Carbon emission monitoring method and system based on AI

Abstract

The invention discloses a carbon emission monitoring method and system based on AI, and relates to the technical field of carbon emission monitoring, and the monitoring method comprises the following steps: obtaining an emission plan and an actual emission condition stored in any carbon emission log, and obtaining a standard reaching condition of the carbon emission log; capturing an abnormal time point when abnormal carbon emission exists from any carbon emission log which does not reach the standard; comparing the plan difference between the abnormal time point and the adjacent time point, and extracting a feature event; analyzing the influence degree of each characteristic event in any carbon emission log on the emission plan, performing risk assessment, and determining a risk threshold for judging whether the emission plan is reasonable or not; when an enterprise formulates a new emission plan for the next period, risk assessment is carried out on the new emission plan; and if the obtained risk value exceeds a risk threshold value, carrying out risk early warning on the new emission plan, and providing plan adjustment suggestions for an enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and specifically to an AI-based carbon emission monitoring method and system. Background Art

[0002] Carbon emissions refer to the amount of greenhouse gas emissions generated during the production, transportation, use and recycling of a product. The most important greenhouse gas is carbon dioxide. Carbon emissions are one of the main factors leading to global warming and have serious impacts on ecosystems, human health and social economy. As one of the main targets of carbon emissions, enterprises need to be strictly regulated. Each enterprise should clarify its own carbon reduction targets and promote sustainable development.

[0003] The carbon emission standards of each company are usually set by national or regional laws and regulations. Companies can also formulate stricter internal standards based on their own circumstances and goals. However, due to the lack of sufficient data support when setting carbon emission targets, the target setting may lack scientific basis, resulting in deviations in the plan; or overly aggressive emission reduction targets may be set without fully considering their actual capabilities, resulting in the failure of the plan. Summary of the invention

[0004] The purpose of the present invention is to provide an AI-based carbon emissions monitoring method and system to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a carbon emissions monitoring method based on AI, the monitoring method comprising the following steps:

[0006] Step S100: Monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emissions monitoring system to record the emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log;

[0007] Step S200: extracting the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, capturing the abnormal time points with abnormal carbon emissions; obtaining the emission plan at each time point, comparing the plan differences between the abnormal time point and the adjacent time points, and extracting the characteristic events that affect the emission plan;

[0008] Step S300: Analyze the impact of each characteristic event in any carbon emission log on the emission plan, and conduct risk assessment on the emission plan in the carbon emission log; compare the differences between the risk assessments of various emission plans, and determine the risk threshold for judging whether the emission plan is reasonable;

[0009] Step S400: When the enterprise formulates a new emission plan for the next cycle, characteristic events in the new emission plan are extracted and risk assessment is performed on the new emission plan; if the obtained risk value exceeds the risk threshold, a risk warning is issued for the new emission plan, and plan adjustment suggestions are provided to the enterprise.

[0010] Furthermore, step S100 includes the following steps:

[0011] Step S101: Selecting an emission plan formulated by an enterprise for any period, wherein the emission plan includes: dividing the period into a number of time points, monitoring the carbon emission events and expected emissions at each time point and the allowable error of the emissions, and generating a carbon emission log corresponding to the period;

[0012] Step S102: Set the expected total emissions recorded in the carbon emission log as E, obtain the actual emissions of each carbon emission event occurring at any time point, and obtain the actual total emissions of the carbon emission log as E. ac ; Assume that the allowable error of the emission is θ, if (E ac -E) / E>θ, the carbon emission log is marked with features; the feature mark is used to mark the abnormal situation in the carbon emission log; the enterprise sets an allowable error according to its own situation, and if the allowable error is exceeded, it can be said that the carbon emissions are abnormal.

[0013] Further, step S200 includes the following steps:

[0014] Step S201: Set the carbon emission log with characteristic mark as the characteristic log, and select a characteristic log at random; obtain the actual emission amount at each time point in the characteristic log, wherein the actual emission amount at the ith time point is set as (m i ) ac ;

