An AI-based carbon emission data analysis system

Abstract

This invention relates to the field of artificial intelligence technology, specifically to an AI-based carbon emission data analysis system. The AI-based carbon emission data analysis system includes a data acquisition unit, an artificial intelligence platform, an emission management unit, and an enterprise terminal unit. By preprocessing carbon emission data, this invention can reasonably balance data from various sensors, avoiding carbon emission calculation deviations caused by large errors in individual sensors, thus greatly improving the accuracy of subsequent carbon emission analysis and management. Furthermore, by integrating human and economic benefits into carbon emission benefit calculations, it optimizes resource allocation and, with a refined, gradual approval and suspension mechanism, ensures orderly emissions. Simultaneously, by allocating carbon emission requests based on carbon emission benefits and release capacity, it directs carbon emission resources towards more profitable enterprises.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to an artificial intelligence-based carbon emission data analysis system. Background Technology

[0002] Currently, artificial intelligence (AI) is experiencing rapid development and demonstrating enormous potential and application prospects in multiple fields. For example, in intelligent manufacturing, AI technology optimizes production processes, improves manufacturing efficiency, reduces costs, and achieves automation and intelligence. In intelligent transportation, AI technology enables intelligent driving, traffic flow prediction, and intelligent scheduling, thereby improving traffic safety and efficiency.

[0003] With increasing global attention to climate change, the analysis and management of carbon emission data has become crucial. Carbon emissions are a general term for greenhouse gas emissions, with carbon dioxide being the most significant component. Increased carbon emissions lead to rising sea levels, glacial retreat, and abnormal climate, impacting the natural environment. Given this situation, carbon emission data analysis systems are becoming increasingly important.

[0004] A carbon emission data analysis system based on artificial intelligence is disclosed in patent application number "CN202410658145.4". Its key feature is that a carbon emission data acquisition module divides the collected carbon emission data into regions and transmits the data to a carbon emission data analysis module. The carbon emission data analysis module analyzes the collected carbon emission data and transmits it to a carbon emission anomaly index calculation module. The carbon emission anomaly index calculation module calculates the carbon emission anomaly index and transmits the detected carbon emission anomaly results to a carbon emission anomaly location module. The carbon emission anomaly location module identifies and marks the carbon emission anomaly areas and transmits the location information of the located carbon emission anomalies to a carbon emission anomaly feedback module. The carbon emission anomaly feedback module generates an anomaly data report, which, along with the received carbon emission anomaly area location information, is sent to the terminal of the nearest monitoring personnel. This improves the accuracy of carbon emission data analysis and the efficiency of data acquisition.

[0005] While the carbon emission data analysis system provided in the aforementioned patent documents has advantages such as recording abnormal carbon emission data, it lacks the ability to optimize the allocation of carbon emission resources. Therefore, this invention provides an artificial intelligence-based carbon emission data analysis system to address the aforementioned technical problems. Summary of the Invention

[0006] The purpose of this invention is to provide an artificial intelligence-based carbon emission data analysis system. By preprocessing carbon emission data, it can reasonably weigh the data from various sensors, avoiding carbon emission calculation deviations caused by large errors in individual sensors, thus greatly improving the accuracy of subsequent carbon emission analysis and management. Furthermore, by integrating human and economic benefits into carbon emission benefit calculations, it optimizes resource allocation and, with a refined step-by-step approval and suspension mechanism, ensures orderly emissions. At the same time, it allocates carbon emission requests based on carbon emission benefits and release capacity, allowing carbon emission resources to flow to more profitable enterprises.

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

[0008] This invention provides an artificial intelligence-based carbon emission data analysis system, comprising a data acquisition unit, an artificial intelligence platform, an emission management unit, and an enterprise terminal unit, wherein:

[0009] The data acquisition unit is used to collect carbon emission data from various carbon-emitting enterprises within the detection area and to preprocess the collected carbon emission data.

