Coal supply and demand balance scheduling method, electronic equipment and computer program product

Abstract

The invention discloses a coal supply and demand balance scheduling method, electronic equipment and a computer program product, and belongs to the technical field of resource planning, dispatching, distribution or scheduling, the coal supply and demand balance scheduling is carried out based on historical completion conditions and coal annual supply and demand plans, and the method comprises the following steps: obtaining coal supply chain management data, forming business related constraints and line combination constraints from the coal supply chain management data according to a coal annual supply and demand plan; and establishing an optimization model, performing calculation based on the optimization model, and generating a corresponding coal supply and demand balance scheduling plan scheme. According to the method, the coal supply chain management data is acquired, the optimization model is established in combination with the business constraint and the line combination constraint, the line screening conforming to the business logic is comprehensively considered, and the contract relation is mapped into the line transportation relation, so that the coal transportation constraint is more refined, and the fine leveling of the coal supply and demand balance plan scheme is realized.

Description

Technical Field

[0001] The invention relates to a coal supply and demand balance scheduling method, electronic equipment and computer program product, belonging to the technical field of resource planning, dispatching, allocation or scheduling. Background Art

[0002] The Chinese invention patent application with publication number CN115759578A provides a monthly plan preparation method, device and system that considers the full-process operation and management of the energy supply chain. The invention patent application can be briefly described as follows: the monthly plan preparation method includes obtaining the initial monthly plan declaration data of the subordinate units of each industrial sector for the next month, and the initial monthly plan declaration data is generated based on various plan data of the subordinate units of each industrial sector for the next month; analyzing and verifying all the initial monthly plan declaration data, screening out the monthly plan declaration data that do not meet the requirements, and obtaining the intermediate monthly plan declaration data; calculating the balance of each industrial sector; adjusting the intermediate monthly plan declaration data according to the balance of each industrial sector based on the business indicators and business indicator thresholds of each industrial sector, and forming a compiled monthly plan operation plan. The invention can improve the overall operational efficiency of the energy supply chain.

[0003] The defects of the above-mentioned Chinese invention patent application CN115759578A are: relying on monthly rolling updates to calculate the monthly coal plan for the next month, only using the data of the next month as input for leveling, without considering the impact of contracts signed at the beginning of the year and the completion of supply and demand in historical months of the year on subsequent plans, resulting in an incomplete monthly plan; the entire monthly plan leveling process is carried out according to the set business logic, including data verification, inter-sector adjustments, etc., and still requires a lot of manual comparison and judgment, and the degree of automated judgment is low; in the process of generating the monthly coal plan, there is no mention of the conflict priority of data verification and the adjustment and optimization goals of the plan, and it is impossible to compare and judge the advantages and disadvantages of different plans.

[0004] Through the analysis of the above-mentioned prior application documents, it can be seen that the existing technology can no longer meet people's requirements and is in urgent need of improvement. Summary of the invention

[0005] The purpose of the present invention is to provide a coal supply and demand balance scheduling method, electronic equipment and computer program product to solve the technical problems that the coal supply and demand translation scheduling method pays less attention to historical completion status and data conflict priority.

[0006] The present invention provides the following scheme:

[0007] A method for balancing coal supply and demand is provided, which balances coal supply and demand based on historical completion status and an annual coal supply and demand plan, and comprises:

[0008] Acquire coal supply chain management data, and transform the coal supply chain management data into business-related constraints and line combination constraints according to the annual coal supply and demand plan;

[0009] Establishing an optimization model, and setting corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the business-related constraints and line combination constraints;

[0010] Calculations are performed based on the optimization model to generate the corresponding coal supply and demand balance scheduling plan.

[0011] Furthermore, the coal supply chain management data includes: power plant demand planning data, historical monthly production demand completion data information, early year contract signing information, and contract priority weight information;

[0012] The business-related constraints include: node traffic flow direction constraints, total production demand plan constraints, and contract completion deviation constraints;

[0013] The line combination constraints include: the transportation routes in the supply relationship and the sum of the annual transportation volumes of all lines.

[0014] Furthermore, in the process of performing line combination constraints, there are one-to-many, one-to-one, many-to-one, and many-to-many contract relationship quantity inputs;

[0015] Perform route combination constraint operations on the contracts and supply volumes signed at the beginning of the year, convert the contractual relationship into the transportation route corresponding to the supply relationship, and form a transportation route constraint relationship;

[0016] The contract volume is converted into a constraint corresponding to the sum of the annual transportation volumes of all routes, forming a constraint relationship for the annual transportation volumes of all routes.

[0017] Furthermore, in the process of establishing the optimization model:

[0018] The set parameters include a set and a parameter, the set is specifically a set of identifiers of different entities involved in coal supply chain management, and the parameter is specifically a supply chain parameter for performing coal supply and demand balance scheduling in coal supply chain management;

[0019] The decision variables are specifically coal supply chain scheduling variables, which are used to provide multi-dimensional parameters of coal type, time, and route, as well as slack variables for processing constraint conditions;

[0020] Constructing a corresponding objective function according to the decision variables, wherein the objective function is used to minimize the transportation cost and the contract deviation penalty;

[0021] Based on the objective function, the corresponding transportation cost is calculated according to the product of the coal supply from the coal mine to the power plant on a specific route and the unit tonnage cost of the corresponding route;

[0022] The contract deviation penalty is obtained by multiplying the coefficients of all contracts and the corresponding slack variables and combining the penalty coefficient of contract completion deviation;

[0023] According to the transportation cost and contract deviation penalty, combined with the actual situation of contract deviation and contract priority, it is detected whether the coal production meets the contract requirements, the objective function is adjusted accordingly, and different penalty coefficients are set according to the coal production.

