Cross-department carbon data real-time collaborative collection method and system based on edge computing

Abstract

This invention relates to the field of real-time collaborative carbon data acquisition technology, and discloses a method and system for real-time collaborative carbon data acquisition across departments based on edge computing. The method includes: performing the following operations on each deployed node department in a set of deployed node departments: acquiring a multi-source heterogeneous parameter set; obtaining a multi-source heterogeneous standard parameter set; acquiring energy department data packets; calculating data packet transmission priority scores and encrypted data packets; summarizing the data packet transmission priority scores and encrypted data packets respectively to obtain a data packet transmission priority score set and an encrypted data packet set corresponding to the set of deployed node departments; transmitting the encrypted data packet set to a pre-built cloud collaboration layer according to the data packet transmission priority score set to obtain a sent data packet set; and completing the real-time collaborative acquisition of cross-departmental carbon data based on edge computing based on the sent data packet set. This invention can improve the real-time performance, accuracy, and collaborative efficiency of carbon data acquisition.

Description

Technical Field

[0001] This invention relates to the field of real-time collaborative carbon data acquisition technology, and in particular to a cross-departmental real-time collaborative carbon data acquisition method and system based on edge computing. Background Technology

[0002] Edge computing refers to the deployment of industrial gateways or embedded servers with computing, storage, and encryption capabilities at the field side, close to the functional departments responsible for carbon emission sources. "Cross-department" refers to covering multiple carbon emission-related functional departments, including production, energy, logistics, and construction. Real-time collaborative carbon data acquisition means that edge nodes complete carbon emission data acquisition, quality verification, priority scheduling, and encrypted transmission at a rate of seconds, while the cloud-based collaborative layer synchronously aggregates data from multiple departments and provides real-time feedback on cross-departmental carbon intensity benchmarks and energy-saving and carbon-reduction strategies.

[0003] In traditional multi-sectoral real-time carbon emission accounting systems, each department typically builds its own monitoring system, employing heterogeneous communication protocols and data formats. This results in ineffective data exchange across departments and makes it difficult to establish a unified accounting standard. Furthermore, existing systems generally use fixed-period upload mechanisms without differentiated transmission scheduling based on data criticality. This causes high-value, urgent data to compete with ordinary data for limited channel resources, increasing network congestion risks and leading to higher packet loss rates and compromised transmission timeliness for critical data. Therefore, improving the real-time performance, accuracy, and collaborative efficiency of carbon data acquisition is an urgent technical challenge. Summary of the Invention

[0004] This invention provides a cross-departmental real-time collaborative acquisition method for carbon data based on edge computing and a computer-readable storage medium. Its main purpose is to improve the real-time performance, accuracy, and collaborative efficiency of carbon data acquisition.

[0005] To achieve the above objectives, this invention provides a cross-departmental collaborative carbon data acquisition method based on edge computing, comprising:

[0006] Once the set of functional departments for carbon emission sources is identified, edge computing nodes are deployed for each functional department of the carbon emission source set to obtain the set of departments with deployed nodes.

[0007] Perform the following operations for each deployed node department in the deployed node department set:

[0008] Based on the multi-source heterogeneous parameter set collected by the deployed node departments, a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set.

[0009] The system acquires data packets from the energy sector based on a multi-source heterogeneous standard parameter set, receives collaborative optimization strategy instructions, and calculates data packet transmission priority scores and encrypted data packets based on the energy sector data packets and collaborative optimization strategy instructions.

[0010] By summarizing the data packet sending priority scores and encrypted data packets respectively, we can obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node department set;

[0011] The encrypted data packet set is transmitted to the pre-built cloud collaboration layer according to the data packet sending priority score set to obtain the sent data packet set;

[0012] Real-time collaborative collection of cross-departmental carbon data based on edge computing was completed based on the sent data packet set.

[0013] Optionally, obtaining the multi-source heterogeneous standard parameter set based on the multi-source heterogeneous parameter set includes:

[0014] A set of data tuples is matched from a multi-source heterogeneous parameter set based on a pre-built association rule base. The set of data tuples includes multiple data tuples, and each data tuple includes independent variable values ​​and dependent variable values.

[0015] Data tuples are extracted sequentially from the data tuple set, and the target rule model is identified from the association rule base based on the extracted data tuples.

[0016] Substitute the independent variables from the extracted data tuples into the target rule model to obtain the expected value, and calculate the relative deviation based on the expected value and the dependent variable value from the extracted data tuples.

[0017] If the relative deviation is greater than the preset tolerance threshold, the independent variable value and dependent variable value in the extracted data tuple will be regarded as suspicious independent variable value and suspicious dependent variable value, respectively.

[0018] The confidence weights of the independent and dependent variables are obtained based on the values ​​of the questionable independent and dependent variables.

[0019] The values ​​of the calibrated dependent variable or calibrated independent variable are determined based on the confidence weights of the independent and dependent variables.

[0020] Based on the calibration dependent variable value or calibration independent variable value, the multi-source heterogeneous standard parameters are identified, and the multi-source heterogeneous standard parameters are summarized to obtain the multi-source heterogeneous standard parameter set.

[0021] Optionally, the step of obtaining energy sector data packets based on a multi-source heterogeneous standard parameter set includes:

[0022] Multi-source heterogeneous standard parameters are extracted sequentially from the multi-source heterogeneous standard parameter set, and field mapping transformation operations are performed on the extracted multi-source heterogeneous standard parameters to obtain unified standard parameters;

[0023] The data energy type is determined based on unified standard parameters, and then matched with a pre-built carbon emission factor library to obtain the type carbon emission factor.

[0024] The unified standard parameters are transformed into unified dimensional parameters based on the type carbon emission factor. The carbon dioxide equivalent value is then calculated based on the unified dimensional parameters and the type carbon emission factor.

[0025] The carbon dioxide equivalent values ​​and data energy types are summarized separately to obtain the carbon dioxide equivalent value set and data energy type set corresponding to the multi-source heterogeneous standard parameter set. The total carbon emissions are calculated based on the carbon dioxide equivalent value set, where the total carbon emissions are the sum of multiple carbon dioxide equivalent values ​​in the carbon dioxide equivalent value set.

[0026] Obtain the total output of the deployed node departments, and calculate the carbon intensity per unit product based on the total carbon emissions and total output.

[0027] The carbon intensity of a unit product and the data energy type set are encapsulated to obtain an energy sector data package.

[0028] Optionally, the step of calculating the data packet transmission priority score and encrypting the data packet based on the energy sector data packet and the collaborative optimization strategy instruction includes:

[0029] The criticality level of the data was determined based on the energy sector's data package and collaborative optimization strategy instructions;

[0030] The target transmission strategy is obtained by matching data based on its criticality level within a pre-built transmission strategy library.

[0031] Based on the target transmission strategy and the data packets obtained from the energy sector, compressed data packets are acquired, and data encryption is performed on the compressed data packets to obtain encrypted data packets. The data packet transmission priority score is determined according to the data criticality level.

[0032] Optionally, the step of determining the data criticality level based on energy sector data packets and collaborative optimization strategy instructions includes:

[0033] Meta-information fields are parsed from the energy sector data packets. These fields include: data generation timestamp, data source department identifier, data energy type set, and carbon intensity per unit product.

[0034] Based on the collaborative optimization strategy instructions, obtain the current system time and historical unit product carbon intensity set, and calculate the criticality score and numerical anomaly score based on the data energy type set and historical unit product carbon intensity set.

[0035] The time offset is calculated based on the data generation timestamp and the current system time, and the timeliness sensitivity score is calculated based on the time offset.

[0036] The comprehensive criticality score is calculated based on a preset set of scoring weights, time sensitivity score, criticality score, and numerical anomaly score.

[0037] The criticality level of the data is determined based on the comprehensive criticality score.

[0038] Optionally, the calculation of criticality score and numerical anomaly score based on the data energy type set and historical unit product carbon intensity set includes:

[0039] The energy type contribution weight set is obtained based on the data energy type set. The energy type contribution weight set includes multiple energy type contribution weights, and the energy type contribution weights correspond one-to-one with the data energy types.