[0010] The artificial intelligence platform performs a first intelligent judgment on each carbon-emitting enterprise based on the carbon emission data received from each carbon-emitting enterprise, and performs a second intelligent judgment on the excess requests of each carbon-emitting enterprise based on the excess request information, the first carbon emission result and the carbon emission benefits of the enterprise, and performs artificial intelligence sorting and release instruction feedback on the carbon emission requests. The artificial intelligence platform is connected to the data acquisition unit, the emission management unit and the enterprise terminal unit.

[0011] The emission management unit issues corresponding warning information based on the received warning information and release instruction information, uploads preset threshold information and updates the carbon emission setting information of each carbon emitting enterprise, and displays the received information. The emission management unit is connected to the data acquisition unit, the artificial intelligence platform and the enterprise terminal unit.

[0012] The enterprise terminal unit is used to provide the operating information of carbon emitting enterprises, upload excess emission request information, and receive corresponding excess emission request feedback information. The enterprise terminal unit is connected to both the artificial intelligence platform and the emission management unit.

[0013] The present invention is further configured such that: the data acquisition unit includes a data acquisition module, a data preprocessing module, and a first communication module, wherein:

[0014] The data acquisition module is used to collect carbon emission data from various carbon-emitting enterprises within the detection area.

[0015] The data preprocessing module is used to preprocess the carbon emission data collected from each carbon-emitting enterprise. The data preprocessing module is connected to the data acquisition module, and the preprocessing process is as follows:

[0016] Calculate the relative accuracy values ​​of sensors used at different data collection points for the same time t and the same carbon-emitting enterprise h. In the formula, e i Let be the error range of the i-th sensor;

[0017] The relative accuracy of each sensor is normalized to obtain the corresponding accuracy weight. In the formula, n is the number of sensors. This is the sum of the relative accuracies of all sensors;

[0018] Calculate the carbon emissions of carbon-emitting enterprise h at time t. In the formula, x i Let be the carbon emissions collected by the i-th sensor at time t;

[0019] The first communication module is used to realize information interaction between the data acquisition unit and the artificial intelligence platform, and the first communication module is connected to the data preprocessing module.

[0020] The present invention is further configured such that: the artificial intelligence platform includes a second communication module, an intelligent evaluation module, and a database module, wherein:

[0021] The second communication module is used to realize information interaction between the artificial intelligence platform and the data acquisition unit, the emission management unit, and the enterprise terminal unit;

[0022] The intelligent assessment module makes a first intelligent judgment on each carbon emission enterprise based on the received carbon emission data of each carbon emission enterprise, and makes a second intelligent judgment on the excess request of each carbon emission enterprise based on the excess request information of each carbon emission enterprise, the first carbon emission result and the carbon emission benefits of the enterprise. The intelligent assessment module is connected to the second communication module.

[0023] The database module is used to store preset threshold information, information of each carbon-emitting enterprise retrieved, and collected carbon emission data. The database module is connected to both the second communication module and the intelligent assessment module.

[0024] The present invention is further configured such that the process of the first intelligent determination is as follows:

[0025] X the carbon emissions of each carbon-emitting enterprise h h Carbon emission quota D of the corresponding enterprise h Compare;

[0026] If carbon emissions X h≤Carbon emission index D h If so, it is determined that the carbon-emitting enterprise h has not exceeded the emission limit;

[0027] Otherwise, the carbon-emitting enterprise will be deemed to be exceeding its emission limits, and an early warning will be issued to the emission management unit.

[0028] The present invention is further configured such that the process of the second intelligent determination is as follows:

[0029] If, in the results of the first intelligent judgment, all carbon-emitting enterprises h have not exceeded their emission limits, calculate the total carbon emission quota for each carbon-emitting enterprise h.

[0030] Next, calculate the amount of carbon emissions that can be released, D. 释放 =D 阈值 -D 总 In the formula, D 阈值 A threshold for carbon emissions;

[0031] Based on the excess emission request information of carbon-emitting enterprise h, calculate the carbon emission benefit E of the corresponding carbon-emitting enterprise h. h =βZ 1h +γZ 2h In the formula, ω ih As the weight of human benefit, I ih ω is an indicator of human resource efficiency. jh As a weight for economic benefits, I jh For economic benefit indicators, β and γ are both weighting coefficients, and β+γ=1;

[0032] Based on the carbon emission benefits of each carbon-emitting enterprise h and the requested carbon emission amount of the corresponding carbon-emitting enterprise h, the carbon emission requests of the corresponding carbon-emitting enterprises h are approved sequentially. The approval is granted only when the newly added carbon emissions from the first to the jth carbon-emitting enterprises are less than or equal to the releasable amount D. 释放 And the new carbon emissions from the first carbon-emitting enterprise to the (j+1)th carbon-emitting enterprise are greater than the releasable amount D. 释放 When this happens, the carbon emission requests of the (j+1)th carbon-emitting enterprise and subsequent carbon-emitting enterprises are suspended.

