Carbon emission data monitoring and analyzing system and method

Abstract

The invention relates to the technical field of data monitoring analysis, in particular to a carbon emission data monitoring analysis system and method.The carbon emission data monitoring analysis system comprises a source point extraction module, an on-chain confirmation module, an access frequency sorting module, a block positioning adjustment module and an integration module; and confirming a record on a certificate broadcast verification construction chain, sorting access frequency calculation intensity sorting numbers, positioning and adjusting a link to draw a deployment atlas, and collecting concentration frequency integration fluctuation to establish an archive set. According to the method, a unique compressed tracking code is generated based on time and structure identification, node comparison consistency is written into a linked list, access frequency constructs intensity sorting division areas, paths are adjusted, time sequence fluctuation sections are extracted and classified into sets, a filing structure is formed in combination with labels, and the method is suitable for trend arrangement and continuous calling of carbon emission monitoring. And the data reading efficiency and the retrieval continuity are improved.

Description

Technical Field

[0001] This invention relates to the field of data monitoring and analysis technology, and in particular to a carbon emission data monitoring and analysis system and method. Background Technology

[0002] The field of data monitoring and analysis technology encompasses methods and systems for collecting and analyzing data using various sensors and devices. Its core focus is on real-time monitoring and data mining of various environmental, industrial, and energy data. Through data collection, transmission, storage, and analysis, it provides support for decision-making. It is widely applied in environmental monitoring, health monitoring, industrial production, energy management, and other fields, aiming to achieve effective data management and real-time feedback, optimize resource utilization, improve efficiency, and support sustainable development. With the increasing levels of intelligence and automation, data monitoring and analysis technology is gradually developing towards greater intelligence and efficiency.

[0003] The carbon emission data monitoring and analysis system refers to a technical solution for the real-time monitoring, recording, and analysis of carbon emission data. It encompasses the collection of carbon emission-related data through specialized equipment and technologies, and the calculation and analysis of carbon emissions using sensors, data processing algorithms, and data transmission mechanisms. Specifically, it proposes concrete implementation methods for carbon emission monitoring, ensuring data integrity and accuracy through real-time collection, transmission, and storage of carbon emission-related environmental data. Specific data processing methods are used to extract effective information from the carbon emission data, providing support for further analysis and decision-making. The system includes a real-time monitoring and analysis process for carbon emission data, and covers the specific steps and technical paths for data collection, analysis, and output.

[0004] Existing technologies lack sequential control methods for the structure of carbon emission record data. Raw data is labeled only through parameter calling during multi-device acquisition, lacking the ability to compress and combine data for identification. This leads to the risk of duplicate and confused record labels, making it impossible to accurately reconstruct corresponding operational information under multi-node data flow conditions. Access behavior records do not have a joint processing method for access frequency and time interval, making it impossible to construct a data access priority classification structure. Access paths respond chaotically during changes in data distribution density. At the analysis level, there is no data classification and aggregation logic based on time evolution and trend differences, resulting in mixed stacking of data from adjacent segments, affecting the identification, judgment, and stratified analysis of continuous emission characteristics. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a carbon emission data monitoring and analysis system and method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a carbon emission data monitoring and analysis system, the system comprising: The source extraction module obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment, splices them together to form a data segment, performs compression and truncation operations to obtain the basic string value, combines the work area number with the current task label to form a tracking code, and generates a set of carbon emission tracking numbers. The on-chain verification module, based on the carbon emission tracking number set, broadcasts encrypted text to multiple verification nodes, performs structural consistency judgment on the content returned by each node, combines the completely matching text content and attaches it to the end of the current chain list segment to generate a carbon emission on-chain record fragment group. The access frequency sorting module extracts the access time and frequency corresponding to the number of the recorded segment group on the carbon emission chain according to the number index, performs a product integration of the access interval and access frequency to construct the access intensity sorting value, generates a number arrangement sequence according to the intensity sorting position, and generates a high-frequency segment distribution list. The block positioning and adjustment module identifies the segments with higher access intensity as high-frequency blocks and sets priority paths according to the access order in the high-frequency segment distribution list, while segments with lower access intensity are set as regular paths. It also organizes all path mapping information and generates a monitoring data link allocation map.

[0007] As a further embodiment of the present invention, the carbon emission tracking number set includes a collection process identifier, a device binding code, and a task stage marker; the carbon emission chain record fragment group includes a verification node index, a chain storage identifier, and a signature information digest; the high-frequency segment distribution list includes access frequency level, active time period division, and data fragment priority; and the monitoring data link allocation map includes fragment path classification, link level division, and call pointing identifier.

[0008] As a further embodiment of the present invention, the carbon emission tracking number generation module includes a data splicing submodule, a serial value compression submodule, and a tracking code construction submodule; The data splicing submodule obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment records, splices them in the order of timestamp, equipment number, and sensor component name to form a single data segment, filters out missing fields and structurally abnormal items, processes them into a standard field format, adds a serial number identifier to each data segment, and generates a field splicing sequence set. The string compression submodule determines whether the total length of each sequence in the field concatenation sequence set exceeds the truncation threshold. If the length exceeds the threshold, the first and last four characters and the middle three characters are extracted and concatenated together, keeping the character order unchanged. At the same time, the number of character types is introduced as the truncation complement and appended to the end to obtain a list of truncation encoded values. The tracking code construction submodule, based on the extracted code value list, inserts the work area number and task tag sequentially before and after the code value to form a three-segment tracking structure, and establishes a corresponding index structure according to the work area number, removes items without area numbers, retains all complete structural items as valid number records, and generates a carbon emission tracking number set.

