Pressure detection early warning method and system based on pipe network data

Abstract

The invention belongs to the technical field of pipe network detection, and provides a pressure detection early warning method and system based on pipe network data, and the method comprises the steps: carrying out the position comparison analysis of a historical abnormal pipe network detection node and a set pipe network detection node, finding out a pipe network missing detection node, and carrying out the detection of the pipe network missing detection node; and through pressure abnormity time point prediction of the pipe network missing detection node, whether the pipe network missing detection node has pressure abnormity in the current pipe network detection period is judged, the accuracy of pipe network pressure detection is improved, the pressure distribution of the pipe network under different working conditions is simulated, and the detection accuracy is improved. The normal pressure range of each pipe network detection node under different working conditions is found, the problem that pipe network pressure detection is inaccurate or misjudged due to different working conditions and different normal pressure ranges of the pipe network detection nodes is solved, and the necessary early-warning pipe network detection nodes of the emergency early-warning pipe network detection nodes are identified. And the timeliness of pipe network pressure detection early warning is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipe network detection, and in particular to a pressure detection and early warning method and system based on pipe network data. Background Art

[0002] There are many problems in the existing technology in terms of setting up pipeline network detection nodes and pressure detection. For example, in the setting up of pipeline network detection nodes, there is a lack of system position comparison analysis between historical abnormal nodes and established nodes, making it difficult to accurately detect missed detection nodes, resulting in some key locations not being effectively monitored, making it impossible to fully grasp the operation status of the pipeline network. In addition, the existing technology does not predict the time point of pressure anomalies at the missed detection nodes, and cannot determine in advance whether these nodes will have pressure anomalies during the current detection cycle, which is not conducive to the reasonable planning of detection work and resource allocation. In terms of pipeline network pressure detection, since the pipeline network pressure distribution is different under different working conditions, and the existing technology fails to simulate the pipeline network pressure distribution for each working condition, and determine the normal pressure range of each detection node under different working conditions, it is easy to cause inaccurate or misjudged pressure detection due to changes in working conditions, and it is impossible to detect pipeline network pressure anomalies in a timely and accurate manner. At the same time, the existing technology can only identify pipeline detection nodes where pressure abnormalities have occurred, lacks predictive analysis of pressure changes, and cannot identify nodes where pressure abnormalities may occur in advance, resulting in untimely pipeline pressure detection warnings and inability to take effective measures in the early stages of pressure abnormalities, which may cause the problem to expand and affect the safe and stable operation of the pipeline network.

[0003] To this end, the present invention provides a pressure detection and early warning method and system based on pipeline network data. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0005] The technical solution adopted by the present invention to solve its technical problem is: A pressure detection and early warning method based on pipe network data, comprising: Compare the locations of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify missed detection nodes in the pipe network; Through the time series of pressure anomaly intervals of the missed detection nodes in the historical operation cycle of the pipeline network, it is predicted whether the missed detection nodes in the pipeline network will have pressure anomalies in the current pipeline network detection cycle. If so, the missed detection nodes in the pipeline network are marked as necessary detection nodes in the pipeline network. Add new pipe network detection nodes according to the locations of necessary detection nodes in the pipe network and optimize the settings of pipe network detection nodes; After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; The detected pressure of each pipeline network detection node is compared with the normal pressure range of each pipeline network detection node under the corresponding working conditions to identify the emergency warning pipeline network detection nodes and the necessary warning pipeline network detection nodes.

[0006] Preferably, the method for identifying the missing detection nodes of the pipe network is: The historical abnormal pipeline network detection nodes that are different from the positions of all the set pipeline network detection nodes are marked as pipeline network missed detection nodes.

[0007] Preferably, the process of predicting whether a pipeline network missed detection node will have abnormal pressure in the current pipeline network detection cycle is: Perform stationarity analysis on the pressure anomaly interval time series to determine the predicted value of the anomaly interval time, and determine the pressure anomaly prediction time point in combination with the last pressure anomaly time point of the missed detection node in the pipeline network; If the pressure anomaly prediction time point is within the current pipe network detection cycle, it means that the missed detection node of the pipe network will cause pressure anomaly within the current pipe network detection cycle.

[0008] Preferably, the process of performing stationarity analysis on the pressure anomaly interval time series is: The standard deviation and mean of the pressure anomaly interval time series are calculated, and the standard deviation and mean are proportionally calculated to obtain the stable value of the pressure anomaly interval time series; If the stability value is greater than or equal to the stability threshold, it means that it is not stable; then the LSTN network is used to process the pressure abnormal interval time series to determine the abnormal interval time prediction value If the stable value is less than the stable threshold, it means stability, and the historical average method is used to process the pressure anomaly interval time series to determine the predicted value of the anomaly interval time.

[0009] Preferably, the normal pressure range of each pipe network detection node under the different working conditions is determined as follows: By simulating the pressure distribution of the pipeline network under different working conditions, the maximum and minimum pressures of each pipeline network detection node under normal operation of the pipeline network under different working conditions are obtained, and the normal pressure range of each pipeline network detection node under different working conditions is constructed.

[0010] Preferably, the emergency warning pipe network detection node and the necessary warning pipe network detection node are identified as follows: If the pressure of the pipeline network detection node is not within the normal pressure range, the pipeline network detection node is marked as an emergency warning pipeline network detection node, otherwise, the pipeline network detection node is marked as a non-emergency warning pipeline network detection node; Perform critical pressure analysis on non-emergency warning pipeline network detection nodes to determine whether pressure prediction is needed. If necessary, predict whether the pressure of the non-emergency warning pipeline network detection node will reach the edge of the normal pressure range endpoint during the current pipeline network detection cycle. If so, mark the non-emergency warning pipeline network detection node as a necessary warning pipeline network detection node.

