A carbon emission optimization control system based on artificial intelligence

Abstract

The present invention relates to the field of artificial intelligence technology, and specifically to an artificial intelligence-based carbon emission optimization and control system; the artificial intelligence-based carbon emission optimization and control system includes an emission data unit, an artificial intelligence unit and an optimization control unit; the present invention collects environmental data from detection points around emission sources, corrects the original carbon emission data, effectively eliminates measurement deviations caused by environmental factors, and can reflect the true situation of carbon emissions. At the same time, it uses time series to accurately predict the future carbon emissions of each enterprise based on historical carbon emission data, and performs artificial intelligence anomaly judgment on current and predicted carbon emissions respectively. It not only compares the emission threshold of each enterprise itself, but also considers the total carbon emission threshold of the region. It adopts a dual judgment mechanism, which greatly improves the artificial intelligence recognition ability of carbon emission anomalies, so that management personnel can optimize and control carbon emissions.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based carbon emission optimization control system. Background Art

[0002] Artificial intelligence is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. This broad field encompasses robotics, speech recognition, image recognition, natural language processing, expert systems, machine learning, and computer vision.

[0003] With the acceleration of the global industrialization process, the emission of greenhouse gases such as carbon dioxide has increased sharply. The resulting global warming problem has become a serious challenge facing all mankind.

[0004] Carbon emission control is an important measure to achieve sustainable energy utilization. The design of a carbon emission optimization control system can achieve intelligent regulation and optimization of the emission process, achieve the goal of reducing carbon emissions, and further improve air quality and protect the ecological environment.

[0005] Based on this, the present invention provides a carbon emission optimization control system based on artificial intelligence to solve the technical problems raised above. Summary of the Invention

[0006] The purpose of the present invention is to provide a carbon emission optimization and control system based on artificial intelligence. By collecting environmental data from detection points around emission sources, the original carbon emission data is corrected, which effectively eliminates the measurement deviation caused by environmental factors and can reflect the true situation of carbon emissions. At the same time, the future carbon emissions of each enterprise are accurately predicted based on historical carbon emission data by using time series, and artificial intelligence anomaly judgments are made on current and predicted carbon emissions respectively. Not only the emission thresholds of each enterprise are compared, but also the regional total carbon emission thresholds are considered. The dual judgment mechanism greatly improves the artificial intelligence recognition ability of carbon emission anomalies, so that managers can optimize and control carbon emissions.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The present invention provides a carbon emission optimization control system based on artificial intelligence, comprising an emission data unit, an artificial intelligence unit, and an optimization control unit, wherein:

[0009] The emission data unit is used to collect carbon emission data of emission sources of each enterprise in the detection area and detection data of detection points around the corresponding emission sources, and to correct the carbon emission data of each enterprise using the carbon emission model based on the detection data;

[0010] The artificial intelligence unit performs artificial intelligence abnormality determination on the current carbon emissions of each enterprise based on the carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset total carbon emissions threshold of the region, and predicts the carbon emissions of each enterprise. It also performs intelligent abnormality determination on the predicted carbon emissions of each enterprise based on the predicted carbon emissions value of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset total carbon emissions threshold of the region. At the same time, according to the determination result, a corresponding optimization control instruction is generated and fed back to the optimization control unit. The artificial intelligence unit is connected to the optimization control unit;

[0011] The optimization control unit is used to receive optimization control instructions, display the received information, formulate control strategies based on the optimization control instructions, and feed back to the corresponding enterprise, as well as upload preset information. The optimization control unit is connected to the artificial intelligence unit.

[0012] The present invention is further configured as follows: the emission data unit includes an emission collection module, a data correction module and a first communication module, wherein:

[0013] The emission collection module is used to collect carbon emission data from emission sources of each enterprise in the detection area, as well as detection data from detection points around the corresponding emission sources;

[0014] The data correction module corrects the carbon emission data of each enterprise using the carbon emission model based on the detection data, and the data correction module is connected to the emission collection module;

[0015] The first communication module is used to realize information interaction between the emission data unit and the artificial intelligence unit, and the first communication module is connected to the data correction module.

