Carbon emission optimization control system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a carbon emission optimization control system based on artificial intelligence. The carbon emission optimization control system based on artificial intelligence comprises an emission data unit, an artificial intelligence unit and an optimization control unit. According to the method, the environmental data of the detection points around the emission source are collected, the original carbon emission data are corrected, the measurement deviation caused by environmental factors is effectively eliminated, the real condition of carbon emission can be reflected, meanwhile, the future carbon emission of each enterprise is accurately predicted according to the historical carbon emission data by using the time sequence, and the carbon emission quality of the enterprise is improved. According to the method, the current carbon emission and the predicted carbon emission are subjected to artificial intelligence anomaly judgment, the emission threshold values of all enterprises are compared, the total carbon emission threshold value of the region is considered, a dual judgment mechanism is adopted, the artificial intelligence recognition capacity of the carbon emission anomaly condition is greatly improved, and management personnel can conveniently carry out optimization control on the carbon emission.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and specifically provides a carbon emission optimization control system based on artificial intelligence. Background Art

[0002] Artificial intelligence is a new technical science that studies, develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Artificial intelligence is a very broad science, including robots, speech recognition, image recognition, natural language processing, expert systems, machine learning, computer vision, etc.

[0003] With the accelerated advancement of the global industrialization process, the emissions of greenhouse gases such as carbon dioxide have increased sharply, and the resulting global warming problem has become a severe challenge faced by all mankind.

[0004] Carbon emission control is an important measure to achieve sustainable energy utilization. Designing a carbon emission optimization control system can achieve intelligent adjustment and optimization of the emission process, achieve the goal of reducing carbon emissions, and thus 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 above-mentioned technical problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a carbon emission optimization control system based on artificial intelligence. By collecting environmental data of detection points around emission sources, the original carbon emission data is corrected, effectively eliminating measurement deviations caused by environmental factors and reflecting the true situation of carbon emissions. At the same time, using time series, the future carbon emissions of each enterprise are accurately predicted based on historical carbon emission data, and artificial intelligence anomaly determination is performed on both current and predicted carbon emissions. Not only comparing the emission thresholds of each enterprise itself, but also considering the total regional carbon emission threshold, a dual determination mechanism is adopted, greatly improving the artificial intelligence recognition ability of carbon emission anomalies, so as to facilitate managers to optimize the control of 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, including 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 correct the carbon emission data of each enterprise by using a carbon emission model according to the detection data;

[0010] The artificial intelligence unit makes an artificial intelligence-based abnormal determination of the current carbon emissions of each enterprise according to the carbon emissions of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold, predicts the carbon emissions of each enterprise, and makes an intelligent abnormal determination of the predicted carbon emissions of each enterprise according to the predicted carbon emission values of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold. 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 the optimization control instruction, display the received information, formulate a control strategy according to the optimization control instruction, feed it back to the corresponding enterprise, and upload the 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, where:

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

[0014] The data correction module corrects the carbon emission data of each enterprise by using a carbon emission model according to the detection data. The data correction module is connected to the emission collection module;

[0015] The first communication module is used to realize the information interaction between the emission data unit and the artificial intelligence unit. 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 by using a carbon emission model according to the detection data is as follows:

[0017] Assume that the coordinates of the detection point are (x i , y i , z i ), and a target function is constructed In the formula, 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 diffusion parameter in the horizontal direction, 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] Derive 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] Using the carbon emission model formula, obtain the corrected emissions of the 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 interaction between the artificial intelligence unit and the emission data unit and the optimization control unit;

[0023] The intelligent prediction module predicts the carbon emissions of each enterprise according to the received carbon emission 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 the 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] Obtain the carbon emission time series X of the enterprise (0) ={x (0) (1), x (0) (2), …, x (0) (n)}, where x (0) (n) is the carbon emission;

[0027] Perform a first-order accumulation on it to obtain X (1) ={x (1) (1), x (1) (2), …, x (1) (n)}, where

[0028] k = 1, 2, …, n;

[0029] Establish the equation where a and b are both parameters, solve the equation to obtain the prediction formula

[0030]

[0031] Then obtain the predicted value through inverse accumulation

[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 makes an artificial intelligence-based abnormal determination of the current carbon emissions of each enterprise according to the carbon emissions of each enterprise received, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold. The first intelligent module is connected to the second communication module;

[0034] The second intelligent module makes an intelligent abnormal determination of the predicted carbon emissions of each enterprise according to the predicted carbon emission values of each enterprise obtained, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission 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] A further setting of the present invention is that the process of making an artificial intelligence-based abnormal determination of the current carbon emissions of each enterprise is as follows:

[0037] Compare the current carbon emissions of each enterprise with the corresponding carbon emission thresholds of each enterprise respectively to obtain a first determination result;

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

[0039] A further setting of the present invention is that the process of making an intelligent abnormal determination of the predicted carbon emissions of each enterprise is as follows:

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

[0041] Then calculate the sum of the predicted carbon emissions of each enterprise and compare the sum of the predicted carbon emissions with the corresponding total regional carbon emission threshold to obtain a fourth determination result.