[0011] Preferably, the pressure critical analysis process is: If the pressure of the non-emergency warning pipe network detection node is not within the critical pressure range, a proximity analysis signal is generated; Based on the proximity analysis signal, if the pressure value of the non-emergency warning pipeline network detection node exceeds the maximum value in the critical pressure range, the pressure value of the non-emergency warning pipeline network detection node is processed with the maximum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipeline network detection node; If the pressure value of the non-emergency warning pipe network detection node is lower than the minimum value in the critical pressure range, the pressure value of the non-emergency warning pipe network detection node is processed with the minimum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipe network detection node; If the pressure critical value is less than or equal to the pressure critical threshold, a prediction signal is generated.

[0012] Preferably, the process of predicting whether the pressure of the non-emergency warning pipe network detection node will reach the edge of the normal pressure range endpoint within the current pipe network detection cycle includes: The separation time point is obtained by detecting the time point at which the pressure value of the non-emergency warning pipe network detection node separates from the critical pressure range; Fit the pressure values ​​of the non-emergency warning pipe network detection nodes in the period between the separation time point and the prediction signal generation time point to obtain the pressure fitting straight line and calculate the determination coefficient; The determination coefficient is used to determine whether the pressure value of the non-emergency warning pipe network detection node changes linearly or nonlinearly and to determine the time point when the pressure edge arrives; If the pressure edge arrival time point is within the current pipe network detection cycle, the non-emergency warning pipe network detection node will be marked as a necessary warning pipe network detection node.

[0013] Preferably, the pressure edge arrival time point is determined in the following manner: If it changes linearly, find the time point corresponding to the endpoint value of the normal pressure range closest to the pressure value of the non-emergency warning pipeline network detection node on the pressure fitting line, that is, the pressure edge arrival time point; If it changes nonlinearly, the pressure change rate of the non-emergency warning pipeline network detection nodes at adjacent moments is calculated, and the fastest pressure change rate is extracted; The critical pressure value and the fastest rate of pressure change are proportionally calculated to obtain the critical remaining pressure duration, and the pressure edge arrival time is determined in combination with the predicted signal generation time point.

[0014] A pressure detection and early warning system based on pipe network data, characterized in that it includes: Missed detection analysis module: compare the positions of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify missed detection nodes of the pipe network; Necessary detection analysis module: through the pressure anomaly interval time series of the pipeline network missed detection node in the historical operation cycle of the pipeline network, it is predicted whether the pipeline network missed detection node will have pressure anomaly in the current pipeline network detection cycle. If so, the pipeline network missed detection node is marked as a necessary detection node of the pipeline network; Detection node optimization module: add new pipeline network detection nodes according to the necessary detection node positions of the pipeline network, and optimize the settings of pipeline network detection nodes; Working condition pressure simulation module: After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; Pressure detection and early warning module: compares the detected pressure of each pipeline network detection node with the normal pressure range of each pipeline network detection node under the corresponding working conditions, and identifies the emergency early warning pipeline network detection nodes and the necessary early warning pipeline network detection nodes.

[0015] The beneficial effects of the present invention are as follows: 1. By comparing and analyzing the positions of historical abnormal pipeline network detection nodes with the already set pipeline network detection nodes, the missed detection nodes of the pipeline network are found, and the abnormal pressure time points of the missed detection nodes of the pipeline network are predicted to determine whether the missed detection nodes of the pipeline network will have abnormal pressure in the current pipeline network detection cycle, thereby completing the setting optimization of the pipeline network detection nodes, improving the rationality of the setting of the pipeline network detection nodes, optimizing the resource cost of the pipeline network pressure detection, and improving the accuracy of the pipeline network pressure detection.