[0016] The present invention is further configured as follows: the process of correcting the carbon emission data of each enterprise using the carbon emission model based on the detection data is as follows:

[0017] Assume that the coordinates of the detection point are (x i ,y i , z i ), construct the objective function Where C i is the carbon dioxide concentration value of the detection point around the emission source, K is the carbon emission of the emission source, λ y is the horizontal diffusion parameter, λ z is the diffusion parameter in the vertical direction, H is the height of the emission source, and v is the average wind speed;

[0018] Derivative E with respect to K and set the derivative to 0 to obtain the carbon emission model formula of the corrected emission source

[0019]

[0020] Use the carbon emission model formula to obtain the corrected emissions of carbon emission data.

[0021] The present invention is further configured as follows: the artificial intelligence unit includes a second communication module, an intelligent prediction module and a database module, wherein:

[0022] The second communication module is used to realize information exchange between the artificial intelligence unit, the emission data unit and the optimization control unit;

[0023] The intelligent prediction module predicts the carbon emissions of each enterprise based on the received carbon emissions data of each enterprise, and the intelligent prediction module is connected to the second communication module;

[0024] The database module is used to store the received carbon emission data and preset control threshold information, and the database module is connected to both the second communication module and the intelligent prediction module.

[0025] The present invention is further configured as follows: the process of predicting the carbon emissions of each enterprise is as follows:

[0026] Get the carbon emissions time series X of the enterprise (0) ={x (0) (1), x (0) (2),…,x (0) (n)}, where x (0) (n) is carbon emissions;

[0027] Accumulate it once and get X (1) ={x (1) (1), x (1) (2),…,x (1) (n)}, where

[0028] k = 1, 2, ..., n;

[0029] Establishing the equation In the formula, a and b are parameters. Solve the equation to get the prediction formula

[0030]

[0031] Then the predicted value is obtained by cumulative reduction

[0032] The present invention is further configured as follows: the artificial intelligence unit further includes a first intelligent module, a second intelligent module and an information feedback module, wherein:

[0033] The first intelligent module performs an artificial intelligence-based abnormality determination on the current carbon emissions of each enterprise based on the received carbon emissions of each enterprise, a preset carbon emissions threshold for each enterprise, and a preset regional total carbon emissions threshold. The first intelligent module is connected to a second communication module;

[0034] The second intelligent module performs intelligent anomaly determination on the predicted carbon emissions of each enterprise based on the obtained predicted carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset regional total carbon emissions threshold. The second intelligent module is connected to the intelligent prediction module.

[0035] The information feedback module generates an optimization control instruction according to the obtained determination result and feeds it back to the optimization control unit. The information feedback module is connected to the second communication module, the database module, the first intelligent module and the second intelligent module.

[0036] The present invention is further configured as follows: the process of performing artificial intelligence abnormality determination on the current carbon emissions of each enterprise is as follows:

[0037] Comparing the current carbon emissions of each enterprise with the corresponding carbon emissions threshold of each enterprise to obtain a first determination result;

[0038] Then, the sum of the current carbon emissions of each enterprise is calculated, and the sum of the current carbon emissions is compared with the corresponding regional total carbon emission threshold to obtain a second determination result.

[0039] The present invention is further configured as follows: the process of performing intelligent abnormality determination on the predicted carbon emissions of each enterprise is as follows:

[0040] Comparing the predicted carbon emissions of each enterprise with the corresponding carbon emission threshold of each enterprise to obtain a third determination result;

[0041] Then, the predicted carbon emission sum of each enterprise is obtained, and the predicted carbon emission sum is compared with the corresponding regional total carbon emission threshold to obtain a fourth determination result.

[0042] The present invention is further configured as follows: the process of generating the optimization control instruction is as follows:

[0043] If the first determination result is that any one of the current carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, then an optimization control instruction for the corresponding enterprise is generated; otherwise, no optimization control instruction is generated;

[0044] If the second determination result is that the current carbon emissions are greater than the corresponding regional total carbon emissions threshold, then an optimization control instruction is generated for each enterprise; otherwise, no optimization control instruction is generated;

[0045] If the third determination result is that any one of the predicted carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, a prediction optimization control instruction is generated for the corresponding enterprise; otherwise, no prediction optimization control instruction is generated;

[0046] If the fourth determination result is that the predicted carbon emissions are greater than the corresponding regional total carbon emissions threshold, a prediction optimization control instruction is generated for each enterprise; otherwise, no prediction optimization control instruction is generated.