[0009] As a further aspect of the present invention, the on-chain verification module includes an encrypted broadcast submodule, a node verification submodule, and a chain-linked submodule; The encrypted broadcast submodule, based on the carbon emission tracking number set, concatenates the character-encoded text corresponding to each number with the task tag to form encrypted text, broadcasts all encrypted text sequentially to multiple verification nodes within a set range, records the returned text content and corresponding number of each node, and generates a node return text group. The node verification submodule compares the returned text of each node with the paragraph structure length and character order according to the text group returned by the node, filters the returned text with equal paragraph length and consistent character arrangement, marks the matching status of the corresponding node number and the original number, and calls the returned text records that have been successfully compared to form a verification sample to obtain a paragraph consistency sample group. The chain-linking submodule, based on the paragraph consistency sample group, links each group of sample data as new paragraph content to the end of the current linked list, records the corresponding verification node number and number index position in the linked list structure, removes linked items missing any field, arranges valid segments according to the linking order, and establishes a carbon emission chain recording segment group.

[0010] As a further embodiment of the present invention, the access frequency sorting module includes a log extraction submodule, a strength construction submodule, and a distribution generation submodule; The log extraction submodule filters out access records with corresponding numbers from the access behavior log based on the number index in the record segment group on the carbon emission chain, extracts the access time and counts the number of accesses for each number, establishes a time list and a total frequency record table for each number, and generates an access behavior extraction value set. The intensity construction submodule calculates the average interval between two adjacent visits based on the time list of each number in the access behavior extraction value set, then multiplies the interval with the corresponding access frequency, sorts the product values ​​of all numbers in ascending order, obtains their corresponding position index sequence, and establishes an access intensity sorting sequence. The distribution generation submodule, based on the number index of the first-ranked number in the access intensity sorting sequence, locates the segment number to which the access belongs from the original access record, accumulates the frequency of the same segment number, and generates a number grouping table by arranging the numbers from high to low frequency, thus establishing a high-frequency segment distribution list.

[0011] As a further embodiment of the present invention, the block positioning adjustment module includes a frequency marking submodule, a link setting submodule, and a path sorting submodule; The frequency marking submodule, based on the access sorting information in the high-frequency segment distribution list, marks the segments with the highest access intensity as high-frequency blocks and the remaining segments as inactive blocks. At the same time, it records the linked list position index and access frequency identifier corresponding to each number and generates a block hierarchical identifier table. The link setting submodule, based on the block type of each number in the block hierarchy identifier table, sets priority link identifiers and configures forward jump fields for high-frequency blocks, sets regular link identifiers and supplements basic jump information for inactive blocks, and marks link attribute fields and access order numbers to obtain a link node label set. The path organization submodule, based on the link attribute fields and access order numbers in the link node annotation set, calls the path index information in the record segment group on the carbon emission chain, rearranges and synchronizes the numbering of all link nodes, organizes them into a unified structure mapping relationship, and establishes a monitoring data link allocation map.

[0012] As a further aspect of the present invention, the system further includes: The aggregation module calls the block number content under the priority path in the monitoring data link allocation map, extracts the carbon dioxide concentration, emission frequency and total release value in the block, performs trend division and labeling on the extracted items, archives them into a time-series record structure, and generates a carbon emission monitoring archive collection. The carbon emission monitoring archive includes emission indicator groups, archive time blocks, and tracking number bindings.

[0013] As a further embodiment of the present invention, the aggregation module includes a path extraction submodule, a fluctuation classification submodule, and a numbering summary submodule; The path extraction submodule calls the block number content marked as the priority path in the monitoring data link allocation map, extracts carbon dioxide concentration, emission frequency and total release value from the block corresponding to each number, establishes a data list for the extracted items according to the number and records the time field, and generates an emission index extraction set. The fluctuation classification submodule extracts the time field of each indicator in the set according to the emission indicators, constructs time series groups of adjacent records according to the block number order, judges the difference of the indicator value change trend in each time series, and divides the fluctuation interval according to the direction of the difference change to obtain the emission trend segmentation structure. The numbering and summarizing submodule, based on the block number and time range to which each fluctuation interval belongs in the emission trend segmentation structure, sets continuous interval affiliation labels and adds a number identification field, merges and organizes all structures, establishes a complete collection list, and generates a carbon emission monitoring archive collection.