[0024] Furthermore, in the process of establishing the optimization model, the constraints include:

[0025] The coal mine flow balance constraint is used to obtain the coal production volume by summing the coal transportation output and spot volume of the coal mine in each month, satisfying the following formula:

[0026]

[0027] I is the coal mine number set, K is the coal type number set, CX i，k，t is the export volume of coal type k from coal mine i in month t, where: i∈I, k∈K i , t∈T;

[0028] FLOW i，j，k，t，r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I, j∈J i , k∈K i，j , t∈T, r∈R i，j ;

[0029] C i，k，t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T;

[0030] The power plant flow balance constraint is used to obtain the power plant coal demand in each month. The power plant coal demand is equal to the sum of the power plant's external procurement and coal transportation input, and satisfies the following formula:

[0031]

[0032] I is the set of coal mine numbers, K is the set of coal type numbers;

[0033] PX i，k，t is the amount of coal type k purchased by power plant j in month t, where j∈J, k∈K j , t∈T;

[0034] FLOW i，j，k，t，r is the supply of coal type k from coal mine i to power plant j on line r in month t;

[0035] P j，k，t is the actual demand for coal type k at power plant j in month t;

[0036] The monthly output fluctuation constraint of coal mines is used to characterize the fluctuation of coal mine output around the standard production capacity and satisfies the following formula:

[0037]

[0038] I is the coal mine number set, K is the coal type number set, and T is the future optimization month number set;

[0039] is the fluctuation coefficient of the total production of coal mine in month i;

[0040] TC i，k，t is the standard output of coal mine i in month t, or the output that has been completed;

[0041] C i，k，t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T.

[0042] Furthermore, in the process of establishing the optimization model, the constraints include:

[0043] The annual output fluctuation constraint of coal mines is used to constrain the annual total output of coal mines to fluctuate around the standard production capacity and satisfy the following formula:

[0044]

[0045] I is the coal mine number set, K is the coal type number set, T is the future optimization month number set, T past is a collection of past month numbers;

[0046] is the fluctuation coefficient of the total production of the coal mine in year i;

[0047] TC i，k is the standard total output of coal mine i in the whole year;

[0048] is the actual production of coal type k by coal mine i in the past month t of this year;

[0049] C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T;

[0050] The annual demand fluctuation constraint of the power plant is used to constrain the total annual coal demand of the power plant to fluctuate around the standard demand, satisfying the following formula:

[0051]

[0052] J is the set of power plant numbers, K is the set of coal type numbers, T past is the set of past month numbers, and T is the set of future optimized month numbers;

[0053] is the fluctuation coefficient of the total amount of power plant received in year j;

[0054] is the demand satisfaction of coal type k by power plant j in the past month t of this year;

[0055] P j，k，t is the actual demand for coal type k at power plant j in month t, where j∈J, k∈K j , t∈T;

[0056] The contract deviation relaxation constraint is used to characterize the completion of the contract and satisfies the following formula:

[0057]

[0058] K is the set of coal type numbers, and C is the set of contract numbers;

[0059] Cons c,k is the signed supply of contract c;

[0060] slack c，k is the slack variable for constraint breakthrough;

[0061] is the supply completion volume of contract c in the past month;

[0062] FLOW i，j，k，t，r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I, j∈J i , k∈K i，j ,t∈T,r∈R i，j .

[0063] A coal supply and demand balance scheduling system, used to implement the coal supply and demand balance scheduling method, comprising:

[0064] A coal supply chain management data acquisition module is used to acquire coal supply chain management data and transform the coal supply chain management data into business-related constraints and line combination constraints according to the annual coal supply and demand plan;

[0065] An optimization model building module is used to build an optimization model, and set corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the business-related constraints and line combination constraints;

[0066] The coal supply and demand balance scheduling plan generation module is used to calculate based on the optimization model and generate the corresponding coal supply and demand balance scheduling plan.

[0067] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0068] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0069] A computer program product comprises a computer program, and when the computer program is executed by a processor, the method described is implemented.

[0070] Compared with the prior art, the present invention has the following advantages:

[0071] The present invention acquires coal supply chain management data, establishes an optimization model in combination with business constraints and line combination constraints, comprehensively considers line screening that conforms to business logic, maps contractual relationships into line transportation relationships, and makes coal transportation constraints more refined, thereby achieving fine-tuning of the coal supply and demand balance plan.

[0072] The present invention fully considers the completion status of coal supply and demand in historical months, as well as the weight priority of the contract, and while generating the monthly coal plan, comprehensively considers the business-related constraints and line combination constraints of the node, constrains the annual total production plan of the supply node and the annual total demand plan of the demand node to remain unchanged, complies with the business management rules of coal supply and demand and the actual production index capabilities of the supplier, and ensures that under the premise of deviations in contract completion, a monthly coal supply and demand plan with a smaller violation cost is obtained according to the input weight priority, reflecting the potential value of intelligent decision-making.

[0073] The present invention calculates the corresponding transportation cost by using the objective function, obtains the contract deviation penalty by combining the contract coefficient and the slack variable with the penalty coefficient of the contract completion deviation, and then makes corresponding adjustments to the objective function, and sets different penalty coefficients according to the coal output, so that business personnel can use the slack variables of the contract with automatically obtained result output to judge the expected completion status of important contracts and make timely supply and demand adjustments to the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 It is a system architecture diagram of the coal supply and demand balance debugging method in a specific application scenario.

[0076] Figure 2 It is a flow chart of the coal supply and demand balance debugging method.

[0077] Figure 2A It is a flow chart of the optimization technical solution from step S21 to step S26.

[0078] Figure 3 This is the architecture diagram of the coal supply and demand balance debugging system.

[0079] Figure 4 Specific implementation methods of some embodiments of the present invention.