[0033] If all carbon-emitting enterprises h exceed their emission limits in the first intelligent judgment, then the carbon emission requests of all carbon-emitting enterprises h will be rejected.

[0034] If some carbon-emitting enterprises h exceed the emission limits in the first intelligent judgment result, then carbon-emitting enterprises that do not exceed the emission limits are selected, and the judgment is made in the same way as if all carbon-emitting enterprises h did not exceed the emission limits in the first intelligent judgment result.

[0035] The present invention is further configured such that: the artificial intelligence platform further includes an intelligent sorting module and a release feedback module, wherein:

[0036] The intelligent sorting module, based on the result of the second intelligent judgment, sorts the carbon emission requests using artificial intelligence according to the size of the carbon emission benefits of the approved carbon emission enterprise h. The intelligent sorting module is connected to the intelligent evaluation module.

[0037] The release feedback module sends a release instruction to the emission management unit based on the received sorting information. The release feedback module is connected to the second communication module, the database module, and the intelligent sorting module.

[0038] The present invention is further configured such that: the emission management unit includes a third communication module and an information display module, wherein:

[0039] The third communication module is used to realize information interaction between the emission management unit, the data acquisition unit, the artificial intelligence platform, and the enterprise terminal unit.

[0040] The information display module is used to display the received information, and the information display module is connected to the third communication module.

[0041] The present invention is further configured such that: the emission management unit further includes an information publishing module and an early warning reminder module, wherein:

[0042] The information publishing module is used to upload preset threshold information and update the carbon emission setting information of each carbon emitting enterprise. The information publishing module is connected to both the third communication module and the information display module.

[0043] The early warning and reminder module issues corresponding early warning information based on the received early warning information and release command information. The early warning and reminder module is connected to both the third communication module and the information display module.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention significantly improves the accuracy of final carbon emission calculations by preprocessing carbon emission data, specifically by calculating the relative accuracy values ​​and accuracy weights of sensors and comprehensively considering the measurement errors of different sensors. It rationally balances the data from various sensors, avoiding calculation deviations caused by large errors in individual sensors, thus greatly enhancing the accuracy of subsequent carbon emission analysis and management. Furthermore, by integrating human and economic benefits into the calculation of carbon emission benefits, it achieves multi-factor fusion, more accurately reflecting the actual impact and value of corporate carbon emissions. This approach guides companies to focus on improving human efficiency while pursuing economic benefits, achieving an organic combination of corporate development and carbon emission management. Carbon emission requests are allocated based on carbon emission benefits and release capacity, directing carbon emission resources to companies with higher efficiency, thereby promoting resource utilization efficiency across the entire industry. In addition, the gradual approval of carbon emission requests according to corporate carbon emission benefits and requested emission volumes ensures stable and orderly carbon emission management. Attached Figure Description

[0046] Figure 1 This is a system diagram of an artificial intelligence-based carbon emission data analysis system according to the present invention.

[0047] Figure 2 This is a system diagram of the data acquisition unit in an artificial intelligence-based carbon emission data analysis system of the present invention.

[0048] Figure 3 This is a system diagram of an artificial intelligence platform in an artificial intelligence-based carbon emission data analysis system of the present invention.

[0049] Figure 4 This is a system diagram of an emission management unit in an artificial intelligence-based carbon emission data analysis system according to the present invention.

