Carbon quota determination method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a carbon quota determination method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining big data information of a to-be-quoted enterprise; performing first clustering analysis on the daily electricity consumption information to obtain daily target load electricity consumption information of the enterprise to be quoted; on the basis of the yield information and the capacity information, obtaining enterprise correction coefficient information of the to-be-quoted enterprise; based on the daily electricity consumption information, the capacity information, the enterprise correction coefficient information and the daily target load electricity consumption information, obtaining daily activity level information of the to-be-quoted enterprise; based on the daily activity level information and the carbon emission factor information, obtaining daily carbon emission information of the to-be-quoted enterprise; and based on the carbon emission date information and the daily carbon emission information, obtaining the carbon quota information of the enterprise to be quoted in the first target time period. The carbon quota is determined based on the big data information including the electric power big data, and reliability and accuracy are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of carbon emission technology, and in particular to a carbon quota determination method, device, electronic device, storage medium and program product. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] As one of the means to achieve carbon neutrality, carbon trading can reduce carbon dioxide emissions at a lower cost. The initial allocation of carbon quotas is the core and foundation of the carbon quota trading mechanism.

[0004] In the related art, when initially allocating carbon quotas, quotas are usually allocated based on the historical emissions of the emission units, or based on the production activities of the emission units multiplied by a benchmark value set by the industry.

[0005] However, in the above plan, giving more quotas to companies with high historical carbon emissions will result in low motivation for high-emission companies to reduce emissions; while giving very few carbon quotas to companies with low historical carbon emissions or no historical carbon emissions data will also undermine the motivation of companies to reduce emissions, which is not conducive to reducing carbon emissions. Summary of the invention

[0006] In view of this, the purpose of the present disclosure is to propose a carbon quota determination method, device, electronic device, storage medium and program product, which at least to a certain extent solve one of the technical problems in the related art.

[0007] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a carbon quota determination method, including:

[0008] Obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period;

[0009] Performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota;

[0010] Based on the output information and the capacity information, obtaining the enterprise correction coefficient information of the enterprise to be allocated a quota;

[0011] Based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information, the daily activity level information of the enterprise to be allocated is obtained;

[0012] Based on the daily activity level information and the carbon emission factor information, obtaining the daily carbon emission information of the enterprise to be allocated;

[0013] Based on the carbon emission date information and the daily carbon emission information, the carbon quota information of the enterprise to be allocated within the first target time period is obtained.

[0014] In some exemplary embodiments, the step of obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, includes:

[0015] Obtaining the first original daily electricity consumption information of several electricity users;

[0016] Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas;

[0017] Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information;

[0018] Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

[0019] In some exemplary embodiments, performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated power includes:

[0020] Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively;

[0021] The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

[0022] In some exemplary embodiments, the production information includes production information within a second target time period, and the capacity information includes capacity information within the second target time period;

[0023] The obtaining of the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information includes:

[0024] The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

[0025] In some exemplary embodiments, obtaining the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information includes:

[0026] The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

[0027] In some exemplary embodiments, obtaining the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information includes:

[0028] The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

[0029] In some exemplary embodiments, obtaining the carbon quota information of the enterprise to be allocated carbon quota within the first target time period based on the carbon emission date information and the daily carbon emission information includes:

[0030] The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0031] Based on the same inventive concept, a second aspect of the exemplary embodiment of the present disclosure provides a carbon quota determination device, including:

[0032] A big data information acquisition module is configured to acquire big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period;

[0033] A daily target load power consumption information determination module is configured to perform a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota;

[0034] An enterprise correction coefficient information determination module is configured to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information;

[0035] A daily activity level information determination module is configured to obtain the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information;

[0036] A daily carbon emission information determination module is configured to obtain the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information;

[0037] The carbon quota information determination module is configured to obtain the carbon quota information of the enterprise to be allocated within the first target time period based on the carbon emission date information and the daily carbon emission information.