[0040] The criticality score is obtained by weighted summation based on the energy type contribution weight set and the data energy type set;

[0041] The historical average carbon intensity and the historical carbon intensity standard deviation are calculated based on the historical unit product carbon intensity set.

[0042] The standard deviation multiple is calculated based on the historical average carbon intensity and the historical carbon intensity standard deviation. The numerical anomaly score is then mapped based on the standard deviation multiple and a pre-constructed anomaly score table.

[0043] Optionally, the calculation of timeliness sensitivity score based on time offset includes:

[0044] Obtain the carbon allowance accounting period based on the data source department identification, and calculate the decay coefficient and critical period growth coefficient based on the carbon allowance accounting period;

[0045] The timeliness sensitivity score is calculated based on the time offset, decay coefficient, critical period growth coefficient, and carbon quota accounting cycle. The formula for calculating the timeliness sensitivity score is as follows:

[0046] ;

[0047] in, Indicates the timeliness sensitivity score. This indicates the preset maximum score. Indicates the attenuation coefficient. Indicates the time offset. Indicates the carbon quota accounting cycle. This represents the natural exponential function. This represents the preset critical period gain coefficient. This represents the growth coefficient during the critical period.

[0048] Optionally, determining the data packet transmission priority score based on the data criticality level includes:

[0049] Based on the data criticality level, a basic priority score is determined, and the current available network status indicator set is obtained. The current available network status indicator set includes: channel delay, available bandwidth, and packet loss rate.

[0050] The channel quality score is calculated based on channel delay and packet loss rate, and the bandwidth margin is calculated based on available bandwidth and preset historical peak bandwidth.

[0051] The channel state comprehensive score is calculated based on the channel quality score and bandwidth margin, and the corrected channel score is calculated based on the basic priority score and the channel state comprehensive score.

[0052] The survival time urgency coefficient is calculated based on the time offset and carbon quota accounting cycle. Based on the data source department identifier, a query is performed in the pre-constructed departmental collaboration relationship graph to obtain the downstream departmental dependency weight set. The overall departmental dependency is calculated based on the downstream departmental dependency weight set.

[0053] The packet transmission priority score is calculated based on the department's overall dependency, the corrected channel score, and the survival time urgency coefficient.

[0054] Optionally, the step of calculating the data packet transmission priority score based on departmental comprehensive dependency, modified channel score, and lifetime urgency coefficient includes:

[0055] The collaboration tightness weighting factor is calculated based on the department's comprehensive dependency. The ideal distance and negative ideal distance are calculated based on the basic priority score, the corrected channel score, the survival time urgency coefficient, and the collaboration tightness weighting factor.

[0056] Calculate the data packet transmission priority score based on the ideal distance and negative ideal distance.

[0057] To achieve the above objectives, the present invention also provides a cross-departmental collaborative carbon data acquisition system based on edge computing, comprising:

[0058] The edge node deployment module is used to identify the set of functional departments of carbon emission sources, and to deploy edge computing nodes for each functional department of carbon emission sources in the set of functional departments of carbon emission sources, so as to obtain the set of departments with deployed nodes.

[0059] The energy data package generation module is used to perform the following operations on each deployed node department in the deployed node department set: collect multi-source heterogeneous parameter sets based on the deployed node departments, and obtain multi-source heterogeneous standard parameter sets based on the multi-source heterogeneous parameter sets;

[0060] The priority score calculation module is used to obtain energy sector data packets based on a multi-source heterogeneous standard parameter set, receive collaborative optimization strategy instructions, calculate data packet transmission priority scores and encrypted data packets according to energy sector data packets and collaborative optimization strategy instructions, and summarize data packet transmission priority scores and encrypted data packets to obtain the data packet transmission priority score set and encrypted data packet set corresponding to the deployed node sector set.

[0061] The cloud-based collaborative transmission module is used to transmit encrypted data packets to a pre-built cloud-based collaborative layer according to the data packet sending priority set, obtain the sent data packet set, and complete the real-time collaborative collection of cross-departmental carbon data based on edge computing based on the sent data packet set.

[0062] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0063] Memory, storing at least one instruction;

[0064] The processor executes the instructions stored in the memory to implement the aforementioned cross-departmental collaborative carbon data acquisition method based on edge computing.

[0065] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned cross-departmental collaborative carbon data acquisition method based on edge computing.

[0066] To address the problems described in the background art, this invention identifies a set of functional departments responsible for carbon emission sources. For each functional department within this set, edge computing nodes are deployed, resulting in a deployed node department set. This invention achieves comprehensive digital perception and coverage of carbon emission sources, ensuring the integrity of data collection from the source and laying the physical foundation for building a full life-cycle carbon data system. For each deployed node department in the deployed node department set, the following operations are performed: A multi-source heterogeneous parameter set is collected based on the deployed node department; a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set. This invention solves the data silo problem caused by inconsistent sensor protocols and data formats, achieving interconnection and deep fusion of multi-source data, greatly improving data quality and usability. Data packets from the energy sector are obtained based on the multi-source heterogeneous standard parameter set, and received... The collaborative optimization strategy instructions calculate data packet transmission priority scores and encrypted data packets based on energy sector data packets and the collaborative optimization strategy instructions. This invention generates high-value-density data products and provides decision-making basis for intelligent transmission through priority assessment. Encryption ensures the security and privacy of commercially sensitive data during transmission. By summarizing the data packet transmission priority scores and encrypted data packets, a set of data packet transmission priority scores and encrypted data packets corresponding to the deployed node department set is obtained. This invention forms a global transmission scheduling view, enabling the system to optimize network resource allocation from a holistic perspective. Based on the data packet transmission priority score set, the encrypted data packet set is transmitted to the pre-built cloud collaboration layer to obtain the sent data packet set. High-priority data is prioritized in this invention, effectively reducing its transmission latency and packet loss risk, and ensuring the real-time performance of critical business operations. Based on the sent data packet set, real-time collaborative collection of cross-departmental carbon data based on edge computing is completed. Therefore, this invention can improve the real-time performance, accuracy, and collaborative efficiency of carbon data collection. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a cross-departmental collaborative carbon data acquisition method based on edge computing, provided in an embodiment of the present invention.

[0068] Figure 2 This is a functional block diagram of a cross-departmental collaborative carbon data acquisition system based on edge computing, provided in an embodiment of the present invention.

[0069] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the cross-departmental collaborative carbon data acquisition method based on edge computing, according to an embodiment of the present invention.

[0070] Explanation of reference numerals in the attached figures:

[0071] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0072] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0074] This application provides a method for real-time collaborative acquisition of cross-departmental carbon data based on edge computing. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0075] Reference Figure 1 The diagram shown is a flowchart illustrating a cross-departmental collaborative carbon data acquisition method based on edge computing, according to an embodiment of the present invention. In this embodiment, the cross-departmental collaborative carbon data acquisition method based on edge computing includes:

[0076] S1. Identify the set of functional departments for carbon emission sources, and deploy edge computing nodes for each functional department in the set of functional departments for carbon emission sources to obtain the set of departments with deployed nodes.

[0077] It should be explained that the set of functional departments directly related to carbon emissions refers to the collection of all functional departments directly involved in carbon emissions. For example, these functional departments could be production departments (such as factories and workshops), energy management departments (such as electricity, gas, and heating systems), transportation departments (such as logistics and fleets), or building maintenance departments (such as HVAC and lighting systems). Edge computing node deployment refers to installing edge computing devices (such as industrial gateways and embedded servers) at the site of each carbon emission source functional department (such as factory workshops and building power distribution rooms) to achieve real-time local processing of sensor data and reduce cloud transmission latency. The set of departments with deployed nodes refers to the collection of all departments with deployed nodes.

[0078] S2. Perform the following operations on each deployed node department in the deployed node department set.