[0080] Figure 5 It is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0081] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In the current coal supply chain management, coal enterprises are usually faced with complex supply and demand balance issues. The traditional method of preparing coal monthly plans mainly relies on forecast data for the next month, while ignoring the long-term contracts signed at the beginning of the year and the actual supply and demand completion in historical months, which often leads to incomplete monthly plans and cannot accurately reflect the actual operation of the entire supply chain. Due to the lack of consideration of historical data, enterprises cannot make full use of existing supply and demand information when formulating monthly plans, which may lead to unreasonable and inefficient resource allocation.

[0083] In addition, the existing monthly plan leveling process is highly dependent on manual operations and has a low degree of automation. Steps such as data verification and inter-sector adjustment require a lot of manual comparison and judgment, which not only increases the workload, but also reduces the accuracy and response speed of decision-making. In a rapidly changing market environment, processes that rely solely on manual operations are difficult to meet the company's needs for flexibility and agility. Therefore, there is an urgent need for a method that can automate and intelligently process monthly plans to improve decision-making efficiency and accuracy.

[0084] In view of the above problems, the present invention proposes a method for balancing the supply and demand of coal, which improves the integrity and accuracy of the monthly plan by comprehensively considering the contract plan at the beginning of the year and the supply and demand completion of historical months, as well as through automated data acquisition and optimization model algorithms. The scope of application of the present invention includes but is not limited to the supply chain management department, production planning department and sales department of coal enterprises. Through the technical solution of the present invention, coal plants, power plants and other supply chain participants in the coal supply chain can more accurately predict and plan the supply and demand of coal, optimize resource allocation, and improve the overall efficiency of the supply chain. Through automated data processing and intelligent optimization algorithms, manual intervention is reduced, the error rate is reduced, and the decision-making speed is accelerated, so that enterprises can adapt to market changes more quickly and improve their competitiveness.

[0085] like Figure 1 As shown, in Figure 1 The application scenario shown comprehensively applies methods such as operations research, mathematical programming, mathematical modeling, and solvers to achieve supply and demand balance scheduling of the coal production and marketing system.

[0086] The coal supply and demand balance debugging method involves coal supply chain management data 100. By obtaining the coal supply chain management data 100, business-related constraints 101 and line combination constraints 102 are formed according to the annual coal supply and demand plan, and the business-related constraints 101 and line combination constraints 102 are input into the optimization model 103. The corresponding set parameters, decision variables, objective functions and constraints are set in the optimization model 103 to generate a coal supply and demand balance scheduling plan 104. Figure 1This is the basic application scenario of the present invention. Based on this basic application scenario, some other specific implementations of the present invention can be expanded. For example, the coal supply chain management data 100 can be coal mine production data, power plant demand data, contract data, historical supply and demand data, transportation data, coal type data, etc. Business-related constraints 101 can be production constraints, demand constraints, inventory constraints, transportation constraints, and contract completion constraints, etc. Line combination constraints 102 can be route availability constraints, contract route constraints, supply relationship constraints between coal mines and power plants, etc. Configuration items in the optimization model 103 can be set parameters, decision variables, objective functions, and constraints. The final generated coal supply and demand balance scheduling plan 104 can include monthly production plans, monthly supply plans, monthly demand plans, transportation plans, contract completion status, and so on. In Figure 1 The basic application scenario shown provides a method for balancing coal supply and demand that takes multiple factors into consideration. By collecting coal supply chain management data and optimizing it using an optimization model, the supply efficiency and responsiveness of the coal supply chain are improved, providing basic information for subsequent supply and demand planning and decision-making, ensuring the feasibility of the coal supply and demand balancing scheduling plan, and determining the rationality and predictability of contract performance.

[0087] In order to better understand the present invention, multiple embodiments are listed in the specification of the present invention, and the following theoretical concepts may be involved in different embodiments:

[0088] Operations research: Its main purpose is to provide scientific basis for personnel when making decisions. It is one of the important methods to achieve effective management, correct decision-making and modern management. This discipline is an interdisciplinary study of applied mathematics and formal science, using statistics, mathematical models and algorithms to find the best or approximately the best solution to complex problems. Operations research is often used to solve complex problems in real life, especially to improve or optimize the efficiency of existing systems. The basic knowledge of operations research includes real analysis, matrix theory, stochastic processes, discrete mathematics and algorithm foundations. In terms of application, operations research is mostly related to warehousing, logistics, algorithms and other fields. Therefore, operations research is also closely related to applied mathematics, industrial engineering, computer science, economic management and other majors.

[0089] Mathematical programming: the problem of finding the best solution according to a certain measurement index while meeting the requirements of a given goal. The requirements of the given goal that must be met are called constraints, and the measurement index is called the objective function. To describe it in mathematical language is to find the extreme value of the objective function under certain constraints.

[0090] Mathematical modeling: Establish a mathematical model based on actual problems, solve the mathematical model, and then solve the actual problem based on the results. When it is necessary to analyze and study a practical problem from a quantitative perspective, it is necessary to establish a mathematical model based on in-depth investigation and research, understanding the object information, making simplified assumptions, analyzing the internal laws, etc., using mathematical symbols and language to express it.

[0091] Solver: is a software tool used to solve operations research and optimization problems. These solvers are widely used in various fields, including logistics management, production planning, resource allocation, transportation planning, financial modeling, etc. They help users find the best solution to a problem, usually maximizing or minimizing an objective function while satisfying a set of constraints.

[0092] Coal transportation: refers to the transportation of qualified coal to destinations, including ports, power plants and chemical plants, by rail, road, coastal and inland waterway after it is mined. Coal transportation includes belt, rail, water and road, or single mode direct transportation, or railway, road and water multimodal transportation.