[0050] Explanation of icon numbers:

[0051] 100. Data Acquisition Unit; 110. Data Acquisition Module; 120. Data Preprocessing Module; 130. First Communication Module; 200. Artificial Intelligence Platform; 210. Second Communication Module; 220. Intelligent Assessment Module; 230. Database Module; 240. Intelligent Sorting Module; 250. Release Feedback Module; 300. Emission Management Unit; 310. Third Communication Module; 320. Information Display Module; 330. Information Release Module; 340. Early Warning and Reminder Module; 400. Enterprise Terminal Unit. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0053] Example:

[0054] like Figures 1-4 As shown, this embodiment provides an artificial intelligence-based carbon emission data analysis system, including a data acquisition unit 100, an artificial intelligence platform 200, an emission management unit 300, and an enterprise terminal unit 400. The data acquisition unit 100 collects carbon emission data from various carbon-emitting enterprises within the detection area and preprocesses the collected data. The artificial intelligence platform 200 performs a first intelligent judgment on each carbon-emitting enterprise based on the received carbon emission data, and a second intelligent judgment on the excess requests of each enterprise based on the excess request information, the first carbon emission result, and the enterprise's carbon emission benefits. It also performs artificial intelligence sorting of the carbon emission requests and provides release instruction feedback. The AI ​​platform 200 is connected to the data acquisition unit 100, the emission management unit 300, and the enterprise terminal unit 400. The emission management unit 300 issues corresponding warning information based on the received warning and release instruction information, uploads preset threshold information, and updates the carbon emission setting information for each carbon-emitting enterprise. Simultaneously, it displays the received information. The emission management unit 300 is connected to the data acquisition unit 100, the AI ​​platform 200, and the enterprise terminal unit 400. The enterprise terminal unit 400 provides operational information of carbon-emitting enterprises, uploads excessive emission request information, and receives corresponding excessive emission request feedback information. The enterprise terminal unit 400 is connected to both the AI ​​platform 200 and the emission management unit 300.

[0055] In this embodiment, it should be noted that the data acquisition unit 100 collects carbon emissions from each carbon-emitting enterprise through sensors at different detection points, preprocesses the collected carbon emission data, and uploads the preprocessed carbon emission data to the artificial intelligence platform 200. The artificial intelligence platform 200 first compares the acquired carbon emission data with the corresponding carbon emission quota for each enterprise. If the carbon emission quota is exceeded, it indicates that the enterprise is illegally emitting carbon emissions, and the information is fed back to the emission management unit 300. Managers can view the information through the emission management unit 300 and can, including but not limited to, notify the illegally emitting carbon enterprise to stop the excessive emissions. The AI ​​platform 200 will also make a second intelligent judgment on the excess requests of each carbon-emitting enterprise based on the excess request information, the first carbon emission result, and the carbon emission benefits of the enterprise. Then, it will sort the carbon emission requests according to the size of the carbon emission benefits of the approved carbon-emitting enterprises and feed back the release instruction information to the emission management unit 300. The management personnel can set the carbon emission quota of each carbon-emitting enterprise according to the release instruction and update the stored carbon emission quota of each carbon-emitting enterprise. The set enterprise terminal unit 400 can send excess emission requests to the management personnel. After approval, carbon emissions can be carried out according to the given carbon emission quota.

[0056] In this invention, the data acquisition unit 100 includes a data acquisition module 110, a data preprocessing module 120, and a first communication module 130, wherein: the data acquisition module 110 is used to acquire carbon emission data of each carbon-emitting enterprise within the detection area; the data preprocessing module 120 is used to preprocess the acquired carbon emission data of each carbon-emitting enterprise, and the data preprocessing module 120 is connected to the data acquisition module 110, wherein the preprocessing process is as follows:

[0057] Calculate the relative accuracy values ​​of sensors used at different data collection points for the same time t and the same carbon-emitting enterprise h. In the formula, e i Let be the error range of the i-th sensor.

[0058] The relative accuracy of each sensor is normalized to obtain the corresponding accuracy weight. In the formula, n is the number of sensors. This is the sum of the relative accuracies of all sensors.

[0059] Calculate the carbon emissions of carbon-emitting enterprise h at time t. In the formula, x i Let be the carbon emissions collected by the i-th sensor at time t.