[0047] The present invention is further configured as follows: the optimization control unit includes a third communication module, an information display module, a control feedback module and an information import module, wherein:

[0048] The third communication module is used to realize information exchange between the optimization control unit and the artificial intelligence unit;

[0049] The information display module is used to display the received information, and the information display module is connected to the third communication module;

[0050] The control feedback module formulates a carbon emission optimization strategy based on the received optimization control instruction and feeds back the strategy to the corresponding enterprise. The control feedback module is connected to both the third communication module and the information display module.

[0051] The information import module is used to upload information preset by the management personnel, and the information import module is connected to both the third communication module and the information display module.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention collects environmental data from detection points around emission sources and corrects the original carbon emission data, effectively eliminating measurement deviations caused by environmental factors, reflecting the true situation of carbon emissions, and providing rich data dimensions for the system's intelligent analysis. At the same time, it uses time series to accurately predict the future carbon emissions of each enterprise based on historical carbon emission data, and performs artificial intelligence anomaly judgment on current and predicted carbon emissions respectively. It not only compares the emission threshold of each enterprise itself, but also considers the regional total carbon emission threshold. The dual judgment mechanism greatly improves the artificial intelligence recognition ability of carbon emission anomalies. In addition, the optimization control instructions will be fed back to management personnel to formulate optimization control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a system diagram of an artificial intelligence-based carbon emission optimization control system of the present invention.

[0055] Figure 2 This is a system diagram of emission data units in an artificial intelligence-based carbon emission optimization control system of the present invention.

[0056] Figure 3 This is a system diagram of the artificial intelligence unit in an artificial intelligence-based carbon emission optimization control system of the present invention.

[0057] Figure 4 This is a system diagram of an optimization control unit in an artificial intelligence-based carbon emission optimization control system of the present invention.

[0058] Description of Figure Numbers:

[0059] 100. Emission data unit; 110. Emission collection module; 120. Data correction module; 130. First communication module; 200. Artificial intelligence unit; 210. Second communication module; 220. Intelligent prediction module; 230. Database module; 240. First intelligent module; 250. Second intelligent module; 260. Information feedback module; 300. Optimization control unit; 310. Third communication module; 320. Information display module; 330. Control feedback module; 340. Information import module. DETAILED DESCRIPTION

[0060] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0061] Example:

[0062] like Figures 1-4As shown, this embodiment provides a carbon emission optimization control system based on artificial intelligence, including an emission data unit 100, an artificial intelligence unit 200 and an optimization control unit 300, wherein: the emission data unit 100 is used to collect carbon emission data of emission sources of each enterprise in the detection area and detection data of detection points around the corresponding emission sources, and according to the detection data, use the carbon emission model to correct the carbon emission data of each enterprise; the artificial intelligence unit 200 performs artificial intelligence abnormality judgment on the current carbon emissions of each enterprise according to the carbon emissions of each enterprise, the preset carbon emission threshold of each enterprise and the preset total carbon emission threshold of the region, and The carbon emissions of each enterprise are predicted, and intelligent abnormality judgment is made on the predicted carbon emissions of each enterprise based on the predicted carbon emissions value of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset regional total carbon emissions threshold. At the same time, according to the judgment result, the corresponding optimization control instruction is generated and fed back to the optimization control unit 300. The artificial intelligence unit 200 is connected to the optimization control unit 300; the optimization control unit 300 is used to receive the optimization control instruction, display the received information, and formulate a control strategy based on the optimization control instruction, and feed it back to the corresponding enterprise, as well as upload preset information. The optimization control unit 300 is connected to the artificial intelligence unit 200.