[0015] Step S202: Obtain the expected emission at the ith time point in the emission plan of the characteristic log as m i , the emission error at the i-th time point is calculated to be Δm i =[(m i ) ac -m i ] / m i ; Calculate the average value of the emission error at each time point, and obtain the average error per unit time point in the characteristic log (Δm) ave ; If (Δm) ave ≤θ and Δm i >θ, or (Δm) ave >θ and Δm i >(Δm) ave, then the i-th time point is taken as the abnormal time point in the characteristic log; according to the difference relationship between the allowable error of the emission set by the enterprise and the average error, if the average error is less than the set error, it means that the set error is reasonable. Therefore, as long as the error of each time point is greater than the set error, it is an abnormal time point; but if the average error is greater than the set error, it means that the error set by the enterprise is unreasonable, and it is necessary to use the average error as a reference standard to judge the abnormal time point;

[0016] Step S203: Set the i-th time point in the characteristic log as an abnormal time point, obtain the carbon emission event occurring at the i-th time point, and obtain the event set of the i-th time point; obtain the event set of the i-1-th time point adjacent to the i-th time point, compare the carbon emission events in the two event sets with each other, extract the different events in the two event sets, and obtain the characteristic event of the i-th time point; under normal circumstances, the carbon emission events at two adjacent time points are similar. If there is an abnormality in the latter time point, it means that the newly generated carbon emission event has an impact on the carbon emission plan;

[0017] Step S204: If the carbon emission events in the two event sets are the same, randomly select a carbon emission event from the event set, count the number of occurrences of the carbon emission event as a characteristic event in each characteristic log as x, and obtain the occurrence frequency η of the carbon emission event; sort the carbon emission events in the event set from high to low according to the occurrence frequency, and generate a carbon emission event queue;

[0018] For the characteristic events that lead to deviations in the carbon emission plan, the specific emissions of each carbon emission event are not directly obtained. Firstly, it is very troublesome to detect the specific emissions, and the funds and energy consumed will increase exponentially. Secondly, the detection of specific emissions will be inaccurate, and each carbon emission event will affect each other, resulting in more inaccurate amounts after subsequent aggregation.

[0019] Step S205: Select a carbon emission event from the carbon emission event queue in order, and obtain the emission error Δm corresponding to the abnormal time point when the carbon emission event is used as a characteristic event in any other carbon emission log. ’ , if Δm ’ ≤Δm i , then the carbon emission event is set as a characteristic event. If Δm ’ >Δm i , then continue to select the next carbon emission event until Δm is satisfied ’ ≤Δm i So far, all the ’ ≥Δm iThe carbon emission events are all set as characteristic events; and a number of characteristic events at the i-th time point in the characteristic log are obtained;

[0020] When the characteristic events at two time points are the same, it is necessary to analyze each characteristic event according to the error caused by each characteristic event to the plan in other characteristic logs. The greater the impact, the greater the probability of abnormality in each carbon emission event, and the more likely it is to become a characteristic event;

[0021] Step S206: Summarize the characteristic events at each time point in the characteristic log to obtain a characteristic event set of the characteristic log.

[0022] Further, step S300 includes the following steps:

[0023] Step S301: randomly select a carbon emission log, obtain a characteristic event set of the emission plan in the carbon emission log; randomly select the jth characteristic event from the characteristic event set, and obtain the occurrence frequency of the jth characteristic event in each carbon emission log as η j , the frequency of occurrence can reflect the impact of characteristic events; the expected total emissions of the carbon emission log are set as E and the actual total emissions are set as E ac , get the emission error θ of the carbon emission log ’ ; According to the formula:

[0024]

[0025] Among them, IF() is a judgment function. If θ ’ >0, then IF(θ ’ >0)=1, otherwise, IF(θ ’ >0)=0; n is the number of characteristic events in the characteristic event set; the risk value F of the emission plan of the carbon emission log is calculated; to evaluate the risk value of an emission plan, it is necessary to first consider whether it exceeds the established carbon emission standard, and then combine the occurrence frequency of each characteristic event in the historical emission log to reasonably reflect the risk level of the plan;

[0026] Step S302: Obtain the risk value of each characteristic log, and select the risk value with the smallest value as the risk threshold F for judging whether the emission plan is reasonable. min .

