Enterprise green low-carbon management method based on cloud platform

Abstract

The invention provides an enterprise green low-carbon management method based on a cloud platform, and relates to the technical field of carbon emission management, and the method comprises the steps: receiving and preprocessing first environment data, and obtaining first processing information; performing information analysis on the first processing information to obtain a first analysis result, and identifying a plurality of carbon emission nodes; the enterprise low-carbon management requirement serves as the constraint, the minimum energy consumption, the minimum carbon emission and the minimum waste quantity serve as the target, a plurality of emission reduction strategies are generated and sent to the strategy evaluation module for evaluation, and the optimal emission reduction strategy is selected for enterprise green low-carbon management. Through the enterprise low-carbon management method and device, the technical problem of low management efficiency caused by low efficiency of processing a large amount of complex data in enterprise low-carbon management in the prior art can be solved, automation and intelligence of data collection, analysis and strategy evaluation are realized by building the enterprise low-carbon management cloud platform, and the efficiency of enterprise low-carbon management is improved.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission management, and in particular to a green and low-carbon management method for enterprises based on a cloud platform. Background Art

[0002] With the increasing attention paid to environmental protection and sustainable development around the world, it has become a trend for enterprises to implement green and low-carbon management. At present, most enterprises generally adopt automated monitoring systems, such as energy management systems and intelligent monitoring systems, to collect energy consumption data in real time, automatically monitor and report energy usage, and greatly reduce the reliance on manual data collection. However, there are still some problems in practical applications. Since a large amount of data may come from different sources and formats, high-performance computing power and analysis tools are required for processing and analysis, while existing automated tools often lack sufficient intelligence and cannot accurately interpret complex data relationships, making it difficult to meet the needs of efficient processing of large amounts of data.

[0003] To sum up, there are technical problems in the existing technology that the data processing and analysis capabilities of enterprises are limited when conducting green and low-carbon management, making it difficult to meet the needs of fast and accurate environmental management, resulting in low management efficiency. Summary of the invention

[0004] The purpose of this application is to provide a cloud platform-based enterprise green and low-carbon management method to solve the technical problems in the prior art that the enterprise has limited data processing and analysis capabilities when conducting green and low-carbon management, making it difficult to meet the needs of fast and accurate environmental management, resulting in low management efficiency.

[0005] In view of the above problems, the present application provides a cloud platform-based enterprise green and low-carbon management method, wherein the cloud platform-based enterprise green and low-carbon management method includes: receiving first environmental data through the data collection module, and preprocessing the first environmental data through the preprocessing channel in the data collection module to obtain first processing information; performing information analysis on the first processing information through the data analysis module to obtain a first analysis result, and identifying multiple carbon emission nodes based on the first analysis result; taking the enterprise low-carbon management needs as constraints, and taking the lowest energy consumption, the lowest carbon emissions and the lowest amount of waste as goals, generating multiple emission reduction strategies based on the multiple carbon emission nodes, and sending the multiple emission reduction strategies to the strategy evaluation module; evaluating the multiple emission reduction strategies through the strategy evaluation module, and selecting the optimal emission reduction strategy for enterprise green and low-carbon management.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The first environmental data is received through the data collection module, and the first environmental data is preprocessed through the preprocessing channel in the data collection module to obtain the first processing information; the first processing information is analyzed through the data analysis module to obtain the first analysis result, and multiple carbon emission nodes are identified based on the first analysis result; based on the enterprise's low-carbon management needs, with the lowest energy consumption, the lowest carbon emissions and the lowest amount of waste as the goal, multiple emission reduction strategies are generated based on the multiple carbon emission nodes, and the multiple emission reduction strategies are sent to the strategy evaluation module; the multiple emission reduction strategies are evaluated through the strategy evaluation module, and the optimal emission reduction strategy is selected to perform green and low-carbon management of the enterprise. In other words, by building an enterprise low-carbon management cloud platform, automatic data collection and preprocessing are realized, big data analysis and artificial intelligence algorithms are used to process data, multiple carbon emission nodes are identified, multiple emission reduction strategies are generated based on the carbon emission nodes, and the enterprise's low-carbon management needs are constrained to evaluate and select the optimal strategy, thereby improving the efficiency of the enterprise's low-carbon management.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a flow chart of the green and low-carbon management method for enterprises based on the cloud platform of this application;

