Carbon emission power monitoring method and system based on big data processing

Abstract

The present application relates to the technical field of power monitoring, and more particularly to a carbon emission power monitoring method and system based on big data processing, which analyzes the carbon emission state of power equipment based on energy structure data, power consumption data and equipment energy efficiency data, analyzes the power supply state of power equipment based on system load data and power consumption data, evaluates the operation risk of power equipment according to the carbon emission state analysis result and the power supply state analysis result of power equipment, and gives an operation risk optimization early warning to the operation and maintenance personnel according to the operation risk evaluation result of power equipment. Through real-time data fusion and multi-dimensional analysis of big data processing, high-load and high-carbon emission equipment can be accurately identified, and energy allocation and equipment scheduling can be coordinated to realize safe and low-carbon operation of the industrial park power system.

Description

Technical Field

[0001] This invention relates to the field of power monitoring technology, and in particular to a method and system for monitoring carbon emissions in power based on big data processing. Background Technology

[0002] With the increasing severity of global climate change, carbon emissions have received widespread attention from the international community. As one of the major sectors emitting carbon, the accurate monitoring and effective control of carbon emissions from the power industry are crucial for achieving global carbon reduction targets. The need for monitoring and managing carbon emissions from the power sector is increasingly urgent, but traditional monitoring methods are no longer sufficient to meet current demands.

[0003] Carbon emissions from the power industry come from a variety of sources, but the most significant are the combustion of fossil fuels and energy losses during electricity transmission. Fossil fuels, such as coal, oil, and natural gas, release large amounts of greenhouse gases like carbon dioxide during combustion, exacerbating global warming. While energy losses during electricity transmission contribute less to carbon emissions than combustion, they are still substantial.

[0004] However, current technologies often treat carbon emissions and power supply status as two independent indicators for separate analysis, neglecting the inherent connection and mutual influence between them. Existing technologies fail to consider the close relationship between the operating conditions of power equipment and carbon emissions, and how these factors jointly affect the normal operation of the equipment. For example, current technologies do not account for the fact that under high load conditions, power equipment consumes more energy, thus exacerbating carbon emissions and potentially leading to increased power supply pressure and overload risks.

[0005] To address these issues, this application presents a carbon emission power monitoring method and system based on big data processing. Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a carbon emission power monitoring method and system based on big data processing. By comprehensively analyzing the carbon emission status and power supply status of power equipment, the operating risks of power equipment can be assessed and quantified, thereby improving the operating efficiency and stability of power equipment.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a carbon emission electricity monitoring method based on big data processing, comprising the following steps:

[0009] S1. Obtain the energy efficiency data and power consumption data of the power equipment, and at the same time obtain the energy structure data and system load data of the power system;

[0010] S2. Analyze the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data;

[0011] S3. Analyze the power supply status of power equipment based on system load data and power consumption data;

[0012] S4. Assess the operational risks of power equipment based on the carbon emission status analysis results and power supply status analysis results;

[0013] S5. Based on the operational risk assessment results of power equipment, provide early warnings to operation and maintenance personnel regarding optimized operation risks of power equipment.

[0014] In one implementation of the present invention, step S2 involves analyzing the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data, including the following specific steps:

[0015] S21. Extract the energy efficiency data and power consumption data of the power equipment, and at the same time extract the energy structure data of the power system;

[0016] S22. Based on energy structure data, electricity consumption data, and equipment energy efficiency data, analyze the carbon emission status of power equipment to obtain the carbon emission status analysis results of power equipment.

[0017] In one implementation of the present invention, step S22 includes the following specific steps:

[0018] S221. Based on energy structure data, analyze the carbon emission status of the energy structure and obtain the analysis results of the carbon emission status of the energy structure.

[0019] S222. Based on power consumption data and equipment energy efficiency data, analyze the carbon emission status of equipment energy efficiency and obtain the analysis results of equipment energy efficiency carbon emission status.

[0020] S223. Based on the analysis results of carbon emission status of energy structure and carbon emission status of equipment energy efficiency, analyze the carbon emission status of power equipment to obtain the analysis results of carbon emission status of power equipment.