[0060] In addition, the first communication module 130 is used to realize information interaction between the data acquisition unit 100 and the artificial intelligence platform 200, and the first communication module 130 is connected to the data preprocessing module 120.

[0061] In this embodiment, it should be noted that an artificial intelligence model is used for carbon emission data preprocessing. Artificial intelligence models typically possess high versatility and generalization capabilities, and can be applied to fields such as natural language processing, image recognition, speech recognition, and data correction. Their applications are widespread and will not be elaborated upon further here. This embodiment utilizes the data acquisition module 110 to collect comprehensive data from various carbon-emitting enterprises within the detection area, providing a data foundation for subsequent analysis. The collected data is then uploaded to the data preprocessing module 120. The data preprocessing module 120 calculates the relative accuracy values ​​and accuracy weights of the sensors, comprehensively considering the measurement errors of different sensors, significantly improving the accuracy of the final carbon emission calculation. It can reasonably balance the data from each sensor, avoiding carbon emission calculation deviations caused by large errors in individual sensors. The preprocessed carbon emission data is then uploaded to the artificial intelligence platform 200 via the first communication module 130 for management personnel to view.

[0062] As an example of preprocessing, suppose that at time t, carbon-emitting enterprise h has two sensors at different collection points. The error ranges of sensor 1 and sensor 2 are e1 = 1 and e2 = 1, respectively. Then the relative accuracies of sensor 1 and sensor 2 are 1 and 1, respectively, and the corresponding accuracy weights are 0.5 and 0.5, respectively. Suppose that the carbon emissions collected by sensor 1 and sensor 2 at time t are 100 and 120, respectively. Then the carbon emissions of carbon-emitting enterprise h at time t are 110.

[0063] The preprocessing method provided in this embodiment calculates carbon emissions by considering the relative accuracy of sensors at different acquisition points and assigning corresponding weights. This avoids the excessive influence of errors or biases that may exist in a single sensor on the results. At the same time, this method does not rely on a single calculation mode, but calculates relative accuracy and weights based on the actual measurement error range, thereby better adapting to various complex monitoring scenarios.

[0064] In this invention, the artificial intelligence platform 200 includes a second communication module 210, an intelligent evaluation module 220, and a database module 230. The second communication module 210 enables information interaction between the artificial intelligence platform 200 and the data acquisition unit 100, the emission management unit 300, and the enterprise terminal unit 400. The intelligent evaluation module 220 performs a first intelligent judgment on each carbon-emitting enterprise based on the received carbon emission data, and performs a second intelligent judgment on the excess requests of each carbon-emitting enterprise based on the excess request information, the first carbon emission result, and the enterprise's carbon emission benefits. The intelligent evaluation module 220 is connected to the second communication module 210. The database module 230 stores preset threshold information, retrieved information about each carbon-emitting enterprise, and collected carbon emission data. The database module 230 is connected to both the second communication module 210 and the intelligent evaluation module 220.

[0065] In this embodiment, it should be noted that the preprocessed carbon emission data is received by the second communication module 210 and uploaded to the intelligent assessment module 220, which then performs the first intelligent judgment and the second intelligent judgment.

[0066] The process of the first intelligent determination is as follows:

[0067] X the carbon emissions of each carbon-emitting enterprise h h Carbon emission quota D of the corresponding enterprise h Compare them.

[0068] If carbon emissions X h ≤Carbon emission index D h If so, it is determined that the carbon-emitting enterprise h has not exceeded the emission limit.

[0069] Otherwise, the carbon-emitting enterprise will be deemed to be exceeding its emission limits, and an early warning will be issued to the emission management unit 300.

[0070] Furthermore, the process of the second intelligent determination is as follows:

[0071] If, in the results of the first intelligent judgment, all carbon-emitting enterprises h have not exceeded their emission limits, calculate the total carbon emission quota for each carbon-emitting enterprise h.

[0072] Next, calculate the amount of carbon emissions that can be released, D. 释放 =D 阈值 -D 总 In the formula, D 阈值 This is a threshold for limiting carbon emissions.