[0016] 2. Simulate the pressure distribution of the pipeline network under different working conditions, find the normal pressure range of each pipeline network detection node under different working conditions, solve the problem of inaccurate or misjudgment of pipeline network pressure detection due to different working conditions and different normal pressure ranges of pipeline network detection nodes, and identify emergency warning pipeline network detection nodes and necessary warning pipeline network detection nodes through pressure comparison. Not only can the pipeline network detection nodes with abnormal pressure be identified, but also the pipeline network detection nodes that may have abnormal pressure can be identified through predictive analysis of pressure changes, and necessary marking can be performed to improve the timeliness of pipeline network pressure detection and warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 It is a flowchart of the steps of a pressure detection and early warning method based on pipe network data according to an embodiment of the present invention; Figure 2 It is a flowchart of a pressure detection and early warning system based on pipe network data described in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0020] Example 1 See also Figure 1 As shown, a pressure detection and early warning method based on pipe network data according to an embodiment of the present invention comprises the following steps: Step 1: Compare the locations of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify the missed detection nodes of the pipe network; In step 1, the set pipe network detection nodes are set in advance by those skilled in the art. For example, the setting of the pipe network detection nodes includes but is not limited to the following methods: Method 1: In a tree-like and one-way water supply network structure, such as a tree-like water supply system in mountainous areas, the tip is prone to low pressure due to the large elevation difference in the mountainous area; the bifurcation reflects the change in water flow distribution pressure. The setting of the network detection nodes includes but is not limited to: water plant outlet (1 point), main pipe bifurcation (2 points), and the entrance of the highest village (1 point), etc. Method 2: In a ring-shaped and multi-directional water supply network structure, such as an urban ring-shaped water supply system, it is necessary to detect the pressure fluctuation at the intersection of the ring trunk line, track the dispatch response before and after the pump station, and calibrate the water distribution deviation at the boundary line. The setting of the network detection nodes includes but is not limited to: the intersection of the ring trunk line (3 points), before and after the secondary pressure pump station (2 points), and the water supply boundary line (2 points). Method 3: In the drainage network structure, such as the commercial area drainage network system, the water quality needs to be monitored due to the high oil content of catering wastewater; the flow rate at the inlet of the regulating reservoir reflects the drainage efficiency. The setting of the network detection nodes includes but is not limited to: the drainage outlet of the catering concentration area (2 points), the intersection of the rainwater pipe (2 points), the inlet of the regulating reservoir (3 points), etc.; In step 1, the historical abnormal pipe network detection node represents the pipe network detection node where pressure abnormality occurs during the historical operation cycle of the pipe network; In step 1, the process of identifying the missing detection nodes of the pipe network includes: Compare the locations of the set pipe network detection nodes with the historical abnormal pipe network detection nodes. Mark the historical abnormal pipeline network detection nodes that are different from all the set pipeline network detection nodes as pipeline network missed detection nodes; On the contrary, if the historical abnormal pipe network detection node is at the same position as any set pipe network detection node, the historical abnormal pipe network detection node is marked as a non-pipeline network missed detection node; Step 2: Through the time series of pressure anomaly intervals of the missed detection nodes in the historical operation cycle of the pipeline network, it is predicted whether the missed detection nodes in the pipeline network will have pressure anomalies in the current pipeline network detection cycle. If so, the missed detection nodes in the pipeline network are marked as necessary detection nodes in the pipeline network; In step 2, the pressure anomaly interval time series of the pipe network omission detection node in the pipe network historical operation cycle is constructed by the interval time when the pressure anomaly occurs at the pipe network omission detection node in the pipe network historical operation cycle; For example, it is assumed that the time points at which the pressure anomalies of the missed detection nodes of the pipeline network occur during the historical operation cycle of the pipeline network are (ysj1, ysj2, ysj3...ysj n ), where ysjn represents the time point when the pressure anomaly occurs for the nth time at the missed detection node in the historical operation cycle of the pipeline network. The constructed pressure anomaly interval time series is: (ysj2-ysj1, ysj3-ysj2, ... ysj n -ysj n-1 ) In step 2, the process of predicting whether the missed detection node of the pipeline network will have abnormal pressure in the current pipeline network detection cycle includes: The standard deviation and mean of the pressure anomaly interval time series of the pipeline network omission detection node in the historical operation cycle of the pipeline network are calculated, and the standard deviation and the mean are proportionally calculated to obtain the stable value of the pressure anomaly interval time series of the pipeline network omission detection node in the historical operation cycle of the pipeline network; It can be understood that the calculation of the ratio of the standard deviation and the mean and the calculation of the stable value of the pressure anomaly interval time series of the pipe network missed detection node in the historical operation cycle of the pipe network by obtaining the coefficient of variation are to determine the stability of the pressure anomaly of the pipe network missed detection node in the historical operation cycle of the pipe network. Its role is conducive to the subsequent prediction of whether the pipe network missed detection node will have pressure anomaly in the current pipe network detection cycle according to the stability of the pressure anomaly of the pipe network missed detection node in the historical operation cycle of the pipe network. In some preferred embodiments, the stable value of the pressure anomaly interval time series of the missed detection node of the pipeline network within the historical operation cycle of the pipeline network is compared with the stable threshold value; If the stability value is greater than or equal to the stability threshold, it means that the pressure anomaly interval time series of the missed detection node in the pipeline network during the historical operation cycle of the pipeline network is not stable; If the stability value is less than the stability threshold, it means that the pressure anomaly interval time series of the missed detection node in the pipeline network is stable during the historical operation cycle of the pipeline network; If the pressure anomaly interval time series of the missed detection node in the pipeline network is stable during the historical operation cycle of the pipeline network, the pressure anomaly interval time series is processed by the historical average method, and the mean of the pressure anomaly interval time series is used as the predicted value of the anomaly interval time; If the pressure anomaly interval time series of the missed detection node in the pipeline network is not stable during the historical operation cycle of the pipeline network, the LSTN network is used to process the pressure anomaly interval time series to determine the predicted value of the anomaly interval time; Exemplarily, the process of processing the pressure anomaly interval time series using the LSTN network includes: Assume that the pressure anomaly interval time series is: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11] (unit: day); Convert the pressure anomaly interval time series into LSTM input format. For example, use the first three pressure anomaly intervals to predict the fourth pressure anomaly interval to form a training sample: Input sequence: [[2,3,4],[3,4,5],...,[8,9,10]]; Output labels: [5,6,7,8,9,10,11]; Normalization: Scale the data to the range [0,1] (e.g., normalize using the maximum-minimum value); Design a neural network with LSTM layers: Input layer: 3 nodes (corresponding to sliding window length 3); LSTM layer: 64 units; Fully connected layer: 1 node (outputs the predicted pressure anomaly interval); Model training; Divide the data set: the first 70% of the samples are used as the training set, and the last 30% are used as the validation set; Training parameters: set the number of training rounds (epochs=50) and batch size (batch_size=32); Predict the next interval; Input the last 3 intervals [9, 10, 11] into the trained LSTM model; Output prediction value: The model outputs the predicted pressure abnormal interval time after the 11th interval, and obtains the abnormal interval time prediction value; Obtain the last abnormal pressure time point of the pipe network missed detection node before the current pipe network detection cycle, and sum it with the predicted value of the abnormal interval time to obtain the subsequent abnormal pressure prediction time point of the pipe network missed detection node; If the pressure anomaly prediction time point is within the current pipe network detection cycle, it means that the pipe network missed detection node will have pressure anomaly within the current detection cycle, and the pipe network missed detection node is marked as a necessary detection node of the pipe network; If the pressure anomaly prediction time point is not within the current pipe network detection cycle, it means that the pipe network missed detection node will not have pressure anomaly within the current detection cycle, and the pipe network missed detection node is marked as a non-necessary detection node for the pipe network; It can be understood that by predicting whether the omitted detection nodes of the pipeline network will have abnormal pressure in the current detection cycle and identifying the necessary detection nodes of the pipeline network according to the prediction results, it is beneficial to optimize the newly set settings of the already set pipeline network detection nodes according to the necessary detection nodes of the pipeline network, and improve the reasonable accuracy of the setting of the pipeline network detection nodes. In addition, through the necessity analysis of the omitted detection nodes of the pipeline network, it is beneficial to improve the reasonable accuracy of the setting of the pipeline network detection nodes while optimizing the resource cost of the setting of the pipeline network detection nodes; Step 3: Add new pipe network detection nodes according to the necessary detection node locations of the pipe network and optimize the pipe network detection node settings; Specifically, a new pipe network detection node is set at a position corresponding to a necessary detection node of the pipe network; The technical solution and benefits of the embodiments of the present application are: positionally comparing historical abnormal pipeline network detection nodes with already set pipeline network detection nodes, identifying pipeline network missed detection nodes, and predicting whether the pipeline network missed detection nodes will have pressure abnormalities in the current pipeline network detection cycle through the pressure abnormality interval time series of the pipeline network missed detection nodes in the historical operation cycle of the pipeline network. If so, the pipeline network missed detection nodes are marked as necessary pipeline network detection nodes, and new pipeline network detection nodes are added according to the necessary pipeline network detection node settings to optimize the pipeline network detection node settings. The present application finds the pipeline network missed detection nodes by positionally comparing and analyzing the historical abnormal pipeline network detection nodes with the already set pipeline network detection nodes, and determines whether the pipeline network missed detection nodes will have pressure abnormalities in the current pipeline network detection cycle through the pressure abnormality time point prediction of the pipeline network missed detection nodes, thereby completing the setting optimization of the pipeline network detection nodes, improving the rationality of the pipeline network detection node settings, optimizing the resource cost of pipeline network pressure detection, and improving the accuracy of pipeline network pressure detection.