[0021] In one implementation of the present invention, step S3 analyzes the power supply status of the power equipment based on system load data and power consumption data, including the following specific steps:

[0022] S31. Extract power consumption data of power equipment and system load data of power system;

[0023] S32. Based on system load data and power consumption data, analyze the power supply status of power equipment to obtain the power supply status analysis results of power equipment.

[0024] In one implementation of the present invention, step S32 includes the following specific steps:

[0025] S321. Based on system load data and power consumption data, analyze the power supply and demand balance status of power equipment and obtain the power supply and demand balance status analysis results of power equipment.

[0026] S322. Based on system load data and power consumption data, analyze the load pressure status of power equipment and obtain the load pressure status analysis results of power equipment.

[0027] S323. The power supply and demand balance analysis results and the load pressure analysis results of the power equipment are weighted and summed to obtain the power supply status analysis results of the power equipment.

[0028] In one implementation of the present invention, step S4 assesses the operational risk of the power equipment based on the carbon emission status analysis results and the power supply status analysis results, including the following specific steps:

[0029] S41. Obtain the carbon emission status analysis results and power supply status analysis results of the power equipment obtained from the analysis;

[0030] S42. Based on the carbon emission status analysis results and power supply status analysis results of the power equipment, the operational risk of the power equipment is assessed, and the operational risk assessment results of the power equipment are obtained.

[0031] In one implementation of the present invention, step S5 involves providing an early warning of power equipment operation risk optimization to maintenance personnel based on the power equipment operation risk assessment results, including the following specific content:

[0032] S51. Obtain the operational risk assessment results of the power equipment obtained from the analysis;

[0033] S52. Preset operation risk threshold. When the operation risk assessment result of the power equipment is greater than the operation risk threshold, provide early warning of power equipment operation risk optimization to the operation and maintenance personnel.

[0034] Secondly, embodiments of the present invention also provide a carbon emission power monitoring system based on big data processing, including:

[0035] The data acquisition module is used to acquire equipment energy efficiency data and power consumption data of power equipment, as well as energy structure data and system load data of the power system.

[0036] The carbon emission status analysis module is used to analyze the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data.

[0037] The power supply status analysis module is used to analyze the power supply status of power equipment based on system load data and power consumption data.

[0038] The operational risk assessment module is used to assess the operational risks of power equipment based on the analysis results of the carbon emission status and power supply status of the power equipment.

[0039] The operation risk optimization and early warning module is used to provide early warnings to operation and maintenance personnel about the operation risks of power equipment based on the results of the operation risk assessment.

[0040] The control module is used to control the operation of the data acquisition module, carbon emission status analysis module, power supply status analysis module, operation risk assessment module, and operation risk optimization and early warning module.

[0041] Thirdly, an electronic device provided by an embodiment of the present invention includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a carbon emission power monitoring method based on big data processing by calling the computer program stored in the memory.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a carbon emission power monitoring method based on big data processing.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] This invention analyzes the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data; it analyzes the power supply status of power equipment based on system load data and electricity consumption data; based on the analysis results of the carbon emission status and power supply status of power equipment, it assesses the operational risks of power equipment; and based on the operational risk assessment results, it provides early warnings to operation and maintenance personnel regarding optimized operation risks of power equipment. Through real-time data fusion and multi-dimensional analysis using big data processing, it can accurately identify high-load, high-carbon-emission equipment and coordinate energy allocation and equipment scheduling to achieve safe and low-carbon operation of the power system in industrial parks. Attached Figure Description

[0045] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0046] Figure 1 This is a schematic diagram of the overall process of the carbon emission power monitoring method based on big data processing of the present invention;

[0047] Figure 2 This is a flowchart of step S2 in the carbon emission power monitoring method based on big data processing of the present invention;

[0048] Figure 3 This is a flowchart of step S3 in the carbon emission power monitoring method based on big data processing of the present invention;

[0049] Figure 4 This is a schematic diagram of the carbon emission power monitoring system based on big data processing according to the present invention. Detailed Implementation

[0050] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0051] Example 1

[0052] The carbon emission power monitoring method based on big data processing provided in this embodiment is applicable to the management of large industrial parks or urban power grids. It can comprehensively acquire equipment energy efficiency data, power consumption data, and power system energy structure and load data. Through in-depth analysis of this data, this embodiment can accurately assess the carbon emission status and power supply status of power equipment, and thus comprehensively judge the operational risks of power equipment. It effectively solves the problem of the separation between carbon emission and power supply status analysis in traditional monitoring methods, which makes it difficult to comprehensively reflect the equipment operating status. It provides maintenance personnel with timely and accurate early warning of power equipment operation risks, helps to improve energy utilization efficiency, reduce carbon emissions, and ensure the stable operation of the power system.

