Natural gas consigner path optimization method based on operation planning optimization technology

Abstract

The invention relates to a natural gas consigner path optimization method based on an operation planning optimization technology, and the method comprises the steps: initializing node information and customer information, and configuring a natural gas supply amount and a natural gas demand amount of a current month; configuring various limiting parameters and carrying out rationality test; selecting constraint conditions, decision variables and a target function to construct a mixed integer programming model; and solving the mixed integer programming model, if there is a solution, obtaining an output result, and if there is no solution, reconfiguring limiting parameters. The method has the beneficial effects that on the premise that customer requirements are met, gas source supply is reasonably distributed and allocated, automatic matching of monthly transportation paths of a natural gas pipeline network and intelligent calculation of monthly gas transmission amount of each path are achieved, the working efficiency of business personnel is improved, and the operation cost of enterprises is reduced.

Description

Technical Field

[0001] The present invention relates to the field of natural gas transportation, and more specifically, to a natural gas consignor path optimization method based on operations optimization technology. Background Art

[0002] At present, the selection of pipeline transportation routes and transportation plans of natural gas shippers are basically manually formulated by business personnel based on actual experience. There are the following difficulties in manually matching pipeline transportation routes: First, it is difficult for manual analysis to consider the mutual influence between pipelines, and it is difficult to carry out global analysis and matching. When balancing the resource market and allocating the transmission volume, it is necessary to comprehensively consider the newly planned pipelines and the existing pipeline network, and achieve the balance of the entire network through a system analysis method. The current pipeline network planning work mainly focuses on the pipeline topology layout, and lacks the analysis of the impact of the planned pipeline layout on the system flow direction. Second, the scale of the pipeline network is huge, and the workload of global analysis is large. At present, the total mileage of natural gas pipelines in China has reached about 110,000 kilometers, and the scale of the pipeline network continues to increase. The workload of traditional manual statistics is getting larger and larger, which is difficult to meet the requirements of global analysis. Third, there are many upload points and download points, and the pipeline network topology is complex. Since the national natural gas pipeline network presents a complex topology structure with multiple inlets, multiple outlets, and multiple loops, it is necessary to comprehensively consider the impact of multiple facilities such as pipelines, LNG receiving stations, and gas storage facilities. However, the manual analysis method cannot consider too many influencing factors and restrictions, and it is difficult to analyze the complex pipeline network in detail. Fourth, the traditional manual formulation model relies on paper documents and manual operations, which is inefficient and prone to errors. Summary of the invention

[0003] The purpose of the present invention is to address the deficiencies of the prior art and propose a natural gas shipper path optimization method based on operations research optimization technology.

[0004] In the first aspect, a method for optimizing the path of a natural gas shipper based on operations research optimization technology is provided, comprising: Step 1, initialize node information and customer information, the nodes include supply points and demand points; and express the network topology relationship from supply points to demand points in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; Step 2: Allocate the natural gas supply for the month according to the natural gas supply plan submitted by each gas source, and allocate the natural gas demand for the month according to the natural gas demand submitted by each customer; Step 3: Configure various restriction parameters according to the actual situation of the month; Step 4: Perform a rationality check on the restriction parameters; Step 5: When the test is passed, select constraints, decision variables and objective functions to build a mixed integer programming model; Step 6: Solve the mixed integer programming model, obtain the output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

[0005] As a preference, it also includes: Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal path matching and gas transmission plan for reference by business personnel. Otherwise, reconfigure the restriction parameters.

[0006] Preferably, in step 3, pipeline gas transmission volume restriction, daily average target flow, pipeline optimal gas transmission volume, and demand point configurable path quantity restriction parameters are configured, and fixed path matching relationship and fixed path non-matching relationship are set.

[0007] Preferably, in step 4, the rationality check includes: the total gas supply of the month should be greater than or equal to the total customer demand; the upper limit of the path gas supply must be greater than the lower limit of the path gas supply; the manually set flow rate shall not exceed the path gas supply limit and shall not be greater than the supply of the corresponding supply point of the path; the paths in the fixed matching relationship and the fixed non-matching relationship cannot overlap.