[0038] In some exemplary embodiments, the big data information acquisition module is configured to:

[0039] Obtaining the first original daily electricity consumption information of several electricity users;

[0040] Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas;

[0041] Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information;

[0042] Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

[0043] In some exemplary embodiments, the daily target load power consumption information determination module is configured as follows:

[0044] Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively;

[0045] The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

[0046] In some exemplary embodiments, the production information includes production information within a second target time period, and the capacity information includes capacity information within the second target time period;

[0047] The enterprise correction coefficient information determination module is configured as follows:

[0048] The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

[0049] In some exemplary embodiments, the daily activity level information determination module is configured to:

[0050] The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

[0051] In some exemplary embodiments, the daily carbon emission information determination module is configured to:

[0052] The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

[0053] In some exemplary embodiments, the carbon quota information determination module is configured to:

[0054] The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0055] Based on the same inventive concept, a third aspect of the exemplary embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0056] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0057] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product, including computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method described in the first aspect.

[0058] From the above, it can be seen that the carbon quota determination method, device, electronic device, storage medium and program product provided by the embodiment of the present disclosure include: obtaining big data information of the enterprise to be allocated, the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product and carbon emission date information within the first target time period; performing a first cluster analysis on the daily electricity consumption information to obtain the daily target load electricity consumption information of the enterprise to be allocated; based on the output information and the production capacity information, obtaining the enterprise correction coefficient information of the enterprise to be allocated; based on the daily electricity consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load electricity consumption information, obtaining the daily activity level information of the enterprise to be allocated; based on the daily activity level information and the carbon emission factor information, obtaining the daily carbon emission information of the enterprise to be allocated; based on the carbon emission date information and the daily carbon emission information, obtaining the carbon quota information of the enterprise to be allocated within the first target time period. The present disclosure determines carbon quotas based on big data information including electric power big data, which improves reliability and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A schematic diagram of an application scenario of a carbon quota determination method provided for an exemplary embodiment of the present disclosure;

[0061] Figure 2 A schematic diagram of a process flow of a carbon quota determination method provided for an exemplary embodiment of the present disclosure;

[0062] Figure 3 A structural schematic diagram of a carbon quota determination device provided for an exemplary embodiment of the present disclosure;

[0063] Figure 4 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, usage scenarios, etc. of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0065] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0066] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0067] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of the present application. Other methods that meet the relevant laws and regulations may also be applied to the implementation method of the present application.

[0068] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0069] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0071] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0072] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0073] As described in the background technology, carbon trading, as one of the means to achieve carbon neutrality, can reduce carbon dioxide emissions at a lower cost. The initial allocation of carbon quotas is the core of the carbon quota trading mechanism and the basis of the carbon quota trading mechanism.

[0074] Specifically, global warming has brought many negative impacts on the environment and socio-economy, and one of the reasons for the global temperature rise is caused by anthropogenic carbon dioxide (CO2) emissions. Carbon quota is an environmental policy tool used to limit and reduce greenhouse gas emissions, especially carbon dioxide. It sets emission caps for emitters and encourages emission reductions through market mechanisms. In the carbon quota trading system, emitters can buy or sell their quotas as an economic incentive to adjust their emission levels.

[0075] In the related art, when initially allocating carbon quotas, quotas are usually allocated based on the historical emissions of the emission units, or based on the production activities of the emission units multiplied by a benchmark value set by the industry.

[0076] Specifically, the calculation methods of carbon quotas are mainly "Historical Method (Grandfathering)" and "Benchmarking Method (Benchmarking)". The historical method is also called the "grandfathering method". It allocates quotas based on the historical emissions of emission units. It is easy to operate and can increase the acceptance of many companies in the early stage of the establishment of the carbon market. The historical method uses the historical emissions of enterprises as a benchmark, so that the quotas issued can basically meet the normal production activities of each enterprise. Therefore, many enterprises will not have a great aversion to it, which is conducive to the establishment of the carbon market. The benchmark method allocates quotas based on the production activities of emission units multiplied by the benchmark value set by the industry, where the benchmark value can be the average level of the industry or product or the advanced level of the industry or product.