[0053] The carbon emission power monitoring method based on big data processing provided in this embodiment is also applicable to smart grids requiring efficient management. This embodiment can collect and analyze data from various power devices in the smart grid in real time and accurately, such as solar panels, wind turbines, traditional thermal power plants, and distribution networks. By deeply integrating multi-dimensional information such as energy structure, power consumption, equipment energy efficiency, and system load, it can help managers comprehensively understand the current carbon emission intensity and power supply-demand balance of the power system; providing solid data support for formulating scientific and reasonable energy dispatch strategies, energy conservation and emission reduction measures, and contingency plans for sudden power events.

[0054] like Figure 1 As shown, this embodiment provides a carbon emission electricity monitoring method based on big data processing, which specifically includes the following steps:

[0055] S1. Obtain the energy efficiency data and power consumption data of the power equipment, and at the same time obtain the energy structure data and system load data of the power system;

[0056] S2. Analyze the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data;

[0057] S3. Analyze the power supply status of power equipment based on system load data and power consumption data;

[0058] S4. Assess the operational risks of power equipment based on the carbon emission status analysis results and power supply status analysis results;

[0059] S5. Based on the operational risk assessment results of power equipment, provide early warnings to operation and maintenance personnel regarding optimized operation risks of power equipment.

[0060] like Figure 2 As shown, in this embodiment, step S2 analyzes the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data, including the following specific steps:

[0061] S21. Extract the energy efficiency data and power consumption data of the power equipment, and at the same time extract the energy structure data of the power system;

[0062] S22. Based on energy structure data, electricity consumption data, and equipment energy efficiency data, analyze the carbon emission status of power equipment to obtain the carbon emission status analysis results of power equipment.

[0063] In this embodiment, step S22 includes the following specific steps:

[0064] S221. Based on energy structure data, analyze the carbon emission status of the energy structure to obtain the analysis results. This step, by summing and analyzing the carbon emissions during power generation from different types of energy sources, reflects the impact of the proportion of the energy structure in the power system on the carbon emissions per unit of electricity, thus reflecting the impact of the energy structure on carbon emissions during the power consumption process of electrical equipment. The formula for calculating the carbon emission status of the energy structure can be: Wherein, Cng represents the carbon emission status of the energy structure, ai represents the proportion of the i-th type of power generation energy in the power system where the power equipment is located in the energy structure data, ci represents the carbon emission coefficient per unit of power generation of the i-th type of power generation energy in the power system where the power equipment is located in the energy structure data, amax represents the proportion of the power generation energy with the largest share in the power system where the power equipment is located in the energy structure data, cmax represents the carbon emission coefficient per unit of power generation of the power generation energy with the largest share in the power system where the power equipment is located in the energy structure data, n represents the total number of power generation energy in the power system where the power equipment is located in the energy structure data, and i is any one from 1 to n.

[0065] S222. Based on power consumption data and equipment energy efficiency data, analyze the carbon emission status of equipment energy efficiency to obtain the analysis results. This step reflects the impact of equipment energy efficiency on carbon emissions during operation by comprehensively analyzing the equipment's energy efficiency and power consumption. The formula for calculating the carbon emission status of equipment energy efficiency is as follows: Wherein, Cnx represents the equipment's energy efficiency carbon emission status, P represents the electrical energy input into the power equipment from the power consumption data, and Po represents the electrical energy output from the power equipment from the equipment's energy efficiency data;

[0066] S223. Based on the analysis results of carbon emission status of energy structure and carbon emission status of equipment energy efficiency, the carbon emission status of power equipment is analyzed to obtain the analysis results of carbon emission status of power equipment. In this step, by combining the energy cleanliness of the power system where the power equipment is located and the energy efficiency status of the power equipment itself, a comprehensive analysis is conducted to quantify the carbon emission status of the equipment. This allows for the rapid location of high-carbon equipment identified in the analysis. The formula for calculating the carbon emission status of power equipment is: Cdl=a×Cng+b×Cnx, where Cdl is the carbon emission status of the power equipment, and a and b are the influence weights of energy structure and equipment energy efficiency, respectively.

