Industrial cluster-oriented collaborative carbon inclusive management method and system

Abstract

This invention relates to the field of carbon management technology, specifically to a collaborative carbon inclusive management method and system for industrial clusters. The method includes the following steps: constructing an emission intensity queue by combining the total carbon emissions of multiple enterprises with industrial output; calculating adjacent intensity differences to generate congested enterprise segments; generating carbon credit sequences based on the percentage of clustering exceeding a threshold; generating a ranking change set based on the ranking change difference; and constructing a carbon inclusive collaborative control instruction by adding compensation credits to enterprises that have experienced degradation. In this invention, emission tiers are constructed by integrating measured flue gas parameters and output; congested ranking segments with clustering characteristics are screened based on adjacent intensity differences; control actions are triggered based on specific percentages; degradation and improvement indicators are quantified based on cross-cycle ranking change trajectories; compensation credits are assigned to entities that continuously optimize efficiency; monitoring is shifted from static accounting to a dynamic relative competition level; a multi-dimensional assessment dimension combining production and density is established; the incentive misalignment dilemma is resolved; and the precision of collaborative governance is improved.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology, and in particular to a collaborative carbon inclusive management method and system for industrial clusters. Background Technology

[0002] The field of carbon management technology involves a technical system for recording, accounting for, monitoring, statistically analyzing, and managing carbon emission activities. It mainly includes carbon emission data collection, emission accounting rules, emission source classification management, enterprise or regional carbon emission statistics, carbon emission reduction target management, and carbon trading and carbon incentive mechanism management. Its technical system usually revolves around energy consumption data recording, production activity emission statistics, emission factor matching calculation, regional emission total aggregation, and information sharing mechanisms among management entities. In industrial parks, industrial clusters, and regional economic systems, information platforms are used to uniformly record and manage emission data from multiple entities, thereby forming a regional-level carbon emission statistics and governance system.

[0003] Among them, the traditional collaborative carbon credit management method and system for industrial clusters refers to the technical solution for unified management of carbon emission reduction activities of multiple enterprises in an industrial park or industrial cluster. It usually involves collecting energy consumption data such as enterprise electricity consumption records, gas usage records, production equipment operation records, and transportation fuel usage records, calculating the enterprise's carbon emissions according to a preset emission factor table, establishing an enterprise emission ledger according to the list of enterprises in the park, registering enterprise energy-saving renovation records, clean energy usage records, and low-carbon production behaviors, and assigning carbon credits to carbon emission reduction behaviors according to preset credit rules. Subsequently, the enterprise carbon emission and emission reduction data are aggregated through a centralized database, and the management agency registers, counts, and distributes credits according to the enterprise emission reduction records, thereby forming a carbon emission record and inclusive incentive management method within the industrial cluster.

[0004] Traditional carbon management mechanisms rely on static energy records and preset emission factors for single-dimensional accounting. They typically distribute points in isolation based on specific emission reduction behaviors, lacking analysis of the dynamic differences in emission intensity and relative evolution trends within clusters. This makes it difficult to make targeted adjustments based on overall distribution characteristics. The single static model causes the accounting results to deviate from actual production efficiency, making it difficult to accurately identify and incentivize clustered enterprises that demonstrate sustained emission reduction potential. This results in misallocation of incentive resources and low overall governance efficiency, failing to build a scientific collaborative guidance mechanism. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a collaborative carbon inclusive management method for industrial clusters, comprising the following steps: S1: Collect the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculate the total carbon emissions of a single enterprise. Collect the industrial output value of multiple enterprises and combine it with the total carbon emissions of a single enterprise to analyze the carbon emission intensity and construct an emission intensity queue. S2: Calculate the adjacent intensity difference of carbon emission intensity for adjacent enterprises in the emission intensity queue, filter out continuously arranged enterprises that have not reached the preset congestion determination threshold, and generate congested enterprise segments. S3: Obtain the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, extract the associated congested enterprise segment for point allocation and generate a cluster carbon point sequence. S4: Based on the emission intensity queue, read the cross-cycle ranking of a single enterprise and calculate the ranking change difference. Filter the ranking change difference corresponding to the decrease or increase of the ranking number. Accumulate and calculate the cumulative value of ranking degradation and the cumulative value of ranking improvement, and integrate them to generate a ranking change set. S5: Based on the ranking change set, for enterprises whose cumulative ranking improvement value exceeds the cumulative ranking decline value, extract the corresponding points located within the cluster carbon point sequence and add them to a preset fixed compensation value to construct a carbon inclusive collaborative regulation instruction.

[0006] As a further embodiment of the present invention, the emission intensity queue includes enterprise emission intensity ranking, enterprise emission intensity level identifier, and enterprise emission intensity interval label; the congested enterprise segment includes a segment enterprise identifier set and a continuous interval of segment intensity; the cluster carbon credit sequence includes enterprise carbon credit, enterprise credit level division, and credit segment distribution identifier; the ranking change set includes enterprise ranking change identifier, cumulative enterprise ranking improvement, and cumulative enterprise ranking decline; and the carbon inclusive collaborative regulation instruction includes enterprise carbon compensation credit, enterprise regulation implementation identifier, and enterprise collaborative regulation level.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Acquire the analog voltage signal output by the carbon dioxide sensor in the exhaust pipe of multiple enterprises in the industrial cluster to obtain the carbon dioxide concentration in the flue gas, align the data with the exhaust volume output by the same frequency exhaust flow meter, calculate the total carbon emissions of a single enterprise and aggregate enterprise identifiers to generate an enterprise carbon emission dataset. S102: Collect the industrial output value of multiple enterprises and perform enterprise identification matching, call the enterprise carbon emission dataset and perform corresponding item retrieval based on the enterprise identification, calculate the ratio between emissions and industrial output value, and sort the identification mapping results of all enterprise ratio calculations to obtain the enterprise emission intensity table. S103: Extract the enterprise identifier field and emission intensity value field from the enterprise emission intensity table to form an identifier intensity combination sequence, sort the emission intensity values ​​in ascending order and record the sorting index position, and reassemble the enterprise identifier corresponding to the sorting index and the emission intensity array in order to establish an emission intensity queue.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Extract the enterprise number sequence and carbon emission intensity from the emission intensity queue, perform adjacent position difference operation according to the index order of the emission intensity in ascending order, subtract the carbon emission intensity of the previous enterprise from the carbon emission intensity of the next enterprise, and serialize and arrange according to the original number order to obtain the emission intensity difference sequence. S202: Based on the emission intensity difference sequence, record the location index where the difference value does not reach the preset congestion judgment threshold, and at the same time, determine the continuity of the corresponding enterprise number according to the location index order, and sequentially concatenate the enterprise number where the index difference is equal to one to generate a continuous enterprise identifier sequence. S203: Extract adjacent numbers from the continuous enterprise identifier sequence and perform segment boundary identification operation according to the number order. When the interval between enterprise numbers is greater than one, record the segment breakpoint position. At the same time, perform segment splitting and recombination operation on the continuous enterprise identifier sequence according to the breakpoint position to generate crowded enterprise segments.

