CN118171500B

CN118171500B - Steady state tracing monitoring analysis method, device, equipment and medium for natural gas pipe network

Info

Publication number: CN118171500B
Application number: CN202410594530.7A
Authority: CN
Inventors: 魏海东; 金威; 陈宝; 栾星; 周翔
Original assignee: Shanghai Three Zero Four Zero Technology Co ltd
Current assignee: Shanghai Three Zero Four Zero Technology Co ltd
Priority date: 2024-05-14
Filing date: 2024-05-14
Publication date: 2024-07-16
Anticipated expiration: 2044-05-14
Also published as: CN118171500A

Abstract

The application discloses a steady state traceability monitoring and analyzing method, device, equipment and medium of a natural gas pipe network, which relate to the technical field of natural gas pipe network simulation and comprise the steps of carrying out flow monitoring, pipeline data filtering and error rate calculation on steady state simulation results obtained by steady state simulation calculation of the natural gas pipe network to obtain an absolute flow balance error rate, and judging whether the absolute flow balance error rate is larger than total input and total output deviation multiplying power; if the total deviation rate is larger than the total deviation rate, performing flow balance treatment on the filtered pipeline data; judging whether the first matrix of the node continuity equation set is a singular matrix or not; if yes, adding a plurality of minimum bias items to obtain a first matrix of the target node continuity equation set, solving a sparse matrix of a second matrix of the node continuity equation set to obtain an air source occupation ratio, and performing steady-state tracing monitoring analysis. The application can realize steady-state traceability monitoring analysis of the natural gas pipeline network and improve stability, rationality and accuracy of the traceability monitoring analysis.

Description

Steady state tracing monitoring analysis method, device, equipment and medium for natural gas pipe network

Technical Field

The invention relates to the technical field of natural gas pipeline network simulation, in particular to a steady-state traceability monitoring and analyzing method, device, equipment and medium of a natural gas pipeline network.

Background

At present, the information of node components, heat values and the like in urban fuel gas topology can be divided into the following two modes by software and hardware: (1) By installing special detection instruments such as ultrasonic flow meters, gas analyzers and the like at specific positions of the pipe network. Can be directly arranged on a pipeline, and can also utilize a non-contact measurement method; (2) And constructing a gas flow direction directed graph of the urban gas pipe network based on the pipe network simulation calculation result, traversing to each end node by taking each gas source node as a starting point in a depth-first mode, and further calculating the duty ratio of each node from different gas sources. The two above-mentioned drawbacks are as follows: (1) The detection instrument is arranged at a specific position of the pipe network and can only provide data in a limited monitoring range, so that data acquisition can be discontinuous, the data can be lost or inaccurate in areas among the installation positions, so that the analysis of the overall condition of the pipeline system is deviated, the installation position of the instrument can be influenced by environmental factors, such as temperature change, humidity, vibration and the like, the accuracy and the stability of the instrument can be influenced, the deviation of the data is caused, and the real-time monitoring data is lacking in other areas of the pipeline system; (2) Obtaining a ring network in a directed graph of the gas flow direction based on a simulation calculation result, and failing to perform tracing calculation; in a large-scale mould pressing urban fuel pipe network, a tracing result is difficult to obtain in a short time, and is greatly influenced by a simulation measuring and calculating result, and the tracing effect is poor in stability and rationality.

From the above, how to realize steady-state traceability monitoring analysis of the natural gas pipe network, reduce inaccuracy and deviation of data, thereby improving stability, rationality and accuracy of traceability monitoring analysis, reducing accident occurrence risk of the natural gas pipe network, and guaranteeing stable operation of the natural gas pipe network is a problem to be solved in the field.

Disclosure of Invention

In view of the above, the invention aims to provide a steady-state tracing monitoring analysis method, a device, equipment and a medium for a natural gas pipe network, which can realize steady-state tracing monitoring analysis on the natural gas pipe network, reduce inaccuracy and deviation of data, and further improve stability, rationality and accuracy of tracing monitoring analysis, thereby reducing accident occurrence risk of the natural gas pipe network and guaranteeing stable operation of the natural gas pipe network. The specific scheme is as follows:

In a first aspect, the application discloses a steady-state traceability monitoring analysis method for a natural gas pipeline network, which comprises the following steps:

Obtaining a steady-state simulation result obtained based on steady-state simulation calculation of a natural gas pipe network, performing flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of a pipe, determining a current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power;

if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, performing flow balance processing on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, calculating the total outlet total inlet deviation multiplying power of the pipeline, and performing flow balance processing on the filtered pipeline data by utilizing the total outlet total inlet deviation multiplying power to obtain a target steady-state simulation result;

Respectively constructing a first matrix of the node continuity equation set and a second matrix of the node continuity equation set by using the target steady-state simulation result, and judging whether the first matrix of the node continuity equation set is a singular matrix or not;

if the first matrix of the node continuity equation set is not a singular matrix, carrying out sparse matrix solving on the first matrix of the node continuity equation set and the second matrix of the node continuity equation set, if the first matrix of the node continuity equation set is a singular matrix, adding a plurality of minimum bias terms to the first matrix of the node continuity equation set to obtain a first matrix of a target node continuity equation set, and carrying out sparse matrix solving on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set to obtain an air source occupation ratio;

And carrying out steady-state traceability monitoring analysis on the natural gas pipe network by using the gas source occupation ratio.

Optionally, the performing flow monitoring, pipe data filtering and error rate calculation on the pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of the pipe includes:

Monitoring the flow of the pipeline data in the steady-state simulation result, judging whether the flow in the pipeline data is smaller than 0, if the flow in the pipeline data is smaller than 0, exchanging the initial node number and the end node number of the pipeline data corresponding to the flow to obtain the exchanged pipeline data, and then filtering and deleting the pipeline data after the exchange to obtain the steady-state simulation result after the filtering;

if the flow in the pipeline data is not less than 0, directly filtering and deleting the pipeline data to obtain the filtered steady-state simulation result;

And calculating the total inlet flow and the total outlet flow of the pipeline according to the filtered steady-state simulation result, calculating the error rate of the total inlet flow and the total outlet flow to obtain a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate.

