Optimized control method, device and equipment for carbon emission and medium

Abstract

The invention provides a carbon emission optimization control method and device, equipment and a medium, and the method comprises the steps: obtaining the production data of at least two production conditions of a target project, and obtaining the carbon emission data; performing clustering analysis on the production data to obtain a condition combination with the lowest carbon emission; standardizing the production data to obtain standardized data; calculating a carbon emission contribution rate corresponding to each production condition; according to the standardized data, calculating a carbon emission combined influence coefficient between the production conditions; determining an optimization control target according to the carbon emission contribution rate and the carbon emission combined influence coefficient to obtain optimization control parameters; performing optimization control on the target project according to the optimization control parameters; and in the optimization control process, the production data is operated in a range corresponding to the condition combination. According to the method, the combined influence among all production condition factors is considered, and the adjustment efficiency of the carbon emission of the target project can be effectively improved.

Description

Technical Field

[0001] The present invention application relates to the field of carbon emission data processing, and particularly to an optimization control method, device, equipment and medium for carbon emissions. Background Art

[0002] With the development of economy and technology and the increase in carbon emissions, in order to achieve goals such as "carbon peak" and "carbon neutrality", all industries have actively responded to promote the reduction of carbon emissions. Industrial production is one of the main sources of carbon emissions and is crucial for achieving carbon emission reduction. However, existing technologies often adjust and control carbon emissions for individual production condition factors. After completing the carbon emission adjustment based on a production condition factor, further carbon emission adjustment is carried out for another production condition factor. Since these production condition factors are independent of each other, this adjustment method ignores the combined influence between production condition factors, resulting in low carbon emission adjustment efficiency. Summary of the Invention

[0003] The present invention application provides an optimization control method, device, equipment and medium for carbon emissions to solve the technical problem of how to improve the adjustment efficiency of carbon emissions.

[0004] To solve the above technical problem, the present invention application provides an optimization control method for carbon emissions, including:

[0005] Obtain production data of at least two production conditions of a target project, and obtain carbon emission data of the target project;

[0006] Perform clustering analysis on the production data according to the carbon emission data to obtain a condition combination with the lowest carbon emissions;

[0007] Perform standardization processing on the production data corresponding to each production condition to obtain standardized data; calculate the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculate the carbon emission combined influence coefficient between each production condition according to the standardized data;

[0008] Determine an optimization control target according to the carbon emission contribution rate and the carbon emission combined influence coefficient to obtain optimization control parameters; perform optimization control on the target project according to the optimization control parameters; wherein, during the optimization control process, the production data of the target project runs within the range of the production data corresponding to the condition combination.

[0009] As a preferred solution, the performing standardization processing on the production data corresponding to each production condition to obtain standardized data includes: performing standardization processing on the production data corresponding to each production condition according to the maximum and minimum values of the production condition in the production data to obtain standardized data.

[0010] As a preferred solution, calculating the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data includes:

[0011] Obtain a preset regression model;

[0012] Solve the regression model according to the standardized data and the carbon emission data to obtain the regression coefficients corresponding to each production condition, and then obtain the carbon emission contribution rate corresponding to each production condition based on the regression coefficients; where

[0013] The regression model includes:

[0014] C = β0 + β1Z1 + β2Z2 + … + β n Z n + ε;

[0015] where ε is the error term, C represents the carbon emission data; β1, β2, …, β n are the regression coefficients corresponding to each production data A1, A2, …, A n respectively, and β0 is a constant.

[0016] As a preferred solution, obtaining the carbon emission contribution rate corresponding to each production condition based on the regression coefficients includes:

[0017] Calculate the carbon emission contribution rate corresponding to the production condition according to the following formula:

[0018]

[0019] where R i is the carbon emission contribution rate corresponding to the production data A i , and n is the total number of production conditions.

[0020] As a preferred solution, calculating the carbon emission joint influence coefficient between each production condition according to the standardized data includes:

[0021] Calculate the carbon emission joint influence coefficient according to the following formula:

[0022]

[0023] where Z j is the value after standardized processing of the production data A j , Z ik is the value of Z i at the kth data point, Z jk is the value of Z j at the kth data point, is Z i 's average value, is Zj The average value, m is the total number of data points, R ij is production data A i and production data A j is the combined carbon emission impact coefficient between them.

