Carbon neutralization path recommendation method driven by digital assets

Abstract

The invention relates to the technical field of carbon emission optimization, in particular to a digital asset-driven carbon neutralization path recommendation method, which comprises the following steps of: acquiring carbon neutralization path records in a digital asset full life cycle, screening frequent combinations, evaluating carbon factors and response efficiency, eliminating invalid paths, extracting intersection segments, optimizing and reserving, and generating a recommendation result. According to the method, high-frequency mode recognition of carbon neutralization path execution behaviors can be achieved through combination frequency statistics and sorting of path execution records in the digital asset life cycle, an evaluation system is constructed based on multi-dimensional indexes such as carbon factor fluctuation, task response time and path execution density, the actual effect of each path combination can be quantified, and the evaluation accuracy is improved. According to the method, low-efficiency or invalid path pairs are screened out through an evaluation result, the accuracy and effectiveness of path recommendation are guaranteed, path intersection segments are extracted in combination with continuous and consistent path number sequences in active digital assets, and path combinations identified as low-quality paths can be eliminated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission optimization, in particular to a digital asset driven carbon neutral path recommendation method. BACKGROUND

[0002] The technical field of carbon emission optimization relates to a systematic method for quantifying, analyzing and managing carbon emission activities, including carbon emission data collection and accounting, emission source identification, carbon quota allocation and trading, carbon emission and carbon neutral path modeling and analysis, carbon emission reduction measure evaluation and carbon neutral strategy formulation.

[0003] Among them, the carbon neutral path recommendation method refers to the possibility of a certain organization or project to achieve carbon neutral in the future, based on historical carbon emission data, energy consumption, carbon sink capacity and other factors, to analyze the life cycle, and to build emission reduction schemes and optimization decisions.

[0004] In the prior art, only historical carbon emission data, energy consumption and carbon sink capacity are used for life cycle analysis, without considering the time sequence and frequency characteristics of carbon neutral paths in real execution scenarios. The lack of targeted evaluation indicators in path optimization often leads to inaccurate matching of digital assets in different stages of dynamic demand. In a complex environment of multiple tasks and asset execution path branches, the efficiency fluctuations of repeated execution of paths are ignored. Some paths have been used historically but cannot adapt to the adjustment direction of the current carbon neutral strategy, which may cause low-efficiency or ineffective paths in path recommendation, resulting in unstable carbon factor control effect, increased task response time delay, and blind reuse of path selection, ultimately affecting the pace and resource allocation efficiency of the organization's carbon neutral strategy. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art and to provide a digital asset driven carbon neutral path recommendation method.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a digital asset driven carbon neutral path recommendation method, comprising the following steps: S1: obtaining each carbon neutral path execution record formed in the life cycle of digital assets and sorting and combining, counting the execution frequency of each pair of carbon neutral paths in the entire record, and screening the frequently executed carbon neutral path combination; S2: obtaining the carbon factor, task response time and task execution frequency of each group of carbon neutral paths in the frequently executed carbon neutral path combination before and after execution, calculating the evaluation index of each group of carbon neutral paths, and obtaining the carbon neutral path evaluation result; S3: According to the carbon neutral path evaluation result, compared with the preset carbon neutral path evaluation index determination threshold, the invalid carbon neutral path pair is selected to obtain the pre-excluded carbon neutral path; S4: Obtain the digital asset number and the corresponding carbon neutral path number in the current life cycle, select the active asset number according to the carbon neutral path execution record of each number, and compare whether the continuous active asset number and the corresponding carbon neutral path number are completely consistent to identify the carbon neutral path intersection section. S5: Remove the pre-excluded carbon neutral path from the carbon neutral path intersection section, and retain the remaining carbon neutral path combination to obtain the carbon neutral path recommendation result.

[0007] As a further scheme of the application, the carbon neutral path combination includes carbon neutral path pairs, execution frequency, and sorting relationship, the carbon neutral path evaluation result includes carbon factor fluctuation, task response efficiency, and carbon neutral path execution density, the pre-excluded carbon neutral path is specifically an evaluation substandard carbon neutral path pair, an invalid task carbon neutral path, and a low-frequency execution carbon neutral path, and the carbon neutral path intersection section includes continuous active asset number, carbon neutral path number consistency, and carbon neutral path overlapping section. The carbon neutral path recommendation result specifically refers to an optimized carbon neutral path combination, an effective carbon neutral path section, and a recommended carbon neutral path number.

[0008] As a further scheme of the application, the acquisition step of the frequently executed carbon neutral path combination is specifically: S111: Obtain each carbon neutral path execution record formed in the digital asset life cycle, extract the task number, carbon neutral path number and trigger time field, classify according to the task number and sort in ascending order according to the trigger time field, and based on the sorting result, construct a combination according to every three continuous carbon neutral path numbers as a group to generate a carbon neutral path number sequence combination; S112: Call the three carbon neutral path numbers in the carbon neutral path number sequence combination as a combination identifier, count the number of occurrences of the combination in all carbon neutral path execution records, compare the statistical frequency with the minimum frequency threshold to obtain the compared carbon neutral path number combination; S113: Based on the carbon neutral path number content in the compared carbon neutral path number combination, an association relationship between carbon neutral path combinations is constructed by an Apriori association rule algorithm, and a frequently executed carbon neutral path combination is extracted.

