Coal purchasing and allocation method and device for thermal power enterprises

Abstract

The application discloses a coal purchasing and allocation method for thermal power enterprises. The method comprises the following steps: acquiring multi-source heterogeneous data, wherein the multi-source heterogeneous data comprises at least one index data; pre-processing the multi-source heterogeneous data to obtain pre-processed multi-source heterogeneous data, wherein the pre-processed multi-source heterogeneous data comprises at least one pre-processed index data; determining a plurality of hierarchical early warning events according to the at least one pre-processed index data, wherein the plurality of hierarchical early warning events comprises a stock early warning event; determining a preliminary regional thermal power enterprise purchasing plan according to the at least one pre-processed index data, the stock early warning event and a purchasing demand prediction model; determining a final regional thermal power enterprise purchasing plan according to the preliminary regional thermal power enterprise purchasing plan and a purchasing decision model; and allocating the final regional thermal power enterprise purchasing plan according to a multi-target optimization allocation model to determine a purchasing plan of each thermal power plant under a regional thermal power enterprise. The application reduces the purchasing cost and optimizes the stock structure.

Description

Technical Field

[0001] This invention belongs to the field of fuel management and information technology for thermal power plants, and specifically relates to a method and apparatus for coal procurement and allocation in thermal power plants. Background Technology

[0002] Thermal power generation is a core pillar of my country's power supply. Fuel costs (mainly coal) typically account for over 70% of the total power generation costs of thermal power plants, and in some high-coal-consuming plants, this figure can reach 80%. Therefore, the sophistication of fuel management and the scientific nature of decision-making directly determine the economic benefits and market competitiveness of thermal power enterprises (hereinafter referred to as thermal power companies), and are also related to the stability and security of regional power supply. Currently, most thermal power companies in my country (especially those with multiple thermal power plants under regional power generation companies) still face many prominent technical problems in fuel management due to limitations in technical architecture and management models. These problems are as follows: 1) Data fragmentation and information silos are prominent: Each subordinate thermal power plant independently deploys its fuel management system, and the system suppliers, data standards, and data collection methods are inconsistent across different plants. This results in core data such as procurement contract data, fuel inventory data (inventory quantity, coal type, and storage location), incoming coal quality testing data (key indicators such as calorific value, sulfur content, ash content, moisture content, and volatile matter), unit coal consumption data, and coal quality testing data being stored in the local systems of each power plant, making real-time uploading, sharing, and synchronization impossible. As the overall management entity, the regional power generation company struggles to grasp real-time global information such as actual fuel consumption, inventory status, and coal quality compliance at each power plant. This leads to a lack of unified, comprehensive, and real-time data support for procurement decisions and dispatching, easily resulting in chaotic procurement practices where each power plant operates independently, hindering the coordinated and optimized allocation of regional resources. 2) Procurement and transportation decisions rely heavily on human experience and lack scientific rigor: Currently, most regional power generation companies and their subordinate thermal power plants rely primarily on the human experience of relevant management personnel for core decisions such as fuel procurement planning, coal source selection, and transportation allocation. There is a lack of systematic integration, analysis, and quantitative calculation of multi-source data. Specifically, the decision-making process fails to fully consider the interconnected impact of multiple dimensions of data, including market price fluctuations (such as differences between pithead prices, port prices, long-term contract prices, and market prices), the inventory structure of each power plant (coal type suitability, inventory margin), changes in unit load demand, transportation capacity (matching of road, rail, and waterway capacity, transportation costs), and supplier fulfillment capabilities. This leads to a disconnect between procurement plans and actual demand, resulting in either excessively high procurement costs (e.g., failure to capture market low-price windows in a timely manner) or an unreasonable inventory structure (e.g., stockpiling of certain coal types or shortages of certain suitable coal types), and even redundant cross-regional transportation, increasing transportation costs and wasting resources. 3) Lack of dynamic early warning and closed-loop control mechanisms, resulting in delayed risk response: Throughout the fuel management process, the existing management model lacks standardized and automated hierarchical response and closed-loop handling procedures after events such as abnormal inventory (e.g., below the safety stock level or above the reasonable stock limit), abnormal coal quality (e.g., the quality of coal delivered to the plant deviates significantly from the contract), abnormal supplier performance (e.g., failure to deliver according to plan or substandard quality of delivered goods), and abnormal transportation occur. In most cases, abnormalities must be detected manually, reported level by level, and manually approved and handled, leading to delayed response. This not only easily causes coal shortages and shutdowns (due to failure to promptly warn and handle situations where inventory is below the safety stock threshold) and fuel stockpiling that ties up capital (due to failure to promptly allocate funds when inventory exceeds the upper limit), but may also lead to decreased unit operating efficiency, excessive environmental emissions, and even uncontrolled costs due to the failure to promptly address abnormal coal quality.

[0003] In summary, the current fuel management model of thermal power plants suffers from numerous technical pain points, such as lack of data sharing, unscientific decision-making, and lack of closed-loop control, which cannot meet the needs of regional power generation companies for intensive and intelligent management of fuel in their subordinate thermal power plants. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide a method and apparatus for coal procurement and allocation in thermal power plants.

[0005] In a first aspect, embodiments of the present invention provide a method for coal procurement and allocation in thermal power plants, comprising: Acquire multi-source heterogeneous data, wherein the multi-source heterogeneous data includes at least one indicator data; The multi-source heterogeneous data is preprocessed to obtain preprocessed multi-source heterogeneous data, which includes at least one preprocessed index data. Based on the at least one preprocessed indicator data and the corresponding at least one indicator warning threshold, several graded warning events are determined, including inventory warning events. Based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model, a preliminary regional thermal power enterprise procurement plan is determined. Based on the preliminary regional thermal power enterprise procurement plan and procurement decision model, the final regional thermal power enterprise procurement plan is determined. Based on the multi-objective optimization allocation model, the final regional-level thermal power enterprise procurement plan is allocated to determine the procurement plans of each thermal power plant under the regional-level thermal power enterprise.