[0093] Coal supply and demand balance scheduling: refers to a series of management mechanisms in the coal production and marketing system that adjusts transportation plans in a timely manner according to the balance between downstream market demand and coal supply capacity to ensure the supply and demand balance of the coal production and marketing system and to ensure the efficient, stable and safe operation of the coal production and marketing system.

[0094] like Figure 2 The coal supply and demand balance scheduling method shown in the figure performs a balanced scheduling of coal supply and demand based on the historical completion status and the annual coal supply and demand plan, including:

[0095] Step S1, obtaining coal supply chain management data, and forming business-related constraints and line combination constraints from the coal supply chain management data according to the annual coal supply and demand plan.

[0096] The channels for obtaining coal supply chain management data in step S1 can be internal databases (production data and contract data, for example: production data obtained when sensors deployed in coal mines and power plants and their automation equipment perform real-time production monitoring), market industry reports, regulatory agency data, logistics platforms, trading platforms, etc. It can also be data provided by third-party data service providers, data provided by supply chain partners, etc.

[0097] The annual coal supply and demand plan in step S1 can be a monthly plan or a full-year production plan and a full-year demand plan for power plants. The monthly plan refers to a plan that fully considers the supply and demand completion status of historical months and the supply relationship and supply volume of contracts signed at the beginning of the year, the full-year production plan of comprehensive coal mines, and the full-year demand plan of power plants. The plan is implemented and generated under the premise of minimizing transportation costs and minimizing the completion deviation rate of contracts at the beginning of the year that take weight priorities into consideration. The coal supply and demand are adjusted and scheduled using operations research knowledge combined with mathematical models to achieve and generate a monthly coal plan.

[0098] Step S2, establishing an optimization model, and setting corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the service-related constraints and line combination constraints.

[0099] Step S3, performing calculations based on the optimization model to generate a corresponding coal supply and demand balance scheduling plan.

[0100] The optimization technical solution provided in steps S1 to S3 provides an intelligent coal supply and demand balance scheduling method, forms corresponding constraints through the precise collection of coal supply chain management data, and then realizes efficient utilization and optimized use of the coal supply chain through the application of the optimization model, thereby improving the accuracy of the coal supply and demand balance scheduling plan and its adaptability to future changes, reducing manual intervention and improving the automation and intelligence of the coal supply and demand balance scheduling plan, realizing the optimal allocation of coal resources, minimizing related line transportation costs and contract deviations, providing business personnel with automated result output, helping to adjust the corresponding supply and demand relationship in a timely manner, and increasing decision-making support capabilities.

[0101] In step S1, the coal supply chain management data includes: power plant demand planning data, historical monthly production demand completion data information, contract signing information at the beginning of the year, and contract priority weight information, wherein the historical monthly production demand completion data information is usually the historical monthly production demand completion data information of coal mines and power plants.

[0102] The business-related constraints include: node traffic flow direction constraints, total production demand plan constraints, and contract completion deviation constraints. The node traffic flow direction constraints are usually node traffic flow direction constraints of coal mines and power plants, and the total production demand plan constraints are usually total production demand plan constraints of coal mines and power plants combined with historical months.

[0103] The line combination constraints include: the transportation routes in the supply relationship and the sum of the annual transportation volumes of all lines, wherein the transportation route refers to the transportation route obtained by converting the contractual relationship into the supply relationship, and the sum of the annual transportation volumes of all lines is obtained by converting the contractual volume. By converting the contracts signed at the beginning of the year and the supply volume into line combination constraints, that is, converting the contractual volume into a constraint corresponding to the sum of the annual transportation volumes of all lines, a constraint relationship for the annual transportation volumes of all lines is formed. In the process of finally forming the line combination constraints, there are four types of contractual relationship volume inputs: one-to-many, one-to-one, many-to-one, and many-to-many.

[0104] Acquiring coal supply chain management data and forming business-related constraints and line combination constraints in step S1 are the key to ensuring that the subsequent optimization model can accurately reflect the actual situation and generate an effective scheduling plan. The coal supply chain management data may include power plant demand plans, historical production demand completion data, contract signing information at the beginning of the year, and contract priority weights. The above data provides basic information for the optimization model, ensuring that the input data of the optimization model is comprehensive and has historical continuity. The specific requirements of business operations and the influence of historical data are reflected through business-related constraints, ensuring the feasibility of the plan and the fulfillment of the contract. The line combination constraints are formed by converting the contract relationship into the transportation route obtained by the supply relationship, and the sum of the annual transportation volume converted from the contract volume. Under the premise of fully considering the input of different contract relationship quantities, the accuracy of the transportation plan and the proper fulfillment of the contract are ensured, the transportation route and cost are optimized, and the efficiency of resource allocation is improved, so that the subsequent optimization model can flexibly respond to different business scenarios and improve the responsiveness of the supply chain to market changes.

[0105] like Figure 2A As shown, in the process of establishing the optimization model in step S2, it further includes:

[0106] Step S21, the set parameters include a set and a parameter, the set is specifically a set of identifiers of different entities involved in coal supply chain management, and the parameters are specifically supply chain parameters for coal supply and demand balance scheduling in coal supply chain management.

[0107] Step S22, the decision variables are specifically coal supply chain scheduling variables, which are used to provide multi-dimensional parameters of coal type, time, and route, as well as slack variables for processing constraint conditions. Among them, the coal supply chain scheduling variables further include: coal type supply, actual coal mine output, coal mine export volume, actual demand of power plants, and external procurement volume of power plants.

[0108] Step S23, constructing a corresponding objective function according to the decision variables, wherein the objective function is used to minimize the transportation cost Cost and the contract deviation penalty Penalty.

[0109] Step S24, based on the objective function, the corresponding transportation cost Cost is calculated according to the product of the coal supply from the coal mine to the power plant on a specific route and the unit ton cost of the corresponding route.