[0063] In this embodiment, it should be noted that the emission data unit 100 obtains the carbon emissions of the carbon emission sources of each enterprise, as well as the detection data of the detection points set around the emission sources. The carbon emissions are corrected using the detection data, and the corrected carbon emissions data are uploaded to the artificial intelligence unit 200. The artificial intelligence unit 200 will, on the one hand, make an abnormal judgment on the current carbon emissions, and on the other hand, calculate and obtain the predicted value of the carbon emissions, and make an abnormal judgment on the predicted value, so as to detect the current and future carbon emissions, and according to the judgment results, timely adjust the carbon emissions of each enterprise to optimize the control, and feed back the optimization control instructions to the optimization control unit 300. After receiving the optimization control instructions, the management personnel can view them through the optimization control unit 300 and formulate corresponding optimization control information, which can be fed back to the corresponding enterprise to achieve the effect of optimizing the control of carbon emissions.

[0064] In the present invention, the emission data unit 100 includes an emission collection module 110, a data correction module 120 and a first communication module 130, wherein: the emission collection module 110 is used to collect carbon emission data of emission sources of each enterprise in the detection area, and uses the detection data of detection points around the corresponding emission sources; the data correction module 120 corrects the carbon emission data of each enterprise based on the detection data using a carbon emission model, and the data correction module 120 is connected to the emission collection module 110; the first communication module 130 is used to realize information interaction between the emission data unit 100 and the artificial intelligence unit 200, and the first communication module 130 is connected to the data correction module 120.

[0065] In this embodiment, it should be noted that the emission collection module 110 uploads the collected information to the data correction module 120, which corrects the carbon emission data of the enterprise and uploads the corrected carbon emission data to the artificial intelligence unit 200 through the first communication module 130.

[0066] In the present invention, the process of correcting the carbon emission data of each enterprise using the carbon emission model based on the detection data is as follows:

[0067] Assume that the coordinates of the detection point are (x i ,y i , z i ), construct the objective function Where C i is the carbon dioxide concentration value of the detection point around the emission source, K is the carbon emission of the emission source, λ y is the horizontal diffusion parameter, λ z is the diffusion parameter in the vertical direction, H is the height of the emission source, and v is the average wind speed;

[0068] Derivative E with respect to K and set the derivative to 0 to obtain the carbon emission model formula of the corrected emission source

[0069]

[0070] Use the carbon emission model formula to obtain the corrected emissions of carbon emission data.

[0071] In this embodiment, it should be noted that artificial intelligence models generally have a high degree of versatility and generalization capabilities and can be applied to fields such as natural language processing, image recognition, speech recognition, and data correction. These applications are widespread and will not be elaborated upon here. This embodiment, by comprehensively collecting carbon emission data from enterprise emission sources and environmental data from monitoring points surrounding the emission sources, and then correcting the collected carbon emission data, can effectively reduce measurement errors caused by environmental factors, ensure the high accuracy of the data input into the system, and provide a solid and reliable foundation for subsequent analysis and decision-making.

[0072] In the present invention, the artificial intelligence unit 200 includes a second communication module 210, an intelligent prediction module 220 and a database module 230, wherein: the second communication module 210 is used to realize information interaction between the artificial intelligence unit 200 and the emission data unit 100 and the optimization control unit 300; the intelligent prediction module 220 predicts the carbon emissions of each enterprise based on the received carbon emission data of each enterprise, and the intelligent prediction module 220 is connected to the second communication module 210; the database module 230 is used to store the received carbon emission data and preset control threshold information, and the database module 230 is connected to both the second communication module 210 and the intelligent prediction module 220.

[0073] In this embodiment, it should be noted that the corrected carbon emission data is received through the second communication module 210 and uploaded to the intelligent prediction module 220. At the same time, the intelligent prediction module 220 retrieves the preset control threshold information from the database module 230. The intelligent prediction module 220 will predict the carbon emissions of each enterprise and obtain the predicted carbon emissions value of each enterprise.