[0006] In one possible implementation, the levels of the aforementioned tiered early warning events include system strict control, company approval, and escalation control.

[0007] In one possible implementation, determining several graded early warning events based on the at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold includes: Based on the preprocessed inventory data and the corresponding inventory threshold, the inventory warning event is determined and the level of the inventory warning event is determined to be system strong control. The inventory threshold is the sum of the first safety inventory threshold and the buffer inventory.

[0008] In one possible implementation, determining the inventory warning event and classifying the level of the inventory warning event as a system-wide strict control based on the preprocessed inventory data and the corresponding inventory threshold includes: The preprocessed inventory data is determined using the following formula: I represents the preprocessed inventory data of regional thermal power enterprises in cycle t+1. t D represents the initial coal inventory of regional thermal power enterprises in period t. t C represents the amount of coal delivered to regional thermal power plants during cycle t. t This represents the coal consumption of regional thermal power plants during cycle t. The inventory threshold is determined by the following formula: R represents the inventory threshold, which is the value of the preprocessed inventory data. When inventory levels drop to or below the inventory threshold R, a purchase requisition is triggered. L represents the purchase lead time, which is the average number of days from order placement to warehousing, and A represents the average daily consumption. represents buffer stock, and S represents the first safety stock threshold. When the preprocessed inventory data Less than or equal to the corresponding inventory threshold When this occurs, an inventory warning event is triggered, and the level of the inventory warning event is determined to be system strict control.

[0009] In one possible implementation, the at least one preprocessed indicator data includes the unit price of the i-th type of coal in period t. And the sum of transportation costs and warehousing costs ; The step of determining a preliminary regional-level thermal power enterprise procurement plan based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model includes: When an inventory warning event is triggered, the predicted purchase quantity of type i coal in period t is determined by minimizing the first summation result according to the formula of the following procurement demand forecasting model. : The first summation result is the sum of the purchase cost, transportation cost, and storage cost of the i-th type of coal in period t. The summation result, Let represent the unit price of the i-th type of coal in period t. Let Z represent the sum of transportation and storage costs for the i-th type of coal in period t, and let Z represent the first summation result. This represents the predicted purchase quantity of the i-th type of coal in period t.

[0010] In one possible implementation, determining the final regional-level thermal power enterprise procurement plan based on the preliminary regional-level thermal power enterprise procurement plan and procurement decision model includes: Based on the formula of the procurement decision model, while minimizing the second summation result, the preliminary regional-level thermal power enterprise procurement plan is adjusted to determine the actual procurement quantity of the i-th type of coal in period t. Inventory at the end of period t : The second summation result is the sum of the purchase cost and inventory holding cost of the i-th type of coal in period t. The summation result, This represents the second summation result, where T represents the periodic set and C represents the coal type set. Let represent the unit price of the i-th type of coal in period t. This represents the actual quantity of type i coal purchased in period t. Represents the unit inventory holding cost. This represents the inventory level at the end of period t.

[0011] In one possible implementation, the step of allocating the final regional-level thermal power enterprise procurement plan according to the multi-objective optimization allocation model, and determining the procurement plans of each thermal power plant under the regional-level thermal power enterprise, includes: Based on the formula of the multi-objective optimization allocation model, the final regional-level thermal power enterprise procurement plan is allocated. Under the conditions of minimizing the third summation result, maximizing the total inventory safety margin, and maximizing the overall coal quality compatibility, the procurement quantity of the i-th type of coal purchased from coal source k in period t, and transported to power plant j via path l and carrier m, is determined. and the coal inventory of power plant j at the end of period t. : The third summation result is the sum of the procurement cost, transportation cost, and storage cost of the i-th type of coal in period t. This represents the third summation result. This represents the pithead / FOB unit price of purchasing the i-th type of coal from coal source point k in period t. This represents the unit transportation cost of transporting type i coal from coal source k through path l and carrier m to power plant j in period t. This represents the quantity of type i coal purchased from coal source k in period t, transported via path l and carrier m to power plant j. This represents the unit inventory holding cost of power plant j; in, Represents the total inventory safety margin. This represents the coal inventory of power plant j at the end of period t. This represents the second safety stock threshold for power plant j in period t; Introducing auxiliary variables ≥0 and constraints ≥ - Then the goal becomes: in, Represents overall coal quality compatibility. This represents the net calorific value of the i-th type of coal on a received basis. This represents the design calorific value of the coal used in power plant unit J. improve Maximize the environmental and economic benefits of coal quality: in, and They are respectively , The weighting coefficients, , The weighted average sulfur and ash content of the coal delivered to power plant j in period t are used as intermediate variables and defined using linear constraints: in, This represents the sulfur content of the i-th type of coal. The ash content represents the i-th type of coal; Overall objective function: Where α, β, γ are weighting coefficients, and Norm1, Norm2, Norm3 are normalization factors.

[0012] Secondly, embodiments of the present invention provide a coal procurement and allocation device for thermal power plants, comprising: The data acquisition module is used to acquire multi-source heterogeneous data, which includes at least one indicator data. The data preprocessing module is used to preprocess the multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data, wherein the preprocessed multi-source heterogeneous data includes at least one preprocessed indicator data. The early warning event determination module is used to determine a number of graded early warning events based on the at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold, wherein the number of graded early warning events includes inventory early warning events. The initial procurement plan determination module is used to determine the initial regional thermal power enterprise procurement plan based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model. The final procurement plan determination module is used to determine the final regional thermal power enterprise procurement plan based on the preliminary regional thermal power enterprise procurement plan and procurement decision model. The procurement plan determination module for each thermal power plant is used to allocate the final regional thermal power enterprise procurement plan based on a multi-objective optimization allocation model, and to determine the procurement plans of each thermal power plant under the regional thermal power enterprise.