[0024] As a preferred solution, based on the carbon emission data, performing cluster analysis on the production data to obtain a combination of conditions with the lowest carbon emissions, including:

[0025] Performing cluster analysis on the production data to respectively determine the range parameters corresponding to each production condition;

[0026] Based on each of the range parameters, eliminating the production data outside each of the range parameters;

[0027] Grouping the production data after elimination according to each production condition to obtain a combination of conditions with the lowest carbon emissions.

[0028] As a preferred solution, obtaining the production data of at least two production conditions of the target project, including:

[0029] Obtaining, through sensors, the production data of at least two production conditions among the temperature, pressure, running time, noise, flow rate, and humidity of the target project.

[0030] Correspondingly, the present invention application also provides an optimized control device for carbon emissions, including an acquisition module, a clustering module, a calculation module, and an optimized control module;

[0031] The acquisition module is used to obtain the production data of at least two production conditions of the target project and obtain the carbon emission data of the target project;

[0032] The clustering module is used to perform cluster analysis on the production data according to the carbon emission data to obtain a combination of conditions with the lowest carbon emissions;

[0033] The calculation module is used to perform standardization processing on the production data corresponding to each production condition to obtain standardized data; calculating the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculating the combined carbon emission impact coefficient between each production condition according to the standardized data;

[0034] The optimized control module is used to determine an optimized control target according to the carbon emission contribution rate and the combined carbon emission impact coefficient to obtain optimized control parameters; realizing optimized control of the target project according to the optimized control parameters; wherein, during the optimized control process, the production data of the target project runs within the production data range corresponding to the condition combination.

[0035] As a preferred solution, the calculation module standardizes the production data corresponding to each production condition to obtain standardized data, including: the calculation module standardizes the production data corresponding to each production condition according to the maximum and minimum values of the production condition in the production data to obtain standardized data.

[0036] As a preferred solution, the calculation module calculates the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data, including:

[0037] The calculation module obtains a preset regression model;

[0038] According to the standardized data and the carbon emission data, solve the regression model to obtain the regression coefficients corresponding to each production condition, and then obtain the carbon emission contribution rate corresponding to each production condition based on the regression coefficients; where,

[0039] The regression model includes:

[0040] C = β0 + β1Z1 + β2Z2 + … + β n Z n + ε;

[0041] where ε is the error term, C represents the carbon emission data; β1, β2, …, β n are the regression coefficients corresponding to each production data A1, A2, …, A n and β0 is a constant.

[0042] As a preferred solution, the calculation module obtains the carbon emission contribution rate corresponding to each production condition based on the regression coefficients, including:

[0043] The calculation module calculates the carbon emission contribution rate corresponding to the production condition according to the following formula:

[0044]

[0045] where, R i is the carbon emission contribution rate corresponding to the production data A i and n is the total number of production conditions.

[0046] As a preferred solution, the calculation module calculates the carbon emission joint influence coefficient between each production condition according to the standardized data, including:

[0047] The calculation module calculates the carbon emission joint influence coefficient according to the following formula:

[0048]

[0049] where, Z j is the production data Aj The value of Z after standardization ik is Z i The value of Z at the k-th data point jk is Z j The value at the k-th data point is Z i The average value of is Z j The average value of, where m is the total number of data points, R ij For production data A i And production data A j The combined carbon emission impact coefficient between them

[0050] As a preferred solution, the clustering module performs clustering analysis on the production data according to the carbon emission data to obtain the combination of conditions with the lowest carbon emission, including:

[0051] The clustering module performs clustering analysis on the production data and respectively determines the range parameters corresponding to each production condition;

[0052] Based on each of the range parameters, the production data outside each of the range parameters is excluded;

[0053] The production data after exclusion is grouped according to each production condition to obtain the combination of conditions with the lowest carbon emission.