Optionally, the calculating the total inlet flow and the total outlet flow of the pipeline according to the filtered steady-state simulation result, calculating an error rate of the total inlet flow and the total outlet flow to obtain a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate, including:

Screening out pipeline data with an initial node number of an air source node and pipeline data with a terminal node number of a user node from the filtered steady-state simulation result, and respectively summing to obtain total inlet flow and total outlet flow of the pipeline;

Calculating the difference between the total inlet flow and the total outlet flow, dividing the difference by the total inlet flow to calculate a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate.

Optionally, if the absolute flow balance error rate is not greater than the total in-total out deviation multiplying power, performing flow balance processing on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total in-total out deviation multiplying power, calculating the total in-total deviation multiplying power of the pipeline, and performing flow balance processing on the filtered pipeline data by using the total in-total deviation multiplying power to obtain a target steady-state simulation result, including:

If the absolute flow balance error rate is not greater than the total input and total output deviation multiplying power, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to finish flow balance processing of the filtered pipeline data;

If the absolute flow balance error rate is larger than the total inlet and outlet deviation multiplying power, calculating the total inlet deviation multiplying power according to the total inlet flow and the total outlet flow, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to finish flow balance processing of the total inlet deviation multiplying power, so as to obtain a target steady-state simulation result.

Optionally, constructing a first matrix of the node continuity equation set by using the target steady-state simulation result includes:

Filtering pipeline data with the initial node number of the target steady-state simulation result being an air source node to obtain non-air source node pipeline data;

Taking a starting node number and a terminating node number in the non-air source node pipeline data as a first row index and a first column index respectively to generate a first compression list;

Classifying the termination nodes in the non-air source node pipeline data, summing the flow data corresponding to the classified termination nodes, and generating a second compression list based on the first compression list;

combining the first compression list and the second compression list to obtain a third compression list;

and constructing and generating a first matrix of the node continuity equation set according to the third compression list.

Optionally, constructing a second matrix of the node continuity equation set by using the target steady-state simulation result, including:

screening air source node pipeline data with an initial node number of an air source node from the target steady-state simulation result;

generating a second row index and a second column index based on the gas source node pipeline data and the node continuity equation set first matrix;

traversing the air source node pipeline data, and constructing a node continuity equation set second matrix by using the second row index and the second column index.

Optionally, adding a plurality of minimum bias terms to the first matrix of the node continuity equation set to obtain a first matrix of the target node continuity equation set includes:

Constructing a diagonal matrix of minimum bias terms corresponding to the first matrix of the node continuity equation set;

and adding a plurality of minimum bias terms to diagonal position elements of the first matrix of the node continuity equation set by using the minimum bias term diagonal matrix so as to obtain the first matrix of the target node continuity equation set.

In a second aspect, the application discloses a steady-state traceability monitoring and analyzing device of a natural gas pipeline network, which comprises:

The preprocessing and calculating module is used for obtaining a steady-state simulation result obtained based on steady-state simulation calculation of the natural gas pipe network, carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of a pipe, determining a current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power;

the flow balance processing module is used for carrying out flow balance processing on the filtered pipeline data based on the current deviation multiplying power if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, calculating the total inlet and outlet deviation multiplying power of the pipeline if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, and carrying out flow balance processing on the filtered pipeline data by utilizing the total inlet and outlet deviation multiplying power so as to obtain a target steady-state simulation result;

the matrix construction module is used for respectively constructing a first matrix of the node continuity equation set and a second matrix of the node continuity equation set by utilizing the target steady-state simulation result, and judging whether the first matrix of the node continuity equation set is a singular matrix or not;

The sparse matrix solving module is used for carrying out sparse matrix solving on the first node continuity equation set matrix and the second node continuity equation set matrix if the first node continuity equation set matrix is not a singular matrix, adding a plurality of minimum bias terms to the first node continuity equation set matrix to obtain a first target node continuity equation set matrix, and carrying out sparse matrix solving on the first target node continuity equation set matrix and the second node continuity equation set matrix to obtain an air source occupation ratio;

and the monitoring analysis module is used for carrying out steady-state traceability monitoring analysis on the natural gas pipe network by utilizing the gas source occupation ratio.

In a third aspect, the present application discloses an electronic device, comprising:

a memory for storing a computer program;

and the processor is used for executing the computer program to realize the steady-state traceability monitoring and analysis method of the natural gas pipe network.

In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; the method comprises the steps of a steady state traceability monitoring analysis method of a natural gas pipe network, wherein the steady state traceability monitoring analysis method of the natural gas pipe network is disclosed.