[0027] Further, step S400 includes the following steps:

[0028] Step S401: Obtain the enterprise's new emission plan for the next cycle, and obtain a characteristic event set of the new emission plan; obtain the occurrence frequency of each characteristic event in the characteristic event set, and obtain the risk value F of the new emission plan.new ;

[0029] Step S402: Set the risk threshold to F min , if F new >F min , then a risk warning is issued for the new emission plan; arbitrarily select the kth characteristic event in the characteristic event set, obtain the emission error of the abnormal event point when the kth characteristic event is used as a characteristic event in each characteristic log, and obtain the average emission error of the kth characteristic event as (Δm k ) ave , set the occurrence frequency of the kth characteristic event to η k , according to the formula:

[0030] Y=η k ×(Δm k ) ave ;

[0031] Calculate the adjustment priority of the kth characteristic event in the new emission plan; sort them from large to small according to the adjustment priority, and provide plan adjustment suggestions to the enterprise; the unreasonable aspects of the enterprise need to be improved, but the improvement process needs to be gradual, so it is necessary to set priorities to make a reasonable arrangement of the order of adjustments, and give priority to optimizing events with greater impact, so as to better implement subsequent further optimization.

[0032] In order to better implement the above method, an AI-based carbon emissions monitoring system is also proposed. The monitoring system includes an emission log analysis module, a characteristic event analysis module, a plan risk assessment module, and an emission plan adjustment module;

[0033] The emission log analysis module is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log;

[0034] The characteristic event analysis module is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, and capture the abnormal time points with abnormal carbon emissions; obtain the emission plan at each time point, compare the plan differences between the abnormal time point and the adjacent time points, and extract the characteristic events that affect the emission plan;

[0035] The plan risk assessment module is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan, and to conduct risk assessment on the emission plan in the carbon emission log; to compare the differences between the risk assessments of various emission plans, and to determine the risk threshold for judging whether the emission plan is reasonable;

[0036] The emission plan adjustment module is used to extract characteristic events in the new emission plan when the enterprise formulates a new emission plan for the next cycle, and conduct a risk assessment on the new emission plan; if the obtained risk value exceeds the risk threshold, a risk warning is issued for the new emission plan, and plan adjustment suggestions are provided to the enterprise.

[0037] Further, the emission log analysis module includes an emission log generation unit and a compliance analysis unit;

[0038] The emission log generation unit is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; the compliance analysis unit is used to obtain the emission plan and actual emissions stored in any carbon emission log to obtain the compliance status of the carbon emission log.

[0039] Furthermore, the characteristic event analysis module includes an abnormal event point capture unit and a characteristic event extraction unit;

[0040] The abnormal time point capture unit is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standards, and capture the abnormal time points where abnormal carbon emissions exist; the characteristic event extraction unit is used to obtain the emission plan at each time point, compare the plan differences between the abnormal time point and the adjacent time points, and extract the characteristic events that affect the emission plan.

[0041] Further, the plan risk assessment module includes an impact degree analysis unit and a risk threshold setting unit;

[0042] The impact degree analysis unit is used to analyze the impact degree of each characteristic event in any carbon emission log on the emission plan, and to perform risk assessment on the emission plan in the carbon emission log; the risk threshold setting unit is used to compare the differences in risk assessment situations between various emission plans, and to determine the risk threshold for judging whether the emission plan is reasonable.

[0043] Further, the emission plan adjustment module includes a preset plan evaluation unit and a plan adjustment analysis unit;

[0044] A preset plan evaluation unit is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan, and to perform risk assessment on the emission plan in the carbon emission log; a plan adjustment analysis unit is used to issue a risk warning for the new emission plan if the risk value obtained exceeds the risk threshold, and to provide plan adjustment suggestions to the enterprise.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention analyzes the rationality of the carbon emission plan formulated by the enterprise, helps the enterprise to obtain a carbon emission plan that meets the enterprise's own situation based on its own historical carbon emission situation, which is conducive to helping the enterprise optimize energy use and improve operating efficiency;

[0047] 2. The present invention analyzes the difference between the emission logs, extracts the characteristic events that affect the carbon emission plan without directly monitoring the specific emission amount of each carbon emission event, effectively analyzes the impact of each characteristic event, helps to accurately analyze the carbon emission plan, and helps enterprises improve their carbon emission plans;

[0048] 3. The present invention helps enterprises directly reflect the rationality of the plans by conducting risk assessment on various carbon emission plans, provides data support for the enterprises to set carbon emission targets in the future, helps enterprises to formulate reasonable plans, enables enterprises to effectively complete the formulated plans, and improves the operating efficiency of the enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the steps of an AI-based carbon emissions monitoring method;

[0050] Figure 2 A structural diagram of an AI-based carbon emissions monitoring system. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0052] Example: Figure 1 to Figure 2 As shown, the present invention provides an AI-based carbon emissions monitoring method, the monitoring method comprising the following steps:

[0053] Step S100: Monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emissions monitoring system to record the emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log;

[0054] Wherein, step S100 includes the following steps:

[0055] Step S101: Selecting an emission plan formulated by an enterprise for any period, wherein the emission plan includes: dividing the period into a number of time points, monitoring the carbon emission events and expected emissions at each time point and the allowable error of the emissions, and generating a carbon emission log corresponding to the period;

[0056] Step S102: Set the expected total emissions recorded in the carbon emission log as E, obtain the actual emissions of each carbon emission event occurring at any time point, and obtain the actual total emissions of the carbon emission log as E. ac ; Assume that the allowable error of the emission is θ, if (E ac -E) / E>θ, then the carbon emission log is marked with features;

[0057] Example 1: The expected total emission recorded in a carbon emission log is set to 100, while the actual total emission is 150, and the emission tolerance is set to 10%, because (150-100) / 100=50%, so the carbon emission log needs to be marked with features;

[0058] Step S200: extracting the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, capturing the abnormal time points with abnormal carbon emissions; obtaining the emission plan at each time point, comparing the plan differences between the abnormal time point and the adjacent time points, and extracting the characteristic events that affect the emission plan;

[0059] Wherein, step S200 includes the following steps:

[0060] Step S201: Set the carbon emission log with characteristic mark as the characteristic log, and select a characteristic log at random; obtain the actual emission amount at each time point in the characteristic log, wherein the actual emission amount at the ith time point is set as (m i ) ac ;

[0061] Step S202: Obtain the expected emission at the ith time point in the emission plan of the characteristic log as m i , the emission error at the i-th time point is calculated to be Δm i =[(m i )ac -m i ] / m i ; Calculate the average value of the emission error at each time point, and obtain the average error per unit time point in the characteristic log (Δm) ave ; If (Δm) ave ≤θ and Δm i >θ, or (Δm) ave >θ and Δm i >(Δm) ave , then the i-th time point is taken as the abnormal time point in the feature log;

[0062] Example 2: The emission error at the i-th time point is set to 13%, and the allowable error of the emission amount is 10%. The average value of the emission errors at each time point is calculated to be 15%. Since 15%>10%, it is shown that the emission error range established is unreasonable. The average emission error of 15% is used as the standard. Since 13%<15%, the i-th time point is not an abnormal time point.

[0063] Step S203: setting the i-th time point in the characteristic log as an abnormal time point, acquiring the carbon emission event occurring at the i-th time point, and obtaining the event set of the i-th time point; acquiring the event set of the i-1-th time point adjacent to the i-th time point, comparing the carbon emission events in the two event sets, extracting the different events in the two event sets, and obtaining the characteristic event of the i-th time point;

[0064] Step S204: If the carbon emission events in the two event sets are the same, randomly select a carbon emission event from the event set, count the number of occurrences of the carbon emission event as a characteristic event in each characteristic log as x, and obtain the occurrence frequency η of the carbon emission event; sort the carbon emission events in the event set from high to low according to the occurrence frequency, and generate a carbon emission event queue;

[0065] Step S205: Select a carbon emission event from the carbon emission event queue in order, and obtain the emission error Δm corresponding to the abnormal time point when the carbon emission event is used as a characteristic event in any other carbon emission log. ’ , if Δm ’ ≤Δm i , then the carbon emission event is set as a characteristic event. If Δm ’ >Δm i , then continue to select the next carbon emission event until Δm is satisfied ’ ≤Δm i So far, all the ’ ≥Δm iThe carbon emission events are all set as characteristic events; and a number of characteristic events at the i-th time point in the characteristic log are obtained;

[0066] Step S206: Summarize the characteristic events at each time point in the characteristic log to obtain a characteristic event set of the characteristic log.