[0077] However, the inventors of the present disclosure discovered that, in the above scheme, giving more quotas to enterprises with high historical carbon emissions will result in low motivation for high-emission enterprises to reduce emissions; while giving very few carbon quotas to enterprises with low historical carbon emissions or no historical carbon emissions data will also undermine the motivation of enterprises to reduce emissions, which is not conducive to reducing carbon emissions.

[0078] Specifically, taking the allocation of quotas in the power industry as an example, the power (thermal power generation) industry, which is included in the quota management of the national carbon emission trading market, now uses the benchmark method to determine the carbon quota. The steel, chemical, aviation, nonferrous metals and other industries that are gradually included in the trading market still use the historical method to determine the carbon quota. The historical method gives more quotas to companies with high historical emissions, which will lead to low motivation for high-emission companies to reduce emissions. It is very unfair for emerging companies that use high-tech technologies, because they get very few carbon quotas because of low historical carbon emissions or no historical carbon emissions data. This will also hit the motivation of companies to reduce emissions, which is not conducive to the research and development and promotion of low-carbon technologies.

[0079] In order to solve the above problems, the present disclosure provides a carbon quota determination solution, which specifically includes:

[0080] Obtain big data information of the enterprise to be allocated, the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of products and carbon emission date information within the first target time period; perform a first cluster analysis on the daily electricity consumption information to obtain the daily target load electricity consumption information of the enterprise to be allocated; based on the output information and the production capacity information, obtain the enterprise correction factor information of the enterprise to be allocated; based on the daily electricity consumption information, the production capacity information, the enterprise correction factor information and the daily target load electricity consumption information, obtain the daily activity level information of the enterprise to be allocated; based on the daily activity level information and the carbon emission factor information, obtain the daily carbon emission information of the enterprise to be allocated; based on the carbon emission date information and the daily carbon emission information, obtain the carbon quota information of the enterprise to be allocated within the first target time period. The present disclosure determines carbon quotas based on big data information including electric power big data, thereby improving reliability and accuracy.

[0081] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0082] refer to Figure 1 , which is a schematic diagram of an application scenario of the carbon quota determination method provided by an exemplary embodiment of the present disclosure.

[0083] This application scenario includes a terminal device 101, a server 102, and a data storage system 103. The terminal device 101, the server 102, and the data storage system 103 may be connected via a wired or wireless communication network to achieve data interaction.

[0084] The terminal device 101 may be an electronic device with data transmission and multimedia input / output functions close to the user side, including but not limited to a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a personal digital assistant (PDA) or other electronic devices capable of realizing the above functions. The electronic device may include a processor and a display screen with a touch input function, the display screen is used to present a graphical user interface, the graphical user interface can display an application interface, and the processor is used to process application data, generate a graphical user interface, and control the display of the graphical user interface on the display screen.

[0085] Server 102 and data storage system 103 can both be independent physical servers, or a server cluster or distributed system composed of multiple physical servers. They can also be cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0086] In some exemplary embodiments, the carbon quota determination method may be executed on the server 102 .

[0087] When the carbon quota determination method is run on the server 102, the server 102 is used to provide a carbon quota determination service to the user of the terminal device 101. The terminal device 101 is installed with a client that communicates with the server 102. The user can input at least one enterprise to be allocated quota through the client. After clicking the allocation button, the client sends the enterprise to be allocated quota to the server 102. The server 102 obtains the big data information of the enterprise to be allocated quota, and the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product and carbon emission date information within the first target time period; the daily electricity consumption information is first aggregated Class analysis is performed to obtain the daily target load power consumption information of the enterprise to be allocated; based on the production information and the production capacity information, the enterprise correction coefficient information of the enterprise to be allocated is obtained; based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information, the daily activity level information of the enterprise to be allocated is obtained; based on the daily activity level information and the carbon emission factor information, the daily carbon emission information of the enterprise to be allocated is obtained; based on the carbon emission date information and the daily carbon emission information, the carbon quota information of the enterprise to be allocated in the first target time period is obtained. The server 102 sends the carbon quota information of the enterprise to be allocated in the first target time period to the client, and the client displays the carbon quota information of the enterprise to be allocated in the first target time period to the user to help the user allocate carbon quotas.

[0088] The data storage system 103 stores big data information of enterprises to be allocated quotas, and the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of products, and carbon emission date information within the first target time period.