[0009] As a further scheme of the application, the acquisition step of the carbon neutral path evaluation result is specifically: S211: Extract the task number corresponding to each carbon neutral path combination of the frequently executed carbon neutral path combination, obtain the carbon factor reading value sequence within a specified time before and after the execution of the carbon neutral path, and count the maximum difference in the sequence as the carbon factor fluctuation amplitude set; S212: Call the task number corresponding to each carbon neutral path combination in the carbon factor fluctuation amplitude value set, extract the task response time field and the carbon neutral path execution frequency field, merge the three numerical values corresponding to each other, and generate a carbon neutral path evaluation index set; S213: Based on all numerical fields in the carbon neutral path evaluation index set, construct the sorting index corresponding to each carbon neutral path by TOPSIS sorting algorithm, and obtain the carbon neutral path evaluation result.

[0010] As a further scheme of the present application, the obtaining step of the pre-excluded carbon neutral path is specifically: S311: Extract the index of each carbon neutral path combination in the carbon neutral path evaluation result, compare it with the preset carbon neutral path evaluation index determination threshold, determine the carbon neutral path combination less than the carbon neutral path evaluation index determination threshold, and generate a low-index carbon neutral path combination set; S312: Extract the three carbon neutral path numbers corresponding to each carbon neutral path combination in the low-index carbon neutral path combination set, register and classify them according to the task number, and obtain the carbon neutral path combination registration record. S313: Call all carbon neutral path number combinations in the carbon neutral path combination registration record, filter the corresponding carbon neutral path pair structure, mark it as the current target to be excluded, and obtain the pre-excluded carbon neutral path.

[0011] As a further scheme of the present application, the obtaining step of the carbon neutral path intersection segment is specifically: S411: Obtain all digital asset numbers in the current life cycle, call the carbon neutral path execution record corresponding to each digital asset number, filter the digital asset numbers with carbon neutral path jump records, and generate an active digital asset number set; S412: Extract the continuous carbon neutral path number sequence from the carbon neutral path number sequence corresponding to each number in the active digital asset number set, and obtain a carbon neutral path number sequence mapping set; S413: Based on the carbon neutral path number sequence mapping set of all digital assets, extract whether there are two or more carbon neutral path numbers completely consistent in any two groups and the corresponding task number, and obtain the carbon neutral path intersection segment.

[0012] As a further scheme of the present application, the obtaining step of the carbon neutral path recommendation result is specifically: S511: Call each set of carbon neutral path number combination in the carbon neutral path intersection section, extract the corresponding task number information, and match and compare with the pre-excluded carbon neutral path number combination to generate a matching carbon neutral path number combination; S512: Based on the matching carbon neutral path number combination, all matching successful carbon neutral path number combinations are excluded from the carbon neutral path intersection section, and the unmatched carbon neutral path number combination is retained to generate a remaining carbon neutral path number combination set; S513: Call all carbon neutral path number contents in the remaining carbon neutral path number combination and corresponding task number information to generate a carbon neutral path recommendation result.

[0013] Compared with the prior art, the advantages and positive effects of the present application are: In the present application, by combining the frequency statistics and sorting of path execution records in the life cycle of digital assets, high-frequency pattern recognition of carbon neutral path execution behavior can be realized, and an evaluation system is constructed relying on carbon factor fluctuation, task response time and path execution density, etc. Multi-dimensional indicators can quantify the actual effectiveness of each path combination. Through the evaluation result, low-efficiency or invalid path pairs are excluded to ensure the accuracy and effectiveness of the path recommendation. In combination with the continuous and consistent path number sequence in the active digital assets, the path intersection section is extracted, further excluding the path combinations identified as low-quality, and only the stable execution and clear effectiveness path pairs are retained. The constructed path recommendation result has higher execution density, better carbon factor control ability and stronger task response synergy, which helps to more efficiently screen and reuse high-quality carbon neutral paths in carbon emission reduction management, enhances the adaptability of the recommended scheme in multi-task and multi-cycle asset management, and improves the overall quality of path selection and the efficiency of achieving carbon neutral targets. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The present application is a main step schematic diagram; Figure 2 The present application is a flowchart of step S1; Figure 3 The present application is a flowchart of step S2; Figure 4 The present application is a flowchart of step S3; Figure 5 The present application is a flowchart of step S4; Figure 6 The present application is a flowchart of step S5. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0016] Please refer to Figure 1 The present application provides a technical scheme: a digital asset driven carbon neutral path recommendation method, comprising the following steps: S1: obtaining each carbon neutral path execution record formed in the life cycle of a digital asset and sorting and combining, counting the execution frequency of each pair of carbon neutral paths in the entire record, and screening the frequently executed carbon neutral path combination; S2: obtaining the carbon factor, task response time and task execution frequency within a specified time before and after the execution of each group of carbon neutral paths in the frequently executed carbon neutral path combination, calculating the evaluation index of each group of carbon neutral paths, and obtaining the carbon neutral path evaluation result; S3: comparing the carbon neutral path evaluation result with the preset carbon neutral path evaluation index determination threshold, selecting invalid carbon neutral path pairs, and obtaining pre-excluded carbon neutral paths; S4: obtaining the digital asset number and the corresponding carbon neutral path number in the current life cycle, selecting active asset numbers according to the carbon neutral path execution record of each number, comparing whether the consecutive active asset numbers and the corresponding carbon neutral path numbers are completely consistent, and identifying the carbon neutral path intersection section; S5: excluding the pre-excluded carbon neutral paths from the carbon neutral path intersection section, retaining the remaining carbon neutral path combination, and obtaining the carbon neutral path recommendation result; The carbon neutral path combination includes carbon neutral path pairs, execution frequency and sorting relationship, the carbon neutral path evaluation result includes carbon factor fluctuation, task response efficiency and carbon neutral path execution density, the pre-excluded carbon neutral path is specifically a carbon neutral path pair that does not meet the evaluation, an invalid task carbon neutral path and a low-frequency execution carbon neutral path, the carbon neutral path intersection section includes consecutive active asset numbers, carbon neutral path number consistency and carbon neutral path overlapping section, and the carbon neutral path recommendation result specifically refers to the optimized carbon neutral path combination, effective carbon neutral path section and recommended carbon neutral path number.