The application provides a steady-state traceability monitoring analysis method of a natural gas pipe network, which comprises the steps of obtaining a steady-state simulation result obtained based on steady-state simulation calculation of the natural gas pipe network, carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of a pipe, determining a current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power; if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, performing flow balance processing on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, calculating the total outlet total inlet deviation multiplying power of the pipeline, and performing flow balance processing on the filtered pipeline data by utilizing the total outlet total inlet deviation multiplying power to obtain a target steady-state simulation result; Respectively constructing a first matrix of the node continuity equation set and a second matrix of the node continuity equation set by using the target steady-state simulation result, and judging whether the first matrix of the node continuity equation set is a singular matrix or not; if the first matrix of the node continuity equation set is not a singular matrix, carrying out sparse matrix solving on the first matrix of the node continuity equation set and the second matrix of the node continuity equation set, if the first matrix of the node continuity equation set is a singular matrix, adding a plurality of minimum bias terms to the first matrix of the node continuity equation set to obtain a first matrix of a target node continuity equation set, and carrying out sparse matrix solving on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set to obtain an air source occupation ratio; And carrying out steady-state traceability monitoring analysis on the natural gas pipe network by using the gas source occupation ratio. The application obtains absolute flow balance error rate by carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in a steady-state simulation result, then judges whether the absolute flow balance error rate is larger than total input and total output deviation multiplying power, then carries out flow balance treatment to obtain a target steady-state simulation result, determines the target steady-state simulation result through a flow balance adjustment mechanism, can reduce inaccuracy and deviation of data, for example, can filter a very small flow pipe, an air source abnormal air inlet pipe and a user abnormal air outlet pipe, ensures rationality of a tracing result, judges whether a first matrix of a constructed node continuity equation set is a singular matrix, if so, The method adds a plurality of minimum bias terms for the first matrix of the node continuity equation set, ensures that matrix singularities are eliminated, effectively carries out subsequent sparse matrix solving under the condition of not influencing calculation results, carries out sparse matrix solving to obtain an air source occupation ratio, realizes steady-state tracing monitoring analysis on the natural gas pipe network, can realize steady-state tracing monitoring analysis on the natural gas pipe network, does not need additional hardware and other equipment support, greatly improves the operation efficiency of a tracing algorithm by using sparse matrix calculation to replace the existing graph network mode, saves a large amount of calculation resource expenditure, simultaneously skillfully solves the ring network problem which is difficult to solve in the prior art by using the matrix storage form of pipe network data, therefore, the stability, rationality and accuracy of traceability monitoring analysis can be improved, the accident occurrence risk of the natural gas pipe network is reduced, and the stable operation of the natural gas pipe network is ensured.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a steady state traceability monitoring and analyzing method of a natural gas pipeline network disclosed by the application;

FIG. 2 is a flow chart of preprocessing steady-state simulation results disclosed by the application;

FIG. 3 is a flow chart of a flow balance adjustment mechanism data processing disclosed in the present application;

FIG. 4 is a diagram showing a comparison of conventional trace-to-steady trace-to-trace runtime disclosed in the present application;

FIG. 5 is a flow chart of a steady-state traceability monitoring and analyzing method of a natural gas pipeline network disclosed by the application;

FIG. 6 is a flow chart of a first matrix for constructing a system of node continuity equations in accordance with the present disclosure;

FIG. 7 is a flow chart of a second matrix for constructing a system of node continuity equations in accordance with the present disclosure;

FIG. 8 is a schematic structural diagram of a steady-state traceability monitoring and analyzing device of a natural gas pipeline network disclosed by the application;

fig. 9 is a block diagram of an electronic device according to the present application.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

At present, the information of node components, heat values and the like in urban fuel gas topology can be divided into the following two modes by software and hardware: (1) By installing special detection instruments such as ultrasonic flow meters, gas analyzers and the like at specific positions of the pipe network. Can be directly arranged on a pipeline, and can also utilize a non-contact measurement method; (2) And constructing a gas flow direction directed graph of the urban gas pipe network based on the pipe network simulation calculation result, traversing to each end node by taking each gas source node as a starting point in a depth-first mode, and further calculating the duty ratio of each node from different gas sources. The two above-mentioned drawbacks are as follows: (1) The detection instrument is arranged at a specific position of the pipe network and can only provide data in a limited monitoring range, so that data acquisition can be discontinuous, the data can be lost or inaccurate in areas among the installation positions, so that the analysis of the overall condition of the pipeline system is deviated, the installation position of the instrument can be influenced by environmental factors, such as temperature change, humidity, vibration and the like, the accuracy and the stability of the instrument can be influenced, the deviation of the data is caused, and the real-time monitoring data is lacking in other areas of the pipeline system; (2) Obtaining a ring network in a directed graph of the gas flow direction based on a simulation calculation result, and failing to perform tracing calculation; in a large-scale mould pressing urban fuel pipe network, a tracing result is difficult to obtain in a short time, and is greatly influenced by a simulation measuring and calculating result, and the tracing effect is poor in stability and rationality. From the above, how to realize steady-state traceability monitoring analysis of the natural gas pipe network, reduce inaccuracy and deviation of data, thereby improving stability, rationality and accuracy of traceability monitoring analysis, reducing accident occurrence risk of the natural gas pipe network, and guaranteeing stable operation of the natural gas pipe network is a problem to be solved in the field.

Referring to fig. 1, the embodiment of the invention discloses a steady-state traceability monitoring and analyzing method for a natural gas pipeline network, which specifically comprises the following steps:

Step S11: and obtaining a steady-state simulation result obtained based on steady-state simulation calculation of the natural gas pipe network, carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of the pipe, determining a current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power.

In this embodiment, a steady-state simulation result obtained based on steady-state simulation calculation of a natural gas pipe network is obtained, flow monitoring is performed on pipe data in the steady-state simulation result, whether the flow in the pipe data is smaller than 0 is judged, if the flow in the pipe data is smaller than 0, an initial node number and a termination node number of the pipe data corresponding to the flow are exchanged to obtain exchanged pipe data, and then pipe data filtering and deleting are performed on the exchanged pipe data to obtain a filtered steady-state simulation result; if the flow in the pipeline data is not less than 0, directly filtering and deleting the pipeline data to obtain the filtered steady-state simulation result; and calculating the total inlet flow and the total outlet flow of the pipeline according to the filtered steady-state simulation result, calculating error rates of the total inlet flow and the total outlet flow to obtain a flow balance error rate, taking absolute values of the flow balance error rate to obtain an absolute flow balance error rate, determining the current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than the total inlet and total outlet deviation multiplying power.

The flow of calculating the absolute flow balance error rate is as follows: screening out pipeline data with an initial node number of an air source node and pipeline data with a terminal node number of a user node from the filtered steady-state simulation result, and respectively summing to obtain total inlet flow and total outlet flow of the pipeline; calculating the difference between the total inlet flow and the total outlet flow, dividing the difference by the total inlet flow to calculate a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate.