[0067] Step S300: Analyze the impact of each characteristic event in any carbon emission log on the emission plan, and conduct risk assessment on the emission plan in the carbon emission log; compare the differences between the risk assessments of various emission plans, and determine the risk threshold for judging whether the emission plan is reasonable;

[0068] Wherein, step S300 includes the following steps:

[0069] Step S301: randomly select a carbon emission log, obtain a characteristic event set of the emission plan in the carbon emission log; randomly select the jth characteristic event from the characteristic event set, and obtain the occurrence frequency of the jth characteristic event in each carbon emission log as η j , set the expected total emissions of the carbon emission log to E and the actual total emissions to E ac , get the emission error θ of the carbon emission log ’ ; According to the formula:

[0070]

[0071] Among them, IF() is a judgment function. If θ ’ >0, then IF(θ ’ >0)=1, otherwise, IF(θ ’ >0)=0; n is the number of characteristic events in the characteristic event set; the risk value F of the emission plan of the carbon emission log is calculated;

[0072] Example 3: Obtain three characteristic events in a carbon emission log, with occurrence frequencies of 20%, 30% and 40% respectively, set the emission error of the carbon emission log to 10%, and calculate the risk value F = (1 + 0.1) × (1 + 0.2) × (1 + 0.3) × (1 + 0.4) = 2.4024;

[0073] Step S302: Obtain the risk value of each characteristic log, and select the risk value with the smallest value as the risk threshold F for judging whether the emission plan is reasonable. min .

[0074] Step S400: When the enterprise formulates a new emission plan for the next cycle, characteristic events in the new emission plan are extracted to perform risk assessment on the new emission plan; if the obtained risk value exceeds the risk threshold, a risk warning is issued for the new emission plan, and plan adjustment suggestions are provided to the enterprise;

[0075] Wherein, step S400 includes the following steps:

[0076] Step S401: Obtain the enterprise's new emission plan for the next cycle, and obtain a characteristic event set of the new emission plan; obtain the occurrence frequency of each characteristic event in the characteristic event set, and obtain the risk value F of the new emission plan. new ;

[0077] Step S402: Set the risk threshold to F min , if F new >F min , then a risk warning is issued for the new emission plan; arbitrarily select the kth characteristic event in the characteristic event set, obtain the emission error of the abnormal event point when the kth characteristic event is used as a characteristic event in each characteristic log, and obtain the average emission error of the kth characteristic event as (Δm k ) ave , set the occurrence frequency of the kth characteristic event to η k , according to the formula:

[0078] Y=η k ×(Δm k ) ave ;

[0079] The adjustment priority of the kth characteristic event in the new emission plan is calculated; the adjustment priorities are sorted from large to small, and plan adjustment suggestions are provided to the enterprise.

[0080] An AI-based carbon emissions monitoring system, the monitoring system includes an emission log analysis module, a characteristic event analysis module, a plan risk assessment module and an emission plan adjustment module;

[0081] The emission log analysis module is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log;

[0082] The characteristic event analysis module is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, and capture the abnormal time points with abnormal carbon emissions; obtain the emission plan at each time point, compare the plan differences between the abnormal time point and the adjacent time points, and extract the characteristic events that affect the emission plan;

[0083] The plan risk assessment module is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan, and to conduct risk assessment on the emission plan in the carbon emission log; to compare the differences between the risk assessments of various emission plans, and to determine the risk threshold for judging whether the emission plan is reasonable;

[0084] The emission plan adjustment module is used to extract characteristic events in the new emission plan when the enterprise formulates a new emission plan for the next cycle, and conduct a risk assessment on the new emission plan; if the obtained risk value exceeds the risk threshold, a risk warning is issued for the new emission plan, and plan adjustment suggestions are provided to the enterprise.

[0085] Among them, the emission log analysis module includes an emission log generation unit and a compliance analysis unit;

[0086] The emission log generation unit is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; the compliance analysis unit is used to obtain the emission plan and actual emissions stored in any carbon emission log to obtain the compliance status of the carbon emission log.

[0087] Among them, the characteristic event analysis module includes an abnormal event point capture unit and a characteristic event extraction unit;

[0088] The abnormal time point capture unit is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standards, and capture the abnormal time points where abnormal carbon emissions exist; the characteristic event extraction unit is used to obtain the emission plan at each time point, compare the plan differences between the abnormal time point and the adjacent time points, and extract the characteristic events that affect the emission plan.

[0089] Among them, the planning risk assessment module includes an impact degree analysis unit and a risk threshold setting unit;

[0090] The impact degree analysis unit is used to analyze the impact degree of each characteristic event in any carbon emission log on the emission plan, and to perform risk assessment on the emission plan in the carbon emission log; the risk threshold setting unit is used to compare the differences in risk assessment situations between various emission plans, and to determine the risk threshold for judging whether the emission plan is reasonable.