[0009] As a further aspect of the present invention, the congestion determination threshold is determined by obtaining the carbon emission intensity numerical sequence of all enterprises in the industrial cluster, summing the numerical sequence and dividing it by the total number of enterprises to obtain the intensity benchmark mean, calculating the standard deviation of the carbon emission intensity value relative to the intensity benchmark mean, and then multiplying the intensity benchmark mean and the standard deviation by a proportionality coefficient.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Read the congested enterprise segment to obtain the number of enterprises in the segment, collect the cluster enterprise registration list in the same period to perform total statistics to obtain the total number of enterprises in the cluster, calculate the enterprise number ratio between the number of enterprises in the segment and the total number of enterprises in the cluster and normalize it to establish the enterprise clustering ratio. S302: Based on the enterprise clustering ratio, extract the segment identifier corresponding to the enterprise clustering ratio value exceeding the preset control trigger threshold, perform segment sequence aggregation processing on all segment identifiers, and bind and encode the segment identifiers with the corresponding enterprise index sequence to generate a congested segment index set. S303: Extract the enterprise index sequence within the segment based on the congested segment index set, establish an enterprise arrangement sequence and decrease the integral by a preset fixed step size, assign an initial integral to the first enterprise and assign values ​​by decreasing the integral by a fixed step size, and serialize the integral values ​​of all enterprises to generate a cluster carbon integral sequence.

[0011] As a further embodiment of the present invention, the control trigger threshold is determined by obtaining the total number of enterprises in the cluster and the number of enterprises in the congested section within the period, calculating the enterprise clustering ratio sequence of multiple periods, performing mean calculation to obtain the benchmark clustering ratio, and multiplying the benchmark clustering ratio with a preset fluctuation coefficient.

[0012] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Extract the cluster emission intensity queue for three consecutive cycles, read the cross-cycle ranking of a single enterprise, perform a difference operation on the ranking sequence number of the same enterprise in the first and second cycles to obtain the first ranking difference, perform a difference operation on the ranking sequence number in the second and third cycles to obtain the second ranking difference, and establish a cross-cycle ranking difference sequence. S402: Based on the cross-period ranking difference sequence, sign identification is performed on the values ​​in the sequence that do not reach zero difference and a ranking improvement set is constructed. The values ​​in the sequence that are less than or equal to zero difference are kept at their original values ​​and a ranking degradation set is constructed. The data are then grouped and compiled according to the enterprise number to obtain the enterprise ranking change record sequence. S403: Based on the enterprise ranking change record sequence, retrieve the corresponding ranking improvement sets of multiple enterprises, perform cumulative calculation to obtain the cumulative ranking improvement value, perform item-by-item cumulative calculation on the ranking deterioration set of the same enterprise to obtain the cumulative ranking deterioration value, merge the cumulative ranking improvement values ​​and cumulative ranking deterioration values ​​of all enterprises, and generate the ranking change set.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the ranking change set, subtract the cumulative ranking decline value from the cumulative ranking improvement value of multiple enterprises to obtain the enterprise ranking difference sequence, and logically determine and record the enterprise identifiers with a difference greater than zero in the enterprise ranking difference sequence to generate an enterprise ranking difference identifier sequence. S502: Based on the enterprise ranking difference identifier sequence, perform index matching operation on enterprises with a difference greater than zero, map the corresponding enterprise index to the internal integral value of the cluster carbon integral sequence, and reconstruct the sequence of integral value set to generate an internal integral arrangement vector. S503: Based on the internal integral arrangement vector, the integral values ​​of multiple enterprises in the vector are numerically superimposed with the preset fixed compensation value to obtain a compensation integral value sequence, and the compensation integral value sequence is processed by instruction encoding to establish a carbon inclusive collaborative control instruction.

[0014] A collaborative carbon inclusion management system for industrial clusters includes: The emission accounting module collects the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculates the total carbon emissions of a single enterprise. It also collects the industrial output value of multiple enterprises and analyzes the carbon emission intensity in combination with the total carbon emissions of a single enterprise. Finally, it constructs an emission intensity queue and transmits it to the congestion determination module. The congestion determination module calculates the adjacent intensity difference of carbon emission intensity of adjacent enterprises in the emission intensity queue, filters out continuously arranged enterprises that have not reached the preset congestion determination threshold, generates congested enterprise segments and passes them to the integral allocation module. The points allocation module obtains the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, it extracts the associated congested enterprise segment for points allocation, generates a cluster carbon points sequence, and transmits it to the ranking change module. The ranking change module reads the cross-cycle ranking of a single enterprise based on the emission intensity queue and calculates the ranking change difference. It filters the ranking change difference corresponding to the decrease or increase of the ranking number, accumulates and calculates the cumulative value of ranking degradation and the cumulative value of ranking improvement, integrates them, generates a ranking change set, and transmits it to the collaborative control module. The collaborative regulation module, based on the ranking change set, extracts the corresponding integral located within the cluster carbon integral sequence of enterprises whose cumulative ranking improvement value exceeds the cumulative ranking decline value, and adds it to a preset fixed compensation value to construct a carbon inclusive collaborative regulation instruction.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, measured flue gas parameters and industrial output are integrated to construct a horizontal emission hierarchy. Adjacent intensity differences are used to screen congested emission zones with clustering characteristics. Targeted control actions are triggered based on specific quantity proportions. By quantifying the cumulative indicators of degradation and improvement through cross-cycle ranking changes, the main body for continuous optimization of efficiency is accurately identified and given compensation points. This shifts emission monitoring from static and isolated accounting to a dynamic and relatively competitive level, establishing a multi-dimensional assessment dimension that combines actual production and distribution density. This effectively solves the dilemma of misaligned incentive resources and significantly improves the precision of carbon inclusive collaborative governance. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] Please see Figure 1 This invention provides a collaborative carbon inclusive management method for industrial clusters, comprising the following steps: S1: Collect the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculate the total carbon emissions of a single enterprise. Collect the industrial output value of multiple enterprises and combine it with the total carbon emissions of a single enterprise to analyze the carbon emission intensity and construct an emission intensity queue. S2: Calculate the carbon emission intensity difference between adjacent enterprises in the emission intensity queue, filter out continuously arranged enterprises that have not reached the preset congestion determination threshold, and generate congested enterprise segments; S3: Obtain the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, extract the associated congested enterprise segment for point allocation and generate the cluster carbon point sequence. S4: Based on the emission intensity queue, read the cross-cycle ranking of a single enterprise and calculate the ranking change difference. Filter the ranking change difference corresponding to the decrease or increase of the ranking number. Accumulate and calculate the cumulative value of ranking degradation and the cumulative value of ranking improvement and integrate them to generate a set of ranking changes. S5: Based on the ranking change set, for enterprises whose cumulative ranking improvement value exceeds the cumulative ranking decline value, extract the corresponding points located within the cluster carbon point sequence and add them to the preset fixed compensation value to construct a carbon inclusive collaborative regulation instruction.

[0021] The emission intensity queue includes enterprise emission intensity ranking, enterprise emission intensity level identifier, and enterprise emission intensity interval label. The congested enterprise segment includes the segment enterprise identifier set and the continuous intensity interval of the segment. The cluster carbon credit sequence includes enterprise carbon credit, enterprise credit level division, and credit segment distribution identifier. The ranking change set includes enterprise ranking change identifier, cumulative enterprise ranking improvement, and cumulative enterprise ranking decline. The carbon inclusive coordinated regulation instruction includes enterprise carbon compensation credit, enterprise regulation implementation identifier, and enterprise coordinated regulation level.

[0022] Please see Figure 2 The specific steps of S1 are as follows: S101: Acquire the analog voltage signal output by the carbon dioxide sensor in the exhaust pipe of multiple enterprises in the industrial cluster to obtain the carbon dioxide concentration in the flue gas, align the data with the exhaust volume output by the same frequency exhaust flow meter, calculate the total carbon emissions of a single enterprise and aggregate enterprise identifiers to generate an enterprise carbon emission dataset. The system acquires analog voltage signals from an infrared carbon dioxide sensor inside the exhaust pipe, collecting data 10 times per second. This data is converted into carbon dioxide concentration data, and mean filtering is used to remove outlier data exceeding three times the standard deviation of the mean. The system also acquires the exhaust volume value output from a synchronous exhaust flow meter. A maximum timestamp alignment error threshold is set, and this is determined through comparison of 100 synchronous sensor test data. When the alignment error threshold is set to 5 milliseconds, the data matching success rate reaches 99.8%. Therefore, the allowable error is forcibly fixed at 5 milliseconds to complete the timestamp alignment operation. Finally, the carbon dioxide concentration data and the exhaust volume value at the same time point are multiplied to obtain the carbon emission data.