[0079] It should be explained that the deployed node departments refer to the carbon emission source functional departments that have completed the installation of edge computing nodes. These departments can output standardized parameters in real time (such as carbon emissions per unit output and energy consumption benchmark values) to provide a data closed loop for the group-level carbon neutrality path.

[0080] S3. Collect multi-source heterogeneous parameter sets based on the deployed node departments, and obtain multi-source heterogeneous standard parameter sets based on the multi-source heterogeneous parameter sets.

[0081] Specifically, the process of obtaining a multi-source heterogeneous standard parameter set based on a multi-source heterogeneous parameter set includes:

[0082] A set of data tuples is matched from a multi-source heterogeneous parameter set based on a pre-built association rule base. The set of data tuples includes multiple data tuples, and each data tuple includes independent variable values ​​and dependent variable values.

[0083] Data tuples are extracted sequentially from the data tuple set, and the target rule model is identified from the association rule base based on the extracted data tuples.

[0084] Substitute the independent variables from the extracted data tuples into the target rule model to obtain the expected value, and calculate the relative deviation based on the expected value and the dependent variable value from the extracted data tuples.

[0085] If the relative deviation is greater than the preset tolerance threshold, the independent variable value and dependent variable value in the extracted data tuple will be regarded as suspicious independent variable value and suspicious dependent variable value, respectively.

[0086] The confidence weights of the independent and dependent variables are obtained based on the values ​​of the questionable independent and dependent variables.

[0087] The values ​​of the calibrated dependent variable or calibrated independent variable are determined based on the confidence weights of the independent and dependent variables.

[0088] Based on the calibration dependent variable value or calibration independent variable value, the multi-source heterogeneous standard parameters are identified, and the multi-source heterogeneous standard parameters are summarized to obtain the multi-source heterogeneous standard parameter set.

[0089] It should be explained that the association rule base refers to a pre-built database containing multiple rule models that describe the causal relationships between parameters in a carbon emission scenario. For example, rule models include, but are not limited to, the product of boiler steam production and steam enthalpy being equal to the product of natural gas consumption, gas calorific value, and estimated efficiency. A data tuple set refers to data pairs matched from a multi-source heterogeneous parameter set that have association rules. The target rule model refers to the association rule corresponding to the current data tuple. The expected value refers to the numerical value obtained by substituting the independent variable value into the target rule model. The tolerance threshold refers to the maximum relative deviation between the pre-set expected value and the measured dependent variable value. The relative deviation is the value obtained by dividing the absolute difference between the expected value and the dependent variable value by the expected value. The independent variable confidence weight is a quantitative score of the reliability of the independent variable data source (e.g., a pump's ammeter). The dependent variable confidence weight is a quantitative score of the reliability of the dependent variable data source (e.g., a cooling water flow meter). All these quantitative scores are numerical measures of the reliability, accuracy, and stability of the data source equipment (sensors, instruments, or business systems). For example, if the cooling water flow meter was calibrated within the past month, the quantitative score is set to 0.95; if it was not calibrated within the past month, the quantitative score is set to 0.60. If the relative deviation exceeds a preset tolerance threshold, it indicates that two data points that should follow a strict correlation do not conform to known scientific laws or the normal operating state of the equipment. Therefore, the independent variable values ​​in the data tuple are considered suspicious independent variable values, and the dependent variable values ​​are considered suspicious dependent variable values. Association rules and confidence weights are used to correct the suspicious independent and dependent variable values, thereby ensuring the accuracy and reliability of subsequent carbon accounting data. Independent variable values ​​refer to the measured values ​​that serve as inputs or causes in the target rule model. Dependent variable values ​​refer to the measured values ​​that serve as outputs or results in the target rule model. For example, if the target rule model is: cooling water flow rate equals a coefficient multiplied by the pump operating current, then the pump operating current is the independent variable value, and the cooling water flow rate equals the dependent variable value.

[0090] Importantly, the step of identifying the calibrated dependent variable value or calibrated independent variable value based on the confidence weights of the independent and dependent variables is as follows: If the confidence weight of the independent variable is greater than the confidence weight of the dependent variable, then the suspected independent variable value is taken as the true independent variable value, and the true independent variable value is substituted into the target rule model to obtain the calibrated dependent variable value; if the confidence weight of the dependent variable is greater than the confidence weight of the independent variable, then the suspected dependent variable value is taken as the true dependent variable value, and the true dependent variable value is substituted into the target rule model to obtain the calibrated independent variable value. The true independent variable refers to a suspected independent variable value whose relative deviation is greater than the tolerance threshold and whose independent variable confidence weight is higher than the dependent variable confidence weight. The calibrated dependent variable value is the value calculated after substituting the true independent variable value into the target rule model. A multi-source heterogeneous standard parameter set refers to a collection composed of all multi-source heterogeneous standard parameters. When the system detects a numerical contradiction between pairs of data that should follow a target rule model, violating the established relationship, it automatically selects the optimal correction scheme based on the reliability of the data source: if the independent variable (causal data) has higher reliability, its true value is used as a benchmark to recalculate and calibrate the dependent variable (outcome data) through the target rule model; conversely, if the dependent variable has higher reliability, its true value is used as a benchmark to reverse-engineer and calibrate the independent variable data. This step achieves intelligent arbitration and self-repair of data contradictions.

[0091] S4. Obtain energy sector data packets based on multi-source heterogeneous standard parameter sets, receive collaborative optimization strategy instructions, and calculate data packet transmission priority scores and encrypted data packets based on energy sector data packets and collaborative optimization strategy instructions.

[0092] In detail, the acquisition of energy sector data packets based on multi-source heterogeneous standard parameter sets includes:

[0093] Multi-source heterogeneous standard parameters are extracted sequentially from the multi-source heterogeneous standard parameter set, and field mapping transformation operations are performed on the extracted multi-source heterogeneous standard parameters to obtain unified standard parameters;

[0094] The data energy type is determined based on unified standard parameters, and then matched with a pre-built carbon emission factor library to obtain the type carbon emission factor.

[0095] The unified standard parameters are transformed into unified dimensional parameters based on the type carbon emission factor. The carbon dioxide equivalent value is then calculated based on the unified dimensional parameters and the type carbon emission factor.

[0096] The carbon dioxide equivalent values ​​and data energy types are summarized separately to obtain the carbon dioxide equivalent value set and data energy type set corresponding to the multi-source heterogeneous standard parameter set. The total carbon emissions are calculated based on the carbon dioxide equivalent value set, where the total carbon emissions are the sum of multiple carbon dioxide equivalent values ​​in the carbon dioxide equivalent value set.

[0097] Obtain the total output of the deployed node departments, and calculate the carbon intensity per unit product based on the total carbon emissions and total output.

[0098] The carbon intensity of a unit product and the data energy type set are encapsulated to obtain an energy sector data package.

[0099] It should be explained that the field mapping and transformation operation refers to the operation of mapping the extracted multi-source heterogeneous standard parameters according to a preset field lookup table, thereby generating unified standard parameters with consistent structure. Unified standard parameters refer to energy consumption quantities that, after field mapping and transformation, have unified field names, unified data types, and can be directly used for carbon emission factor retrieval. Data energy type refers to the energy type corresponding to the data, including but not limited to: raw coal, washed coal, natural gas, diesel, fuel oil, electricity, heat, and biomass. The carbon emission factor library refers to a pre-built relational database stored on edge nodes, which records the lower heating value, carbon content per unit calorific value, and oxidation rate corresponding to various data energy types. The product of the lower heating value, carbon content per unit calorific value, and oxidation rate corresponding to various data energy types is calculated, and then the value of this product is converted into the molecular weight of carbon dioxide to obtain the carbon emission factor. Carbon content per unit calorific value refers to the carbon mass corresponding to each unit of lower heating value under standard conditions (0℃, 101.325kPa). Type carbon emission factor refers to the carbon emission factor corresponding to the confirmed data energy type, used as a coefficient to convert energy consumption into carbon dioxide emissions. Dimensional conversion refers to the process of converting the original units of measurement in the unified standard parameters into numerical values ​​matching the denominator units of the carbon emission factor, according to the units of measurement required by the type carbon emission factor, through fixed conversion relationships or lower heating values. Unified dimensional parameter refers to the energy consumption whose units of measurement are consistent with the dimensions of the corresponding type carbon emission factor after dimensional conversion. Carbon dioxide equivalent value is the value obtained by multiplying the unified dimensional parameter by the type carbon emission factor. The carbon dioxide equivalent value is used to convert the emissions of a certain greenhouse gas into equivalent carbon dioxide emissions based on its global warming potential. The carbon dioxide equivalent value set is the set of all carbon dioxide equivalent values. The data energy type set is the set of all data energy types. Total output refers to the physical quantity of qualified products read by the field programmable logic controller or manufacturing execution system within the corresponding accounting period for deployed node departments. Unit product carbon intensity is the value obtained by dividing total carbon emissions by total output. Energy sector data packets refer to data packets that encapsulate unit product carbon intensity, data energy type set, carbon dioxide equivalent value set, total carbon emissions, and timestamp information according to the JSON or MQTT protocol.