[0089] Combine the following Figure 1 The carbon quota determination method according to the exemplary embodiment of the present disclosure is described by using the application scenario. It should be noted that the above application scenario is only shown to facilitate understanding of the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0090] refer to Figure 2 , which is a flow chart of a carbon quota determination method provided by an exemplary embodiment of the present disclosure.

[0091] In an exemplary embodiment, the carbon quota determination method includes the following steps:

[0092] Step S210: Obtain big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period.

[0093] In some exemplary embodiments, obtaining big data information of the enterprise to be allocated quota includes:

[0094] Obtain big data information of the target year for the enterprises to be allocated quotas.

[0095] In the specific implementation, since the carbon quota of the enterprise to be allocated in the target year is usually determined after the end of the target year, big data information of the target year can be obtained.

[0096] In some exemplary embodiments, big data information includes daily electricity consumption information. The present disclosure takes into account that electricity consumption data is real-time, covers various industries, has a certain correlation with the level of corporate activities, and can reflect the production status of the enterprise to a certain extent. Based on this, an electricity, production and carbon emission calculation model is provided. The purpose is to utilize the characteristics of electricity data such as real-time, full coverage and reflection of the production status of the enterprise, and design a carbon quota calculation method based on electricity big data.

[0097] Next, we will introduce the daily power consumption information in the power data, specifically:

[0098] In some exemplary embodiments, the step of obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, includes:

[0099] Obtaining the first original daily electricity consumption information of several electricity users;

[0100] Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas;

[0101] Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information;

[0102] Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

[0103] In this exemplary embodiment, the data table includes:

[0104] Based on the daily electricity consumption data of each electricity user provided by the power grid company's data center, a data list or data table is generated by matching multiple underlying data tables, including at least six dimensions: user ID, meter ID, administrative region, industry, date and electricity consumption.

[0105] In this exemplary embodiment, non-enterprise electricity users include:

[0106] Rural residents and urban residents.

[0107] In a specific implementation, based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas, including the following steps:

[0108] Data table acquisition: First, it is necessary to obtain a data table containing the first original daily power consumption information. This data table should contain the daily power consumption information of all power users.

[0109] Identify non-enterprise electricity users: In the data table, there needs to be a field or column to distinguish which are enterprise electricity users and which are non-enterprise electricity users. This is usually determined by unit type, name or specific identification.

[0110] Eliminate non-enterprise electricity users: Once non-enterprise electricity users are identified, the next step is to remove the second raw daily electricity consumption information of these units from the data table. This can be achieved by data filtering, deletion, or creating a new data set.

[0111] Get the third original daily electricity consumption information: After excluding non-enterprise electricity users, the remaining data is the third original daily electricity consumption information of the enterprises to be allocated quotas.

[0112] In a specific implementation, performing a second cluster analysis on the third original daily power consumption information, cleaning the abnormal values ​​in the third original daily power consumption information, and obtaining the cleaned daily power consumption information includes the following steps:

[0113] Data preparation: Standardize or normalize the third original daily electricity consumption information.

[0114] Select clustering algorithm: select a suitable second clustering algorithm according to the characteristics of the third original daily electricity consumption information, such as K-means, hierarchical clustering, DBSCAN, etc.

[0115] Determine the number of clusters (K value): For algorithms that require specifying the number of clusters (such as K-means), you can use methods such as the elbow rule and silhouette coefficient to determine the optimal number of clusters.

[0116] Perform clustering: Apply clustering algorithm to perform cluster analysis on the third original daily electricity consumption information.

[0117] Analyze clustering results: Check the points in each cluster to determine whether the clustering is reasonable and whether there are any abnormal clusters.

[0118] Identify outliers: Outliers are usually located far away from the cluster center or do not belong to any cluster. For density-based clustering algorithms such as DBSCAN, noise points can be directly identified as outliers.

[0119] Handling outliers: Based on the business logic and the specific circumstances of the data, decide whether to delete outliers, replace outliers, or keep them. When replacing outliers, you can use statistics such as cluster center, mean, median, etc.

