipelines, it suffers from low efficiency and lacks a comprehensive understanding of the entire pipeline network's operational status. Furthermore, it neglects the guiding role of the pipeline network topology in data acquisition, leading to a lack of scientific rigor in monitoring point selection, thus affecting the representativeness of the monitoring data and the accuracy of the analytical models. Simultaneously, it suffers from problems such as limited parameter dimensions and simplified calculation models, making it difficult to accurately assess the operational status in complex water environments. Therefore, improving the efficiency and automation of water supply network monitoring, and enhancing the accuracy of water status assessments, has become a pressing technical challenge. Summary of the Invention

[0005] This invention provides an intelligent office assistance method based on a large water affairs model and a computer-readable storage medium. Its main purpose is to improve the efficiency and automation of monitoring water supply networks and improve the accuracy of water affairs status assessment.

[0006] To achieve the above objectives, this invention provides an intelligent office assistance method based on a large-scale water resources model, comprising:

[0007] Acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, coefficient of linear expansion of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value.

[0008] The carbon coefficient and reduction coefficient were confirmed based on multiple sets of historical water quality data.

[0009] The water sample monitoring agency and the pipeline monitoring agency were identified. The water sample monitoring agency includes: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor.

[0010] Total organic carbon and redox potential values ​​were obtained using water sample monitoring agencies.

[0011] The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value.

[0012] Multiple key nodes of the water supply network topology were obtained;

[0013] Perform the following operation on each of the multiple critical pipeline nodes:

[0014] Pipeline monitoring agencies are used to monitor key nodes in the pipeline to obtain node flow rates, node pressure values, and node temperatures.

[0015] Based on pipeline data and node temperatures, the pressure loss index, temperature influence index, and stress influence index were identified.

[0016] The node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index are input into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score.

[0017] By summarizing the comprehensive water status scores, multiple comprehensive water status scores are obtained;

[0018] An office assistance report set is generated based on a comprehensive score of multiple water conditions. The office assistance report set is then sent to a pre-built smart office terminal to complete the smart office assistance.

[0019] Optionally, the carbon coefficient and reduction coefficient are determined based on multiple historical water quality data sets, including:

[0020] Multiple historical water quality scores, multiple historical total organic carbon values, and multiple historical reduction potential values ​​were extracted from multiple historical water quality data sets.

[0021] The carbon coefficient is calculated based on multiple historical water quality scores and multiple historical total organic carbon values, using the following formula:

[0022] ;

[0023] in, Indicates the carbon coefficient. This indicates the number of historical water quality scores among multiple historical water quality scores. This represents the first of multiple historical total organic carbon values. Historical total organic carbon value, This indicates the first of multiple historical water quality scores. A historical water quality score, Indicates taking the absolute value;

[0024] The reduction coefficient is obtained based on multiple historical water quality scores and multiple historical reduction potential values.

[0025] Optionally, obtaining the total organic carbon value and redox potential value using a water sample monitoring agency includes:

[0026] Obtain initial water samples;

[0027] The initial water sample is filtered using a filtration device in the water sample monitoring facility to obtain a filtered water sample;

[0028] The flow cell to be tested was obtained based on filtered water samples, a micro water pump, and a water sample flow cell.

[0029] The total organic carbon value was obtained by monitoring the flow cell under test using a total organic carbon sensor.

[0030] The redox potential value is obtained by monitoring the flow cell under test using a redox potential sensor.

[0031] Optionally, the formula for calculating the water quality impact index is as follows:

[0032] ;

[0033] in, Indicates the water quality impact index. Indicates the total organic carbon value. Indicates the redox potential value. Represents the reduction coefficient. It is the natural logarithm. It is a natural constant.

[0034] Optionally, the step of obtaining multiple key pipeline nodes based on the water supply network topology map includes:

[0035] Multiple water supply nodes are combined in a binary manner to obtain multiple node pairs, wherein each node pair includes a starting node and an ending node.

[0036] Perform the following operation on each of the multiple node pairs:

[0037] The shortest path is determined based on the starting node, the ending node, and multiple water supply pipelines. The shortest path is the shortest path from the starting node to the ending node.

[0038] Identify the set of intermediate nodes in the shortest path;

[0039] Summarize the intermediate node sets to obtain multiple intermediate node sets, and extract multiple analysis nodes from the multiple intermediate node sets;

[0040] The total number of analyses is determined based on multiple analysis nodes, where the total number of analyses is the number of analysis nodes among the multiple analysis nodes;

[0041] A classification operation is performed on multiple analysis nodes to obtain multiple target node sets, where each target node set corresponds one-to-one with a water supply node;

[0042] For each of the multiple target node sets, perform the following operation:

[0043] The number of duplicates is determined based on the target node set, where the number of duplicates is the number of nodes analyzed in the target node set;

[0044] The node recurrence rate is calculated based on the number of repetitions and the total number of analyses, using the following formula:

[0045] ;

[0046] in, Indicates the node recurrence rate. Indicates the number of repetitions. Indicates the total number of analyses;

[0047] The node reproducibility rates are summarized to obtain multiple node reproducibility rates, where each node reproducibility rate corresponds one-to-one with a water supply node;

[0048] Perform the following operation on each of the multiple water supply nodes:

[0049] The node degree is determined based on the water supply node and multiple water supply pipelines. The node degree is the number of water supply pipelines connected to the water supply node among the multiple water supply pipelines.

[0050] The importance of a node is calculated based on its recurrence rate and degree, as shown in the following formula:

[0051] ;

[0052] in, Indicates the importance of nodes. Indicates the degree of a node. Indicates the node recurrence rate;

[0053] Compare the node importance with a preset importance threshold. If the node importance is greater than or equal to the importance threshold, then the key nodes of the pipeline are identified based on the water supply nodes.

[0054] Summarize the key nodes of the pipeline to obtain multiple key pipeline nodes.

[0055] Optionally, the step of using a pipeline monitoring agency to monitor key pipeline nodes and obtain node flow rates, node pressure values, and node temperatures includes:

[0056] The flow rate at key nodes of the pipeline is monitored using flow meters in the pipeline monitoring system to obtain the node flow rate value;

[0057] Pressure sensors are used to monitor the pressure at key nodes in the pipeline to obtain node pressure values;

[0058] Temperature sensors are used to monitor the temperature of key nodes in the pipeline to obtain the node temperature.