[0054] As a preferred solution, the clustering module obtains production data of at least two production conditions of the target project, including:

[0055] The clustering module obtains production data of at least two production conditions such as temperature, pressure, operating time, noise, flow rate, and humidity of the target project through sensors.

[0056] Correspondingly, the present invention application also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the optimized control method of carbon emission as described above is implemented.

[0057] Correspondingly, the present invention application also provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. Wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimized control method of carbon emission as described above.

[0058] Compared with the prior art, the present invention application has the following beneficial effects:

[0059] The present invention application provides an optimized control method, device, equipment and medium for carbon emissions. The method includes: obtaining production data of at least two production conditions of a target project, and obtaining carbon emission data of the target project; performing cluster analysis on the production data according to the carbon emission data to obtain a condition combination with the lowest carbon emission; performing standardization processing on the production data corresponding to each production condition to obtain standardized data; calculating the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculating the carbon emission joint influence coefficient between each production condition according to the standardized data; determining an optimized control target according to the carbon emission contribution rate and the carbon emission joint influence coefficient to obtain optimized control parameters; and performing optimized control on the target project according to the optimized control parameters; wherein, during the optimized control process, the production data of the target project runs within the production data range corresponding to the condition combination. Implementing the present application, by obtaining the production data and carbon emission data of at least two production conditions of the target project, performing cluster analysis, and obtaining a condition combination with the lowest carbon emission; performing standardization processing on the production data, calculating the carbon emission contribution rate corresponding to the production condition and the carbon emission joint influence coefficient between each production condition according to the standardized data and the carbon emission data, so as to determine an optimized control target for the carbon emissions of the target project and obtain optimized control parameters. Compared with the prior art, it also considers the joint influence between each production condition factor on the basis of considering the contribution of a single production condition factor; performing optimized control on the target project according to the optimized control parameters, and at the same time making the production data of the target project run within the production data range corresponding to the condition combination during the optimized control process, that is, considering the production condition combination with the lowest carbon emission, and can perform combined adjustment or joint adjustment of at least two production conditions for the target project, effectively improving the adjustment efficiency of the carbon emissions of the target project. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 : It is a schematic flowchart of an embodiment of the optimized control method for carbon emissions provided by the present invention application.

[0061] Figure 2 : It is a schematic principle diagram of an embodiment of the optimized control method for carbon emissions provided by the present invention application.

[0062] Figure 3 : It is a schematic diagram of an embodiment of data extraction of the optimized control method for carbon emissions provided by the present invention application.

[0063] Figure 4 : It is a schematic principle diagram of an embodiment of obtaining a condition combination of the optimized control method for carbon emissions provided by the present invention application.

[0064] Figure 5: A schematic structural diagram of an embodiment of the optimized control device for carbon emissions provided by this invention application. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0066] Embodiment 1

[0067] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an embodiment of an optimized control method for carbon emissions provided by this invention application. The optimized control method for carbon emissions includes steps S101 to S104, and each step is described in detail as follows:

[0068] Step S101, obtain production data of at least two production conditions of the target project, and obtain the carbon emission data of the target project.

[0069] In this step, as Figure 2 described, the production process of the target project can be detailedly divided, and each production process is split into multiple independent processes (such as Figure 2 Process 1, Process 2, Process 3,..., Process n shown), and through carbon data acquisition sensors (such as smart electricity meters, smart water meters, etc.), the carbon emission data of each process is collected and analyzed in real time to obtain carbon emission data, so as to achieve refined and modular management and optimization.

[0070] Furthermore, on the basis of dividing the process flow, identify and determine the main production condition parameters affecting carbon emissions, such as temperature, pressure, running time, noise, flow rate, and humidity, etc. And through sensors and real-time monitoring systems, ensure multi-dimensional acquisition of parameters, and each parameter can be associated with a specific process flow to form a corresponding relationship of condition - process - carbon emissions.

[0071] Therefore, through sensors, production data of at least two production conditions of the target project, such as temperature, pressure, running time, noise, flow rate, and humidity, can be obtained.