[0120] In the specific implementation, based on the KNN algorithm, the missing values ​​of the cleaned daily electricity consumption information are filled to obtain the daily electricity consumption information of the enterprise to be allocated quota, including the following steps:

[0121] Select K value: Determine the K value, which is the number of nearest neighbors used to fill the gaps. The selection of K value depends on the size of the daily electricity consumption information after cleaning and the dimension of the feature.

[0122] Feature preprocessing: Standardization / normalization: Since the KNN algorithm is sensitive to the scale of features, the features need to be standardized or normalized. Encoding category features: Since the daily power consumption information after cleaning contains category features, appropriate encoding is required, and One-Hot Encoding can be used.

[0123] Build KNN model: Use the complete dataset (without missing values) to build the KNN model.

[0124] Find nearest neighbors: For each missing value, use the KNN model to find the K nearest neighbors in the cleaned daily electricity consumption information.

[0125] Calculate missing values: Estimate missing values ​​based on the values ​​of the K nearest neighbors. This can be done by calculating the average of the corresponding feature values ​​of these neighbors.

[0126] Fill missing values: fill the estimated missing values ​​into the corresponding positions in the original cleaned daily electricity consumption information.

[0127] In this exemplary embodiment, the first target time period may be a season, a year, or the like.

[0128] In specific implementation, after obtaining the big data information of the enterprise to be allocated quota, the method further includes:

[0129] The industry classification of each enterprise to be allocated quota shall be standardized according to the national economic industry classification standards.

[0130] Step S220: Perform a first cluster analysis on the daily electricity consumption information to obtain the daily target load electricity consumption information of the enterprise to be allocated a quota.

[0131] In some exemplary embodiments, performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated power includes:

[0132] Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively;

[0133] The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

[0134] When implementing:

[0135] Based on the daily electricity consumption data in the data list, considering the production characteristics of each industry, cluster analysis is used to classify the total electricity consumption of each enterprise into three categories, corresponding to the maintenance period, stable production period and unstable production period, and the maximum daily electricity consumption data of the stable production period is selected as the maximum production load electricity consumption, that is, the daily target load electricity consumption information, and the daily target load electricity consumption information is defined as 100% load electricity consumption G 100％ .

[0136] Step S230: Based on the output information and the capacity information, obtain the enterprise correction coefficient information of the enterprise to be allocated quota.

[0137] In some exemplary embodiments, the production information includes production information within a second target time period, and the capacity information includes capacity information within the second target time period;

[0138] The obtaining of the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information includes:

[0139] The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

[0140] In this exemplary embodiment, the second target time period may be a season, a year, or the like.

[0141] As a specific example, the second target time period is a year, that is, the output and capacity are determined in units of years.

[0142] In the specific implementation, considering that the actual production situation of the enterprise often cannot reach the target load production, the actual output will be slightly less than the production capacity. In order to prevent errors, it is necessary to make corrections based on the output and production capacity information. The formula is as follows:

[0143]

[0144] Among them, X Y,i is the correction coefficient of enterprise i, A Y,i is the annual output data of enterprise i, N Y,i is the annual production capacity of enterprise i.

[0145] Step S240: Based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information, obtain the daily activity level information of the enterprise to be allocated.

[0146] In some exemplary embodiments, obtaining the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information includes:

[0147] The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

[0148] In specific implementation, based on the daily electricity consumption data in the data list, the maximum production load daily electricity consumption is screened after data processing, and the daily activity level of the enterprise is calculated in combination with the capacity correction coefficient, as shown in the following formula:

[0149]

[0150] Among them, A D,i is the activity level of enterprise i on day D, X Y,i is the annual capacity correction coefficient of enterprise i, G D,i is the daily electricity consumption of enterprise i, G 100％ is the maximum production load power consumption of enterprise i, D is the number of production days in a year, and can be 330.

[0151] Step S250: Based on the daily activity level information and the carbon emission factor information, obtain the daily carbon emission information of the enterprise to be allocated.