[0067] like Figure 3 As shown, in this embodiment, step S3 analyzes the power supply status of the power equipment based on system load data and power consumption data, including the following specific steps:

[0068] S31. Extract power consumption data of power equipment and system load data of power system;

[0069] S32. Based on system load data and power consumption data, analyze the power supply status of power equipment to obtain the power supply status analysis results of power equipment.

[0070] In this embodiment, step S32 includes the following specific steps:

[0071] S321. Based on system load data and power consumption data, analyze the power supply and demand balance state of the power equipment to obtain the analysis results. This step quantifies the power supply and demand balance state of the power equipment by analyzing the matching degree between the power demand of the power equipment and the real-time power supply capacity of the power system, and can determine whether the power equipment can obtain sufficient power supply. The formula for calculating the power supply and demand balance state can be: Wherein, Rs represents the power supply and demand balance state of the power equipment, Ed represents the current power consumption of the power equipment in the power consumption data, Cs represents the total power supply capacity of the power system in the system load data, and Ls represents the current total load of the power system in the system load data;

[0072] S322. Based on system load data and power consumption data, analyze the load pressure status of power equipment to obtain the load pressure status analysis results. This step comprehensively analyzes the proportion of power equipment in the system load and its impact on power system stability. It sums the proportion of power consumption of power equipment to the current total system load and the ratio of the current system load to historical peak load, thereby comprehensively quantifying the load pressure of power equipment and reducing the risk to power supply stability. The formula for calculating the load pressure status is: Wherein, PL represents the load pressure status of the power equipment, and Lp represents the historical peak load of the power system in which the power equipment is located in the system load data;

[0073] S323. The power supply and demand balance analysis results and the load pressure analysis results of the power equipment are weighted and summed to obtain the power supply status analysis results of the power equipment. The calculation formula for the power supply status is: GY=q1×Rs+q2×PL, where GY is the power supply status of the power equipment, and q1 and q2 are the influence weights of the supply and demand balance status and the load pressure status, respectively.

[0074] In this embodiment, step S4 assesses the operational risk of the power equipment based on the carbon emission status analysis results and the power supply status analysis results, including the following specific steps:

[0075] S41. Obtain the carbon emission status analysis results and power supply status analysis results of the power equipment obtained from the analysis;

[0076] S42. Based on the carbon emission status analysis results and power supply status analysis results of the power equipment, the operational risk of the power equipment is assessed to obtain the operational risk assessment result of the power equipment. This step, by weighting and summing the carbon emission status and power supply status, can comprehensively assess the environmental and equipment risks during the operation of the power equipment, avoiding the one-sidedness of single indicators in existing technologies. It takes into account the correlation between carbon emissions and power supply status, and can be closer to the actual operation and maintenance scenario. The assessment formula for the operational risk of power equipment is: R = α × Cdl + β × GY, where R is the operational risk of the power equipment, and α and β are the influence weights of carbon emission status and power supply status, respectively.

[0077] In this embodiment, step S5 involves providing early warnings to maintenance personnel regarding operational risks of the power equipment based on the operational risk assessment results. This includes the following specific details:

[0078] S51. Obtain the operational risk assessment results of the power equipment obtained from the analysis;

[0079] S52. A preset operation risk threshold is established. When the operation risk assessment result of the power equipment exceeds the operation risk threshold, an early warning for power equipment operation risk optimization is issued to the operation and maintenance personnel. It should be noted that the setting parameters (e.g., weights and thresholds) in this embodiment are obtained experimentally by those skilled in the art. The specific experimental method is as follows: historical power equipment energy efficiency data and power consumption data are obtained, along with corresponding historical power system energy structure data and system load data. These are substituted into each step of this embodiment to assess the historical power equipment operation risk. At the same time, the judgment result of whether there is risk during the historical power equipment operation is obtained. The judgment result of whether there is risk during the historical power equipment operation and the historical power equipment operation risk assessment result obtained from each step are imported into fitting software for continuous fitting to obtain the values ​​of the setting parameters (e.g., weights and thresholds) that meet the maximum operation risk judgment accuracy.

