Urban water supply equipment operation supervision system based on big data analysis

Abstract

The invention belongs to the field of water supply equipment supervision, relates to a data analysis technology, and aims to solve the problems that an existing urban water supply equipment operation supervision system cannot accurately monitor key parameters of all pipe network nodes and cannot integrate, analyze and optimize the key parameters. The urban water supply equipment operation supervision system based on big data analysis comprises a data acquisition module, a flow analysis module, a pressure analysis module, a fusion analysis module and a supervision optimization module. The data acquisition module, the flow analysis module, the pressure analysis module, the fusion analysis module and the supervision optimization module are in communication connection in sequence; the data acquisition module is used for acquiring pressure and flow velocity, the flow analysis module and the pressure analysis module are used for analyzing the pressure and flow, the fusion analysis module is used for carrying out fusion analysis on pipe flow and pipe pressure signals, and the supervision optimization module is used for carrying out supervision optimization on supervision nodes; according to the invention, the reliability and efficiency of supervision can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of water supply equipment supervision and management, relates to data analysis technology, and specifically is a city water supply equipment operation supervision system based on big data analysis. Background Art

[0002] Urban water supply equipment refers to a series of facilities and devices used in urban water supply systems to ensure the effective transportation, distribution and use of water resources, including water pumping stations, water towers, high-level water tanks, variable frequency speed regulation water supply equipment, etc. Its function is to transport purified water to the user end through the pipeline network and ensure appropriate water pressure.

[0003] With the rapid development of cities and the continuous growth of population, the scale of urban water supply systems is expanding and the structure is becoming more complex. The stable operation of water supply equipment is crucial to ensuring the normal life of urban residents and the smooth progress of industrial production. Traditional urban water supply equipment supervision methods have many limitations and cannot meet the high requirements of modern cities for water supply safety and reliability.

[0004] Existing urban water supply equipment operation supervision systems often find it difficult to accurately monitor key parameters such as pressure and flow rate at each pipeline node in real time. They are also unable to integrate and analyze key parameters and optimize supervision objects, resulting in the inability to timely detect potential pipeline leaks, abnormal pressure, uneven flow distribution and other problems.

[0005] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0006] The purpose of the present invention is to provide a city water supply equipment operation supervision system based on big data analysis, which is used to solve the problem that the existing city water supply equipment operation supervision system is unable to accurately monitor key parameters such as pressure and flow rate of each pipe network node in real time, and is unable to integrate and analyze key parameters and optimize supervision;

[0007] The technical problem to be solved by the present invention is: how to provide an urban water supply equipment operation supervision system based on big data analysis that can accurately monitor key parameters such as pressure and flow rate of each pipeline node in real time, and at the same time integrate and analyze key parameters and optimize supervision.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A city water supply equipment operation supervision system based on big data analysis includes a data acquisition module, a flow analysis module, a pressure analysis module, a fusion analysis module, and a supervision optimization module; the data acquisition module, the flow analysis module, the pressure analysis module, the fusion analysis module, and the supervision optimization module are sequentially connected in communication;

[0010] The data acquisition module is used to collect the pressure and flow rate of the monitoring node: generate a monitoring cycle with a fixed duration of T, mark the intersection of the pipes in the water supply network as the monitoring node i, where i represents the number of monitoring nodes, i = 1, 2, ..., m, m is a positive integer; mark the pressure of the monitoring node i as the pipe pressure value, and mark the flow rate of the monitoring node as the pipe velocity value;

[0011] The flow analysis module is used to analyze the flow size of the supervision node: draw a pipe speed value-time curve within the supervision period, calculate the flow rate ratio LFi based on the pipe speed value-time curve, and generate a pipe flow normal signal and a pipe flow abnormal signal by judging the flow rate ratio LFi;

[0012] The pressure analysis module is used to analyze the pressure of the supervision node: obtain the pipe pressure average value and pipe pressure floating value of the supervision node i during the supervision cycle, obtain the pipe pressure normal signal and pipe pressure abnormal signal by analyzing the pipe pressure average value, and obtain the pipe pressure stable signal and pipe pressure floating signal by analyzing the pipe pressure floating value;