[0014] A carbon emission data monitoring and analysis method, wherein the carbon emission data monitoring and analysis method is executed based on the aforementioned carbon emission data monitoring and analysis system, includes the following steps: S1: Collect the timestamp, equipment number and sensor component name from the carbon emission monitoring equipment, concatenate the three items to generate a structure string, extract and compress it, and combine it with the collection area number and task tag to generate a set of carbon emission tracking numbers. S2: Construct encrypted text based on the carbon emission tracking number set, broadcast the text to multiple verification nodes, compare the structure and order of the node feedback information, splice the consistent content and attach it to the end of the chain to generate a carbon emission chain record fragment group. S3: Extract index logs from the recorded fragment groups on the carbon emission chain, obtain the access time and number of calls for each fragment, calculate the interval between the current time and the access time, multiply the interval and the number of calls to form the access intensity, sort the intensity values ​​to obtain the fragment number order, divide the sorted sequence into access segments, and generate a high-frequency segment distribution list. S4: Based on the sorting position in the high-frequency segment distribution list, mark the first segment as an active block and set a priority path, mark the last segment as an inactive block and set a regular path, organize the path pointing mapping, and generate a monitoring data link allocation map. S5: Call the priority path number segment in the monitoring data link allocation map, extract carbon dioxide concentration, emission frequency and total value, classify the values ​​according to time series and bind number tags to compile, and generate a carbon emission monitoring archive.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the data tracking code generated based on device time information and structural identifiers possesses uniqueness and compression characteristics. After comparing the text structure consistency between nodes, the binding result is written into a linked list, enhancing the complete association capability of data in the registration process. A sorting mechanism is established by combining access intervals and call frequencies to generate access intensity values, effectively dividing access density areas and adjusting the response level of access paths. After the link organization is completed, fluctuating change segments are identified through segment extraction and time-series classification operations, and an archive structure set is formed by combining tags and tracking marks. This enables structured aggregation and sequential comparison of data in multiple rounds of access, making it suitable for carbon emission monitoring scenarios with continuous change characteristics. It supports centralized organization and numbered calls of emission trends in segments, improving the targeting of data reading and the coherence of retrieval paths. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of the source point extraction module of the present invention. Figure 3 This is a flowchart illustrating the acquisition process of the on-chain verification module of the present invention. Figure 4 This is a flowchart illustrating the acquisition process of the access frequency sorting module in this invention. Figure 5 This is a flowchart illustrating the acquisition process of the block positioning adjustment module of the present invention. Figure 6 This is a flowchart illustrating the acquisition process of the aggregation module in this invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] 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.

[0022] Please see Figure 1 This invention provides a technical solution: a carbon emission data monitoring and analysis system, the system comprising: The source extraction module obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment records. It then splices each item in sequence to form a data segment, performs compression and truncation operations on the data segment to obtain the basic string value, combines the work area number with the current task label to establish a tracking code, and generates a set of carbon emission tracking numbers. The on-chain verification module, based on the carbon emission tracking number set, broadcasts the encrypted text generated by the sender to multiple verification nodes. It compares the paragraph structure and text order of the content returned by each node, and combines the texts that match completely into a chain binding structure and attaches it to the end of the current chain list paragraph to generate a carbon emission on-chain record fragment group. The access frequency sorting module extracts access behavior logs based on the number index of the recorded segment group on the carbon emission chain, obtains the access time and access frequency corresponding to each number, performs a product integration of the access interval and access frequency to construct the access intensity ranking value, generates a number arrangement sequence based on the intensity ranking position, and generates a high-frequency segment distribution list. The block positioning and adjustment module, based on the access order in the high-frequency segment distribution list, marks segments with high access intensity as high-frequency blocks and sets priority links, marks segments with low access intensity as inactive blocks and sets regular links, synchronizes and organizes all path mapping information, and generates a monitoring data link allocation map. The aggregation module calls the block number content under the priority path in the monitoring data link allocation map, extracts the carbon dioxide concentration, emission frequency and total release value in the block, performs time-series integration and segmented fluctuation classification on the extracted items, adds numbered tags to the classification structure and creates an overview list, and generates a carbon emission monitoring archive.

[0023] The carbon emission tracking number set includes data collection process identifiers, device binding codes, and task stage markers. The carbon emission chain record fragment group includes verification node indexes, chained storage identifiers, and signature information digests. The high-frequency segment distribution list includes access frequency levels, active time period divisions, and data fragment priorities. The monitoring data link allocation map includes fragment path classification, link level division, and call pointer identifiers. The carbon emission monitoring archive set includes emission indicator groups, archive time blocks, and tracking number bindings.

[0024] Please see Figure 2 The carbon emission tracking number generation module includes a data splicing submodule, a string compression submodule, and a tracking code construction submodule; The data splicing submodule obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment records, splices them in the order of timestamp, equipment number, and sensor component name to form a single data segment, filters out missing fields and structurally abnormal items, processes them into a standard field format, adds a serial number identifier to each data segment, and generates a field splicing sequence set. The process retrieves carbon emission monitoring equipment records. For example, a record might contain a timestamp "YYYYMMDD111500", equipment number "SN-F2-1001", and sensor name "CO2-Sensor-A". Before concatenating these records, the integrity and format of each field are verified. For instance, the timestamp field is checked to ensure it is a 14-digit numeric string; the equipment number field is checked to ensure it conforms to the preset format of "alphanumeric-numeric"; and the sensor name field is checked to ensure it is not empty. For a record with a timestamp of "YYYY-MM-DD11:15:00", a format conversion function is called to uniformly process it to "YYYYMMDD111500". For a record missing an equipment number, it is marked as a structural anomaly and removed from the current processing flow. Then, the field content that has passed verification and has a uniform format is concatenated in strict accordance with the predetermined order of timestamp, device number, and sensor component name to obtain a single data fragment "YYYYMMDD111500SN-F2-1001CO2-Sensor-A". At the same time, a sequence number identifier starting from 1 is added to this data fragment, such as "1". Another record "YYYYMMDD111600", "SN-F2-1002", and "CO2-Sensor-B" are concatenated into "YYYYMMDD111600SN-F2-1002CO2-Sensor-B" and a sequence number identifier "2" is added. All processed data fragments and their sequence number identifiers are stored together in an ordered set to generate a field concatenation sequence set.

