Thermal power enterprise plant furnace mark single difference intelligent analysis method and related device

Abstract

The invention discloses a thermal power enterprise plant furnace mark single difference intelligent analysis method and a related device, and relates to the field of thermal power plant fuel management, and the method comprises the steps: collecting and preprocessing coal information, production operation information, cost information and supply information; calculating a factory furnace standard single difference according to the pre-processed factory coal information and the pre-processed production operation information, analyzing influence factors of the factory furnace standard single difference, and constructing a factor analysis model of the factory furnace standard single difference according to the influence factors; and verifying the factor analysis model based on the pre-processed coal information, production operation information, cost information and supply information to obtain a single difference value of the plant furnace mark, a single difference time sequence change trend of the plant furnace mark and a single difference influence factor proportion of the plant furnace mark, and performing visual display to realize intelligent analysis of the single difference of the plant furnace mark. According to the method, the problem that the dynamic association process among links of thermal power enterprise fuel management cannot be identified due to the fact that the change trend of the factory furnace mark single difference cannot be accurately monitored in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of fuel management in thermal power plants, and particularly to an intelligent analysis method for the single - difference between factory and furnace standard prices in thermal power enterprises and related devices. Background Art

[0002] Driven by the dual factors of power market reform and energy structure transformation, thermal power enterprises, as the core pillar of power supply, are facing unprecedented cost - control pressures and technological - upgrading challenges. Coal costs account for 70% - 80% of the total costs of thermal power enterprises, and the unit fuel cost directly determines the market competitiveness and sustainable development ability of the enterprises. At a given energy - consumption level, continuously reducing the standard coal price per unit entering the furnace has become the key path for thermal power enterprises to enhance their core competitiveness and achieve cost reduction and efficiency improvement.

[0003] Existing research mainly focuses on the prediction of the standard coal price per unit in the fuel - procurement link or the improvement of the combustion efficiency in the furnace, but does not deeply integrate the data in links such as procurement, storage, blending, and combustion. Such fragmented analysis can only identify cost anomalies in a single link, and it is difficult to correlate the collaborative effects of multiple links, resulting in the inability to identify the dynamic correlations between multiple links. The single - difference between factory and furnace standard prices is the difference between the standard coal price per unit entering the factory and the standard coal price per unit entering the furnace, which comprehensively reflects the cost fluctuations of fuel in the entire chain of procurement, transportation, storage, and processing in the form of a difference, and can more accurately locate the weak links in management. Therefore, it is necessary to accurately analyze the change trend and influencing factors of the single - difference between factory and furnace forms to provide an effective decision - making basis for the fuel management of thermal power plant enterprises. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent analysis method for the single - difference between factory and furnace standard prices in thermal power enterprises and related devices, so as to solve the problem that the existing technology cannot accurately monitor the change trend of the single - difference between factory and furnace standard prices, resulting in the inability to identify the dynamic correlation process among the various links of fuel management in thermal power enterprises.

[0005] To achieve the above - mentioned purpose, the present invention adopts the following technical solutions: In the first aspect, an intelligent analysis method for the single - difference between factory and furnace standard prices in thermal power enterprises includes the following steps: Collect information on coal entering the factory, production operation information, cost information, and supply information and perform pre - processing; After calculating the single - difference between factory and furnace standard prices based on the pre - processed information on coal entering the factory and production operation information, analyze the influencing factors of the single - difference between factory and furnace standard prices, and construct a factor - analysis model for the single - difference between factory and furnace standard prices according to the influencing factors; Verify the factor - analysis model based on the pre - processed information on coal entering the factory, production operation information, cost information, and supply information, obtain the single - difference value between factory and furnace standard prices, the time - series change trend of the single - difference between factory and furnace standard prices, and the proportion of influencing factors of the single - difference between factory and furnace standard prices, and perform visual display to realize the intelligent analysis of the single - difference between factory and furnace standard prices.