[0021] Example 2 See also Figure 1 As shown, based on the technical solution of optimizing the setting of the pipeline network detection nodes recorded in the above embodiment 1, the embodiment of the present invention performs pipeline network operation simulation under different working conditions after optimizing the setting of the pipeline network detection nodes, and compares the simulation with the actual operating conditions of the pipeline network in the current pipeline network detection cycle, so as to more accurately realize the pipeline network pressure detection and early warning. Therefore, a pressure detection and early warning method based on pipeline network data described in an embodiment of the present invention includes the following steps: Step 4: After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; In step 4, the network pressure model is constructed as follows: A1. Collecting pipe network information: Obtain the topological structure of the pipe network, clarify the connection relationship between each node and pipe segment, for example, a ring pipe network contains multiple water source nodes, several intermediate nodes and a large number of terminal nodes, and record the pipe diameter, pipe material and other information of each pipe segment. For example, the pipe diameter of a certain pipe segment is 400mm and the pipe material is ductile iron pipe; A2: Analyze historical water consumption data to determine water consumption patterns in different time periods. For example, the peak water consumption periods are 7-9 a.m. and 17-19 p.m. on weekdays. Water consumption on weekends is relatively dispersed but generally higher than non-peak periods on weekdays. Water consumption in summer is higher than in winter due to high temperatures. A3, collects pressure data of each detection node in the pipe network at different times, including pressure values ​​in different seasons, different time periods, and under normal and abnormal conditions; A4, select mathematical model: Based on the principle of fluid mechanics, the Darcy-Weisbach formula is used in combination with the node flow balance equation to establish the pipe network pressure model; In step 4, the process of simulating the pressure distribution of the pipeline network under different working conditions and determining the normal pressure range of each pipeline network detection node under different working conditions includes: Set a variety of working conditions, for example, including but not limited to peak working conditions on summer working days and low working conditions on winter weekends, input the preset working condition parameters into the pipe network pressure model, and calculate the pressure distribution of each detection node in the pipe network under the working condition; According to the simulation results, the maximum and minimum pressure values ​​of each pipeline network detection node under normal operation of the pipeline network under different working conditions are obtained, and the normal pressure range of each pipeline network detection node under different working conditions is constructed based on the maximum and minimum pressure values; It should be noted that each pipe network detection node corresponds to a normal pressure range under each operating condition; Step 5: Compare the detected pressure of each pipeline network detection node with the normal pressure range of each pipeline network detection node under the corresponding working conditions to identify the emergency warning pipeline network detection node and the necessary warning pipeline network detection node; In step 5, it should be noted that the corresponding working condition means that the working condition of the pipeline network detection node in the current pipeline network detection cycle is consistent with the working condition of the pipeline network detection node corresponding to the compared normal pressure range; In step 5, the process of identifying the emergency warning network detection nodes and the necessary warning network detection nodes includes: Compare the pressure of the pipe network detection node with the normal pressure range of the pipe network detection node under the corresponding working condition; If the pressure of the pipeline network detection node is within the normal pressure range, the pipeline network detection node is marked as a non-emergency warning pipeline network detection node; If the pressure of the pipeline network detection node is not within the normal pressure range, the pipeline network detection node is marked as an emergency warning pipeline network detection node; It should be noted that the emergency warning pipeline network detection node means that the pipeline network detection node has left the normal pressure range, and an emergency warning is required for this pipeline network detection node to prevent pipeline network accidents; Based on the non-emergency warning pipe network detection node, the pressure of the non-emergency warning pipe network detection node is compared with the critical pressure range within the normal pressure range; If the pressure of the non-emergency warning pipe network detection node is within the critical pressure range, no processing will be performed; If the pressure of the non-emergency warning pipe network detection node is not within the critical pressure range, a proximity analysis signal is generated; It should be noted that the critical pressure range is set by those skilled in the art, the maximum value in the critical pressure range is less than the maximum value in the normal pressure range, and the minimum value in the critical pressure range is greater than the minimum value in the normal pressure range. For example, for a certain terminal pipe network detection node, under peak conditions on past summer working days, the normal pressure range is between 0.22-0.28 MPa, and the set critical pressure range may be 0.24-0.26 MPa; Based on the proximity analysis signal, if the pressure value of the non-emergency warning pipeline network detection node exceeds the maximum value within the critical pressure range, the pressure value of the non-emergency warning pipeline network detection node is processed by taking the absolute value of the difference between the pressure value of the non-emergency warning pipeline network detection node and the maximum value within the normal pressure range to obtain the critical pressure value of the non-emergency warning pipeline network detection node; For example, assuming that the pressure value of the non-emergency warning pipe network detection node is 0.27MPa, which exceeds the maximum value of 0.26MPa in the critical pressure range, the difference between 0.27MPa and 0.28MPa is processed to obtain the absolute value, and the obtained critical pressure value is 0.01MPa; If the pressure value of the non-emergency warning pipe network detection node is lower than the minimum value in the critical pressure range, the pressure value of the non-emergency warning pipe network detection node is taken as the absolute value of the difference between the minimum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipe network detection node; For example, assuming that the pressure value of the non-emergency warning pipe network detection node is 0.235MPa, which is lower than the minimum value of the critical pressure range of 0.24MPa, the difference between 0.235MPa and 0.22MPa is processed to obtain the absolute value, and the obtained critical pressure value is 0.015MPa; comparing the pressure critical value to the pressure critical threshold; If the critical pressure value is less than or equal to the critical pressure threshold, a prediction signal is generated; If the critical pressure value is greater than the critical pressure threshold, no processing is performed; It should be noted that the meaning of the generation of the prediction signal is that the prediction signal is generated because the pressure critical value is less than or equal to the pressure critical threshold value. Since the pressure critical value is less than or equal to the pressure critical threshold value, it means that the pressure value of the non-emergency warning pipeline network detection node has approached the edge of the normal pressure range endpoint of