[0025] The string compression submodule determines whether the total length of each sequence in the field concatenation sequence set exceeds the truncation threshold. For the excess part, the first and last four characters are extracted and combined with the middle three characters to concatenate them while keeping the character order unchanged. At the same time, the number of character types is introduced as the truncation complement and appended to the end to obtain a list of truncation encoded values. For each sequence in the field concatenation sequence set, such as the sequence "YYYYMMDD111500SN-F2-1001CO2-Sensor-A" with the sequence number "1", its total character length is calculated to be 36. This length is compared with the truncation threshold. The truncation threshold is set with reference to the statistical analysis of the lengths of 100,000 historical standard data segments. The analysis results show that 98.6% of the data segment lengths are concentrated between 35 and 45 characters. In order to preserve the uniqueness of the data and improve processing efficiency, the median of the distribution range, 40, is selected as the truncation threshold. Since 36 does not exceed 40, no truncation operation is required, and its encoded value is itself. For another sequence with a total character length of 48, "YYYYMMD",... The string "D111700SN-F3-2001Temp-Humidity-Sensor-XYZ" has a length of 48, exceeding the threshold of 40. Therefore, a truncation operation is performed. Specifically, the first four characters "YYYY", the last four characters "-XYZ", and the three characters "01T" in the middle (positions 23, 24, and 25) are extracted and concatenated in their original order to form "YYYY01T-XYZ". Then, the number of character types in the truncated sequence "YYYYMMDD111700SN-F3-2001Temp-Humidity-Sensor-XYZ" is counted. The count shows that it contains numbers (0, 1, 2, 3, 5, 7, 9, ...). The string contains 25 different characters (Y, M, D), uppercase letters (S, N, F, T, H, X, Y, Z), lowercase letters (e, m, p, u, i, d, t, y, s, o, r), and special symbols (-). The count value 25 is appended to the end of the newly generated string as the two's complement, resulting in the truncated encoding value "YYYY01T-XYZ25". All the processed encoding values ​​are then organized in their original order to generate a list of truncated encoding values.

[0026] The tracking code construction submodule inserts the work area number and task tag before and after the code value according to the list of extracted code values ​​to form a three-segment tracking structure. It also establishes a corresponding index structure according to the work area number, removes items without area numbers, and retains all items with complete structures as valid records of numbers to generate a set of carbon emission tracking numbers. Based on each coded value in the extracted coded value list, such as "YYYY01T-XYZ25", extract its corresponding job area number "WA-03" and task tag "Batch-77B". Perform a tracking code construction operation, which inserts the job area number at the beginning of the coded value and the task tag at the end, forming a three-part tracking structure consisting of the job area number, the extracted coded value, and the task tag, i.e., "WA-03YYYY01T-XYZ25Batch-77B". Based on the job area number "WA-03" in this tracking structure, create an index structure with the job area number as the key and the complete tracking structure as the value, such as {'WA-03': ['WA-03YYYY01T-XYZ25Batch-77B', During the construction process, the integrity of all generated tracking structures is verified. For example, it checks for missing area number entries due to missing source data. If the original record corresponding to a coding value is missing the work area number, the tracking structure it forms, such as "YYYY01T-XYZ25Batch-77B", will be considered incomplete and removed. All tracking structures that pass the verification and have complete structures are retained as valid records for numbering. These valid records are then collected to generate a carbon emission tracking number set.

[0027] Please see Figure 3 The on-chain verification module includes an encrypted broadcast submodule, a node verification submodule, and a chain-linked submodule. The encrypted broadcast submodule, based on the carbon emission tracking number set, concatenates the character-encoded text corresponding to each number with the task tag to form encrypted text, broadcasts all encrypted text sequentially to multiple verification nodes within a set range, records the returned text content and corresponding number of each node, and generates a node return text group. Based on each carbon emission tracking number set, such as "WA-03YYYY01T-XYZ25Batch-77B", the character-encoded text "WA-03YYYY01T-XYZ25" and the task tag "Batch-77B" are extracted and concatenated to form the encrypted text "WA-03YYYY01T-XYZ25Batch-77B". This encrypted text is then broadcast via network interface to five verification nodes within a preset range (Node-A, Node-B, Node-C, Node-D, ...). For each broadcast action (Node-E), the system records a unique transmission sequence number and starts a timer to wait for responses from each node. Within the set 3000 millisecond timeout period, if the system receives return text from Node-A, Node-B, Node-C, and Node-E, but Node-D does not return, the system records the content of the successfully returned text and its source node number. For example, Node-A returns "WA-03YYYY01T-XYZ25Batch-77B", and Node-B returns "WA-0 3YYYY01T-XXZ25Batch-77B (data error exists), Node-C returns "WA-03YYYY01T-XYZ25Batch-77B", Node-E returns "WA-03YYYY01T-XYZ25Batch-77B", and associates these records with the original sent number "WA-03YYYY01T-XYZ25Batch-77B", forming a data structure set containing the original number, the returned node, and the returned text, generating the node return text group.

[0028] The node verification submodule compares the returned text of each node with the paragraph structure length and character order according to the text group returned by the node. It filters the returned text with equal paragraph length and consistent character arrangement, marks the matching status of the corresponding node number and the original number, and calls the returned text records that have been successfully compared to form a verification sample to obtain the paragraph consistency sample group. Based on the records in the returned text group from the nodes, multiple returned texts received with the same original number are compared. Taking the number "WA-03YYYY01T-XYZ25Batch-77B" as an example, it received four returned texts from Node-A, Node-B, Node-C, and Node-E. First, a paragraph structure length comparison is performed. The original encrypted text is 27 characters long, and the texts returned by Node-A, Node-B, Node-C, and Node-E are all 27 characters long. This round of comparison passes. Then, a character-by-character comparison is performed, comparing the original text with each returned text character by character, starting from the first character. The comparison is performed between Node-A, Node-C, and Node-E. When comparing the returned text of de-E, all 27 characters are completely identical, the comparison passes, and Node-A, Node-C, and Node-E are marked as successful matches. When comparing the returned text of Node-B, an inconsistency is found at the 15th character (originally 'Y', returned as 'X'), the comparison fails, and Node-B is marked as a failed match. After filtering, there are three returned texts with equal paragraph length and consistent character arrangement, from Node-A, Node-C, and Node-E respectively. The system then calls these three successfully matched returned text records and combines them into a set of verification samples. This sample contains the original number and a list of all nodes that successfully verified that number, resulting in a paragraph consistency sample group.