[0011] Figure 2 This is a flow chart of identifying carbon emission nodes in the green and low-carbon management method for enterprises based on a cloud platform in this application. DETAILED DESCRIPTION

[0012] This application provides a cloud-based enterprise green and low-carbon management method to solve the technical problem in the prior art that the data processing and analysis capabilities of enterprises are limited when conducting green and low-carbon management, which makes it difficult to meet the needs of rapid and accurate environmental management, resulting in low management efficiency. By building an enterprise low-carbon management cloud platform, the automation and intelligence of data collection, analysis and strategy evaluation are realized, which improves the efficiency of enterprise low-carbon management.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] For examples, please see the attached Figure 1 The present application provides a cloud platform-based enterprise green and low-carbon management method, which is applied to an enterprise low-carbon management cloud platform. The enterprise low-carbon management cloud platform includes a data collection module, a data analysis module, and a strategy evaluation module. The cloud platform-based enterprise green and low-carbon management method specifically includes the following steps:

[0015] Step 1: Receive first environmental data through the data collection module, and preprocess the first environmental data through a preprocessing channel in the data collection module to obtain first processing information.

[0016] Specifically, build an enterprise low-carbon management cloud platform to collect data, analyze data, evaluate and optimize emission reduction strategies. Clarify the purpose, target users, expected effects, etc. of the cloud platform, as well as the functional requirements of the cloud platform. Deploy infrastructure and set up interfaces for the data collection module to receive environmental data from mobile phones inside and outside the enterprise; the data analysis module uses big data analysis tools to mine and analyze data; the strategy evaluation module is responsible for evaluating the generated emission reduction strategies and selecting the most effective strategy for implementation. The data collection module is the core component of the cloud platform. It collects data related to energy consumption, carbon emissions and waste from various departments of the enterprise, including electricity consumption, water consumption, fuel usage, production batch records, equipment operating time, logistics and transportation records, etc., to obtain energy consumption, carbon emissions and waste generation, namely the first environmental data. The first environmental data refers to the original environmental data collected from inside and outside the enterprise, including real-time data and historical data. Integrate data from different sources and formats into a unified data management system to ensure data integrity and consistency. The collected first environment data is preprocessed, including data cleaning (removing errors and outliers), data formatting (unifying data format), data conversion (such as converting data into a format that can be used for analysis), standardization processing (such as converting all energy consumption data into the same unit), etc. The first environment data is received through the data collection module and processed using the preprocessing channel, thereby improving the quality and availability of the data.

[0017] Step 2: Perform information analysis on the first processing information through the data analysis module to obtain a first analysis result, and identify multiple carbon emission nodes based on the first analysis result.

[0018] Specifically, the first processed information after preprocessing is transmitted to the data analysis module for analysis. Through the analysis, the first analysis result is obtained to reveal the key factors and patterns of energy consumption, carbon emissions and waste generation. Through energy consumption analysis, equipment and departments with high energy consumption are identified, which are usually key nodes of carbon emissions. Understand the distribution of carbon emissions, such as which links or activities generate the most carbon emissions, and identify links or activities with large carbon emissions, which are usually key nodes of carbon emissions. Classify waste according to its type and composition, understand its composition and source, analyze waste generation trends and resource utilization rates, and identify links and types of waste with large waste generation.

[0019] According to the analysis results, the main nodes of carbon emissions are identified, which are the focus of the formulation of emission reduction strategies. Specifically, the energy consumption information is converted into quantifiable numerical data, and statistical analysis is performed on it. The maximum value is calculated and found, and the link associated with the maximum value is used as the first node; based on the carbon emission data, a trend chart is constructed to visualize the changes in carbon emissions, the extreme points of carbon emissions are identified, and the link corresponding to the highest value is used as the second node; waste information is classified according to the type of waste, and the amount of each type of waste is calculated, and the link with the largest amount of waste is used as the third node. Integrating these three nodes to obtain multiple carbon emission nodes is the key node of corporate carbon emissions. By identifying multiple carbon emission nodes, enterprises can concentrate resources and strategies to achieve the greatest emission reduction effect, and formulate and implement emission reduction strategies in a targeted manner.