[0023] Table 1. Enterprise Exhaust Carbon Emission Monitoring Data 1 0.15 10 2 0.16 10 3 0.15 11 As shown in Table 1, the structure of the enterprise exhaust carbon emission monitoring data table is used to extract the participating items. The carbon dioxide concentration value of 0.15 kg / m³ is multiplied by the exhaust volume of 10 m³ / s, resulting in an instantaneous carbon emission of 1.5 kg / s. Based on a 90-day quarter, the 3600 instantaneous carbon emission values ​​within a single hour are summed, yielding a total hourly carbon emission of 5400 kg. The unified social credit code of the target enterprise is extracted, and the aforementioned total hourly carbon emission value is mapped to this code using a key-value pair, completing the enterprise identification aggregation operation. The advantage of this calculation logic is that, through strict high-precision alignment and high-frequency instantaneous data multiplication, it reduces the cumulative error caused by nonlinear fluctuations in exhaust flow.

[0024] S102: Collect the industrial output value of multiple enterprises and perform enterprise identification matching, call the enterprise carbon emission dataset and perform corresponding item retrieval based on the enterprise identification, calculate the ratio between emissions and industrial output value, and sort the identification mapping results of all enterprise ratio calculations to obtain the enterprise emission intensity table. Extract the target company's industrial output statistical report data from the previous quarter, remove and clean up any null values ​​in the reports, and convert all currency units to the standard format of ten thousand yuan. Traverse the preceding dataset, perform a full string matching search based on the company's unified social credit code, and retrieve the associated target quarterly carbon emission total value. Set an industry output value correction weight parameter, and use a grid search algorithm with a step size of 0.1 within the range of 0.5 to 1.5 to perform fitting and verification calculations on the cross-industry output ratio characteristics of 100 historical quarters. Verification data shows that when the weight parameter is set to 1.2, it significantly reduces the valuation dispersion of high-energy-consuming, low-output industries; therefore, this weight parameter is fixed at 1.2. The total carbon emissions for the quarter (2160 hours) are calculated by retrieving the aforementioned hourly emissions of 5400 kg, which is equivalent to 11664 tons. This is then converted to an industrial output value of 50 million yuan (in ten-thousand-yuan terms). This industrial output value is multiplied by a weighting parameter of 1.2, resulting in a revised industrial output value of 60 million yuan. The total quarterly carbon emissions of 11664 tons are divided by the revised industrial output value of 60 million yuan to obtain an emission intensity per unit output value of 1.944 tons per ten thousand yuan. Following the precision retention rule, this value is truncated to 1.94 tons per ten thousand yuan. This emission intensity per unit output value is then serialized and stored side-by-side with the target enterprise's unified social credit code in an in-memory hash table. The advantage of this calculation logic is that it introduces industry output value correction weights generated based on historical fitting results to participate in the ratio calculation, which smooths out the inherent distribution differences in product added value among enterprises of different sizes. The comparison results are stable within the normal emission range of the industry, ranging from 1.0 to 3.0 tons per 10,000 yuan, which directly reflects the true energy efficiency rating level of the corresponding enterprise entity. Based on this full mapping relationship, a complete structured summary table is formed.

[0025] S103: Extract the enterprise identification field and emission intensity value field from the enterprise emission intensity table to form an identification intensity combination sequence, sort the emission intensity values ​​in ascending order and record the sorting index position, and reassemble the enterprise identification and emission intensity array corresponding to the sorting index in order to establish an emission intensity queue. Based on the output enterprise emission intensity table, the enterprise unified social credit code field and the unit output value emission intensity numerical field are separated and transformed into two parallel independent sequences. A quick sort comparison operation is performed on the extracted emission intensity numerical sequences, specifying the element at the center of the sequence as the comparison benchmark value. The emission intensity value 1.94 calculated in the previous process is retrieved, and the related intensity values ​​1.50 and 2.10 extracted in the same batch are newly introduced. 1.94 is selected as the benchmark comparison object. A subtraction comparison is performed between 1.50 and 1.94, determining that 1.50 is smaller and shifting it to the left. A subtraction comparison is performed between 2.10 and 1.94, determining that 2.10 is larger and shifting it to the right. This process of layer-by-layer positional manipulation generates a strictly ascending numerical sequence of 1.50, 1.94, and 2.10. During the numerical swapping synchronization cycle, the initial arrangement position of each data point is extracted, and an index change tracking table is established. The initial value 1.50 corresponds to starting index 2, 1.94 corresponds to starting index 1, and 2.10 corresponds to starting index 3. The mapping structure after ascending order reset is recorded as 2, 1, and 3. Based on this structure, parallel enterprise code sequences are called, and the code fields are rearranged and updated in the absolute order of 2, 1, and 3. The rearranged code fields are then joined with the ascending numerical columns at the row level. A timeout check threshold for reorganization is set. After 50 rounds of stress testing with millions of data points, when the processing time exceeds 150 milliseconds, the system's concurrent throughput significantly decreases, causing queuing and blocking of subsequent real-time computing tasks. Therefore, the timeout safety threshold is set to 120 milliseconds. The advantage of this operation logic is that it uses a sequence stripping and separate sorting combined with pointer index tracking reconstruction processing mechanism, avoiding the high overhead of performing full memory replacement on complex multidimensional objects.

[0026] Please see Figure 3 The specific steps of S2 are as follows: S201: Extract the enterprise number sequence and carbon emission intensity from the emission intensity queue, perform adjacent position difference operation according to the index order of emission intensity in ascending order, subtract the carbon emission intensity of the previous enterprise from the carbon emission intensity of the next enterprise, and serialize and arrange according to the original number order to obtain the emission intensity difference sequence. The generated emission intensity queue is retrieved, and structured data records containing enterprise IDs and emission intensity values ​​per unit of output value are extracted sequentially. All extracted enterprise IDs are then loaded into a key-value array in memory, sorted in ascending order. Simultaneously, the corresponding carbon emission intensity values ​​are loaded into a parallel single-precision floating-point data list. The difference step size parameter is set to 1, and an empty data list with a length equal to the original sequence length minus 1 is initialized to store the difference results. A loop traversal pointer is created, with its initial position set to the first index node of the sequence. Within a single loop cycle, the carbon emission intensity value of the previous enterprise corresponding to the current node position and the carbon emission intensity value of the next enterprise corresponding to the pointer position plus 1 are read synchronously through the pointer. The carbon emission intensity value of the next enterprise is then... The carbon emission intensity value is subtracted from the previous company's carbon emission intensity value to obtain the difference in carbon emission intensity between adjacent companies. This difference is then substituted into the ascending sequence of parameters. For the first company (number 101) and its emission intensity of 1.50 tons per 10,000 yuan, and the second company (number 102) and its emission intensity of 1.94 tons per 10,000 yuan, the latter emission intensity of 1.94 is subtracted from the former emission intensity of 1.50, resulting in a difference of 0.44 tons per 10,000 yuan for the first adjacent company. Similarly, for the second company (number 102) and its emission intensity of 1.94, and the third company (number 103) and its emission intensity of 2.10, the difference is subtracted from 1.94, resulting in a difference of 0.16 tons per 10,000 yuan for the second adjacent company.