[0017] Please refer to Figure 2 The acquisition step of the frequently executed carbon neutral path combination is specifically: S111: obtaining each carbon neutral path execution record formed in the life cycle of a digital asset, extracting the task number, carbon neutral path number and trigger time field, classifying according to the task number and sorting in ascending order according to the trigger time field, constructing a combination based on the sorting result, taking every three consecutive carbon neutral path numbers as a group, and generating a carbon neutral path number sequence combination; To obtain the execution records of each carbon neutralization path formed during the life cycle of digital assets, first, it is necessary to clarify that "digital assets" refer to digital information units generated on a blockchain, Internet of Things, or digital platform, such as NFTs, digital certificates, electronic contracts, etc., which can track the entire process of their creation, use, and destruction. Carbon neutralization paths refer to specific implementation processes developed to offset the carbon emissions generated during the life cycle of digital assets, such as using wind power, photovoltaics, battery energy storage, and carbon sinks to offset in stages. The execution records include fields such as task ID (task_id), path ID (path_id), and trigger time (trigger_time). These fields need to be extracted from on-chain logs or background databases. Subsequently, according to the task ID, all records are classified into separate groups for different tasks, such as task numbers T001, T002, etc. Each execution sequence is then sorted in ascending order according to the trigger_time field, i.e., the actual time of path invocation. Finally, the combined identifier is formed in the order of path numbers.

[0018] S112: Call the three carbon neutralization path numbers in the carbon neutralization path number sequence combination as the combined identifier, count the number of occurrences of the combination in all carbon neutralization path execution records, and compare the statistical frequency with the minimum frequency threshold to obtain the carbon neutralization path number combination after comparison. Call the three path numbers in the above path number combination as the combined identifier, i.e., use "P001-P003-P004" as an analysis unit to query the combination in the database for its frequency of occurrence in all carbon neutralization path execution records. All execution sequences containing the combination are filtered through string matching. Each search needs to scan the path combination sequence of each task in the entire database and count the number of occurrences. The counting method is as follows: initialize a combination counter dictionary, extract the combination string row by row, and if the combination exists, increment the count. Otherwise, initialize it to 1. After counting is complete, compare the combination occurrence frequency freq with the preset minimum frequency threshold min_freq. If freq ≥ min_freq, retain it, otherwise, discard it. The minimum frequency threshold min_freq can be adjusted based on the size of the path data and the strategy, for example, set min_freq = 5 to indicate that the combination must appear at least 5 times in the execution records to be considered valid. Set the reference source to the P30 quantile of the combination frequency in historical task data. Assuming that the distribution of combination frequencies in 5000 tasks is [1, 2, 2, 3, 3, 4, 5, 6, 7, 10…], the P30 is the 1500th value, which is 5, i.e., min_freq = 5. In actual examples, if the combination P001-P003-P004 appears 7 times in the records, the combination is retained, and the combination P002-P003-P006 appears 3 times and is discarded.

[0019] S113: Based on the carbon neutralization path number content in the compared carbon neutralization path number combinations, construct the association relationship between carbon neutralization path combinations using the Apriori association rule algorithm, and extract frequently executed carbon neutralization path combinations; For the frequent carbon neutralization path combinations obtained after path number filtering, it is necessary to further explore whether there are significant execution correlations among these path combinations. In this context, a transaction structure is constructed to perform Apriori association rule analysis, combining the path call records formed by multiple carbon neutralization execution tasks of digital assets throughout their lifecycle. Each transaction corresponds to a complete carbon neutralization task instance, and the elements in the transaction are path groups consisting of three consecutive path numbers sorted by trigger time. Integrating all the path groups corresponding to all task numbers constitutes a transaction set. Among them, transaction sets This is the input set for the entire analysis phase, derived from the combined data extracted in the first two steps, grouped into sets of three path numbers under each task number category, in the following format: , It is the total set of all path combinations and transactions in the overall execution process of the digital asset carbon neutrality task; each It is the first A set of path number combinations extracted under each task number, for example ; This represents the total number of task IDs involved in the construction, equivalent to the total number of lifecycle carbon neutrality execution records of the digital asset during the analysis period.

[0020] In this transaction set, if a certain path number combination (such as...) If a combination appears multiple times, it's necessary to determine whether it constitutes a frequent itemset. To do this, the support of the combination is calculated, mathematically expressed as: ; In this formula: It is a combination of carbon neutralization path numbers (e.g.: ), which comes from the path sequence extracted using the sliding window method; It is the path combination In transaction set The number of times it appears in the context, i.e., how many transaction records under each task number actually use it; It is the total number of transactions in the transaction set, that is, the total number of task numbers involved in the computation; This is the result of the formula, representing the path combination. The relative frequency of occurrence of carbon neutralization tasks across all digital asset lifecycles is used to determine whether path combinations are "frequent enough" to proceed to the next step of analysis.

[0021] when At that time, among them For manually set support thresholds (e.g., 0.2), the combination is indicated. If it appears in at least 20% of the tasks, the combination is reserved for building association rules between paths.

[0022] If a path combination Once identified as a frequent itemset, it can be split into two non-empty subsets. (Setting...) Therefore, it can be broken down into: : Indicates the path number group that is executed first in the task; : indicates the path number that follows immediately.

[0023] at this time Establishment, i.e., complete combination It is a prefix and suffix Formed through merger.