Specifically, a steady-state simulation result is firstly obtained, wherein the steady-state simulation result mainly comprises a steady-state calculation result and node type data, and the steady-state calculation result comprises: the steady state calculations are shown in table 1 and mainly include various attributes of the pipeline: pipe_code (pipeline number), source (pipeline start node number), target (pipeline end node number), and q (pipeline natural gas flow in units ofS) from steady state simulation calculations run once per hour; the node type data is shown in table 2, and is composed of node types corresponding to different node IDs (Identity document, unique identification numbers), including gas (source), user (user), and common_node (common node) types:

TABLE 1

TABLE 2

In this embodiment, after a steady-state simulation result is obtained, data preprocessing is performed on the steady-state simulation result, and the method mainly includes two steps: abnormal value processing (i.e., flow monitoring, pipe data filtering) and flow balance control in step S11. The main flow of data preprocessing is shown in fig. 2, and the abnormal value processing can be divided into a negative value and a minimum value, firstly, the sign of the flow in the simulation calculation is considered to represent the flow direction of the fuel gas in the pipeline, so that the flow negative value is converted into a scalar to be calculated, and meanwhile, the source and the target at the tail end of the pipeline are required to be exchanged, thereby achieving the effects of adjusting the flow of the pipeline to be positive and facilitating the tracing steady state calculation. That is, after each pipe data of the steady-state simulation result is traversed, judging whether the flow in the pipe data is smaller than 0, if so, exchanging the source and target of the pipe, and by observing that the minimum values of 1e-20, 1e-32 and the like exist in the flow in the simulation calculation result, under the real scene, the flow is not different from 0, and has no calculation value, so after exchanging the source and target of the pipe, filtering and deleting the pipe data with the flow smaller than Q_threshold_ ALGORITHM (the lowest flow THRESHOLD of the filtered pipe data), obtaining the filtered steady-state simulation result, and calculating the absolute flow balance error rate of the pipe according to the filtered steady-state simulation result so as to carry out flow balance regulation and control later. Meanwhile, the pipe network scale is reduced to a certain extent, and the improvement of the node component calculation efficiency is facilitated.

Step S12: if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, flow balance processing is carried out on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, the total outlet total inlet deviation multiplying power of the pipeline is calculated, and flow balance processing is carried out on the filtered pipeline data by utilizing the total outlet total inlet deviation multiplying power, so that a target steady-state simulation result is obtained.

In this embodiment, if the absolute flow balance error rate is not greater than the total input/total output deviation multiplying power, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to complete flow balance processing on the filtered pipeline data; if the absolute flow balance error rate is larger than the total inlet and outlet deviation multiplying power, calculating the total inlet deviation multiplying power according to the total inlet flow and the total outlet flow, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to finish flow balance processing of the total inlet deviation multiplying power, so as to obtain a target steady-state simulation result.

Because the simulation calculation has good calculation effect in a real scene, but the conditions that the simulation is not converged and the calculation result is error are still difficult to avoid exist, the filtered steady-state simulation result obtained after the abnormal value processing also needs to be subjected to flow balance regulation and control, wherein the method comprises the steps of avoiding two pipeline situations and adjusting total inlet and total outlet flow balance in a targeted way: the pipeline taking the air source node as the target and the pipeline taking the user node as the source are filtered in the data preprocessing link, so that the influence of the unreasonable event on the calculation of the subsequent node components is avoided. In addition, the air source is used as an air outlet end and the user is used as an air inlet end, and the air source and the user are in a flow balance state in the steady-state simulation calculation process, namely the total air inlet and the total air outlet are the same at the same moment. Therefore, the patent adopts a flow balance adjustment mechanism to process the data of the total in and the total out for the steady-state simulation result which is not balanced. The flow of data processing of a specific flow balance adjustment mechanism is shown in fig. 3: screening out pipeline data with source as an air source node and pipeline data with target as a user node from the filtered steady-state simulation result, and respectively summing to obtain total inlet flow and total outlet flow of the pipeline; then obtaining a FLOW balance ERROR rate through (total inlet FLOW rate-total outlet FLOW rate)/total inlet FLOW rate, taking an absolute value, obtaining an absolute FLOW balance ERROR rate, updating the current deviation multiplying power to 0, judging whether the absolute FLOW balance ERROR rate is larger than FLOW_SUM_ERROR (total inlet and outlet deviation multiplying power), if so, calculating the total inlet deviation multiplying power through (total inlet FLOW rate/total outlet FLOW rate-1), traversing the filtered pipeline data, and multiplying by 1+ current deviation multiplying power respectively to obtain a target steady-state simulation result; if the deviation multiplying power is not greater than the current deviation multiplying power +1, traversing the filtered pipeline data, and multiplying the pipeline data by the current deviation multiplying power +1 to obtain a target steady-state simulation result.

Step S13: and respectively constructing a first matrix of the node continuity equation set and a second matrix of the node continuity equation set by using the target steady-state simulation result, and judging whether the first matrix of the node continuity equation set is a singular matrix.

Step S14: if the first matrix of the node continuity equation set is not a singular matrix, sparse matrix solving is carried out on the first matrix of the node continuity equation set and the second matrix of the node continuity equation set, if the first matrix of the node continuity equation set is a singular matrix, a plurality of minimum bias terms are added for the first matrix of the node continuity equation set so as to obtain a first matrix of the target node continuity equation set, and sparse matrix solving is carried out on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set so as to obtain an air source occupation ratio.

Step S15: and carrying out steady-state traceability monitoring analysis on the natural gas pipe network by using the gas source occupation ratio.

The method for solving the duty ratio of each node outside the relief air source from different air sources based on the steady-state simulation result obtained by the steady-state simulation calculation of the natural gas pipe network and the node data sparse matrix can solve the problem that the trace calculation cannot be carried out on the ring network compared with the traditional trace tracing mode by adopting graph search. Meanwhile, the calculation time of steady-state tracing can be greatly shortened by adopting the matrix calculation mode, and the running time pairs of pipe networks in different scales between the traditional tracing and the tracing of the application are shown in fig. 4. The relation between the air supply quantity of each air source and the node flow and the pipe section flow can be qualitatively and quantitatively solved through calculation and analysis of the flow components in each node and the pipe section in the multi-air source pipe network, so that the air supply proportion of each air source is scheduled and optimized according to the change of the air working condition of the pipe network, and the effect of various air sources in the operation of the annular pipe network is exerted to the maximum extent.

