A gas pipeline network simulation analysis system

Abstract

The present invention discloses a gas pipeline network simulation and analysis system, comprising a gas pipeline network data acquisition unit, a pipeline network topology structure construction unit, a pipeline network data value influencing factor analysis unit, a gas pipeline network data prediction model construction unit, a gas pipeline network data periodicity prediction unit, a gas pipeline network data control unit, a risk warning unit, and a gas pipeline network data storage unit. The present invention realizes gas pipeline network simulation by constructing a pipeline network topology structure in a virtual environment; at the same time, it realizes periodicity prediction of gas pipeline network data through a time recurrent neural network model, compares the real data value collected on the gas pipeline network with the gas pipeline network data periodicity prediction range value, and judges the risk of the gas pipeline network. If the real data value exceeds the gas pipeline network data periodicity prediction range value, the valves on the branches and branch nodes in each component of the virtual pipeline network are adjusted to realize the real data value of the gas pipeline network exceeding the preset threshold value and control.

Description

Technical Field

[0001] The present invention belongs to the field of digital simulation systems, and in particular, relates to a gas pipe network simulation analysis system. Background Art

[0002] With the acceleration of urbanization, urban gas pipeline networks are expanding, operating on a massive scale and with complex underground pipelines. Traditional manual management models are no longer sufficient to meet the needs of urban development. Digital gas pipeline network simulation technology analyzes and calculates pipeline network data in real time, understanding the overall network's operating conditions and promptly identifying and predicting potential risks. Based on a high-precision pipeline network system model and powerful algorithms, it continuously analyzes and calculates pipeline network data online, improving the accuracy and real-time nature of analysis. Through data fusion models, enterprises can comprehensively analyze and perceive dynamic changes in the pipeline network. Through a technology platform, digital silos are bridged, internal and external data is coordinated, and data resources are effectively mobilized. This technology not only improves the safety and efficiency of gas management but also helps enterprises make informed decisions, optimize pipeline network structure and configuration, and enhance the safety and cost-effectiveness of the network.

[0003] A Chinese patent application numbered CN202310113798.X discloses a simulation method for a multi-level urban gas pipeline network, providing a first-level to a third-level system with decreasing operational authority. Verify user operational authority. For first-level authority, a first-level system is used to establish a city backbone model, and simulation analysis is performed to obtain simulation data for each backbone node. For second-level authority, a second-level system is used to establish a district model; simulation data for the corresponding backbone nodes in the city backbone model is obtained, and simulation analysis is performed using this data as boundary conditions to obtain simulation data for district nodes. For third-level authority, a third-level system is used to establish a management unit model; simulation data for the corresponding district nodes in the district model is obtained, and simulation analysis is performed using this data as boundary conditions to obtain simulation data for management unit nodes. In this way, nested hierarchical simulation and operation can be performed according to the hierarchical management architecture of the urban gas pipeline network, fully utilizing data from all levels of the pipeline network across levels, achieving efficient overall control, and significantly reducing computing costs.

[0004] The defect of the above-mentioned patent is that although the existing gas pipeline network simulation system or method realizes the simulation of gas pipeline network node data for users with different operating permissions, it lacks the prediction of the load value of the relevant data of the gas pipeline network nodes, resulting in the existing gas pipeline network simulation system being unable to intuitively regulate the operating trend of the gas pipeline network at the next moment and provide effective guiding result prediction values ​​for risk warning. Summary of the Invention

[0005] Aiming at the problem that the existing gas pipeline network simulation system lacks the prediction of the load value of gas pipeline network node-related data, the present invention provides a gas pipeline network simulation and analysis system.

[0006] In order to achieve the above technical objectives, the technical solutions adopted by the present invention are as follows:

[0007] A gas pipeline network simulation and analysis system includes a gas pipeline network data acquisition unit, a pipeline network topology structure construction unit, a pipeline network data value influencing factor analysis unit, a gas pipeline network data prediction model construction unit, a gas pipeline network data periodicity prediction unit, a gas pipeline network data control unit, a risk warning unit, and a gas pipeline network data storage unit;

[0008] The gas network data acquisition unit is in communication connection with the gas network data storage unit, and collects corresponding gas network data through the SCADA monitoring system on the gas network;

