Intelligent analysis and decision-making system for monitoring operation and maintenance of regional carbon emission

Abstract

The invention relates to the technical field of data analysis, in particular to a regional carbon emission monitoring operation and maintenance intelligent analysis decision-making system which comprises a regional monitoring module, a data processing module, a decision-making issuing module and an operation and maintenance adjusting module. Acquisition points are analyzed through the data processing module, the acquisition points are marked as conventional acquisition points and abnormal acquisition points, abnormal results of the abnormal acquisition points are obtained according to the abnormal analysis model, and feature factors and non-feature factors corresponding to the abnormal results are obtained through the decision issuing module. According to the method, the characteristic factors and the non-characteristic factors are acquired, corresponding decision-making schemes are issued and implemented based on the characteristic factors and the non-characteristic factors, and the decision-making schemes implemented at the abnormal acquisition points are adjusted through the operation and maintenance adjustment module, so that the problem of unbalanced carbon emission in the aspects of regional agricultural planting and animal husbandry breeding is effectively solved, autonomous supervision of regional carbon emission is realized; and a decision scheme is made according to supervision, and carbon emission of the region is operated and maintained based on the decision scheme, so that supervision of the carbon emission is more intelligent.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to an intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance. Background Art

[0002] At present, in order to adhere to the energy development strategy of giving priority to energy conservation, it is necessary to control carbon dioxide emissions. This makes carbon emissions an important indicator for measuring the green development of a region, especially in the agricultural planting and livestock farming industries in the region. Its carbon emissions are even a key factor in evaluating the quality of planting or breeding, and it is necessary to promote energy conservation, emission reduction and new energy utilization in the planting and livestock industries.

[0003] In the prior art, the patent document with the application number 202311256380.0 discloses an energy and carbon anomaly processing method, device and electronic device. The method first classifies the energy and carbon data of each sub-region of each client in the energy consumption area within a predetermined time period to obtain the energy and carbon emission data and energy and carbon consumption data of each sub-region of each client, and then processes the energy and carbon emission data and energy and carbon consumption data of each sub-region of each client to determine the abnormal data of each client. Finally, according to the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined, and the identifier of the abnormal sub-region is sent to the server, so that the client can know which sub-regions are abnormal sub-regions, improving the analysis efficiency of energy and carbon data, and thus solving the problem of low analysis efficiency caused by the need for manual analysis and processing of energy and carbon data analysis and management of each client in the energy consumption area in the existing solution.

[0004] The patent document with the application number 202411081504.0 discloses an enterprise energy collection and carbon emission intelligent management system based on the Internet of Things. The system includes: a communication establishment module for establishing digital communication between the Internet of Things monitoring device set and the data center; a data collection module for controlling the Internet of Things monitoring device to collect data; a preprocessing module for receiving demand tasks and performing task parsing to establish a functional parsing result; an intelligent decision-making module for activating an energy selection optimization network to generate a selection optimization result; and a management module for receiving the selection optimization result.

[0005] However, there are significant differences in carbon emissions from agriculture and animal husbandry in different regions, and the supervision and decision-making on carbon emissions are not in place, resulting in poor implementation effects of carbon emission reduction programs, and further leading to the situation of unbalanced regional carbon emissions, which is not conducive to the comprehensive and green development of agriculture and animal husbandry in the region. How to obtain abnormal data of abnormal areas of carbon emissions in agriculture and animal husbandry; at the same time, how to analyze the abnormal data, extract the characteristic factors and non-characteristic factors corresponding to the abnormal results, and issue and implement corresponding decision-making programs for abnormal collection points according to the characteristic factors and non-characteristic factors and trace and adjust the programs has become an urgent problem to be solved. Summary of the Invention

[0006] The object of the present invention is to solve the problems in the background technology, and a regional carbon emission monitoring, operation and maintenance intelligent analysis and decision-making system is proposed.