[0024] Subsequently, the path combination was evaluated. When executed, is the execution path frequently used? The strength of this logical relationship can be calculated using the confidence formula: ; In this expression: It is the prefix part of the path combination; It is the suffix part of the path combination; This is the complete three-path combination mentioned earlier. ; It is a path combination The relative frequency of occurrence in the transaction set, i.e., the aforementioned The value; It is a prefix combination The frequency of occurrence of a single instance within a transaction set; This is the result of the formula, representing all paths that were executed. The path was actually executed in the task. The probability is used to quantify the degree of execution dependency between paths.

[0025] Once a path combination rule has a high confidence level, it is still necessary to determine whether it truly has structural significance, i.e., whether its frequency of occurrence is higher than the theoretical probability of the independent path distribution. Therefore, the lift index is introduced to eliminate randomness, and its formula is as follows: ; Here: It is a combination of path numbers The proportion of instances appearing alone in a transaction set; The result of this expression represents the execution path. Does it significantly improve the path? The probability of being called; if its value is greater than 1, it indicates... and There is a strong logical connection between them.

[0026] In conclusion, , and These are three core computational results, which respectively measure the frequency of path combinations across all tasks, the dependency between paths, and whether that dependency exceeds the level of natural random occurrence. Each computational result is directly related to the carbon neutrality path numbering data structure provided in the first two paragraphs and is based on the actual carbon compensation execution process within the digital asset lifecycle.

[0027] There are 6 carbon neutralization tasks for digital assets, denoted as tasks T1 to T6, and they each executed the following path number combinations: T1: T2: T3: T4: T5: T6: These combinations are the transaction sets obtained after the "sliding extraction of three-path combinations" process in the first two paragraphs, and are used to construct the transaction set. Therefore, at this point we have: Select the path number combination: .

[0028] Calculate support In 6 transactions, combine It appears in tasks T2, T4, and T6, and appears 3 times. Therefore: , , .

[0029] This means that this path combination appears in 50% of the tasks.

[0030] Set prefix ,suffix Calculate the confidence level: Check for prefix combinations What are the tasks: T1: → Contains P003 and P004, but not P006, therefore does not constitute a complete prefix; T2: →Contains A; T3: →Same as T1; T4: →Contains A; T5: →Excluding A; T6: →Contains A; Therefore, A appears in T2, T4, and T6 a total of 3 times. ; Given: ; therefore: ; This means that whenever paths P003 and P004 are executed, P006 will definitely be executed next.

[0031] Calculation lift : Now we need to calculate This refers to the proportion of transactions that include single-path P006.

[0032] Path P006 appears in the following task: T2: T4: T5: T6: There were 4 times in total, therefore: ; Given: ; therefore: ; In this example: , , .

[0033] These values ​​reflect that in the six tasks, paths P003 and P004 are almost always executed in conjunction with P006. This combination not only appears frequently (support 0.5) but also possesses strong reasoning ability (confidence 1.0) and high dependency (lift 1.5). First, determining whether a path number combination is "frequently occurring" depends on whether its relative occurrence ratio reaches a preset minimum support standard. Support represents the proportion of a path combination appearing in all tasks. The basis for setting the support threshold is usually the percentile of the frequency of all path combinations in the sample data or a reasonable proportion set based on business experience. For example, in this example, with a total of 6 tasks, if the minimum support threshold is set to 0.33, it means that the combination must appear in at least one-third of the tasks to be considered frequent. The support of combinations P003, P004, and P006 is 0.5, exceeding 0.33, therefore it is considered "frequently occurring." Next, determining whether a path combination possesses "strong reasoning ability" depends on whether the suffix path always appears given the presence of a prefix path. This is achieved by calculating the confidence level. The confidence score ranges from 0 to 1, where 1 indicates that when a prefix path appears, the suffix path always appears as well. In data mining tasks, a confidence threshold of 0.7 or 0.8 is typically used as a criterion; if the confidence score of a path combination is greater than or equal to this value, the inference between the paths is considered highly reliable. In this example, the value is 1.0, far exceeding the commonly used threshold, hence the term "strong inference ability." Finally, lift is used to determine whether there is a "high dependency" between paths. Lift measures whether, excluding random events, the prefix path significantly increases the probability of the suffix path appearing. A lift of 1 indicates no effect, less than 1 indicates a negative correlation, and greater than 1 indicates a positive correlation, with larger values ​​indicating stronger positive correlations. In practical analysis, 1.0 is usually used as the baseline; a lift greater than 1 is considered a positive correlation, and a lift greater than 1.2 or even 1.5 can be considered a strong dependency. The lift here is 1.5, so it is reasonable to infer that the occurrence of paths P003 and P004 significantly increases the probability of P006 appearing together, indicating that there is a significant linkage between the three.

[0034] Please see Figure 3 The specific steps for obtaining the carbon neutrality pathway assessment results are as follows: S211: Extract the task number corresponding to each carbon neutralization path combination that is frequently executed, obtain the carbon factor reading value sequence within a specified time before and after the execution of the carbon neutralization path, and count the maximum difference in the sequence as the carbon factor fluctuation amplitude set. After identifying frequent carbon neutrality path number combinations, it is necessary to further associate these combinations with their corresponding task numbers to form an index mapping from path combinations to task numbers, serving as the basis for data classification. Using the frequent path combinations retained from the previous mining phase, such as the path number sequence {P003, P004, P006}, we retrieve their execution positions within different task numbers, for example, being fully triggered in task numbers T2, T4, and T6. Then, each task number is processed. First, the initial execution time and the completion time of the last path of the path combination are read, and a fixed-length time window is established around this time period to read carbon factor change data. The window length is set to 5 minutes before and after the execution time period, i.e., a total of 10 minutes, with 10 carbon factor readings obtained by sampling once per minute. The carbon factor refers to the carbon emission equivalent caused by a unit of execution action, usually expressed as kgCO2e / kWh. Specific sampled values ​​are, for example... The maximum and minimum values ​​are calculated within this data sequence to obtain the carbon factor fluctuation amplitude for this path combination under this task number. Let this sampling sequence be... its subscript If the time sampling point is represented in minutes, then the formula for calculating the maximum difference is: ,in, This represents the fluctuation range of the carbon factor, expressed in kgCO2e / kWh. This indicates the maximum value of the carbon factor within that time period. This represents the minimum carbon factor. It is a sequence of carbon factor values ​​collected within a time window. If , ,but This process is repeated for all task numbers associated with the path combination, yielding fluctuation ranges of 0.061, 0.052, and 0.073 for tasks T2, T4, and T6, respectively. These values ​​are then assigned to the carbon factor fluctuation range set, denoted as... Each of them Each task corresponds to a specific task number.