In the embodiment, a steady-state simulation result obtained based on steady-state simulation calculation of a natural gas pipe network is obtained, flow monitoring, pipe data filtering and error rate calculation are carried out on pipe data in the steady-state simulation result, so that an absolute flow balance error rate of a pipe is obtained, a current deviation multiplying power is determined, and whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power is judged; if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, performing flow balance processing on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, calculating the total outlet total inlet deviation multiplying power of the pipeline, and performing flow balance processing on the filtered pipeline data by utilizing the total outlet total inlet deviation multiplying power to obtain a target steady-state simulation result; Respectively constructing a first matrix of the node continuity equation set and a second matrix of the node continuity equation set by using the target steady-state simulation result, and judging whether the first matrix of the node continuity equation set is a singular matrix or not; if the first matrix of the node continuity equation set is not a singular matrix, carrying out sparse matrix solving on the first matrix of the node continuity equation set and the second matrix of the node continuity equation set, if the first matrix of the node continuity equation set is a singular matrix, adding a plurality of minimum bias terms to the first matrix of the node continuity equation set to obtain a first matrix of a target node continuity equation set, and carrying out sparse matrix solving on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set to obtain an air source occupation ratio; And carrying out steady-state traceability monitoring analysis on the natural gas pipe network by using the gas source occupation ratio. The application obtains absolute flow balance error rate by carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in a steady-state simulation result, then judges whether the absolute flow balance error rate is larger than total input and total output deviation multiplying power, then carries out flow balance treatment to obtain a target steady-state simulation result, determines the target steady-state simulation result through a flow balance adjustment mechanism, can reduce inaccuracy and deviation of data, for example, can filter a very small flow pipe, an air source abnormal air inlet pipe and a user abnormal air outlet pipe, ensures rationality of a tracing result, judges whether a first matrix of a constructed node continuity equation set is a singular matrix, if so, The method adds a plurality of minimum bias terms for the first matrix of the node continuity equation set, ensures that matrix singularities are eliminated, effectively carries out subsequent sparse matrix solving under the condition of not influencing calculation results, carries out sparse matrix solving to obtain an air source occupation ratio, realizes steady-state tracing monitoring analysis on the natural gas pipe network, can realize steady-state tracing monitoring analysis on the natural gas pipe network, does not need additional hardware and other equipment support, greatly improves the operation efficiency of a tracing algorithm by using sparse matrix calculation to replace the existing graph network mode, saves a large amount of calculation resource expenditure, simultaneously skillfully solves the ring network problem which is difficult to solve in the prior art by using the matrix storage form of pipe network data, therefore, the stability, rationality and accuracy of traceability monitoring analysis can be improved, the accident occurrence risk of the natural gas pipe network is reduced, and the stable operation of the natural gas pipe network is ensured.

Referring to fig. 5, the embodiment of the invention discloses a steady-state traceability monitoring and analyzing method for a natural gas pipeline network, which specifically comprises the following steps:

Step S21: and obtaining a steady-state simulation result obtained based on steady-state simulation calculation of the natural gas pipe network, carrying out flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of the pipe, determining a current deviation multiplying power, and judging whether the absolute flow balance error rate is larger than a total input and total output deviation multiplying power.

Step S22: if the absolute flow balance error rate is not greater than the total inlet and outlet deviation multiplying power, flow balance processing is carried out on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total inlet and outlet deviation multiplying power, the total outlet total inlet deviation multiplying power of the pipeline is calculated, and flow balance processing is carried out on the filtered pipeline data by utilizing the total outlet total inlet deviation multiplying power, so that a target steady-state simulation result is obtained.

Step S23: filtering pipeline data with an initial node number of an air source node in the target steady-state simulation result to obtain non-air source node pipeline data, respectively taking the initial node number and a termination node number in the non-air source node pipeline data as a first row index and a first column index to generate a first compression list, classifying termination nodes in the non-air source node pipeline data, summing flow data corresponding to the classified termination nodes, generating a second compression list based on the first compression list, combining the first compression list and the second compression list to obtain a third compression list, and constructing and generating a first matrix of a node continuity equation set according to the third compression list.

In this embodiment, the flow of constructing and generating the first matrix a of the node continuity equation set is shown in fig. 6, and the first matrix a of the node continuity equation set necessary for solving the air source duty ratio is mainly constructed. The first matrix A of the node continuity equation set mainly consists of the total output flow of all nodes of the non-air source nodes and the output flow of each branch pipeline. The matrix A comprises sum data of output flow of nodes except the air source node and input branch flow data, the sum of the output flow can be used for quickly obtaining a second compression list (namely a compression list 2 in fig. 6) through target classification summation in pipeline data, the input branch flow data can be quickly obtained through a parallelization traversing reading mode, then a first compression list (namely a compression list 1 in fig. 6) is obtained, the first compression list and the second compression list are combined to obtain a third compression list (namely a compression list 3 in fig. 6), and a node continuity equation set first matrix is constructed and generated according to the third compression list. In order to improve the matrix operation efficiency, rows, columns and numerical values of the matrix A are respectively stored in a compressed mode by using a list, and finally, a first matrix A (namely a flow matrix A) of the node continuity equation set is generated through row-column index and numerical value inversion.

Step S24: and screening air source node pipeline data with an initial node number of an air source node from the target steady-state simulation result, generating a second row index and a second column index based on the air source node pipeline data and the first matrix of the node continuity equation set, traversing the air source node pipeline data, and constructing a second matrix of the node continuity equation set by utilizing the second row index and the second column index.