[0100] Importantly, the above steps aim to scientifically calculate total carbon emissions based on carbon dioxide equivalent values ​​from multi-source, heterogeneous raw carbon data through standardization, carbon emission factor matching, and dimensional unification. This is further combined with production data to generate unit product carbon intensity, and finally packaged into a structured energy sector data package. The purpose is to solve the problem of carbon data being difficult to directly integrate and accurately calculate due to its diverse sources and varying units, transforming disorganized basic parameters into measurable, comparable, and manageable decision-making information. The benefits of this approach are twofold: firstly, it achieves precise quantification and real-time calculation of carbon emissions, providing a reliable data foundation for carbon footprint tracking and emission reduction assessment; secondly, the generated standardized data package breaks down departmental data barriers, forming cross-departmental comparable carbon efficiency indicators linked to production performance, thus improving the refinement, scientific rigor, and collaborative level of corporate carbon management.

[0101] Specifically, the calculation of data packet transmission priority scores and encryption of data packets based on energy sector data packets and collaborative optimization strategy instructions includes:

[0102] The criticality level of the data was determined based on the energy sector's data package and collaborative optimization strategy instructions;

[0103] Based on the data criticality level, a matching is performed in a pre-built transmission strategy library to obtain the target transmission strategy, which includes: periodic transmission strategy, event-driven transmission strategy, or data aggregation transmission strategy.

[0104] Based on the target transmission strategy and the data packets obtained from the energy sector, compressed data packets are acquired, and data encryption is performed on the compressed data packets to obtain encrypted data packets. The data packet transmission priority score is determined according to the data criticality level.

[0105] It should be explained that the collaborative optimization strategy instruction refers to the instruction issued by the system to dynamically adjust the data upload behavior of deployed node departments in order to achieve the collaborative goal of optimal global carbon emissions and optimal communication resources. The transmission strategy library refers to a pre-built relational table placed in the non-volatile memory of the edge node. The target transmission strategy refers to the transmission strategy uniquely matched in the transmission strategy library based on the data criticality level of the current energy department data packet. The steps for obtaining compressed data packets based on the target transmission strategy are as follows: if the target transmission strategy is a periodic transmission strategy, the energy department data packet is stored in a pre-built periodic transmission buffer, and a pre-built periodic transmission timer is started to obtain the start time; when the start time reaches the preset periodic transmission interval, the energy department data packet is extracted from the periodic transmission buffer, and a data packet compression operation is performed to obtain a compressed data packet; if the target transmission strategy is an event-driven transmission strategy, the energy department data packet is treated as an emergency data packet, and a data packet compression operation is immediately triggered to obtain a compressed data packet. The periodic transmission buffer refers to a FIFO buffer area opened in the RAM of the edge node, used only for temporarily storing energy department data packets marked as periodic transmission strategies. The periodic transmission timer refers to a hardware timer created by the edge node operating system. The periodic transmission interval refers to the pre-set time interval for periodic transmission. The start time refers to the time corresponding to the start of the periodic transmission timer. The compressed data packet refers to the compressed data packet from the energy sector. The emergency data packet refers to the data packet from the energy sector corresponding to an event-driven transmission strategy. Performing data encryption on the compressed data packet refers to performing data encryption on the compressed data packet using a symmetric encryption algorithm. The encrypted data packet refers to the encrypted data packet, which serves as input for calculating the data packet transmission priority score. Through data encryption, the security and privacy of sensitive carbon data are ensured during transmission. Simultaneously, this invention automates the process from data criticality identification to transmission strategy execution, reducing the need for manual intervention.

[0106] It should be noted that this invention designs three differentiated transmission strategies to address different data types and business needs in cross-departmental carbon data collection: The periodic transmission strategy is suitable for stable, continuous routine monitoring data (such as environmental parameters), optimizing network and storage resources through timed batch transmission. The event-driven transmission strategy is specifically for abnormal or critical event data (such as energy consumption exceeding thresholds), ensuring real-time early warning through immediate transmission triggering. The data aggregation transmission strategy is geared towards high-frequency but low-value-density raw data (such as second-level vibration signals), significantly reducing network load by aggregating and refining data at the edge before transmission. These three strategies together constitute an adaptive intelligent transmission system, achieving a synergistic effect of prioritizing critical data, efficiently transmitting routine data, and optimizing the processing of massive amounts of data, thereby comprehensively improving system resource utilization efficiency while ensuring business real-time performance.

[0107] Specifically, the determination of data criticality level based on energy sector data packets and collaborative optimization strategy instructions includes:

[0108] Meta-information fields are parsed from the energy sector data packets. These fields include: data generation timestamp, data source department identifier, data energy type set, and carbon intensity per unit product.

[0109] Based on the collaborative optimization strategy instructions, obtain the current system time and historical unit product carbon intensity set, and calculate the criticality score and numerical anomaly score based on the data energy type set and historical unit product carbon intensity set.

[0110] The time offset is calculated based on the data generation timestamp and the current system time, and the timeliness sensitivity score is calculated based on the time offset.

[0111] The comprehensive criticality score is calculated based on a preset set of scoring weights, time sensitivity score, criticality score, and numerical anomaly score.

[0112] The criticality level of the data is determined based on the comprehensive criticality score.

[0113] It should be explained that the data generation timestamp refers to the time when the carbon intensity per unit product is calculated in the energy sector data packet, used to mark the actual generation time of the carbon intensity data. The data source department identifier is a UTF-8 string whose content matches the deployed node department number in the edge node registry, used to uniquely identify the carbon emission source functional department that generated the energy sector data packet. The current system time refers to the time corresponding to the edge node parsing the data packet. The time offset is the absolute difference between the data generation timestamp and the current system time. The weighted summation based on the energy type contribution weight set and the data energy type set refers to the operation of multiplying each element of the data energy type set within the energy sector data packet by its corresponding energy type contribution weight and then summing the results. The accounting criticality score is the value obtained after weighted summation. The higher the accounting criticality score, the higher the weight of the data packet in the overall carbon accounting. The historical unit product carbon intensity set refers to the set of all unit product carbon intensity values ​​stored by the edge node under the same data source department identifier within the past accounting period. The historical carbon intensity average and historical carbon intensity standard deviation refer to the average and standard deviation of the historical unit product carbon intensity set, respectively. The comprehensive criticality score refers to the numerical value obtained by linearly weighting the timeliness sensitivity score, accounting criticality score, and numerical anomaly score according to the score weight configuration set. The data criticality level refers to the result of mapping the comprehensive criticality score to discrete levels. For example, a comprehensive criticality score of [0, 40) is low, [40, 70) is medium, [70, 90) is high, and [90, 100] is urgent.

[0114] In detail, the calculation of criticality score and numerical anomaly score based on the data energy type set and historical unit product carbon intensity set includes:

[0115] The energy type contribution weight set is obtained based on the data energy type set. The energy type contribution weight set includes multiple energy type contribution weights, and the energy type contribution weights correspond one-to-one with the data energy types.