[0029] The chain-linking submodule, based on the paragraph consistency sample group, links each group of sample data as new paragraph content to the end of the current linked list, records the corresponding verification node number and number index position in the linked list structure, removes linked items that are missing any field, arranges valid segments according to the linking order, and establishes a segment group recorded on the carbon emission chain. Based on each sample data group in the segment consistency sample group, a chain-linking operation is performed. Taking the sample data with the number "WA-03YYYY01T-XYZ25Batch-77B" jointly verified by Node-A, Node-C, and Node-E as an example, the system treats this sample data as a new data segment (or block) and appends it to the end of the current linked list structure. Assuming the current linked list already has 10 segments, the index position of the new segment is 11. At the same time as appending, the list of node numbers that successfully verified the data segment is precisely recorded in the metadata area of ​​the linked list segment: "[Node-A, Node-C, After the node is attached, the system performs an integrity check on the newly generated linked list segment to verify whether it contains the three fields: sample data, verification node number list, and number index position. If any field is missing due to system abnormality during processing, such as only recording sample data and index position but omitting node number, the attached item is considered invalid and removed from the linked list. All valid attached segments that pass the integrity check are arranged in the order they were added to the linked list to establish a carbon emission chain recording segment group.

[0030] Please see Figure 4 The access frequency processing module includes a log extraction submodule, a strength construction submodule, and a distribution generation submodule. The log extraction submodule filters out access records with corresponding numbers from the access behavior log based on the number index in the record segment group on the carbon emission chain, extracts the access time and counts the number of accesses for each number, creates a time list and a total frequency record table for each number, and generates an access behavior extraction value set. The access behavior extraction value set is shown in Table 1, with specific content as an example of the access behavior log.

[0031] Table 1: Sample Access Behavior Log Table As shown in Table 1, based on the index of the record segment group in the carbon emission chain, for example, index "11", all records with the "index" field "11" are filtered from the access behavior log database. From the filtered records, the value of the "access timestamp" field is extracted one by one to form a time list [YYYYMMDD142005, YYYYMMDD142515, YYYYMMDD143000]. At the same time, the total number of these records is counted, and the total access frequency of index "11" is 3. This time list and the total frequency are stored in a record table. The same operation is performed on another index "27" to extract its time list [YYYYMMDD150530, YYYYMMDD161040] and the total frequency 2. This operation is performed on the index of all record segments in the chain. Finally, the time list and the total frequency record table corresponding to all numbers are collected to generate the access behavior extraction value set.

[0032] The intensity construction submodule calculates the average interval between two adjacent visits based on the time list of each number in the access behavior extraction value set. Then, it multiplies the interval with the corresponding access frequency and integrates them. After sorting the product values ​​of all numbers from smallest to largest, it obtains their corresponding position index sequence and establishes the access intensity sorting sequence. Based on the time list of each ID in the access behavior extraction value set, the access interval is calculated. Taking ID index "11" as an example, its time list is [142005, 142515, 143000] (dates are omitted for simplicity). The first interval is 142515 - 142005 = 510 seconds, and the second interval is 143000 - 142515 = 485 seconds. The average interval is calculated as (510 + 485) / 2 = 497.5 seconds. Then, this average interval value of 497.5 is multiplied by the total access frequency of 3 corresponding to this ID, resulting in a product value of 497.5 * 3 = 1492.5. For ID index "27", its access interval is 161040 - 150530 = Since there is only one interval, the average interval is 10510 seconds, and the access frequency is 2. The calculated product value is 10510 * 2 = 21020. After calculating the product value for all numbers, these values ​​are sorted in ascending order. For example, if there are four numbers with product values ​​[1492.5, 21020, 850.0, 5600.8], the sorted value is [850.0, 1492.5, 5600.8, 21020]. Based on this sorting result, the position index of the original number in the sorted list is obtained. Assuming the original number order is [A, B, C, D], the corresponding product value is [1492.5, 21020, 850.0, 5600.8], then the sorted position index sequence is [3, 1, 4, ...]. [2] (C ranked 1st, A ranked 2nd, D ranked 3rd, B ranked 4th), establish a ranking sequence based on access intensity.

[0033] The distribution generation submodule locates the segment number of the access record from the original access record based on the number index of the first number in the access intensity sorting sequence, accumulates the frequency of the same segment number, and generates a number grouping table by arranging the numbers from high to low frequency to establish a high-frequency segment distribution list. Based on the access intensity ranking sequence, the index of the number that ranks first is defined as the number in the top 20% of the ranking. If there are 100 numbers in total, it refers to the numbers ranked from 1 to 20. The segment numbers to which these numbers belong are located from the original access records. For example, through access intensity calculation, the ranking position of the number indices "11", "18", and "25" is determined to be in the top 20%. By querying the original records, it is found that the work area number of number "11" is "WA-03", number "18" is "WA-05", and number "25" is "WA-03". Next, the frequency of occurrence of the same segment number is accumulated and counted. In this example, "WA-03" appears twice and "WA-05" appears once. All statistical results are arranged in descending order of frequency to form a number grouping table, such as "WA-03: 2 times" and "WA-05: 1 time". This sorted grouping table is solidified to establish a high-frequency segment distribution list.