[0008] Preferably, in step 5, the constraints include: path gas transmission volume restriction constraint, upper and lower limit constraints on the number of demand point matching paths, gas source coverage constraint, supply point supply volume balance constraint, customer demand volume satisfaction constraint, fixed path matching relationship constraint, fixed path mismatch relationship constraint, path and previous month consistency constraint, supply point exclusion relationship constraint, minimum flow / ratio constraint and daily average flow target value constraint.

[0009] Preferably, in step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: supply point Transport to the point of demand Gas volume , Supply Point and demand points The average daily traffic volume does not reach the daily traffic target value and supply points and demand points The path flow does not meet the value of the minimum flow requirement of the demand point ; The Boolean variables include and , Used to indicate supply points and demand points Is it connected? Used to indicate supply points and demand points Whether the connection relationship is inconsistent with last month.

[0010] Preferably, the optimization objectives corresponding to the objective function include: minimizing the weighted distance of gas transmission from the gas transmission point to the user, minimizing the value of the path flow after the path is activated that does not reach the daily flow target, minimizing the value of the path flow after the path is activated that does not reach the minimum flow requirement set at the demand point, minimizing the difference between the path matching relationship of this month and the path matching relationship of last month, and minimizing the proportion of the gas transmission volume of the adjustable gas source point to the demand of the demand point.

[0011] In a second aspect, a natural gas consignor path optimization system based on operations optimization technology is provided, which is used to execute any of the methods described in the first aspect, including: An initialization module is used to initialize node information and customer information, wherein the nodes include supply points and demand points; and the network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; The first configuration module is used to configure the natural gas supply volume of the month according to the natural gas supply plan submitted by each gas source, and configure the natural gas demand volume of the month according to the natural gas demand submitted by each customer; The first configuration module is used to configure various restriction parameters according to the actual situation of the month; A testing module, used to test the rationality of the restriction parameters; A construction module is used to select constraints, decision variables and objective functions to construct a mixed integer programming model when the test passes; A solution module is used to solve the mixed integer programming model, obtain an output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

[0012] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0013] In a fourth aspect, an electronic device is provided, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement any method as described in the first aspect.

[0014] The beneficial effects of the present invention are: 1. The present invention uses mixed integer programming modeling technology to establish an operations optimization model and uses a mathematical solver to solve it. It can comprehensively and fully consider the specific conditions of various paths, gas sources, upload points, download points and customers. On the premise of meeting customer needs, it can reasonably allocate and deploy gas supply, realize automatic matching of monthly transportation paths of the natural gas pipeline network and intelligent calculation of monthly gas transmission volume of each path, improve the work efficiency of business personnel, and reduce enterprise operating costs.

[0015] 2. The operations optimization model proposed in the present invention can maximize the flexibility and stability of the system by reasonably configuring the adjustable gas source, under the premise of ensuring that the upper and lower limits of the path transmission volume are met, the customer demand constraints are fully met, and the gas source supply is fully consumed. It can reduce the actual transportation cost by minimizing the weighted distance of the gas transmission of the transportation path, and reduce the actual scheduling operation cost by keeping the path matching relationship consistent with the previous month as much as possible. Finally, the optimal path matching plan is formulated and the gas transmission volume of each path is automatically calculated. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for optimizing the path of a natural gas shipper based on operations research optimization technology provided by an embodiment of the present invention; Figure 2 A framework diagram of an operations research optimization model provided by an embodiment of the present invention; Figure 3 A flow chart of another method for optimizing the path of a natural gas shipper based on operations research optimization technology provided by an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a natural gas shipper path optimization system based on operations research optimization technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0018] Embodiment 1: In order to solve the problems of the prior art, Example 1 of the present application provides a natural gas shipper path optimization method based on operations optimization technology. The purpose of this application is to determine the optimal path matching relationship between supply points and demand points and the monthly planned gas transmission volume based on data such as user monthly demand, monthly gas source supply, pipeline network topology and distance information, with the optimization goals of satisfying supply and demand balance, minimizing weighted gas transmission distance, and maximizing system flexibility and stability.