[0035] S212: Call the task number corresponding to each group of carbon neutralization path combinations in the carbon factor fluctuation amplitude value set, extract the task response time field and the carbon neutralization path execution frequency field, merge the three values ​​accordingly, and generate a set of carbon neutralization path evaluation indicators. After constructing the carbon factor fluctuation amplitude set, the next step is to further extract data from the execution records of each frequent path combination across different task numbers. The goal is to summarize three key values ​​for each task number: the carbon factor fluctuation amplitude value, the task response time, and the execution frequency of the path combination within that task. First, the task response time field is obtained, defined as the interval between the start of the task's lifecycle (from the task trigger event) and the formal scheduling and execution of the first path in the path combination. The value of this field is denoted as... The unit is seconds, and its calculation method is as follows: ,in, Indicates the timestamp at which task scheduling began. This represents the timestamp when the first path number in the path combination was scheduled. If task T2 is triggered at 08:00:00, and path P003 starts at 08:00:12, then... Next, extract the path combination execution frequency field, denoted as... This frequency is defined as the number of times a path combination that completely matches a given path number is executed within a task. The frequency is calculated using a sliding window method, which searches the task execution log for and counts completely matching path number sequences. If the path combination {P003, P004, P006} appears twice in task T2, then... Meanwhile, the carbon factor fluctuation amplitude value calculated above is denoted as... The unit is kgCO2e / kWh, and , Perform merge operations to construct triples. For example, for task T2, the ternary index set for this path combination is (0.061, 12, 2), which represents the carbon factor fluctuation amplitude of 0.061, the response time of 12 seconds, and the execution frequency of 2 times for this path combination in this task. This structure is then constructed for all task numbers associated with the path combination to form a set of carbon neutrality path evaluation indicators, denoted as: {(0.061, 12, 2), (0.052, 10, 3), (0.073, 14, 1)}, where each data structure consists of three quantities and corresponds to a specific task number.

[0036] S213: Based on all numerical fields in the carbon neutrality pathway assessment index set, construct the ranking index corresponding to each group of carbon neutrality pathways using the TOPSIS ranking algorithm, and obtain the carbon neutrality pathway assessment results. After obtaining the set of carbon neutrality pathway assessment indicators, the first step is to standardize the three numerical fields corresponding to each carbon neutrality pathway in the set. During execution, the carbon factor fluctuation amplitude, task response time, and execution frequency are extracted as independent column vectors. The minimum and maximum values ​​of each column are then read sequentially, and the values ​​are converted to the range of zero to one using a linear scaling method. For example, the carbon factor fluctuation amplitudes for tasks T2, T4, and T6 are 0.061, 0.052, and 0.073, respectively, with a minimum of 0.052 and a maximum of 0.073. After calculation, the standardized value for T2 is 0.43, T4 is 0, and T6 is 1. The task response time field takes values ​​of 12, 10, and 14 seconds, and the execution frequency field takes values ​​of 2, 3, and 1 times, respectively. The same linear method is used to complete the standardization process, ensuring that all field data are uniformly comparable.

[0037] After standardization, to ensure the relative importance of the indicators is reflected, weights need to be assigned to the three indicators. The weight allocation is based on the statistical characteristics of historical task data: carbon factor fluctuation amplitude is weighted at 0.5, task response time at 0.3, and execution frequency at 0.2. If the sample size is insufficient, industry experience can be used as a reference for setting these weights. Subsequently, for each set of standardized data, two reference standards are constructed: an ideal value and a minimum value. The ideal value is the maximum standardized value of each field, and the minimum value is the minimum standardized value of each field. Taking the three sets of data in this example, the ideal value is (1,1,1), and the minimum value is (0,0,0).

[0038] Next, we need to calculate the difference between each task number combination and the ideal and minimum values. Taking task T2 as an example, its standardized data is (0.43, 0.5, 0.5). The difference from the ideal value, after weighting, is approximately 0.278, and the difference from the minimum value is approximately 0.454. The two distance values ​​for task T4 are 0.63 and 0.20, and for task T6 they are 0.18 and 0.82. By comparing the distance of each combination relative to the ideal and minimum values, we can determine the quality of the path combinations. The relative distance ratio of each group is converted into a proximity index to represent its degree of closeness to the ideal state. According to the calculation results, the proximity index for task T2 is 0.62, for task T4 it is 0.24, and for task T6 it is 0.82.

[0039] To facilitate hierarchical management, interval standards need to be set: path groups with a proximity score greater than or equal to 0.7 are classified as high-level (excellent), those between 0.4 and 0.7 as medium-level (acceptable), and those below 0.4 as low-level (unacceptable). In this example, combination T6 is classified as high-level, T2 as medium-level, and T4 as low-level. The final carbon neutrality path assessment result table includes the task number, three original indicator values, standardized values, assigned weights, ideal value difference, minimum value difference, proximity score result, and level identifier. Throughout the execution process, the path number index, task number index, and indicator matrix are sequentially called, and the ranking results are gradually obtained from the original indicator data through multi-level data recursion.