[0152] In some exemplary embodiments, obtaining the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information includes:

[0153] The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

[0154] In specific implementation, the daily CO2 emissions of enterprises are calculated by calculating the daily activity level of the enterprise using the emission factor method, as shown in the following formula:

[0155] E D，i =A D，i ×EF×(1-η);

[0156] Among them, E D,i is the CO2 emissions of enterprise i on day D, EF is the CO2 emission factor of the corresponding product, which is taken from the CEADs database (China Emission Accounts and Datasets), and η is the emission reduction efficiency of the enterprise. In specific implementation, the emission reduction efficiency of the enterprise can be determined by the enterprise itself, or it can be determined based on the emission reduction technology adopted by the enterprise and the relevant literature corresponding to the emission reduction technology. If there is no relevant data information, the value is 0.

[0157] Step S260: Based on the carbon emission date information and the daily carbon emission information, obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0158] In some exemplary embodiments, obtaining the carbon quota information of the enterprise to be allocated carbon quota within the first target time period based on the carbon emission date information and the daily carbon emission information includes:

[0159] The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0160] During the specific implementation, based on the obtained enterprise CO2 accounting results, according to the influence of different production conditions, staggered production and emission reduction strategies, the calculation results of each enterprise's daily CO2 emissions will be added up to different time scales. For example, if the time scale is set to quarter, then the quarterly carbon quota of each enterprise in this target year will be calculated.

[0161] The specific implementation includes the following steps:

[0162] Data collection: It is necessary to collect daily carbon emissions data of the enterprises to be allocated quotas during the first target period.

[0163] Data collation: Ensure that the collected data is accurate and sorted by date to facilitate subsequent addition operations.

[0164] Sum calculation: For each enterprise, add up the daily carbon emissions during the first target period. You can use spreadsheet software (such as Microsoft Excel, Google Sheets, etc.) to perform the operation and use the SUM function to sum the carbon emissions within the specified date range.

[0165] Data verification: After the addition calculation is completed, the result needs to be verified to ensure that there are no calculation errors.

[0166] Result recording: The calculated total carbon emissions are recorded, which is the carbon quota information of the enterprise in the first target time period.

[0167] Comparison with actual carbon emissions: Compare the calculated carbon quota with the actual carbon emissions to determine whether the company meets the emission requirements.

[0168] Follow-up actions: If the carbon emissions of an enterprise exceed the quota, it needs to consider taking measures to reduce emissions, such as improving production processes, using clean energy, etc. If the enterprise has surplus quotas, it can consider selling the quotas through the carbon trading market.

[0169] From the above, it can be seen that the carbon quota determination method provided by the embodiment of the present disclosure includes: obtaining big data information of the enterprise to be allocated, the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of products and carbon emission date information within the first target time period; performing a first cluster analysis on the daily electricity consumption information to obtain the daily target load electricity consumption information of the enterprise to be allocated; based on the output information and the production capacity information, obtaining the enterprise correction coefficient information of the enterprise to be allocated; based on the daily electricity consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load electricity consumption information, obtaining the daily activity level information of the enterprise to be allocated; based on the daily activity level information and the carbon emission factor information, obtaining the daily carbon emission information of the enterprise to be allocated; based on the carbon emission date information and the daily carbon emission information, obtaining the carbon quota information of the enterprise to be allocated within the first target time period. The present disclosure determines carbon quotas based on big data information including electric power big data, which improves reliability and accuracy.

[0170] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0171] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0172] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a carbon quota determination device.

[0173] refer to Figure 3 , a carbon quota determination device, comprising:

[0174] The big data information acquisition module 310 is configured to acquire big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period;

[0175] The daily target load power consumption information determination module 320 is configured to perform a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota;

[0176] The enterprise correction coefficient information determination module 330 is configured to obtain the enterprise correction coefficient information of the enterprise to be allocated quota based on the output information and the capacity information;

[0177] The daily activity level information determination module 340 is configured to obtain the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information;

[0178] The daily carbon emission information determination module 350 is configured to obtain the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information;

[0179] The carbon quota information determination module 360 ​​is configured to obtain the carbon quota information of the enterprise to be allocated within the first target time period based on the carbon emission date information and the daily carbon emission information.