[0009] The pipe network topology structure building unit is connected to the gas pipe network data acquisition unit, and each component of the pipe network is built according to the relationship and location of the SCADA monitoring system acquisition equipment on the gas pipe network;

[0010] The pipe network data value influencing factor analysis unit is in communication with the gas pipe network data acquisition unit, and performs correlation analysis on each single type of gas pipe network data collected to obtain data related to factors affecting changes in gas pipe network data;

[0011] The gas network data prediction model building unit is connected to the network data value influencing factor analysis unit, a time recurrent neural network model (LSTM) is selected as the gas network data prediction model, and the input gate, forget gate, and output gate of the time recurrent neural network model are determined;

[0012] The gas network data periodicity prediction unit is in communication with the gas network data prediction model building unit, and completes the prediction of the gas network data periodicity by adjusting the input gate value of the time recurrent neural network model within a preset period;

[0013] The risk warning unit is connected to the gas network data periodicity prediction unit in communication, and determines whether the gas network data should be warned of risks by comparing the real data value within the collected gas network data period with the preset threshold value;

[0014] The gas network data control unit is connected to the risk warning unit in communication, and controls the gas network data when it exceeds the preset threshold by adjusting the valves on the branches and branch nodes in each component of the virtual network;

[0015] The gas pipe network data storage unit is communicatively connected to the gas pipe network data periodicity prediction unit and is used to store historical gas pipe network data.

[0016] Furthermore, the components of the pipe network include branches, boundaries and branch points.

[0017] Furthermore, the gas pipeline network data includes methane gas concentration, pressure and flow, and a pipeline network monitoring point is set corresponding to each branch point in the pipeline network.

[0018] Furthermore, detailed steps for correlation analysis of each single type of gas pipeline network data collected are as follows:

[0019] First, a single data type in the gas pipeline network data is selected as the object to be analyzed;

[0020] Then, the gas pipe environment or weather data values ​​that may affect a single data type are selected as the objects of correlation analysis;

[0021] Then, the correlation analysis method is used to obtain the correlation coefficient between the two variables of the object to be judged and the object of correlation analysis;

[0022] Based on the absolute value of the correlation coefficient, the correlation analysis objects with strong correlation are selected as the relevant data of the factors affecting the changes in gas pipeline network data; the correlation analysis objects with strong correlation are: the absolute value of the correlation coefficient in the range of [0.5, 0.8) is significantly correlated. The absolute value of the correlation coefficient in the range of [0.8, 1) is strongly correlated.

[0023] Finally, repeat the above steps until the correlation analysis of all different types of gas pipeline network data is completed.

[0024] Furthermore, the correlation analysis method corresponds to the calculation formula:

[0025]

[0026] Where: r x,y ——Correlation coefficient between variables x and y, n——The number of variables x and y, x i ——the i-th value of variable x, - the mean of variables x, y i ——the i-th value of variable y, ——the mean value of variable y;

[0027] The correlation coefficient ranges from -1 to 1. The closer its absolute value is to 1, the stronger the correlation, while the closer it is to 0, the weaker the correlation. Correlation coefficient values ​​in the range [0, 0.3) indicate a very weak correlation. Correlation coefficient values ​​in the range [0.3, 0.5) indicate a weak correlation. Correlation coefficient values ​​in the range [0.5, 0.8) indicate a significant correlation. Correlation coefficient values ​​in the range [0.8, 1) indicate a strong correlation.

[0028] Furthermore, the input parameters of the time-recurrent neural network model include the cell state C at the previous moment t-1 , hidden state ht-1 and the current input information h t ; The output parameter is the current moment information C t and hidden state h t ;

[0029] The time-recurrent neural network model includes a forget gate, an input gate, and an output gate;

[0030] Forget gate: determine the cell state C at the previous moment t-1 Information that needs to be retained and information that needs to be discarded; t-1 and x t Enter the forget gate and pass it to the Sigmoid function; the specific calculation formula is:

[0031] f t =σ(W f h t-1 +U f x t +b f )

[0032] Where: σ——activation function Sigmoid, output value range [0, 1], controls the propagation of information;

[0033] W f 、U f ——The weight matrix of the forget gate;

[0034] b f ——Forget gate bias;

[0035] Input gate: used to determine the importance of the gas network data input at the current moment, and retain the important gas network data at the current moment and use it to update the cell state; the calculation formula is:

[0036] i t =σ(W i h t-1 +U i x t +b i )

[0037]

[0038] Where: tanh - activation function, output value range [-1, 1];

[0039] W i 、U i ——Input gate weight matrix;

[0040] W C 、U C ——Candidate layer weight matrix;

[0041] bi 、b C ——Input gate, candidate layer bias;

[0042] Output gate: confirm the current cell state C t How much information is lost to the value h of the hidden state at the next moment t The calculation formula is:

[0043] O t =σ(W o h t-1 +U o x t +b t )

[0044] h t =tanh(C t )

[0045] Where: W O 、U O ——The weight matrix of the output layer;

[0046] b t ——Output gate bias;

[0047] Cell state: used to update and store information; specific calculation formula:

[0048]

[0049] Furthermore, by adjusting the input gate value of the time recurrent neural network model within a preset period:

[0050] The historical gas network data collected within a preset period has multiple data values ​​as input gate values;

[0051] Sort multiple data values ​​and select the maximum and minimum values ​​as the input gate values ​​of the time recurrent neural network model;

[0052] The maximum and minimum values ​​of the input gate of the time recurrent neural network model are input to obtain the maximum and minimum values ​​predicted by the output gate, which are used as the periodic preset thresholds of the gas pipeline network data.

[0053] Furthermore, the gas network data exceeds the preset threshold value of the real data value and performs a control method:

[0054] S701, mapping the real data values ​​collected during the gas pipe network data cycle to each component of the constructed pipe network in a 1:1 ratio;

[0055] S702, adjusting the methane gas concentration, pressure, and flow valve openings set on the branches and branch points;

[0056] S703, using the adjusted methane gas concentration, pressure, and flow valve opening as input gate values ​​for the time recurrent neural network model to predict gas pipeline network data at the next moment;

[0057] S704: Determine whether the predicted value of the gas network data at the next moment is within a preset threshold; if so, proceed to step S705; if not, repeat step S703;

[0058] S705: End the adjustment process and send the corresponding methane gas concentration, pressure, and flow valve opening to the system administrator for gas network maintenance personnel to adjust and maintain parameters during network maintenance.

[0059] Furthermore, it also includes a gas pipe network data preprocessing unit, which is communicatively connected to the gas pipe network data acquisition unit and is used to preprocess the acquired gas pipe network data.

[0060] Furthermore, the pre-processing method for the collected gas pipeline network data includes:

[0061] Identification and handling of missing data;

[0062] Identification and handling of outlier data.

[0063] Furthermore, the detailed steps for identifying and processing missing data are as follows:

[0064] By comparing the number of data fields of different data types, determine whether a single data type is missing;

[0065] If missing data occurs, data interpolation is used to fill the missing values, or the entire gas pipeline network data collected at the corresponding time of the missing data is deleted.

[0066] Furthermore, the detailed steps for identifying and processing outlier data are as follows:

[0067] Outliers can be identified by checking whether individual data under a single data type is out of the data range of the single data type or whether the collected data has a value of zero (except for zero temperature or zero natural gas usage, which are not identified as outliers). Box plot analysis can also be used to identify outliers:

[0068] Sort a set of data from small to large. Find the first quartile Q1, median M, and third quartile Q3 of the data set;

[0069] Let IQR = Q3 - Q1, draw a rectangle with IQR (interquartile range) as the length of the rectangle, and the positions on both sides of the rectangle correspond to the first quartile Q1 and the third quartile Q3 of the group of data respectively. Draw a center line on the long side of the rectangle, corresponding to the median M of the group of data. This line is called the median line.

[0070] Draw two straight lines at Q3+1.5IQR and Q1-1.5IQR, respectively, to distinguish outliers from normal values. The range between the two lines is called the inner limit. Draw two straight lines at Q3+3IQR and Q1-3IQR to distinguish extreme outliers from mild outliers. The range outside Q3+3IQR and Q1-3IQR is called the outer limit. Data outside the inner limits are considered outliers, data between the inner and outer limits are considered mild outliers, and data outside the outer limits are considered extreme outliers.