[0013] Thirdly, embodiments of the present invention provide an electronic device, comprising: The system includes a memory and a processor, which communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the steps of the method described in the second aspect and various possible implementations.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect and various possible implementations.

[0015] Fifthly, embodiments of the present invention provide a computer program product containing instructions that, when the computer program product is run on a computer, cause the steps of the method described in the first aspect and various possible implementations to be executed by the computer.

[0016] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: 1) Significantly reduced procurement costs: Through multi-source data fusion analysis, the window of coal market price fluctuations can be captured in real time. Combined with procurement demand forecasting models, procurement decision-making models, and multi-objective optimization allocation models, the overall regional procurement costs can be minimized. At the same time, through unified centralized procurement, the bargaining power with suppliers can be improved, and the unit price of procurement can be reduced. In addition, transportation routes can be optimized to reduce cross-regional transportation redundancy and reduce transportation costs. 2) Optimized inventory structure: The dynamic inventory early warning mechanism can monitor the inventory status of each power plant in real time, trigger early warnings and initiate disposal procedures in a timely manner, effectively avoiding coal shortage shutdowns and ensuring the safe and stable operation of the units; at the same time, by optimizing procurement plans and allocation schemes, fuel inventory backlog has been reduced and capital occupation has been reduced. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a method for coal procurement and allocation in a thermal power plant, provided as an embodiment of the present invention; Figure 2 A schematic block diagram of a coal procurement and allocation device for a thermal power plant, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if monitoring (the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when monitoring (the stated condition or event)," or "in response to monitoring (the stated condition or event)."

[0022] This invention provides a method for coal procurement and allocation in thermal power plants. A flowchart of this method is shown below. Figure 1 As shown. Figure 1 The method may include the following steps: Step 101: Obtain multi-source heterogeneous data, which includes at least one indicator data.

[0023] Step 102: Preprocess the multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data, which includes at least one preprocessed indicator data.

[0024] Step 103: Based on at least one preprocessed indicator data and the corresponding at least one indicator warning threshold, determine several graded warning events, including inventory warning events.

[0025] Step 104: Based on at least one preprocessed indicator data, inventory warning events, and procurement demand forecasting models, determine the preliminary regional thermal power enterprise procurement plan.

[0026] Step 105: Based on the preliminary regional thermal power enterprise procurement plan and procurement decision model, determine the final regional thermal power enterprise procurement plan.

[0027] Step 106: Based on the multi-objective optimization allocation model, allocate the final regional-level thermal power enterprise procurement plan and determine the procurement plans of each thermal power plant under the regional-level thermal power enterprise.

[0028] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments of the present invention.

[0029] First, the above step 101, namely "acquiring multi-source heterogeneous data, wherein the multi-source heterogeneous data includes at least one indicator data", will be described in detail with reference to the embodiments of the present invention.

[0030] In this embodiment of the invention, multi-source heterogeneous data is acquired. This multi-source heterogeneous data includes power plant-side data (including but not limited to real-time fuel inventory data (inventory quantity, coal type, storage location, inventory turnover days) collected by interfacing with existing fuel management systems, unit monitoring systems, and coal quality testing systems of various thermal power plants), incoming coal quality testing data (key indicators such as calorific value, sulfur content, ash content, moisture, volatile matter, and ash fusion point), furnace-feeding coal quality data, unit load plans (daily load, weekly load, monthly load), historical coal consumption data (daily coal consumption, weekly coal consumption, monthly coal consumption, proportion of coal consumption for different coal types), and unit maintenance plans (affecting coal demand)). Market-side data includes real-time coal market price information (pithead price, port price, long-term contract price, market price, and price fluctuation trends) collected by accessing third-party data sources such as coal trading platforms, logistics and transportation platforms, and meteorological departments. This includes data on coal production capacity, transportation capacity (supply of road, rail, and waterway transport capacity, transportation costs, and transportation cycles), meteorological information (weather conditions affecting transportation such as rainfall, snowfall, and high temperatures), supplier-side data (including but not limited to performance data of each coal supplier collected through the supplier management system, such as delivery timeliness, delivery quality compliance rate, and contract fulfillment rate), coal source information (coal type, production capacity, and geographical location), qualification information, quotation information, and after-sales service information), and transportation-side data (including but not limited to equipping vehicles performing transportation tasks with vehicle-mounted positioning terminals (supporting GPS / BeiDou dual-mode positioning) to collect real-time data such as vehicle location, driving trajectory, driving speed, parking time, and loading / unloading status, while also collecting auxiliary data such as carrier information, transportation route information, and loading / unloading time).

[0031] The aforementioned at least one indicator data includes, but is not limited to, the various indicator data in the power plant-side data, the various indicator data in the market-side data, the various indicator data in the supplier-side data, and the various indicator data in the transportation-side data.

[0032] For example, the data collection process automatically starts from 00:00 to 02:00 every day. Through interface integration, it collects fuel inventory data (inventory quantity, coal type, and storage location) from each thermal power plant at the end of the previous day, the daily unit load plan, and the weather forecast for the next three days (focusing on weather conditions affecting transportation such as rain, snow, and fog). It also collects pithead and port price trends from major coal-producing regions (such as Shanxi, Shaanxi, and Inner Mongolia) and the supply of road and rail transport capacity through coal trading and logistics platforms. Furthermore, it collects recent contract performance data and pricing information from each supplier through the supplier management system, and real-time location and trajectory data of all vehicles en route through vehicle positioning terminals. After collection, the data is automatically cleaned, transformed, and linked to a unified database to ensure accuracy and timeliness.

[0033] The following describes step 102, namely, "preprocessing multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data, wherein the preprocessed multi-source heterogeneous data includes at least one preprocessed index data", in conjunction with an embodiment of the present invention.