[0074] In the present invention, the process of predicting the carbon emissions of each enterprise is as follows:

[0075] Get the carbon emissions time series X of the enterprise (0) ={x (0) (1), x (0) (2),…,x (0) (n)}, where x (0) (n) is carbon emissions;

[0076] Accumulate it once and get X (1) ={x (1) (1), x (1) (2),…,x (1) (n)}, where

[0077] k = 1, 2, ..., n;

[0078] Establishing the equation In the formula, a and b are parameters. Solve the equation to get the prediction formula

[0079]

[0080] Then the predicted value is obtained by cumulative reduction

[0081] In this embodiment, it should be noted that a method for predicting the carbon emissions of each enterprise is provided, which can accurately obtain the predicted carbon emissions of each enterprise, and thus know the future carbon emissions situation in advance, so as to subsequently perform artificial intelligence-based abnormal judgment on the current carbon emissions of each enterprise.

[0082] In the present invention, the artificial intelligence unit 200 also includes a first intelligent module 240, a second intelligent module 250 and an information feedback module 260, wherein: the first intelligent module 240 performs an artificial intelligence abnormality judgment on the current carbon emissions of each enterprise based on the received carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise and the preset regional total carbon emissions threshold, and the first intelligent module 240 is connected to the second communication module 210; the second intelligent module 250 performs an intelligent abnormality judgment on the predicted carbon emissions of each enterprise based on the obtained carbon emissions forecast value of each enterprise and the preset carbon emissions threshold of each enterprise and the preset regional total carbon emissions threshold, and the second intelligent module 250 is connected to the intelligent prediction module 220; the information feedback module 260 generates an optimization control instruction based on the obtained judgment result and feeds it back to the optimization control unit 300, and the information feedback module 260 is connected to the second communication module 210, the database module 230, the first intelligent module 240 and the second intelligent module 250.

[0083] The process of AI-based abnormality determination of each company's current carbon emissions is as follows:

[0084] Comparing the current carbon emissions of each enterprise with the corresponding carbon emissions threshold of each enterprise to obtain a first determination result;

[0085] Then, the sum of the current carbon emissions of each enterprise is calculated, and the sum of the current carbon emissions is compared with the corresponding regional total carbon emission threshold to obtain a second determination result.

[0086] The present invention is further configured as follows: the process of intelligently determining abnormalities in the predicted carbon emissions of each enterprise is as follows:

[0087] Comparing the predicted carbon emissions of each enterprise with the corresponding carbon emission threshold of each enterprise to obtain a third determination result;

[0088] Then, the predicted carbon emission sum of each enterprise is obtained, and the predicted carbon emission sum is compared with the corresponding regional total carbon emission threshold to obtain a fourth determination result.

[0089] In addition, the process of generating optimization control instructions is as follows:

[0090] If the first determination result is that any one of the current carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, then an optimization control instruction for the corresponding enterprise is generated; otherwise, no optimization control instruction is generated;

[0091] If the second determination result is that the current carbon emissions are greater than the corresponding regional total carbon emissions threshold, then an optimization control instruction is generated for each enterprise; otherwise, no optimization control instruction is generated;

[0092] If the third determination result is that any one of the predicted carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, a prediction optimization control instruction is generated for the corresponding enterprise; otherwise, no prediction optimization control instruction is generated;

[0093] If the fourth determination result is that the predicted carbon emissions are greater than the corresponding regional total carbon emissions threshold, a prediction optimization control instruction is generated for each enterprise; otherwise, no prediction optimization control instruction is generated.

[0094] In the present invention, it should be noted that the first intelligent module 240 receives the data uploaded through the second communication module 210, combines the carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise and the preset regional total carbon emissions threshold, performs artificial intelligence abnormality judgment on the current carbon emissions of each enterprise, and uploads the judgment result to the information feedback module 260. At the same time, the second intelligent module 250 also receives the data uploaded through the second communication module 210, combines the predicted carbon emissions of each enterprise with the preset carbon emissions threshold of each enterprise and the preset regional total carbon emissions threshold, performs intelligent abnormality judgment on the predicted carbon emissions of each enterprise, and uploads the above judgment result to the information feedback module 260.