[0040] Please see Figure 4 The specific steps for obtaining the pre-excluded carbon neutralization pathways are as follows: S311: Extract the indicators of each carbon neutralization path combination from the carbon neutralization path evaluation results, compare them with the preset carbon neutralization path evaluation indicator judgment threshold, determine the carbon neutralization path combinations that are less than the carbon neutralization path evaluation indicator judgment threshold, and generate a low-indicator carbon neutralization path combination set. After obtaining the evaluation results of all carbon neutrality path combinations across different tasks, the ranking evaluation value corresponding to each path combination is read one by one and compared with a pre-defined judgment benchmark. During the comparison, path combinations with ranking values ​​lower than the judgment benchmark are marked. The judgment benchmark should be set based on the distribution of all ranking values; for example, it can be determined by the overall average level of all ranking values ​​or the lower-middle concentration range of ranking values. The comparison method between the ranking value and the judgment benchmark is a direct comparison. Each comparison result is a judgment result of whether it is less than the threshold. All path combinations that meet the less than relationship are grouped into one category, and this category as a whole is regarded as a path structure with insufficient performance. In this process, it is also necessary to ensure the consistency of the comparison operation, such as using a unified scoring model and a unified normalization standard to avoid inconsistencies in the dimensionality of the ranking results under different tasks. The final set of path combinations is the set of combinations identified as having low evaluation indicators.

[0041] S312: Extract the three carbon neutralization path numbers corresponding to each carbon neutralization path combination in the low-index carbon neutralization path combination set, register and classify them according to the task number, and obtain the carbon neutralization path combination registration record. After identifying the path combinations with low evaluation results, the three carbon neutralization path numbers within each combination are sequentially read, and the task numbers that each combination has appeared in are statistically analyzed. During the statistical analysis, each path combination is associated with the task in which it was actually executed. This registration process must be based on task numbers, uniformly registering all path combinations appearing under the same task number and establishing the attribution relationship between path combinations and task numbers. The registration process must ensure the integrity of the path combination structure, meaning that the three path numbers are indeed executed consecutively within the same task, and there should be no scattered path numbers in the task log. The classification result must completely record key information such as the path combination itself, its associated task number, the frequency of the combination's appearance, and whether it is the first execution in the task.

[0042] S313: Call all carbon neutralization path number combinations in the carbon neutralization path combination registration record, filter the corresponding carbon neutralization path pair structure, mark it as the current target to be excluded, and obtain the carbon neutralization path to be excluded; After obtaining all registered path combinations and task assignment records, the path number sequences within each combination need further analysis. Each combination is broken down into several consecutive connection structures between adjacent path numbers, called path pair structures. This structure indicates a direct, continuous calling relationship between two carbon neutralization paths, potentially posing a path coupling risk. After all path combinations are broken down into multiple path pair structures, a set of path pair structures is formed. Within this set, the frequency of each path pair is statistically analyzed to determine if it repeatedly appears in multiple path combinations with low evaluation results. If a path pair repeatedly appears in most combinations marked as low quality, it indicates that this path pair may be a critical segment causing a decline in path quality. Based on this, these path pair structures that frequently appear in low-evaluation combinations are identified as objects to be excluded in the current stage. Their path numbers, frequency of occurrence, and associated task numbers are recorded, forming a set of paths to be excluded.

[0043] Please see Figure 5 The specific steps for obtaining the intersection segment of the carbon neutralization path are as follows: S411: Obtain all digital asset IDs within the current lifecycle, call the carbon neutralization path execution record corresponding to each digital asset ID, filter digital asset IDs with carbon neutralization path jump records, and generate a set of active digital asset IDs. Within the current lifecycle, the unique identifier of all digital assets must first be obtained as the entry point for processing path execution data. For each digital asset ID, a complete carbon neutrality path execution log must be extracted from its lifecycle-related records. The log must contain basic fields such as path ID, trigger time, and execution status. During the reading process, the focus is on determining whether each record exhibits path jump behavior, i.e., whether there is a non-sequential change in path ID between two consecutive path executions. Path jumps are typically identified by discontinuous path ID sequences, non-linear path ID connections, or structural features such as backtracking or branching. Digital asset records that meet these change characteristics are considered to have dynamic path jumps. All digital asset IDs that meet the jump characteristics are filtered and grouped into a unified set to identify changes, adjustments, or interventions in the path structure of the digital asset within its lifecycle. This set is the active digital asset ID set.