[0059] Optionally, the determination of the pressure loss index, temperature influence index, and stress influence index based on pipeline data and node temperature includes:

[0060] The pressure loss index is calculated based on the pipe's inner diameter, length, and material density from the pipe data. The calculation formula is shown below:

[0061] ;

[0062] in, Indicates the pressure loss index. Indicates the inner diameter of the pipe. Indicates the length of the pipe. Indicates the density of the material. The preset friction factor;

[0063] The temperature influence index is calculated based on the pipe length and node temperature data in the pipeline data. The calculation formula is as follows:

[0064] ;

[0065] in, Indicates the effect of temperature. This represents the coefficient of linear expansion of the material. Indicates node temperature. This is the preset reference temperature;

[0066] The stress influence index is calculated based on the pipe inner diameter, pipe wall thickness, and maximum tensile strength of the material from the pipe data. The calculation formula is as follows:

[0067] ;

[0068] in, Indicates the stress influence index. Indicates the inner diameter of the pipe. Indicates the pipe wall thickness. This indicates the maximum tensile strength of the material.

[0069] Optionally, the water analysis model is as follows:

[0070] ;

[0071] in, This represents a water analysis model. Indicates the node traffic value. This represents the node pressure value.

[0072] Optionally, the step of generating an office auxiliary report set based on multiple comprehensive water status scores includes:

[0073] For each comprehensive water condition score in the multiple comprehensive water condition scores, perform the following operations:

[0074] Compare the comprehensive water condition score with the preset health threshold;

[0075] If the overall water status score is less than or equal to the health threshold, the pre-built first auxiliary report will be used as the office auxiliary report.

[0076] If the overall water condition score is greater than the health threshold, then the overall water condition score is compared with the preset damage threshold.

[0077] If the comprehensive water condition score is less than or equal to the damage threshold, the pre-built second auxiliary report will be used as the office auxiliary report.

[0078] If the comprehensive water condition score is greater than the damage threshold, the pre-built third auxiliary report will be used as the office auxiliary report.

[0079] Compile office auxiliary reports to obtain a set of office auxiliary reports.

[0080] To achieve the above objectives, the present invention also provides an intelligent office assistance system based on a large-scale water resources model, comprising:

[0081] The basic data acquisition module is used to acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, linear expansion coefficient of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value. The carbon coefficient and reduction coefficient are determined based on multiple historical water quality data sets.

[0082] The water source quality monitoring module is used to identify water sample monitoring institutions and pipeline monitoring institutions. The water sample monitoring institutions include: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring institutions include: a flow meter, a pressure sensor, and a temperature sensor. The total organic carbon value and redox potential value are obtained using the water sample monitoring institutions. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value.

[0083] The critical node monitoring module is used to obtain multiple critical nodes of the pipeline based on the water supply network topology map. For each critical node of the multiple critical nodes, the following operations are performed: the critical node of the pipeline is monitored by the pipeline monitoring agency to obtain the node flow value, node pressure value and node temperature. Based on the pipeline data and node temperature, the pressure loss index, temperature influence index and stress influence index are determined.

[0084] The intelligent scoring assistance module is used to input node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index into a pre-built water affairs analysis model to obtain a comprehensive water affairs status score. The comprehensive water affairs status scores are then aggregated to obtain multiple comprehensive water affairs status scores. Based on the multiple comprehensive water affairs status scores, an office assistance report set is generated and sent to a pre-built intelligent office terminal to complete the intelligent office assistance.

[0085] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0086] Memory, storing at least one instruction; and

[0087] The processor executes the instructions stored in the memory to implement the intelligent office assistance method based on the water affairs big data model described above.

[0088] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned intelligent office assistance method based on a large water resources model.

[0089] To address the problems described in the background section, this invention acquires pipeline data, a water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, inner diameter, wall thickness, maximum tensile strength of the material, coefficient of linear expansion of the material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality scores, historical total organic carbon values, and historical reduction potential values. Therefore, this invention, by acquiring pipeline data, facilitates the subsequent calculation of pressure loss index, temperature influence index, and stress influence index based on the pipeline data and node temperatures. By acquiring the water supply network topology map, it facilitates the subsequent identification of multiple key pipeline nodes, thereby obtaining… Multiple historical water quality data sets are collected, and then the carbon coefficient and reduction coefficient are confirmed based on these data sets. This embodiment of the invention calculates the carbon coefficient and reduction coefficient, which reflect the correlation between historical water quality scores and historical total organic carbon (TOC) and historical reduction potential (RPP) values, using pre-acquired historical water quality data sets. This facilitates the subsequent calculation of the water quality impact index based on the carbon coefficient and reduction coefficient, improving the accuracy of water status assessment. The invention also confirms the water sample monitoring agency and pipeline monitoring agency. The water sample monitoring agency includes: a filtration device, a micro-pump, a water sample flow cell, a TOC sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor. This embodiment of the invention confirms the water sample monitoring... The testing mechanism facilitates subsequent monitoring of water samples, and the pipeline monitoring device facilitates subsequent monitoring of key pipeline nodes. The total organic carbon (TOC) and redox potential (OPP) values ​​are obtained using the water sample monitoring mechanism. Therefore, this embodiment of the invention improves the automation level of water supply network monitoring by utilizing the TOC and OPP sensors in the water sample monitoring mechanism to obtain TOC sensor values ​​and OPP values ​​in the water sample. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, TOC value, and OPP value. This embodiment of the invention considers the varying degrees of influence of TOC content and OPP on water quality, and therefore, based on the pre-calculated carbon coefficient and reduction coefficient, it combines the TOC sensor values ​​and OPP values. The potential value is used to calculate the water quality impact index, improving the accuracy of water status assessment. Based on the water supply network topology map, multiple key pipeline nodes are obtained. This embodiment of the invention analyzes the water supply network topology map to obtain multiple key pipeline nodes. For each key pipeline node, the following operations are performed: the key pipeline node is monitored using a pipeline monitoring agency to obtain node flow rate, node pressure, and node temperature. This embodiment of the invention utilizes information from the water supply network topology map to obtain the node recurrence rate and node degree of each node in the water supply network, and calculates the node importance to reflect the degree of node importance, thereby enabling targeted monitoring of key pipeline nodes and improving the efficiency of water supply network monitoring.Based on pipeline data and node temperatures, pressure loss index, temperature influence index, and stress influence index are identified. This embodiment of the invention considers the influence of pipeline length, inner diameter, wall thickness, maximum tensile strength, linear expansion coefficient, and density of the materials used to manufacture the pipeline on the operating status of the water supply pipeline, and calculates the pressure loss index, temperature influence index, and stress influence index. This improves the accuracy of water status assessment. Node flow rate values, node pressure values, water quality influence index, pressure loss index, temperature influence index, and stress influence index are input into a pre-built water analysis model to obtain a comprehensive water status score. This embodiment of the invention automatically calculates the comprehensive water status score using the water analysis model, achieving an overall assessment of the operating status of the water supply pipeline, improving the automation and accuracy of water status assessment. Multiple comprehensive water status scores are summarized, and an office auxiliary report set is generated based on these scores. This office auxiliary report set is sent to a pre-built intelligent office terminal to complete intelligent office assistance. This embodiment of the invention achieves intelligent assistance for water affairs office work by sending the office auxiliary report set to the office terminal, improving the automation of water supply network monitoring. Therefore, this invention can improve the efficiency and automation of monitoring water supply networks, and enhance the accuracy of water condition assessment. Attached Figure Description