[0072] Then, the obtained production data and carbon emission data can be transmitted to the intelligent gateway through the device interface. The intelligent gateway can perform preliminary screening and integration on the data. After eliminating abnormal data, it uploads the data to the central processing module or the cloud data processing center. The data is denoised through a preset algorithm model to lay a foundation for subsequent analysis. In addition, based on the integrated production condition data and the real-time collected carbon emission data, combined with international or industry standards, the carbon emissions of each process can be accurately calculated using the preset carbon emission accounting formula and carbon emission factors.

[0073] Step S102: According to the carbon emission data, perform clustering analysis on the production data to obtain the condition combination with the lowest carbon emissions.

[0074] In this step, according to the carbon emission data, the production data with the least carbon emissions can be screened out. For example, the 1 / 4 production data with the least carbon emissions is obtained, and a multi-dimensional production condition data set is formed to create a preliminary data pool, as Figure 3 shown.

[0075] Perform clustering analysis on the production data in the above preliminary data pool, and respectively determine the range parameters corresponding to each production condition; based on each of the range parameters, eliminate the production data outside each of the range parameters; group the production data after elimination according to each production condition to obtain the condition combination with the lowest carbon emissions.

[0076] For example, for the production condition number i, i = 1, 2, 3,..., n. Ai is the production data corresponding to the production condition i.

[0077] Through clustering analysis, determine the range of the production data corresponding to each production condition. For example:

[0078] Ai > Ai min and / or Ai < Ai max ;

[0079] where, Ai max and Ai min respectively represent the maximum and minimum values allowed for Ai. Then, eliminate the production data outside the range.

[0080] As Figure 4 shown, the production data can be grouped according to the production conditions.

[0081] For example, after clustering, three groups are obtained:

[0082] Group 1: Temperature 310°C to 320°C, pressure 8.5 MPa to 9.0 MPa.

[0083] Group 2: Temperature 330°C to 340°C, pressure 8.0 MPa to 8.5 MPa.

[0084] Group 3: temperature 320°C to 330°C, pressure 8.8 MPa to 9.2 MPa.

[0085] And select the group with the smallest carbon emissions among them to obtain the combination of conditions with the lowest carbon emissions.

[0086] Step S103: Standardize the production data corresponding to each production condition to obtain standardized data; calculate the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculate the combined carbon emission influence coefficient between each production condition according to the standardized data.

[0087] In this step, the production data corresponding to each production condition can be standardized according to the maximum and minimum values of the production data in the production conditions to obtain standardized data.

[0088] Exemplarily, a standardization method based on Min - Max can be adopted, as shown in the following formula:

[0089]

[0090] where Ai j represents the production data of production condition i at data point j, min(Ai) represents the minimum value of production condition i in the production data, max(Ai) represents the maximum value of production condition i in the production data, and Z i is the value after standardizing the production data Ai of production condition i.

[0091] Using the above Min - Max standardization method, the standardized values Z1, Z2, Z3,..., Z i ,..., Z n-1 , Z n .

[0092] Furthermore, according to the standardized data and the carbon emission data, calculate the carbon emission contribution rate corresponding to each production condition. Specifically:

[0093] Obtain a preset regression model;

[0094] Solve the regression model according to the standardized data and the carbon emission data to obtain the regression coefficients corresponding to each production condition, and then obtain the carbon emission contribution rate corresponding to each production condition based on the regression coefficients; where

[0095] the regression model includes:

[0096] C = β0 + β1Z1 + β2Z2 +... + β n Zn + ε;

[0097] where ε is the error term, and C represents carbon emission data; β1, β2, …, β n are the regression coefficients corresponding to each production data A1, A2, …, A n , and β0 is a constant.

[0098] Then, the carbon emission contribution rate corresponding to the production conditions is calculated according to the following formula:

[0099]

[0100] where, R i is the carbon emission contribution rate corresponding to production data A i , and n is the total number of production conditions.