In this embodiment, as shown in fig. 7, a flow of constructing a second matrix b of the node continuity equation set is shown in fig. 7, according to a preprocessed steady-state simulation result, pipeline data with a node with a type of air source as a source is first screened out, then a b matrix with a row index identical to that of the matrix a and a column index identical to that of the node ID with the air source and a value of 0 is generated based on the row of the matrix a and the source with the type of air source, and then each pipeline data with the source screened out by traversing as a node with the type of air source is filled into the matrix b with the source as a column index and the target as a row index, so as to generate the second matrix b of the node continuity equation set required by tracing calculation.

Step S25: and judging whether the first matrix of the node continuity equation set is a singular matrix or not.

Step S26: if the first matrix of the node continuity equation set is not a singular matrix, sparse matrix solving is carried out on the first matrix of the node continuity equation set and the second matrix of the node continuity equation set, if the first matrix of the node continuity equation set is a singular matrix, a plurality of minimum bias terms are added for the first matrix of the node continuity equation set so as to obtain a first matrix of the target node continuity equation set, and sparse matrix solving is carried out on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set so as to obtain an air source occupation ratio.

In this embodiment, if the first matrix of the node continuity equation set is a singular matrix, a diagonal matrix of an extremely small bias term corresponding to the first matrix of the node continuity equation set is constructed, a plurality of extremely small bias terms are added for diagonal position elements of the first matrix of the node continuity equation set by using the diagonal matrix of the extremely small bias term to obtain a first matrix of the target node continuity equation set, and sparse matrix solving is performed on the first matrix of the target node continuity equation set and the second matrix of the node continuity equation set to obtain an air source occupation ratio.

In this embodiment, X may be solved by performing sparse matrix operation ax=b on the first matrix a of the node continuity equation set and the second matrix b of the node continuity equation set, and then the air source occupation ratio of each node from different air sources may be calculated. Note that, in the case of the matrix a, ax=b can be successfully found X in the case of a singular matrix, whereas X cannot be found. Therefore, considering the uncertainty of the singularity of the matrix A, the application adds a plurality of tiny bias items of different orders of magnitude at the diagonal position of the flow matrix A by using LM_ COEFFICIENT (bias quantity, [1e-30, 1e-27, 1e-24, 1e-21], dimensionless)For example:

；

is composed of a very small number plus element (e.g ) A diagonal matrix is formed. Knowing the traffic matrix a andThe diagonal elements are positive numbers, the diagonal elements of the added matrix are constant positive numbers, and the flow matrix A added with the bias term can be determined to have non-singular characteristics according to the characteristics that the directional and non-diagonal position elements of the flow matrix of the pipe network are constant non-positive numbers. The adding method of the minimum bias term effectively eliminates the possible singularities of the flow matrix A under the condition of not influencing the overall calculation result, and ensures the reliability and the robustness of the subsequent matrix calculation. In addition, the method uses sparse matrix, parallel reading calculation and other modes in matrix calculation, so that space utilization and calculation time are greatly reduced, and efficient and light tracing calculation is ensured.

Step S27: and carrying out steady-state traceability monitoring analysis on the natural gas pipe network by using the gas source occupation ratio.

The application constructs a first matrix of a node continuity equation set, a second matrix of the node continuity equation set and a theoretical part for solving a sparse matrix, wherein the theoretical part is as follows:

(1) Node continuity equation. According to the fact that the inflow and outflow of gas of any node in the natural gas pipe network are equal, the continuity equation of any node i in the multi-gas source pipe network can be obtained as follows:

；

Wherein, Node traffic representing node i; representing the flow of each pipe section flowing out of the node i; Representing the flow of each pipe section flowing into the node i from each adjacent air source point; representing the pipe segment traffic flowing into node i from adjacent nodes (except the source point).

(2) A single source to node continuity equation. Let a certain air source point be s and a certain node be i, the continuity equation of the flow of the air source s to the node i can be expressed as:

；

Wherein, Representing the fraction of the node flow out of node i provided by gas source s; representing the part provided by the air source s in the flow of each pipe section flowing out of the node i; representing the flow of each pipe section flowing into the node i from each adjacent gas source point s, and being 0 when the flow is not present; representing the fraction of the flow of gas source s from each pipe segment flowing into node i from adjacent nodes (except for the source point).

3) The continuity equation of multiple gas sources to a node. Assuming that the multi-air source pipe network has n air source points, the continuity equation of each air source to the node i can be expressed as:

；

further, the following relation can be obtained:

；

Assuming that the flow provided by each air source at a certain node in the multi-air source pipe network forms a certain proportion, and each air source is fully mixed at the node, the proportion of each air source is kept unchanged when the air flows out from the node, and the proportion is called as a distribution coefficient and is expressed by 'C', the method can be used for obtaining:

；

representing the distribution coefficient of the air source s at the node i; Representing the distribution coefficient of the air source s in the pipe section i, j; the sum of distribution coefficients of each air source at a certain node (pipe section) is 1, and the relation is as follows:

；

Three formulas of the distribution coefficient are substituted If the flow continuity equation of a certain gas source s to the node i can be expressed as:

；

Wherein, Representing the total outflow of node i, the flow continuity equation for n source supply nodes i is:

；

...

；

....

；

(4) And establishing a continuity equation set. Assuming that the number of nodes in the pipe network is m, establishing a continuity equation set for the flow of a certain air source s into m nodes, wherein the continuity equation set is as follows:

；

Wherein, For the distribution coefficient of the gas source s at node i,For the entire outflow of node i,For each pipe segment flow from an adjacent source point s into node i, 0 when not present,For each pipe segment flow from each node (except the source point) into node i, the distribution coefficient of each node is multiplied, wherein the summation is the summation of the inflow node number k.

From this, it can be deduced that the system of continuity equations for the air source s flowing into the m nodes is:

；

Node 1: ；

node 2: ；

Node 3: ；

and (3) unfolding to obtain:

；

i and k represent nodes of the pipeline, so that the maximum value of i and k is the total number of nodes, and the two subscripts of Q have no physical meaning and are zero when the subscripts are identical. So from the linear matrix ax=b, it is possible to write:

；

The order of the linear equation set is equal to the number of nodes except the source points in the pipe network, the coefficients A and b can be obtained through steady-state calculation results, and the distribution coefficients of the source points to each node and pipe section can be obtained by solving the equation set.