[0019] Specifically, Figure 1 As shown, a natural gas consignor path optimization method based on operations research optimization technology includes: Step 1, initialize node information and customer information, the nodes include supply points and demand points; and use the distance matrix to represent the network topology relationship from the supply point to the demand point; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer.

[0020] During pipeline network transportation, the natural gas supplied by the gas source point is transported to the pipeline system at the upload point. After being transported through the pipeline network, the natural gas is received from the pipeline system at the download point and delivered to the customer. The tuple consisting of the gas source and the upload point (gas source, upload point) is defined as the supply point, and the tuple consisting of the download point and the customer (customer, download point) is defined as the demand point. The relationship between the gas source and the upload point and the download point and the customer is a many-to-many relationship. The path refers to the connection relationship from the supply point to the demand point.

[0021] During initialization, it is necessary to obtain the basic information of the gas source, the basic information of the customer, the basic information of the download point, and the basic information of the upload point, and maintain the distance information between the upload and download points. The basic information includes the name, abbreviation, (customer) contact person and contact number, (gas source) whether it is an adjustable gas source, and other related information. The network topology relationship from the supply point to the demand point is represented in the form of a distance matrix. The matrix columns represent the supply points, the rows represent the demand points, and the matrix values ​​are the distances from the corresponding supply points to the demand points.

[0022] Step 2: Allocate the natural gas supply for the month according to the natural gas supply plan submitted by each gas source, and allocate the natural gas demand for the month according to the natural gas demand submitted by each customer.

[0023] Step 3: Configure various restriction parameters according to the actual situation of the month.

[0024] According to the actual situation of the month, configure parameters such as pipeline gas transmission volume limit, daily average target flow, optimal pipeline gas transmission volume, and the number of configurable paths for demand points, and set fixed path matching relationships and fixed path mismatching relationships. Based on actual business rules, this embodiment makes the following provisions for demand point categories: when the daily average demand of a demand point is less than or equal to 20, the lower limit of the number of configurable paths is set to 1; when the demand is greater than 20 and less than or equal to 50, the lower limit of the number of configurable paths is set to 2; when the demand is greater than 50, the lower limit of the number of configurable paths is set to 3. The lower limit of the daily average flow of the enabled path is set to 0.01 / day, and the expected daily flow target is set to 1 / day.

[0025] Step 4: Perform a rationality check on the restriction parameters.

[0026] Check whether all kinds of parameters are configured reasonably. For example, the total supply of gas source points in the month should be greater than or equal to the total demand of customers, otherwise the supply and demand balance cannot be achieved; the upper limit of the path gas transmission volume must be greater than the lower limit of the path gas transmission volume; the manually set flow rate must not exceed the path gas transmission volume limit and must not be greater than the supply volume of the corresponding supply point of the path; the paths in the fixed matching relationship and the fixed non-matching relationship cannot overlap, etc. If the parameter rationality test cannot be met, return to step 2.

[0027] Step 5: When the test is passed, Figure 2 As shown, constraints, decision variables and objective functions are selected to construct a mixed integer programming model.

[0028] The present invention mainly accepts structured data from different data sources for optimal path matching and gas transmission volume setting. The data used by the model mainly include: Natural gas demand: the natural gas demand of each natural gas customer for the month. When the model is actually used for path optimization, the demand actually reported by the natural gas customer is used here; when making plans in advance, the forecast results of the natural gas sales forecast model can be used here, or the business personnel can adjust according to the actual situation; Natural gas supply: the natural gas supply of each gas source point in the current month. When the model is actually used for path optimization, the supply actually reported by the gas source point is used here; when planning in advance, the business personnel can adjust this according to the actual situation; Basic information of natural gas procurement chain: including the name and type of gas source, customer, upload point, download point and other basic information; Natural gas procurement chain topology: including the topological network relationship between gas sources, customers, upload points, download points, and the actual distance between supply points and demand points; Natural gas pipeline network transportation capacity: including pipeline gas transmission volume restrictions, pipeline optimal gas transmission volume, configurable number of paths for demand points, and manually set path information that must be matched or disconnected.