[0034] In this embodiment of the invention, the collected multi-source heterogeneous data is cleaned (abnormal data and missing data are removed), transformed (data format is unified and indicator caliber is unified), and correlated (relationships between different data sources are established, such as the correlation between coal source points and transportation routes, and between power plant inventory and coal consumption demand), and finally a unified fuel management database is formed, providing standardized data support for subsequent procurement forecasting, allocation optimization and risk control.

[0035] The following describes in detail step 103, namely, "determining several graded early warning events based on at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold, wherein the several graded early warning events include inventory early warning events," in conjunction with embodiments of the present invention.

[0036] In this embodiment of the invention, several graded early warning events can be determined based on at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold. The several graded early warning events include inventory early warning events.

[0037] As one possible implementation, based on the preprocessed inventory data and the corresponding inventory threshold, an inventory warning event is determined and its level is set as strong system control. The inventory threshold is the sum of the first safety stock threshold and the buffer stock.

[0038] Understandably, the 49 types of management events that may occur throughout the fuel management process (covering inventory anomalies, coal quality anomalies, supplier performance anomalies, transportation anomalies, testing anomalies, etc.) are categorized into three types—"System-Controlled," "Company-Approved," and "Escalated Control"—based on their risk level and scope of impact. The triggering conditions, approval processes, handling timelines, and responsible parties for each level of event are clearly defined. System-wide strict control (4 items): mainly for high-risk and high-impact events (such as inventory falling below the inventory threshold or coal quality seriously exceeding standards), the system will automatically trigger mandatory control measures (such as suspending the purchase of the coal source or emergency transportation of fuel), without the need for manual approval, ensuring rapid response; Company approval (3 items): mainly for medium-risk events (such as adjustments to procurement plans, early warnings of abnormal supplier performance), which require approval from the relevant departments of the regional company before the disposal measures can be implemented; Upgraded control (42 items): mainly for general risk events (such as vehicles slightly deviating from the route, inventory approaching the upper limit), which are handled by the power plant or relevant responsible departments. The handling results are reported to the system for record-keeping to ensure that the events are traceable.

[0039] Through standardized, hierarchical, and closed-loop management, various incidents can be handled in a standardized and automated manner, shortening the handling time and reducing management risks.

[0040] As one possible implementation, determining inventory warning events and their level as mandatory system control based on preprocessed inventory data and corresponding inventory thresholds can be achieved through the following steps: Step 1: The pre-processed inventory data is determined using the following formula: I represents the preprocessed inventory data of regional thermal power enterprises in cycle t+1. t D represents the initial coal inventory of regional thermal power enterprises in period t. t C represents the amount of coal delivered to regional thermal power plants during cycle t. t This represents the coal consumption of regional thermal power plants during cycle t. Step two, the inventory threshold is determined using the following formula: R represents the inventory threshold, which is the value of the preprocessed inventory data. When inventory levels drop to or below the inventory threshold R, a purchase requisition is triggered. L represents the purchase lead time, which is the average number of days from order placement to warehousing, and A represents the average daily consumption. represents buffer stock, and S represents the first safety stock threshold. Step 3, when the pre-processed inventory data Less than or equal to the corresponding inventory threshold When this occurs, an inventory warning event is triggered, and the level of the inventory warning event is determined to be system-wide strict control.

[0041] The following describes in detail step 104, namely, "determining a preliminary regional thermal power enterprise procurement plan based on at least one preprocessed indicator data, inventory early warning event, and procurement demand forecasting model," in conjunction with an embodiment of the present invention.

[0042] In this embodiment of the invention, when an inventory warning event occurs, a preliminary regional thermal power enterprise procurement plan can be determined based on at least one preprocessed indicator data and a procurement demand prediction model.

[0043] As one possible implementation, at least one preprocessed indicator data includes the unit price of the i-th type of coal in period t. And the sum of transportation costs and warehousing costs ; Based on at least one pre-processed indicator data, inventory warning events, and a procurement demand forecasting model, the preliminary regional-level thermal power enterprise procurement plan can be determined through the following steps: When an inventory warning event is triggered, the predicted purchase quantity of type i coal in period t is determined by minimizing the first summation result according to the formula of the following procurement demand forecasting model. : The first summation result is the sum of the purchase cost, transportation cost, and storage cost of the i-th type of coal in period t. The summation result, Let represent the unit price of the i-th type of coal in period t. Let Z represent the sum of transportation and storage costs for the i-th type of coal in period t, and let Z represent the first summation result. This represents the predicted purchase quantity of the i-th type of coal in period t. According to... The predicted total purchase volume for all coal types in period t can be determined. The constraints include: Calorific value constraint Where, x' it V represents the predicted purchase quantity of the i-th type of coal in period t. i This indicates the received lower heating value (kcal / kg) of coal type i. V min V max These represent the minimum and maximum permissible calorific value limits for the boiler, respectively.

[0044] Sulfur content / environmental constraints Among them, S i S represents the received sulfur content (%) of coal type i. max The maximum sulfur content allowed by environmental regulations (usually stricter than the design limit). Inventory capacity constraints Where I0 represents the beginning inventory, C t I represents the amount of coal consumed in period t. min ,I max represents the minimum / maximum inventory capacity, respectively, and k represents the cumulative capacity up to the kth period.

[0045] Purchase quantity upper and lower limits constraints Among them, L it Indicates the minimum purchase quantity, U it This indicates the maximum purchase quantity.

[0046] The following describes step 105, namely, "determining the final regional thermal power enterprise procurement plan based on the preliminary regional thermal power enterprise procurement plan and procurement decision model," in conjunction with embodiments of the present invention.