[0013] The fusion analysis module is used to perform fusion analysis on the pipe flow signal and the pipe pressure signal: the normal pipe flow signal and the abnormal pipe flow signal are used as the first analysis point, the floating pipe pressure signal and the stable pipe pressure signal are used as the second analysis point, and the normal pipe pressure signal and the abnormal pipe pressure signal are used as the third analysis point to create a third-order analysis network, and obtain the preset results and the corresponding supervision report signal through the third-order analysis network;

[0014] The supervision optimization module is used to perform supervision optimization on the supervision nodes: generate a supervision area, and use the obtained supervision alarm signal as a replacement for the supervision alarm signal of all supervision nodes i in the supervision area.

[0015] Furthermore, the water flows to a certain supervision node i and the supervision node adjacent to it is marked as the upper node of the supervision node i, and the node to which the upper node flows and is adjacent is the lower node of the upper node; the pressure and flow rate of the water supply network are collected by installing a flow meter and a pressure sensor at the supervision node, and the pressure size of the supervision node i is marked as the pipe pressure value, the flow rate size of the supervision node is marked as the pipe velocity value, and the pipe velocity value is sent to the flow analysis module, and the pipe pressure value is sent to the pressure analysis module.

[0016] Furthermore, the pipe velocity value of the supervision node i within the supervision cycle is obtained, a rectangular coordinate system is established with time as the X-axis and the pipe velocity value as the Y-axis, and a pipe velocity value-time curve within the supervision cycle is plotted; the area enclosed by the pipe velocity value-time curve and the X-axis is integrated to obtain the pipe displacement value GMi; the cross-sectional area S of the pipeline network corresponding to the supervision node i is obtained, and the pipe displacement value GMi and the cross-sectional area S are numerically calculated using the formula GLi=GMi*S to obtain the pipe flow value GLi of the supervision node i within the supervision cycle.

[0017] Furthermore, the pipe flow value GLi of the supervision node i is marked as the upper-level total SZi, the pipe flow values GLi of all lower-level nodes of the supervision node i are obtained and summed up to obtain the lower-level total XZi, the lower-level total XZi is ratio-calculated with the upper-level total SZi to obtain the flow ratio LFi of the supervision node i, and the flow ratio LFi is compared with the preset flow ratio threshold LFmax: if the flow ratio LFi is greater than or equal to the flow ratio threshold LFmax, a normal pipe flow signal is generated; if the flow ratio LFi is less than the flow ratio threshold LFmax, an abnormal pipe flow signal is generated; the flow analysis module sends the normal pipe flow signal and the abnormal pipe flow signal to the fusion analysis module.

[0018] Furthermore, the variance of the pipe pressure value is calculated to obtain the pipe pressure floating value; the pipe pressure floating value is compared with the preset pipe pressure floating threshold: if the pipe pressure floating value is greater than or equal to the pipe pressure floating threshold, a pipe pressure floating signal is generated; if the pipe pressure floating value is less than the pipe pressure floating threshold, a pipe pressure stable signal is generated.

[0019] Furthermore, the actual node distance JSi and the node vertical distance JCi from the supervision node i to any subordinate node are obtained, where the actual node distance JSi is the pipe length between the upper node and the lower node, and the node vertical distance JCi is the difference in height between the upper node and the lower node from the horizontal plane; the pipe pressure values are summed and averaged to obtain the mean pipe pressure, the mean pipe pressure of the supervision node i is marked as the upper pipe pressure SYi, and the mean pipe pressure of the lower node of the supervision node i is marked as the lower pipe pressure XYi; the node actual distance JSi and the node vertical distance JCi as well as the upper pipe pressure SYi and the lower pipe pressure XYi are numerically calculated to obtain the upper and lower pressure difference coefficients YCi of the supervision node i; the upper and lower pressure difference coefficients YCi of the supervision node i are compared with the preset upper and lower pressure difference thresholds YCmax: if the upper and lower pressure difference coefficients YCi are greater than or equal to the upper and lower pressure difference thresholds YCmax, an abnormal pipe pressure signal is generated; if the upper and lower pressure difference coefficients YCi are less than the upper and lower pressure difference thresholds YCmax, a normal pipe pressure signal is generated.