[0007] The present invention adopts the following technical solutions: a regional carbon emission monitoring, operation and maintenance intelligent analysis and decision-making system, including a regional monitoring module, a data processing module, a decision-making and publishing module, and an operation and maintenance adjustment module;

[0008] The regional monitoring module includes a monitoring unit and a number of collection points. The monitoring unit is used to obtain the annual material procurement volume and the total product sales price of different collection points, and calculate the carbon emission value coefficient of the region according to the annual material procurement volume and the total product sales price; it also includes obtaining regional energy data and calculating according to the regional energy data to obtain the standard value coefficient;

[0009] The data processing module is used to analyze the collection points according to the carbon emission value coefficient and the standard value coefficient, add normal or abnormal marks to the collection points, regard the collection points added with normal and abnormal marks as normal collection points and abnormal collection points respectively, and obtain the abnormal results of the abnormal collection points according to the abnormal analysis model;

[0010] The decision-making and publishing module is used to analyze the abnormal results of the abnormal collection points, obtain the characteristic factors and non-characteristic factors corresponding to the abnormal results, and publish the decision-making plan according to the characteristic factors and non-characteristic factors;

[0011] The operation and maintenance adjustment module is used to perform operation and maintenance on the abnormal collection points according to the decision-making plan, and adjust the decision-making plan implemented for the abnormal collection points according to the characteristic factors and non-characteristic factors.

[0012] As a further solution of the present invention, the carbon emission value coefficient C is calculated by the formula It is calculated; Ei represents the amount of purchased materials, which is used as the carbon source amount, i represents the carbon source amount number, i ∈ [1, N], N represents the total number of carbon source amounts, △i represents the emission coefficient of the carbon source, and F represents the total product sales price;

[0013] The regional energy data includes the annual electricity price income and the annual coal resource consumption of the power plant supplying electricity to the region, and the standard value coefficient B is calculated by the formula It is calculated, XH is the annual coal resource consumption, NS is the annual electricity price income, and k is the emission coefficient of coal.

[0014] As a further solution of the present invention, the method for analyzing the collection points is as follows:

[0015] Compare the carbon emission value coefficients of several reference points in the area with the standard value coefficient; if the difference in carbon emission value coefficients is not greater than the standard value coefficient, add a normal mark to the collection point corresponding to the difference in carbon emission value coefficients; if the difference in carbon emission value coefficients is greater than the standard value coefficient, add an abnormal mark to the collection point corresponding to the difference in carbon emission value coefficients.

[0016] As a further solution of the present invention, the method for obtaining the abnormal result of the abnormal collection point is:

[0017] Obtain the annual surplus materials and total loss price of the collection point with the abnormal mark added and mark them as Rj and L respectively, and through the formula Calculate the carbon emission coefficient of loss D, △j represents the emission coefficient of surplus materials, j represents the surplus material number, j ∈ [1, M], and M is the total number of surplus materials;

[0018] Obtain the abnormal analysis value of the abnormal collection point through the abnormal analysis model. The expression of the abnormal analysis model is:

[0019] P is the abnormal analysis value;

[0020] If P = 1, the collection point does not have an abnormal result; if P = 2, the collection point has an abnormal result.

[0021] As a further solution of the present invention, the method for obtaining the characteristic factors corresponding to the abnormal result is:

[0022] If the abnormal collection point has an abnormal result, obtain the total market transaction amount and market average price of the collection point, and calculate the ratio of the total market transaction amount and the market average price to obtain the market activity;

[0023] Obtain the average activity by accumulating and calculating the average value of the market activities of several regular collection points and use it as the standard activity. Mark the market activity of the abnormal collection point as the target activity and compare the target activity with the standard activity;

[0024] If the target activity is not less than the standard activity, it is determined that the abnormal collection point has a price anomaly, and the price anomaly is used as the priority characteristic factor of the abnormal collection point; if the target activity is less than the standard activity, it is determined that the abnormal collection point has a price anomaly and a product anomaly, and the price anomaly and the product anomaly are used as the ordinary characteristic factor and the priority characteristic factor of the abnormal collection point respectively.

[0025] As a further solution of the present invention, the decision-making and publishing module is also connected to an equipment coverage monitoring unit, and the equipment coverage monitoring unit is used to obtain the production plan of the collection point;

[0026] Through the formula Calculate the device coverage analysis values SF and SC z For production plan α z For the carbon content quantification coefficient of the production plan, z is the production plan number, z ∈ [1, Q], and Q is the total number of production plans;

[0027] The method for obtaining the non-characteristic factors corresponding to the abnormal results is as follows:

[0028] Accumulate the device analysis values of the regular collection points and calculate the average value to obtain the average device coverage analysis value, which is used as the standard device coverage analysis value. Mark the device coverage analysis value of the abnormal collection point as the target analysis value, and compare the target analysis value with the standard analysis value. If the target analysis value is not less than the standard analysis value, it is determined that there is an abnormal device quantity at the abnormal collection point, and the abnormal device quantity is used as the priority non-characteristic factor of the abnormal collection point; if the target analysis value is less than the standard analysis value, it is determined that there are abnormal device quantity and abnormal device coverage at the abnormal collection point, and the abnormal device quantity and abnormal device coverage are used as the ordinary non-characteristic factor and priority non-characteristic factor of the abnormal collection point respectively.