[0047] As one possible implementation, based on the formula of the procurement decision model, the preliminary regional-level thermal power enterprise procurement plan is adjusted to determine the actual procurement quantity of the i-th type of coal in period t, while minimizing the second summation result. Inventory at the end of period t : The second summation result is the sum of the purchase cost and inventory holding cost of the i-th type of coal in period t. The summation result, This represents the second summation result, where T represents the periodic set and C represents the coal type set. Let represent the unit price of the i-th type of coal in period t. This represents the actual quantity of type i coal purchased in period t. Represents the unit inventory holding cost. This represents the inventory level at the end of period t. The constraints include: Inventory balance constraints (core dynamic link): Where Inv_0 = Inv_Init(known beginning inventory (tons)). Here it is assumed that procurement and consumption are completed within the same cycle, but in practice this can be adjusted according to the lead time. This represents the ending inventory level (decision variable) for period t. y represents the ending inventory of period t-1 (known or the previous period's decision variable). i,t Let C represent the quantity of coal type i consumed in period t, C represent the set of all coal types, and T represent the set of periods.

[0048] Demands satisfy constraints: Physical meaning: The total amount of coal consumed in each cycle must equal the power generation demand. t This represents the total coal demand forecast for period t (from a procurement demand forecasting model). After determining the purchase quantity of each type of coal in period t, add them together to obtain the total demand.

[0049] Inventory capacity constraints: Physical meaning: The inventory level must be between the minimum and maximum capacity of the coal yard. This indicates the minimum safety stock (to prevent coal shortages). This indicates the maximum physical inventory (coal yard capacity).

[0050] Coal blending constraints: Calorific value constraint: Physical meaning: The weighted average calorific value of the coal fed into the boiler must be within the safe and economical operating range of the boiler. and These represent the minimum and maximum values ​​of the weighted average calorific value of the coal fed into the furnace, respectively.

[0051] Environmental constraints: Physical significance: To control SO2 emissions to meet standards.

[0052] Purchase quantity constraints: Long-term contract volume constraints: Physical meaning: Long-term contract coal must be purchased within the minimum and maximum quantities stipulated in the contract. and These represent the minimum / maximum monthly purchase volume (tons) for coal type i (long-term contract).

[0053] Market supply capacity constraints: in, This represents the maximum amount (in tons) of coal type i that can be supplied during period t.

[0054] f) Nonnegativity constraint: .

[0055] It should be noted that a weighted scoring method is used to comprehensively evaluate each coal source, constructing a scientific and quantitative procurement decision-making model. The scoring indicators comprehensively cover four dimensions: economy, safety, efficiency, and environmental protection. The weights of each indicator can be dynamically adjusted according to the management priorities of regional thermal power enterprises (such as cost priority or environmental priority). The specific scoring indicators are as follows: Economic indicators: These mainly include the price of coal delivered to the plant (including purchase price and transportation price), cost fluctuation range, cost-effectiveness, etc. Safety indicators mainly include the stability of coal supply (historical delivery fulfillment rate), the geographical security of coal source locations (transportation convenience, natural disaster risk), and the reliability of supplier qualifications. Efficiency indicators mainly include transportation distance, transportation cycle, and loading and unloading efficiency. Environmental indicators: These mainly include key environmental indicators such as sulfur content, ash content, and volatile matter in coal, ensuring compliance with the unit's environmental emission requirements.

[0056] By comprehensively evaluating the data, the optimal coal source is selected, avoiding the pursuit of low prices at the expense of quality, safety, and other risks, thus improving the scientific nature of procurement decisions.

[0057] The following describes in detail step 106, namely, "allocating the final regional thermal power enterprise procurement plan according to the multi-objective optimization allocation model, and determining the procurement plan of each thermal power plant under the regional thermal power enterprise," with reference to the embodiments of the present invention.

[0058] As one possible implementation, step 106 is achieved through the following steps: Step 1: Based on the formula of the multi-objective optimization allocation model, allocate the final regional-level thermal power enterprise procurement plan. While minimizing the third summation result, maximizing the total inventory safety margin, and maximizing overall coal quality compatibility, determine the procurement quantity of the i-th type of coal from coal source k in period t, and its transportation to power plant j via path l and carrier m. and the coal inventory of power plant j at the end of period t. : The third summation result is the sum of the procurement cost, transportation cost, and storage cost of the i-th type of coal in period t. This represents the third summation result. This represents the pithead / FOB unit price of purchasing the i-th type of coal from coal source point k in period t. This represents the unit transportation cost of transporting type i coal from coal source k through path l and carrier m to power plant j in period t. This represents the quantity of type i coal purchased from coal source k in period t, transported via path l and carrier m to power plant j. This represents the unit inventory holding cost of power plant j; in, Represents the total inventory safety margin. This represents the coal inventory of power plant j at the end of period t. This represents the second safety stock threshold for power plant j in period t; Introducing auxiliary variables ≥0 and constraints ≥ - Then the goal becomes: in, Represents overall coal quality compatibility. This represents the net calorific value of the i-th type of coal on a received basis. This represents the design calorific value of the coal used in power plant unit J. improve Maximize the environmental and economic benefits of coal quality: in, and They are respectively , The weighting coefficients, , The weighted average sulfur and ash content of the coal delivered to power plant j in period t are used as intermediate variables and defined using linear constraints: in, This represents the sulfur content of the i-th type of coal. The ash content represents the i-th type of coal; Step 2, synthesize the objective function: Where α, β, γ are weighting coefficients, and Norm1, Norm2, Norm3 are normalization factors.

[0059] The constraints include: Demand and inventory balance constraints (dynamic core): in, This is the average calorific value of the coal consumed in this period, and 7000 is the calorific value of standard coal.

[0060] Physical meaning: This is a comprehensive manifestation of material conservation, energy conservation, and time delay, and is the most complex dynamic constraint. This represents the physical inventory (tons) of power plant j at the end of period t. This indicates that coal dispatched in period t-τ(l) arrives in period t, where τ(l) represents the transportation time (days / number of periods) for path l. / This indicates a conversion of standard coal demand into physical coal demand. This indicates the average received lower heating value of coal. This represents the predicted standard coal consumption (tons) of power plant j during period t.