[0073] Based on the excess emission request information of carbon-emitting enterprise h, calculate the carbon emission benefit E of the corresponding carbon-emitting enterprise h. h =βZ1h +γZ 2h In the formula, ω ih As the weight of human benefit, I ih ω is an indicator of human resource efficiency. jh As a weight for economic benefits, I jh For economic benefit indicators, β and γ are both weighting coefficients, and β+γ=1.

[0074] Based on the carbon emission benefits of each carbon-emitting enterprise h and the requested carbon emission amount of the corresponding carbon-emitting enterprise h, the carbon emission requests of the corresponding carbon-emitting enterprises h are approved sequentially. The approval is granted only when the newly added carbon emissions from the first to the jth carbon-emitting enterprises are less than or equal to the releasable amount D. 释放 And the new carbon emissions from the first carbon-emitting enterprise to the (j+1)th carbon-emitting enterprise are greater than the releasable amount D. 释放 When the carbon emission request of the (j+1)th carbon-emitting enterprise and subsequent carbon-emitting enterprises is suspended.

[0075] If all carbon-emitting companies h exceed their emission limits in the first intelligent judgment, then all carbon emission requests from companies h will be rejected.

[0076] If some carbon-emitting enterprises h exceed the emission limits in the first intelligent judgment result, then carbon-emitting enterprises that do not exceed the emission limits are selected, and the judgment is made in the same way as if all carbon-emitting enterprises h did not exceed the emission limits in the first intelligent judgment result.

[0077] As an example, suppose there are three carbon-emitting companies, namely Company A, Company B, and Company C, and the carbon emission quotas and emission data of each company are shown in Table 1.

[0078] Table 1: Carbon Emission Indicators and Emission Data for Each Enterprise

[0079] enterprise Carbon emission quota (tons) Emissions (tons) Company A 100 80 Company B 120 130 Company C 90 70

[0080] The first intelligent judgment is as follows:

[0081] Company A: Its carbon emissions of 80 tons are less than the carbon emission target of 100 tons, therefore Company A is not considered to have exceeded the emission limit.

[0082] Company B: Its carbon emissions of 130 tons exceed the carbon emission target of 120 tons. Company B is deemed to be exceeding the emission limit, and an early warning is issued to the emission management unit 300.

[0083] Company C: Its carbon emissions of 70 tons are less than the carbon emission target of 90 tons, so Company C is judged not to have exceeded the emission limit.

[0084] The second intelligent determination is as follows:

[0085] Assume the carbon emission limit is set at 500 tons.

[0086] Calculate the total carbon emission quota: 100 tons for Company A + 120 tons for Company B + 90 tons for Company C = 310 tons.

[0087] Calculate the amount of carbon emissions that can be released: 500 - 310 = 190 tons.

[0088] Suppose companies A, B, and C each submit carbon emission requests. Company B's request is rejected because it exceeds the emission limit. Company A's labor benefit index is 80 with a weight of 0.4; its economic benefit index is 90 with a weight of 0.6. Company B's labor benefit index is 75 with a weight of 0.4; its economic benefit index is 85 with a weight of 0.6. Calculating the carbon emission benefits, company A's is 86 and company B's is 81. Assume company A requests 20 tons of excess carbon emissions, and company C requests 30 tons of excess carbon emissions. Based on the carbon emission benefits from highest to lowest, company A's request is approved first, resulting in 20 tons of new carbon emissions, which is less than 190 tons. Then company C's request is approved, resulting in 50 tons of new carbon emissions, which is also less than 190 tons.

[0089] In addition, it should be noted that the methods for setting labor efficiency indicators and economic efficiency indicators include, but are not limited to, determining them through expert scoring.

[0090] In this invention, the artificial intelligence platform 200 further includes an intelligent sorting module 240 and a release feedback module 250, wherein: the intelligent sorting module 240 sorts carbon emission requests according to the carbon emission benefits of approved carbon emission enterprises h based on the results of the second intelligent judgment, and the intelligent sorting module 240 is connected to the intelligent evaluation module 220; the release feedback module 250 feeds back release instruction information to the emission management unit 300 according to the received sorting information, and the release feedback module 250 is connected to the second communication module 210, the database module 230 and the intelligent sorting module 240.