[0110] Step S25, according to the product of the coefficients of all contracts and the corresponding slack variables, combined with the penalty coefficient of contract completion deviation, the contract deviation penalty Penalty is obtained.

[0111] Step S26, based on the transportation cost and the contract deviation penalty, combined with the actual situation of the contract deviation and the contract priority, detect whether the coal production meets the contract requirements, make corresponding adjustments to the objective function, and set different penalty coefficients according to the coal production. In step S26, detect whether the coal production meets the contract requirements, that is, detect the deviation between the contract execution and the expectation, make corresponding adjustments to the objective function according to the deviation between the contract execution and the expectation, and then set different penalty coefficients according to the coal production.

[0112] The optimization technical solution provided in steps S21 to S26 describes how to achieve efficient management of the coal supply chain by establishing an optimization model in the coal supply and demand balance scheduling method, clarifies the identification set of different entities involved in coal supply chain management and the supply chain parameters required for coal supply and demand balance scheduling, defines coal supply chain scheduling variables, provides multi-dimensional parameters of coal type, time, and route, and slack variables for processing constraints, and constructs an objective function that minimizes transportation cost Cost and contract deviation penalty Penalty, calculates transportation cost Cost according to specific activities of the coal supply chain, and calculates contract deviation penalty Penalty according to the completion status and priority of the contract, adjusts the objective function according to whether the coal output meets the contract requirements, and sets different penalty coefficients to reflect the actual situation of contract execution.

[0113] In the optimization technical solution provided in steps S21 to S26, the objective function degenerates into minimizing the transportation cost, which means that the objective function is approximately equivalent to minimizing the contract deviation after considering the weight, which means that the objective function is simplified to a simpler form. When all contracts have no deviation, that is, the actual execution is completely consistent with the contract provisions, the penalty term for contract deviation will be zero, because there is no deviation that needs to be punished. If all contracts have no deviation, the objective function degenerates into minimizing only the transportation cost; if the coal production and demand cannot fully meet all contract requirements, and there is a deviation between the contract execution and the expectation, then by setting a larger penalty coefficient, combined with the input contract weight coefficient, the objective function is approximately equivalent to minimizing the contract deviation after considering the weight. In actual work, business personnel can use the optimization technical solution provided in steps S1 to S26 to automatically obtain the slack variables of the contract output, judge the expected completion of important contracts, and make timely adjustments to the supply and demand of the business.

[0114] The optimization technical solution provided in step S21 to step S26 enables the optimization model to adapt to different business scenarios and market changes by considering contract deviations and priorities, thereby improving the flexibility and adaptability of the supply chain. When all contracts are met, the optimization model can be simplified to only minimize transportation costs, thereby improving cost-effectiveness. When contracts cannot be fully met, by introducing penalty coefficients, the model can optimize contract deviations and ensure the fulfillment of key contracts. Business personnel can use this method to automatically obtain contract slack variables, quickly determine the expected completion of contracts, and make timely supply and demand adjustments, thereby improving the efficiency and accuracy of decision-making. By introducing contract deviation penalties and priorities, the optimization model is used to help enterprises better manage supply chain risks and achieve optimization of coal supply chain management.

[0115] In the process of establishing the optimization model in step S2, the constraints include:

[0116] The coal mine flow balance constraint is used to obtain the coal production volume by summing the coal transportation output and spot volume of the coal mine in each month, satisfying the following formula:

[0117]

[0118] I is the set of coal mine numbers, K is the set of coal type numbers, CX i,k,t is the export volume of coal type k from coal mine i in month t, where: i∈I, k∈K i , t∈T;

[0119] FLOW i，j，k，t，r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I, j∈J i , k∈K i，j , t∈T, r∈Ri，j ;

[0120] C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T;

[0121] The power plant flow balance constraint is used to obtain the power plant coal demand in each month. The power plant coal demand is equal to the sum of the power plant's external procurement and coal transportation input, and satisfies the following formula:

[0122]

[0123] I is the set of coal mine numbers, K is the set of coal type numbers;

[0124] PX i,k,t is the amount of coal type k purchased by power plant j in month t, where j∈J, k∈K j , t∈T;

[0125] FLOW i，j，k，t，r is the supply of coal type k from coal mine i to power plant j on line r in month t;

[0126] P j，k，t is the actual demand for type k of coal at power plant j in month t;

[0127] The monthly output fluctuation constraint of coal mines is used to characterize the fluctuation of coal mine output around the standard production capacity and satisfies the following formula:

[0128]

[0129] I is the coal mine number set, K is the coal type number set, and T is the future optimization month number set;

[0130] is the fluctuation coefficient of the total production of the coal mine in month i;

[0131] TC i,k,t is the standard output of coal mine i in month t, or: the output that has been completed;

[0132] C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T.

[0133] The annual output fluctuation constraint of coal mines fully considers the actual coal mine output in historical months and is used to constrain the annual total output of coal mines to fluctuate around the standard production capacity, satisfying the following formula:

[0134]

[0135] I is the coal mine number set, K is the coal type number set, T is the future optimization month number set, T past is a collection of past month numbers;

[0136] is the fluctuation coefficient of the total production of the coal mine in year i;

[0137] TC i，k is the standard total output of coal mine i in the whole year;

[0138] is the actual production of coal type k by coal mine i in the past month t of this year;

[0139] C i，k，t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T;

[0140] The annual demand fluctuation constraint of the power plant fully considers the actual demand of the power plant in historical months, and is used to constrain the total annual coal demand of the power plant to fluctuate around the standard demand, satisfying the following formula:

[0141]

[0142] J is the set of power plant numbers, K is the set of coal type numbers, T past is the set of past month numbers, and T is the set of future optimized month numbers;

[0143] is the fluctuation coefficient of the total amount of power plant received in year j;

[0144] is the demand satisfaction of coal type k by power plant j in the past month t of this year;

[0145] P j，k，t is the actual demand for coal type k at power plant j in month t, where j∈J, k∈K j , t∈T;

[0146] The contract deviation relaxation constraint is used to characterize the completion of the contract. Specifically, the contract completion volume of the historical month converts the contracts signed at the beginning of the year into the sum of the line transportation volume, which satisfies the following formula:

[0147]

[0148] K is the set of coal type numbers, and C is the set of contract numbers;

[0149] Cons c，k is the signed supply of contract c;

[0150] slack c，k is the slack variable for constraint breakthrough;

[0151] is the supply completion volume of contract c in the past month;

[0152] FLOW i，j，k，t，r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I, j∈J i , k∈K i，j , t∈T, r∈R i，j .