[0027] Table 2. Storage table of emission intensity difference sequences 1 101 102 0.44 2 102 103 0.16 As shown in Table 2, the emission intensity difference sequence storage table is used to sequentially write the calculated difference values ​​into a pre-initialized empty data list according to the current position index of the traversal pointer. The obtained values ​​of 0.44 and 0.16 represent the local convergence degree of the efficiency ladder among enterprises. The smaller the value, the more homogeneous the emission characteristics among enterprises are. This difference result directly constitutes the basic set of values ​​for subsequent determination of local cluster congestion effect, and the entire mapping association with the original enterprise number is maintained to complete the serialization arrangement, thus obtaining the emission intensity difference sequence.

[0028] S202: Based on the emission intensity difference sequence, record the location index where the difference value does not reach the preset congestion judgment threshold. At the same time, determine the continuity of the corresponding enterprise number according to the location index order. Concatenate the enterprise number where the index difference is equal to a certain number to generate a continuous enterprise identifier sequence. Based on the generated emission intensity difference sequence, a two-dimensional retrieval matrix containing the difference values ​​and corresponding starting node indices is established. A congestion determination threshold is set, and 1000 sets of historical emission intensity difference data from the same industry over the past 12 months are extracted and sorted in ascending order. The data point at the 25th percentile is selected as the benchmark reference value. Actual calculations show that the difference value corresponding to this percentile is 0.25 tons per 10,000 yuan. To ensure the inclusiveness of the determination interval, this benchmark reference value is multiplied by a relaxation coefficient of 1.2, ultimately calculating the congestion determination threshold to be 0.30 tons per 10,000 yuan. All intensity difference values ​​in the retrieval matrix are traversed, and each is compared with the congestion determination threshold of 0.30. When the difference value is less than or equal to the threshold, an index recording operation is triggered. The node position index in the current two-dimensional matrix is ​​extracted separately and pushed into the marker stack. The first difference value of 0.44 and the second difference value of 0.16 calculated in the previous process are then used to determine the threshold. Comparing 4 with 0.30, it is determined that 4 is greater than the threshold, so the index of that node is discarded. Comparing 0.16 with 0.30, it is determined that 0.16 is less than the threshold, so the starting position index 2 corresponding to the difference is recorded. After completing the full screening, for all position indices saved in the tag stack, the corresponding enterprise numbers are called for sequential alignment. A continuity comparison cursor is set, and adjacent two position index values ​​are extracted one by one and subtracted. It is determined whether the difference between the two indices is equal to 1. If the previous index that meets the condition is 2 and the next index that meets the condition is 3, the difference between the two is indeed 1. Then it is determined that the enterprise numbers associated with these two indices have absolute adjacent continuity in the original sorting. The enterprise numbers corresponding to the continuous nodes with index differences equal to 1 are extracted. According to the ascending index order rule from small to large, a string-level first and last concatenation operation is performed. The character linked list that has been concatenated is further transformed into a standard one-dimensional continuous enterprise identifier sequence for output.

[0029] S203: Extract adjacent numbers from the continuous enterprise identifier sequence and perform segment boundary identification operation according to the number order. When the interval between enterprise numbers is greater than one, record the segment breakpoint position. At the same time, perform segment splitting and recombination operation on the continuous enterprise identifier sequence based on the breakpoint position to generate crowded enterprise segments. Based on the generated continuous enterprise identifier sequence, an array of null breakpoint record pointers and a dynamic doubly linked list for caching temporary segments are initialized. A character-by-character traversal read operation is performed on the continuous enterprise identifier sequence. Two physically adjacent enterprise numbers are extracted as the judgment targets. The last three digits of the extracted two adjacent enterprise numbers are extracted and subtracted to perform segment boundary recognition. Substituting these into the associated enterprise number set integrated in the previous steps, numbers 102 and 103, and numbers 105 and 106 are extracted from the preceding continuous enterprise identifier sequence. The number jump judgment trigger condition is set to an absolute difference greater than 1. Number 102, which is at the beginning, and its adjacent number 103 are extracted. Subtracting 103 from 102 yields a difference of 1. Since the judgment result is not greater than 1, the breakpoint trigger operation is not performed. These two numbers are directly written into the currently active dynamic doubly linked list, and the traversal read pointer is moved. Extract number 103 and its immediate successor number 105, subtract 105 from 103 to calculate the difference of 2. Compare this difference of 2 with the preset jump trigger condition value of 1. If the difference is greater than 1, the segment breakpoint recording mechanism is triggered. Obtain the memory address offset of the element that caused the breakpoint, and write the absolute index position of the sequence where number 103 is located as the segment breakpoint position into the breakpoint record pointer array. During the synchronization phase of recording the breakpoint position, slice the current continuous enterprise identifier sequence, and perform a truncation operation along the physical boundary between number 103 and number 105. Terminate the current dynamic doubly linked list writing action and encapsulate it into an independent data segment. Then, create a new active linked list for number 105 and number 106 after the breakpoint for subsequent data writing, thus completing the segment splitting and reorganization operation. Finally, these independently encapsulated data linked lists are integrated into a standard format congested enterprise segment.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Read the congested enterprise segment to obtain the number of enterprises in the segment, collect the cluster enterprise registration list in the same period to perform total statistics to obtain the total number of enterprises in the cluster, calculate the enterprise number ratio between the number of enterprises in the segment and the total number of enterprises in the cluster and normalize it to establish the enterprise clustering ratio. The congested enterprise segments are retrieved from the output. A line-by-line parsing operation is performed on the dynamic doubly linked list storing these segments to locate and extract the independent data segments in the currently active linked list. The memory read pointer is then used to traverse the segment sequentially from its first node. Whenever the pointer reads a character set containing a valid enterprise ID, the internally set segment enterprise counter is incremented by 1. This counter is then substituted into the specific independent data segment containing enterprise IDs 102 and 103 obtained in the previous step. The pointer traverses this segment, triggering two increment operations, thereby obtaining the number of enterprises within that segment through counting. With a quantity of 2, a secure network communication channel is established. The list of clustered enterprises registered with the Administration for Industry and Commerce within the same period is retrieved via an interface. Null value filtering and removal / cleaning operations are performed on this list to eliminate entries with abnormal cancellation status. For the remaining valid entity data rows after cleaning, a total row count summation operation is performed to determine the total number of clustered enterprises in the current period as 50. The previously obtained enterprise count value of 2 and the total number of enterprises in the cluster value of 50 are loaded into a floating-point arithmetic unit. The value of 2 is divided by the value of 50 to perform a ratio calculation operation, resulting in an initial enterprise clustering ratio of 0.04. Historical data from the past 60 months is then retrieved. The statistical database file is used to extract enterprise clustering ratio records for all historical statistical periods. These records are then sorted in descending order. The highest-ranking value (0.10) is extracted and set as the historical maximum clustering ratio, and the lowest-ranking value (0.01) is extracted and set as the historical minimum clustering ratio. The currently calculated initial enterprise clustering ratio (0.04) and the historical minimum clustering ratio (0.01) are retrieved, and the difference between 0.04 and 0.01 is calculated to obtain a ratio deviation numerator of 0.03. Finally, the historical maximum clustering ratio (0.1) is retrieved. The historical minimum clustering ratio of 0.01 is calculated by subtracting 0.01 from 0.10, resulting in a denominator of 0.09 for the ratio deviation. The numerator of the calculated ratio deviation, 0.03, is then divided by the denominator of the ratio deviation, 0.09, and rounded to two decimal places according to the precision retention rule. The normalized clustering ratio is calculated to be 0.33. This normalized result directly replaces the original unprocessed ratio data and serves as a standardized indicator to determine whether the execution process triggers subsequent control and scheduling mechanisms, thus completing the establishment of the enterprise's clustering ratio.