[0061] Supply capacity constraints: Physical meaning: The total amount of coal of type i purchased from coal source k cannot exceed its supply capacity. This represents the maximum amount (in tons) of coal type i that coal source point k can supply during period t. Transportation capacity constraints: Physical meaning: The total capacity of each transport route and each carrier is limited. This represents the maximum total capacity (in tons) of carrier m on path l within period t.

[0062] Power plant inventory capacity constraints: Physical meaning: The inventory of each power plant must be between zero and maximum capacity. This represents the inventory level of power plant j in period t. This represents the maximum storage capacity (tons) of power plant j.

[0063] Coal quality and environmental protection constraints (hard constraints, must be met): Weighted average sulfur content constraint: Weighted average ash content constraint: Calorific value range constraints: in, and These represent the environmental protection emission limits for sulfur and the ash control requirements for power plant j, respectively. and These represent the lower and upper limits of the coal quality (calorific value, sulfur content, ash content) for the i-th type of coal in the power plant unit design, respectively.

[0064] All three of the above are fractional linear constraints, which can be transformed into linear constraints through linearization techniques (such as multiplying by the denominator), provided that the denominator (total arrivals) is greater than 0.

[0065] Nonnegativity constraint: It should be noted that the procurement plans of each thermal power plant under the regional thermal power enterprise are recommended to be transferred online through the system and submitted to the procurement and finance departments of the regional thermal power enterprise for approval. After approval, the system will automatically distribute the procurement plans of each thermal power plant under the regional thermal power enterprise to the corresponding suppliers and carriers, generate transportation tasks, bind the corresponding transportation vehicles, and specify core information such as transportation routes, loading and unloading times, and coal quality requirements, and synchronize them to the vehicle positioning terminal and the power plant unloading system.

[0066] It should also be noted that all abnormal events (inventory warnings, transportation anomalies, coal quality anomalies, etc.) and their handling results are automatically archived by the system to form a daily event log, and weekly and monthly reports are generated regularly to summarize the fuel management situation, abnormal event handling situation, cost control situation, etc. in the region, for the safety supervision, finance and other departments of regional thermal power enterprises to check and review, and to provide data support for subsequent optimization of procurement strategies and adjustment of control indicators.

[0067] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: 1) Significantly reduced procurement costs: Through multi-source data fusion analysis, the window of coal market price fluctuations can be captured in real time. Combined with a multi-objective optimization allocation model, the overall regional procurement costs can be minimized. At the same time, through unified centralized procurement, the bargaining power with suppliers can be improved, and the unit purchase price can be reduced. In addition, the transportation routes can be optimized to reduce cross-regional transportation redundancy and reduce transportation costs. 2) Optimized inventory structure: The dynamic inventory early warning mechanism can monitor the inventory status of each power plant in real time, trigger early warnings in a timely manner and initiate disposal procedures, effectively avoiding coal shortage shutdowns and ensuring the safe and stable operation of the units; at the same time, by optimizing procurement plans and allocation schemes, fuel inventory backlog has been reduced and capital occupation has been lowered. 3) The hierarchical event-driven closed-loop management mechanism enables automated early warning, transfer, handling, and archiving of abnormal events, eliminating the need for manual contact and reporting. The event closed-loop handling time has been reduced from an average of 48 hours to less than 8 hours, significantly improving handling efficiency. At the same time, the system automatically completes data collection, statistics, analysis, and report generation, replacing the traditional manual report statistics work. The workload of manual report statistics has been reduced by more than 70%, freeing up management manpower and improving the level of management refinement. 4) By monitoring supplier performance, coal quality indicators, and inventory status in real time, various risks can be identified in advance, enabling early detection and handling of risks, reducing risks such as coal shortages, substandard coal quality, and uncontrolled costs, and improving the safety and reliability of fuel management.

[0068] According to another embodiment, a fuel procurement and allocation device for thermal power plants is provided. Figure 2 A schematic block diagram of a fuel procurement and allocation device for a thermal power plant according to one embodiment is shown. Figure 2 As shown, the device 200 may include: a data acquisition module 201, a data preprocessing module 202, an early warning event determination module 203, a primary procurement plan determination module 204, a final procurement plan determination module 205, and a procurement plan determination module for each thermal power plant 206. The main functions of each component module are as follows: The data acquisition module 201 is used to acquire multi-source heterogeneous data, which includes at least one indicator data. The data preprocessing module 202 is used to preprocess the multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data, wherein the preprocessed multi-source heterogeneous data includes at least one preprocessed indicator data. The early warning event determination module 203 is used to determine a number of graded early warning events based on the at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold, wherein the number of graded early warning events includes inventory early warning events; The initial procurement plan determination module 204 is used to determine the initial regional thermal power enterprise procurement plan based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model. The final procurement plan determination module 205 is used to determine the final regional thermal power enterprise procurement plan based on the preliminary regional thermal power enterprise procurement plan and procurement decision model. The procurement plan determination module 206 for each thermal power plant is used to allocate the final regional thermal power enterprise procurement plan based on the multi-objective optimization allocation model, and determine the procurement plan of each thermal power plant under the regional thermal power enterprise.

[0069] In one possible implementation, the levels of the aforementioned tiered early warning events include system-wide strict control, company approval, and escalation management.

[0070] In one possible implementation, the early warning event determination module includes: an early warning event determination submodule.

[0071] The early warning event determination submodule is used to determine the inventory early warning event based on the preprocessed inventory data and the corresponding inventory threshold, and to determine the level of the inventory early warning event as system strong control. The inventory threshold is the sum of the first safety stock threshold and the buffer stock.