[0101] On the other hand, while / before / after calculating the contribution rate, the carbon emission combined influence coefficient between each production condition can also be calculated according to the standardized data, specifically:

[0102] The carbon emission combined influence coefficient is calculated according to the following formula:

[0103]

[0104] where, Z j is the value after the standardized processing of production data A j , Z ik is the value of Z i at the kth data point, Z jk is the value of Z j at the kth data point, is the average value of Z i , is the average value of Z j , m is the total number of data points, and R ij is the carbon emission combined influence coefficient between production data A i and production data A j .

[0105] In this embodiment, if the correlation coefficient between two conditions is close to 1 or -1, it indicates that they have a strong linear relationship. For example, if the correlation coefficient between temperature and pressure is very close to 1, it indicates that the change trends of temperature and pressure are very similar. If the correlation coefficient is close to 0, it indicates that the linear relationship between these two conditions is weak, and they may not be the key factors affecting carbon emissions.

[0106] Step S104, determine the optimization control target according to the carbon emission contribution rate and the carbon emission combined influence coefficient, and obtain the optimization control parameters; implement the optimization control on the target project according to the optimization control parameters.

[0107] Among them, the production data of the target project in the optimization control process runs within the production data range corresponding to the combination of conditions.

[0108] In this step, the optimization control target can be determined according to the carbon emission contribution rate and the combined carbon emission influence coefficient, and the optimization control parameters can be obtained. For example, the optimization control parameters can be described by priority.

[0109] Exemplarily: Select the optimization focus according to the contribution rate ranking, determine the key optimization conditions, and obtain the first priority order; according to the combined influence coefficient, preferentially select the conditions with a high carbon emission contribution rate and a strong correlation with other conditions as the optimization target, and obtain the second priority order; thus, by using an intelligent algorithm to process the first priority order and the second priority order, the optimization control target can be obtained, and then the specific optimization control parameters for the target project can be obtained under the optimization control target.

[0110] This embodiment can establish a real-time monitoring system, and use sensors and intelligent algorithms to automatically adjust each production condition according to the actual production situation. For example, if the carbon emission is too high, the system can automatically adjust conditions such as temperature and pressure to restore them to the optimized range, so as to ensure that the production data of each production condition of the target project in the optimization control process runs within the production data range corresponding to the combination of conditions.

[0111] Correspondingly, as Figure 5 shown, the present invention application also provides an optimization control device 500 for carbon emissions, including an acquisition module 501, a clustering module 502, a calculation module 503, and an optimization control module 504;

[0112] The acquisition module 501 is used to acquire the production data of at least two production conditions of the target project and acquire the carbon emission data of the target project;

[0113] The clustering module 502 is used to perform clustering analysis on the production data according to the carbon emission data to obtain the combination of conditions with the lowest carbon emission;

[0114] The calculation module 503 is used to perform standardization processing on the production data corresponding to each production condition to obtain standardized data; calculate the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculate the combined carbon emission influence coefficient between each production condition according to the standardized data;

[0115] The optimization control module 504 is configured to determine an optimization control target based on the carbon emission contribution rate and the combined carbon emission impact coefficient, and obtain optimization control parameters; and perform optimization control on the target project according to the optimization control parameters; wherein, during the optimization control process, the production data of the target project runs within the production data range corresponding to the condition combination.

[0116] As a preferred solution, the calculation module 503 performs standardization processing on the production data corresponding to each production condition to obtain standardized data, including: the calculation module 503 performs standardization processing on the production data corresponding to each production condition according to the maximum and minimum values of the production data in the production condition, and obtains standardized data.

[0117] As a preferred solution, the calculation module 503 calculates the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data, including:

[0118] The calculation module 503 obtains a preset regression model;

[0119] According to the standardized data and the carbon emission data, the regression model is solved to obtain the regression coefficients corresponding to each production condition, and then the carbon emission contribution rate corresponding to each production condition is obtained based on the regression coefficients; wherein,

[0120] The regression model includes:

[0121] C = β0 + β1Z1 + β2Z2 + … + β n Z n + ε;

[0122] where ε is an error term, C represents carbon emission data; β1, β2, …, β n are the regression coefficients corresponding to each production data A1, A2, …, A n respectively, and β0 is a constant.