[0090] Figure 1 A flowchart illustrating an intelligent office assistance method based on a large water resources model, provided in an embodiment of the present invention;

[0091] Figure 2 A functional module diagram of an intelligent office assistance system based on a large water resources model provided in an embodiment of the present invention;

[0092] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the intelligent office assistance method based on a large water resources model, according to an embodiment of the present invention.

[0093] Explanation of reference numerals in the attached figures:

[0094] 1. Electronic device; 10. Processor; 11. Storage device; 12. Bus.

[0095] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0096] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0097] This application provides an intelligent office assistance method based on a large-scale water resources model. The executing entity of this intelligent office assistance method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the intelligent office assistance method based on a large-scale water resources model can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0098] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent office assistance method based on a large-scale water resources model according to an embodiment of the present invention. In this embodiment, the intelligent office assistance method based on a large-scale water resources model includes:

[0099] S1. Obtain pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, coefficient of linear expansion of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value.

[0100] In this embodiment of the invention, the pipeline refers to the water pipe through which tap water is transported from the pumping station to the user end, and the water affairs big model in this embodiment of the invention is used to assist the tap water company in evaluating the operating status of various facilities in the water supply network.

[0101] It should be explained that pipeline data refers to data related to the pipeline, including: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of the material, coefficient of linear expansion of the material, and material density. Pipeline length refers to the length of each independent pipeline unit within a multi-segment system used to form a water supply pipeline. In water supply pipelines, the lengths of interconnected independent pipeline units are usually consistent to facilitate standardized construction and subsequent operation and maintenance. Pipeline inner diameter refers to the inner diameter of the pipeline; pipeline wall thickness refers to the wall thickness of the pipeline; maximum tensile strength of the material refers to the maximum tensile strength of the material used to manufacture the pipeline; coefficient of linear expansion of the material refers to the coefficient of linear expansion of the material used to manufacture water supply pipelines; and material density refers to the density of the material used to manufacture water supply pipelines. A water supply network topology diagram is a directed graph structure created by a water utility company based on the distribution map of water supply pipelines. The graph uses water supply pipelines as edges and nodes (such as valves, tees, and pump stations) as vertices. Vertices in the topology diagram correspond to actual water sources, control points, branch points, or user points, while edges correspond to actual water supply pipelines. The connections between vertices and edges reflect the relationships between water sources, control points, branch points, or user points and the actual water supply pipelines. Arrows indicate the direction of water flow, thus forming a directed graph structure. Water supply pipelines refer to the edges in the topology diagram, and water supply nodes refer to the vertices. Historical water quality data sets refer to the datasets recorded by the water utility company, including indicators such as water quality scores, total organic carbon (TOC) values, and redox potential (RPP) values. Historical water quality scores refer to the water quality scores in the historical data sets, historical TOC values ​​refer to the total organic carbon values ​​in the historical data sets, and historical RPP values ​​refer to the RPP values ​​in the historical data sets.

[0102] S2. The carbon coefficient and reduction coefficient were confirmed based on multiple historical water quality data sets.

[0103] Specifically, the carbon coefficient and reduction coefficient, identified based on multiple historical water quality data sets, include:

[0104] Multiple historical water quality scores, multiple historical total organic carbon values, and multiple historical reduction potential values ​​were extracted from multiple historical water quality data sets.

[0105] The carbon coefficient is calculated based on multiple historical water quality scores and multiple historical total organic carbon values, using the following formula:

[0106] ;

[0107] in, Indicates the carbon coefficient. This indicates the number of historical water quality scores among multiple historical water quality scores. This represents the first of multiple historical total organic carbon values. Historical total organic carbon value, This indicates the first of multiple historical water quality scores. A historical water quality score, Indicates taking the absolute value;

[0108] The reduction coefficient is obtained based on multiple historical water quality scores and multiple historical reduction potential values.

[0109] It is understood that extracting multiple historical water quality scores, multiple historical total organic carbon values ​​and multiple historical reduction potential values ​​from multiple historical water quality data sets means: extracting the historical water quality score, historical total organic carbon value and historical reduction potential value from each data set from multiple historical data sets, and summarizing the historical water quality score, historical total organic carbon value and historical reduction potential value respectively to obtain multiple historical water quality scores, multiple historical total organic carbon values ​​and multiple historical reduction potential values.

[0110] It should be explained that the carbon coefficient reflects the degree of correlation between historical water quality scores and historical total organic carbon values. The larger the carbon coefficient, the stronger the correlation between historical water quality scores and historical total organic carbon values. The reduction coefficient reflects the degree of correlation between historical water quality scores and historical reduction potential values. The larger the reduction coefficient, the stronger the correlation between historical water quality scores and historical reduction potential values.

[0111] It should be understood that the method for obtaining the reduction coefficient based on multiple historical water quality scores and multiple historical reduction potential values ​​is the same as the method for calculating the carbon coefficient based on multiple historical water quality scores and multiple historical total organic carbon values, and will not be repeated here.

[0112] S3. Identify the water sample monitoring agency and the pipeline monitoring agency. The water sample monitoring agency includes: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor.