[0091] In this embodiment, it should be noted that the intelligent sorting module 240 receives information from the intelligent assessment module 220, sorts the carbon emission requests, and uploads the sorting information to the release feedback module 250. The release feedback module 250 then feeds back the release instruction information to the emission management unit 300 through the second communication module 210.

[0092] In this invention, the emission management unit 300 includes a third communication module 310 and an information display module 320, wherein: the third communication module 310 is used to realize information interaction between the emission management unit 300 and the data acquisition unit 100, the artificial intelligence platform 200 and the enterprise terminal unit 400; the information display module 320 is used to display the received information, and the information display module 320 is connected to the third communication module 310.

[0093] In addition, the emission management unit 300 also includes an information release module 330 and an early warning module 340. The information release module 330 is used to upload preset threshold information and update the carbon emission setting information of each carbon emitting enterprise. The information release module 330 is connected to both the third communication module 310 and the information display module 320. The early warning module 340 issues corresponding early warning information based on the received early warning information and release instruction information. The early warning module 340 is connected to both the third communication module 310 and the information display module 320.

[0094] In this embodiment, it should be noted that the release instruction information from the artificial intelligence platform 200 is received by the third communication module 310 and uploaded to the information display module 320. The information display module 320 displays the release instruction information for management personnel to view. After the management personnel confirm it, the third communication module 310 feeds back to the enterprise terminal unit 400. At the same time, the preset carbon emission index of the carbon emission enterprise is updated by the information release module 330, that is, the carbon emission setting information of each carbon emission enterprise is updated. In addition, when the early warning and reminder module 340 receives the early warning information and the release instruction information, it will issue corresponding information to provide management personnel with information.

[0095] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0096] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An artificial intelligence-based carbon emission data analysis system, characterized by, The system comprises a data acquisition unit (100), an artificial intelligence platform (200), an emission management unit (300) and an enterprise terminal unit (400), wherein: The data acquisition unit (100) is configured to collect carbon emission data of each carbon emission enterprise in a detection area and pre-process the collected carbon emission data; The artificial intelligence platform (200) is configured to perform first intelligent determination on each carbon emission enterprise according to the received carbon emission data of each carbon emission enterprise, perform second intelligent determination on the excess request of each carbon emission enterprise based on the excess request information, the first carbon emission result and the carbon emission benefit of each carbon emission enterprise, and perform artificial intelligence sorting and release instruction feedback on the carbon emission request. The first intelligent determination is performed as follows: The carbon emission amount X of each carbon emission enterprise h h The carbon emission index amount D of the corresponding enterprise h Comparison is made; If the carbon emission amount X h ≤ the carbon emission index amount D h , it is determined that the carbon emission enterprise h does not overemission. If yes, it is determined that the carbon emission enterprise exceeds the emission limit and feedback warning information to the emission management unit (300); The second intelligent determination is performed as follows: In the first intelligent determination result, all carbon emission enterprises h do not over discharge, calculate the total carbon emission index of each carbon emission enterprise h Recalculating the releasable amount D of carbon emissions 释放 = D 阈值 - D 总 , in which D 阈值 is a defined threshold value for the amount of carbon emissions According to the excess emission request information of the carbon emission enterprise h, the carbon emission benefit E of the corresponding carbon emission enterprise h is calculated h = βZ 1h + γZ 2h , wherein, ω ih is the artificial benefit weight, I ih is the artificial benefit index, ω jh is the economic benefit weight, I jh is the economic benefit index, and β and γ are weight coefficients, and β+γ=1; According to the carbon emission benefit of each carbon emission enterprise h and the corresponding carbon emission request emission amount of the carbon emission enterprise h, the carbon emission request of the corresponding carbon emission enterprise h is approved in turn, when the newly added carbon emission amount of the first to jth carbon emission enterprises approved to be emitted is ≤ the releasable amount D 释放 , and the newly added carbon emission amount of the first to j+1th carbon emission enterprises is > the releasable amount D 释放 , the carbon emission request of the j+1th and subsequent carbon emission enterprises is suspended. If all the carbon emission enterprises h exceed the emission limit in the result of the first intelligent determination, it is determined that the carbon emission request of all the carbon emission enterprises h is not approved; If some of the carbon emission enterprises h exceed the emission limit in the result of the first intelligent determination, the carbon emission enterprises that do not exceed the emission limit are selected and determined in the same way as when all the carbon emission enterprises h do not exceed the emission limit in the result of the first intelligent determination; The emission management unit (300) is configured to send corresponding warning information according to the received warning information and release instruction information, upload preset threshold information and update the carbon emission amount setting information of each carbon emission enterprise, and display the received information. The enterprise terminal unit (400) is configured to provide operating information of the carbon emission enterprise, upload the excess emission request information and receive the corresponding excess emission request feedback information.