[0071] The outlier data value is replaced by the data replacement method, or the entire gas pipeline network data collected at the corresponding time of the outlier data is deleted.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] By building the pipeline network topology structure in a virtual environment, gas pipeline network simulation is realized; at the same time, the time recurrent neural network model is used to realize the periodic prediction of gas pipeline network data. The real data values ​​collected on the gas pipeline network are compared with the periodic prediction range values ​​of the gas pipeline network data to judge the risk of the gas pipeline network. If the real data value exceeds the periodic prediction range value of the gas pipeline network data, the valves on the branches and branch nodes in each component of the virtual pipeline network are adjusted to realize the regulation of the real data value of the gas pipeline network that exceeds the preset threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a block diagram of the overall structure of a gas pipe network simulation and analysis system according to an embodiment of the present invention;

[0075] Figure 2 Detailed flowchart of the steps for performing correlation analysis on gas network data in an embodiment of the present invention;

[0076] Figure 3 This is a flow chart of a method for regulating gas network data when it exceeds a preset threshold value according to an embodiment of the present invention.

[0077] Explanation of the marks in the figure: 10-gas pipeline network data acquisition unit, 20-pipeline network topology structure construction unit, 30-pipeline network data value influencing factor analysis unit, 40-gas pipeline network data prediction model construction unit, 50-gas pipeline network data periodicity prediction unit, 60-gas pipeline network data control unit, 70-risk warning unit, 80-gas pipeline network data storage unit. DETAILED DESCRIPTION

[0078] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and drawings. The contents mentioned in the embodiments are not intended to limit the present invention.

[0079] like Figure 1 As shown, this embodiment provides a gas pipeline network simulation and analysis system, including a gas pipeline network data acquisition unit 10, a pipeline network topology structure building unit 20, a pipeline network data value influencing factor analysis unit 30, a gas pipeline network data prediction model building unit 40, a gas pipeline network data periodicity prediction unit 50, a gas pipeline network data control unit 60 and a risk warning unit 70;

[0080] The gas network data acquisition unit 10 collects corresponding gas network data through the SCADA monitoring system on the gas network;

[0081] The pipe network topology building unit 20 builds each component of the pipe network according to the relationship and location of the SCADA monitoring system collection equipment on the gas pipe network;

[0082] The pipe network data value influencing factor analysis unit 30 performs correlation analysis on each single type of gas pipe network data collected to obtain data related to influencing factors that affect changes in gas pipe network data;

[0083] The gas network data prediction model building unit 40 selects a time-recurrent neural network model (LSTM) as the gas network data prediction model and determines an input gate, a forget gate, and an output gate of the time-recurrent neural network model;

[0084] The gas network data periodicity prediction unit 50 completes the prediction of the gas network data periodicity by adjusting the input gate value of the time recurrent neural network model within a preset period;

[0085] The risk warning unit 70 compares the real data value of the collected gas pipeline network data within the period with the preset threshold value to determine whether the gas pipeline network data needs to be warned of risk;

[0086] The gas network data control unit 60 controls the gas network data when it exceeds a preset threshold value by adjusting valves on branches and branch nodes in each component of the virtual network.

[0087] The components of a pipe network include branches, boundaries, and branch points.

[0088] For each pipe segment or non-pipe element on a branch, if it is a pipe segment, mesh it according to the spatial step and the length of the pipe segment. Let N be a positive integer, and record the spatial step Then the spatial grid node on the pipe segment is x j =jΔx, j=0,1.…,N. If it is a non-pipe element, first generate a node upstream and downstream of the element. After all the upstream and downstream nodes of all non-pipe elements are generated, if two non-pipe elements are adjacent, they are merged to generate upstream nodes or downstream nodes, until there is only one node between any adjacent non-pipe elements.

[0089] For the boundaries, the completeness of the pipe network shows that the upstream or downstream of each boundary must be connected to a node obtained from the branch sequence, that is, the boundary does not generate new nodes when the space is divided.

[0090] The situation is similar for branch points. No new nodes are generated when the space is divided. Each branch point must be connected to a node obtained from the branch sequence.

[0091] Gas pipeline network data includes methane gas concentration, pressure and flow, and a pipeline network monitoring point is set at each branch point in the pipeline network.