[0029] The above information is abstracted into specific parameters to input into the model. Table 1 defines all the parameters used in the optimization model: Table 1 Model parameters

[0030] In addition, the output results of the present invention are the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume. These output results are defined as decision variables for model optimization. Table 2 defines all decision variables used in the optimization model: Table 2 Decision variables

[0031] The decision variables are the optimization objects of the model. This application designs appropriate decision variables to ensure that the model correctly outputs the path matching plan and the path gas transmission plan.

[0032] In descending order of priority, the optimization objectives of the operations research model used in the present invention include: (1) Minimize the weighted distance from the gas transmission point to the user (with the gas transmission volume as the weight).

[0033] (2) Once a path is activated, the path flow should reach the daily flow target as much as possible, that is, the value of the part of the path flow that does not reach the daily flow target after the path is activated is minimized.

[0034] (3) Once a path is enabled, the path flow should try to reach the minimum flow requirement set at the demand point, that is, minimize the value of the path flow that does not reach the minimum flow requirement set at the demand point after the path is enabled.

[0035] (4) The path matching relationship of this month is kept consistent with that of last month as much as possible, that is, the difference between the path matching relationship of this month and that of last month is minimized.

[0036] (5) Minimize the ratio of the gas supply volume at the adjustable gas source point to the gas demand at the demand point.

[0037] According to the above optimization objectives, the objective function of the optimization model is defined as:

[0038] Each term in the formula measures one of the above optimization objectives respectively. The overall optimization goal of the model is to find the appropriate decision variable value so that the value of the objective function reaches the minimum value under the premise of satisfying the constraints.

[0039] The design formula of the objective function measures indicators such as weighted gas transmission distance, system flexibility, and system stability. The model will output a solution that minimizes the value of the objective function while satisfying the constraints. In addition, the penalty coefficients of each item in the objective function are optimally set after repeated experimental tests. Appropriate penalty coefficients can enable the model to achieve better results.

[0040] All constraints involved in the natural gas shipper path optimization problem are defined as follows: (1) Path gas volume restriction constraint. If a supply point is matched with a demand point, the gas volume on the matching path must be greater than or equal to the minimum flow requirement of the path, and must be less than or equal to the demand of the user at the demand point; if a supply point is not matched with a demand point, the gas volume on the corresponding path is 0.

[0041]

[0042] (2) The upper and lower limits of the number of paths that the demand point matches. The demand points are divided into different categories according to the size of the demand, and different upper and lower limits of the number of paths are configured for different categories of demand points. The upper and lower limits of the number of paths that can be configured for the demand point d are and express.

[0043]

[0044] (3) Gas source coverage constraint, that is, to cover as many demand points as possible with adjustable gas sources. The specific rules are: for daily average demand greater than the minimum daily average demand limit of system flexibility For every demand point, there must be at least one supply point with an adjustable gas source connected to the demand point.

[0045]

[0046] (4) Supply quantity balance constraint of supply point. The sum of the quantity delivered from the supply point to each demand point should be equal to the supply quantity reported by the supply point.

[0047]

[0048] (5) Customer demand meets the constraint. The sum of the volumes delivered from each supply point to a demand point should be equal to the demand reported by the demand point, so that the customer demand is fully met.

[0049]

[0050] (6) Fixed path matching relationship constraint. This constraint is manually configured by business personnel to meet some special business requirements. Supply point and demand points The fixed matching relationship between the two specifies the demand points for this month. Part of the natural gas demand must be supplied by the supply point Provided, and the supply quantity is specified as .

[0051]

[0052] (7) Fixed path mismatch relationship constraint. This constraint is manually configured by business personnel to deal with special situations such as path maintenance. Supply point and demand points The fixed mismatch relationship between the two specifies the demand points for this month. The natural gas demand cannot be determined by the supply point Provided, the path between the two must be disconnected.

[0053]

[0054] (8) Path consistency constraint with the previous month. This constraint is used to define the decision variables , this decision variable is used in the objective function to measure the similarity between the path matching relationship of this month and the path matching relationship of last month. Trying to keep the path matching relationship consistent with that of last month and reducing adjustments and changes will help reduce the operating costs of the enterprise.