[0020] Furthermore, the pipe flow signal and the pipe pressure signal of the supervision node i are obtained and analyzed and compared in sequence at the first analysis point, the second analysis point and the third analysis point through the third-order analysis network to obtain the preset result at the end of the third-order analysis network; based on the preset result obtained, a corresponding supervision report signal for the supervision node i is generated, and the supervision report signal is sent to the mobile phone terminal of the manager.

[0021] Furthermore, the supervision reporting signal of the supervision node i and the supervision reporting signals of its upper and lower supervision nodes are obtained. If the supervision reporting signals of the supervision node i and the upper and lower supervision nodes are all generated by the same preset results, then the supervision node i and its upper and lower nodes are classified into the same supervision area, and unified data collection and operation supervision are performed on the supervision area, and the obtained supervision alarm signal is used as a substitute for the supervision alarm signal of all supervision nodes i in the supervision area.

[0022] A method for monitoring the operation of urban water supply equipment based on big data analysis, comprising the following steps:

[0023] Step 1: Generate a fixed monitoring cycle of length T, mark the intersection of the pipes in the water supply network as the monitoring node i, and collect the pipe pressure and pipe velocity values of the monitoring node i;

[0024] Step 2: Draw the pipe velocity value-time curve to obtain the lower-level total amount XZi and the upper-level total amount SZi. Calculate the ratio of the lower-level total amount XZi to the upper-level total amount SZi to obtain the flow ratio LFi of the supervision node i. Generate the normal pipe flow signal and the abnormal pipe flow signal by judging the flow ratio LFi.

[0025] Step 3: Obtain the average value and floating value of the pipe pressure, analyze the average value to generate a pipe pressure abnormal signal and a pipe pressure normal signal; analyze the floating value of the pipe pressure to generate a pipe pressure floating signal and a pipe pressure stable signal;

[0026] Step 4: Obtain the pipe flow signal and pipe pressure signal of the monitoring node i and analyze and compare them through the third-order analysis network to obtain the preset result at the end of the third-order analysis network and the corresponding monitoring alarm signal;

[0027] Step 5: Generate a supervision region, and use the obtained supervision alarm signal as a replacement for the supervision alarm signal of all supervision nodes i in the supervision region.

[0028] The present invention has the following beneficial effects:

[0029] 1. Through accurate collection and in-depth analysis of the pressure and flow rate at the monitoring nodes, the operating status of the water supply network can be fully understood in real time, and potential problems such as abnormal flow and pressure imbalance can be discovered in a timely manner;

[0030] 2. By accurately identifying normal and abnormal conditions of pipe flow and pressure, it helps to take preventive measures in advance, ensure the continuous and stable supply of urban water, and reduce the impact of emergencies such as water outages on residents' lives and industrial production;

[0031] 3. Through the fusion analysis module, the relevant signals of pipe flow and pipe pressure are comprehensively evaluated to provide more comprehensive and accurate supervision results, reduce the misjudgment that may be caused by single factor analysis, and improve the reliability and accuracy of supervision;

[0032] 4. The supervision optimization module can classify and uniformly manage supervision nodes, reduce duplication of work, improve supervision efficiency, and carry out targeted optimization based on regional characteristics to reduce management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0035] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention;

[0036] Figure 3 Schematic diagram of a third-order analysis network in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0038] Example 1: Figure 1 As shown, a city water supply equipment operation supervision system based on big data analysis includes a data acquisition module, a flow analysis module, a pressure analysis module, a fusion analysis module and a supervision optimization module; the data acquisition module, the flow analysis module, the pressure analysis module, the fusion analysis module and the supervision optimization module are communicated in sequence.