[0020] Step 3: Taking the enterprise's low-carbon management needs as constraints, and aiming at minimizing energy consumption, carbon emissions and waste, generate multiple emission reduction strategies based on the multiple carbon emission nodes, and send the multiple emission reduction strategies to the strategy evaluation module.

[0021] Specifically, the low-carbon management needs of enterprises include energy consumption, carbon emissions and the amount of waste. The lowest energy consumption, carbon emissions and waste are set as the goal, and strategies are formulated for multiple carbon emission nodes, including improving process flow, replacing high-efficiency equipment, optimizing energy use, and recycling waste. The formulated multiple strategies are randomly combined to form multiple emission reduction strategies, including different technical combinations or implementation sequences. Through the wireless network, the generated multiple emission reduction strategies are synchronously transmitted to the strategy evaluation module for evaluation. By generating strategies based on the specific low-carbon management needs of the enterprise, the effectiveness and applicability of the strategies are ensured.

[0022] Step 4: Evaluate the multiple emission reduction strategies through the strategy evaluation module and select the optimal emission reduction strategy for green and low-carbon management of the enterprise.

[0023] Specifically, the strategy evaluation module is responsible for evaluating the multiple emission reduction strategies generated and determining the corresponding evaluation criteria according to the company's low-carbon management needs. According to the importance of each evaluation criterion, a corresponding weight is assigned to it. Each emission reduction strategy is simulated, and its implementation effect is scored and calculated according to the evaluation criteria and weight allocation to obtain multiple evaluation values. The calculated multiple evaluation values ​​are sorted in descending order to form an evaluation value sequence, and the emission reduction strategy corresponding to the first order of the evaluation value sequence is extracted as the optimal emission reduction strategy. This strategy performs best among all evaluation strategies and best meets the company's low-carbon management needs. Evaluating multiple emission reduction strategies through the strategy evaluation module and selecting the optimal emission reduction strategy for corporate green and low-carbon management will help companies use resources more effectively and achieve the goal of energy conservation and emission reduction.

[0024] Further, as attached Figure 2 As shown, step 2 of this application includes:

[0025] The first processing information includes energy consumption information, carbon emission information, and waste information; a descriptive analysis is performed on the energy consumption information based on data statistics to obtain multiple energy consumption amounts, and the link corresponding to the maximum energy consumption amount among the multiple energy consumption amounts is used as the first node; a carbon emission trend chart is constructed based on the carbon emission information, and a peak identification result is extracted based on the extreme value identification result obtained by marking the extreme values ​​of the carbon emission trend chart, and the link corresponding to the peak identification result is used as the second node; the waste information is classified based on the waste type, the waste generation amount is calculated according to the classification result, and the link corresponding to the maximum generation amount among the waste generation amount is used as the third node; the first node, the second node, and the third node are integrated to obtain the multiple carbon emission nodes.

[0026] Specifically, the first processed information includes energy consumption information, carbon emission information, and waste information. Through data statistical methods, the energy consumption information is analyzed, and the energy consumption data is converted into a unified quantitative indicator to obtain multiple energy consumption amounts for easy comparison and analysis. The collected energy consumption data is associated with each operating link of the enterprise, the energy consumption of each link is clarified, and the main sources of energy consumption are identified. Compare the energy consumption of different time periods, departments or equipment, find the maximum value, extract the link with the largest consumption, and use it as the first node.

[0027] Based on the collected carbon emission data, a carbon emission trend chart is constructed through data visualization technology to show the changing trend of carbon emissions over time. On the carbon emission trend chart, the data is analyzed for extreme values ​​to identify the maximum and minimum values ​​of carbon emissions, that is, the key change points of carbon emissions. In the extreme value identification results, special attention is paid to the peak value, that is, the point with the highest carbon emissions, which indicates the link or period with the largest carbon emissions, and the link corresponding to the peak identification result is identified as the second node.

[0028] Waste information is classified according to the type of waste (such as recyclables, hazardous waste, wet waste, etc.), and the amount of waste generated by each type is calculated. Among the calculated waste generation, the link with the largest generation is identified and used as the third node. The first node is the link with the largest energy consumption identified based on energy consumption information; the second node is the link with the largest carbon emission identified by extreme value identification and peak extraction in the carbon emission trend chart constructed based on carbon emission information; the third node is the link with the largest waste generation identified after waste information classification and waste generation calculation. These three nodes are integrated to obtain multiple carbon emission nodes, which together constitute the main sources and key management points of carbon emissions of the enterprise. According to factors such as carbon emission intensity, energy efficiency and waste generation, the priority order is determined for each link. By integrating multiple nodes, the key sources of carbon emissions can be comprehensively identified and managed, so as to formulate more effective emission reduction strategies.