[0006] In some embodiments, the information of incoming coal includes coal type, quantity of incoming coal, received basis low calorific value of incoming coal, sulfur content, ash content, volatile content, transportation mode, arrival time, and unit price of standard coal for incoming coal; The production operation information includes coal consumption for power generation and heating, calorific value of coal entering the furnace, unit price of standard coal for coal entering the furnace, and unit operation parameters; The cost information includes fuel procurement cost, transportation cost, and in-plant cost; The supply information includes suppliers, coal production areas, and mining processes.

[0007] In some embodiments, the preprocessing is to remove duplicate data, abnormal data, and incomplete data from the information of incoming coal, production operation information, and cost information, and to verify the supply information.

[0008] In some embodiments, the step of calculating the difference in standard unit price between the factory and the furnace based on the preprocessed information of incoming coal and production operation information specifically includes: Calculating the difference between the unit price of standard coal for coal entering the furnace and the unit price of standard coal for incoming coal to obtain the difference in standard unit price between the factory and the furnace; Unit price of standard coal for incoming coal = (fuel procurement cost + transportation cost) / (quantity of incoming coal × received basis low calorific value of incoming coal / 7000 kcal / kg).

[0009] In some embodiments, the influencing factors include: beginning inventory, storage loss, other consumption, calorific value difference, and oil conversion; The variables involved in the beginning inventory include quantity of coal in the beginning inventory, calorific value, and price; The variables involved in the storage loss include quantity of storage loss and price; The variables involved in the other consumption include other fuel costs not included in coal consumption; The variables involved in the calorific value difference include calorific value difference of incoming coal, calorific value difference of coal entering the furnace, and coal price; The variables involved in the oil conversion include cost of consumed crude oil and quantity of standard coal converted from oil.

[0010] In some embodiments, the step of constructing a factor analysis model for the difference in standard unit price between the factory and the furnace based on the influencing factors specifically includes: Calculating the contribution degree of the beginning inventory to the difference in standard unit price between the factory and the furnace based on the quantity of coal in the beginning inventory, calorific value, and price; Calculating the contribution degree of the storage loss to the difference in standard unit price between the factory and the furnace based on the quantity of storage loss and price; Calculating the contribution degree of the other consumption to the difference in standard unit price between the factory and the furnace based on other fuel costs not included in coal consumption; Calculating the contribution degree of the calorific value difference to the difference in standard unit price between the factory and the furnace based on the calorific value difference of incoming coal, calorific value difference of coal entering the furnace, and coal price; Calculate the contribution degree of the oil conversion to the plant furnace standard unit difference based on the consumed crude oil cost and the oil conversion to standard coal quantity; Based on all the above contribution degrees, obtain the proportional relationship of each influencing factor of the plant furnace standard unit difference, and construct a factor analysis model of the plant furnace standard unit difference based on the proportional relationship.

[0011] In a second aspect, an intelligent analysis system for the plant furnace standard unit difference of a thermal power enterprise, characterized by comprising: A multi-source data acquisition and preprocessing module, configured to acquire information on incoming coal, production operation information, cost information, and supply information and perform preprocessing; A plant furnace standard unit difference analysis model construction module, configured to calculate the plant furnace standard unit difference based on the preprocessed incoming coal information and production operation information, analyze the influencing factors of the plant furnace standard unit difference, and construct a factor analysis model of the plant furnace standard unit difference according to the influencing factors; A visualization display and decision support module, configured to verify the factor analysis model based on the preprocessed incoming coal information, production operation information, cost information, and supply information, obtain the plant furnace standard unit difference value, the time series change trend of the plant furnace standard unit difference, and the proportion of the influencing factors of the plant furnace standard unit difference, and perform visualization display to realize intelligent analysis of the plant furnace standard unit difference.

[0012] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, the steps of the intelligent analysis method for the plant furnace standard unit difference of a thermal power enterprise are implemented.