[0095] In the present invention, the optimization control unit 300 includes a third communication module 310, an information display module 320, a control feedback module 330 and an information import module 340, wherein: the third communication module 310 is used to realize information interaction between the optimization control unit 300 and the artificial intelligence unit 200; 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; the control feedback module 330 formulates a carbon emission optimization strategy based on the received optimization control instructions and feeds back to the corresponding enterprise, and the control feedback module 330 is connected to both the third communication module 310 and the information display module 320; the information import module 340 is used to upload information preset by the management personnel, and the information import module 340 is connected to both the third communication module 310 and the information display module 320.

[0096] In this embodiment, it should be noted that the optimization control instructions are received by the third communication module 310 and uploaded to the information display module 320, and the received information is displayed by the information display module 320 so that the management personnel can view the optimization control instructions. At the same time, the management personnel can formulate carbon emission optimization strategies based on the received optimization control instructions through the control feedback module 330, and feed them back to the corresponding enterprises. In addition, the management personnel can also use the information import module 340 to upload preset information, and upload it to the artificial intelligence unit 200 through the third communication module 310 and store it in the database module 230.

[0097] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0098] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A carbon emission optimization control system based on artificial intelligence, characterized in that: The system comprises an emission data unit (100), an artificial intelligence unit (200) and an optimization control unit (300), wherein: The emission data unit (100) is used to collect carbon emission data of emission sources of each enterprise in the detection area and detection data of detection points around the corresponding emission sources, and to correct the carbon emission data of each enterprise using a carbon emission model based on the detection data; The artificial intelligence unit (200) performs artificial intelligence abnormality determination on the current carbon emissions of each enterprise based on the carbon emissions of each enterprise, a preset carbon emissions threshold for each enterprise, and a preset total carbon emissions threshold for the region, and predicts the carbon emissions of each enterprise. It also performs intelligent abnormality determination on the predicted carbon emissions of each enterprise based on the predicted carbon emissions of each enterprise, the preset carbon emissions threshold for each enterprise, and the preset total carbon emissions threshold for the region. At the same time, based on the determination result, a corresponding optimization control instruction is generated and fed back to the optimization control unit (300). The artificial intelligence unit (200) is connected to the optimization control unit (300); The artificial intelligence unit (200) includes a second communication module (210), an intelligent prediction module (220) and a database module (230), wherein: The second communication module (210) is used to implement information interaction between the artificial intelligence unit (200), the emission data unit (100), and the optimization control unit (300); The intelligent prediction module (220) predicts the carbon emissions of each enterprise based on the received carbon emissions data of each enterprise, and the intelligent prediction module (220) is connected to the second communication module (210); The database module (230) is used to store the received carbon emission data and preset control threshold information, and the database module (230) is connected to both the second communication module (210) and the intelligent prediction module (220); The process of predicting the carbon emissions of each enterprise is as follows: Get the carbon emissions time series X of the enterprise (0) ={x (0) (1), x (0) (2),…,x (0) (n)}, where x (0) (n) is carbon emissions; Accumulate it once and get X (1) ={x (1) (1), x (1) (2),…,x (1) (n)}, where Establishing the equation In the formula, a and b are parameters. Solve the equation to get the prediction formula Then the predicted value is obtained by cumulative reduction The optimization control unit (300) is used to receive optimization control instructions, display received information, formulate control strategies based on the optimization control instructions, feed back to corresponding enterprises, and upload preset information. The optimization control unit (300) is connected to the artificial intelligence unit (200).

2. The carbon emission optimization control system based on artificial intelligence according to claim 1 is characterized in that: The emission data unit (100) comprises an emission collection module (110), a data correction module (120) and a first communication module (130), wherein: The emission collection module (110) is used to collect carbon emission data of emission sources of each enterprise within the detection area, and to use detection data of detection points around the corresponding emission sources; The data correction module (120) corrects the carbon emission data of each enterprise using a carbon emission model based on the detection data, and the data correction module (120) is connected to the emission collection module (110); The first communication module (130) is used to realize information interaction between the emission data unit (100) and the artificial intelligence unit (200), and the first communication module (130) is connected to the data correction module (120).