2. The artificial intelligence-based carbon emission data analysis system of claim 1, wherein, The data acquisition unit (100) comprises a data collection module (110), a data pre-processing module (120) and a first communication module (130), wherein: The data collection module (110) is configured to collect carbon emission data of each carbon emission enterprise in a detection area; The data pre-processing module (120) is configured to pre-process the collected carbon emission data of each carbon emission enterprise, and the pre-processing process is as follows: The relative accuracy value of the sensor used by the same carbon emission enterprise h at different collection points at the same time t In the formula, e i Error range of the i-th sensor normalize the relative accuracy of each sensor to obtain a corresponding accuracy weight where n is the number of sensors, is the sum of the relative accuracy of all sensors; Calculate the carbon emission amount of the enterprise h at the time t In the formula, x i The carbon emission amount collected by the i th sensor at the time t; The first communication module (130) is configured to realize information interaction between the data acquisition unit (100) and the artificial intelligence platform (200), and is connected with the data pre-processing module (120).

3. The artificial intelligence-based carbon emission data analysis system of claim 1, wherein, The artificial intelligence platform (200) comprises a second communication module (210), an intelligent evaluation module (220) and a database module (230), wherein: The second communication module (210) is used to realize information interaction between the artificial intelligence platform (200) and the data acquisition unit (100), the emission management unit (300) and the enterprise terminal unit (400); The intelligent evaluation module (220) performs first intelligent determination on each carbon emission enterprise according to the received carbon emission data of each carbon emission enterprise, and performs second intelligent determination on the excess request of each carbon emission enterprise based on the excess request information, the first carbon emission result and the carbon emission benefit of each carbon emission enterprise, and the intelligent evaluation module (220) is connected with the second communication module (210); The database module (230) is used to store the preset threshold information, the information of each carbon emission enterprise and the collected carbon emission data, and the database module (230) is connected with the second communication module (210) and the intelligent evaluation module (220).

4. The artificial intelligence-based carbon emission data analysis system of claim 3, wherein, The artificial intelligence platform (200) further comprises an intelligent sorting module (240) and a release feedback module (250), wherein: The intelligent sorting module (240) sorts the carbon emission request according to the size of the carbon emission benefit of the approved carbon emission enterprise h based on the result of the second intelligent determination, and the intelligent sorting module (240) is connected with the intelligent evaluation module (220); The release feedback module (250) feeds back the release instruction information to the emission management unit (300) according to the received sorting information, and the release feedback module (250) is connected with the second communication module (210), the database module (230) and the intelligent sorting module (240).

5. The artificial intelligence-based carbon emission data analysis system of claim 1, wherein The emission management unit (300) comprises a third communication module (310) and an information display module (320), wherein: The third communication module (310) is used to realize information interaction between the emission management unit (300) and the data acquisition unit (100), the artificial intelligence platform (200) and the enterprise terminal unit (400); The information display module (320) is used to realize the received information, and the information display module (320) is connected with the third communication module (310).

6. The artificial intelligence-based carbon emission data analysis system of claim 5, wherein, The emission management unit (300) further comprises an information publishing module (330) and a warning reminding module (340), wherein: The information publishing module (330) is used to upload the preset threshold information and update the carbon emission amount setting information of each carbon emission enterprise, and the information publishing module (330) is connected with the third communication module (310) and the information display module (320); The warning reminding module (340) sends corresponding warning information according to the received warning information and release instruction information, and the warning reminding module (340) is connected with the third communication module (310) and the information display module (320).

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