[0029] Furthermore, the present application also includes the following steps:

[0030] The energy consumption information is numerically processed to obtain the multiple energy consumption amounts; the maximum value of the multiple energy consumption amounts is calculated to obtain the maximum energy consumption amount; the multiple energy consumption amounts are respectively mapped and associated with the corresponding multiple links, and the link corresponding to the maximum energy consumption amount is extracted as the first node.

[0031] Specifically, energy consumption information comes from energy metering equipment, billing records or enterprise resource planning systems, etc., including consumption data of various types of energy used by the enterprise (such as electricity, natural gas, oil, etc.). The energy consumption information is digitized and converted into quantifiable numerical data, that is, different types of energy consumption information are converted into the same unit. At the same time, digitization also includes steps such as data cleaning (removing errors and outliers), data conversion (such as converting from fuel weight to energy value) and data standardization (such as scaling data to the same range). Statistical analysis is performed on the converted data to obtain multiple energy consumption amounts, each of which corresponds to a specific link or equipment.

[0032] Calculate multiple energy consumptions, compare the energy consumption of different time periods, departments or equipment, find the maximum value, and characterize the highest energy consumption level in the enterprise operation. Map and associate each energy consumption data by department, equipment, process flow or other relevant classifications, clarify the energy consumption of each link, and accurately identify the main sources of energy consumption. For example, associate the energy consumption of the production line with the production line link, and associate the energy consumption of the office building with the office building link. Identify the link corresponding to the maximum energy consumption and define it as the first node. This node is the main source of energy consumption for the enterprise and is the key goal of energy conservation and emission reduction strategy formulation. By accurately identifying the link with the highest energy consumption, targeted energy conservation and emission reduction measures can be formulated and implemented to improve energy efficiency and promote green and low-carbon management.

[0033] Further, step three of this application includes:

[0034] The enterprise's low-carbon management needs include the lowest energy consumption, the lowest carbon emissions and the lowest amount of waste; based on the multiple carbon emission nodes, multiple strategy plans are generated with the enterprise's low-carbon management needs as constraints; the multiple strategy plans are randomly combined to obtain the multiple emission reduction strategies; the multiple emission reduction strategies are synchronized to the strategy evaluation module via wireless transmission for strategy evaluation.

[0035] Specifically, determine the low-carbon management needs of the enterprise, including energy consumption reduction (such as the percentage of energy consumption that needs to be reduced within a certain period of time), carbon emissions reduction (such as the specific amount or percentage of carbon emissions reduction), and waste reduction (such as the specific constraint target of the amount of waste generated during the production process). At the same time, it is necessary to consider factors such as industry standards and the company's own capabilities to set specific emission reduction targets, such as reducing carbon emissions by 20% and reducing energy consumption by 13.5%. Set a time frame for achieving these goals, such as short-term (1 to 2 years), medium-term (3 to 5 years) and long-term (more than 5 years). According to multiple carbon emission nodes, formulate multiple strategic plans that meet the low-carbon management needs of the enterprise, including technology improvement, process optimization, management measures, etc. The formulation of these strategies takes into account both the specific situation of each node and the global optimality. The generated multiple strategic plans are randomly combined to form multiple emission reduction strategies, including different technology combinations or implementation sequences. The use of random combinations increases the diversity and adaptability of strategies to cope with different operating environments and needs. The generated multiple emission reduction strategies are synchronously transmitted to the strategy evaluation module through the wireless network. The strategy evaluation module receives the transmitted multiple emission reduction strategies and evaluates them. Based on multiple carbon emission nodes, multiple emission reduction strategies are generated and synchronized to the strategy evaluation module through network transmission for evaluation, so as to select the optimal emission reduction strategy, which helps enterprises maximize emission reduction benefits while achieving low-carbon management goals.