[0029] As a further solution of the present invention, traverse the non-characteristic factors of the abnormal collection points. If there is no priority non-characteristic factor at the abnormal collection point, traverse the characteristic factors of the abnormal collection point. If there is no ordinary characteristic factor at the abnormal collection point, generate a decision-making plan according to the price abnormality of the abnormal collection point; if there is an ordinary characteristic factor at the abnormal collection point, generate a decision-making plan according to the price abnormality and product abnormality of the abnormal collection point;

[0030] If there is a priority non-characteristic factor at the abnormal collection point, determine whether there is an ordinary non-characteristic factor at the abnormal collection point. If there is no ordinary non-characteristic factor at the abnormal collection point, generate a decision-making plan according to the abnormal device quantity of the abnormal collection point; if there is an ordinary non-characteristic factor at the abnormal collection point, generate a decision-making plan according to the abnormal device quantity and abnormal device coverage of the abnormal collection point.

[0031] As a further solution of the present invention, the method for implementing the scheme adjustment for the abnormal collection point is as follows:

[0032] If the abnormal collection point has ordinary characteristic factors, adjust the priority characteristic factors in the decision-making plan; if the abnormal collection point has ordinary non-characteristic factors, adjust the priority non-characteristic factors in the decision-making plan.

[0033] Compared with the existing technologies, the advantages of the present invention are as follows: By analyzing the collection points through the data processing module, the collection points are marked as regular collection points and abnormal collection points, and the abnormal results of the abnormal collection points are obtained according to the abnormal analysis model. By analyzing the abnormal results through the decision-making and publishing module, the characteristic factors and non-characteristic factors corresponding to the abnormal results are obtained, and corresponding decision-making schemes are published and implemented for the abnormal collection points according to the characteristic factors and non-characteristic factors. By adjusting the decision-making schemes implemented for the abnormal collection points through the operation and maintenance adjustment module, the problem of unbalanced carbon emissions in regional agricultural planting and livestock farming is effectively solved, the autonomous supervision of regional carbon emissions is realized, and decision-making schemes are formulated based on the supervision. Based on the decision-making schemes, the operation and maintenance of regional carbon emissions are carried out, making the supervision of carbon emissions more intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is the module flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0036] Referring to Figure 1 , a regional carbon emission monitoring, operation and maintenance intelligent analysis and decision-making system includes a regional monitoring module, a data processing module, a decision-making and publishing module, and an operation and maintenance adjustment module;

[0037] The regional monitoring module includes a monitoring unit and a number of collection points. The monitoring unit is used to obtain the annual material purchase volume and the total product sales price of different collection points, and calculate the carbon emission value coefficient of the region according to the annual material purchase volume and the total product sales price;

[0038] The carbon emission value coefficient C is calculated by the formula ; Ei represents the purchased material quantity and is used as the carbon source quantity, i represents the carbon source quantity number, i ∈ [1, N], N represents the total number of carbon source quantities, △i represents the emission coefficient of the carbon source, and F represents the total product sales price;

[0039] It should be noted that any agricultural planting point and livestock farming point in the region is regarded as a collection point. The monitoring unit can be local distributors and market supervision agencies. The material purchase volume of each collection point is obtained through the distributors, and the total product sales price is obtained through the market supervision agencies;

[0040] It also includes obtaining regional energy data and calculating according to the regional energy data to obtain the standard value coefficient;

[0041] The regional energy data includes the annual electricity price income and the annual coal resource consumption of the power plants supplying electricity to the region. Through the formula the standard value coefficient B is calculated. XH is the annual coal resource consumption in kilograms, NS is the annual electricity price income, and k is the emission coefficient of coal;

[0042] The data processing module is used to analyze the collection points according to the carbon emission value coefficient and the standard value coefficient, add normal or abnormal marks to the collection points, regard the collection points added with normal and abnormal marks as normal collection points and abnormal collection points respectively, and obtain the abnormal results of the abnormal collection points according to the abnormal analysis model;

[0043] The method for analyzing the collection points is as follows:

[0044] Compare the carbon emission value coefficients of several reference points in the region with the standard value coefficient; if the difference in carbon emission value coefficients is not greater than the standard value coefficient, add a normal mark to the collection point corresponding to the difference in carbon emission value coefficients; if the difference in carbon emission value coefficients is greater than the standard value coefficient, add an abnormal mark to the collection point corresponding to the difference in carbon emission value coefficients;

[0045] The method for obtaining the abnormal results of the abnormal collection points is as follows:

[0046] Obtain the annual surplus materials and total loss price of the collection points added with abnormal marks and mark them as Rj and L respectively. Through the formula calculate the loss carbon emission coefficient D. △j represents the emission coefficient of surplus materials, j represents the surplus material number, j ∈ [1, M], and M is the total number of surplus materials;

[0047] Obtain the abnormal analysis value of the abnormal collection points through the abnormal analysis model. The expression of the abnormal analysis model is:

[0048] P is the abnormal analysis value;

[0049] If P = 1, the collection point does not have abnormal results; if P = 2, the collection point has abnormal results;

[0050] The decision-making and publishing module is used to analyze the abnormal results of the abnormal collection points, obtain the characteristic factors and non-characteristic factors corresponding to the abnormal results, and publish the decision-making scheme according to the characteristic factors and non-characteristic factors;

[0051] The method for obtaining the characteristic factors corresponding to the abnormal results is as follows:

[0052] If the abnormal collection point has abnormal results, obtain the total market transaction volume and market average price of the collection point, and calculate the ratio of the total market transaction volume and market average price to obtain the market activity;

[0053] The average activity is obtained by accumulating and calculating the average value of the market activities of several regular collection points, and it is used as the standard activity. The market activity of the abnormal collection point is marked as the target activity, and the target activity is compared with the standard activity;

[0054] If the target activity is not less than the standard activity, it is determined that there is a price anomaly at the abnormal collection point, and the price anomaly is used as the priority characteristic factor of the abnormal collection point; if the target activity is less than the standard activity, it is determined that there are price anomalies and product anomalies at the abnormal collection point, and the price anomaly and product anomaly are used as the ordinary characteristic factor and priority characteristic factor of the abnormal collection point respectively;

[0055] The decision-making release module is also connected to a device coverage monitoring unit, and the device coverage monitoring unit is used to obtain the production plan of the collection point;

[0056] Through the formula The device coverage analysis values SF and SC are calculated z is the production plan, and α z is the carbon quantification coefficient of the production plan, z is the production plan number, z ∈ [1, Q], and Q is the total number of production plans; it should be noted that the production plans include fully automated, semi-automated, and de-automated. The carbon quantification coefficient of fully automated is greater than that of semi-automated, and the carbon quantification coefficient of semi-automated is greater than that of de-automated. De-automated can be understood as completely manual production;

[0057] The method for obtaining the non-characteristic factors corresponding to the abnormal results is as follows:

[0058] The device analysis values of the regular collection points are accumulated and averaged to obtain the average device coverage analysis value, which is used as the standard device coverage analysis value. The device coverage analysis value of the abnormal collection point is marked as the target analysis value, and the target analysis value is compared with the standard analysis value. If the target analysis value is not less than the standard analysis value, it is judged that there is an abnormal device quantity at the abnormal collection point, and the abnormal device quantity is used as the priority non-characteristic factor of the abnormal collection point; if the target analysis value is less than the standard analysis value, it is judged that there are abnormal device quantity and abnormal device coverage at the abnormal collection point, and the abnormal device quantity and abnormal device coverage are used as the ordinary non-characteristic factor and priority non-characteristic factor of the abnormal collection point respectively;

[0059] Traverse the non-characteristic factors of the abnormal collection points. If there are no priority non-characteristic factors in the abnormal collection points, then traverse the characteristic factors of the abnormal collection points. If there are no common characteristic factors in the abnormal collection points, then generate a decision-making plan based on the price anomaly of the abnormal collection points. For example, the decision-making plan based on the price anomaly can be that the products are uniformly recycled by a designated institution, and the product prices are set by the designated institution to keep the prices at a normal level, preventing the product prices from competing with each other or developing independently due to excessive production or other factors, which may lead to a decline in product prices. The decision-making plan includes but is not limited to this, and will not be elaborated further;

[0060] If there are common characteristic factors in the abnormal collection points, then generate a decision-making plan based on the price anomaly and product anomaly of the abnormal collection points. For example, the decision-making plans based on product anomalies include but are not limited to existing technical means such as interfering with the production environment, protecting the health of the production environment, and adjusting the production mode, which will not be elaborated here;