[0039] The data acquisition module is used to collect the pressure and flow rate of the supervision node: a supervision cycle of a fixed duration of T is generated, and the operation of the water supply network in the urban water supply equipment is supervised within the supervision cycle. The intersection of the pipes in the water supply network is marked as the supervision node i, where i represents the number of supervision nodes, i = 1, 2, ..., m, and m is a positive integer; the supervision node to which the water flows and is adjacent to a certain supervision node is marked as the parent node of the supervision node, and the node to which the parent node flows and is adjacent is the child node of the parent node; the pressure and flow rate of the water supply network are collected by installing flow meters and pressure sensors at the supervision nodes, and the pressure size of the supervision node i is marked as the pipe pressure value, and the flow rate size of the supervision node is marked as the pipe velocity value. The pipe velocity value is sent to the flow analysis module, and the pipe pressure value is sent to the pressure analysis module; by accurately collecting and deeply analyzing the pressure and flow rate of the supervision nodes, data support is provided for real-time and comprehensive understanding of the operation status of the water supply network and timely discovery of potential problems.

[0040] The flow analysis module is used to analyze the flow size of the supervision node: obtain the pipe velocity value of the supervision node i within the supervision period, establish a rectangular coordinate system with time as the X-axis and the pipe velocity value as the Y-axis, and draw the pipe velocity value-time curve within the supervision period; integrate the area enclosed by the pipe velocity value-time curve and the X-axis to obtain the pipe displacement value GMi; obtain the cross-sectional area S of the pipe network corresponding to the supervision node i, and perform numerical calculations on the pipe displacement value GMi and the cross-sectional area S through the formula GLi=GMi*S to obtain the pipe flow value GLi of the supervision node i within the supervision period. The value of the pipe flow value GLi represents the flow size of the supervision node i; the flow status of the supervision node i is analyzed through the pipe flow value GLi. Row analysis: Mark the pipe flow value GLi of the supervision node i as the upper-level total SZi, obtain the pipe flow values GLi of all subordinate nodes of the supervision node i and sum them to obtain the subordinate total XZi, calculate the ratio of the subordinate total XZi to the upper-level total SZi to obtain the flow ratio LFi of the supervision node i, and compare the flow ratio LFi with the preset flow ratio threshold LFmax: if the flow ratio LFi is greater than or equal to the flow ratio threshold LFmax, a normal pipe flow signal is generated; if the flow ratio LFi is less than the flow ratio threshold LFmax, an abnormal pipe flow signal is generated; the flow analysis module sends the normal pipe flow signal and the abnormal pipe flow signal to the fusion analysis module.

[0041] The pressure analysis module is used to analyze the pressure of the supervision node: obtain the pipe pressure value of the supervision node i within the supervision cycle, sum and average the pipe pressure values to obtain the pipe pressure mean, and calculate the variance of the pipe pressure values to obtain the pipe pressure floating value;

[0042] Analyze the mean pipe pressure of the supervisory node i: obtain the actual node distance JSi and the node vertical distance JCi from the supervisory node i to any subordinate node, where the actual node distance JSi is the pipe length between the upper node and the subordinate node, and the node vertical distance JCi is the difference in height between the upper node and the subordinate node from the horizontal plane; mark the mean pipe pressure of the supervisory node i as the upper pipe pressure SYi, and mark the mean pipe pressure of the subordinate nodes of the supervisory node i as the subordinate pipe pressure XYi; through the formula Obtain the upper and lower pressure difference coefficients YCi of the supervisory node i, where k1 and k2 are both proportional coefficients, and k1>k2>1; compare the upper and lower pressure difference coefficients YCi of the supervisory node i with the preset upper and lower pressure difference thresholds YCmax: if the upper and lower pressure difference coefficients YCi are greater than or equal to the upper and lower pressure difference thresholds YCmax, generate a pipe pressure abnormality signal; if the upper and lower pressure difference coefficients YCi are less than the upper and lower pressure difference thresholds YCmax, generate a pipe pressure normal signal;

[0043] Analyze the pipe pressure fluctuation value of supervisory node i: compare the pipe pressure fluctuation value with the preset pipe pressure fluctuation threshold. If the pipe pressure fluctuation value is greater than or equal to the pipe pressure fluctuation threshold, generate a pipe pressure fluctuation signal; if the pipe pressure fluctuation value is less than the pipe pressure fluctuation threshold, generate a pipe pressure stability signal.