[0042] A further setting of the present invention is that the process of generating the optimization control instruction is as follows:

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

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

[0046] If the fourth determination result is that the sum of the predicted carbon emissions is greater than the corresponding total regional carbon emission threshold, a predicted optimization control instruction for each enterprise is generated; otherwise, no predicted 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, where:

[0048] The third communication module is used to realize information interaction 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 according to the received optimization control instruction and feeds it back 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 the information preset by the management personnel. The information import module is connected to both the third communication module and the information display module.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] By collecting the environmental data of the detection points around the emission sources, the present invention corrects the original carbon emission data, effectively eliminates the measurement deviation caused by environmental factors, can reflect the true situation of carbon emissions, provides rich data dimensions for the intelligent analysis of the system. At the same time, using time series, it accurately predicts the future carbon emissions of each enterprise according to the historical carbon emission data, and respectively performs artificial intelligence anomaly determination on the current and predicted carbon emissions. It not only compares the emission thresholds of each enterprise itself, but also considers the total regional carbon emission threshold, adopting a dual determination mechanism, which greatly improves the artificial intelligence recognition ability of carbon emission anomalies. In addition, the optimization control instruction will be fed back to the management personnel for formulating optimization control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a system diagram of a carbon emission optimization control system based on artificial intelligence according to the present invention.

[0055] Figure 2 It is a system diagram of the emission data unit in a carbon emission optimization control system based on artificial intelligence according to the present invention.

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

[0058] Explanation of the reference numerals in the attached drawings:

[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 implementation manners

[0060] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0061] Embodiment:

[0062] As Figures 1-4As shown in the figure, this embodiment provides an artificial intelligence-based carbon emission optimization control system, including an emission data unit 100, an artificial intelligence unit 200, and an optimization control unit 300, where: The emission data unit 100 is used to collect the carbon emission data of each enterprise emission source in the detection area and the detection data of the detection points around the corresponding emission source, 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-based abnormal determination on the current carbon emissions of each enterprise according to the carbon emissions of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold, and predicts the carbon emissions of each enterprise, and according to the predicted carbon emission values of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold, performs intelligent abnormal determination on the predicted carbon emissions of each enterprise. At the same time, according to the determination result, generates corresponding optimization control instructions and feeds them 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 instructions, display the received information, formulate control strategies according to the optimization control instructions, feed them back to the corresponding enterprise, and 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 each enterprise emission source and the detection data of the detection points set around the emission source, uses the detection data to correct the carbon emissions, and uploads the corrected carbon emission data to the artificial intelligence unit 200. On the one hand, the artificial intelligence unit 200 will perform abnormal determination on the current carbon emissions, and on the other hand, it will also calculate and obtain the predicted value of the carbon emissions, and perform abnormal determination on this predicted value, so as to detect the current and future carbon emissions, and according to the determination result, timely adjust and optimize the carbon emissions of each enterprise, and feed the optimization control instructions back to the optimization control unit 300. After receiving the optimization control instructions, the optimization control unit 300 allows managers to view 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, where: the emission collection module 110 is used to collect the carbon emission data of each enterprise emission source in the detection area, as well as the detection data of the detection points around the corresponding emission source; the data correction module 120 corrects the carbon emission data of each enterprise using a carbon emission model according to 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 the 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, and the data correction module 120 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 a carbon emission model according to the detection data is as follows:

[0067] Assume that the coordinates of the detection point are (x i , y i , z i ), and a target function is constructed In the formula, 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 diffusion parameter in the horizontal direction, 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] Derive 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 the carbon emission data.

[0071] In this embodiment, it should be noted that the artificial intelligence model usually has high generality and generalization ability, and can be applied to fields such as natural language processing, image recognition, speech recognition, and data correction. It has a wide range of applications and will not be elaborated here. In this embodiment, by comprehensively collecting the carbon emission data of enterprise emission sources and the environmental data of the detection points around the emission sources, and then correcting the collected carbon emission data, the measurement error caused by environmental factors can be effectively reduced, ensuring that the data input into the system is highly accurate, and providing a solid and reliable basis 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, where: the second communication module 210 is used to implement 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 according to 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, and the intelligent prediction module 220 will predict the carbon emissions of each enterprise to obtain the carbon emission prediction values of each enterprise.

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

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

[0076] Perform a first-order accumulation on it to obtain X (1) ={x (1) (1), x (1) (2), …, x (1) (n)}, where

[0077] k = 1, 2, …, n;

[0078] Establish the equation where a and b are both parameters, solve the equation to obtain the prediction formula

[0079]

[0080] Then obtain the prediction value through inverse accumulation

[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 values of the carbon emissions of each enterprise, and then know the future carbon emission situation in advance, so as to subsequently perform artificial intelligence-based abnormal determination on the current carbon emissions of each enterprise.