3. The carbon emission optimization control system based on artificial intelligence according to claim 2 is characterized in that: The process of correcting the carbon emission data of each enterprise using the carbon emission model based on the detection data is as follows: Assume that the coordinates of the detection point are (x i ,y i , z i ), construct the objective function Where C i is the carbon dioxide concentration value of the detection point around the emission source, K is the carbon emission of the emission source, λ y is the horizontal diffusion parameter, λ z is the diffusion parameter in the vertical direction, H is the height of the emission source, and v is the average wind speed; Derivative E with respect to K and set the derivative to 0 to obtain the carbon emission model formula of the corrected emission source Use the carbon emission model formula to obtain the corrected emissions of carbon emission data.

4. The carbon emission optimization control system based on artificial intelligence according to claim 1 is characterized in that: The artificial intelligence unit (200) further comprises a first intelligent module (240), a second intelligent module (250) and an information feedback module (260), wherein: The first intelligent module (240) performs artificial intelligence abnormality determination on the current carbon emissions of each enterprise based on the received carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset regional total carbon emissions threshold. The first intelligent module (240) is connected to the second communication module (210); The second intelligent module (250) performs intelligent abnormality determination on the predicted carbon emissions of each enterprise based on the obtained predicted carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset regional total carbon emissions threshold. The second intelligent module (250) is connected to the intelligent prediction module (220); The information feedback module (260) generates an optimization control instruction based on the obtained judgment result and feeds it back to the optimization control unit (300). The information feedback module (260) is connected to the second communication module (210), the database module (230), the first intelligent module (240) and the second intelligent module (250).

5. The carbon emission optimization control system based on artificial intelligence according to claim 4 is characterized in that: The process of artificial intelligence-based abnormality determination of each enterprise's current carbon emissions is as follows: Comparing the current carbon emissions of each enterprise with the corresponding carbon emissions threshold of each enterprise to obtain a first determination result; Then, the sum of the current carbon emissions of each enterprise is calculated, and the sum of the current carbon emissions is compared with the corresponding regional total carbon emission threshold to obtain a second determination result.

6. The carbon emission optimization control system based on artificial intelligence according to claim 5 is characterized in that: The process of intelligently determining anomalies in the predicted carbon emissions of each enterprise is as follows: Comparing the predicted carbon emissions of each enterprise with the corresponding carbon emission threshold of each enterprise to obtain a third determination result; Then, the predicted carbon emission sum of each enterprise is obtained, and the predicted carbon emission sum is compared with the corresponding regional total carbon emission threshold to obtain a fourth determination result.

7. The carbon emission optimization control system based on artificial intelligence according to claim 6 is characterized in that: The process of generating the optimization control instruction is as follows: If the first determination result is that any one of the current carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, then an optimization control instruction for the corresponding enterprise is generated; otherwise, no optimization control instruction is generated; If the second determination result is that the current carbon emissions are greater than the corresponding regional total carbon emissions threshold, then an optimization control instruction is generated for each enterprise; otherwise, no optimization control instruction is generated; If the third determination result is that any one of the predicted carbon emissions of each enterprise is greater than the corresponding carbon emission threshold of each enterprise, a prediction optimization control instruction is generated for the corresponding enterprise; otherwise, no prediction optimization control instruction is generated; If the fourth determination result is that the predicted carbon emissions are greater than the corresponding regional total carbon emissions threshold, a prediction optimization control instruction is generated for each enterprise; otherwise, no prediction optimization control instruction is generated.

8. The carbon emission optimization control system based on artificial intelligence according to claim 1 is characterized in that: The optimization control unit (300) includes a third communication module (310), an information display module (320), a control feedback module (330) and an information import module (340), wherein: The third communication module (310) is used to implement information interaction between the optimization control unit (300) and the artificial intelligence unit (200); 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); The control feedback module (330) formulates a carbon emission optimization strategy based on the received optimization control instruction and feeds back the strategy to the corresponding enterprise. The control feedback module (330) is connected to both the third communication module (310) and the information display module (320); The information import module (340) is used to upload information preset by the management personnel, and the information import module (340) is connected to both the third communication module (310) and the information display module (320).

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