[0031] S302: Based on the enterprise clustering ratio, extract the segment identifier corresponding to the enterprise clustering ratio value exceeding the preset control trigger threshold, perform segment sequence aggregation processing on all segment identifiers, and bind and encode the segment identifiers with the corresponding enterprise index sequence to generate a congested segment index set. The normalized value of the enterprise clustering ratio is extracted, and a preset control trigger threshold parameter is configured. Four sets of historical congestion data records from the previous year's quarterly warnings due to excessive sewage discharge are retrieved from the safety management database. The critical clustering ratio values ​​for triggering early warnings in these four sets of historical warning states are extracted: 0.26, 0.28, 0.25, and 0.29, respectively. These four critical values ​​are loaded into an accumulation register and continuously summed, resulting in a total sum of 1.08. This total sum of 1.08 is divided by the total number of warning groups (parameter 4) to calculate the average, yielding a basic average triggering ratio of 0.2. 7. To reserve sufficient policy buffer space for intervention in actual control, a safety redundancy coefficient parameter of 1.1 is set. The calculated basic average trigger ratio of 0.27 is multiplied by this safety redundancy coefficient of 1.1 to perform a multiplication amplification operation, resulting in a preset control trigger threshold of 0.297. Following the rule of retaining two decimal places, a rounding operation is performed, ultimately fixing the preset control trigger threshold at 0.30. A numerical comparison and judgment execution mechanism is established, where the previously substituted normalized aggregation ratio value of 0.33 and the fixed preset control trigger threshold of 0.30 are compared in a numerical comparator. The output result is determined to be 0.33, which is greater than 0.30. Since the result exceeds the threshold, the underlying execution environment immediately activates a segment identifier extraction forced instruction. It traces upwards along the physical path of the data bus to the original memory block address associated with the aggregation percentage, extracts the physical storage start address code of that block as a unique identifier, and converts the start address into a segment identifier digital feature sequence 11 using a preset mapping encoding dictionary. In concurrent scenarios where multiple segments exceed the threshold, all segment identifiers that meet the triggering conditions are concatenated according to their generation sequence, thus completing the process for all exceptions. The segment sequence aggregation process is performed, and then a new key-value pair mapping level is opened in the relational mapping table. The previously extracted segment identifier string 11 is forcibly set as the underlying hash retrieval primary key. The consecutive enterprise index sequence containing enterprise number 102 and enterprise number 103 is set as the associated mapping value. The memory-level binding write operation is performed through dynamic hash encryption algorithm. The binding encoding operation between the segment identifier and the corresponding enterprise index sequence is realized at the physical storage layer. This directly provides a targeted and tamper-proof basic data set for the subsequent targeted application of punitive scoring. The final congested segment index set is directly output.

[0032] S303: Extract the enterprise index sequence within the segment based on the congested segment index set, establish an enterprise arrangement sequence and decrease the integral by a preset fixed step size, assign an initial integral to the first enterprise and assign values ​​by decreasing them sequentially by a fixed step size, and serialize the integral values ​​of all enterprises to generate a cluster carbon integral sequence. The generated congested segment index set is parsed, and the underlying hash primary key is used for reverse lookup and unpacking operations to peel off the encrypted outer data shell. The internal enterprise index sequence deeply bound to the segment identifier string 11 is accurately extracted, and enterprise numbers 102 and 103 within this sequence container are obtained. A memory snapshot of the timing from the preceding differential judgment stage is retrieved. Based on the strictly ascending temporal arrangement characteristic of the preceding emission intensity, enterprise number 102, at the beginning position, is established as the first ranking position, and the immediately following enterprise number 103 is established as the second ranking position. This absolute physical arrangement order is used to establish a directional continuous enterprise permutation sequence. An initial baseline integral parameter and a preset fixed step size parameter were set for managing carbon efficiency rankings. The baseline carbon emission access score for the same industry as stipulated in national environmental protection standards was retrieved and directly set as the initial baseline integral value of 100 points. The average emission reduction cost sensitivity index records of similar heavily polluting enterprises were extracted. A linear regression fitting calculation was performed on data from 100 historical violation penalty cases using the least squares method. Experimental verification showed that when the score difference between adjacent lagging enterprises was set to 5 points, it could maximize the enterprises' willingness to rectify emissions while avoiding the risk of industry-wide bankruptcies. Therefore, the decreasing step size parameter was forcibly fixed at 5 points, and the starting sequence was initiated. The column traversal assignment mechanism extracts the first company (102) from the established company sequence and directly writes its initial baseline score of 100 into the score field of this first company to complete the initial assignment. Then, the internal write pointer moves to the second-ranked company (103), retrieves the score of 100 held by the previous-ranked company, and subtracts it from the fixed decreasing step size of 5. The calculated difference is 95 points, which is then written into the score field of the second-ranked company (103). If other company nodes exist after this sequence, the process continues in a loop. The process involves retrieving the previous integral and subtracting the step size parameter 5, then traversing the cursor until it reaches the end of the sequence. The integral value of 100 corresponding to enterprise number 102 and the integral value of 95 corresponding to enterprise number 103 are fully extracted. Strictly following the original sequential arrangement rules from front to back, these two sets of data carrying key-value mapping features are processed by compact string concatenation and queuing. The resulting decreasing integral sequence value directly represents the remaining legal carbon emission rights of each enterprise after being forcibly reduced under the overcrowding state. This assignment and arrangement result with absolute penalty gradient finally generates a cluster carbon integral sequence with strong administrative constraints.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Extract the emission intensity queue of clusters for three consecutive cycles, read the cross-cycle ranking of a single enterprise, perform a difference operation on the ranking sequence number of the same enterprise in the first and second cycles to obtain the first ranking difference, perform a difference operation on the ranking sequence number in the second and third cycles to obtain the second ranking difference, and establish a cross-cycle ranking difference sequence. Extract the cluster emission intensity queue for three consecutive observation periods, and traverse the data queue to read the specific ranking of individual enterprises across these three periods. For example, extract the emission ranking sequence of target enterprises, namely enterprise number 102 and enterprise number 103, over the past three consecutive calendar months. Enterprise number 102's ranking number is 10 in the first period, 8 in the second period, and 11 in the third period. For enterprise number 102, its ranking number 8 in the second period and its ranking number 10 in the first period are loaded into the calculation node. The difference between the second and first period ranking numbers is calculated as -2. Subsequently, the enterprise's ranking number 11 in the third period and its ranking number 8 in the second period are obtained, and the difference between the third and second period ranking numbers is calculated as +3. Simultaneously, the ranking sequence of enterprise number 103 is read as 15 in the first period, 12 in the second period, and 9 in the third period. Subtracting the first period's ranking sequence 15 from the second period's rank sequence 12 yields a first ranking difference of -3. Similarly, subtracting the second period's ranking sequence 12 from the third period's rank sequence 9 yields a second ranking difference of -3. All these calculated ranking differences are then arranged in a one-dimensional permutation according to their chronological order. For enterprise number 102, the calculated first ranking difference of -2 and the second ranking difference of +3 are concatenated sequentially. For enterprise number 103, the first ranking difference of -3 and the second ranking difference of -3 are concatenated sequentially. This completes the establishment of corresponding sequences for each specific entity.