[0123] As a preferred solution, the calculation module 503 obtains the carbon emission contribution rate corresponding to each production condition based on the regression coefficients, including:

[0124] The calculation module 503 calculates the carbon emission contribution rate corresponding to the production condition according to the following formula:

[0125]

[0126] where, R i is the carbon emission contribution rate corresponding to the production data A i , and n is the total number of production conditions.

[0127] As a preferred solution, the calculation module 503 calculates the combined carbon emission impact coefficients between production conditions according to the standardized data, including:

[0128] The calculation module 503 calculates the combined carbon emission impact coefficient according to the following formula:

[0129]

[0130] where Z j is the value after standardized processing of production data A j , Z ik is the value of Z i at the k-th data point, Z jk is the value of Z j at the k-th data point, is the average value of Z i , is the average value of Z j , m is the total number of data points, and R ij is the combined carbon emission impact coefficient between production data A i and production data A j .

[0131] As a preferred solution, the clustering module 502 performs clustering analysis on the production data according to the carbon emission data to obtain the condition combination with the lowest carbon emission, including:

[0132] The clustering module 502 performs clustering analysis on the production data and respectively determines the range parameters corresponding to each production condition;

[0133] Based on each of the range parameters, the production data outside each of the range parameters is excluded;

[0134] The production data after exclusion is grouped according to each production condition to obtain the condition combination with the lowest carbon emission.

[0135] As a preferred solution, the clustering module 502 obtains production data of at least two production conditions of the target project, including:

[0136] The clustering module 502 obtains production data of at least two production conditions among temperature, pressure, running time, noise, flow rate, and humidity of the target project through sensors.

[0137] Correspondingly, the present invention application also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the optimized control method for carbon emission as described above is implemented.

[0138] The so-called 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal and connects various parts of the entire terminal through various interfaces and circuits.

[0139] The memory can be used to store the computer program. The processor realizes various functions of the terminal by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0140] Correspondingly, the present invention application also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimized control method for carbon emissions described above.

[0141] Among them, if the modules integrated in the optimized control device / terminal for carbon emissions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0142] Compared with the prior art, the present invention application has the following beneficial effects:

[0143] The present invention provides an optimized control method, device, equipment and medium for carbon emissions. The method includes: obtaining production data of at least two production conditions of a target project and obtaining carbon emission data of the target project; performing clustering analysis on the production data according to the carbon emission data to obtain a condition combination with the lowest carbon emission; performing standardization processing on the production data corresponding to each production condition to obtain standardized data; calculating the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data; calculating the carbon emission joint influence coefficient between each production condition according to the standardized data; determining an optimized control target according to the carbon emission contribution rate and the carbon emission joint influence coefficient to obtain optimized control parameters; implementing optimized control on the target project according to the optimized control parameters; wherein, during the optimized control process, the production data of the target project runs within the production data range corresponding to the condition combination. Implementing the present application, by obtaining the production data and carbon emission data of at least two production conditions of the target project, performing clustering analysis to obtain a condition combination with the lowest carbon emission; performing standardization processing on the production data, calculating the carbon emission contribution rate corresponding to the production condition and the carbon emission joint influence coefficient between each production condition according to the standardized data and the carbon emission data, so as to determine an optimized control target for the carbon emissions of the target project and obtain optimized control parameters. Compared with the prior art, it also considers the joint influence between each production condition factor on the basis of considering the contribution of a single production condition factor; implementing optimized control on the target project through the optimized control parameters, and at the same time making the production data of the target project run within the production data range corresponding to the condition combination during the optimized control process, that is, considering the production condition combination with the lowest carbon emission, and can perform combined adjustment or joint adjustment of at least two production conditions for the target project, effectively improving the adjustment efficiency of the carbon emissions of the target project.