the pipeline network detection node. In order to prevent the pressure value of the non-emergency warning pipeline network detection node from exceeding the normal pressure range, the pressure prediction of the non-emergency warning pipeline network detection node is carried out in advance, which is conducive to the early warning of the pipeline network pressure detection and improves the timeliness of the warning. Based on the prediction signal, the time point at which the pressure value of the non-emergency warning pipe network detection node deviates from the critical pressure range is obtained to obtain the deviating time point; It should be noted that the pressure value of the non-emergency warning network detection node is out of the critical pressure range, which means that the pressure value of the non-emergency warning network detection node exceeds the maximum value within the critical pressure range or the pressure value of the non-emergency warning network detection node is lower than the minimum value within the critical pressure range; Obtain the pressure values ​​of the non-emergency warning pipe network detection nodes at different times during the period from the separation time point to the prediction signal generation time point, and integrate them into a pressure data set; The least squares method is used to fit the pressure values ​​of the non-emergency warning pipe network detection nodes at different times in the pressure data set to obtain the pressure fitting line and calculate the determination coefficient R of the pressure fitting line. 2 ; For example, the coefficient of determination R 2 The calculation method can be: The pressure values ​​of the non-emergency warning pipe network detection nodes at different times in the pressure data set are fitted by the least squares method, and the pressure fitting line and the pressure fitting line model (y=kx+b) can be obtained, where y represents pressure, x represents time, and b represents intercept; different times are input as independent variables to obtain the pressure prediction values ​​at different times; three square sums are calculated according to the pressure values ​​of the non-emergency warning pipe network detection nodes at different times and the pressure prediction values ​​at different times, including the total deviation square sum (SST), regression square sum (SSR) and residual square sum (SSE), and the determination coefficient is calculated by the three square sums. The specific calculation formula is as follows: The coefficient of determination R of the pressure fitting straight line 2 Compare with the coefficient of determination threshold; If the determination coefficient of the pressure fitting straight line is R 2If it is greater than or equal to the determination coefficient threshold, it means that the pressure fitting linear fitting effect is good, and the pressure value of the non-emergency warning pipeline network detection node changes linearly; If the determination coefficient of the pressure fitting straight line is R 2 If it is less than the determination coefficient threshold, it means that the pressure fitting linear fitting effect is good, and the pressure value of the non-emergency warning pipeline network detection node changes nonlinearly; Based on the linear change of the pressure value of the non-emergency warning pipe network detection node, find the time point corresponding to the endpoint value of the normal pressure range closest to the pressure value of the non-emergency warning pipe network detection node on the pressure fitting line, that is, the pressure edge arrival time point; If the pressure edge arrival time point is within the current pipe network detection cycle, it means that the pressure value of the non-emergency warning pipe network detection node will reach the edge of the normal pressure range endpoint within the current pipe network detection cycle, and an early warning is required. In this case, the non-emergency warning pipe network detection node is marked as a necessary warning pipe network detection node; If the pressure edge arrival time point is within the current pipe network detection cycle, it means that the pressure value of the non-emergency warning pipe network detection node will not reach the edge of the normal pressure range endpoint within the current pipe network detection cycle, and no operation is performed; Based on the non-linear change of the pressure value of the non-emergency warning pipeline network detection node, the pressure change rate of the non-emergency warning pipeline network detection node at adjacent moments in the pressure data set is calculated, and the fastest pressure change rate is extracted, where the fastest pressure change rate includes the fastest pressure increase rate and the fastest pressure decrease rate; where the pressure change rate is obtained by proportionally calculating the pressure difference of the non-emergency warning pipeline network detection node at adjacent moments and the time interval between adjacent moments; The critical pressure value is proportional to the fastest pressure change rate to obtain the critical remaining pressure duration, and combined with the predicted signal generation time point, the pressure edge arrival time point is obtained by summing up, that is, the pressure edge arrival time point = predicted signal generation time point + residual pressure critical duration; It should be noted that if the pressure value of the non-emergency warning network detection node is lower than the minimum value in the critical pressure range when the prediction signal is generated, the critical pressure value and the fastest pressure reduction rate are proportionally calculated; if the pressure value of the non-emergency warning network detection node exceeds the maximum value in the critical pressure range when the prediction signal is generated, the critical pressure value and the fastest pressure increase rate are proportionally calculated; If the pressure edge arrival time point is within the current pipe network detection cycle, it means that the pressure value of the non-emergency warning pipe network detection node will reach the edge of the normal pressure range endpoint within the current pipe network detection cycle, and an early warning is required. In this case, the non-emergency warning pipe network detection node is marked as a necessary warning pipe network detection node; If the pressure edge arrival time point is within the current pipe network detection cycle, it means that the pressure value of the non-emergency warning pipe network detection node will not reach the edge of the normal pressure range endpoint within the current pipe network detection cycle, and no operation is performed; The technical solution and benefits of the embodiments of the present application are as follows: after the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model, and the normal pressure range of each pipeline network detection node under different working conditions is determined. In the current pipeline network detection cycle, the pressure of each pipeline network detection node is detected and compared with the normal pressure range of each pipeline network detection node under the corresponding working condition, and the emergency warning pipeline network detection node and the necessary warning pipeline network detection node are identified. The present invention simulates the pressure distribution of the pipeline network under different working conditions, finds the normal pressure range of each pipeline network detection node under different working conditions, solves the problem of inaccurate or misjudgment of pipeline pressure detection caused by different normal pressure ranges of pipeline network detection nodes due to different working conditions, and identifies the emergency warning pipeline network detection nodes and the necessary warning pipeline network detection nodes through pressure comparison. Not only the pipeline network detection nodes with abnormal pressure are identified, but also the pipeline network detection nodes that may have abnormal pressure are identified through predictive analysis of pressure changes, and the necessity is marked, thereby improving the timeliness of pipeline pressure detection and warning.