[0180] In some exemplary embodiments, the big data information acquisition module 310 is configured to:

[0181] Obtaining the first original daily electricity consumption information of several electricity users;

[0182] Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas;

[0183] Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information;

[0184] Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

[0185] In some exemplary embodiments, the daily target load power consumption information determination module 320 is configured as follows:

[0186] Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively;

[0187] The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

[0188] In some exemplary embodiments, the production information includes production information within a second target time period, and the capacity information includes capacity information within the second target time period;

[0189] The enterprise correction coefficient information determination module 330 is configured to:

[0190] The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

[0191] In some exemplary embodiments, the daily activity level information determination module 340 is configured to:

[0192] The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

[0193] In some exemplary embodiments, the daily carbon emission information determination module 350 is configured to:

[0194] The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

[0195] In some exemplary embodiments, the carbon quota information determination module 360 ​​is configured to:

[0196] The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0197] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0198] The device of the above embodiment is used to implement the corresponding carbon quota determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0199] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the carbon quota determination method described in any of the above embodiments is implemented.

[0200] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0201] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0202] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0203] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0204] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0205] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0206] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0207] The electronic device of the above embodiment is used to implement the corresponding carbon quota determination method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0208] The memory 1020 stores machine-readable instructions executable by the processor 1010. When the electronic device is running, the processor 1010 communicates with the memory 1020 via the bus 1030, so that the processor 1010 executes the following instructions when running:

[0209] Obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period;

[0210] Performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota;

[0211] Based on the output information and the capacity information, obtaining the enterprise correction coefficient information of the enterprise to be allocated a quota;

[0212] Based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information, the daily activity level information of the enterprise to be allocated is obtained;

[0213] Based on the daily activity level information and the carbon emission factor information, obtaining the daily carbon emission information of the enterprise to be allocated;

[0214] Based on the carbon emission date information and the daily carbon emission information, the carbon quota information of the enterprise to be allocated within the first target time period is obtained.

[0215] In a possible implementation manner, in the instructions executed by the processor 1010, the acquiring of big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, includes:

[0216] Obtaining the first original daily electricity consumption information of several electricity users;

[0217] Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas;

[0218] Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information;

[0219] Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

[0220] In a possible implementation manner, in the instructions executed by the processor 1010, performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated includes:

[0221] Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively;

[0222] The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

[0223] In a possible implementation, in the instruction executed by the processor 1010, the production information includes production information within the second target time period, and the production capacity information includes production capacity information within the second target time period;

[0224] The obtaining of the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information includes:

[0225] The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

[0226] In a possible implementation manner, in the instructions executed by the processor 1010, obtaining the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information, and the daily target load power consumption information includes:

[0227] The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

[0228] In a possible implementation manner, in the instructions executed by the processor 1010, obtaining the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information includes:

[0229] The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

[0230] In a possible implementation, in the instructions executed by the processor 1010, obtaining the carbon quota information of the enterprise to be allocated carbon quota within the first target time period based on the carbon emission date information and the daily carbon emission information includes:

[0231] The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

[0232] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the carbon quota determination method as described in any of the above embodiments.

[0233] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0234] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0235] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the carbon quota determination method described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0236] Based on the same inventive concept, corresponding to the carbon quota determination method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the carbon quota determination method. Corresponding to the execution subject corresponding to each step in each embodiment of the carbon quota determination method, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0237] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the carbon quota determination method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0238] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." In addition, in some embodiments, the present disclosure may also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0239] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive examples) of computer-readable storage media may include, for example: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0240] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0241] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0242] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0243] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified in the boxes in the flowchart and / or block diagram.

[0244] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable medium produce a product that includes an instruction device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0245] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable device to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0246] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0247] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0248] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0249] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0250] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0251] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0252] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0253] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A method for determining a carbon quota, characterized in that: include: Obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period; Performing a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota; Based on the output information and the capacity information, obtaining the enterprise correction coefficient information of the enterprise to be allocated a quota; Based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information, the daily activity level information of the enterprise to be allocated is obtained; Based on the daily activity level information and the carbon emission factor information, obtaining the daily carbon emission information of the enterprise to be allocated; Based on the carbon emission date information and the daily carbon emission information, the carbon quota information of the enterprise to be allocated within the first target time period is obtained.