[0044] S412: Extract a continuous carbon neutralization path number sequence from the carbon neutralization path number sequence corresponding to each number in the active digital asset number set to obtain a carbon neutralization path number sequence mapping set. From the set of active digital asset IDs, the execution records of the carbon neutralization path corresponding to each digital asset are read one by one, and the complete sequence of path IDs executed in chronological order is extracted. For each asset ID, only the path ID records with valid execution status, complete sequence, and comparable timestamps are retained, and this sequence is treated as a time-directed carbon neutralization path chain. Further, continuously called path segments in the path ID sequence are identified, i.e., continuous path combinations without interruptions, insertions, or jumps. This extraction operation can be completed using sliding window technology or adjacency index judgment. A multi-window partitioning operation is performed on each path ID sequence, dividing each group of three to five consecutive path ID segments into a unit, and sequentially numbering them to form a set of path ID segment pieces. Each digital asset ID corresponds to one or more sets of path ID segment pieces. This set can be regarded as the path execution trajectory formed by the digital asset during its lifecycle, structurally forming a mapping structure from digital assets to path segment pieces, i.e., a carbon neutralization path ID sequence mapping set. S413: Based on the carbon neutralization path number sequence of all digital assets in the carbon neutralization path number sequence mapping set, extract whether there are two or more consecutive carbon neutralization path number segments with completely consistent carbon neutralization path numbers and corresponding task numbers in any two groups, and obtain the carbon neutralization path intersection segment. After mapping the path number sequences corresponding to all active digital assets, the next step is to perform a cross-comparison operation to determine whether there are two or more sets of path number segments that are structurally identical. This identification operation uses a group-by-group path sequence cross-comparison mechanism, performing equal-length segment sliding alignment on any two sets of path number sequence segments corresponding to different digital assets. Each sliding window compares three or more consecutive path numbers to determine whether the path numbers are completely identical in order and content. If there are two or more different digital asset path number sequences where two or more consecutive path number segments are structurally identical, it can be determined that the carbon neutrality path execution behavior has a repetitive structure. For all successfully matched path segments, it is necessary to further trace back to their respective task numbers and establish a matching mapping between path number segments and task numbers. The finally extracted path number segments constitute the carbon neutrality path intersection segment, and the source digital asset number, the matched task number, and the starting position of the path segment in the task are output.

[0045] Please see Figure 6 The specific steps for obtaining the carbon neutrality pathway recommendation results are as follows: S511: Call each group of carbon neutralization path number combinations in the intersection segment of carbon neutralization paths, extract the corresponding task number information, and match and compare it with the carbon neutralization path number combinations in the pre-excluded carbon neutralization paths to generate matching carbon neutralization path number combinations. For the intersection of carbon neutrality pathways, it is necessary to read the pathway number combinations group by group, extract the task number information associated with each combination, and use the task number as the basis for comparison to match each item with the set of pathway numbers marked as pre-exclusion in the previous stage. This matching process is considered valid only if both the pathway number combination structure and the source task number are consistent. The matching method adopts the principle of complete consistency of pathway numbers, that is, the order and content of the three pathway numbers must be strictly the same as any combination in the pre-exclusion set. At the same time, it is necessary to determine whether the combination appears in the same or cross-referenced task numbers. To ensure matching accuracy, a precise string comparison method should be used, combined with the task number index for double retrieval, to exclude occasional duplication of pathway combinations in different tasks. All pathway number combinations that meet the matching conditions will be uniformly classified into the matching carbon neutrality pathway number combination set. The structure of this set should include a combination number, a sequence of three pathway numbers, a task number, and a matching identifier to indicate that the combination has logically overlapped with the pre-exclusion list.

[0046] S512: Based on the matching carbon neutralization path number combinations, remove all successfully matched carbon neutralization path number combinations from the intersection segment of carbon neutralization paths, retain the unmatched carbon neutralization path number combinations, and generate the remaining set of carbon neutralization path number combinations. After matching and identifying the path number combinations in the intersection segment with the pre-excluded path combinations, a difference operation must be performed on the intersection segment structure immediately. All successfully matched path number combinations must be removed from the original intersection segment structure, retaining only the unmatched combinations. This operation requires traversing all path combination numbers in the intersection segment structure, sequentially determining whether a completely identical path number structure exists in the matching set. If it exists, it is marked as matched and removed from the structure; otherwise, it is retained as the current remaining path. The path number comparison process still uses three-path structure integrity verification, ensuring consistent number order, consistent field values, and consistent task numbers or the ability to cross-index each other. After the removal operation is completed, a new set of carbon neutralization path number combinations is formed. This set represents the path behavior fragments retained after removing all known structures to be excluded from the intersection segment. The structure of this set is a mapping from path combination numbers to task numbers, identifying path segments that have not yet overlapped with low-index structures in history, and retaining their original information such as execution records, sequence positions, and task relevance.

[0047] S513: Call up all carbon neutralization path number contents and corresponding task number information in the remaining carbon neutralization path number combinations to generate carbon neutralization path recommendation results; After preserving the remaining path number combinations, each combination needs to be parsed individually to obtain its path number content and corresponding task number data. This data is then organized and standardized into a standard output format for generating path recommendations. During this process, each remaining path number combination must retain its original structural order. The three path numbers are indivisible and should be recommended as a whole. The task number field is added as an auxiliary dimension to each combination record to indicate the context in which the combination was scheduled in historical tasks. The recommendation results must consider that the path combination does not intersect with low-quality structures, treating it as a relatively independent and valuable path segment. The output recommendation results should include fields such as the path combination number, the structure of the three path numbers, historical usage frequency, the list of associated task numbers, and its starting position in the task structure, to form a complete and context-clear set of path recommendation candidates.

[0048] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A digital asset-driven method for recommending carbon-neutral pathways, characterized in that, Includes the following steps: S1: Obtain the execution records of each carbon neutralization path formed during the lifecycle of digital assets, sort and combine them, count the execution frequency of each carbon neutralization path pair in the entire record, and filter the frequently executed carbon neutralization path combinations. S2: Obtain the carbon factor, task response time, and task execution frequency for each carbon neutralization path combination before and after execution within a specified time period, calculate the evaluation index for each carbon neutralization path, and obtain the carbon neutralization path evaluation result. S3: Based on the carbon neutralization pathway evaluation results, compare them with the preset carbon neutralization pathway evaluation index judgment threshold, select invalid carbon neutralization pathway pairs, and obtain the carbon neutralization pathways to be excluded. S4: Obtain the digital asset ID and corresponding carbon neutralization path ID within the current lifecycle. Select the active asset ID based on the carbon neutralization path execution record for each ID. Compare whether consecutive active asset IDs and corresponding carbon neutralization path IDs are completely consistent and identify the intersection segment of carbon neutralization paths. S5: Remove the pre-excluded carbon neutralization paths from the intersection of the carbon neutralization paths, retain the remaining carbon neutralization path combinations, and obtain the carbon neutralization path recommendation results.