[0036] Furthermore, the present application also includes the following steps:

[0037] The multiple strategy schemes are encoded and identified to establish a strategy scheme matrix, wherein the rows of the strategy scheme matrix represent the multiple carbon emission nodes and the columns represent the strategy schemes corresponding to each carbon emission node; the strategy scheme matrix is ​​traversed, the multiple strategy schemes are randomly selected for combination, and the multiple emission reduction strategies are generated.

[0038] Specifically, a unique code or identifier is assigned to each strategy scheme, and the code identifier records the detailed information of the strategy scheme, including key information such as the name, source, target, and measures of the strategy. Each code identifier corresponds to a specific strategy scheme, so that it can be accurately identified and selected when randomly combined. Create a matrix, the rows of the matrix represent carbon emission nodes, the columns represent the strategy schemes generated for each node, and each cell in the matrix represents a possible strategy. Traverse the strategy scheme matrix, that is, check each strategy scheme row by row or column by column. Use random combination methods, such as random sampling, random permutation, etc., to select strategy combinations from the combination matrix to ensure that each strategy combination is unique and without duplication. Generate multiple emission reduction strategies based on the results of the random combination. Ensure that each emission reduction strategy is unique and without duplication. The multiple emission reduction strategies are synchronized to the strategy evaluation module through network transmission for evaluation. By managing and combining strategy schemes, a variety of emission reduction strategies are generated, so as to implement green and low-carbon management more effectively.

[0039] Further, step 4 of this application includes:

[0040] Based on the enterprise's low-carbon management needs, an evaluation standard is determined; according to the evaluation standard combined with weight distribution, multiple evaluation values ​​of the multiple emission reduction strategies are calculated; the multiple evaluation values ​​are sorted in descending order to obtain an evaluation value sequence, and the emission reduction strategy corresponding to the first order of the evaluation value sequence is extracted as the optimal emission reduction strategy.

[0041] Specifically, according to the low-carbon management needs of enterprises, the evaluation criteria are determined, such as the reduction of source consumption, the reduction of carbon emissions, and the reduction of waste. According to the importance of each evaluation criterion, a corresponding weight is assigned to it. Multiple emission reduction strategies are imported into the strategy evaluation module for simulation execution. According to the evaluation criteria and weight allocation, the evaluation value of each emission reduction strategy is calculated. The calculated multiple evaluation values ​​are sorted in descending order to form an evaluation value sequence. The evaluation value score is usually obtained by multiplying the evaluation result of each indicator by its corresponding weight and then adding these products. The emission reduction strategy corresponding to the first order of the evaluation value sequence is taken as the optimal emission reduction strategy. This strategy is the best choice under the current conditions, that is, the emission reduction strategy with the highest score is implemented as the optimal strategy. The first order refers to the strategy with the highest ranking or the largest evaluation value among multiple emission reduction strategies. Determining the evaluation criteria based on the low-carbon management needs of enterprises, calculating the evaluation values ​​of multiple emission reduction strategies, and sorting these evaluation values ​​in descending order to select the optimal emission reduction strategy will help enterprises to systematically evaluate and select the optimal emission reduction strategy, and also help enterprises to select the best solution to achieve the greatest environmental and economic benefits.

[0042] Furthermore, the present application also includes the following steps:

[0043] Perform weight calculation on the evaluation criteria to obtain a weight allocation factor; collect multiple emission reduction data after the execution of the multiple emission reduction strategies, wherein the multiple emission reduction data include multiple energy consumption amounts, multiple carbon emissions amounts, and multiple waste generation amounts; based on the multiple emission reduction data, combined with the weight allocation factor, obtain the multiple evaluation values.

[0044] Specifically, the evaluation criteria are weighted to obtain the weight allocation factor. The purpose of the weight calculation is to quantify the importance of each evaluation criterion to the overall low-carbon management goal of the enterprise. Different criteria have different impacts on enterprises, so weights are needed to reflect this difference. The weight allocation factor is the relative importance of each evaluation criterion, expressed in numerical form. Collect data on the execution of multiple emission reduction strategies, including energy consumption, carbon emissions, and waste generation, to reflect the actual effect of the strategy. Combine the collected emission reduction data with the weight allocation factor, and calculate the evaluation value of each emission reduction strategy by weighted average, weighted summation, etc. to obtain multiple evaluation values. The evaluation value is a comprehensive indicator that reflects the performance of the strategy on all evaluation criteria. Through quantitative data and systematic evaluation methods, the effect of emission reduction strategies can be accurately evaluated, so as to more effectively select and implement green and low-carbon management strategies.