[0072] In one possible implementation, the early warning event determination submodule is specifically used for: The preprocessed inventory data is determined using the following formula: I represents the preprocessed inventory data of regional thermal power enterprises in cycle t+1. t D represents the initial coal inventory of regional thermal power enterprises in period t. t C represents the amount of coal delivered to regional thermal power plants during cycle t. t This represents the coal consumption of regional thermal power plants during cycle t. The inventory threshold is determined by the following formula: R represents the inventory threshold, which is the value of the preprocessed inventory data. When inventory levels drop to or below the inventory threshold R, a purchase requisition is triggered. L represents the purchase lead time, which is the average number of days from order placement to warehousing, and A represents the average daily consumption. represents buffer stock, and S represents the first safety stock threshold. When the preprocessed inventory data Less than or equal to the corresponding inventory threshold When this occurs, an inventory warning event is triggered, and the level of the inventory warning event is determined to be system strict control.

[0073] In one possible implementation, the at least one preprocessed indicator data includes the unit price of the i-th type of coal in period t. And the sum of transportation costs and warehousing costs ; Module 204, which determines the preliminary procurement plan, is specifically used for: When an inventory warning event is triggered, the predicted purchase quantity of type i coal in period t is determined by minimizing the first summation result according to the formula of the following procurement demand forecasting model. : The first summation result is the sum of the purchase cost, transportation cost, and storage cost of the i-th type of coal in period t. The summation result, Let represent the unit price of the i-th type of coal in period t. Let Z represent the sum of transportation and storage costs for the i-th type of coal in period t, and let Z represent the first summation result. This represents the predicted purchase quantity of the i-th type of coal in period t.

[0074] In one possible implementation, the final procurement plan determination module 205 is specifically used for: Based on the formula of the procurement decision model, while minimizing the second summation result, the preliminary regional-level thermal power enterprise procurement plan is adjusted to determine the actual procurement quantity of the i-th type of coal in period t. Inventory at the end of period t : The second summation result is the sum of the purchase cost and inventory holding cost of the i-th type of coal in period t. The summation result, This represents the second summation result, where T represents the periodic set and C represents the coal type set. Let represent the unit price of the i-th type of coal in period t. This represents the actual quantity of type i coal purchased in period t. Represents the unit inventory holding cost. This represents the inventory level at the end of period t.

[0075] In one possible implementation, the procurement plan determination module 206 for each thermal power plant is specifically used for: Based on the formula of the multi-objective optimization allocation model, the final regional-level thermal power enterprise procurement plan is allocated. Under the conditions of minimizing the third summation result, maximizing the total inventory safety margin, and maximizing the overall coal quality compatibility, the procurement quantity of the i-th type of coal purchased from coal source k in period t, and transported to power plant j via path l and carrier m, is determined. and the coal inventory of power plant j at the end of period t. : The third summation result is the sum of the procurement cost, transportation cost, and storage cost of the i-th type of coal in period t. This represents the third summation result. This represents the pithead / FOB unit price of purchasing the i-th type of coal from coal source point k in period t. This represents the unit transportation cost of transporting type i coal from coal source k through path l and carrier m to power plant j in period t. This represents the quantity of type i coal purchased from coal source k in period t, transported via path l and carrier m to power plant j. This represents the unit inventory holding cost of power plant j; in, Represents the total inventory safety margin. This represents the coal inventory of power plant j at the end of period t. This represents the second safety stock threshold for power plant j in period t; Introducing auxiliary variables ≥0 and constraints ≥ - Then the goal becomes: in, Represents overall coal quality compatibility. This represents the net calorific value of the i-th type of coal on a received basis. This represents the design calorific value of the coal used in power plant unit J. improve Maximize the environmental and economic benefits of coal quality: in, and They are respectively , The weighting coefficients, , The weighted average sulfur and ash content of the coal delivered to power plant j in period t are used as intermediate variables and defined using linear constraints: in, This represents the sulfur content of the i-th type of coal. The ash content represents the i-th type of coal; Overall objective function: Where α, β, and γ are weighting coefficients, and Norm1, Norm2, and Norm3 are normalization factors. The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0076] In addition, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0077] And an electronic device, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0078] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0079] in, Figure 3The architecture of an electronic device is illustrated by way of example, which may include a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, and a memory 320. The processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320 can communicate with each other via a communication bus 330.

[0080] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0081] The memory 320 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 320 can store the operating system 321 for controlling the operation of the electronic device 300, and the basic input / output system (BIOS) 322 for controlling the low-level operations of the electronic device 300. Additionally, it can store a web browser 323, a data storage management system 324, and a fuel procurement and allocation device 325 for thermal power plants, etc. The aforementioned fuel procurement and allocation device 325 for thermal power plants can be the application program that specifically implements the aforementioned steps in this embodiment of the invention. In summary, when the technical solution provided in this embodiment of the invention is implemented through software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310.

[0082] Input / output interface 313 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0083] Network interface 314 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0084] Bus 330 includes a pathway for transmitting information between various components of the device, such as processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, and memory 320.

[0085] It should be noted that although the above-described device only shows the processor 310, video display adapter 311, disk drive 312, input / output interface 313, network interface 314, memory 320, bus 330, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions 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 coal procurement and allocation in thermal power plants, characterized in that, include: Acquire multi-source heterogeneous data, wherein the multi-source heterogeneous data includes at least one indicator data; The multi-source heterogeneous data is preprocessed to obtain preprocessed multi-source heterogeneous data, wherein the preprocessed multi-source heterogeneous data includes at least one preprocessed index data. Based on the at least one preprocessed indicator data and the corresponding at least one indicator warning threshold, several graded warning events are determined, including inventory warning events. Based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model, a preliminary regional thermal power enterprise procurement plan is determined. Based on the preliminary regional thermal power enterprise procurement plan and procurement decision model, the final regional thermal power enterprise procurement plan is determined. Based on the multi-objective optimization allocation model, the final regional-level thermal power enterprise procurement plan is allocated to determine the procurement plans of each thermal power plant under the regional-level thermal power enterprise.

2. The method according to claim 1, characterized in that, The levels of the aforementioned tiered early warning events include system-wide strict control, company approval, and escalation control.