[0080] Example 2

[0081] like Figure 4 As shown, this embodiment provides a carbon emission power monitoring system based on big data processing, including:

[0082] The data acquisition module is used to acquire equipment energy efficiency data and power consumption data of power equipment, as well as energy structure data and system load data of the power system.

[0083] The carbon emission status analysis module is used to analyze the carbon emission status of power equipment based on energy structure data, electricity consumption data, and equipment energy efficiency data.

[0084] The power supply status analysis module is used to analyze the power supply status of power equipment based on system load data and power consumption data.

[0085] The operational risk assessment module is used to assess the operational risks of power equipment based on the analysis results of the carbon emission status and power supply status of the power equipment.

[0086] The operation risk optimization and early warning module is used to provide early warnings to operation and maintenance personnel about the operation risks of power equipment based on the results of the operation risk assessment.

[0087] The control module is used to control the operation of the data acquisition module, carbon emission status analysis module, power supply status analysis module, operation risk assessment module, and operation risk optimization and early warning module.

[0088] The steps for implementing the corresponding functions of each parameter and each unit module in the carbon emission power monitoring system based on big data processing of the present invention can be referred to the parameters and steps in the embodiments of the carbon emission power monitoring method based on big data processing mentioned above, and will not be repeated here.

[0089] Example 3

[0090] An electronic device according to an embodiment of the present invention includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a carbon emission power monitoring method based on big data processing by calling the computer program stored in the memory. It should be noted that all computer programs for the carbon emission power monitoring method based on big data processing are implemented using the C language. The data acquisition module, carbon emission status analysis module, power supply status analysis module, operational risk assessment module, operational risk optimization and early warning module, and control module are all controlled by a remote server.

[0091] Example 4

[0092] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0093] When the computer program runs on the computer device, it causes the computer device to perform the aforementioned carbon emission power monitoring method based on big data processing.

[0094] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for big data processing devices and media are relatively simple in description because they are fundamentally similar to the method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0095] The systems, media, and methods provided in the embodiments of the present invention are in one-to-one correspondence. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. 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. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0100] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0101] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0103] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A carbon emission power monitoring method based on big data processing, characterized in that, The method comprises the following steps: S1, obtaining device energy efficiency data and power consumption data of the power equipment, and simultaneously obtaining energy structure data and system load data of the power system; S2, analyzing the carbon emission state of the power equipment based on the energy structure data, the power consumption data and the device energy efficiency data; Specifically, the energy structure carbon emission state and the device energy efficiency carbon emission state are calculated; The calculation formula of the energy structure carbon emission state is: ; wherein, Cng is the energy structure carbon emission state, ai is the proportion of the i-th power generation energy in the power system where the power equipment is located in the energy structure data, ci is the unit power generation carbon emission coefficient of the i-th power generation energy in the power system where the power equipment is located in the energy structure data, amax is the proportion corresponding to the power generation energy with the largest proportion in the power system where the power equipment is located in the energy structure data, cmax is the unit power generation carbon emission coefficient corresponding to the power generation energy with the largest proportion in the power system where the power equipment is located in the energy structure data, n is the total number of power generation energies of the power system where the power equipment is located in the energy structure data, and i is any one of 1 to n. The calculation formula of the device energy efficiency carbon emission state is: Wherein, Cnx is the device energy efficiency carbon emission state, P is the electric energy input into the power device in the electric power consumption data, and Po is the electric energy output from the power device in the device energy efficiency data. S3, analyzing the power supply state of the power equipment based on the system load data and the power consumption data; Specifically, the power supply and demand balance state and the load pressure state are calculated; Wherein, the power supply and demand balance state calculation formula is: Wherein, Rs is the power supply and demand balance state of the power equipment, Ed is the current power consumption of the power equipment in the power consumption data, Cs is the total power supply capacity of the power system in the system load data, and Ls is the current total load of the power system in the system load data. The calculation formula of the load pressure state is: Wherein, PL is the load pressure state of the power equipment, and Lp is the historical peak load of the power system in which the power equipment is located in the system load data. S4, evaluating the operation risk of the power equipment according to the carbon emission state analysis result and the power supply state analysis result of the power equipment; S5, giving an operation risk optimization warning to the operation and maintenance personnel according to the operation risk evaluation result of the power equipment.