[0153] Through further limiting the constraints in the process of establishing the optimization model, it can be seen that in the process of establishing the optimization model of the coal supply and demand balance scheduling plan, a series of constraints are used to ensure the practical applicability of the optimization model and the feasibility of the scheduling plan. The above constraints cover the key links of the coal supply chain, including the flow balance of coal mines and power plants, the fluctuation of production and demand, and the completion of contracts. By limiting and constraining multiple constraints, the flow balance of coal mines and power plants is ensured, and the balance between the production of coal mines and the demand of power plants in each month is ensured, which conforms to the basic operation mechanism of the coal supply chain, and further considers the changes in production and demand of coal mines and power plants on a monthly and annual time scale, allowing certain fluctuations around standard production capacity or standard demand to adapt to the uncertainty of actual production and demand. The contracts signed at the beginning of the year are converted into the sum of line transportation volumes to characterize and adjust the completion of contracts, allowing for certain flexibility in the execution of contracts when necessary.

[0154] Through the above-mentioned multiple constraints, the balance between supply and demand in the coal supply chain is ensured, resource waste or supply shortage is avoided, the optimization model can adapt to the changes in actual production and demand, and the flexibility and adaptability of the scheduling plan are improved. The production and demand data of historical months are fully considered, making the optimization model closer to reality, improving the accuracy of the scheduling plan, and providing a certain flexibility for the performance of the contract through slack, while ensuring that key contracts can be executed first, enhancing the reliability of the supply chain. The reasonable configuration of multiple constraints provides stability and flexibility, ensuring that key contracts can be executed first, enhancing the reliability of the supply chain, improving the rationality of coal resource configuration, and achieving the refinement of coal supply chain management.

[0155] like Figure 3 The coal supply and demand translation debugging system shown is used to implement the coal supply and demand balance scheduling method described in any one of the embodiments of the present invention, including:

[0156] The coal supply chain management data acquisition module is used to acquire coal supply chain management data and form business-related constraints and line combination constraints from the coal supply chain management data according to the annual coal supply and demand plan.

[0157] The optimization model establishment module is used to establish the optimization model and set corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the business-related constraints and line combination constraints.

[0158] The coal supply and demand balance scheduling plan generation module is used to calculate based on the optimization model and generate the corresponding coal supply and demand balance scheduling plan.

[0159] The implementation methods of the system described above are merely illustrative, for example, the various functional modules, units or subsystems in the system may or may not be physically separated, or may or may not be physical units, that is, they may be located in the same place, or they may be distributed to multiple different systems and their subsystems or modules. Those skilled in the art may select some or all of the functional modules, units or subsystems according to actual needs to achieve the purpose of the embodiments of the present invention, and those of ordinary skill in the art may understand and implement the above-mentioned situations without creative work.

[0160] like Figure 4 Some specific implementation methods of the present invention are shown. The embodiments of the present invention are based on the coal supply and demand requirements and take into account the supply and demand completion status of historical months, as well as the supply relationship and supply quantity of the contract signed at the beginning of the year, and the overall annual production plan of the coal mine and the overall annual demand plan of the power plant remain unchanged. In order to minimize the transportation cost and minimize the completion deviation rate of the contract at the beginning of the year taking into account the weight priority, the operations optimization knowledge is used to construct a mixed integer programming model to adjust and schedule the coal supply and demand, so as to realize the generation of the coal monthly plan.

[0161] The specific implementation steps of the embodiment of the present invention are:

[0162] Step 1, obtain input data: obtain relevant input data such as coal mine production plan data, power plant demand plan data, historical monthly production demand completion data information of coal mines and power plants, contract signing information at the beginning of the year, contract priority weight information, etc.

[0163] Step 2: Define relevant constraints: Conduct business research to determine business-related constraints that take into account historical completion: node flow direction constraints between coal mines and power plants, total production demand plan constraints for coal mines and power plants based on historical months, contract completion deviation constraints, etc.

[0164] Step 3: Line combination constraint conversion: For the contracts and supply quantities signed at the beginning of the year, the line combination constraint module is used to convert the contractual relationship into the transportation routes involved in the supply relationship, and the contract quantity is converted into the constraint corresponding to the sum of the annual transportation quantities of all routes. This line combination constraint module supports four types of contractual relationship quantity inputs: one-to-many, one-to-one, many-to-one, and many-to-many. The example table is as follows:

[0165] Contract ID Supplier ID Demander ID Contract volume Contract 1 Coal Mine A Power Plant A 10 Contract 2 Coal Mine A & Coal Mine B Power Plant A 20 Contract 3 Coal Mine B Power Plant A & Power Plant B 30 Contract 4 Coal Mine A & Coal Mine B Power Plant A & Power Plant B 40

[0166] A combination constraint module is used to read the supplier and demander fields of each contract, and search for historical supply routes in the road network or generate new possible supply routes. The routes that do not meet the contract requirements or business logic are removed with reference to the special notes of the contract, and a route combination constraint table is generated. The table structure and result samples are as follows:

[0167] Contract ID Contract volume Line ID Contract 1 10 Line 1 Contract 1 10 Route 2 Contract 2 20 Line 3 Contract 3 30 Line 4

[0168] The route combination constraint table finally generated by this module provides data support for the contract completion deviation constraint of the model. The constraints on the specified contract are completed by summing up the transportation volume of all routes under the contract number.