[0044] The pressure analysis module sends the generated normal pipe pressure signal, abnormal pipe pressure signal, floating pipe pressure signal and stable pipe pressure signal to the fusion analysis module; by accurately identifying the normal and abnormal conditions of pipe flow and pipe pressure, it helps to take measures to prevent faults in advance, ensure the continuous and stable urban water supply, and reduce the impact of emergencies such as water outages on residents' lives and industrial production.

[0045] The fusion analysis module is used to perform fusion analysis on the pipe flow signal and the pipe pressure signal: the normal pipe flow signal and the abnormal pipe flow signal are used as the first analysis point, the pipe pressure floating signal and the pipe pressure stable signal are used as the second analysis point, and the normal pipe pressure signal and the abnormal pipe pressure signal are used as the third analysis point to create a Figure 3 The third-order analysis network shown in the figure obtains the pipe flow signal and the pipe pressure signal of the supervision node i and performs analysis and comparison at the first analysis point, the second analysis point and the third analysis point in sequence through the third-order analysis network to obtain the preset result at the end of the third-order analysis network; generates a corresponding supervision report signal for the supervision node i according to the preset result obtained, and sends the supervision report signal to the mobile phone terminal of the manager; comprehensively evaluates the relevant signals of the pipe flow and pipe pressure through the fusion analysis module to provide more comprehensive and accurate supervision results, reduce the misjudgment that may be caused by single-factor analysis, and improve the reliability and accuracy of supervision.

[0046] The supervision optimization module is used to optimize the supervision of supervision nodes: obtain the supervision report signal of supervision node i and the supervision report signals of its upper and lower supervision nodes. If the supervision report signals of supervision node i and its upper and lower nodes are generated by the same preset results, then the supervision node i and its upper and lower nodes are classified into the same supervision area, and unified data collection and operation supervision are carried out on the supervision area, and the obtained supervision alarm signal is used as a substitute for the supervision alarm signal of all supervision nodes i in the supervision area; through the supervision optimization module, supervision nodes can be classified and uniformly managed, duplication of work can be reduced, supervision efficiency can be improved, and targeted optimization can be carried out according to regional characteristics to reduce management costs.

[0047] Example 2: Figure 2 As shown, a method for supervising the operation of urban water supply equipment based on big data analysis includes the following steps:

[0048] Step 1: Generate a fixed monitoring cycle of duration T. Mark the intersection of the water supply network pipes as monitoring node i. Mark the monitoring nodes that flow toward a monitoring node and are adjacent to it as the parent node of the monitoring node. The nodes that flow toward and are adjacent to the parent node are the child nodes of the parent node. Collect the pipe pressure and pipe velocity values of monitoring node i.

[0049] Step 2: Draw a pipe velocity value-time curve within the supervision cycle, and calculate the pipe flow value GLi from the pipe velocity value-time curve. Use the pipe flow value GLi to obtain the lower-level total amount XZi and the upper-level total amount SZi. Calculate the ratio of the lower-level total amount XZi to the upper-level total amount SZi to obtain the flow ratio LFi of the supervision node i. By determining the flow ratio LFi, a normal pipe flow signal and an abnormal pipe flow signal are generated.

[0050] Step 3: Obtain the pipe pressure values of the supervision node i during the supervision cycle, sum and average the pipe pressure values to obtain the pipe pressure mean value, and calculate the pipe pressure variance to obtain the pipe pressure floating value; analyze the pipe pressure mean value to generate a pipe pressure abnormality signal and a pipe pressure normal signal; analyze the pipe pressure floating value to generate a pipe pressure floating signal and a pipe pressure stable signal;

[0051] Step 4: Obtain the pipe flow signal and pipe pressure signal of the monitoring node i and analyze and compare the first analysis point, the second analysis point, and the third analysis point in sequence through the third-order analysis network to obtain the preset result at the end of the third-order analysis network and the corresponding monitoring alarm signal;

[0052] Step 5: Generate a supervision area, conduct unified data collection and operation supervision on the supervision area, and use the obtained supervision alarm signal as a substitute for the supervision alarm signal of all supervision nodes i in the supervision area.