[0022] Example 3 See also Figure 2 As shown, the present invention, based on the pressure detection and early warning method based on pipeline network data described in the above-mentioned embodiment 1 and embodiment 2, further provides a pressure detection and early warning system based on pipeline network data, including the following modules: Missed detection analysis module: compare the positions of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify missed detection nodes of the pipe network; The process of identifying the missing detection nodes of the pipe network includes: Compare the locations of the set pipe network detection nodes with the historical abnormal pipe network detection nodes. Mark the historical abnormal pipeline network detection nodes that are different from all the set pipeline network detection nodes as pipeline network missed detection nodes; On the contrary, if the historical abnormal pipe network detection node is at the same position as any set pipe network detection node, the historical abnormal pipe network detection node is marked as a non-pipeline network missed detection node; Necessary detection analysis module: through the pressure anomaly interval time series of the pipeline network missed detection node in the historical operation cycle of the pipeline network, it is predicted whether the pipeline network missed detection node will have pressure anomaly in the current pipeline network detection cycle. If so, the pipeline network missed detection node is marked as a necessary detection node of the pipeline network; The pressure anomaly interval time series of the pipe network omission detection node in the pipe network historical operation cycle is constructed by the interval time when the pressure anomaly occurs at the pipe network omission detection node in the pipe network historical operation cycle; The process of predicting whether a pipe network missed detection node will have abnormal pressure in the current pipe network detection cycle includes: The standard deviation and mean of the pressure anomaly interval time series of the missed detection nodes in the pipeline network during the historical operation cycle of the pipeline network are calculated, and the standard deviation and the mean are proportionally calculated to obtain the stable value of the pressure anomaly interval time series of the missed detection nodes in the pipeline network during the historical operation cycle of the pipeline network; If the stability value is greater than or equal to the stability threshold, it means that the pressure anomaly interval time series of the missed detection node in the pipeline network during the historical operation cycle of the pipeline network is not stable; If the stability value is less than the stability threshold, it means that the pressure anomaly interval time series of the missed detection node in the pipeline network is stable during the historical operation cycle of the pipeline network; If the pressure anomaly interval time series of the missed detection node in the pipeline network is stable during the historical operation cycle of the pipeline network, the pressure anomaly interval time series is processed by the historical average method, and the mean of the pressure anomaly interval time series is used as the predicted value of the anomaly interval time; If the pressure anomaly interval time series of the missed detection node in the pipeline network is non-stationary during the historical operation cycle of the pipeline network, the LSTN network is used to process the pressure anomaly interval time series to determine the predicted value of the anomaly interval time; Obtain the last abnormal pressure time point of the pipe network missed detection node before the current pipe network detection cycle, and sum it with the predicted value of the abnormal interval time to obtain the subsequent abnormal pressure prediction time point of the pipe network missed detection node; If the pressure anomaly prediction time point is within the current pipe network detection cycle, it means that the pipe network missed detection node will have pressure anomaly within the current detection cycle, and the pipe network missed detection node is marked as a necessary detection node of the pipe network; If the pressure anomaly prediction time point is not within the current pipe network detection cycle, it means that the pipe network missed detection node will not have pressure anomaly within the current detection cycle, and the pipe network missed detection node is marked as a non-necessary detection node for the pipe network; Detection node optimization module: add new pipeline network detection nodes according to the necessary detection node positions of the pipeline network, and optimize the settings of pipeline network detection nodes; Set up new pipe network detection nodes at the locations corresponding to the necessary detection nodes of the pipe network; Working condition pressure simulation module: After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; According to the simulation results, the maximum and minimum pressure values ​​of each pipeline network detection node under normal operation of the pipeline network under different working conditions are obtained, and the normal pressure range of each pipeline network detection node under different working conditions is constructed based on the maximum and minimum pressure values; Pressure detection and early warning module: compares the detected pressure of each pipeline network detection node with the normal pressure range of each pipeline network detection node under the corresponding working conditions, and identifies the emergency early warning pipeline network detection nodes and the necessary early warning pipeline network detection nodes.