[0092] like Figure 2 As shown in the figure, the detailed steps for correlation analysis of each single type of gas pipeline network data collected are as follows:

[0093] S301, first select a single data type in the gas pipeline network data as an analysis object;

[0094] S302, then selecting gas pipe environment or weather data values ​​that may affect a single data type as a correlation analysis object;

[0095] S303, using the correlation analysis method to calculate the correlation coefficient between the two variables of the object to be judged and the correlation analysis object;

[0096] S304. Selecting a correlation analysis object with a stronger correlation according to the absolute value of the correlation coefficient as the relevant data of the influencing factors affecting the change of the gas pipeline network data;

[0097] S305. Finally, repeat the above steps until the correlation analysis of all different types of gas pipeline network data is completed.

[0098] The corresponding calculation formula for the correlation analysis method is:

[0099]

[0100] Where: r x,y ——Correlation coefficient between variables x and y, n——The number of variables x and y, x i ——the i-th value of variable x, - the mean of variables x, y i ——the i-th value of variable y, ——the mean value of variable y;

[0101] The correlation coefficient ranges from -1 to 1. The closer its absolute value is to 1, the stronger the correlation, while the closer it is to 0, the weaker the correlation. Correlation coefficient values ​​in the range [0, 0.3) indicate a very weak correlation. Correlation coefficient values ​​in the range [0.3, 0.5) indicate a weak correlation. Correlation coefficient values ​​in the range [0.5, 0.8) indicate a significant correlation. Correlation coefficient values ​​in the range [0.8, 1) indicate a strong correlation.

[0102] The input parameters of the time-recurrent neural network model include the cell state C at the previous moment t-1 , hidden state h t-1 and the current input information h t ; The output parameter is the current moment information C t and hidden state h t ;

[0103] Forget gate: determine the cell state C at the previous moment t-1 Information that needs to be retained and information that needs to be discarded; t-1 and x t Enter the forget gate and pass it to the Sigmoid function; the specific calculation formula is:

[0104] f t =σ(W f h t-1 +U f x t +b f )

[0105] Where: σ——activation function Sigmoid, output value range [0, 1], controls the propagation of information;

[0106] W f 、U f ——The weight matrix of the forget gate;

[0107] b f ——Forget gate bias;

[0108] Input gate: used to determine the importance of the gas network data input at the current moment, and retain the important gas network data at the current moment and use it to update the cell state; the calculation formula is:

[0109] i t =σ(W i h t-1 +U i x t +b i )

[0110]

[0111] Where: tanh - activation function, output value range [-1, 1];

[0112] W i 、U i ——Input gate weight matrix;

[0113] W C 、U C ——Candidate layer weight matrix;

[0114] b i 、b C ——Input gate, candidate layer bias;

[0115] h t-1 and X t Enter the input gate and pass it to the Sigmoid function. At the same time, h t-1 and x t Enter the candidate layer and pass it to the function tanh. If the number multiplied by the output information of the candidate layer is 0, then this part of the current input information needs to be discarded; if the number multiplied by the output information of the candidate layer is 1, then this part of the current input information is completely retained and the cell state C is updated. t If the number multiplied by the candidate layer output information is between (0, 1), then this part of the current input information is not completely retained in the new cell state.

[0116] Output gate: confirm the current cell state C t How much information is lost to the value h of the hidden state at the next moment t The calculation formula is:

[0117] O t =σ(W o h t-1 +U o x t +b t )

[0118] h t =tanh(C t )

[0119] Where: W O 、U O ——The weight matrix of the output layer;

[0120] b t ——Output gate bias;

[0121] h t-1 and x tEnter the output gate and pass it to the sigmoid function. At the same time, the updated cell state C t Pass it to the function tanh. Multiply the output values ​​on both sides point by point to get the hidden state h at the current moment t In the time-recurrent neural network model, the hidden state h at the current moment is t and cell state C t The cell state C at the previous moment t-1 , hidden state h t-1 and the current input information x t Joint decision-making;

[0122] Cell state: used to update and store information; specific formula:

[0123]

[0124] First, the cell state C at the previous moment t-1 and the vector f output by the forget gate t Multiply point by point, if it is multiplied by a value f close to 0 t , then this information needs to be discarded if it is multiplied by a value f close to 1 t , then the information is fully preserved if it is multiplied by the value f t If the value is between (0, 1), the information can only be partially retained; finally, the value of the previous cell state is added to the output value of the input gate point by point to update the cell state at the current moment.