2. The big data processing based carbon emission power monitoring method according to claim 1, wherein, In the step S2, the carbon emission state of the power equipment is analyzed based on the energy structure data, the power consumption data and the device energy efficiency data, which comprises the following specific steps: S21, extracting the device energy efficiency data and the power consumption data of the power equipment, and simultaneously extracting the energy structure data of the power system; S22, analyzing the carbon emission state of the power equipment based on the energy structure data, the power consumption data and the device energy efficiency data, to obtain the carbon emission state analysis result of the power equipment.

3. The big data processing based carbon emission power monitoring method according to claim 2, wherein, The step S22 comprises the following specific steps: S221, analyzing the energy structure carbon emission state based on the energy structure data, to obtain the energy structure carbon emission state analysis result; S222, analyzing the device energy efficiency carbon emission state based on the power consumption data and the device energy efficiency data, to obtain the device energy efficiency carbon emission state analysis result; S223, analyzing the carbon emission state of the power equipment according to the energy structure carbon emission state analysis result and the device energy efficiency carbon emission state analysis result, to obtain the carbon emission state analysis result of the power equipment.

4. The big data processing based carbon emission power monitoring method according to claim 3, wherein, In the step S3, the power supply state of the power equipment is analyzed based on the system load data and the power consumption data, which comprises the following specific steps: S31, extracting the power consumption data of the power equipment and the system load data of the power system; S32, analyzing the power supply state of the power equipment based on the system load data and the power consumption data, to obtain the power supply state analysis result of the power equipment.

5. The big data processing based carbon emission power monitoring method according to claim 4, wherein, The step S32 comprises the following specific steps: S321, analyzing the power supply and demand balance state of the power equipment based on the system load data and the power consumption data, to obtain the power supply and demand balance state analysis result of the power equipment; S322, analyzing the load pressure state of the power equipment based on the system load data and the power consumption data, to obtain the load pressure state analysis result of the power equipment; S323, weighting and summing the power supply and demand balance state analysis result and the load pressure state analysis result of the power equipment, to obtain the power supply state analysis result of the power equipment.

6. The big data processing based carbon emission power monitoring method according to claim 5, wherein, In the step S4, the operation risk of the power equipment is evaluated according to the carbon emission state analysis result and the power supply state analysis result of the power equipment, which comprises the following specific steps: S41, obtaining the carbon emission state analysis result and the power supply state analysis result of the power equipment obtained by analysis; S42, based on the carbon emission state analysis result and the power supply state analysis result of the power equipment, evaluating the operation risk of the power equipment to obtain the operation risk evaluation result of the power equipment.

7. The big data processing based carbon emission power monitoring method according to claim 6, wherein, The step S5 includes the following specific contents: S51, obtaining the operation risk evaluation result of the power equipment obtained by analysis; S52, presetting an operation risk threshold, and when the operation risk evaluation result of the power equipment is greater than the operation risk threshold, the operation risk optimization warning of the power equipment is given to the operation and maintenance personnel.

8. A carbon emission power monitoring system based on big data processing, which is implemented based on the carbon emission power monitoring method based on big data processing in any one of claims 1-7, characterized in that, The system comprises: A data acquisition module is configured to acquire the equipment energy efficiency data and the power consumption data of the power equipment, and simultaneously acquire the energy structure data and the system load data of the power system; A carbon emission state analysis module is configured to analyze the carbon emission state of the power equipment based on the energy structure data, the power consumption data and the equipment energy efficiency data; A power supply state analysis module is configured to analyze the power supply state of the power equipment based on the system load data and the power consumption data; An operation risk evaluation module is configured to evaluate the operation risk of the power equipment according to the carbon emission state analysis result and the power supply state analysis result of the power equipment; An operation risk optimization warning module is configured to give the operation risk optimization warning of the power equipment to the operation and maintenance personnel according to the operation risk evaluation result of the power equipment; A control module is configured to control the operation of the data acquisition module, the carbon emission state analysis module, the power supply state analysis module, the operation risk evaluation module and the operation risk optimization warning module.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the carbon emission power monitoring method based on big data processing according to any one of claims 1-7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, Instructions are stored, and when the instructions run on a computer, the computer executes the carbon emission power monitoring method based on big data processing according to any one of claims 1-7.

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