3. The method according to claim 2, characterized in that, The step of determining several graded early warning events based on the at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold includes: Based on the preprocessed inventory data and the corresponding inventory threshold, the inventory warning event is determined and the level of the inventory warning event is determined to be system strong control. The inventory threshold is the sum of the first safety inventory threshold and the buffer inventory.

4. The method according to claim 3, characterized in that, The step of determining the inventory warning event and classifying the level of the inventory warning event as a system-wide strict control based on the preprocessed inventory data and the corresponding inventory threshold includes: The preprocessed inventory data is determined using the following formula: I represents the preprocessed inventory data of regional thermal power enterprises in cycle t+1. t D represents the initial coal inventory of regional thermal power enterprises in period t. t C represents the amount of coal delivered to regional thermal power plants during cycle t. t This represents the coal consumption of regional thermal power plants during cycle t. The inventory threshold is determined by the following formula: R represents the inventory threshold, which is the value of the preprocessed inventory data. When inventory levels drop to or below the inventory threshold R, a purchase requisition is triggered. L represents the purchase lead time, which is the average number of days from order placement to warehousing, and A represents the average daily consumption. represents buffer stock, and S represents the first safety stock threshold. When the preprocessed inventory data Less than or equal to the corresponding inventory threshold When this occurs, an inventory warning event is triggered, and the level of the inventory warning event is determined to be system strict control.

5. The method according to claim 1, characterized in that, The at least one preprocessed index data includes the unit price of the i-th type of coal in period t. And the sum of transportation costs and warehousing costs ; The step of determining a preliminary regional-level thermal power enterprise procurement plan based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model includes: When an inventory warning event is triggered, the predicted purchase quantity of type i coal in period t is determined by minimizing the first summation result according to the formula of the following procurement demand forecasting model. : The first summation result is the sum of the purchase cost, transportation cost, and storage cost of the i-th type of coal in period t. The summation result, Let represent the unit price of the i-th type of coal in period t. Let Z represent the sum of transportation and storage costs for the i-th type of coal in period t, and let Z represent the first summation result. This represents the predicted purchase quantity of the i-th type of coal in period t.

6. The method according to claim 1, characterized in that, The step of determining the final regional-level thermal power enterprise procurement plan based on the preliminary regional-level thermal power enterprise procurement plan and procurement decision model includes: Based on the formula of the procurement decision model, while minimizing the second summation result, the preliminary regional-level thermal power enterprise procurement plan is adjusted to determine the actual procurement quantity of the i-th type of coal in period t. Inventory at the end of period t : The second summation result is the sum of the purchase cost and inventory holding cost of the i-th type of coal in period t. The summation result, This represents the second summation result, where T represents the periodic set and C represents the coal type set. Let represent the unit price of the i-th type of coal in period t. This represents the actual quantity of type i coal purchased in period t. Represents the unit inventory holding cost. This represents the inventory level at the end of period t.

7. The method according to claim 1, characterized in that, The step of allocating the final regional-level thermal power enterprise procurement plan based on the multi-objective optimization allocation model, and determining the procurement plans of each thermal power plant under the regional-level thermal power enterprise, includes: Based on the formula of the multi-objective optimization allocation model, the final regional-level thermal power enterprise procurement plan is allocated. Under the conditions of minimizing the third summation result, maximizing the total inventory safety margin, and maximizing the overall coal quality compatibility, the procurement quantity of the i-th type of coal purchased from coal source k in period t, and transported to power plant j via path l and carrier m, is determined. and the coal inventory of power plant j at the end of period t. : The third summation result is the sum of the procurement cost, transportation cost, and storage cost of the i-th type of coal in period t. This represents the third summation result. This represents the pithead / FOB unit price of purchasing the i-th type of coal from coal source point k in period t. This represents the unit transportation cost of transporting type i coal from coal source k through path l and carrier m to power plant j in period t. This represents the quantity of type i coal purchased from coal source k in period t, transported via path l and carrier m to power plant j. This represents the unit inventory holding cost of power plant j; in, Represents the total inventory safety margin. This represents the coal inventory of power plant j at the end of period t. This represents the second safety stock threshold for power plant j in period t; Introducing auxiliary variables ≥0 and constraints ≥ - Then the goal becomes: in, Represents overall coal quality compatibility. This represents the net calorific value of the i-th type of coal on a received basis. This represents the design calorific value of the coal used in power plant unit J. improve Maximize the environmental and economic benefits of coal quality: in, and They are respectively , The weighting coefficients, , The weighted average sulfur and ash content of the coal delivered to power plant j in period t are used as intermediate variables and defined using linear constraints: in, This represents the sulfur content of the i-th type of coal. The ash content represents the i-th type of coal; Overall objective function: Where α, β, γ are weighting coefficients, and Norm1, Norm2, Norm3 are normalization factors.

8. A fuel procurement and allocation device for thermal power plants, characterized in that, include: The data acquisition module is used to acquire multi-source heterogeneous data, which includes at least one indicator data. The data preprocessing module is used to preprocess the multi-source heterogeneous data to obtain preprocessed multi-source heterogeneous data, wherein the preprocessed multi-source heterogeneous data includes at least one preprocessed indicator data. The early warning event determination module is used to determine a number of graded early warning events based on the at least one preprocessed indicator data and the corresponding at least one indicator early warning threshold, wherein the number of graded early warning events includes inventory early warning events. The initial procurement plan determination module is used to determine the initial regional thermal power enterprise procurement plan based on the at least one preprocessed indicator data, the inventory early warning event, and the procurement demand forecasting model. The final procurement plan determination module is used to determine the final regional thermal power enterprise procurement plan based on the preliminary regional thermal power enterprise procurement plan and procurement decision model. The procurement plan determination module for each thermal power plant is used to allocate the final regional thermal power enterprise procurement plan based on a multi-objective optimization allocation model, and to determine the procurement plans of each thermal power plant under the regional thermal power enterprise.

9. An electronic device, characterized in that, include: The memory and the processor communicate with each other via a bus; The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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