[0113] It should be explained that the water sample monitoring device is an integrated unit comprising a filtration device, a micro water pump, a water sample flow cell, a total organic carbon (TOC) sensor, and a redox potential (OPP) sensor. The filtration device is a Y-type filter; optionally, a Shanghai Shihua SY-I type Y-type filter is used. The micro water pump is a micro diaphragm pump; optionally, a Kamoe KLP180P is used. The water sample flow cell is a small reservoir integrating a TOC sensor and an OPP sensor. The TOC sensor and OPP sensor are both installed at the bottom of the flow cell. The TOC sensor is a TOC analyzer; optionally, a Horiba HT-110 is used. The OPP sensor is a multi-parameter water quality analyzer; optionally, a Horiba LAQUA F-74 is used. The pipeline monitoring device is an integrated unit comprising a flow meter, a pressure sensor, and a temperature sensor. The flow meter is an electromagnetic flow meter; optionally, an ABB flow meter is used. The FSM4000 is used as a flow meter, and its pressure sensor is a piezoresistive pressure sensor. Optionally, the NXP MPX5050DP is used as the pressure sensor. The temperature sensor is a sensor used to measure the internal temperature of critical nodes in a pipeline.

[0114] S4. Obtain the total organic carbon value and redox potential value using water sample monitoring agencies, and calculate the water quality impact index based on the carbon coefficient, reduction coefficient, total organic carbon value and redox potential value.

[0115] Specifically, the acquisition of total organic carbon and redox potential values ​​using a water sample monitoring agency includes:

[0116] Obtain initial water samples;

[0117] The initial water sample is filtered using a filtration device in the water sample monitoring facility to obtain a filtered water sample;

[0118] The flow cell to be tested was obtained based on filtered water samples, a micro water pump, and a water sample flow cell.

[0119] The total organic carbon value was obtained by monitoring the flow cell under test using a total organic carbon sensor.

[0120] The redox potential value is obtained by monitoring the flow cell under test using a redox potential sensor.

[0121] It should be explained that the initial water sample refers to the sample of tap water that has been purified by the water treatment plant and is about to be delivered to the city's water supply pipeline via a booster pump station. Filtering the initial water sample using the filtration device in the water sample monitoring mechanism means introducing the initial water sample into a tap water pipe equipped with the filtration device to achieve filtration; the filtered water sample refers to the initial water sample after filtration. Obtaining the flow cell to be tested based on the filtered water sample, the micro-pump, and the flow cell means using the micro-pump to slowly introduce the filtered water sample into the flow cell at a low flow rate; the flow cell to be tested refers to the flow cell into which the filtered water sample has been introduced. Monitoring the flow cell to be tested using a total organic carbon sensor means using a total organic carbon sensor installed at the bottom of the flow cell to monitor the flow cell; the total organic carbon value refers to the total organic carbon content in the filtered water sample. Monitoring the flow cell to be tested using a redox potential sensor means using a redox potential sensor installed at the bottom of the flow cell to monitor the flow cell; the redox potential value refers to the redox potential value in the filtered water sample.

[0122] In detail, the formula for calculating the water quality impact index is as follows:

[0123] ;

[0124] in, Indicates the water quality impact index. Indicates the total organic carbon value. Indicates the redox potential value. Represents the reduction coefficient. It is the natural logarithm. It is a natural constant.

[0125] It should be explained that the water quality impact index reflects the degree of water pollution; the higher the water quality impact index, the greater the degree of water pollution.

[0126] S5. Obtain multiple key nodes of the water supply network based on the water supply network topology map.

[0127] In detail, the acquisition of multiple key pipeline nodes based on the water supply network topology includes:

[0128] Multiple water supply nodes are combined in a binary manner to obtain multiple node pairs, wherein each node pair includes a starting node and an ending node.

[0129] Perform the following operation on each of the multiple node pairs:

[0130] The shortest path is determined based on the starting node, the ending node, and multiple water supply pipelines. The shortest path is the shortest path from the starting node to the ending node.

[0131] Identify the set of intermediate nodes in the shortest path;

[0132] Summarize the intermediate node sets to obtain multiple intermediate node sets, and extract multiple analysis nodes from the multiple intermediate node sets;

[0133] The total number of analyses is determined based on multiple analysis nodes, where the total number of analyses is the number of analysis nodes among the multiple analysis nodes;

[0134] A classification operation is performed on multiple analysis nodes to obtain multiple target node sets, where each target node set corresponds one-to-one with a water supply node;

[0135] For each of the multiple target node sets, perform the following operation:

[0136] The number of duplicates is determined based on the target node set, where the number of duplicates is the number of nodes analyzed in the target node set;

[0137] The node recurrence rate is calculated based on the number of repetitions and the total number of analyses, using the following formula:

[0138] ;

[0139] in, Indicates the node recurrence rate. Indicates the number of repetitions. Indicates the total number of analyses;

[0140] The node reproducibility rates are summarized to obtain multiple node reproducibility rates, where each node reproducibility rate corresponds one-to-one with a water supply node;

[0141] Perform the following operation on each of the multiple water supply nodes:

[0142] The node degree is determined based on the water supply node and multiple water supply pipelines. The node degree is the number of water supply pipelines connected to the water supply node among the multiple water supply pipelines.

[0143] The importance of a node is calculated based on its recurrence rate and degree, as shown in the following formula:

[0144] ;

[0145] in, Indicates the importance of nodes. Indicates the degree of a node. Indicates the node recurrence rate;

[0146] Compare the node importance with a preset importance threshold. If the node importance is greater than or equal to the importance threshold, then the key nodes of the pipeline are identified based on the water supply nodes.

[0147] Summarize the key nodes of the pipeline to obtain multiple key pipeline nodes.

[0148] It should be explained that the phrase "forming multiple node pairs by binary combination of multiple water supply nodes" means: for the multiple water supply nodes, a binary combination operation is performed, that is, two different nodes are randomly selected from all nodes and combined to form a node pair. In this embodiment of the invention, the binary combination does not consider the node selection order, and the node pairs are mutually exclusive. If the number of water supply nodes is... Then, through the calculation formulas of combinatorial mathematics... The total number of node pairs can be determined.

[0149] For example, when the water supply nodes include node A, node B, node C, and node D, the number of water supply nodes is: By combining binary pairs, we can obtain node pairs {A, B}, {A, C}, {A, D}, {B, C}, {B, D}, and {C, D}, for a total of 6 pairs of nodes.

[0150] For example, in a pair of nodes in a water supply network topology diagram, according to the water flow direction marked on the water supply network topology diagram, if the water flows out from a certain water supply node in the node pair, then that water supply node is the starting node in the node pair, and the other water supply node in the node pair in the direction of water flow is the ending node.