[0055]

[0056] (9) Supply point exclusion constraint. The same demand point can be matched with at most one supply point with which there is an exclusion relationship.

[0057]

[0058] (10) Minimum flow / ratio constraint. This constraint is used to define the flow value that does not meet the minimum flow requirement. , this decision variable is used in the objective function to measure the extent to which the path meets the minimum flow requirement.

[0059]

[0060] (11) Daily average flow target value constraint. Once the path is activated, the daily flow should be as close to the daily flow target value as possible.

[0061]

[0062] Various constraints ensure that the output solution of the model will not exceed various restrictions, ensuring the smooth operation of the natural gas supply chain.

[0063] Step 6: Solve the mixed integer programming model, obtain the output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

[0064] After determining the model parameters, decision variables, objective function, and constraints, a mixed integer programming model is established, and the mathematical programming solver COPT is called to optimize and solve the model to obtain the output results. If the model has no solution (overly demanding parameter settings may lead to no solution for the model), return to step 3 to reconfigure the parameters. Finally, the path matching plan for the month and the gas transmission plan for each path are output. The result of the model output is the value of the aforementioned decision variables. The specific gas transmission plan is also represented by the decision variables. For example, if the model output results show that the distance between the supply point s and the demand point d is If the value is 1, it means that in the plan for that month, the path from s to d needs to be matched, and the corresponding gas volume for that month is .

[0065] Embodiment 2: Based on Example 1, Example 2 of the present application provides a more specific method for optimizing the path of a natural gas consignor based on operations optimization technology. According to the supply and demand information actually obtained by a natural gas consignor in July 2024, based on the optimization model of the present invention, the path matching and gas transmission plan results for that month are generated, and compared and analyzed with the actual plan formulated by the business personnel.

[0066] Specifically, Figure 3 As shown, the method includes: Step 1, initialize node information and customer information, the nodes include supply points and demand points; and use the distance matrix to represent the network topology relationship from the supply point to the demand point; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer.

[0067] Step 2: Allocate the natural gas supply for the month according to the natural gas supply plan submitted by each gas source, and allocate the natural gas demand for the month according to the natural gas demand submitted by each customer.

[0068] Step 3: Configure various restriction parameters according to the actual situation of the month.

[0069] Step 4: Perform a rationality check on the restriction parameters.

[0070] Step 5: When the test passes, select constraints, decision variables and objective functions to build a mixed integer programming model.

[0071] Step 6: Solve the mixed integer programming model, obtain the output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

[0072] Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal path matching and gas transmission plan for reference by business personnel. Otherwise, reconfigure the restriction parameters.

[0073] In step 7, business requirements refer to special requirements that require confirmation by business personnel and are not universal. For example, the number of paths that some demand points need to match limits special requirements that are not specified in the model, such as the demand point must match XX paths.

[0074] In addition, the path matching and gas transmission scheme generated by the model are compared with the scheme manually formulated by business personnel based on experience. The comparison results are shown in Table 3. The total gas transmission weighted distance value and the flow value that does not reach the daily flow target are used to evaluate the model effect. Obviously, compared with the manually formulated scheme, the scheme generated by the model can reduce the total target value by 23.04% and the gas transmission weighted distance by 23.35%, which is a significant effect.

[0075] Table 3 Comparison results

[0076] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0077] Embodiment 3: Based on Example 1, Example 3 of the present application provides a natural gas consignor path optimization system based on operations optimization technology, including: An initialization module is used to initialize node information and customer information, wherein the nodes include supply points and demand points; and the network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; The first configuration module is used to configure the natural gas supply volume of the month according to the natural gas supply plan submitted by each gas source, and configure the natural gas demand volume of the month according to the natural gas demand submitted by each customer; The first configuration module is used to configure various restriction parameters according to the actual situation of the month; A testing module, used for testing the rationality of the limiting parameters; A construction module is used to select constraints, decision variables and objective functions to construct a mixed integer programming model when the test passes; A solution module is used to solve the mixed integer programming model, obtain an output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

[0078] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiment 1. Therefore, the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be repeated in this application.