[0053] A city water supply equipment operation supervision system based on big data analysis generates a supervision cycle with a fixed duration of T during operation, marks the pipe intersection of the water supply network as a supervision node i, and collects the pipe pressure and pipe velocity values of the supervision node i; draws a pipe velocity value-time curve and calculates the flow ratio through processing, and generates a normal pipe flow signal and an abnormal pipe flow signal by judging the flow ratio; obtains the pipe pressure mean and the pipe pressure floating value, analyzes the pipe pressure mean, and generates a pipe pressure abnormality signal and a normal pipe flow signal; analyzes the pipe pressure floating value, and generates a pipe pressure floating signal and a pipe pressure stable signal; obtains the pipe flow signal and the pipe pressure signal of the supervision node i, analyzes and compares them through a third-order analysis network, and obtains a preset result at the end of the third-order analysis network and a corresponding supervision alarm signal; finally, generates a supervision area, and uses the obtained supervision alarm signal as a substitute for the supervision alarm signal of all supervision nodes i in the supervision area.

[0054] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0055] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0056] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A city water supply equipment operation supervision system based on big data analysis, characterized in that: It includes a data acquisition module, a flow analysis module, a pressure analysis module, a fusion analysis module and a supervision optimization module; the data acquisition module, the flow analysis module, the pressure analysis module, the fusion analysis module and the supervision optimization module are sequentially connected in communication; The data acquisition module is used to collect the pressure and flow rate of the monitoring node: generate a monitoring cycle with a fixed duration of T, mark the intersection of the pipes in the water supply network as the monitoring node i, where i represents the number of monitoring nodes, i = 1, 2, ..., m, m is a positive integer; mark the pressure of the monitoring node i as the pipe pressure value, and mark the flow rate of the monitoring node as the pipe velocity value; The flow analysis module is used to analyze the flow size of the supervision node: draw a pipe speed value-time curve within the supervision period, calculate the flow rate ratio LFi based on the pipe speed value-time curve, and generate a pipe flow normal signal and a pipe flow abnormal signal by judging the flow rate ratio LFi; The pressure analysis module is used to analyze the pressure of the supervision node: obtain the pipe pressure average value and pipe pressure floating value of the supervision node i during the supervision cycle, obtain the pipe pressure normal signal and pipe pressure abnormal signal by analyzing the pipe pressure average value, and obtain the pipe pressure stable signal and pipe pressure floating signal by analyzing the pipe pressure floating value; The fusion analysis module is used to perform fusion analysis on the pipe flow signal and the pipe pressure signal: the normal pipe flow signal and the abnormal pipe flow signal are used as the first analysis point, the floating pipe pressure signal and the stable pipe pressure signal are used as the second analysis point, and the normal pipe pressure signal and the abnormal pipe pressure signal are used as the third analysis point to create a third-order analysis network, and obtain the preset results and the corresponding supervision report signal through the third-order analysis network; The supervision optimization module is used to perform supervision optimization on the supervision nodes: generate a supervision area, and use the obtained supervision alarm signal as a replacement for the supervision alarm signal of all supervision nodes i in the supervision area.

2. The urban water supply equipment operation supervision system based on big data analysis according to claim 1 is characterized in that: The water flows to a certain supervision node i and the supervision node adjacent to it is marked as the superior node of the supervision node i, and the node to which the superior node flows and is adjacent is the subordinate node of the superior node; the pressure and flow rate of the water supply network are collected by installing a flow meter and a pressure sensor at the supervision node, and the pressure size of the supervision node i is marked as the pipe pressure value, the flow rate size of the supervision node is marked as the pipe velocity value, and the pipe velocity value is sent to the flow analysis module, and the pipe pressure value is sent to the pressure analysis module.