2. The digital asset-driven carbon neutrality path recommendation method according to claim 1, characterized in that, The carbon neutralization path combination includes carbon neutralization path pairs, execution frequency, and ranking relationship. The carbon neutralization path evaluation results include carbon factor fluctuation, task response efficiency, and carbon neutralization path execution density. The carbon neutralization paths to be excluded are specifically carbon neutralization path pairs that fail to meet the evaluation criteria, invalid task carbon neutralization paths, and low-frequency execution carbon neutralization paths. The carbon neutralization path intersection segment includes continuously active asset IDs, carbon neutralization path ID consistency, and carbon neutralization path overlapping segments. The carbon neutralization path recommendation results specifically refer to the optimized carbon neutralization path combination, effective carbon neutralization path segments, and recommended carbon neutralization path IDs.

3. The digital asset-driven carbon neutrality path recommendation method according to claim 1, characterized in that, The steps for obtaining the frequently executed carbon neutralization pathway combinations are as follows: S111: Obtain the execution record of each carbon neutralization path formed during the lifecycle of the digital asset, extract the task number, carbon neutralization path number and trigger time field, classify according to task number and sort in ascending order according to trigger time field, and construct a combination based on the sorting result by grouping every three consecutive carbon neutralization path numbers to generate carbon neutralization path number sequence combination. S112: Call the three carbon neutralization path numbers in the carbon neutralization path number sequence combination as the combination identifier, count the number of times the combination appears in all carbon neutralization path execution records, and compare the count frequency with the minimum frequency threshold to obtain the carbon neutralization path number combination after comparison. S113: Based on the carbon neutralization path number content in the compared carbon neutralization path number combinations, construct the association relationship between carbon neutralization path combinations using the Apriori association rule algorithm, and extract the frequently executed carbon neutralization path combinations.

4. The digital asset-driven carbon neutrality path recommendation method according to claim 3, characterized in that, The specific steps for obtaining the carbon neutrality pathway assessment results are as follows: S211: Extract the task number corresponding to each carbon neutralization path combination of the frequently executed carbon neutralization path combinations, obtain the carbon factor reading value sequence within a specified time before and after the execution of the carbon neutralization path, and count the maximum difference in the sequence as the carbon factor fluctuation amplitude set. S212: Call the task number corresponding to each group of carbon neutralization path combinations in the carbon factor fluctuation amplitude value set, extract the task response time field and the carbon neutralization path execution frequency field, merge the three values ​​accordingly, and generate a set of carbon neutralization path evaluation indicators. S213: Based on all the numerical fields in the carbon neutrality path evaluation index set, construct the ranking index corresponding to each group of carbon neutrality paths using the TOPSIS ranking algorithm, and obtain the carbon neutrality path evaluation results.

5. The digital asset-driven carbon neutrality path recommendation method according to claim 4, characterized in that, The steps for obtaining the pre-excluded carbon neutralization pathways are as follows: S311: Extract the index of each carbon neutralization path combination in the carbon neutralization path evaluation results, compare it with the preset carbon neutralization path evaluation index judgment threshold, determine the carbon neutralization path combination that is less than the carbon neutralization path evaluation index judgment threshold, and generate a low index carbon neutralization path combination set. S312: Extract the three carbon neutralization path numbers corresponding to each carbon neutralization path combination in the low-index carbon neutralization path combination set, register and classify them according to the task number, and obtain the carbon neutralization path combination registration record. S313: Call all carbon neutralization path number combinations in the carbon neutralization path combination registration record, filter the corresponding carbon neutralization path pair structure, mark it as the current target to be excluded, and obtain the carbon neutralization path to be excluded.

6. The digital asset-driven carbon neutrality path recommendation method according to claim 5, characterized in that, The specific steps for obtaining the intersection segment of the carbon neutralization paths are as follows: S411: Obtain all digital asset IDs within the current lifecycle, call the carbon neutralization path execution record corresponding to each digital asset ID, filter digital asset IDs with carbon neutralization path jump records, and generate a set of active digital asset IDs. S412: Extract a continuous carbon neutralization path number sequence from the carbon neutralization path number sequence corresponding to each number in the active digital asset number set to obtain a carbon neutralization path number sequence mapping set; S413: Based on the carbon neutralization path number sequence of all digital assets in the carbon neutralization path number sequence mapping set, extract whether there are two or more consecutive carbon neutralization path number segments with completely consistent carbon neutralization path numbers and corresponding task numbers in any two groups, and obtain the carbon neutralization path intersection segment.

7. The digital asset-driven carbon neutrality path recommendation method according to claim 6, characterized in that, The specific steps for obtaining the carbon neutrality pathway recommendation results are as follows: S511: Call each group of carbon neutralization path number combinations in the intersection segment of the carbon neutralization paths, extract the corresponding task number information, and match and compare it with the carbon neutralization path number combinations in the pre-excluded carbon neutralization paths to generate matching carbon neutralization path number combinations. S512: Based on the matching carbon neutralization path number combinations, remove all successfully matched carbon neutralization path number combinations from the carbon neutralization path intersection segment, retain the unmatched carbon neutralization path number combinations, and generate the remaining carbon neutralization path number combination set. S513: Call up all carbon neutralization path number contents and corresponding task number information in the remaining carbon neutralization path number combination to generate carbon neutralization path recommendation results.

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