[0125] By adjusting the input gate value of the time recurrent neural network model within the preset period:

[0126] The historical gas network data collected within a preset period has multiple data values ​​as input gate values;

[0127] Sort multiple data values ​​and select the maximum and minimum values ​​as the input gate values ​​of the time recurrent neural network model;

[0128] The time-recurrent neural network model inputs the maximum and minimum values ​​of the input gate and obtains the maximum and minimum values ​​predicted by the output gate, which serve as the periodic preset thresholds for gas pipeline network data. This enables the preset thresholds for the next period of gas pipeline network data to be set, facilitating the comparison between the actual data value and the preset threshold within the gas pipeline network data period.

[0129] like Figure 3 As shown in the figure, the gas network data exceeds the preset threshold value and the real data value is regulated:

[0130] S701, mapping the real data values ​​collected during the gas pipe network data cycle to each component of the constructed pipe network in a 1:1 ratio;

[0131] S702, adjusting the methane gas concentration, pressure, and flow valve openings set on the branches and branch points;

[0132] S703, using the adjusted methane gas concentration, pressure, and flow valve opening as input gate values ​​for the time recurrent neural network model to predict gas pipeline network data at the next moment;

[0133] S704. Determine whether the predicted value of the gas pipeline network data at the next moment is within the preset threshold; if it is within the preset threshold, proceed to step S705; if it is not within the preset threshold, repeat the previous step S703; continue to adjust the methane gas concentration, pressure and flow valve opening set on the branch and branch point until the predicted value of the gas pipeline network data at the next moment is within the preset threshold, then end the process.

[0134] S705: End the adjustment process and send the corresponding methane gas concentration, pressure, and flow valve opening to the system administrator for gas network maintenance personnel to adjust and maintain parameters during network maintenance.

[0135] It also includes a gas pipe network data preprocessing unit, which is in communication with the gas pipe network data acquisition unit 10 and is used to preprocess the acquired gas pipe network data.

[0136] The pre-processing methods for the collected gas pipeline network data include:

[0137] Identification and handling of missing data;

[0138] Identification and handling of outlier data.

[0139] Detailed steps for identifying and handling missing data:

[0140] By comparing the number of data fields of different data types, determine whether a single data type is missing;

[0141] If missing data occurs, data interpolation is used to fill the missing values, or the entire gas pipeline network data collected at the corresponding time of the missing data is deleted.

[0142] Detailed steps for identifying and processing outlier data:

[0143] Outliers can be identified by checking whether individual data under a single data type is out of the data range of the single data type or the data is zero (except for zero temperature or zero natural gas usage, which are not identified as outliers). Box plot analysis can also be used to identify outliers:

[0144] Sort a set of data from small to large. Find the first quartile Q1, median M, and third quartile Q3 of the data set;

[0145] Let IQR = Q3 - Q1, draw a rectangle with IQR (interquartile range) as the length of the rectangle, and the positions on both sides of the rectangle correspond to the first quartile Q1 and the third quartile Q3 of the group of data respectively. Draw a center line on the long side of the rectangle, corresponding to the median M of the group of data. This line is called the median line.

[0146] Draw two straight lines at Q3+1.5IQR and Q1-1.5IQR, respectively, to distinguish outliers from normal values. The range between the two lines is called the inner limit. Draw two straight lines at Q3+3IQR and Q1-3IQR to distinguish extreme outliers from mild outliers. The range outside Q3+3IQR and Q1-3IQR is called the outer limit. Data outside the inner limits are considered outliers, data between the inner and outer limits are considered mild outliers, and data outside the outer limits are considered extreme outliers.

[0147] The outlier data value is replaced by the data replacement method, or the entire gas pipeline network data collected at the corresponding time of the outlier data is deleted.

[0148] Compared with the prior art, the present invention has the following beneficial effects:

[0149] By building the pipeline network topology structure in a virtual environment, gas pipeline network simulation is realized; at the same time, the time recurrent neural network model is used to realize the periodic prediction of gas pipeline network data. The real data values ​​collected on the gas pipeline network are compared with the periodic prediction range values ​​of the gas pipeline network data to judge the risk of the gas pipeline network. If the real data value exceeds the periodic prediction range value of the gas pipeline network data, the valves on the branches and branch nodes in each component of the virtual pipeline network are adjusted to realize the regulation of the real data value of the gas pipeline network that exceeds the preset threshold.