[0082] In the present invention, the artificial intelligence unit 200 further includes a first intelligent module 240, a second intelligent module 250, and an information feedback module 260, where: the first intelligent module 240 performs artificial intelligence-based abnormal determination on the current carbon emissions of each enterprise according to the received carbon emissions of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission threshold, and the first intelligent module 240 is connected to the second communication module 210; the second intelligent module 250 performs intelligent abnormal determination on the predicted carbon emissions of each enterprise according to the obtained predicted values of the carbon emissions of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset total regional carbon emission 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 according to the obtained determination 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] Among them, the process of performing artificial intelligence-based abnormal determination on the current carbon emissions of each enterprise is as follows:

[0084] Compare the current carbon emissions of each enterprise with the corresponding carbon emission thresholds of each enterprise respectively to obtain a first determination result;

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

[0086] The present invention is further configured as follows: the process of performing intelligent abnormal determination on the predicted carbon emissions of each enterprise is as follows:

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

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

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

[0090] If any one of the current carbon emissions of each enterprise in the first determination result is greater than the corresponding carbon emission threshold of each enterprise, 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 sum of the current carbon emissions is greater than the corresponding regional total carbon emission threshold, optimization control instructions for each enterprise are generated; otherwise, no optimization control instructions are 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, predicted optimization control instructions for the corresponding enterprise are generated; otherwise, no predicted optimization control instructions are generated.

[0093] If the fourth determination result is that the sum of the predicted carbon emissions is greater than the corresponding regional total carbon emission threshold, predicted optimization control instructions for each enterprise are generated; otherwise, no predicted optimization control instructions are 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 emission thresholds of each enterprise, and the preset regional total carbon emission threshold, makes an artificial intelligence-based abnormal determination of the current carbon emissions of each enterprise, and uploads the determination 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 emission values of each enterprise, the preset carbon emission thresholds of each enterprise, and the preset regional total carbon emission threshold, makes an intelligent abnormal determination of the predicted carbon emissions of each enterprise, and uploads the above determination results 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, where: 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 according to the received optimization control instructions and feeds it 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 the 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 third communication module 310 receives the optimization control instruction and uploads it to the information display module 320. The information display module 320 displays the received information so that the management personnel can view the optimization control instruction. At the same time, the management personnel can formulate a carbon emission optimization strategy according to the received optimization control instruction through the control feedback module 330 and feedback it to the corresponding enterprise. In addition, the management personnel can also use the information import module 340 to upload the preset information and upload it to the artificial intelligence unit 200 through the third communication module 310 for storage in the database module 230.

[0097] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0098] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can understand and utilize the present invention well. The present invention is only limited 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 according to the carbon emissions of each enterprise, a preset carbon emissions threshold of each enterprise, and a preset total carbon emissions threshold of the region, and predicts the carbon emissions of each enterprise, and performs intelligent abnormality determination on the predicted carbon emissions of each enterprise according to the predicted carbon emissions of each enterprise, the preset carbon emissions threshold of each enterprise, and the preset total carbon emissions threshold of the region, and generates corresponding optimization control instructions according to the determination results, and feeds them back to the optimization control unit (300), and the artificial intelligence unit (200) is connected to the optimization control unit (300); The optimization control unit (300) is used to receive optimization control instructions, display the received information, formulate control strategies according to the optimization control instructions, feed back to the corresponding enterprise, 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 in 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 according to 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 In the formula, 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 diffusion parameter in the horizontal direction, 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) comprises 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), 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).

5. The carbon emission optimization control system based on artificial intelligence according to claim 4 is characterized in that: 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 Setting up 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 6. The carbon emission optimization control system based on artificial intelligence according to claim 4 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, and 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 acquired predicted carbon emissions of each enterprise, 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 determination result, and feeds back the instruction 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).

7. The carbon emission optimization control system based on artificial intelligence according to claim 6 is characterized in that: The process of artificial intelligence abnormality determination of the current carbon emissions of each enterprise is as follows: Compare the current carbon emissions of each enterprise with the corresponding carbon emissions threshold of each enterprise to obtain a first determination result; Then, the current sum of carbon emissions of each enterprise is calculated, and the current sum of carbon emissions is compared with the corresponding regional total carbon emission threshold to obtain a second determination result.

8. The carbon emission optimization control system based on artificial intelligence according to claim 7 is characterized in that: The process of intelligently determining anomalies of the predicted carbon emissions of each enterprise is as follows: Compare the predicted carbon emissions of each enterprise with the corresponding carbon emissions threshold of each enterprise to obtain a third determination result; Then, the predicted carbon emission sum of each enterprise is calculated, and the predicted carbon emission sum is compared with the corresponding regional total carbon emission threshold to obtain a fourth determination result.

9. The carbon emission optimization control system based on artificial intelligence according to claim 8 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, 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 for the corresponding enterprise is generated; 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.

10. The carbon emission optimization control system based on artificial intelligence according to claim 1 is characterized in that: The optimization control unit (300) comprises 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 according to the received optimization control instruction, and feeds back the strategy 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).

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