[0013] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent analysis method for the plant furnace standard unit difference of a thermal power enterprise are implemented.

[0014] In a fifth aspect, a computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the intelligent analysis method for the plant furnace standard unit difference of a thermal power enterprise are implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By deeply integrating multi-source data such as incoming coal, production operation, cost, and supply, the present invention constructs a panoramic data pool, significantly improving the analysis accuracy. And based on the preprocessed incoming coal information, production operation information, cost information, and supply information, the factor analysis model is verified to obtain the plant furnace standard unit difference value, the time series change trend of the plant furnace standard unit difference, and the proportion of the influencing factors of the plant furnace standard unit difference, which can solve the problem that the prior art cannot accurately monitor the change trend of the plant furnace standard unit difference, resulting in the inability to identify the dynamic association process between various links of fuel management in thermal power enterprises.

[0016] Furthermore, the system adopts dynamic correlation analysis technology to reveal the interaction mechanism among various links of fuel management, establishes a full-link dynamic factor analysis model including initial inventory, storage loss, calorific value attenuation, and oil conversion substitution, and quantifies the impact of each factor on the unit price difference between plant and furnace.

[0017] Furthermore, the present invention visualizes the data, which can intuitively display the unit price difference between plant and furnace, the time series evolution of the unit price difference between plant and furnace, and the proportion of each influencing factor, enabling managers to quickly locate abnormal points and take optimization measures, significantly improving management efficiency, dynamically optimizing procurement, inventory, and blending strategies, and effectively reducing fuel costs. Description of the Drawings

[0018] Figure 1 Schematic diagram of the intelligent analysis system for the unit price difference between plant and furnace of thermal power enterprises provided in the embodiment; Figure 2 Data collection and preprocessing process for the intelligent analysis of the unit price difference between plant and furnace of thermal power enterprises provided in the embodiment; Figure 3 Schematic diagram of the analysis of the calorific value difference between plant and furnace provided in the embodiment; Figure 4 Flow chart of the intelligent analysis method for the unit price difference between plant and furnace of thermal power enterprises provided in the embodiment. Detailed Embodiments

[0019] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the drawings. The content described is an explanation of the present invention rather than a limitation.

[0020] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, systems, products, or devices.

[0021] As Figure 4 shown, the present embodiment provides an intelligent analysis method for the unit price difference between plant and furnace of thermal power enterprises, including the following steps: S1: Collect information on incoming coal, production operation information, cost information, and supply information and perform preprocessing, which includes data cleaning to remove duplicate data, abnormal data, and incomplete data in the incoming coal information, production operation information, and cost information, check the supply information, and finally store the preprocessed information.

[0022] Specifically, as Figure 2As shown in the figure, a multi-source data acquisition interface is established and connected to the plant-side fuel management system, production system, financial system, and coal supplier system of the thermal power enterprise. The information on incoming coal is obtained from the plant-side fuel management system, the production operation information is obtained from the production system, the cost information is obtained from the financial system, and the supply information is obtained from the coal supplier system.

[0023] Specifically, the information on incoming coal includes coal type, incoming coal quantity, received base low calorific value of incoming coal, sulfur content, ash content, volatile content, transportation mode, arrival time, and incoming standard coal unit price; The production operation information includes coal consumption for power generation and heating, in-furnace calorific value, in-furnace standard coal unit price, and unit operation parameters; The cost information includes fuel procurement cost, transportation cost, and in-plant cost; The supply information includes suppliers, coal production areas, and mining processes.

[0024] S2: After calculating the plant-furnace standard unit price difference based on the preprocessed incoming coal information and production operation information, analyze the influencing factors of the plant-furnace standard unit price difference, and construct a factor analysis model for the plant-furnace standard unit price difference according to the influencing factors; Specifically, calculate the difference between the in-furnace standard coal unit price and the incoming standard coal unit price to obtain the plant-furnace standard unit price difference; Incoming standard coal unit price = (fuel procurement cost + transportation cost) / (incoming coal quantity × received base low calorific value of incoming coal / 7000 kcal / kg), where 7000 kcal / kg is the calorific value of standard coal.