[0151] Understandably, determining the shortest path based on the starting node, ending node, and multiple water supply pipelines means: for a given starting node and ending node, combining the nodes in the water supply network and the connecting water supply pipelines, using the Dijkstra shortest path algorithm, starting from the starting node and gradually expanding to the ending node, to determine the path with the shortest path length between the starting node and the ending node. The path with the shortest path length is the shortest path. Furthermore, the above process is a publicly available technical solution, and this embodiment of the invention will not elaborate further. Determining the intermediate node set in the shortest path means: based on the obtained shortest path between the starting node and the ending node, identifying all nodes appearing in the shortest path, and summarizing all nodes appearing in the shortest path to obtain the intermediate node set.

[0152] For example, if there are three node pairs, these three node pairs will generate three shortest paths. If the shortest path corresponding to the first node pair contains nodes A, B, C, and D, then nodes A, B, C, and D together form the first intermediate node set. If the shortest path corresponding to the second node pair contains nodes B, C, D, and F, then nodes B, C, D, and F together form the second intermediate node set. If the shortest path corresponding to the third node pair contains nodes C, D, and E, then nodes C, D, and E together form the third intermediate node set. Summarizing these intermediate node sets, we obtain three intermediate node sets. From these three intermediate node sets, we extract all the nodes, i.e., we extract one node A. Two nodes B, three nodes C, three nodes D, one node E, and one node F (or one node A, two nodes B, three nodes C, three nodes D, one node E, and one node F) together constitute the multiple analysis nodes. The analysis nodes within these multiple analysis nodes are either node A, node B, node C, node D, node E, or node F. A classification operation is performed on these multiple analysis nodes to obtain multiple target node sets. This involves grouping analysis nodes with the same node number into a single target node set, thus classifying the multiple analysis nodes into multiple target node sets. Therefore, a total of six target node sets are obtained: {node A}, {node B, node B}, {node C, node C, node C}, {node D, node D, node D}, {node E}, and {node F}. The target nodes in these target node sets are the analysis nodes after the classification operation. The node number refers to the number of the corresponding water supply node in the water supply network topology diagram; for example, the node number of node A is A.

[0153] It is understood that identifying key pipeline nodes based on the water supply nodes means identifying corresponding valves, tees, or pumping stations in the real world based on the water supply nodes on the water supply network topology map. These valves, tees, or pumping stations are the key pipeline nodes.

[0154] It should be understood that the importance of a node reflects its significance within the water supply network; the higher the node importance, the greater its importance within the water supply network.

[0155] Optionally, the importance threshold is 0.15.

[0156] S6. Perform the following operations on each of the multiple critical pipeline nodes: use a pipeline monitoring agency to monitor the critical pipeline node, obtain the node flow rate, node pressure, and node temperature, and determine the pressure loss index, temperature influence index, and stress influence index based on the pipeline data and node temperature.

[0157] In detail, the method of using a pipeline monitoring agency to monitor key pipeline nodes and obtain node flow rates, node pressure values, and node temperatures includes:

[0158] The flow rate at key nodes of the pipeline is monitored using flow meters in the pipeline monitoring system to obtain the node flow rate value;

[0159] Pressure sensors are used to monitor the pressure at key nodes in the pipeline to obtain node pressure values;

[0160] Temperature sensors are used to monitor the temperature of key nodes in the pipeline to obtain the node temperature.

[0161] It should be explained that the node flow rate refers to the water flow rate per unit time in the pipeline where the critical node is located, the node pressure refers to the water pressure inside the pipeline at the critical node, and the node temperature refers to the temperature of the pipeline at the critical node.

[0162] Specifically, the determination of the pressure loss index, temperature influence index, and stress influence index based on pipeline data and node temperatures includes:

[0163] The pressure loss index is calculated based on the pipe's inner diameter, length, and material density from the pipe data. The calculation formula is shown below:

[0164] ;

[0165] in, Indicates the pressure loss index. Indicates the inner diameter of the pipe. Indicates the length of the pipe. Indicates the density of the material. The preset friction factor;

[0166] The temperature influence index is calculated based on the pipe length and node temperature data in the pipeline data. The calculation formula is as follows:

[0167] ;

[0168] in, Indicates the effect of temperature. This represents the coefficient of linear expansion of the material. Indicates node temperature. This is the preset reference temperature;

[0169] The stress influence index is calculated based on the pipe inner diameter, pipe wall thickness, and maximum tensile strength of the material from the pipe data. The calculation formula is as follows:

[0170] ;

[0171] in, Indicates the stress influence index. Indicates the inner diameter of the pipe. Indicates the pipe wall thickness. This indicates the maximum tensile strength of the material.

[0172] It should be explained that the pressure loss index reflects the energy loss caused by friction during water flow in the water supply pipeline. The smaller the pressure loss index, the less energy is lost during water flow. The temperature influence index reflects the degree of influence of the external environment's temperature on the water supply pipeline. The larger the temperature influence index, the greater the influence of the external environment's temperature on the water supply pipeline. The stress influence index reflects the water supply pipeline's ability to resist internal and external loads. The smaller the stress influence index, the stronger the water supply pipeline's ability to resist internal and external loads.

[0173] It should be understood that the friction factor is a value set arbitrarily by the water company based on the roughness of the water supply pipes. It is optional, with a friction factor of 0.02. The reference temperature is a value related to the geographical environment of the water supply pipes, with an optional reference temperature of 25℃.

[0174] S7. Input the node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score. Summarize the comprehensive water affairs status scores to obtain multiple comprehensive water affairs status scores.

[0175] In detail, the water analysis model is as follows:

[0176] ;

[0177] in, This represents a water analysis model. Indicates the node traffic value. This represents the node pressure value.

[0178] It should be explained that the comprehensive water condition score is the value calculated by inputting the node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index into the water analysis model. The comprehensive water condition score is used to assess the safety level of the operation of critical pipeline nodes; the lower the comprehensive water condition score, the safer the operation of the critical pipeline nodes.

[0179] S8. Generate an office assistance report set based on the comprehensive score of multiple water statuses, and send the office assistance report set to the pre-built intelligent office terminal to complete the intelligent office assistance.

[0180] It should be explained that the intelligent office terminal refers to the intelligent work platform built by the water utility company to receive office auxiliary reports.

[0181] In detail, the generation of an office auxiliary report set based on multiple comprehensive water status scores includes:

[0182] For each comprehensive water condition score in the multiple comprehensive water condition scores, perform the following operations:

[0183] Compare the comprehensive water condition score with the preset health threshold;

[0184] If the overall water status score is less than or equal to the health threshold, the pre-built first auxiliary report will be used as the office auxiliary report.