[0034] S402: Based on the cross-period ranking difference sequence, sign identification is performed on the values ​​in the sequence that do not reach zero difference and a ranking improvement set is constructed. For the values ​​in the sequence that are less than or equal to zero difference, the original value is maintained and a ranking degradation set is constructed. The data is then grouped and compiled according to the enterprise number to obtain the enterprise ranking change record sequence. Obtain the cross-period ranking difference sequence data for each enterprise, setting zero as the judgment boundary benchmark. Read the values ​​-2 and +3 contained in the cross-period ranking difference sequence of enterprise number 102, and perform a digit-by-digit comparison operation between each value in the sequence and the zero difference benchmark. For the first ranking difference of negative 2 for enterprise number 102, it is determined that its value is less than the zero difference benchmark, that is, it meets the judgment condition of not reaching zero difference. At this time, the sign recognition logic is activated to extract the negative sign feature of the negative value, indicating that the enterprise's emission ranking in this interval is showing an upward and improving trend. It is separated and classified into a specific positive change data pool, constructing a ranking improvement set containing the single value -2. Subsequently, the second ranking difference of positive 3 for enterprise number 102 is compared and determined that its value is greater than the zero difference benchmark, that is, it meets the judgment condition of exceeding zero difference. At this time, the sign extraction logic is forcibly cut off and the original value preservation writing instruction is triggered, directly recording the value positive 3 as is into the negative change data pool, constructing a ranking degradation set containing the single value positive 3. Similarly, for the two negative 3 values ​​in the difference sequence of enterprise number 103, neither reached zero difference after comparison. Symbol recognition was performed on each, and the results were stored in the positive change data pool, constructing a ranking improvement set containing the two negative 3 values, while the ranking degradation set for this enterprise was set to empty. Then, using the enterprise's unique identifier as the primary key index, the corresponding sets of data distributed in the positive and negative change data pools were linked and integrated. For enterprise number 102, its identifier was used as the search term, and its corresponding ranking improvement set (negative 2) and ranking degradation set (positive 3) were extracted. For enterprise number 103, its identifier was used as the search term, and its corresponding ranking improvement set (two negative 3 values) and empty degradation set were extracted. The data sets that have been grouped and assembled were written into a preset structured multidimensional array, thus completing the task of generating the record sequence. The generated result represents a deeply deconstructed micro-behavioral change archive of enterprises, thus obtaining the enterprise ranking change record sequence.

[0035] S403: Based on the enterprise ranking change record sequence, retrieve the corresponding ranking improvement sets of multiple enterprises, perform cumulative calculation to obtain the cumulative ranking improvement value, perform item-by-item cumulative calculation on the ranking deterioration set of the same enterprise to obtain the cumulative ranking deterioration value, merge all the cumulative ranking improvement values ​​and cumulative ranking deterioration values ​​of all enterprises to generate the ranking change set; Based on the enterprise characteristic data contained within the enterprise ranking change record sequence, the aggregation statistical logic for positive change characteristics is initiated first. The value -2 in the ranking improvement set corresponding to enterprise number 102 is read; since there is only one data point, its cumulative ranking improvement value is directly determined to be -2. The two values ​​-3 in the ranking improvement set corresponding to enterprise number 103 are read, and these multiple similar data points are loaded into the accumulation register for continuous summation, i.e., the values ​​-3 and -3 are summed together to calculate its cumulative ranking improvement value of -6. Then, the aggregation statistical logic for negative change characteristics is switched to. The value +3 in the ranking degradation set corresponding to enterprise number 102 is read; since there is only one data point, the result of summing each item is determined as the cumulative ranking degradation value of +3. Since the ranking degradation set for enterprise number 103 is empty, its cumulative ranking degradation value is directly assigned to zero. After completing the single-item polarity accumulation, a new key-value merging region is allocated in memory. The cumulative improvement and cumulative deterioration values ​​calculated separately for the same enterprise identifier are then absolutely bound and merged. For enterprise number 102, its cumulative improvement value of -2 and cumulative deterioration value of +3 are merged to form the corresponding data pair -2 and +3. For enterprise number 103, its cumulative improvement value of -6 and cumulative deterioration value of zero are merged to form the data pair -6 and zero. To more clearly illustrate the data merging process for some samples at this stage, structured data is introduced for explanation. Table 3 is the ranking change status merging record table. As shown in Table 3, this step uses a strategy of separate accumulation followed by merging to extract complex long-cycle fluctuations into two-dimensional directional features.

[0036] Table 3 Merged Record of Rank Change Status Company Number 102 -2 3 negative 2 and positive 3 Company Number 103 -6 zero -6 and zero As shown in Table 3, the data results intuitively present the bidirectional variation extreme state after merging. The calculated two-dimensional numerical results represent a highly condensed final variation characterization set that retains both positive and negative amplitude characteristics. Using this set of data, the final output generates the precise set of positional variations.

[0037] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the ranking change set, subtract the cumulative ranking decline value from the cumulative ranking improvement value of multiple enterprises to obtain the enterprise ranking difference sequence, and logically determine and record the enterprise identifiers with a difference greater than zero in the enterprise ranking difference sequence to generate the enterprise ranking difference identifier sequence. The generated ranking change set data is retrieved. Using physical addressing, the comprehensive change status pairs corresponding to company number 102 (-2 and +3) and company number 103 (-6 and zero) are extracted and loaded into independent registers for data segmentation. For company number 102, the cumulative ranking improvement value is -2 and the cumulative ranking decline value is +3. For company number 103, the cumulative ranking improvement value is -6 and the cumulative ranking decline value is zero. The absolute value conversion logic is activated. For all extracted negative cumulative ranking improvement values, sign bit reversal and masking operations are performed to forcibly remove the negative sign feature to obtain the absolute value of the actual change magnitude. The conversion yields an absolute improvement value of 2 for company number 102 and an absolute improvement value of 6 for company number 103. The difference subtraction operation logic is activated. The absolute improvement value obtained after conversion for each company is set as the minuend parameter, and the corresponding bound cumulative ranking decline value is set as the subtrahend parameter to perform the difference operation. Substituting the data for company number 102, the absolute improvement value of 2 is subtracted from the cumulative ranking degradation value of 3, resulting in a ranking difference of -1. Substituting the data for company number 103, the absolute improvement value of 6 is subtracted from the cumulative ranking degradation value of zero, resulting in a ranking difference of +6. These continuously calculated difference results are then sequentially written into a dynamic memory queue according to the original time sequence of the company codes, and packaged to generate a sequence of company ranking differences containing the values ​​of -1 and +6. The threshold value of the logic comparator is forced to zero, and the read pointer is invoked to perform a logical comparison operation on all ranking difference data from the beginning of the difference sequence. The ranking difference value of -1 for company number 102 is read and compared with the threshold value of zero. If the result is -1 less than zero, the condition of a difference greater than zero is not met, triggering a memory erase instruction to completely remove and release the company identifier and associated data from the active processing queue. Continue reading the ranking difference value of enterprise number 103, which is positive 6, and compare it with the baseline value of zero again. The judgment result is that positive 6 is greater than zero and fully meets the judgment condition. The record retention append instruction is triggered to copy and extract the identification string of enterprise number 103 separately and store it in the flag register. After traversal and reorganization operation, an enterprise ranking difference identification sequence containing only the independent enterprise number 103 is generated.