[0116] The criticality score is obtained by weighted summation based on the energy type contribution weight set and the data energy type set;

[0117] The historical average carbon intensity and the historical carbon intensity standard deviation are calculated based on the historical unit product carbon intensity set.

[0118] The standard deviation multiple is calculated based on the historical average carbon intensity and the historical carbon intensity standard deviation. The numerical anomaly score is then mapped based on the standard deviation multiple and a pre-constructed anomaly score table.

[0119] Importantly, the step of calculating the standard deviation multiple based on the historical average carbon intensity and the historical carbon intensity standard deviation is as follows: subtract the historical average carbon intensity from the unit product carbon intensity, and then divide the result by the historical carbon intensity standard deviation to obtain the standard deviation multiple. The step of mapping the numerical anomaly score based on the standard deviation multiple and a pre-constructed anomaly scoring table is as follows: find the scoring interval corresponding to the standard deviation multiple in the anomaly scoring table, and confirm the numerical anomaly score based on the scoring interval. The anomaly scoring table refers to a pre-constructed two-dimensional lookup table that includes multiple standard deviation multiple intervals. For example, if the standard deviation multiple is in [2,3), the numerical anomaly score is 60 points; if the standard deviation multiple is in [3,4), the numerical anomaly score is 80 points; and if the standard deviation multiple is greater than 4, the numerical anomaly score is 100 points. The scoring weight configuration set refers to the pre-set weights corresponding to the timeliness sensitivity score, the accounting criticality score, and the numerical anomaly score, and the sum of the weight configuration sets is 1. For example, the weight of the timeliness sensitivity score is 0.3, the weight of the accounting criticality score is 0.4, and the weight of the numerical anomaly score is 0.3.

[0120] It should be noted that the energy type contribution weight set refers to the collection of weights recording the impact of each data energy type on global carbon emissions. The energy type contribution weight is a value derived by experts based on multiple dimensions of factors, including emission factor levels, historical consumption proportions, policy regulatory intensity, and the urgency of emission reduction. This aims to scientifically quantify the actual impact of different energy sources on sectoral carbon emissions. Furthermore, this invention employs a weighted average method for calculation because this method accurately reflects the differentiated contributions of various energy sources to total emissions and possesses good business interpretability, computational stability, and management orientation—it conforms to the physical essence of carbon emission accounting and can adapt to adjustments in management priorities through flexible weight configuration, thus providing a quantitative basis that is both objective and adaptable for data criticality assessment.

[0121] It should be noted that the above steps are based on a weighted calculation of energy type contribution weights, objectively reflecting the actual importance of various energy data in carbon accounting. This invention integrates the accounting criticality score and numerical anomaly score, combined with a timeliness sensitivity score, and utilizes a preset comprehensive score weight configuration set to normalize multi-source, multi-dimensional data characteristics (importance, anomaly, urgency) into a unified, quantified comprehensive criticality score. Subsequently, the system maps this score to a specific data criticality level (e.g., urgent, high, medium, low) based on the threshold range it falls within. This criticality level directly determines which data transmission strategy (e.g., periodic transmission strategy, event-driven transmission strategy, or data aggregation transmission strategy) the system should intelligently select when data is about to be scheduled for transmission. This step provides a cross-departmental, objective priority evaluation standard for carbon data from all departments. Regardless of whether the data originates from the production workshop or the logistics department, the system assesses its transmission urgency using the same algorithm, thereby achieving optimal scheduling across the global network. Meanwhile, intelligent transmission ensures that critical data affecting overall carbon accounting and carbon quotas, as well as sudden abnormal alarm data, are delivered to the cloud collaboration layer with priority and in a timely manner. This enables the cloud to integrate key information more quickly, conduct cross-departmental carbon emission correlation analysis, early warning, and decision-making, while allowing scarce resources such as network bandwidth to be used to transmit the most valuable data, thereby improving the timeliness and efficiency of collaborative carbon data collection, analysis, and response.

[0122] In detail, the calculation of timeliness sensitivity score based on time offset includes:

[0123] Obtain the carbon allowance accounting period based on the data source department identification, and calculate the decay coefficient and critical period growth coefficient based on the carbon allowance accounting period;

[0124] The timeliness sensitivity score is calculated based on the time offset, decay coefficient, critical period growth coefficient, and carbon quota accounting cycle. The formula for calculating the timeliness sensitivity score is as follows:

[0125] ;

[0126] in, Indicates the timeliness sensitivity score. This indicates the preset maximum score. Indicates the attenuation coefficient. Indicates the time offset. Indicates the carbon quota accounting cycle. This represents the natural exponential function. This represents the preset critical period gain coefficient. This represents the growth coefficient during the critical period.

[0127] It should be explained that the carbon allowance accounting cycle refers to the period during which enterprises submit carbon allowance reports to the government or regulatory agencies. For example, the carbon allowance accounting cycle is 30 days. The scoring cap refers to the highest possible score for the timeliness sensitivity score, which is set in advance. By setting an upper limit for the timeliness sensitivity score, it ensures that the timeliness sensitivity scores of all data are within a uniform and comparable range. For example, the scoring cap is 100 points. The attenuation coefficient is a pre-set coefficient. The formula for calculating the attenuation coefficient based on the carbon allowance accounting cycle is as follows:

[0128] ;

[0129] in, This represents a logarithmic function. Before the end date of the carbon quota accounting cycle, the decay coefficient determines the rate at which the data value decreases over time. The larger the decay coefficient, the higher the rate of data timeliness decay. The critical period gain coefficient is a coefficient that immediately and significantly increases the baseline level of the timeliness sensitivity score after the data exceeds the carbon quota accounting cycle. The critical period gain coefficient is set as follows: first, the value constraint range is determined according to the carbon quota regulatory requirements; then, the reuse value of historical data is evaluated by statistically analyzing the reuse ratio and reuse frequency of expired data in the past 3 to 5 accounting cycles to obtain a baseline value; subsequently, it is fine-tuned based on the frequency and importance of data retrospective needs; finally, the fine-tuned value is substituted into the timeliness sensitivity scoring formula described in this application to verify the rationality of the score and determine a unique value, namely the critical period gain coefficient.

[0130] The critical period gain coefficient ensures that overdue data receives sufficient priority, reflecting the urgency of unprocessed data. The critical period growth coefficient adjusts the timeliness sensitivity score of overdue data. In the step of calculating the attenuation coefficient and critical period growth coefficient based on the carbon quota accounting cycle, the critical period growth coefficient is calculated by dividing 3 by the carbon quota accounting cycle. The shorter the carbon quota accounting cycle, the larger the critical period growth coefficient, and the faster the value of the data recovers after exceeding the accounting cycle.

[0131] Furthermore, This indicates that the carbon quota accounting cycle has not been exceeded from the time the data was generated to the present. The timeliness sensitivity score starts from the upper limit of the score and decreases exponentially as the time offset increases. This indicates that the data has exceeded the accounting period. This represents the fraction from data generation to the carbon quota accounting cycle, ensuring a smooth transition of the function from the regular period to the critical period. This means that once the data expires, this portion of the score starts to grow from 0 and eventually approaches the critical period gain coefficient. Once the deadline is exceeded, the function enters the critical reporting period (…). At this point, the timeliness sensitivity of the data spikes dramatically. Although the data is overdue, its timeliness sensitivity score exceeds the upper limit, indicating that the system will immediately transmit this overdue data with the highest priority. This is because avoiding regulatory consequences due to delayed data reporting becomes extremely important. The formula for calculating the timeliness sensitivity score quantifies the time urgency of the data and precisely maps it to a score that can be used for decision-making.

[0132] Specifically, determining the data packet transmission priority score based on the data criticality level includes:

[0133] Based on the data criticality level, a basic priority score is determined, and the current available network status indicator set is obtained. The current available network status indicator set includes: channel delay, available bandwidth, and packet loss rate.

[0134] The channel quality score is calculated based on channel delay and packet loss rate, and the bandwidth margin is calculated based on available bandwidth and preset historical peak bandwidth.