3. The urban water supply equipment operation supervision system based on big data analysis according to claim 2 is characterized in that: Obtain the pipe velocity value of the supervision node i within the supervision cycle, establish a rectangular coordinate system with time as the X-axis and the pipe velocity value as the Y-axis, and draw the pipe velocity value-time curve within the supervision cycle; integrate the area enclosed by the pipe velocity value-time curve and the X-axis to obtain the pipe displacement value GMi; obtain the cross-sectional area S of the pipeline network corresponding to the supervision node i, and numerically calculate the pipe displacement value GMi and the cross-sectional area S using the formula GLi = GMi * S to obtain the pipe flow value GLi of the supervision node i within the supervision cycle.

4. The urban water supply equipment operation supervision system based on big data analysis according to claim 3 is characterized in that: Mark the pipe flow value GLi of the supervisory node i as the upper-level total SZi, obtain the pipe flow values GLi of all subordinate nodes of the supervisory node i and sum them to obtain the subordinate total XZi, calculate the ratio of the subordinate total XZi to the upper-level total SZi to obtain the flow fraction LFi of the supervisory node i, and compare the flow fraction LFi with the preset flow fraction threshold LFmax: if the flow fraction LFi is greater than or equal to the flow fraction threshold LFmax, a pipe flow normal signal is generated; If the flow fraction LFi is less than the flow fraction threshold LFmax, a pipe flow abnormality signal is generated; the flow analysis module sends the pipe flow normal signal and the pipe flow abnormality signal to the fusion analysis module.

5. The urban water supply equipment operation supervision system based on big data analysis according to claim 4 is characterized in that: The variance of the pipe pressure value is calculated to obtain the pipe pressure floating value; the pipe pressure floating value is compared with the preset pipe pressure floating threshold: if the pipe pressure floating value is greater than or equal to the pipe pressure floating threshold, a pipe pressure floating signal is generated; if the pipe pressure floating value is less than the pipe pressure floating threshold, a pipe pressure stable signal is generated.

6. The urban water supply equipment operation supervision system based on big data analysis according to claim 5 is characterized in that: Obtain the actual node distance JSi and node vertical distance JCi from the supervisory node i to any subordinate node, where the actual node distance JSi is the pipe length between the upper and lower nodes, and the node vertical distance JCi is the difference in height between the upper and lower nodes from the horizontal plane; sum and average the pipe pressure values to obtain the mean pipe pressure, mark the mean pipe pressure of the supervisory node i as the upper-level pipe pressure SYi, and mark the mean pipe pressure of the subordinate nodes of the supervisory node i as the lower-level pipe pressure XYi; The actual node distance JSi and the node vertical distance JCi, as well as the upper-level pipe pressure SYi and the lower-level pipe pressure XYi, are numerically calculated to obtain the upper and lower pressure difference coefficient YCi of the supervision node i; the upper and lower pressure difference coefficient YCi of the supervision node i is compared with the preset upper and lower pressure difference thresholds YCmax: if the upper and lower pressure difference coefficient YCi is greater than or equal to the upper and lower pressure difference thresholds YCmax, an abnormal pipe pressure signal is generated; if the upper and lower pressure difference coefficient YCi is less than the upper and lower pressure difference thresholds YCmax, a normal pipe pressure signal is generated.

7. The urban water supply equipment operation supervision system based on big data analysis according to claim 6 is characterized in that: Obtain the pipe flow signal and pipe pressure signal of the supervision node i and perform analysis and comparison of the first analysis point, the second analysis point and the third analysis point in sequence through the third-order analysis network to obtain the preset result at the end of the third-order analysis network; generate the corresponding supervision report signal for the supervision node i according to the preset result obtained, and send the supervision report signal to the mobile terminal of the manager.

8. The urban water supply equipment operation supervision system based on big data analysis according to claim 7 is characterized in that: Obtain the supervision report signal of the supervision node i and the supervision report signals of its upper and lower supervision nodes. If the supervision report signals of the supervision node i and the upper and lower supervision nodes are all generated by the same preset results, then the supervision node i and its upper and lower nodes are classified into the same supervision area, and unified data collection and operation supervision are performed on the supervision area, and the obtained supervision alarm signal is used as a substitute for the supervision alarm signal of all supervision nodes i in the supervision area.

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