[0091] Among them, the emission plan adjustment module includes a preset plan evaluation unit and a plan adjustment analysis unit;

[0092] A preset plan evaluation unit is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan, and to perform risk assessment on the emission plan in the carbon emission log; a plan adjustment analysis unit is used to issue a risk warning for the new emission plan if the risk value obtained exceeds the risk threshold, and to provide plan adjustment suggestions to the enterprise.

[0093] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A carbon emissions monitoring method based on AI, characterized by: The monitoring method comprises the following steps: Step S100: Monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emissions monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log; Step S200: extracting the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, capturing the abnormal time points with abnormal carbon emissions; obtaining the emission plan at each time point, comparing the plan differences between the abnormal time point and the adjacent time points, and extracting the characteristic events that affect the emission plan; Step S300: Analyze the impact of each characteristic event in any carbon emission log on the emission plan, and conduct risk assessment on the emission plan in the carbon emission log; compare the differences between the risk assessments of various emission plans, and determine the risk threshold for judging whether the emission plan is reasonable; Step S400: When the enterprise formulates a new emission plan for the next cycle, characteristic events in the new emission plan are extracted and risk assessment is performed on the new emission plan; if the obtained risk value exceeds the risk threshold, a risk warning is issued for the new emission plan, and plan adjustment suggestions are provided to the enterprise.

2. The AI-based carbon emissions monitoring method according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: Selecting an emission plan formulated by an enterprise for any period, wherein the emission plan includes: dividing the period into a number of time points, monitoring the carbon emission events and expected emissions at each time point and the allowable error of the emissions, and generating a carbon emission log corresponding to the period; Step S102: Set the expected total emissions recorded in the carbon emission log as E, obtain the actual emissions of each carbon emission event occurring at any time point, and obtain the actual total emissions of the carbon emission log as E. ac ; Assume that the allowable error of the emission is θ, if (E ac -E) / E>θ, the carbon emission log is marked with features.

3. The AI-based carbon emissions monitoring method according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: Set the carbon emission log with characteristic mark as the characteristic log, and select a characteristic log at random; obtain the actual emission amount at each time point in the characteristic log, wherein the actual emission amount at the ith time point is set as (m i ) ac ; Step S202: Obtain the expected emission at the ith time point in the emission plan of the characteristic log as m i , the emission error at the i-th time point is calculated to be Δm i =[(m i ) ac -m i ] / m i ; Calculate the average value of the emission error at each time point, and obtain the average error per unit time point in the characteristic log (Δm) ave ; If (Δm) ave ≤θ and Δm i >θ, or (Δm) ave >θ and Δm i >(Δm) ave , then the i-th time point is taken as the abnormal time point in the feature log; Step S203: setting the i-th time point in the characteristic log as an abnormal time point, acquiring the carbon emission event occurring at the i-th time point, and obtaining the event set of the i-th time point; acquiring the event set of the i-1-th time point adjacent to the i-th time point, comparing the carbon emission events in the two event sets, extracting the different events in the two event sets, and obtaining the characteristic event of the i-th time point; Step S204: If the carbon emission events in the two event sets are the same, randomly select a carbon emission event from the event set, count the number of occurrences of the carbon emission event as a characteristic event in each characteristic log as x, and obtain the occurrence frequency η of the carbon emission event; sort the carbon emission events in the event set from high to low according to the occurrence frequency, and generate a carbon emission event queue; Step S205: Select a carbon emission event from the carbon emission event queue in order, and obtain the emission error of the abnormal time point corresponding to the carbon emission event as a characteristic event in any other carbon emission log, which is Δm'. If Δm'≤Δm i , then the carbon emission event is set as a characteristic event, if Δm'>Δm i , then continue to select the next carbon emission event until Δm'≤Δm i So far, all the i The carbon emission events are all set as characteristic events; and a number of characteristic events at the i-th time point in the characteristic log are obtained; Step S206: Summarize the characteristic events at each time point in the characteristic log to obtain a characteristic event set of the characteristic log.

4. The AI-based carbon emissions monitoring method according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: randomly select a carbon emission log, obtain a characteristic event set of the emission plan in the carbon emission log; randomly select the jth characteristic event from the characteristic event set, and obtain the occurrence frequency of the jth characteristic event in each carbon emission log as η j , set the expected total emissions of the carbon emission log to E and the actual total emissions to E ac , get the emission error θ' of the carbon emission log; according to the formula: Wherein, IF() is a judgment function, if θ'>0, then IF(θ'>0)=1, otherwise, IF(θ'>0)=0; n is the number of characteristic events in the characteristic event set; the risk value F of the emission plan of the carbon emission log is calculated; Step S302: Obtain the risk value of each characteristic log, and select the risk value with the smallest value as the risk threshold F for judging whether the emission plan is reasonable. min .