[0061] If there are priority non-characteristic factors in the abnormal collection points, then determine whether there are common non-characteristic factors in the abnormal collection points. If there are no common non-characteristic factors in the abnormal collection points, then generate a decision-making plan based on the equipment quantity anomaly of the abnormal collection points. For example, the decision-making plan based on the equipment quantity anomaly can be to increase the number or quality of equipment, including but not limited to increasing the number of equipment, upgrading the equipment software system, and replacing the equipment, etc.;

[0062] If there are common non-characteristic factors in the abnormal collection points, then generate a decision-making plan based on the equipment quantity anomaly and equipment coverage anomaly of the abnormal collection points. For example, the decision-making plan based on the equipment coverage anomaly can be to increase the equipment coverage, including but not limited to adding different types of relevant equipment in different production links to achieve a fully automated or semi-automated production method and eliminate the automated production method;

[0063] The operation and maintenance adjustment module is used to perform operation and maintenance on the abnormal collection points according to the decision-making plan, and adjust the decision-making plan implemented for the abnormal collection points based on the characteristic factors and non-characteristic factors;

[0064] The method for implementing scheme adjustment for abnormal collection points is as follows:

[0065] If the abnormal collection point has common characteristic factors, then adjust the priority characteristic factors in the decision-making plan; if the abnormal collection point has common non-characteristic factors, then adjust the priority non-characteristic factors in the decision-making plan;

[0066] The standard value coefficient and the carbon emission value coefficient of the region are obtained through the regional monitoring module. The data processing module analyzes the collection points, marks the collection points as regular collection points and abnormal collection points, and obtains the abnormal results of the abnormal collection points according to the abnormal analysis model. The decision-making and release module analyzes the abnormal results to obtain the characteristic factors and non-characteristic factors corresponding to the abnormal results, and issues and implements corresponding decision-making plans for the abnormal collection points according to the characteristic factors and non-characteristic factors. The operation and maintenance adjustment module adjusts the decision-making plans implemented for the abnormal collection points, effectively solves the problem of unbalanced carbon emissions in regional agricultural planting and livestock farming, realizes the autonomous supervision of regional carbon emissions, formulates decision-making plans based on the supervision, and conducts operation and maintenance on the carbon emissions of the region based on the decision-making plans, making the supervision of carbon emissions more intelligent.

[0067] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance, characterized in that: It includes a regional monitoring module, a data processing module, a decision-making and publishing module, and an operation and maintenance adjustment module; The regional monitoring module includes a monitoring unit and a number of collection points. The monitoring unit is used to obtain the annual material procurement volume and the total product sales price of different collection points, and calculate the carbon emission value coefficient of the region based on the annual material procurement volume and the total product sales price; it also includes obtaining regional energy data and calculating according to the regional energy data to obtain a standard value coefficient; The data processing module is used to analyze the collection points according to the carbon emission value coefficient and the standard value coefficient, add normal or abnormal marks to the collection points, regard the collection points added with normal and abnormal marks as normal collection points and abnormal collection points respectively, and obtain the abnormal results of the abnormal collection points according to the abnormal analysis model; The decision-making and publishing module is used to analyze the abnormal results of the abnormal collection points, obtain the characteristic factors and non-characteristic factors corresponding to the abnormal results, and publish a decision-making plan according to the characteristic factors and non-characteristic factors; The operation and maintenance adjustment module is used to perform operation and maintenance on the abnormal collection points according to the decision-making plan, and adjust the decision-making plan implemented for the abnormal collection points according to the characteristic factors and non-characteristic factors.

2. The intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance according to claim 1, wherein: The carbon emission value coefficient C is calculated through the formula and obtained Ei represents the quantity of purchased materials, i represents the carbon source quantity number, i ∈ [1, N], N represents the total number of carbon source quantities, △i represents the emission coefficient of the carbon source, and F represents the total product sales price; The regional energy data includes the annual electricity price revenue and the annual coal resource consumption of the power plants supplying electricity to the region. Through the formula the standard value coefficient B is calculated, where XH is the annual coal resource consumption, NS is the annual electricity price revenue, and k is the emission coefficient of coal.