[0185] If the overall water condition score is greater than the health threshold, then the overall water condition score is compared with the preset damage threshold.

[0186] If the comprehensive water condition score is less than or equal to the damage threshold, the pre-built second auxiliary report will be used as the office auxiliary report.

[0187] If the comprehensive water condition score is greater than the damage threshold, the pre-built third auxiliary report will be used as the office auxiliary report.

[0188] Compile office auxiliary reports to obtain a set of office auxiliary reports.

[0189] It should be explained that the office assistance report refers to a report on the pipeline operation status used to complete intelligent office assistance. For example, after this report is sent to the staff of the water supply company, the staff of the water supply company can promptly handle and repair the corresponding key pipeline nodes based on the report content.

[0190] Understandably, the first auxiliary report is a text message used to remind the water company staff that the water supply pipeline is operating normally, for example, "Pipeline is healthy and operating normally." The second auxiliary report is a text message used to remind the water company staff that the water supply pipeline is experiencing problems, for example, "Pipeline has minor problems and needs regular inspection." The third auxiliary report is a text message used to remind the water company staff that the water supply pipeline is severely damaged, for example, "Pipeline is severely damaged and needs emergency repair." The office auxiliary report set is a collection of office auxiliary reports. The health threshold and damage threshold are values ​​manually set by the water company based on the historical operating status of the water supply pipeline. Optionally, the health threshold is 0.3 and the damage threshold is 0.6.

[0191] To address the problems described in the background section, this invention acquires pipeline data, a water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, inner diameter, wall thickness, maximum tensile strength of the material, coefficient of linear expansion of the material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality scores, historical total organic carbon values, and historical reduction potential values. Therefore, this invention, by acquiring pipeline data, facilitates the subsequent calculation of pressure loss index, temperature influence index, and stress influence index based on the pipeline data and node temperatures. By acquiring the water supply network topology map, it facilitates the subsequent identification of multiple key pipeline nodes, thereby obtaining… Multiple historical water quality data sets are collected, and then the carbon coefficient and reduction coefficient are confirmed based on these data sets. This embodiment of the invention calculates the carbon coefficient and reduction coefficient, which reflect the correlation between historical water quality scores and historical total organic carbon (TOC) and historical reduction potential (RPP) values, using pre-acquired historical water quality data sets. This facilitates the subsequent calculation of the water quality impact index based on the carbon coefficient and reduction coefficient, improving the accuracy of water status assessment. The invention also confirms the water sample monitoring agency and pipeline monitoring agency. The water sample monitoring agency includes: a filtration device, a micro-pump, a water sample flow cell, a TOC sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor. This embodiment of the invention confirms the water sample monitoring... The testing mechanism facilitates subsequent monitoring of water samples, and the pipeline monitoring device facilitates subsequent monitoring of key pipeline nodes. The total organic carbon (TOC) and redox potential (OPP) values ​​are obtained using the water sample monitoring mechanism. Therefore, this embodiment of the invention improves the automation level of water supply network monitoring by utilizing the TOC and OPP sensors in the water sample monitoring mechanism to obtain TOC sensor values ​​and OPP values ​​in the water sample. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, TOC value, and OPP value. This embodiment of the invention considers the varying degrees of influence of TOC content and OPP on water quality, and therefore, based on the pre-calculated carbon coefficient and reduction coefficient, it combines the TOC sensor values ​​and OPP values. The potential value is used to calculate the water quality impact index, improving the accuracy of water status assessment. Based on the water supply network topology map, multiple key pipeline nodes are obtained. This embodiment of the invention analyzes the water supply network topology map to obtain multiple key pipeline nodes. For each key pipeline node, the following operations are performed: the key pipeline node is monitored using a pipeline monitoring agency to obtain node flow rate, node pressure, and node temperature. This embodiment of the invention utilizes information from the water supply network topology map to obtain the node recurrence rate and node degree of each node in the water supply network, and calculates the node importance to reflect the degree of node importance, thereby enabling targeted monitoring of key pipeline nodes and improving the efficiency of water supply network monitoring.Based on pipeline data and node temperatures, pressure loss index, temperature influence index, and stress influence index are identified. This embodiment of the invention considers the influence of pipeline length, inner diameter, wall thickness, maximum tensile strength, linear expansion coefficient, and density of the materials used to manufacture the pipeline on the operating status of the water supply pipeline, and calculates the pressure loss index, temperature influence index, and stress influence index. This improves the accuracy of water status assessment. Node flow rate values, node pressure values, water quality influence index, pressure loss index, temperature influence index, and stress influence index are input into a pre-built water analysis model to obtain a comprehensive water status score. This embodiment of the invention automatically calculates the comprehensive water status score using the water analysis model, achieving an overall assessment of the operating status of the water supply pipeline, improving the automation and accuracy of water status assessment. Multiple comprehensive water status scores are summarized, and an office auxiliary report set is generated based on these scores. This office auxiliary report set is sent to a pre-built intelligent office terminal to complete intelligent office assistance. This embodiment of the invention achieves intelligent assistance for water affairs office work by sending the office auxiliary report set to the office terminal, improving the automation of water supply network monitoring. Therefore, this invention can improve the efficiency and automation of monitoring water supply networks, and enhance the accuracy of water condition assessment.

[0192] like Figure 2 The diagram shown is a functional block diagram of an intelligent office assistance system based on a large water resources model provided in an embodiment of the present invention.

[0193] The intelligent office assistance system 100 based on a large-scale water resources model described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent office assistance system 100 based on a large-scale water resources model may include a basic data acquisition module 101, a water source quality monitoring module 102, a key node monitoring module 103, and an intelligent scoring assistance module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0194] The basic data acquisition module 101 is used to acquire pipeline data, water supply network topology map and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, linear expansion coefficient of material and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value and historical reduction potential value. The carbon coefficient and reduction coefficient are determined based on multiple historical water quality data sets.

[0195] The water source quality monitoring module 102 is used to identify the water sample monitoring agency and the pipeline monitoring agency. The water sample monitoring agency includes a filter device, a micro water pump, a water sample flow tank, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes a flow meter, a pressure sensor, and a temperature sensor. The total organic carbon value and redox potential value are obtained using the water sample monitoring agency. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value.