[0045] Furthermore, the present application also includes the following steps:

[0046] Monitor the implementation of the optimal emission reduction strategy in real time and obtain execution data; transmit the execution data to the data analysis module for analysis and generate a first report; collect feedback information based on the first report and conduct green and low-carbon management of the enterprise.

[0047] Specifically, the optimal emission reduction strategy is implemented in the enterprise operation, the execution of the strategy is continuously monitored through the data collection module, and the execution status is transmitted to the data analysis module for analysis to evaluate the actual effect of the optimal emission reduction strategy. The actual effect is compared with the expected target, and the strategy is continuously optimized and adjusted to achieve the best continuous emission reduction effect. The first report should at least include the comparison of execution data with expected targets, quantitative analysis of emission reduction effects, problems and challenges in strategy implementation, and strategy recommendations that may need to be adjusted. According to the needs of the enterprise, an easy-to-understand report format is generated, which can be a table, chart, text description, etc. The generated first report is distributed to relevant stakeholders, such as management, heads of relevant departments, and possible external partners. Feedback from stakeholders on the report is collected, including understanding of execution data and analysis results, evaluation of strategy execution effects, etc. According to the execution feedback and report analysis results, the optimal emission reduction strategy is adjusted as necessary, and the execution data collection module is updated to ensure that the new data can reflect the implementation of the adjusted strategy. Through real-time monitoring, enterprises continuously monitor the execution effect of their emission reduction strategies and adjust strategies according to actual conditions to ensure the realization of the enterprise's low-carbon management goals.

[0048] In summary, the green and low-carbon management method for enterprises based on the cloud platform provided by this application has the following technical effects:

[0049] The first environmental data is received through the data collection module, and the first environmental data is preprocessed through the preprocessing channel in the data collection module to obtain the first processing information; the first processing information is analyzed through the data analysis module to obtain the first analysis result, and multiple carbon emission nodes are identified based on the first analysis result; based on the enterprise's low-carbon management needs, with the lowest energy consumption, the lowest carbon emissions and the lowest amount of waste as the goal, multiple emission reduction strategies are generated based on the multiple carbon emission nodes, and the multiple emission reduction strategies are sent to the strategy evaluation module; the multiple emission reduction strategies are evaluated through the strategy evaluation module, and the optimal emission reduction strategy is selected to perform green and low-carbon management of the enterprise. In other words, by building an enterprise low-carbon management cloud platform, automatic data collection and preprocessing are realized, big data analysis and artificial intelligence algorithms are used to process data, multiple carbon emission nodes are identified, multiple emission reduction strategies are generated based on the carbon emission nodes, and the enterprise's low-carbon management needs are constrained to evaluate and select the optimal strategy, thereby improving the efficiency of the enterprise's low-carbon management.

[0050] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0051] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A green and low-carbon management method for enterprises based on a cloud platform, wherein the green and low-carbon management method for enterprises based on a cloud platform is applied to an enterprise low-carbon management cloud platform, wherein: The enterprise low-carbon management cloud platform includes a data collection module, a data analysis module, and a strategy evaluation module, and is characterized in that the enterprise green low-carbon management method based on the cloud platform includes: receiving first environmental data through the data collection module, and preprocessing the first environmental data through a preprocessing channel in the data collection module to obtain first processing information; Performing information analysis on the first processing information through the data analysis module to obtain a first analysis result, and identifying a plurality of carbon emission nodes based on the first analysis result; Taking the enterprise's low-carbon management needs as constraints, and taking the lowest energy consumption, the lowest carbon emissions, and the lowest amount of waste as goals, generating multiple emission reduction strategies based on the multiple carbon emission nodes, and sending the multiple emission reduction strategies to the strategy evaluation module; The strategy evaluation module is used to evaluate the multiple emission reduction strategies and select the optimal emission reduction strategy for green and low-carbon management of the enterprise.