[0135] The channel state comprehensive score is calculated based on the channel quality score and bandwidth margin, and the corrected channel score is calculated based on the basic priority score and the channel state comprehensive score.

[0136] The survival time urgency coefficient is calculated based on the time offset and carbon quota accounting cycle. Based on the data source department identifier, a query is performed in the pre-constructed departmental collaboration relationship graph to obtain the downstream departmental dependency weight set. The overall departmental dependency is calculated based on the downstream departmental dependency weight set.

[0137] The packet transmission priority score is calculated based on the department's overall dependency, the corrected channel score, and the survival time urgency coefficient.

[0138] It should be explained that the basic priority score refers to the comprehensive criticality score corresponding to the data criticality level. Channel latency refers to the time required for a data packet to travel from the node to the gateway and back. Available bandwidth refers to the maximum upload speed currently available on the network. Packet loss rate is the ratio of the difference between the total number of probe packets sent by the edge node to the next-hop network address and the number of lost packets that were not acknowledged by the receiver within a statistical time window, to the total number of packets sent. The steps for calculating the channel quality score based on channel latency and packet loss rate are as follows: The latency score is calculated based on the channel latency, the preset maximum tolerable latency, and the preset standard channel latency, as shown in the following formula:

[0139] ;

[0140] in, Indicates delayed scoring. This represents a function that takes the maximum value. Indicates the maximum tolerable delay. Indicates channel delay. Indicates standard channel delay;

[0141] The packet loss rate score is calculated based on the packet loss rate, and the calculation formula is as follows:

[0142] ;

[0143] in, This indicates the packet loss rate score. This indicates the preset maximum tolerable packet loss rate. Indicates packet loss rate. This indicates the preset standard packet loss rate;

[0144] The latency score and packet loss rate score are multiplied to obtain the multiplied score. The square root of this multiplied score yields the channel quality score. The maximum tolerable packet loss rate refers to the pre-defined limit acceptable to the service. When the packet loss rate exceeds the maximum tolerable rate, it indicates that the network quality is too poor to effectively support the normal operation of the application. The standard packet loss rate is a pre-defined value representing the proportion of data packets lost under near-perfect, interference-free network conditions. The standard channel delay is the pre-defined minimum time required for a data packet to travel from the sender to the receiver. When the network delay equals the ideal delay, it means that data transmission has almost no waiting time, and the response is extremely fast. The maximum tolerable delay refers to the pre-defined limit of waiting time acceptable to the service. Exceeding this time makes data transmission too slow and unable to meet the real-time requirements of the service.

[0145] Furthermore, the channel state comprehensive score refers to the numerical value obtained by geometrically averaging the channel quality score and bandwidth margin. The formula for calculating the corrected channel score in the step of calculating the corrected channel score based on the basic priority score and the channel state comprehensive score is as follows:

[0146] ;

[0147] in, This indicates a corrected channel score. Indicates the basic priority score. This represents the comprehensive channel state score. The formula for calculating the survival time urgency coefficient in the step of calculating the survival time urgency coefficient based on the time offset and carbon quota calculation period is as follows:

[0148] ;

[0149] in, This represents the survival time urgency coefficient. The departmental collaboration relationship diagram is a directed weighted graph describing the data dependencies between departments. This graph includes multiple nodes and edges. Each node represents a carbon emission source functional department that has deployed nodes, and each edge corresponds to a weight, representing the strength of a department's data dependency on another department. The downstream department dependency weight set is the set composed of all downstream department dependency weights. The downstream department dependency weight represents the comprehensive dependency strength of a downstream department on data provided by upstream departments in three dimensions: business necessity, timeliness urgency, and impact breadth, when performing its regulated or critical internal carbon accounting and reporting tasks. This comprehensive dependency strength is achieved by integrating the dependency assessment results from multiple dimensions into a single, quantifiable, and comparable overall dependency value through preset rules (such as weighted calculation). The departmental comprehensive dependency degree is the sum of the downstream departmental dependency weight sets. The collaboration tightness weighting factor is the sum of the departmental comprehensive dependency degree and a preset base value (such as 1).

[0150] It should be noted that this invention dynamically adjusts data transmission priority by real-time sensing of channel quality and bandwidth conditions, enabling optimal allocation of network resources under different operating conditions. Building upon this, a survival time urgency coefficient is further introduced, transforming the timeliness value of data into a calculable priority parameter, thereby ensuring that critical data nearing its deadline receives priority processing. Simultaneously, by querying the departmental collaboration graph, the dependency weight set of all downstream departments to which the data packet belongs is obtained, and the overall departmental dependency of the data source department is calculated. This quantifies the cross-departmental collaborative value and business impact of data into a specific weighting factor, ensuring that core data affecting the decision-making and collaborative work of multiple downstream departments receives a priority weight in transmission scheduling commensurate with its global importance. Finally, through this multi-dimensional priority-driven intelligent scheduling mechanism, the transmission delay and packet loss risk of high-value data are effectively reduced, comprehensively ensuring the reliable end-to-end delivery of critical business data.

[0151] In detail, the calculation of the data packet transmission priority score based on departmental comprehensive dependency, modified channel score, and lifetime urgency coefficient includes:

[0152] The collaboration tightness weighting factor is calculated based on the department's comprehensive dependency. The ideal distance and negative ideal distance are calculated based on the basic priority score, the corrected channel score, the survival time urgency coefficient, and the collaboration tightness weighting factor.

[0153] Calculate the data packet transmission priority score based on the ideal distance and negative ideal distance.

[0154] It should be explained that bandwidth margin refers to the value obtained by dividing the available bandwidth by the historical peak bandwidth and then multiplying by 100%. The steps for calculating the ideal distance and negative ideal distance based on the basic priority score, corrected channel score, lifetime urgency coefficient, and coordination tightness weighting factor are as follows: The ideal distance is calculated as the sum of the Euclidean distances of the basic priority score, corrected channel score, lifetime urgency coefficient, and coordination tightness weighting factor with a preset maximum parameter set; the negative ideal distance is calculated as the sum of the Euclidean distances of the basic priority score, corrected channel score, lifetime urgency coefficient, and coordination tightness weighting factor with a preset minimum parameter set. The maximum parameter set includes: the maximum basic priority score, the maximum corrected channel score, the maximum lifetime urgency coefficient, and the maximum coordination tightness weighting factor. The minimum parameter set includes: the minimum basic priority score, the minimum corrected channel score, the minimum lifetime urgency coefficient, and the minimum coordination tightness weighting factor. Both the maximum and minimum parameter sets are preset parameter sets. The formula for calculating the data packet transmission priority score in the step of calculating the data packet transmission priority score based on the ideal distance and negative ideal distance is as follows:

[0155] ;

[0156] in, Indicates the priority score for data packet transmission. Indicates the negative ideal distance. Indicates the ideal distance.

[0157] S5. Summarize the data packet sending priority scores and encrypted data packets respectively to obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node department set.

[0158] It should be explained that the packet transmission priority score set refers to the set consisting of the transmission priority scores of all data packets. The encrypted data packet set refers to the set consisting of all encrypted data packets.

[0159] S6. Based on the data packet sending priority, transmit the encrypted data packet set to the pre-built cloud collaboration layer to obtain the sent data packet set.

[0160] It should be explained that the cloud-based collaboration layer refers to the core processing platform in the system architecture, which realizes the aggregation, processing, and analysis of cross-departmental carbon data through a distributed cloud service architecture. The sent data packet set refers to the encrypted data packet set that has been sent to the cloud-based collaboration layer. By building the cloud-based collaboration layer, unified management and collaborative analysis of cross-departmental carbon data are achieved, solving the data silo problem. Simultaneously, by clearly defining the status management of the sent data packet set, the reliability and traceability of data transmission are ensured, providing a technical foundation for the full lifecycle management of carbon data. This architecture supports horizontal scaling of the system and can adapt to the carbon data collection needs of enterprises of different sizes.

[0161] S7. Based on the sent data packet set, complete the real-time collaborative collection of cross-departmental carbon data using edge computing.