[0196] The key node monitoring module 103 is used to obtain multiple key nodes of the pipeline based on the water supply network topology map. For each key node of the multiple key nodes, the following operations are performed: the key node of the pipeline is monitored by the pipeline monitoring agency to obtain the node flow value, node pressure value and node temperature. Based on the pipeline data and node temperature, the pressure loss index, temperature influence index and stress influence index are confirmed.

[0197] The intelligent scoring assistance module 104 is used to input node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index and stress impact index into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score. The comprehensive water affairs status scores are summarized to obtain multiple comprehensive water affairs status scores. Based on the multiple comprehensive water affairs status scores, an office assistance report set is generated and sent to the pre-built intelligent office terminal to complete the intelligent office assistance.

[0198] In detail, the modules in the intelligent office assistance system 100 based on a large water resources model described in this embodiment of the invention employ the same methods as described above. Figure 1 The method uses the same technical means as the intelligent office assistance method based on the water affairs big model described in the article, and can produce the same technical effect, so it will not be repeated here.

[0199] like Figure 3 The diagram shown is a structural schematic of an electronic device that implements an intelligent office assistance method based on a large water resources model, according to an embodiment of the present invention.

[0200] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a smart office assistance method program based on a large water model.

[0201] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a smart office assistance method program based on a large water management model, but also to temporarily store data that has been output or will be output.

[0202] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a smart office assistance method program based on a large water management model) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0203] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0204] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0205] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0206] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0207] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0208] The intelligent office assistance method program based on a large water resources model, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0209] Acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, coefficient of linear expansion of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value.

[0210] The carbon coefficient and reduction coefficient were confirmed based on multiple sets of historical water quality data.

[0211] The water sample monitoring agency and the pipeline monitoring agency were identified. The water sample monitoring agency includes: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor.

[0212] Total organic carbon and redox potential values ​​were obtained using water sample monitoring agencies.

[0213] The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value.

[0214] Multiple key nodes of the water supply network topology were obtained;

[0215] Perform the following operation on each of the multiple critical pipeline nodes:

[0216] Pipeline monitoring agencies are used to monitor key nodes in the pipeline to obtain node flow rates, node pressure values, and node temperatures.

[0217] Based on pipeline data and node temperatures, the pressure loss index, temperature influence index, and stress influence index were identified.

[0218] The node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index are input into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score.

[0219] By summarizing the comprehensive water status scores, multiple comprehensive water status scores are obtained;

[0220] An office assistance report set is generated based on a comprehensive score of multiple water conditions. The office assistance report set is then sent to a pre-built smart office terminal to complete the smart office assistance.

[0221] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0222] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0223] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0224] Acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, coefficient of linear expansion of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value.

[0225] The carbon coefficient and reduction coefficient were confirmed based on multiple sets of historical water quality data.

[0226] The water sample monitoring agency and the pipeline monitoring agency were identified. The water sample monitoring agency includes: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor.

[0227] Total organic carbon and redox potential values ​​were obtained using water sample monitoring agencies.

[0228] The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value.

[0229] Multiple key nodes of the water supply network topology were obtained;

[0230] Perform the following operation on each of the multiple critical pipeline nodes:

[0231] Pipeline monitoring agencies are used to monitor key nodes in the pipeline to obtain node flow rates, node pressure values, and node temperatures.

[0232] Based on pipeline data and node temperatures, the pressure loss index, temperature influence index, and stress influence index were identified.

[0233] The node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index are input into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score.

[0234] By summarizing the comprehensive water status scores, multiple comprehensive water status scores are obtained;

[0235] An office assistance report set is generated based on a comprehensive score of multiple water conditions. The office assistance report set is then sent to a pre-built smart office terminal to complete the smart office assistance.

[0236] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0237] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0238] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0239] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0240] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart office assistance method based on a large-scale water resources model, characterized in that, The method includes: Acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, coefficient of linear expansion of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value. The carbon coefficient and reduction coefficient were confirmed based on multiple sets of historical water quality data. The water sample monitoring agency and the pipeline monitoring agency were identified. The water sample monitoring agency includes: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring agency includes: a flow meter, a pressure sensor, and a temperature sensor. Total organic carbon and redox potential values ​​were obtained using water sample monitoring agencies. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value. Multiple key nodes of the water supply network topology were obtained; Perform the following operation on each of the multiple critical pipeline nodes: Pipeline monitoring agencies are used to monitor key nodes in the pipeline to obtain node flow rates, node pressure values, and node temperatures. Based on pipeline data and node temperatures, the pressure loss index, temperature influence index, and stress influence index were identified. The node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index are input into the pre-built water affairs analysis model to obtain a comprehensive water affairs status score. By summarizing the comprehensive water status scores, multiple comprehensive water status scores are obtained; An office assistance report set is generated based on a comprehensive score of multiple water conditions. The office assistance report set is then sent to a pre-built smart office terminal to complete the smart office assistance.

2. The intelligent office assistance method based on a large water resources model as described in claim 1, characterized in that, The carbon coefficient and reduction coefficient, identified based on multiple historical water quality data sets, include: Multiple historical water quality scores, multiple historical total organic carbon values, and multiple historical reduction potential values ​​were extracted from multiple historical water quality data sets. The carbon coefficient is calculated based on multiple historical water quality scores and multiple historical total organic carbon values, using the following formula: ； in, Indicates the carbon coefficient. This indicates the number of historical water quality scores among multiple historical water quality scores. This represents the first of multiple historical total organic carbon values. Historical total organic carbon value, This indicates the first of multiple historical water quality scores. A historical water quality score, Indicates taking the absolute value; The reduction coefficient is obtained based on multiple historical water quality scores and multiple historical reduction potential values.

3. The intelligent office assistance method based on a large water resources model as described in claim 2, characterized in that, The method of obtaining total organic carbon and redox potential values ​​using a water sample monitoring agency includes: Obtain initial water samples; The initial water sample is filtered using a filtration device in the water sample monitoring facility to obtain a filtered water sample; The flow cell to be tested was obtained based on filtered water samples, a micro water pump, and a water sample flow cell. The total organic carbon value was obtained by monitoring the flow cell under test using a total organic carbon sensor. The redox potential value is obtained by monitoring the flow cell under test using a redox potential sensor.

4. The intelligent office assistance method based on a large water resources model as described in claim 3, characterized in that, The formula for calculating the water quality impact index is as follows: ； in, Indicates the water quality impact index. Indicates the total organic carbon value. Indicates the redox potential value. Represents the reduction coefficient. It is the natural logarithm. It is a natural constant.