[0023] The process of identifying the emergency warning network detection nodes and the necessary warning network detection nodes includes: If the pressure of the pipeline network detection node is within the normal pressure range, the pipeline network detection node is marked as a non-emergency warning pipeline network detection node; If the pressure of the pipeline network detection node is not within the normal pressure range, the pipeline network detection node is marked as an emergency warning pipeline network detection node; Based on non-emergency early warning pipe network detection nodes; If the pressure of the non-emergency warning pipe network detection node is within the critical pressure range, no processing will be performed; If the pressure of the non-emergency warning pipe network detection node is not within the critical pressure range, a proximity analysis signal is generated; Based on the proximity analysis signal, if the pressure value of the non-emergency warning pipeline network detection node exceeds the maximum value within the critical pressure range, the pressure value of the non-emergency warning pipeline network detection node is processed by taking the absolute value of the difference between the pressure value of the non-emergency warning pipeline network detection node and the maximum value within the normal pressure range to obtain the critical pressure value of the non-emergency warning pipeline network detection node; If the pressure value of the non-emergency warning pipe network detection node is lower than the minimum value in the critical pressure range, the pressure value of the non-emergency warning pipe network detection node is taken as the absolute value of the difference between the minimum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipe network detection node; If the critical pressure value is less than or equal to the critical pressure threshold, a prediction signal is generated; Based on the prediction signal, the time point at which the pressure value of the non-emergency warning pipe network detection node deviates from the critical pressure range is obtained to obtain the deviating time point; Obtain the pressure values ​​of the non-emergency warning pipe network detection nodes at different times during the period from the separation time point to the prediction signal generation time point, and integrate them into a pressure data set; The least squares method is used to fit the pressure values ​​of the non-emergency warning pipe network detection nodes at different times in the pressure data set to obtain the pressure fitting line and calculate the determination coefficient R of the pressure fitting line. 2 ; If the determination coefficient of the pressure fitting straight line is R 2If it is greater than or equal to the determination coefficient threshold, it means that the pressure fitting linear fitting effect is good, and the pressure value of the non-emergency warning pipeline network detection node changes linearly; If the determination coefficient of the pressure fitting straight line is R 2 If it is less than the determination coefficient threshold, it means that the pressure fitting linear fitting effect is good, and the pressure value of the non-emergency warning pipeline network detection node changes nonlinearly; Based on the linear change of the pressure value of the non-emergency warning pipe network detection node, find the time point corresponding to the endpoint value of the normal pressure range closest to the pressure value of the non-emergency warning pipe network detection node on the pressure fitting line, that is, the pressure edge arrival time point; If the pressure edge arrival time point is within the current pipe network detection cycle, it means that the pressure value of the non-emergency warning pipe network detection node will reach the edge of the normal pressure range endpoint within the current pipe network detection cycle, and an early warning is required. In this case, the non-emergency warning pipe network detection node is marked as a necessary warning pipe network detection node; Based on the non-linear change of the pressure value of the non-emergency warning pipeline network detection node, the pressure change rate of the non-emergency warning pipeline network detection node at adjacent moments in the pressure data set is calculated, and the fastest pressure change rate is extracted, where the fastest pressure change rate includes the fastest pressure increase rate and the fastest pressure decrease rate; where the pressure change rate is obtained by proportionally calculating the pressure difference of the non-emergency warning pipeline network detection node at adjacent moments and the time interval between adjacent moments; The critical pressure value is proportional to the fastest pressure change rate to obtain the critical remaining pressure duration, and combined with the predicted signal generation time point, the pressure edge arrival time point is obtained by summing up, that is, the pressure edge arrival time point = predicted signal generation time point + residual pressure critical duration; If the pressure edge arrival time point is within the current pipeline network detection cycle, it means that the pressure value of the non-emergency warning pipeline network detection node will reach the edge of the normal pressure range endpoint within the current pipeline network detection cycle, and early warning is required. In this case, the non-emergency warning pipeline network detection node will be marked as a necessary warning pipeline network detection node.