The innovation point of the application is that: and (1) no extra cost and convenient use. The tracing result is obtained only by using a steady-state simulation result in a numerical calculation mode without additional hardware and other equipment support; and (2) the algorithm is efficient and ingenious. Matrix numerical calculation replaces a graph network mode, so that the tracing speed is greatly improved, and meanwhile, the ring network problem which is difficult to solve in the graph network searching method is skillfully solved in a matrix storage mode of pipe network data; and (3) the occupied space is small, and the calculation time is short. The operations such as matrix reading and calculating are performed in a parallel mode by using the sparse matrix to store the pipe network values, so that the operation efficiency of the traceability algorithm is greatly improved, and a large amount of calculation resource expenditure is saved; and (4) solving stability. Multiple groups of extremely small bias items with different orders of magnitude are added to the diagonal position in the matrix A, so that matrix singularities are eliminated, and under the condition that a calculation result is not influenced, the smooth solving of AX=b is effectively ensured; and (5) reasonably using the calculation result. In order to avoid the situation that the sum of node tracing duty ratio results is not 1 due to unreasonable initial value assignment or deviation of measurement results in steady-state measurement, a pipeline with extremely small flow, an air source abnormal air inlet pipeline and a user abnormal air outlet pipeline are filtered, and the rationality of tracing results is ensured.

Referring to fig. 8, the embodiment of the invention discloses a steady-state tracing monitoring analysis device for a natural gas pipeline network, which specifically comprises the following steps:

The preprocessing and calculating module 11 is configured to obtain a steady-state simulation result obtained based on steady-state simulation calculation of a natural gas pipe network, perform flow monitoring, pipe data filtering and error rate calculation on pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of a pipe, determine a current deviation multiplying power, and determine whether the absolute flow balance error rate is greater than a total input/total output deviation multiplying power;

the flow balance processing module 12 is configured to perform flow balance processing on the filtered pipeline data based on the current deviation multiplying power if the absolute flow balance error rate is not greater than the total in/out deviation multiplying power, calculate a total in/out deviation multiplying power of the pipeline if the absolute flow balance error rate is greater than the total in/out deviation multiplying power, and perform flow balance processing on the filtered pipeline data by using the total in/out deviation multiplying power to obtain a target steady-state simulation result;

the matrix construction module 13 is configured to respectively construct a first matrix of the node continuity equation set and a second matrix of the node continuity equation set according to the target steady-state simulation result, and determine whether the first matrix of the node continuity equation set is a singular matrix;

the sparse matrix solving module 14 is configured to solve, if the first node continuity equation set matrix is not a singular matrix, the sparse matrix for the first node continuity equation set matrix and the second node continuity equation set matrix, and if the first node continuity equation set matrix is a singular matrix, add a plurality of minimum bias terms to the first node continuity equation set matrix to obtain a first target node continuity equation set matrix, and solve, for the first target node continuity equation set matrix and the second node continuity equation set matrix, the sparse matrix to obtain an air source occupation ratio;

And the monitoring and analyzing module 15 is used for carrying out steady-state traceability monitoring and analyzing on the natural gas pipe network by utilizing the gas source occupation ratio.

In some embodiments, the preprocessing and computing module 11 may specifically include:

The node number exchange module is used for monitoring the flow of the pipeline data in the steady-state simulation result, judging whether the flow in the pipeline data is smaller than 0, exchanging the initial node number and the termination node number of the pipeline data corresponding to the flow if the flow in the pipeline data is smaller than 0 to obtain the exchanged pipeline data, and then filtering and deleting the pipeline data after the exchange to obtain the steady-state simulation result after the filtering;

The pipeline data filtering module is used for directly filtering and deleting the pipeline data if the flow in the pipeline data is not less than 0 so as to obtain the steady-state simulation result after filtering;

And the error rate calculation module is used for calculating the total inlet flow and the total outlet flow of the pipeline according to the filtered steady simulation result, calculating the error rate of the total inlet flow and the total outlet flow to obtain a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate.

The screening and summing module is used for screening out pipeline data with the initial node number being an air source node and pipeline data with the terminal node number being a user node from the filtered steady-state simulation result, and respectively summing the pipeline data to obtain the total inlet flow and the total outlet flow of the pipeline;

and the difference value calculation module is used for calculating the difference value between the total inlet flow and the total outlet flow, dividing the difference value by the total inlet flow to calculate a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate.

In some specific embodiments, the flow balance processing module 12 may specifically include:

The first flow balance processing module is used for traversing the filtered pipeline data and multiplying the sum of the current deviation multiplying power and 1 respectively if the absolute flow balance error rate is not greater than the total input and total output deviation multiplying power so as to finish flow balance processing of the filtered pipeline data;

And the second flow balance processing module is used for calculating total input deviation multiplying power according to the total input flow and the total output flow if the absolute flow balance error rate is larger than the total input deviation multiplying power, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to finish flow balance processing on the total input deviation multiplying power and obtain a target steady-state simulation result.

In some specific embodiments, the matrix construction module 13 may specifically include:

the filtering module is used for filtering pipeline data of which the initial node number is an air source node in the target steady-state simulation result so as to obtain non-air source node pipeline data;

The first compression list generation module is used for taking a starting node number and a terminating node number in the non-air source node pipeline data as a first row index and a first column index respectively to generate a first compression list;

The second compression list generation module is used for classifying the termination nodes in the non-air source node pipeline data, summing the flow data corresponding to the classified termination nodes, and generating a second compression list based on the first compression list;

the third compression list generation module is used for combining the first compression list and the second compression list to obtain a third compression list;

And the first matrix construction module is used for constructing and generating a first matrix of the node continuity equation set according to the third compression list.

the pipeline data screening module is used for screening air source node pipeline data with an initial node number of an air source node from the target steady-state simulation result;

The index generation module is used for generating a second row index and a second column index based on the air source node pipeline data and the node continuity equation set first matrix;

And the second matrix construction module is used for traversing the air source node pipeline data and constructing a second matrix of the node continuity equation set by utilizing the second row index and the second column index.