[0038] S502: Based on the enterprise ranking difference identifier sequence, perform index matching operation on enterprises with a difference greater than zero, map the corresponding enterprise index to the internal integral value of the cluster carbon integral sequence, and reconstruct the order of the integral value set to generate an internal integral arrangement vector. Based on the generated enterprise ranking difference identifier sequence, the underlying memory string structure of this sequence is parsed to extract the unique entity identifier, enterprise number 103, which is explicitly determined to satisfy the conditional matching operation. This identifier is then forcibly configured as the primary key mapping parameter for subsequent global data retrieval. A snapshot file of the cluster carbon credit sequence data generated by the previous execution process is retrieved. This encrypted snapshot file fully encapsulates the holding credit value of 100 bound to enterprise number 102 and the holding credit value of 95 bound to enterprise number 103. The underlying bidirectional hash index matching operation logic is initiated, and the enterprise number 103 string, configured as the retrieval primary key, is input into the source address addressing end of the matching algorithm engine. An omnidirectional sliding traversal comparison operation is then performed in the target address mapping pool constructed from the cluster carbon credit sequence. When the memory scanning cursor reaches the data storage node containing enterprise ID 103, a strict underlying character consistency comparison and confirmation operation is performed. After confirming that the physical identifier code completely matches, a lock extraction and penetration instruction is immediately triggered. The corresponding enterprise index is directly mapped to the underlying internal integral value field of the data node, accurately reading and extracting the internal integral value 95 that is strongly dependent on enterprise ID 103. For enterprise ID 102, which does not appear in the preceding identifier sequence but still exists in the integral snapshot cache, since it cannot pass the primary key consistency verification step, a bypass skip and data masking isolation operation is directly performed on its node. After completing the mapping penetration and accurate extraction of the full integral snapshot, all successfully extracted discrete integral values ​​are imported into the sequential reconstruction queuing cache area. Since only the single integral value 95 was successfully retrieved and extracted in this data flow instance, sequential reconstruction is directly performed to load it into the first memory storage position of the structured one-dimensional data queue. After underlying queue linearization and normalization organization and encapsulation, an internal integral arrangement vector containing the value 95 is generated and fixed in a pre-allocated continuous storage area.

[0039] S503: Based on the internal integral arrangement vector, the integral values ​​of multiple enterprises in the vector are numerically superimposed with the preset fixed compensation value to obtain the compensation integral numerical sequence, and the compensation integral numerical sequence is processed by instruction encoding to establish a carbon inclusive collaborative control instruction. The generated internal integral arrangement vector file is retrieved. The high-speed data communication bus is used to extract the multi-enterprise integral value (95) stored in the first active memory address block of the vector, specifically the internal integral value 95 strictly associated with enterprise number 103, and load it into a specific accumulation register of the central floating-point arithmetic unit. Preset fixed compensation value benchmark parameters for executing positive physical incentive allocation are configured. A total of 120 sets of benchmark emission reduction reward quota data issued by the National Carbon Trading Filing Management Center for the same type of fine chemical industry over the past 36-month statistical period are extracted and analyzed for median value calculation. Based on the obtained macro-industry average incentive median, the preset fixed compensation value is precisely set to a constant value of 15. The numerical superposition operation logic mechanism at the bottom layer of the arithmetic logic unit is activated. The internal integral value 95 temporarily read from the current register and the configured fixed compensation value 15 are simultaneously pushed into the addition operation execution channel to perform a basic addition summation operation. Substituting the specific numerical data obtained from the aforementioned settings, the extracted internal integral value 95 is added to the fixed compensation value 15, and the summation process is performed. A rigorous calculation yields a combined quota of 110 after superposition and fusion. After completing the above arithmetic operations, the calculated summation result, 110, is used as the updated and overwritten rights expansion quota and filled back into a specific structured data management table. The establishment and storage of a compensation integral value sequence containing the single final updated value 110 is then completed through memory assembly. Subsequently, the hardware and software instruction encoding and conversion mechanism for the end-point physical execution control mechanism is initiated, extracting the compensation quota value 110 from the generated compensation integral value sequence and calling the pre-configured hexadecimal conversion dictionary and industrial equipment communication protocol encapsulation standard template. During the message construction and assembly phase, a specific hexadecimal opcode sequence segment representing the positive reward nature of carbon inclusiveness is inserted into the header of the output message. The compensated integral value 110 obtained from the core calculation is converted into the corresponding binary machine identification code and loaded into the core area of ​​the message data payload. At the same time, a cyclic redundancy check bit code and a network physical medium access control layer address pointing to the physical valve intelligent controller of enterprise number 103 are appended to the end of the communication message. By performing bit splicing and strong hash encryption packaging and encapsulation processing on all fields, the output carbon inclusiveness collaborative control command is established. Finally, the control command result containing the calculated value 110 is established, which represents the upper limit capacity of the expanded production and sewage discharge flow threshold allowed by the target enterprise after obtaining inclusiveness compensation. This directly triggers the precise caliber displacement ratio of the controlled valve opening response at the execution end.

[0040] Please see Figure 7 A collaborative carbon inclusion management system for industrial clusters, including: The emission accounting module collects the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculates the total carbon emissions of a single enterprise. It also collects the industrial output value of multiple enterprises and analyzes the carbon emission intensity in combination with the total carbon emissions of a single enterprise. Finally, it constructs an emission intensity queue and transmits it to the congestion determination module. The congestion determination module calculates the adjacent intensity difference of carbon emission intensity of adjacent enterprises in the emission intensity queue, filters out continuously arranged enterprises that have not reached the preset congestion determination threshold, generates congested enterprise segments and passes them to the integral allocation module. The points allocation module obtains the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, it extracts the associated congested enterprise segment for points allocation, generates the cluster carbon points sequence, and transmits it to the ranking change module. The ranking change module reads the cross-cycle ranking of a single enterprise based on the emission intensity queue and calculates the ranking change difference. It filters the ranking change difference corresponding to the decrease or increase of the ranking number, accumulates and calculates the cumulative value of ranking degradation and the cumulative value of ranking improvement, integrates them, generates a ranking change set, and transmits it to the collaborative control module. The coordinated regulation module, based on the ranking change set, extracts the corresponding integrals located within the cluster carbon integral sequence of enterprises whose cumulative improvement in ranking exceeds the cumulative decline in ranking, and adds them to a preset fixed compensation value to construct a carbon inclusive coordinated regulation instruction.

[0041] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A collaborative carbon inclusive management method for industrial clusters, characterized in that, Includes the following steps: S1: Collect the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculate the total carbon emissions of a single enterprise. Collect the industrial output value of multiple enterprises and combine it with the total carbon emissions of a single enterprise to analyze the carbon emission intensity and construct an emission intensity queue. S2: Calculate the adjacent intensity difference of carbon emission intensity for adjacent enterprises in the emission intensity queue, filter out continuously arranged enterprises that have not reached the preset congestion determination threshold, and generate congested enterprise segments. S3: Obtain the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, extract the associated congested enterprise segment for point allocation and generate a cluster carbon point sequence. S4: Based on the emission intensity queue, read the cross-cycle ranking of a single enterprise and calculate the ranking change difference. Filter the ranking change differences with a difference greater than zero or less than zero. Accumulate and calculate the cumulative value of ranking degradation and the cumulative value of ranking improvement, and integrate them to generate a ranking change set. S5: Based on the ranking change set, for enterprises whose cumulative ranking improvement value exceeds the cumulative ranking decline value, extract the corresponding points located within the cluster carbon point sequence and add them to a preset fixed compensation value to construct a carbon inclusive collaborative regulation instruction.

2. The industry cluster-oriented collaborative carbon inclusive management method according to claim 1, wherein, The emission intensity queue includes enterprise emission intensity ranking, enterprise emission intensity level identifier, and enterprise emission intensity interval label. The congested enterprise segment includes a segment enterprise identifier set and a continuous interval of segment intensity. The cluster carbon credit sequence includes enterprise carbon credit, enterprise credit level division, and credit segment distribution identifier. The ranking change set includes enterprise ranking change identifier, cumulative enterprise ranking improvement, and cumulative enterprise ranking decline. The carbon inclusive collaborative regulation instruction includes enterprise carbon compensation credit, enterprise regulation implementation identifier, and enterprise collaborative regulation level.