[0034] Please see Figure 5 The block positioning adjustment module includes a frequency marking submodule, a link setting submodule, and a path sorting submodule; The frequency marking submodule, based on the access sorting information in the high-frequency segment distribution list, marks the segments with the highest access intensity as high-frequency blocks and the remaining segments as inactive blocks. At the same time, it records the linked list position index and access frequency identifier corresponding to each number and generates a block hierarchical identifier table. Based on the access sorting information in the high-frequency segment distribution list, segments in the top 20% of the access intensity sorting sequence are numbered, for example, number "11", and marked as high-frequency blocks. Segment numbers in the bottom 80% of the sorting position, for example, number "27", are marked as inactive blocks. While marking, the absolute position index of each number in the linked list structure is recorded, such as the position index of number "11" is 11, as well as its specific access frequency identifier, such as the access frequency of number "11" is 3. This information—segment number, block type (high-frequency / inactive), linked list position index, and access frequency—is integrated into a single record. After completing this operation for all segment numbers, all records are aggregated to generate a block hierarchy identifier table.

[0035] The link setting submodule, based on the block type of each number in the block hierarchy identifier table, sets priority link identifiers and configures forward jump fields for high-frequency blocks, sets regular link identifiers and supplements basic jump information for inactive blocks, and marks link attribute fields and access order numbers to obtain a link node label set. Based on the block type of each number in the block hierarchy identifier table, link attributes are set. For number "11" marked as a high-frequency block, a priority link identifier field with a value of "1" is set, and a forward jump field is configured for it. The content of this field is the linked list position index of the next high-frequency block. For example, if the position index of the next high-frequency block is 18, then the value of the forward jump field is "18". For number "27" marked as an inactive block, a regular link identifier field with a value of "0" is set, and basic jump information is added. This information only points to its next physical node in the linked list, such as node 28. Then, all nodes are labeled with link attribute fields (priority / regular) and an access order number. This number is generated based on the new jump logic. The information of all nodes after the above labeling process is collected to obtain the link node label set.

[0036] The path organization submodule, based on the link attribute fields and access order numbers in the link node annotation set, calls the path index information in the record segment group on the carbon emission chain, rearranges and synchronizes the numbering of all link nodes, organizes them into a unified structure mapping relationship, and establishes a monitoring data link allocation map. Based on the link attribute fields and access order numbers in the link node annotation set, the system retrieves the original, physical order-based path index information from the recorded fragment groups on the carbon emission chain and performs a full link reordering. The system traverses each link node, reads its priority link identifier and forward jump field. If a node is identified as a priority link, its logical next hop directly points to the node specified in its forward jump field, rather than its physical next node. For example, the next hop of node 11 (high frequency) directly points to node 18 (high frequency), skipping the physically adjacent inactive nodes 12, 13, 14, 15, 16, and 17. For inactive blocks, the jump path maintains the original physical order. The system synchronously updates all these new logical jump relationships and organizes them into a unified structure mapping table covering all nodes, indexed by the number and with the logical next hop number as the value, to establish a monitoring data link allocation map.

[0037] Please see Figure 6 The aggregation module includes a path extraction submodule, a fluctuation classification submodule, and a number summary submodule; The path extraction submodule calls the block number content marked as a priority path in the monitoring data link allocation map, extracts carbon dioxide concentration, emission frequency and total release value from the block corresponding to each number, establishes a data list for the extracted items according to the number and records the time field, and generates an emission index extraction set. The emission indicators are extracted into a set, and the specific indicator values ​​are shown in the block data examples in Table 2.

[0038] Table 2: Sample Table of Block Core Data As shown in Table 2, all block numbers marked as priority paths in the monitoring data link allocation map are called, such as numbers "11" and "18". Based on these numbers, the preset emission indicators, namely carbon dioxide concentration, emission frequency and total release value, are extracted from the detailed data recorded on the chain. For block number "11", at 14:30, the carbon dioxide concentration is 520.5 ppm, the emission frequency is 12 times / hour and the total release value is 25.6 kg. At 14:45, the values ​​are 535.8 ppm, 15 times / hour and 28.3 kg. These extracted items are organized uniformly according to the block number and the corresponding time field is recorded. A data list containing data at multiple time points is built for each number. Finally, the data lists of all priority path blocks are collected to generate the emission indicator extraction set.

[0039] The fluctuation classification submodule extracts the time field of each indicator in the set based on the emission indicators, constructs time series groups of adjacent records according to the block number order, judges the difference of the indicator value change trend in each time series, and divides the fluctuation interval according to the direction of the difference change to obtain the emission trend segmentation structure. Based on the time field of each indicator extracted from the emission index set, time series analysis is performed on the data. Taking the carbon dioxide concentration data of block number "11" as an example, its time series is [14:30: 520.5, 14:45: 535.8]. Two adjacent records are constructed into a time series group. The difference between the indicator value at the later time and the indicator value at the previous time is calculated, i.e., 535.8 - 520.5 = 15.3. A threshold for judging the direction of the difference change is set. This threshold is based on the analysis of the standard deviation of the fluctuation of historical monitoring data of similar equipment, and 1.5 times the standard deviation is taken as the threshold. Assuming that the calculated threshold is 5.0 ppm, since the difference 15.3 is greater than 5.0, the fluctuation range of the time period 14:30-14:45 is divided into "significant increase". If the difference is between -5.0 and 5.0, it is divided into "stable". If it is less than -5.0, it is divided into "significant decrease". For the carbon dioxide concentration sequence of block number "18" [480.2, Perform the same operation on 475.1. The difference is 475.1 - 480.2 = -5.1, which is less than -5.0. Therefore, it is classified as "significantly decreased". Perform this difference judgment and interval division on all indicators of all blocks to obtain the segmented structure of emission trends.