2. The method according to claim 1, characterized in that The step of obtaining big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, includes: Obtaining the first original daily electricity consumption information of several electricity users; Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas; Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information; Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

3. The method according to claim 1, characterized in that: The first cluster analysis is performed on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated power, including: Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively; The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

4. The method according to claim 1, characterized in that The production information includes production information within a second target time period, and the production capacity information includes production capacity information within the second target time period; The obtaining of the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information includes: The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

5. The method according to claim 4, characterized in that The obtaining of the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information includes: The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

6. The method according to claim 1, characterized in that The obtaining of the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information includes: The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

7. The method according to claim 1, characterized in that The obtaining, based on the carbon emission date information and the daily carbon emission information, the carbon quota information of the enterprise to be allocated within the first target time period includes: The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

8. A carbon quota determination device, characterized in that: include: A big data information acquisition module is configured to acquire big data information of the enterprise to be allocated quota, wherein the big data information includes daily electricity consumption information, output information, production capacity information, carbon emission factor information of the product, and carbon emission date information within the first target time period; A daily target load power consumption information determination module is configured to perform a first cluster analysis on the daily power consumption information to obtain the daily target load power consumption information of the enterprise to be allocated a quota; An enterprise correction coefficient information determination module is configured to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota based on the output information and the capacity information; A daily activity level information determination module is configured to obtain the daily activity level information of the enterprise to be allocated based on the daily power consumption information, the production capacity information, the enterprise correction coefficient information and the daily target load power consumption information; A daily carbon emission information determination module is configured to obtain the daily carbon emission information of the enterprise to be allocated based on the daily activity level information and the carbon emission factor information; The carbon quota information determination module is configured to obtain the carbon quota information of the enterprise to be allocated within the first target time period based on the carbon emission date information and the daily carbon emission information.

9. The device according to claim 8, characterized in that The big data information acquisition module is configured as follows: Obtaining the first original daily electricity consumption information of several electricity users; Based on the data table, the second original daily electricity consumption information of non-enterprise electricity users in the first original daily electricity consumption information is eliminated to obtain the third original daily electricity consumption information of the enterprises to be allocated quotas; Performing a second cluster analysis on the third original daily power consumption information, cleaning abnormal values ​​in the third original daily power consumption information, and obtaining cleaned daily power consumption information; Based on the KNN algorithm, missing values ​​are filled in the cleaned daily electricity consumption information to obtain the daily electricity consumption information of the enterprise to be allocated.

10. The device according to claim 8, characterized in that The daily target load power consumption information determination module is configured as follows: Perform a first cluster analysis on the daily power consumption information to obtain the maximum daily power consumption information corresponding to the maintenance period, the stable production period, and the unstable production period respectively; The maximum daily power consumption information corresponding to the stable production period is used as the daily target load power consumption information of the enterprise to be allocated.

11. The device according to claim 8, characterized in that The production information includes production information within a second target time period, and the production capacity information includes production capacity information within the second target time period; The enterprise correction coefficient information determination module is configured as follows: The output information within the second target time period is divided by the production capacity information within the second target time period to obtain the enterprise correction coefficient information of the enterprise to be allocated a quota.

12. The device according to claim 11, characterized in that The daily activity level information determination module is configured to: The daily activity level information of the enterprise to be allocated is obtained by dividing the product of the daily power consumption information, the production capacity information and the enterprise correction coefficient information by the product of the daily target load power consumption information and the number of days in the second target time period.

13. The device according to claim 8, characterized in that The daily carbon emission information determination module is configured to: The daily activity level information is multiplied by the carbon emission factor information to obtain the daily carbon emission information of the enterprise to be allocated a quota.

14. The device according to claim 8, characterized in that The carbon quota information determination module is configured to: The daily carbon emission information corresponding to the carbon emission date information is summed up to obtain the carbon quota information of the enterprise to be allocated within the first target time period.

15. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

16. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

17. A computer program product, characterized in that The method comprises computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 7.

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