[0025] The influencing factors include: beginning inventory, storage loss, other consumption, calorific value difference, and oil conversion; The variables involved in the beginning inventory include beginning inventory coal quantity, calorific value, and price; The variables involved in the storage loss include storage loss quantity and price; The variables involved in the other consumption include other fuel costs not included in coal consumption; The variables involved in the calorific value difference include incoming calorific value difference, in-furnace calorific value difference, and coal price; The variables involved in the oil conversion include consumed crude oil cost and oil conversion standard coal quantity.

[0026] Calculate the contribution degree of the beginning inventory to the plant-furnace standard unit price difference according to the beginning inventory coal quantity, calorific value, and price; Calculate the contribution degree of the storage loss to the plant-furnace standard unit price difference according to the storage loss quantity and price; Calculate the contribution degree of the other consumption to the plant-furnace standard unit price difference according to the other fuel costs not included in coal consumption; Calculate the contribution degree of the calorific value difference to the plant-furnace standard unit price difference according to the incoming calorific value difference, in-furnace calorific value difference, and coal price; As Figure 3 shown, the in-plant calorific value difference and the in-furnace calorific value difference together constitute the plant-furnace calorific value difference. The analysis of the plant-furnace calorific value difference includes daily report analysis, monthly report analysis, and annual report analysis. The daily report analysis of the plant-furnace calorific value difference focuses on short-term fluctuations. By calculating the calorific value difference sequence of the current day in real time, it captures immediate influencing factors such as the quality fluctuations of coal batches and the changes in the combustion system state, providing a basis for the dynamic adjustment of combustion parameters for operating personnel. The monthly report analysis of the plant-furnace calorific value difference deepens the trend research, establishes a calorific value difference control chart, identifies the correlation between coal procurement strategies, equipment maintenance cycles, and calorific value loss within the monthly cycle, and supports medium- and long-term procurement plans and maintenance decisions. The annual report analysis of the plant-furnace calorific value difference establishes a multi-dimensional regression model between the calorific value difference, power generation cost, and carbon emissions, evaluates the annual energy efficiency management performance, and guides strategic investment directions such as the optimization of fuel procurement structure and equipment upgrade and transformation.

[0027] Calculate the contribution degree of the oil conversion to the plant-furnace standard unit difference according to the cost of consumed crude oil and the amount of oil converted to standard coal. Based on all the above contribution degrees, obtain the proportional relationship of the influencing factors of the plant-furnace standard unit difference, and construct a factor analysis model of the plant-furnace standard unit difference based on the proportional relationship.

[0028] Verify the factor analysis model through the historical data of the in-plant coal information, production operation information, cost information, and supply information in S1, and continuously optimize the factor analysis module according to the analysis results feedback obtained by the factor analysis module. The analysis results include: the plant-furnace standard unit difference value, the time-series change trend of the plant-furnace standard unit difference, and the proportion of the influencing factors of the plant-furnace standard unit difference.

[0029] S3: Verify the factor analysis model based on the preprocessed in-plant coal information, production operation information, cost information, and supply information, obtain the plant-furnace standard unit difference value, the time-series change trend of the plant-furnace standard unit difference, and the proportion of the influencing factors of the plant-furnace standard unit difference, and perform visual display to realize the intelligent analysis of the plant-furnace standard unit difference.

[0030] Specifically, use a bar chart to compare the magnitudes of the plant-furnace standard unit difference values of different power plants, use a line chart to present the time-series change trend of the plant-furnace standard unit difference, and use a pie chart to present the proportion of the influencing factors of the plant-furnace standard unit difference.