[0040] The numbering summary submodule sets continuous interval affiliation labels and adds number identification fields based on the block number and time range to which each fluctuation interval belongs in the emission trend segmentation structure. It then merges and organizes the entire structure, establishes a complete collection list, and generates a carbon emission monitoring archive collection. Based on the various fluctuation intervals divided in the emission trend segmentation structure, the block number and corresponding time range are extracted. For example, for the "significant increase" interval of block number "11" within the time range of 14:30-14:45, a continuous interval affixation label is assigned, such as "WA03-YYYYMMDD-Up-01", and a unique number identifier field is added, such as UUID "e5a3b2c1-...". The same operation is performed on the "significant decrease" interval of block number "18", assigning the label "WA05-YYYYMMDD-Down-01" and a new number identifier field. All the tagged and numbered segmentation structures are merged and organized to form a clear and complete collection list that can be quickly retrieved and analyzed. Each item in this list clearly records the emission trend, the equipment area involved, the time period, and the specific data pointer, ultimately generating a carbon emission monitoring archive collection.

[0041] A method for monitoring and analyzing carbon emission data includes the following steps: S1: Collect the timestamp, equipment number and sensor component name from the carbon emission monitoring equipment, concatenate the three items to generate a structure string, extract and compress it, and combine it with the collection area number and task tag to generate a set of carbon emission tracking numbers. S2: Construct encrypted text based on the carbon emission tracking number set, broadcast the text to multiple verification nodes, compare the structure and order of the node feedback information, splice the consistent content and attach it to the end of the chain to generate a group of record fragments on the carbon emission chain. S3: Extract index logs from the recorded fragment groups on the carbon emission chain, obtain the access time and number of calls for each fragment, calculate the interval between the current time and the access time, multiply the interval and the number of calls to form the access intensity, sort the intensity values ​​to obtain the fragment number order, divide the sorted sequence into access segments, and generate a high-frequency segment distribution list. S4: Based on the sorting position in the high-frequency segment distribution list, mark the first segment as an active block and set a priority path, mark the last segment as an inactive block and set a regular path, organize the path pointing mapping, and generate a monitoring data link allocation map. S5: Call the priority path number segment in the monitoring data link allocation map, extract carbon dioxide concentration, emission frequency and total value, classify the values ​​according to time series and bind number tags to compile, and generate a carbon emission monitoring archive.

[0042] The above description is merely a specific embodiment 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 carbon emission data monitoring and analysis system, characterized in that, The system includes: The source extraction module obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment, splices them together to form a data segment, performs compression and truncation operations to obtain the basic string value, combines the work area number with the current task label to form a tracking code, and generates a set of carbon emission tracking numbers. The on-chain verification module, based on the carbon emission tracking number set, broadcasts encrypted text to multiple verification nodes, performs structural consistency judgment on the content returned by each node, combines the completely matching text content and attaches it to the end of the current chain list segment to generate a carbon emission on-chain record fragment group. The access frequency sorting module extracts the access time and frequency corresponding to the number of the recorded segment group on the carbon emission chain according to the number index, performs a product integration of the access interval and access frequency to construct the access intensity sorting value, generates a number arrangement sequence according to the intensity sorting position, and generates a high-frequency segment distribution list. The block positioning and adjustment module identifies the segments with higher access intensity as high-frequency blocks and sets priority paths according to the access order in the high-frequency segment distribution list, while segments with lower access intensity are set as regular paths. It also organizes all path mapping information and generates a monitoring data link allocation map.

2. The carbon emission data monitoring and analysis system according to claim 1, characterized in that: The carbon emission tracking number set includes a data collection process identifier, a device binding code, and a task stage marker. The carbon emission chain record fragment group includes a verification node index, a chain storage identifier, and a signature information digest. The high-frequency segment distribution list includes access frequency level, active time period division, and data fragment priority. The monitoring data link allocation map includes fragment path classification, link level division, and call pointing identifier.

3. The carbon emission data monitoring and analysis system according to claim 1, characterized in that, The carbon emission tracking number generation module includes a data splicing submodule, a serial value compression submodule, and a tracking code construction submodule; The data splicing submodule obtains the timestamp, equipment number, and sensor component name from the carbon emission monitoring equipment records, splices them in the order of timestamp, equipment number, and sensor component name to form a single data segment, filters out missing fields and structurally abnormal items, processes them into a standard field format, adds a serial number identifier to each data segment, and generates a field splicing sequence set. The string compression submodule determines whether the total length of each sequence in the field concatenation sequence set exceeds the truncation threshold. If the length exceeds the threshold, the first and last four characters and the middle three characters are extracted and concatenated together, keeping the character order unchanged. At the same time, the number of character types is introduced as the truncation complement and appended to the end to obtain a list of truncation encoded values. The tracking code construction submodule, based on the extracted code value list, inserts the work area number and task tag sequentially before and after the code value to form a three-segment tracking structure, and establishes a corresponding index structure according to the work area number, removes items without area numbers, retains all complete structural items as valid number records, and generates a carbon emission tracking number set.