[0169] Step 4: Establish an optimization model:

[0170] a) Collection parameters. The relevant collection parameters are shown in the following table:

[0171]

[0172]

[0173]

[0174] b) Decision variables. The decision variables are shown in the following table:

[0175]

[0176]

[0177] c) Objective function: While realizing the decomposition of the monthly coal supply and demand plan, it minimizes the transportation cost and the contract deviation after considering the weighted sum. The mathematical expression of the objective function is as follows:

[0178] obj=min(Cost+Penlity)……(1)

[0179]

[0180]

[0181] If all contracts have no deviation, the objective function here degenerates into minimizing the transportation cost; if the coal production and demand cannot fully meet all the contract requirements, and the contract execution deviates from the expectation, then by setting a larger penalty coefficient and combining the input contract weight coefficient, the objective function is approximately equivalent to minimizing the contract deviation after considering the weight. Business personnel can automatically obtain the slack amount of the contract output, judge the expected completion status of important contracts, and make timely adjustments to the supply and demand of the business.

[0182] d) Constraints:

[0183] i. Coal mine flow balance constraints:

[0184] In each month, the sum of the coal transportation output and spot volume of the coal mine is equal to the coal production of the coal mine, which satisfies the following formula:

[0185]

[0186] ii. Power plant flow balance constraints

[0187] In each month, the coal demand of the power plant is equal to the sum of the external coal purchase of the power plant and the coal transportation input, which satisfies the following formula:

[0188]

[0189] iii. Monthly coal mine output fluctuation constraint: Every month, the coal mine output fluctuates around the standard production capacity, specifically satisfying the following formula:

[0190]

[0191] iv. Annual coal mine output fluctuation constraint: Considering the actual coal mine output in historical months, the total annual production of coal mines is constrained to fluctuate around the standard production capacity, specifically satisfying the following formula:

[0192]

[0193] v. Constraints on annual power plant demand fluctuations: Considering the actual power plant demand in historical months, constrain the total annual coal demand of the power plant to fluctuate around the standard demand, specifically satisfying the following formula:

[0194]

[0195] v i. Contract deviation relaxation constraint: For contracts signed at the beginning of the year, the line combination constraint module is used to convert them into the sum of the line transportation volume. Combined with the contract completion volume of historical months, the contract relaxation is used to describe the completion status of each contract, which specifically satisfies the following formula:

[0196]

[0197] The above-mentioned implementation mode of the present invention addresses the problem of less reference to historical completion status when generating the monthly plan for coal supply and demand scheduling, considers the completion status of production demand in historical months, limits fluctuations in production demand in future months, and incorporates the impact of the past on the future into the constraints of the optimization model, so that the optimization results of the optimization model are closer to actual business scenarios. The implementation mode of the present invention solves the problem of less consideration of data conflict priority in monthly plan data verification, can comprehensively consider the contract weight priority, constrain and limit contract completion deviations based on the contract weight priority, consider violations of lower weighted contracts, and generate a coal monthly plan.

[0198] like Figure 5 As shown, the present invention not only provides a method and system for balancing the supply and demand of coal, but also provides corresponding electronic equipment, storage media and computer program products:

[0199] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method steps of any embodiment of the present invention.

[0200] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the method steps of any embodiment of the present invention.

[0201] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the method of any embodiment of the present invention is implemented.

[0202] A structural schematic diagram of an electronic device is provided. The electronic device provided in an embodiment of the present invention includes: one or more processors 710 and a storage device 720; the processor 710 in the electronic device can be one or more, and one processor 710 is taken as an example in the figure; the storage device 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, so that the one or more processors 710 implement the coal supply and demand balancing scheduling method as described in any one of the embodiments of the present invention.

[0203] The electronic device may further include: an input device 730 and an output device 740 .

[0204] The processor 710, storage device 720, input device 730 and output device 740 in the electronic device may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0205] The storage device 720 in the electronic device is a computer-readable storage medium that can be used to store one or more programs, which can be software programs, computer executable programs, and modules, such as program instructions / modules corresponding to the coal supply and demand balance scheduling method provided in the embodiment of the present invention. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the storage device 720, that is, the coal supply and demand balance scheduling method in the above method embodiment is implemented.

[0206] The storage device 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the storage device 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the storage device 720 may further include a memory remotely arranged relative to the processor 710, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0207] The input device 730 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. The output device 740 may include a display device such as a display screen.

[0208] Furthermore, when one or more programs included in the electronic device are executed by the one or more processors 710, the programs execute the coal supply and demand balance scheduling method instructions of steps S1 to S3 in the embodiment of the present invention.

[0209] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, any one of the embodiments claimed in the claims can be used in any combination in the embodiments of the present invention.

[0210] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0211] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0212] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including corresponding claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise explicitly stated, each feature disclosed in this specification (including corresponding claims, abstracts and drawings) can be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for balancing coal supply and demand, characterized in that: include: Acquire coal supply chain management data, and transform the coal supply chain management data into business-related constraints and line combination constraints according to the annual coal supply and demand plan; Establishing an optimization model, and setting corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the business-related constraints and line combination constraints; Calculations are performed based on the optimization model to generate the corresponding coal supply and demand balance scheduling plan.