[0024] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A pressure detection and early warning method based on pipe network data, characterized in that: include: Compare the locations of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify missed detection nodes in the pipe network; Through the time series of pressure anomaly intervals of the missed detection nodes in the historical operation cycle of the pipeline network, it is predicted whether the missed detection nodes in the pipeline network will have pressure anomalies in the current pipeline network detection cycle. If so, the missed detection nodes in the pipeline network are marked as necessary detection nodes in the pipeline network. Add new pipe network detection nodes according to the locations of necessary detection nodes in the pipe network and optimize the settings of pipe network detection nodes; After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; The detected pressure of each pipeline network detection node is compared and predicted and analyzed with the normal pressure range of each pipeline network detection node under the corresponding working conditions to identify the emergency warning pipeline network detection nodes and the necessary warning pipeline network detection nodes.

2. A pressure detection and early warning method based on pipe network data according to claim 1, characterized in that: The method for identifying the missing detection nodes of the pipe network is as follows: The historical abnormal pipeline network detection nodes that are different from the positions of all the set pipeline network detection nodes are marked as pipeline network missed detection nodes.

3. The pressure detection and early warning method based on pipe network data according to claim 1 is characterized in that: The process of predicting whether a pipe network missed detection node will have pressure anomaly within the current pipe network detection cycle is as follows: Perform stationarity analysis on the pressure anomaly interval time series to determine the predicted value of the anomaly interval time, and determine the pressure anomaly prediction time point in combination with the last pressure anomaly time point of the missed detection node in the pipeline network; If the pressure anomaly prediction time point is within the current pipe network detection cycle, it means that the missed detection node of the pipe network will cause pressure anomaly within the current pipe network detection cycle.

4. A pressure detection and early warning method based on pipe network data according to claim 3, characterized in that: The process of performing stationarity analysis on the pressure anomaly interval time series is as follows: The standard deviation and mean of the pressure anomaly interval time series are calculated, and the standard deviation and mean are proportionally calculated to obtain the stable value of the pressure anomaly interval time series; If the stability value is greater than or equal to the stability threshold, it means that it is not stable; then the LSTN network is used to process the pressure abnormal interval time series to determine the abnormal interval time prediction value If the stable value is less than the stable threshold, it means stability, and the historical average method is used to process the pressure anomaly interval time series to determine the predicted value of the anomaly interval time.

5. The pressure detection and early warning method based on pipe network data according to claim 1 is characterized in that: The normal pressure range of each pipe network detection node under different working conditions is determined as follows: By simulating the pressure distribution of the pipeline network under different working conditions, the maximum and minimum pressures of each pipeline network detection node under normal operation of the pipeline network under different working conditions are obtained, and the normal pressure range of each pipeline network detection node under different working conditions is constructed.

6. The pressure detection and early warning method based on pipe network data according to claim 1 is characterized by: The identification method of the emergency warning pipe network detection node and the necessary warning pipe network detection node is: If the pressure of the pipeline network detection node is not within the normal pressure range, the pipeline network detection node is marked as an emergency warning pipeline network detection node, otherwise, the pipeline network detection node is marked as a non-emergency warning pipeline network detection node; Perform critical pressure analysis on non-emergency warning pipeline network detection nodes to determine whether pressure prediction is needed. If necessary, predict whether the pressure of the non-emergency warning pipeline network detection node will reach the edge of the normal pressure range endpoint during the current pipeline network detection cycle. If so, mark the non-emergency warning pipeline network detection node as a necessary warning pipeline network detection node.

7. A pressure detection and early warning method based on pipe network data according to claim 6, characterized in that: The pressure critical analysis process is: If the pressure of the non-emergency warning pipe network detection node is not within the critical pressure range, a proximity analysis signal is generated; Based on the proximity analysis signal, if the pressure value of the non-emergency warning pipeline network detection node exceeds the maximum value in the critical pressure range, the pressure value of the non-emergency warning pipeline network detection node is processed with the maximum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipeline network detection node; If the pressure value of the non-emergency warning pipe network detection node is lower than the minimum value in the critical pressure range, the pressure value of the non-emergency warning pipe network detection node is processed with the minimum value in the normal pressure range to obtain the critical pressure value of the non-emergency warning pipe network detection node; If the pressure critical value is less than or equal to the pressure critical threshold, a prediction signal is generated.

8. The pressure detection and early warning method based on pipe network data according to claim 6 is characterized by: The process of predicting whether the pressure of the non-emergency warning pipe network detection node will reach the edge of the normal pressure range endpoint within the current pipe network detection cycle includes: The separation time point is obtained by detecting the time point at which the pressure value of the non-emergency warning pipe network detection node separates from the critical pressure range; Fit the pressure values ​​of the non-emergency warning pipe network detection nodes in the period between the separation time point and the prediction signal generation time point to obtain the pressure fitting straight line and calculate the determination coefficient; The determination coefficient is used to determine whether the pressure value of the non-emergency warning pipe network detection node changes linearly or nonlinearly and to determine the time point when the pressure edge arrives; If the pressure edge arrival time point is within the current pipe network detection cycle, the non-emergency warning pipe network detection node will be marked as a necessary warning pipe network detection node.

9. A pressure detection and early warning method based on pipe network data according to claim 8, characterized in that: The method for determining the pressure edge arrival time point is: If it changes linearly, find the time point corresponding to the endpoint value of the normal pressure range closest to the pressure value of the non-emergency warning pipeline network detection node on the pressure fitting line, that is, the pressure edge arrival time point; If it changes nonlinearly, the pressure change rate of the non-emergency warning pipeline network detection nodes at adjacent moments is calculated, and the fastest pressure change rate is extracted; The critical pressure value and the fastest rate of pressure change are proportionally calculated to obtain the critical remaining pressure duration, and the pressure edge arrival time is determined in combination with the predicted signal generation time point.

10. A pressure detection and early warning system based on pipe network data, characterized in that: include: Missed detection analysis module: compare the positions of historical abnormal pipe network detection nodes with the set pipe network detection nodes to identify missed detection nodes of the pipe network; Necessary detection analysis module: through the pressure anomaly interval time series of the pipeline network missed detection node in the historical operation cycle of the pipeline network, it is predicted whether the pipeline network missed detection node will have pressure anomaly in the current pipeline network detection cycle. If so, the pipeline network missed detection node is marked as a necessary detection node of the pipeline network; Detection node optimization module: add new pipeline network detection nodes according to the necessary detection node positions of the pipeline network, and optimize the settings of pipeline network detection nodes; Working condition pressure simulation module: After the optimization setting of the pipeline network detection nodes is completed, the pressure distribution of the pipeline network under different working conditions is simulated through the pipeline network pressure model to determine the normal pressure range of each pipeline network detection node under different working conditions; Pressure detection and early warning module: compare and predict the pressure of each pipeline network detection node detected with the normal pressure range of each pipeline network detection node under the corresponding working conditions, and identify emergency early warning pipeline network detection nodes and necessary early warning pipeline network detection nodes.

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