4. The carbon emission data monitoring and analysis system according to claim 1, characterized in that, The on-chain verification module includes an encrypted broadcast submodule, a node verification submodule, and a chain-linked submodule. The encrypted broadcast submodule, based on the carbon emission tracking number set, concatenates the character-encoded text corresponding to each number with the task tag to form encrypted text, broadcasts all encrypted text sequentially to multiple verification nodes within a set range, records the returned text content and corresponding number of each node, and generates a node return text group. The node verification submodule compares the returned text of each node with the paragraph structure length and character order according to the text group returned by the node, filters the returned text with equal paragraph length and consistent character arrangement, marks the matching status of the corresponding node number and the original number, and calls the returned text records that have been successfully compared to form a verification sample to obtain a paragraph consistency sample group. The chain-linking submodule, based on the paragraph consistency sample group, links each group of sample data as new paragraph content to the end of the current linked list, records the corresponding verification node number and number index position in the linked list structure, removes linked items missing any field, arranges valid segments according to the linking order, and establishes a carbon emission chain recording segment group.

5. The carbon emission data monitoring and analysis system according to claim 1, characterized in that, The access frequency processing module includes a log extraction submodule, a strength construction submodule, and a distribution generation submodule. The log extraction submodule filters out access records with corresponding numbers from the access behavior log based on the number index in the record segment group on the carbon emission chain, extracts the access time and counts the number of accesses for each number, establishes a time list and a total frequency record table for each number, and generates an access behavior extraction value set. The intensity construction submodule calculates the average interval between two adjacent visits based on the time list of each number in the access behavior extraction value set, then multiplies the interval with the corresponding access frequency, sorts the product values ​​of all numbers in ascending order, obtains their corresponding position index sequence, and establishes an access intensity sorting sequence. The distribution generation submodule, based on the number index of the first-ranked number in the access intensity sorting sequence, locates the segment number to which the access belongs from the original access record, accumulates the frequency of the same segment number, and generates a number grouping table by arranging the numbers from high to low frequency, thus establishing a high-frequency segment distribution list.

6. The carbon emission data monitoring and analysis system according to claim 1, characterized in that, The block positioning adjustment module includes a frequency marking submodule, a link setting submodule, and a path sorting submodule; The frequency marking submodule, based on the access sorting information in the high-frequency segment distribution list, marks the segments with the highest access intensity as high-frequency blocks and the remaining segments as inactive blocks. At the same time, it records the linked list position index and access frequency identifier corresponding to each number and generates a block hierarchical identifier table. The link setting submodule, based on the block type of each number in the block hierarchy identifier table, sets priority link identifiers and configures forward jump fields for high-frequency blocks, sets regular link identifiers and supplements basic jump information for inactive blocks, and marks link attribute fields and access order numbers to obtain a link node label set. The path organization submodule, based on the link attribute fields and access order numbers in the link node annotation set, calls the path index information in the record segment group on the carbon emission chain, rearranges and synchronizes the numbering of all link nodes, organizes them into a unified structure mapping relationship, and establishes a monitoring data link allocation map.

7. The carbon emission data monitoring and analysis system according to claim 1, characterized in that, The system also includes: The aggregation module calls the block number content under the priority path in the monitoring data link allocation map, extracts the carbon dioxide concentration, emission frequency and total release value in the block, performs trend division and labeling on the extracted items, archives them into a time-series record structure, and generates a carbon emission monitoring archive collection. The carbon emission monitoring archive includes emission indicator groups, archive time blocks, and tracking number bindings.

8. The carbon emission data monitoring and analysis system according to claim 7, characterized in that, The aggregation module includes a path extraction submodule, a fluctuation classification submodule, and a number summary submodule. The path extraction submodule calls the block number content marked as the priority path in the monitoring data link allocation map, extracts carbon dioxide concentration, emission frequency and total release value from the block corresponding to each number, establishes a data list for the extracted items according to the number and records the time field, and generates an emission index extraction set. The fluctuation classification submodule extracts the time field of each indicator in the set according to the emission indicators, constructs time series groups of adjacent records according to the block number order, judges the difference of the indicator value change trend in each time series, and divides the fluctuation interval according to the direction of the difference change to obtain the emission trend segmentation structure. The numbering and summarizing submodule, based on the block number and time range to which each fluctuation interval belongs in the emission trend segmentation structure, sets continuous interval affiliation labels and adds a number identification field, merges and organizes all structures, establishes a complete collection list, and generates a carbon emission monitoring archive collection.

9. A method for monitoring and analyzing carbon emission data, characterized in that, The method is used in the carbon emission data monitoring and analysis system according to any one of claims 1-8, and includes the following steps: S1: Collect the timestamp, equipment number and sensor component name from the carbon emission monitoring equipment, concatenate the three items to generate a structure string, extract and compress it, and combine it with the collection area number and task tag to generate a set of carbon emission tracking numbers. S2: Construct encrypted text based on the carbon emission tracking number set, broadcast the text to multiple verification nodes, compare the structure and order of the node feedback information, splice the consistent content and attach it to the end of the chain to generate a carbon emission chain record fragment group. S3: Extract index logs from the recorded fragment groups on the carbon emission chain, obtain the access time and number of calls for each fragment, calculate the interval between the current time and the access time, multiply the interval and the number of calls to form the access intensity, sort the intensity values ​​to obtain the fragment number order, divide the sorted sequence into access segments, and generate a high-frequency segment distribution list. S4: Based on the sorting position in the high-frequency segment distribution list, mark the first segment as an active block and set a priority path, mark the last segment as an inactive block and set a regular path, organize the path pointing mapping, and generate a monitoring data link allocation map. S5: Call the priority path number segment in the monitoring data link allocation map, extract carbon dioxide concentration, emission frequency and total value, classify the values ​​according to time series and bind number tags to compile, and generate a carbon emission monitoring archive.

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