3. The industry cluster-oriented collaborative carbon inclusive management method according to claim 1, wherein, The specific steps of S1 are as follows: S101: Collect the analog voltage signal output by the carbon dioxide sensor in the exhaust pipe of multiple enterprises in the industrial cluster to obtain the carbon dioxide concentration in the flue gas, align the data with the exhaust volume output by the same frequency exhaust flow meter, calculate the total carbon emissions of a single enterprise and aggregate enterprise identifiers to generate a dataset of enterprise carbon emissions. S102: Collect the industrial output value of multiple enterprises and perform enterprise identification matching, call the enterprise carbon emission dataset and perform corresponding item retrieval based on the enterprise identification, calculate the ratio between emissions and industrial output value, and sort the identification mapping results of all enterprise ratio calculations to obtain the enterprise emission intensity table. S103: Extract the enterprise identifier field and emission intensity value field from the enterprise emission intensity table to form an identifier intensity combination sequence, sort the emission intensity values ​​in ascending order and record the sorting index position, and reassemble the enterprise identifier corresponding to the sorting index and the emission intensity array in order to establish an emission intensity queue.

4. The industry cluster-oriented collaborative carbon inclusive management method according to claim 1, wherein, The specific steps of S2 are as follows: S201: Extract the enterprise number sequence and carbon emission intensity from the emission intensity queue, perform adjacent position difference operation according to the index order of the emission intensity in ascending order, subtract the carbon emission intensity of the previous enterprise from the carbon emission intensity of the next enterprise, and serialize and arrange according to the original number order to obtain the emission intensity difference sequence. S202: Based on the emission intensity difference sequence, record the location index where the difference value has not reached the preset congestion judgment threshold. At the same time, perform continuity judgment on the corresponding enterprise number according to the location index order, and sequentially concatenate the enterprise numbers with an index difference of one to generate a continuous enterprise identifier sequence. S203: Extract adjacent numbers from the continuous enterprise identifier sequence and perform segment boundary identification operation according to the number order. When the interval between enterprise numbers is greater than one, record the segment breakpoint position. At the same time, perform segment splitting and recombination operation on the continuous enterprise identifier sequence according to the breakpoint position to generate crowded enterprise segments.

5. The cluster-oriented collaborative carbon inclusive management method according to claim 4, wherein, The congestion determination threshold is determined by obtaining the carbon emission intensity numerical sequence of all enterprises in the industrial cluster, summing the numerical sequence and dividing it by the total number of enterprises to obtain the intensity benchmark mean, calculating the standard deviation of the carbon emission intensity value relative to the intensity benchmark mean, and then multiplying the intensity benchmark mean and the standard deviation by a proportionality coefficient.

6. The industry cluster-oriented collaborative carbon inclusive management method according to claim 1, wherein, The specific steps for S3 are as follows: S301: Read the congested enterprise segment to obtain the number of enterprises in the segment, collect the cluster enterprise registration list in the same period to perform total statistics to obtain the total number of enterprises in the cluster, calculate the enterprise number ratio between the number of enterprises in the segment and the total number of enterprises in the cluster and normalize it to establish the enterprise clustering ratio. S302: Based on the enterprise clustering ratio, extract the segment identifier corresponding to the enterprise clustering ratio value exceeding the preset control trigger threshold, perform segment sequence aggregation processing on all segment identifiers, and bind and encode the segment identifiers with the corresponding enterprise index sequence to generate a congested segment index set. S303: Extract the enterprise index sequence within the segment based on the congested segment index set, establish an enterprise arrangement sequence and decrease the integral by a preset fixed step size, assign an initial integral to the first enterprise and assign values ​​by decreasing the integral by a fixed step size, and serialize the integral values ​​of all enterprises to generate a cluster carbon integral sequence.

7. The collaborative carbon inclusive management method for industrial clusters according to claim 6, characterized in that, The control trigger threshold is determined by obtaining the total number of enterprises in the cluster and the number of enterprises in the congested section within the period, calculating the enterprise clustering ratio sequence of multiple periods, performing mean calculation to obtain the benchmark clustering ratio, and multiplying the benchmark clustering ratio with a preset fluctuation coefficient.

8. The collaborative carbon inclusive management method for industrial clusters according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Extract the cluster emission intensity queue for three consecutive cycles, read the cross-cycle ranking of a single enterprise, perform a difference operation on the ranking sequence number of the same enterprise in the first and second cycles to obtain the first ranking difference, perform a difference operation on the ranking sequence number in the second and third cycles to obtain the second ranking difference, and establish a cross-cycle ranking difference sequence. S402: Based on the cross-period ranking difference sequence, sign identification is performed on the values ​​in the sequence that do not reach zero difference and a ranking improvement set is constructed. The values ​​in the sequence that are less than or equal to zero difference are kept at their original values ​​and a ranking degradation set is constructed. The data are then grouped and compiled according to the enterprise number to obtain the enterprise ranking change record sequence. S403: Based on the enterprise ranking change record sequence, retrieve the corresponding ranking improvement sets of multiple enterprises, perform cumulative calculation to obtain the cumulative ranking improvement value, perform item-by-item cumulative calculation on the ranking deterioration set of the same enterprise to obtain the cumulative ranking deterioration value, merge the cumulative ranking improvement values ​​and cumulative ranking deterioration values ​​of all enterprises, and generate the ranking change set.

9. The collaborative carbon inclusive management method for industrial clusters according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the ranking change set, subtract the cumulative ranking decline value from the cumulative ranking improvement value of multiple enterprises to obtain the enterprise ranking difference sequence, and logically determine and record the enterprise identifiers with a difference greater than zero in the enterprise ranking difference sequence to generate an enterprise ranking difference identifier sequence. S502: Based on the enterprise ranking difference identifier sequence, perform index matching operation on enterprises with a difference greater than zero, map the corresponding enterprise index to the internal integral value of the cluster carbon integral sequence, and reconstruct the sequence of integral value set to generate an internal integral arrangement vector. S503: Based on the internal integral arrangement vector, the integral values ​​of multiple enterprises in the vector are numerically superimposed with the preset fixed compensation value to obtain a compensation integral value sequence, and the compensation integral value sequence is processed by instruction encoding to establish a carbon inclusive collaborative control instruction.

10. A collaborative carbon credit management system for industrial clusters, characterized in that: The system is used to implement the collaborative carbon inclusive management method for industrial clusters as described in any one of claims 1-9, and the system includes: The emission accounting module collects the flue gas carbon dioxide concentration and exhaust volume of multiple enterprises in the industrial cluster and calculates the total carbon emissions of a single enterprise. It also collects the industrial output value of multiple enterprises and analyzes the carbon emission intensity in combination with the total carbon emissions of a single enterprise. Finally, it constructs an emission intensity queue and transmits it to the congestion determination module. The congestion determination module calculates the adjacent intensity difference of carbon emission intensity of adjacent enterprises in the emission intensity queue, filters out continuously arranged enterprises that have not reached the preset congestion determination threshold, generates congested enterprise segments and passes them to the integral allocation module. The points allocation module obtains the number of enterprises in the congested enterprise segment and the total number of enterprises in the cluster to calculate the enterprise clustering ratio. If it exceeds the preset control trigger threshold, it extracts the associated congested enterprise segment for points allocation, generates a cluster carbon points sequence, and transmits it to the ranking change module. The ranking change module reads the cross-cycle ranking of a single enterprise based on the emission intensity queue and calculates the ranking change difference. It filters the ranking change difference with a value greater than zero or less than zero, accumulates and calculates the cumulative value of ranking degradation and the cumulative value of ranking improvement, integrates them, generates a ranking change set, and transmits it to the collaborative control module. The collaborative regulation module, based on the ranking change set, extracts the corresponding integral located within the cluster carbon integral sequence of enterprises whose cumulative ranking improvement value exceeds the cumulative ranking decline value, and adds it to a preset fixed compensation value to construct a carbon inclusive collaborative regulation instruction.

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