[0150] The above describes in detail the gas pipeline network simulation and analysis system provided by this application. The description of the specific embodiments is intended only to facilitate understanding of the method and core concept of this application. It should be noted that those skilled in the art may make various improvements and modifications to this application without departing from the principles of this application, and such improvements and modifications also fall within the scope of protection of the claims of this application.

Claims

1. A gas pipe network simulation analysis system, characterized in that: It includes a gas pipe network data acquisition unit (10), a pipe network topology structure building unit (20), a pipe network data value influencing factor analysis unit (30), a gas pipe network data prediction model building unit (40), a gas pipe network data periodicity prediction unit (50), a gas pipe network data control unit (60), a risk warning unit (70) and a gas pipe network data storage unit (80); The gas pipe network data acquisition unit (10) is in communication connection with the gas pipe network data storage unit (80), and collects corresponding gas pipe network data through the SCADA monitoring system on the gas pipe network; The pipe network topology structure building unit (20) is in communication connection with the gas pipe network data acquisition unit (10), and builds each component of the pipe network according to the relationship and setting position of the SCADA monitoring system acquisition equipment on the gas pipe network; The pipe network data value influencing factor analysis unit (30) is in communication connection with the gas pipe network data acquisition unit (10), and performs correlation analysis on each single type of gas pipe network data acquired to obtain data related to influencing factors affecting changes in the gas pipe network data; The gas network data prediction model building unit (40) is in communication connection with the network data value influencing factor analysis unit (30), selects a time recurrent neural network model as the gas network data prediction model, and determines an input gate, a forget gate, and an output gate of the time recurrent neural network model; The gas pipe network data periodicity prediction unit (50) is in communication with the gas pipe network data prediction model building unit (40), and completes the prediction of the gas pipe network data periodicity by adjusting the input gate value of the time recurrent neural network model within a preset period; The risk warning unit (70) is in communication with the gas pipe network data periodicity prediction unit (50), and determines whether the gas pipe network data should be subject to risk warning by comparing the real data value within the collected gas pipe network data period with a preset threshold value; The gas pipe network data control unit (60) is in communication with the risk warning unit (70) and controls the gas pipe network data when it exceeds a preset threshold value by adjusting valves on branches and branch nodes in each component of the virtual pipe network; The gas pipe network data storage unit (80) is in communication connection with the gas pipe network data periodicity prediction unit (50) and is used for storing historical gas pipe network data.

2. A gas pipe network simulation analysis system according to claim 1, characterized in that: The components of a pipe network include branches, boundaries, and branch points.

3. A gas pipe network simulation analysis system according to claim 2, characterized in that: Gas pipeline network data includes methane gas concentration, pressure and flow, and a pipeline network monitoring point is set at each branch point in the pipeline network.

4. A gas pipe network simulation analysis system according to claim 3, characterized in that: Detailed steps for correlation analysis of each single type of gas pipeline network data collected: First, a single data type in the gas pipeline network data is selected as the object to be analyzed; Then, the gas pipe environment or weather data values ​​that may affect a single data type are selected as the objects of correlation analysis; Then, the correlation analysis method is used to obtain the correlation coefficient between the two variables of the object to be judged and the object of correlation analysis; According to the absolute value of the correlation coefficient, the correlation analysis object with stronger correlation is selected as the relevant data of the influencing factors affecting the change of gas pipeline network data; Finally, repeat the above steps until the correlation analysis of all different types of gas pipeline network data is completed.

5. A gas pipe network simulation analysis system according to claim 4, characterized in that: The corresponding calculation formula for the correlation analysis method is: Where: r x,y ——Correlation coefficient between variables x and y, n——The number of variables x and y, x i ——the i-th value of variable x, - the mean of variables x, y i ——the i-th value of variable y, ——the mean value of variable y; The correlation coefficient ranges from -1 to 1.