2. The method for balancing coal supply and demand according to claim 1, characterized in that: The coal supply chain management data includes: power plant demand planning data, historical monthly production demand completion data information, early year contract signing information, and contract priority weight information; The business-related constraints include: node traffic flow direction constraints, total production demand plan constraints, and contract completion deviation constraints; The line combination constraints include: the transportation routes in the supply relationship and the sum of the annual transportation volumes of all lines.

3. The method for balancing coal supply and demand according to claim 2, characterized in that: In the process of line combination constraint, there are one-to-many, one-to-one, many-to-one, and many-to-many contract relationship quantity inputs; Perform route combination constraint operations on the contracts and supply volumes signed at the beginning of the year, convert the contractual relationship into the transportation route corresponding to the supply relationship, and form a transportation route constraint relationship; The contract volume is converted into a constraint corresponding to the sum of the annual transportation volumes of all routes, forming a constraint relationship for the annual transportation volumes of all routes.

4. The method for balancing coal supply and demand according to claim 1, characterized in that: In the process of building an optimization model: The set parameters include a set and a parameter, the set is specifically a set of identifiers of different entities involved in coal supply chain management, and the parameter is specifically a supply chain parameter for performing coal supply and demand balance scheduling in coal supply chain management; The decision variables are specifically coal supply chain scheduling variables, which are used to provide multi-dimensional parameters of coal type, time, and route, as well as slack variables for processing constraint conditions; Constructing a corresponding objective function according to the decision variables, wherein the objective function is used to minimize the transportation cost and the contract deviation penalty; Based on the objective function, the corresponding transportation cost is calculated according to the product of the coal supply from the coal mine to the power plant on a specific route and the unit tonnage cost of the corresponding route; The contract deviation penalty is obtained by multiplying the coefficients of all contracts and the corresponding slack variables and combining the penalty coefficient of contract completion deviation; According to the transportation cost and contract deviation penalty, combined with the actual situation of contract deviation and contract priority, it is detected whether the coal production meets the contract requirements, the objective function is adjusted accordingly, and different penalty coefficients are set according to the coal production.

5. The method for balancing coal supply and demand according to claim 1, characterized in that: In the process of establishing the optimization model, the constraints include: The coal mine flow balance constraint is used to obtain the coal production volume by summing the coal transportation output and spot volume of the coal mine in each month, satisfying the following formula: I is the coal mine number set, K is the coal type number set, CX i,k,t is the export volume of coal type k from coal mine i in month t, where: i∈I, k∈K i , t∈T; FLOW i，j，k，t，r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I,j∈J i ,k∈K i,j ,t∈T,r∈R i,j ; C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T; The power plant flow balance constraint is used to obtain the power plant coal demand in each month. The power plant coal demand is equal to the sum of the power plant's external procurement and coal transportation input, and satisfies the following formula: I is the set of coal mine numbers, K is the set of coal type numbers; PX i,k,t is the amount of coal type k purchased by power plant j in month t, where j∈J,k∈K j ,t∈T; FLOW i,j,k,t,r is the supply of coal type k from coal mine i to power plant j on line r in month t; P j,k,t is the actual demand for type k of coal at power plant j in month t; The monthly output fluctuation constraint of coal mines is used to characterize the fluctuation of coal mine output around the standard production capacity and satisfies the following formula: I is the coal mine number set, K is the coal type number set, and T is the future optimization month number set; is the fluctuation coefficient of the total production of the coal mine in month i; TC i,k,k is the standard output of coal mine i in month t, or the output that has been completed; C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I, k∈K i , t∈T.

6. The method for balancing coal supply and demand according to claim 1, characterized in that: In the process of establishing the optimization model, the constraints include: The annual output fluctuation constraint of coal mines is used to constrain the annual total output of coal mines to fluctuate around the standard production capacity and satisfy the following formula: I is the coal mine number set, K is the coal type number set, T is the future optimization month number set, T past is a collection of past month numbers; is the fluctuation coefficient of the total production of the coal mine in year i; TC i,k is the standard total output of coal mine i in the whole year; is the actual production of coal type k by coal mine i in the past month t of this year; C i,k,t is the actual output of coal type k in coal mine i in month t, where i∈I,k∈K i ,t∈T; The annual demand fluctuation constraint of the power plant is used to constrain the total annual coal demand of the power plant to fluctuate around the standard demand, satisfying the following formula: J is the set of power plant numbers, K is the set of coal type numbers, T past is the set of past month numbers, and T is the set of future optimized month numbers; is the fluctuation coefficient of the total amount of power plant received in year j; is the demand satisfaction of coal type k by power plant j in the past month t of this year; P j,k,t is the actual demand for coal type k at power plant j in month t, where j∈J,k∈K j ,t∈T; The contract deviation relaxation constraint is used to characterize the completion of the contract and satisfies the following formula: K is the set of coal type numbers, and C is the set of contract numbers; Cons c,k is the signed supply of contract c; slack c,k is the slack variable for constraint breakthrough; is the supply completion volume of contract c in the past month; FLOW i,j,k,t,r is the amount of coal type k supplied by coal mine i to power plant j on line r in month t, where i∈I, j∈J i , k∈K i，j , t∈T, r∈R i，j .

7. A coal supply and demand balance scheduling system, used to implement the coal supply and demand balance scheduling method according to any one of claims 1 to 6, characterized in that: include: A coal supply chain management data acquisition module is used to acquire coal supply chain management data and transform the coal supply chain management data into business-related constraints and line combination constraints according to the annual coal supply and demand plan; An optimization model building module is used to build an optimization model, and set corresponding set parameters, decision variables, objective functions and constraints in the optimization model according to the business-related constraints and line combination constraints; The coal supply and demand balance scheduling plan generation module is used to calculate based on the optimization model and generate the corresponding coal supply and demand balance scheduling plan.

8. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: It stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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