[0031] As Figure 1 shown, this embodiment provides an intelligent analysis system for the plant-furnace standard unit difference of a thermal power enterprise, including: a multi-source data acquisition and preprocessing module, a plant-furnace standard unit difference analysis model construction module, a visual display and decision support module, a model optimization and verification module, and a system management module; The multi-source data acquisition and preprocessing module is used to collect in-plant coal information, production operation information, cost information, and supply information and perform preprocessing; The plant-furnace standard single-difference analysis model construction module is used to calculate the plant-furnace standard single-difference based on the preprocessed information of incoming coal and production operation information, analyze the influencing factors of the plant-furnace standard single-difference, and construct a factor analysis model of the plant-furnace standard single-difference according to the influencing factors; The visualization display and decision support module is used to verify the factor analysis model based on the preprocessed incoming coal information, production operation information, cost information and supply information, obtain the plant-furnace standard single-difference value, the time-series change trend of the plant-furnace standard single-difference, and the proportion of the influencing factors of the plant-furnace standard single-difference, and perform visualization display to realize the intelligent analysis of the plant-furnace standard single-difference; The model optimization and verification module is used to verify the factor analysis model through the historical data of incoming coal information, production operation information, cost information and supply information, and continuously optimize the factor analysis model according to the analysis results obtained by the factor analysis model. The analysis results include: the plant-furnace standard single-difference value, the time-series change trend of the plant-furnace standard single-difference, and the proportion of the influencing factors of the plant-furnace standard single-difference.

[0032] The system management module dynamically schedules the resource allocation and task priorities among the multi-source data acquisition and preprocessing module, the plant-furnace standard single-difference analysis model construction module, the visualization display and decision support module, and the model optimization and verification module to ensure the efficient cooperation of each module.

[0033] The division of modules in the embodiments of the present invention is illustrative, only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present invention, the functional modules can be integrated in one processor, or can exist separately physically, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software function modules.

[0034] In this embodiment, a computer device is also provided. The computer device includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes a calculation component and an iteration component, capable of performing model calculations and model updates). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of an intelligent analysis method for the single-difference of factory furnace bid lists in thermal power enterprises.

[0035] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, one or more instructions suitable for being loaded and executed by the processor are also stored. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of an intelligent analysis method for the single-difference of factory furnace bid lists in thermal power enterprises in the above embodiment.

[0036] This embodiment also provides a computer program product. The computer program product includes a computer program. When the computer program is executed by the processor, the corresponding steps of an intelligent analysis method for the single-difference of factory furnace bid lists in thermal power enterprises in the above embodiment are implemented.

[0037] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0038] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0039] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An intelligent analysis method for the single-difference of factory furnace standard forms in thermal power enterprises, characterized in that, It includes the following steps: Collect the information of incoming coal, production operation, cost, and supply, and perform preprocessing; After calculating the difference between the factory furnace standard unit price based on the preprocessed incoming coal information and production operation information, analyze the influencing factors of the difference between the factory furnace standard unit price, and construct a factor analysis model for the difference between the factory furnace standard unit price according to the influencing factors; Verify the factor analysis model based on the preprocessed incoming coal information, production operation information, cost information, and supply information, obtain the difference value of the factory furnace standard unit price, the time series change trend of the difference between the factory furnace standard unit price, and the proportion of the influencing factors of the difference between the factory furnace standard unit price, and perform visual display to realize the intelligent analysis of the difference between the factory furnace standard unit price.

2. The intelligent analysis method for the single-difference of factory furnace standard forms of thermal power enterprises according to claim 1, wherein The incoming coal information includes coal type, incoming coal quantity, received base low calorific value of incoming coal, sulfur content, ash content, volatile matter, transportation method, arrival time, and incoming standard coal unit price; The production operation information includes coal consumption for power generation and heating, calorific value of coal entering the furnace, standard coal unit price of coal entering the furnace, and unit operation parameters; The cost information includes fuel procurement cost, transportation cost, and in-plant cost; The supply information includes suppliers, coal production areas, and mining processes.