6. A gas pipe network simulation analysis system according to claim 5, characterized in that: The input parameters of the time-recurrent neural network model include the cell state C at the previous moment t-1 , hidden state h t-1 and the current input information X t ; The output parameter is the current moment information C t and hidden state h t ; The time-recurrent neural network model includes a forget gate, an input gate, and an output gate; Forget gate: determine the cell state C at the previous moment t-1 Information that needs to be retained and information that needs to be discarded; t-1 and x t Enter the forget gate and pass it to the Sigmoid function; the specific calculation formula is: f t =σ(W f h t-1 +U f x t +b f ) Where: σ——activation function Sigmoid, output value range [0, 1], controls the propagation of information; W f 、U f ——The weight matrix of the forget gate; b f ——Forget gate bias; f t is the vector output by the forget gate; Input gate: used to determine the importance of the gas network data input at the current moment, and retain the important gas network data at the current moment and use it to update the cell state; the calculation formula is: i t =σ(W i h t-1 +U i x t +b i ) Where: tanh - activation function, output value range [-1, 1]; W i 、U i ——Input gate weight matrix; ——Candidate layer weight matrix; b i , bc——input gate, candidate layer bias; Output gate: confirm the current cell state C t How much information is lost to the value h of the hidden state at the next moment t The calculation formula is: The t =σ(W o h t-1 +U o x t +b t ) h t =tanh(C t ) Where: W O 、U O ——The weight matrix of the output layer; b t ——Output gate bias; Cell state: used to update and store information; specific calculation formula: Among them, C t-1 is the cell state at the previous moment, f t The vector output by the forget gate; K t The value left for the previous cell state, j t is the output value of the input gate; C t The cell status value at the current moment.

7. A gas pipe network simulation analysis system according to claim 6, characterized in that: By adjusting the input gate value of the time recurrent neural network model within the preset period: The historical gas network data collected within a preset period has multiple data values ​​as input gate values; Sort multiple data values ​​and select the maximum and minimum values ​​as the input gate values ​​of the time recurrent neural network model; The maximum and minimum values ​​of the input gate of the time recurrent neural network model are input to obtain the maximum and minimum values ​​predicted by the output gate, which are used as the periodic preset thresholds of the gas pipeline network data.

8. A gas pipe network simulation analysis system according to claim 7, characterized in that: Gas network data exceeds the preset threshold value and the actual data value is regulated: S701, mapping the real data values ​​collected during the gas pipe network data cycle to each component of the constructed pipe network in a 1:1 ratio; S702, adjusting the methane gas concentration, pressure, and flow valve openings set on the branches and branch points; S703, using the adjusted methane gas concentration, pressure, and flow valve opening as input gate values ​​for the time recurrent neural network model to predict gas pipeline network data at the next moment; S704: Determine whether the predicted value of the gas network data at the next moment is within a preset threshold; If it is within the preset threshold, proceed to step S705; if it is not within the preset threshold, repeat the previous step S703; S705: End the adjustment process and send the corresponding methane gas concentration, pressure, and flow valve opening to the system administrator for gas network maintenance personnel to adjust and maintain parameters during network maintenance.

9. A gas pipe network simulation analysis system according to claim 8, characterized in that: It also includes a gas pipe network data preprocessing unit, which is in communication with the gas pipe network data acquisition unit (10) and is used to preprocess the acquired gas pipe network data; The pre-processing methods for the collected gas pipeline network data include: Identification and handling of missing data: By comparing the number of data fields of different data types, determine whether a single data type is missing; If missing data occurs, data interpolation is used to fill the missing values, or the entire gas pipeline network data collected at the corresponding time of the missing data is deleted; Identification and processing of outlier data: Use box plot analysis to identify outliers: Sort a set of data from small to large values, and find the first quartile Q1, median M, and third quartile Q3 of the data set; Let IQR = Q3 - Q1, draw a rectangle with the IQR interquartile range as the length of the rectangle, and the positions of the two sides of the rectangle correspond to the first quartile Q1 and the third quartile Q3 of the data set respectively. Draw a midline on the long side of the rectangle, corresponding to the median M of the data set. This line is called the median line; Draw two straight lines at Q3+1.5IQR and Q1-1.5IQR respectively. The two straight lines are used to distinguish outliers from normal values, and the range between the two lines is called the inner limit; draw two straight lines at Q3+3IQR and Q1-3IQR respectively to distinguish the boundary between extreme outliers and mild outliers. The range outside Q3+3IQR and Q1-3IQR is called the outer limit. Data outside the inner limit is considered an outlier, data between the inner and outer limits is mild outlier data, and data outside the outer limit is extreme outlier data. Processing of outlier data: Use data replacement method to replace outlier data values, or delete the entire gas pipeline network data collected at the corresponding time of the outlier data.

Images