2. The green and low-carbon management method for enterprises based on a cloud platform according to claim 1 is characterized in that: The first processing information is analyzed by the data analysis module to obtain a first analysis result, and a plurality of carbon emission nodes are identified based on the first analysis result, including: The first processing information includes energy consumption information, carbon emission information, and waste information; Performing a descriptive analysis on the energy consumption information based on data statistics to obtain multiple energy consumption amounts, and taking a link corresponding to a maximum energy consumption amount among the multiple energy consumption amounts as a first node; Constructing a carbon emission trend graph based on the carbon emission information, extracting a peak identification result according to an extreme value identification result obtained by performing extreme value identification on the carbon emission trend graph, and taking a link corresponding to the peak identification result as a second node; Classifying the waste information based on waste types, calculating the waste generation amount according to the classification result, and taking the link corresponding to the maximum generation amount in the waste generation amount as the third node; The first node, the second node, and the third node are integrated to obtain the multiple carbon emission nodes.

3. The green and low-carbon management method for enterprises based on cloud platform according to claim 2 is characterized in that: The energy consumption information is descriptively analyzed based on data statistics to obtain multiple energy consumption amounts, and a link corresponding to a maximum energy consumption amount among the multiple energy consumption amounts is used as a first node, including: Numerically processing the energy consumption information to obtain the multiple energy consumption amounts; Calculating the maximum value of the multiple energy consumptions to obtain the maximum energy consumption; The multiple energy consumption amounts are mapped and associated to the corresponding multiple links respectively, and the link corresponding to the maximum energy consumption amount is extracted as the first node.

4. The green and low-carbon management method for enterprises based on a cloud platform according to claim 1 is characterized in that: Taking the enterprise's low-carbon management requirements as a constraint, generating multiple emission reduction strategies based on the multiple carbon emission nodes, and sending the multiple emission reduction strategies to the strategy evaluation module, including: The low-carbon management requirements of enterprises include the lowest energy consumption, the lowest carbon emissions and the lowest amount of waste; Based on the multiple carbon emission nodes, and with the enterprise's low-carbon management requirements as constraints, generate multiple strategy solutions; Randomly combining the multiple strategy solutions to obtain the multiple emission reduction strategies; The multiple emission reduction strategies are synchronized to the strategy evaluation module through wireless transmission for strategy evaluation.

5. The green and low-carbon management method for enterprises based on cloud platform according to claim 4 is characterized in that: The multiple strategies are randomly combined to obtain the multiple emission reduction strategies, including: Encoding and identifying the multiple strategy solutions, and establishing a strategy solution matrix, wherein the rows of the strategy solution matrix represent the multiple carbon emission nodes, and the columns represent the strategy solutions corresponding to each carbon emission node; The strategy scheme matrix is ​​traversed, and the plurality of strategy schemes are randomly selected for combination to generate the plurality of emission reduction strategies.

6. The green and low-carbon management method for enterprises based on cloud platform according to claim 1 is characterized in that: The strategy evaluation module is used to evaluate multiple emission reduction strategies, including: Determine the evaluation criteria based on the low-carbon management needs of the enterprise; Calculating multiple evaluation values ​​of the multiple emission reduction strategies according to the evaluation criteria combined with weight allocation; The multiple evaluation values ​​are sorted in descending order to obtain an evaluation value sequence, and the emission reduction strategy corresponding to the first order of the evaluation value sequence is extracted as the optimal emission reduction strategy.

7. The green and low-carbon management method for enterprises based on cloud platform according to claim 6 is characterized in that: Calculating multiple evaluation values ​​of the multiple emission reduction strategies according to the evaluation criteria combined with weight allocation includes: Performing weight calculation on the evaluation criteria to obtain a weight allocation factor; Collecting a plurality of emission reduction data after the plurality of emission reduction strategies are executed, wherein the plurality of emission reduction data include a plurality of energy consumption amounts, a plurality of carbon emissions amounts, and a plurality of waste generation amounts; Based on the multiple emission reduction data and combined with the weight allocation factors, the multiple evaluation values ​​are obtained.

8. The green and low-carbon management method for enterprises based on cloud platform according to claim 1 is characterized in that: Also includes: Monitor the implementation of the optimal emission reduction strategy in real time and obtain implementation data; Transmitting the execution data to a data analysis module for analysis to generate a first report; Collect feedback information based on the first report and conduct green and low-carbon management of the enterprise.

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