3. The intelligent analysis method for the single difference of factory furnace labels of thermal power enterprises according to claim 1, wherein The preprocessing is to remove duplicate data, abnormal data, and incomplete data from the incoming coal information, production operation information, and cost information, and check the supply information.

4. The intelligent analysis method for the single difference of plant furnace labels of thermal power enterprises according to claim 2, wherein, The step of calculating the difference between the factory furnace standard unit price based on the preprocessed incoming coal information and production operation information specifically includes: Calculate the difference between the standard coal unit price of coal entering the furnace and the standard coal unit price of incoming coal to obtain the difference between the factory furnace standard unit price; Incoming standard coal unit price = (fuel procurement cost + transportation cost) / (incoming coal quantity × received base low calorific value of incoming coal / 7000 kcal / kg).

5. The intelligent analysis method for the single difference of plant furnace standard list of thermal power enterprises according to claim 1, characterized in that, The influencing factors include: beginning inventory, storage loss, other consumption, calorific value difference, and oil conversion; The variables involved in the beginning inventory include the quantity, calorific value, and price of the beginning inventory coal; The variables involved in the storage loss include the storage loss quantity and price; The variables involved in the other consumption include other fuel costs for consumption not included in the coal consumption; The variables involved in the calorific value difference include the calorific value difference of incoming coal, the calorific value difference of coal entering the furnace, and the coal price; The variables involved in the oil conversion include the cost of consumed crude oil and the oil conversion standard coal quantity.

6. The intelligent analysis method for the single difference of plant furnace standard list of thermal power enterprises according to claim 5, wherein, The step of constructing the factor analysis model for the difference between the factory furnace standard unit price according to the influencing factors specifically includes: Calculate the contribution degree of the beginning inventory to the difference between the factory furnace standard unit price according to the quantity, calorific value, and price of the beginning inventory coal; Calculate the contribution degree of the storage loss to the difference between the factory furnace standard unit price according to the storage loss quantity and price; Calculate the contribution degree of the other consumption to the difference between the factory furnace standard unit price according to the other fuel costs for consumption not included in the coal consumption; Calculate the contribution degree of the calorific value difference to the difference between the factory furnace standard unit price according to the calorific value difference of incoming coal, the calorific value difference of coal entering the furnace, and the coal price; Calculate the contribution degree of the oil conversion to the difference between the factory furnace standard unit price according to the cost of consumed crude oil and the oil conversion standard coal quantity; Based on all the above contribution degrees, obtain the proportional relationship of each influencing factor of the difference between the factory furnace standard unit price, and construct the factor analysis model for the difference between the factory furnace standard unit price based on the proportional relationship.

7. An intelligent analysis system for the single-difference of factory furnace standard forms in thermal power enterprises, characterized in that, It includes: A multi-source data collection and preprocessing module, which is used to collect the information of incoming coal, production operation, cost, and supply, and perform preprocessing; The plant-furnace standard single-difference analysis model construction module is used to calculate the plant-furnace standard single-difference based on the preprocessed in-plant coal information and production operation information, analyze the influencing factors of the plant-furnace standard single-difference, and construct a factor analysis model of the plant-furnace standard single-difference according to the influencing factors; The visualization display and decision support module is used to verify the factor analysis model based on the preprocessed in-plant coal information, production operation information, cost information and supply information, obtain the plant-furnace standard single-difference value, the time-series change trend of the plant-furnace standard single-difference and the proportion of the influencing factors of the plant-furnace standard single-difference, and perform visualization display to realize the intelligent analysis of the plant-furnace standard single-difference.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, the steps of the intelligent analysis method for the plant-furnace standard single-difference of a thermal power enterprise according to any one of claims 1 to 6 are realized.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent analysis method for the plant-furnace standard single-difference of a thermal power enterprise according to any one of claims 1 to 6 are realized.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the intelligent analysis method for the plant-furnace standard single-difference of a thermal power enterprise according to any one of claims 1 to 6 are realized.

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