[0144] The above specific embodiments further elaborate on the purpose, technical solution and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimized control method for carbon emissions, characterized in that, Including: Obtain production data of at least two production conditions of the target project, and obtain the carbon emission data of the target project; According to the carbon emission data, perform cluster analysis on the production data to obtain the condition combination with the lowest carbon emission; Perform standardization processing on the production data corresponding to each production condition to obtain standardized data; according to the standardized data and the carbon emission data, calculate the carbon emission contribution rate corresponding to each production condition; according to the standardized data, calculate the carbon emission joint influence coefficient between each production condition; Determine the optimization control target according to the carbon emission contribution rate and the carbon emission joint influence coefficient to obtain the optimization control parameters; perform optimization control on the target project according to the optimization control parameters; wherein, during the optimization control process, the production data of the target project runs within the production data range corresponding to the condition combination.

2. The optimized control method for carbon emissions according to claim 1, characterized in that, The performing standardization processing on the production data corresponding to each production condition to obtain standardized data includes: performing standardization processing on the production data corresponding to each production condition according to the maximum value and the minimum value of the production data in the production condition to obtain standardized data.

3. The optimized control method for carbon emissions according to claim 2, characterized in that, The calculating the carbon emission contribution rate corresponding to each production condition according to the standardized data and the carbon emission data includes: Obtain a preset regression model; According to the standardized data and the carbon emission data, solve the regression model to obtain the regression coefficients corresponding to each production condition, and then obtain the carbon emission contribution rate corresponding to each production condition based on the regression coefficients; wherein, The regression model includes: C = β0 + β1Z1 + β2Z2 + … + β n Z n + ε; Among them, ε is the error term, and C represents carbon emission data; β1, β2, …, β n are the regression coefficients corresponding to each production data A1, A2, …, A n , and β0 is a constant.

4. The optimized control method for carbon emissions according to claim 3, characterized in that, The obtaining the carbon emission contribution rate corresponding to each production condition based on the regression coefficients includes: Calculate the carbon emission contribution rate corresponding to the production condition according to the following formula: Among them, R i is the corresponding carbon emission contribution rate of production data A i , and n is the total number of production conditions.

5. The optimized control method for carbon emissions according to claim 2, characterized in that The calculating the carbon emission joint influence coefficient between each production condition according to the standardized data includes: Calculate the carbon emission joint influence coefficient according to the following formula: Among them, Z j is the value after standardization of production data A j , Z ik is the value of Z i at the k-th data point, Z jk is the value of Z j at the k-th data point, is the average value of Z i , is the average value of Z j , m is the total number of data points, R ij is the combined carbon emission impact coefficient between production data A i and production data A j .

6. The optimization control method for carbon emissions according to claim 1, characterized in that, The performing cluster analysis on the production data according to the carbon emission data to obtain the condition combination with the lowest carbon emission includes: Perform cluster analysis on the production data, and respectively determine the range parameters corresponding to each production condition; Based on each of the range parameters, eliminate the production data outside each of the range parameters; Group the production data after elimination according to each production condition to obtain the condition combination with the lowest carbon emission.

7. A method for optimizing control of carbon emissions according to any one of claims 1 to 6, characterized in that The obtaining the production data of at least two production conditions of the target project includes: Through sensors, obtain the production data of at least two production conditions among the temperature, pressure, running time, noise, flow rate and humidity of the target project.

8. An optimized control device for carbon emissions, characterized in that, Including an acquisition module, a clustering module, a calculation module and an optimization control module; The acquisition module is used to obtain the production data of at least two production conditions of the target project and obtain the carbon emission data of the target project; The clustering module is used to perform cluster analysis on the production data according to the carbon emission data to obtain the condition combination with the lowest carbon emission; The calculation module is used to perform normalization processing on the production data corresponding to each production condition to obtain normalized data; calculate the carbon emission contribution rate corresponding to each production condition according to the normalized data and the carbon emission data; calculate the carbon emission joint influence coefficient between each production condition according to the normalized data; The optimization control module is used to determine an optimization control target according to the carbon emission contribution rate and the carbon emission joint influence coefficient to obtain optimization control parameters; perform optimization control on the target project according to the optimization control parameters; wherein, during the optimization control process, the production data of the target project runs within the production data range corresponding to the condition combination.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization control method for carbon emissions as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program. Wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the optimization control method for carbon emissions as described in any one of claims 1 to 7.

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