5. The intelligent office assistance method based on a large water resources model as described in claim 4, characterized in that, The method of obtaining multiple key pipeline nodes based on the water supply network topology map includes: Multiple water supply nodes are combined in a binary manner to obtain multiple node pairs, wherein each node pair includes a starting node and an ending node. Perform the following operation on each of the multiple node pairs: The shortest path is determined based on the starting node, the ending node, and multiple water supply pipelines. The shortest path is the shortest path from the starting node to the ending node. Identify the set of intermediate nodes in the shortest path; Summarize the intermediate node sets to obtain multiple intermediate node sets, and extract multiple analysis nodes from the multiple intermediate node sets; The total number of analyses is determined based on multiple analysis nodes, where the total number of analyses is the number of analysis nodes among the multiple analysis nodes; A classification operation is performed on multiple analysis nodes to obtain multiple target node sets, where each target node set corresponds one-to-one with a water supply node; For each of the multiple target node sets, perform the following operation: The number of duplicates is determined based on the target node set, where the number of duplicates is the number of nodes analyzed in the target node set; The node recurrence rate is calculated based on the number of repetitions and the total number of analyses, using the following formula: ； in, Indicates the node recurrence rate. Indicates the number of repetitions. Indicates the total number of analyses; The node reproducibility rates are summarized to obtain multiple node reproducibility rates, where each node reproducibility rate corresponds one-to-one with a water supply node; Perform the following operation on each of the multiple water supply nodes: The node degree is determined based on the water supply node and multiple water supply pipelines. The node degree is the number of water supply pipelines connected to the water supply node among the multiple water supply pipelines. The importance of a node is calculated based on its recurrence rate and degree, as shown in the following formula: ； in, Indicates the importance of nodes. Indicates the degree of a node. Indicates the node recurrence rate; Compare the node importance with a preset importance threshold. If the node importance is greater than or equal to the importance threshold, then the key nodes of the pipeline are identified based on the water supply nodes. Summarize the key nodes of the pipeline to obtain multiple key pipeline nodes.

6. The intelligent office assistance method based on a large water resources model as described in claim 5, characterized in that, The method of using a pipeline monitoring agency to monitor key pipeline nodes and obtain node flow rates, node pressure values, and node temperatures includes: The flow rate at key nodes of the pipeline is monitored using flow meters in the pipeline monitoring system to obtain the node flow rate value; Pressure sensors are used to monitor the pressure at key nodes in the pipeline to obtain node pressure values; Temperature sensors are used to monitor the temperature of key nodes in the pipeline to obtain the node temperature.

7. The intelligent office assistance method based on a large water resources model as described in claim 6, characterized in that, The pressure loss index, temperature influence index, and stress influence index, determined based on pipeline data and node temperatures, include: The pressure loss index is calculated based on the pipe's inner diameter, length, and material density from the pipe data. The calculation formula is shown below: ； in, Indicates the pressure loss index. Indicates the inner diameter of the pipe. Indicates the length of the pipe. Indicates the density of the material. The preset friction factor; The temperature influence index is calculated based on the pipe length and node temperature data in the pipeline data. The calculation formula is as follows: ； in, Indicates the effect of temperature. This represents the coefficient of linear expansion of the material. Indicates node temperature. This is the preset reference temperature; The stress influence index is calculated based on the pipe inner diameter, pipe wall thickness, and maximum tensile strength of the material from the pipe data. The calculation formula is as follows: ； in, Indicates the stress influence index. Indicates the inner diameter of the pipe. Indicates the pipe wall thickness. This indicates the maximum tensile strength of the material.

8. The intelligent office assistance method based on a large water resources model as described in claim 7, characterized in that, The water analysis model is shown below: ； in, This represents a water analysis model. Indicates the node traffic value. This represents the node pressure value.

9. The intelligent office assistance method based on a large water resources model as described in claim 8, characterized in that, The set of office auxiliary reports generated based on multiple comprehensive water status scores includes: For each comprehensive water condition score in the multiple comprehensive water condition scores, perform the following operations: Compare the comprehensive water condition score with the preset health threshold; If the overall water status score is less than or equal to the health threshold, the pre-built first auxiliary report will be used as the office auxiliary report. If the overall water condition score is greater than the health threshold, then the overall water condition score is compared with the preset damage threshold. If the comprehensive water condition score is less than or equal to the damage threshold, the pre-built second auxiliary report will be used as the office auxiliary report. If the comprehensive water condition score is greater than the damage threshold, the pre-built third auxiliary report will be used as the office auxiliary report. Compile office auxiliary reports to obtain a set of office auxiliary reports.

10. An intelligent office assistance system based on a large-scale water resources model, characterized in that: The system includes: The basic data acquisition module is used to acquire pipeline data, water supply network topology map, and multiple historical water quality data sets. The pipeline data includes: pipeline length, pipeline inner diameter, pipeline wall thickness, maximum tensile strength of material, linear expansion coefficient of material, and material density. The water supply network topology map includes: multiple water supply pipelines and multiple water supply nodes. The historical water quality data sets include: historical water quality score, historical total organic carbon value, and historical reduction potential value. The carbon coefficient and reduction coefficient are determined based on multiple historical water quality data sets. The water source quality monitoring module is used to identify water sample monitoring institutions and pipeline monitoring institutions. The water sample monitoring institutions include: a filtration device, a micro water pump, a water sample flow cell, a total organic carbon sensor, and a redox potential sensor. The pipeline monitoring institutions include: a flow meter, a pressure sensor, and a temperature sensor. The total organic carbon value and redox potential value are obtained using the water sample monitoring institutions. The water quality impact index is calculated based on the carbon coefficient, reduction coefficient, total organic carbon value, and redox potential value. The critical node monitoring module is used to obtain multiple critical nodes of the pipeline based on the water supply network topology map. For each critical node of the multiple critical nodes, the following operations are performed: the critical node of the pipeline is monitored by the pipeline monitoring agency to obtain the node flow value, node pressure value and node temperature. Based on the pipeline data and node temperature, the pressure loss index, temperature influence index and stress influence index are determined. The intelligent scoring assistance module is used to input node flow rate, node pressure, water quality impact index, pressure loss index, temperature impact index, and stress impact index into a pre-built water affairs analysis model to obtain a comprehensive water affairs status score. The comprehensive water affairs status scores are then aggregated to obtain multiple comprehensive water affairs status scores. Based on the multiple comprehensive water affairs status scores, an office assistance report set is generated and sent to a pre-built intelligent office terminal to complete the intelligent office assistance.

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