Claims

1. A method for optimizing the path of natural gas shippers based on operations research optimization technology, characterized in that: include: Step 1: Initialize node information and customer information, where the node includes a supply point and a demand point; The network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; Step 2: Allocate the natural gas supply for the month according to the natural gas supply plan submitted by each gas source, and allocate the natural gas demand for the month according to the natural gas demand submitted by each customer; Step 3: Configure various restriction parameters according to the actual situation of the month; Step 4: Perform a rationality check on the restriction parameters; Step 5: When the test is passed, select constraints, decision variables and objective functions to build a mixed integer programming model; Step 6: Solve the mixed integer programming model, obtain the output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

2. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 1 is characterized in that: Also includes: Step 7: determine whether the output result meets the business requirements. If so, use the output result as the optimal path matching and gas transmission plan for reference by business personnel. Otherwise, reconfigure the restriction parameters.

3. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 2 is characterized in that: In step 3, configure the pipeline gas transmission volume limit, the average daily target flow, the optimal pipeline gas transmission volume, the number of configurable paths for the demand point, and set the fixed path matching relationship and the fixed path non-matching relationship.

4. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 3 is characterized in that: In step 4, the rationality check includes: the total gas supply of the month should be greater than or equal to the total customer demand; the upper limit of the path gas supply must be greater than the lower limit of the path gas supply; the manually set flow rate must not exceed the path gas supply limit and must not be greater than the supply of the corresponding supply point of the path; the paths in the fixed matching relationship and the fixed non-matching relationship cannot overlap.

5. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 4 is characterized in that: In step 5, the constraints include: path gas transmission volume restriction constraint, upper and lower limit constraints on the number of demand point matching paths, gas source coverage constraint, supply point supply volume balance constraint, customer demand satisfaction constraint, fixed path matching relationship constraint, fixed path mismatch relationship constraint, path and previous month consistency constraint, supply point exclusion relationship constraint, minimum flow / ratio constraint and daily average flow target value constraint.

6. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 5 is characterized in that: In step 5, the decision variables include continuous variables and Boolean variables; The continuous variables include: supply point Transport to the point of demand Gas volume , Supply Point and demand points The average daily traffic volume does not reach the daily traffic target value and supply points and demand points The path flow does not meet the value of the minimum flow requirement of the demand point ; The Boolean variables include and , Used to indicate supply points and demand points Is it connected? Used to indicate supply points and demand points Whether the connection relationship is inconsistent with last month.

7. The method for optimizing the natural gas consignor path based on operations research optimization technology according to claim 5 is characterized in that: In step 5, the optimization objectives corresponding to the objective function include: minimizing the weighted distance of gas transmission from the gas transmission point to the user, minimizing the value of the path flow after the path is enabled that does not reach the daily flow target, minimizing the value of the path flow after the path is enabled that does not reach the minimum flow requirement set at the demand point, minimizing the difference between the path matching relationship of this month and the path matching relationship of last month, and minimizing the proportion of the gas transmission volume of the adjustable gas source point to the demand of the demand point.

8. A natural gas consignor route optimization system based on operations research optimization technology, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: An initialization module is used to initialize node information and customer information, wherein the nodes include supply points and demand points; and the network topology relationship from the supply point to the demand point is represented in the form of a distance matrix; the supply point is a tuple consisting of a gas source and an upload point, and the demand point is a tuple consisting of a download point and a customer; The first configuration module is used to configure the natural gas supply volume of the month according to the natural gas supply plan submitted by each gas source, and configure the natural gas demand volume of the month according to the natural gas demand submitted by each customer; The first configuration module is used to configure various restriction parameters according to the actual situation of the month; A testing module, used to test the rationality of the restriction parameters; A construction module is used to select constraints, decision variables and objective functions to construct a mixed integer programming model when the test passes; A solution module is used to solve the mixed integer programming model, obtain an output result if there is a solution, and reconfigure the restriction parameters if there is no solution; the output result is the optimal path matching relationship between the supply point and the demand point and the optimal gas transmission volume.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 7.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 7.

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