In some specific embodiments, the sparse matrix solving module 14 may specifically include:

The diagonal matrix construction module is used for constructing a minimum bias term diagonal matrix corresponding to the first matrix of the node continuity equation set;

And the minimum bias term adding module is used for adding a plurality of minimum bias terms for diagonal position elements of the first matrix of the node continuity equation set by utilizing the minimum bias term diagonal matrix so as to obtain the first matrix of the target node continuity equation set.

Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. The memory 22 is configured to store a computer program, where the computer program is loaded and executed by the processor 21 to implement relevant steps in the steady-state traceability monitoring analysis method of the natural gas pipeline network performed by the electronic device disclosed in any of the foregoing embodiments.

In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.

The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon include an operating system 221, a computer program 222, and data 223, and the storage may be temporary storage or permanent storage.

The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the data 223 in the memory 22 by the processor 21, which may be Windows, unix, linux or the like. The computer program 222 may further comprise a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the steady state traceability monitoring analysis method of a natural gas pipeline network performed by the electronic device 20 as disclosed in any of the previous embodiments. The data 223 may include, in addition to the data received by the steady state traceability monitoring and analyzing device of the natural gas pipeline network and transmitted by the external device, the data collected by the self input/output interface 25, and the like.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Further, the embodiment of the application also discloses a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is loaded and executed by a processor, the steady-state traceability monitoring and analyzing method steps of the natural gas pipeline network disclosed in any embodiment are realized.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The steady state traceability monitoring and analyzing method, device, equipment and storage medium of the natural gas pipeline network provided by the invention are described in detail, and specific examples are applied to the explanation of the principle and the implementation mode of the invention, and the explanation of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. The steady-state traceability monitoring and analyzing method for the natural gas pipeline network is characterized by comprising the following steps of:

performing steady-state tracing monitoring analysis on the natural gas pipe network by using the gas source occupation ratio;

And calculating the error rate of the pipeline data in the steady-state simulation result to obtain the absolute flow balance error rate of the pipeline, wherein the error rate calculation comprises the following steps: calculating total inlet flow and total outlet flow of a pipeline according to the filtered steady-state simulation result, calculating error rates of the total inlet flow and the total outlet flow to obtain a flow balance error rate, and taking absolute values of the flow balance error rate to obtain an absolute flow balance error rate;

Calculating the total inlet flow and the total outlet flow of the pipeline according to the filtered steady-state simulation result, calculating the error rate of the total inlet flow and the total outlet flow to obtain a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate, wherein the method comprises the following steps: screening out pipeline data with an initial node number of an air source node and pipeline data with a terminal node number of a user node from the filtered steady-state simulation result, and respectively summing to obtain total inlet flow and total outlet flow of the pipeline; calculating a difference value between the total inlet flow and the total outlet flow, dividing the difference value by the total inlet flow to calculate a flow balance error rate, and taking an absolute value of the flow balance error rate to obtain an absolute flow balance error rate;

If the absolute flow balance error rate is not greater than the total in-total out deviation multiplying power, performing flow balance processing on the filtered pipeline data based on the current deviation multiplying power, if the absolute flow balance error rate is greater than the total in-total out deviation multiplying power, calculating the total out-total in-deviation multiplying power of the pipeline, and performing flow balance processing on the filtered pipeline data by utilizing the total out-total in-deviation multiplying power to obtain a target steady-state simulation result, wherein the method comprises the steps of: if the absolute flow balance error rate is not greater than the total input and total output deviation multiplying power, traversing the filtered pipeline data, and multiplying the sum of the current deviation multiplying power and 1 respectively to finish flow balance processing of the filtered pipeline data; if the absolute flow balance error rate is larger than the total inlet and outlet deviation multiplying power, calculating the total inlet deviation multiplying power according to the total inlet flow and the total outlet flow, traversing the filtered pipeline data, and multiplying the sum of the total inlet deviation multiplying power and 1 respectively to finish flow balance processing of the total inlet deviation multiplying power and obtain a target steady-state simulation result.

2. The method for steady-state traceability monitoring and analysis of a natural gas pipe network according to claim 1, wherein the steps of performing flow monitoring, pipe data filtering and error rate calculation on the pipe data in the steady-state simulation result to obtain an absolute flow balance error rate of the pipe comprise:

If the flow in the pipeline data is not less than 0, the pipeline data is directly filtered and deleted to obtain the steady-state simulation result after filtering.

3. The steady state traceability monitoring analysis method of a natural gas pipe network according to claim 1, wherein constructing a first matrix of a node continuity equation set by using the target steady state simulation result comprises:

4. The steady state traceability monitoring analysis method of a natural gas pipe network according to claim 1, wherein constructing a second matrix of a node continuity equation set by using the target steady state simulation result comprises:

5. The method for steady-state traceability monitoring and analysis of a natural gas pipe network according to any one of claims 1 to 4, wherein adding a plurality of minimum bias terms to the first matrix of node continuity equations to obtain a first matrix of target node continuity equations comprises:

6. Steady state traceability monitoring analysis device of natural gas pipe network, characterized by comprising:

The monitoring analysis module is used for carrying out steady-state traceability monitoring analysis on the natural gas pipe network by utilizing the gas source occupation ratio;

7. An electronic device, comprising:

a memory for storing a computer program;

a processor for executing the computer program to implement the steady state traceability monitoring analysis method of a natural gas pipe network according to any one of claims 1 to 5.

8. A computer-readable storage medium for storing a computer program; the steady state traceability monitoring and analyzing method of the natural gas pipe network according to any one of claims 1 to 5 is realized when the computer program is executed by a processor.