[0162] It should be explained that this invention constructs a complete intelligent carbon data acquisition and transmission system by systematically deploying edge computing nodes. Firstly, it achieves comprehensive digital perception of carbon emission sources and standardized processing of multi-source heterogeneous data, fundamentally solving the data silo problem. Secondly, by generating high-value-density energy data packages and implementing priority-based intelligent transmission scheduling, it achieves a shift from unified transmission to on-demand transmission, effectively ensuring the real-time performance and security of critical data. The resulting unified carbon data asset enables comprehensive cross-departmental carbon data flow, providing a high-quality, timely data foundation for enterprise carbon management and significantly improving the overall efficiency and accuracy of carbon management.

[0163] To address the problems described in the background art, this invention identifies a set of functional departments responsible for carbon emission sources. For each functional department within this set, edge computing nodes are deployed, resulting in a deployed node department set. This invention achieves comprehensive digital perception and coverage of carbon emission sources, ensuring the integrity of data collection from the source and laying the physical foundation for building a full life-cycle carbon data system. For each deployed node department in the deployed node department set, the following operations are performed: A multi-source heterogeneous parameter set is collected based on the deployed node department; a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set. This invention solves the data silo problem caused by inconsistent sensor protocols and data formats, achieving interconnection and deep fusion of multi-source data, greatly improving data quality and usability. Data packets from the energy sector are obtained based on the multi-source heterogeneous standard parameter set, and received... The collaborative optimization strategy instructions calculate data packet transmission priority scores and encrypted data packets based on energy sector data packets and the collaborative optimization strategy instructions. This invention generates high-value-density data products and provides decision-making basis for intelligent transmission through priority assessment. Encryption ensures the security and privacy of commercially sensitive data during transmission. By summarizing the data packet transmission priority scores and encrypted data packets, a set of data packet transmission priority scores and encrypted data packets corresponding to the deployed node department set is obtained. This invention forms a global transmission scheduling view, enabling the system to optimize network resource allocation from a holistic perspective. Based on the data packet transmission priority score set, the encrypted data packet set is transmitted to the pre-built cloud collaboration layer to obtain the sent data packet set. High-priority data is prioritized in this invention, effectively reducing its transmission latency and packet loss risk, and ensuring the real-time performance of critical business operations. Based on the sent data packet set, real-time collaborative collection of cross-departmental carbon data based on edge computing is completed. Therefore, this invention can improve the real-time performance, accuracy, and collaborative efficiency of carbon data collection.

[0164] like Figure 2 The diagram shown is a functional block diagram of a cross-departmental carbon data real-time collaborative acquisition system based on edge computing provided in an embodiment of the present invention.

[0165] The edge computing-based cross-departmental carbon data real-time collaborative acquisition system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the edge computing-based cross-departmental carbon data real-time collaborative acquisition system 100 may include an edge node deployment module 101, an energy data packet generation module 102, a transmission priority score calculation module 103, and a cloud collaborative transmission module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0166] The edge node deployment module 101 is used to identify the set of carbon emission source functional departments, and to deploy edge computing nodes for each carbon emission source functional department in the set of carbon emission source functional departments to obtain the set of deployed node departments.

[0167] The energy data packet generation module 102 is used to perform the following operations on each deployed node department in the deployed node department set: collect multi-source heterogeneous parameter sets according to the deployed node departments, and obtain multi-source heterogeneous standard parameter sets based on the multi-source heterogeneous parameter sets;

[0168] The sending priority score calculation module 103 is used to obtain energy sector data packets based on a multi-source heterogeneous standard parameter set, receive collaborative optimization strategy instructions, calculate data packet sending priority scores and encrypted data packets according to energy sector data packets and collaborative optimization strategy instructions, and summarize data packet sending priority scores and encrypted data packets respectively to obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node sector set.

[0169] The cloud-based collaborative transmission module 104 is used to transmit encrypted data packets to a pre-built cloud-based collaborative layer according to the data packet transmission priority set, thereby obtaining a set of sent data packets. Based on this set of sent data packets, real-time collaborative acquisition of cross-departmental carbon data based on edge computing is completed. Specifically, in this embodiment of the invention, each module in the cross-departmental collaborative acquisition system 100 based on edge computing adopts the same approach as described above. Figure 1 The method used is the same as the real-time collaborative acquisition method for cross-departmental carbon data based on edge computing described in the article, and can produce the same technical effect, so it will not be repeated here.

[0170] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a cross-departmental collaborative carbon data acquisition method based on edge computing, according to an embodiment of the present invention.

[0171] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a cross-departmental carbon data real-time collaborative acquisition method program based on edge computing.

[0172] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a cross-departmental carbon data real-time collaborative acquisition method program based on edge computing, but also to temporarily store data that has been output or will be output.

[0173] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a cross-departmental carbon data real-time collaborative acquisition method program based on edge computing) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0174] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0175] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0176] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0177] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0178] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0179] The program for a cross-departmental collaborative carbon data acquisition method based on edge computing, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0180] Once the set of functional departments for carbon emission sources is identified, edge computing nodes are deployed for each functional department of the carbon emission source set to obtain the set of departments with deployed nodes.

[0181] Perform the following operations for each deployed node department in the deployed node department set:

[0182] Based on the multi-source heterogeneous parameter set collected by the deployed node departments, a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set.

[0183] The system acquires data packets from the energy sector based on a multi-source heterogeneous standard parameter set, receives collaborative optimization strategy instructions, and calculates data packet transmission priority scores and encrypted data packets based on the energy sector data packets and collaborative optimization strategy instructions.

[0184] By summarizing the data packet sending priority scores and encrypted data packets respectively, we can obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node department set;

[0185] The encrypted data packet set is transmitted to the pre-built cloud collaboration layer according to the data packet sending priority score set to obtain the sent data packet set;

[0186] Real-time collaborative collection of cross-departmental carbon data based on edge computing was completed based on the sent data packet set.

[0187] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0188] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0189] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0190] Once the set of functional departments for carbon emission sources is identified, edge computing nodes are deployed for each functional department of the carbon emission source set to obtain the set of departments with deployed nodes.

[0191] Perform the following operations for each deployed node department in the deployed node department set:

[0192] Based on the multi-source heterogeneous parameter set collected by the deployed node departments, a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set.

[0193] The system acquires data packets from the energy sector based on a multi-source heterogeneous standard parameter set, receives collaborative optimization strategy instructions, and calculates data packet transmission priority scores and encrypted data packets based on the energy sector data packets and collaborative optimization strategy instructions.

[0194] By summarizing the data packet sending priority scores and encrypted data packets respectively, we can obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node department set;

[0195] The encrypted data packet set is transmitted to the pre-built cloud collaboration layer according to the data packet sending priority score set to obtain the sent data packet set;

[0196] Real-time collaborative collection of cross-departmental carbon data based on edge computing was completed based on the sent data packet set.

[0197] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0198] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0199] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0200] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cross-departmental collaborative method for real-time carbon data acquisition based on edge computing, characterized in that, The method includes: Once the set of functional departments for carbon emission sources is identified, edge computing nodes are deployed for each functional department of the carbon emission source set to obtain the set of departments with deployed nodes. Perform the following operations for each deployed node department in the deployed node department set: Based on the multi-source heterogeneous parameter set collected by the deployed node departments, a multi-source heterogeneous standard parameter set is obtained based on the multi-source heterogeneous parameter set. The system acquires data packets from the energy sector based on a multi-source heterogeneous standard parameter set, receives collaborative optimization strategy instructions, and calculates data packet transmission priority scores and encrypted data packets based on the energy sector data packets and collaborative optimization strategy instructions. By summarizing the data packet sending priority scores and encrypted data packets respectively, we can obtain the data packet sending priority score set and encrypted data packet set corresponding to the deployed node department set; The encrypted data packet set is transmitted to the pre-built cloud collaboration layer according to the data packet sending priority score set to obtain the sent data packet set; Real-time collaborative collection of cross-departmental carbon data based on edge computing was completed based on the sent data packet set.

2. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 1, characterized in that, The process of obtaining a multi-source heterogeneous standard parameter set based on a multi-source heterogeneous parameter set includes: A set of data tuples is matched from a multi-source heterogeneous parameter set based on a pre-built association rule base. The set of data tuples includes multiple data tuples, and each data tuple includes independent variable values ​​and dependent variable values. Data tuples are extracted sequentially from the data tuple set, and the target rule model is identified from the association rule base based on the extracted data tuples. Substitute the independent variables from the extracted data tuples into the target rule model to obtain the expected value, and calculate the relative deviation based on the expected value and the dependent variable value from the extracted data tuples. If the relative deviation is greater than the preset tolerance threshold, the independent variable value and dependent variable value in the extracted data tuple will be regarded as suspicious independent variable value and suspicious dependent variable value, respectively. The confidence weights of the independent and dependent variables are obtained based on the values ​​of the questionable independent and dependent variables. The values ​​of the calibrated dependent variable or calibrated independent variable are determined based on the confidence weights of the independent and dependent variables. Based on the calibration dependent variable value or calibration independent variable value, the multi-source heterogeneous standard parameters are identified, and the multi-source heterogeneous standard parameters are summarized to obtain the multi-source heterogeneous standard parameter set.

3. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 2, characterized in that, The acquisition of energy sector data packets based on multi-source heterogeneous standard parameter sets includes: Multi-source heterogeneous standard parameters are extracted sequentially from the multi-source heterogeneous standard parameter set, and field mapping transformation operations are performed on the extracted multi-source heterogeneous standard parameters to obtain unified standard parameters; The data energy type is determined based on unified standard parameters, and then matched with a pre-built carbon emission factor library to obtain the type carbon emission factor. The unified standard parameters are transformed into unified dimensional parameters based on the type carbon emission factor. The carbon dioxide equivalent value is then calculated based on the unified dimensional parameters and the type carbon emission factor. The carbon dioxide equivalent values ​​and data energy types are summarized separately to obtain the carbon dioxide equivalent value set and data energy type set corresponding to the multi-source heterogeneous standard parameter set. The total carbon emissions are calculated based on the carbon dioxide equivalent value set, where the total carbon emissions are the sum of multiple carbon dioxide equivalent values ​​in the carbon dioxide equivalent value set. Obtain the total output of the deployed node departments, and calculate the carbon intensity per unit product based on the total carbon emissions and total output. The carbon intensity of a unit product and the data energy type set are encapsulated to obtain an energy sector data package.

4. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 3, characterized in that, The calculation of data packet transmission priority scores and encrypted data packets based on energy sector data packets and collaborative optimization strategy instructions includes: The criticality level of the data was determined based on the energy sector's data package and collaborative optimization strategy instructions; The target transmission strategy is obtained by matching data based on its criticality level within a pre-built transmission strategy library. Based on the target transmission strategy and the data packets obtained from the energy sector, compressed data packets are acquired, and data encryption is performed on the compressed data packets to obtain encrypted data packets. The data packet transmission priority score is determined according to the data criticality level.

5. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 4, characterized in that, The determination of data criticality level based on energy sector data packets and collaborative optimization strategy instructions includes: Meta-information fields are parsed from the energy sector data packets. These fields include: data generation timestamp, data source department identifier, data energy type set, and carbon intensity per unit product. Based on the collaborative optimization strategy instructions, obtain the current system time and historical unit product carbon intensity set, and calculate the criticality score and numerical anomaly score based on the data energy type set and historical unit product carbon intensity set. The time offset is calculated based on the data generation timestamp and the current system time, and the timeliness sensitivity score is calculated based on the time offset. The comprehensive criticality score is calculated based on a preset set of scoring weights, time sensitivity score, criticality score, and numerical anomaly score. The criticality level of the data is determined based on the comprehensive criticality score.

6. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 5, characterized in that, The criticality score and numerical anomaly score calculated based on the data energy type set and historical unit product carbon intensity set include: The energy type contribution weight set is obtained based on the data energy type set. The energy type contribution weight set includes multiple energy type contribution weights, and the energy type contribution weights correspond one-to-one with the data energy types. The criticality score is obtained by weighted summation based on the energy type contribution weight set and the data energy type set; The historical average carbon intensity and the historical carbon intensity standard deviation are calculated based on the historical unit product carbon intensity set. The standard deviation multiple is calculated based on the historical average carbon intensity and the historical carbon intensity standard deviation. The numerical anomaly score is then mapped based on the standard deviation multiple and a pre-constructed anomaly score table.

7. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 6, characterized in that, The calculation of timeliness sensitivity score based on time offset includes: Obtain the carbon allowance accounting period based on the data source department identification, and calculate the decay coefficient and critical period growth coefficient based on the carbon allowance accounting period; The timeliness sensitivity score is calculated based on the time offset, decay coefficient, critical period growth coefficient, and carbon quota accounting cycle. The formula for calculating the timeliness sensitivity score is as follows: ； in, Indicates the timeliness sensitivity score. This indicates the preset maximum score. Indicates the attenuation coefficient. Indicates the time offset. Indicates the carbon quota accounting cycle. This represents the natural exponential function. This represents the preset critical period gain coefficient. This represents the growth coefficient during the critical period.

8. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 7, characterized in that, The process of determining the data packet transmission priority score based on the data criticality level includes: Based on the data criticality level, a basic priority score is determined, and the current available network status indicator set is obtained. The current available network status indicator set includes: channel delay, available bandwidth, and packet loss rate. The channel quality score is calculated based on channel delay and packet loss rate, and the bandwidth margin is calculated based on available bandwidth and preset historical peak bandwidth. The channel state comprehensive score is calculated based on the channel quality score and bandwidth margin, and the corrected channel score is calculated based on the basic priority score and the channel state comprehensive score. The survival time urgency coefficient is calculated based on the time offset and carbon quota accounting cycle. Based on the data source department identifier, a query is performed in the pre-constructed departmental collaboration relationship graph to obtain the downstream departmental dependency weight set. The overall departmental dependency is calculated based on the downstream departmental dependency weight set. The packet transmission priority score is calculated based on the department's overall dependency, the corrected channel score, and the survival time urgency coefficient.

9. The cross-departmental collaborative carbon data acquisition method based on edge computing as described in claim 8, characterized in that, The calculation of data packet transmission priority score based on departmental comprehensive dependency, corrected channel score, and lifetime urgency coefficient includes: The collaboration tightness weighting factor is calculated based on the department's comprehensive dependency. The ideal distance and negative ideal distance are calculated based on the basic priority score, the corrected channel score, the survival time urgency coefficient, and the collaboration tightness weighting factor. Calculate the data packet transmission priority score based on the ideal distance and negative ideal distance.

10. A cross-departmental collaborative carbon data acquisition system based on edge computing, characterized in that, The system includes: The edge node deployment module is used to identify the set of functional departments of carbon emission sources, and to deploy edge computing nodes for each functional department of carbon emission sources in the set of functional departments of carbon emission sources, so as to obtain the set of departments with deployed nodes. The energy data package generation module is used to perform the following operations on each deployed node department in the deployed node department set: collect multi-source heterogeneous parameter sets based on the deployed node departments, and obtain multi-source heterogeneous standard parameter sets based on the multi-source heterogeneous parameter sets; The priority score calculation module is used to obtain energy sector data packets based on a multi-source heterogeneous standard parameter set, receive collaborative optimization strategy instructions, calculate data packet transmission priority scores and encrypted data packets according to energy sector data packets and collaborative optimization strategy instructions, and summarize data packet transmission priority scores and encrypted data packets to obtain the data packet transmission priority score set and encrypted data packet set corresponding to the deployed node sector set. The cloud-based collaborative transmission module is used to transmit encrypted data packets to a pre-built cloud-based collaborative layer according to the data packet sending priority set, obtain the sent data packet set, and complete the real-time collaborative collection of cross-departmental carbon data based on edge computing based on the sent data packet set.

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