3. The intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance according to claim 2, wherein: The method for analyzing the collection points is as follows: Compare the carbon emission value coefficients of several reference points in the region with the standard value coefficient; if the difference in carbon emission value coefficients is not greater than the standard value coefficient, add a normal mark to the collection point corresponding to the difference in carbon emission value coefficients; If the difference in carbon emission value coefficients is greater than the standard value coefficient, add an abnormal mark to the collection point corresponding to the difference in carbon emission value coefficients.

4. The intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance according to claim 3, wherein: The method for obtaining the abnormal results of the abnormal collection points is as follows: Obtain the historical surplus materials and total loss price of the collection points with abnormal marks added, and mark them as Rj and L respectively. Through the formula Calculate the carbon emission coefficient of loss D, △j represents the emission coefficient of surplus materials, j represents the surplus material number, j ∈ [1, M], and M is the total number of surplus materials; Obtain the abnormal analysis value of the abnormal collection point through the abnormal analysis model. The expression of the abnormal analysis model is: P is the abnormal analysis value; If P = 1, then the collection point does not have abnormal results; if P = 2, then the collection point has abnormal results.

5. An intelligent analysis and decision-making system for regional carbon emission monitoring operation and maintenance according to claim 4, characterized in that: The method for obtaining the characteristic factors corresponding to the abnormal results is as follows: If the abnormal collection point has abnormal results, obtain the total market transaction volume and the market average price of the collection point, and calculate the ratio of the total market transaction volume and the market average price to obtain the market activity; Obtain the average activity by accumulating and averaging the market activities of several normal collection points, and use it as the standard activity. Mark the market activity of the abnormal collection point as the target activity, and compare the target activity with the standard activity; If the target activity is not less than the standard activity, it is determined that the abnormal collection point has a price anomaly, and the price anomaly is used as the priority characteristic factor of the abnormal collection point; If the target activity is less than the standard activity, it is determined that the abnormal collection point has a price anomaly and a product anomaly, and the price anomaly and the product anomaly are used as the ordinary characteristic factor and the priority characteristic factor of the abnormal collection point respectively.

6. The intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance according to claim 5, characterized in that: The decision-making and publishing module is also connected to a device coverage monitoring unit, and the device coverage monitoring unit is used to obtain the production plan of the collection point; Through the formula The device coverage analysis values SF and SC are calculated z is the production plan, α z is the carbon content quantification coefficient of the production plan, z is the production plan number, z ∈ [1, Q], and Q is the total number of production plans; The method for obtaining the non-characteristic factors corresponding to the abnormal results is as follows: Accumulate the device analysis values of regular collection points and calculate the mean value to obtain the average device coverage analysis value, which is used as the standard device coverage analysis value. Mark the device coverage analysis value of the abnormal collection point as the target analysis value, and compare the target analysis value with the standard analysis value. If the target analysis value is not less than the standard analysis value, it is determined that there is an abnormal device quantity at the abnormal collection point, and the abnormal device quantity is used as the priority non-characteristic factor of the abnormal collection point; If the target analysis value is less than the standard analysis value, it is determined that there are abnormal device quantity and abnormal device coverage at the abnormal collection point, and the abnormal device quantity and abnormal device coverage are used as the ordinary non-characteristic factor and priority non-characteristic factor of the abnormal collection point respectively.

7. The intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance according to claim 6, characterized in that: Traverse the non-characteristic factors of the abnormal collection point. If there is no priority non-characteristic factor at the abnormal collection point, traverse the characteristic factors of the abnormal collection point. If there is no ordinary characteristic factor at the abnormal collection point, generate a decision plan based on the price anomaly of the abnormal collection point; if there is an ordinary characteristic factor at the abnormal collection point, generate a decision plan based on the price anomaly and product anomaly of the abnormal collection point; If there is a priority non-characteristic factor at the abnormal collection point, determine whether there is an ordinary non-characteristic factor at the abnormal collection point. If there is no ordinary non-characteristic factor at the abnormal collection point, generate a decision plan based on the abnormal device quantity of the abnormal collection point; if there is an ordinary non-characteristic factor at the abnormal collection point, generate a decision plan based on the abnormal device quantity and abnormal device coverage of the abnormal collection point.

8. An intelligent analysis and decision-making system for regional carbon emission monitoring and operation and maintenance, according to claim 6, characterized in that: The method for implementing the scheme adjustment for the abnormal collection point is as follows: If the abnormal collection point has an ordinary characteristic factor, adjust the priority characteristic factor in the decision plan; if the abnormal collection point has an ordinary non-characteristic factor, adjust the priority non-characteristic factor in the decision plan.

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