5. The AI-based carbon emissions monitoring method according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: Obtain the enterprise's new emission plan for the next cycle, and obtain a set of characteristic events for the new emission plan; obtain the occurrence frequency of each characteristic event in the characteristic event set, and obtain the risk value F of the new emission plan. new ; Step S402: Set the risk threshold to F min , if F new >F min , then a risk warning is issued for the new emission plan; arbitrarily select the kth characteristic event in the characteristic event set, obtain the emission error of the abnormal event point when the kth characteristic event is used as a characteristic event in each characteristic log, and obtain the average emission error of the kth characteristic event as (Δm k ) ave , set the occurrence frequency of the kth characteristic event to η k , according to the formula: Y=η k ×(Δm k ) ave ; The adjustment priority of the kth characteristic event in the new emission plan is calculated; the adjustment priorities are sorted from large to small, and plan adjustment suggestions are provided to the enterprise.

6. A carbon emission monitoring system, used to execute the AI-based carbon emission monitoring method according to any one of claims 1 to 5, characterized in that: The monitoring system includes an emission log analysis module, a characteristic event analysis module, a plan risk assessment module and an emission plan adjustment module; The emission log analysis module is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate several carbon emission logs of the enterprise; obtain the emission plan and actual emission situation stored in any carbon emission log, and obtain the compliance status of the carbon emission log; The characteristic event analysis module is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, and capture the abnormal time points with abnormal carbon emissions; obtain the emission plan at each time point, compare the plan differences between the abnormal time point and the adjacent time points, and extract the characteristic events that affect the emission plan; The plan risk assessment module is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan and perform risk assessment on the emission plan in the carbon emission log; Compare the differences in risk assessment between various emission plans and determine the risk threshold for judging whether the emission plan is reasonable; The emission plan adjustment module is used to extract characteristic events in the new emission plan and conduct risk assessment on the new emission plan when the enterprise formulates a new emission plan for the next period; If the obtained risk value exceeds the risk threshold, a risk warning will be issued for the new emission plan, and plan adjustment suggestions will be provided to the enterprise.

7. A carbon emission monitoring system according to claim 6, characterized in that: The emission log analysis module includes an emission log generation unit and a compliance analysis unit; The emission log generation unit is used to monitor the carbon emissions of the enterprise in each period through monitoring equipment, establish a carbon emission monitoring system to record the carbon emissions of the enterprise in each period, and generate a number of carbon emission logs of the enterprise; the compliance analysis unit is used to obtain the emission plan and actual emissions stored in any carbon emission log to obtain the compliance status of the carbon emission log.

8. A carbon emission monitoring system according to claim 6, characterized in that: The characteristic event analysis module includes an abnormal event point capture unit and a characteristic event extraction unit; The abnormal time point capturing unit is used to extract the carbon emission conditions at each time point in any carbon emission log that does not meet the standard, and capture the abnormal time point where abnormal carbon emission exists; The characteristic event extraction unit is used to obtain the emission plan at each time point, compare the plan difference between the abnormal time point and the adjacent time point, and extract the characteristic events that affect the emission plan.

9. A carbon emission monitoring system according to claim 6, characterized in that: The plan risk assessment module includes an impact degree analysis unit and a risk threshold setting unit; The impact degree analysis unit is used to analyze the impact degree of each characteristic event in any carbon emission log on the emission plan, and to perform risk assessment on the emission plan in the carbon emission log; the risk threshold setting unit is used to compare the differences in risk assessment situations between various emission plans, and to determine the risk threshold for judging whether the emission plan is reasonable.

10. A carbon emission monitoring system according to claim 6, characterized in that: The emission plan adjustment module includes a preset plan evaluation unit and a plan adjustment analysis unit; The preset plan evaluation unit is used to analyze the impact of each characteristic event in any carbon emission log on the emission plan and perform risk assessment on the emission plan in the carbon emission log; The plan adjustment analysis unit is used to issue a risk warning for the new emission plan if the obtained risk value exceeds the risk threshold, and provide plan adjustment suggestions to the enterprise.

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