Carbon emission analysis method and system based on Internet of Things equipment, and medium

Abstract

The invention discloses a carbon emission analysis method and system based on Internet of Things equipment, and a medium, mainly relates to the technical field of carbon emission analysis, and is used for solving the problems that an existing scheme lacks a unified standard and is difficult to integrate across systems, an accounting method of static emission factors is relatively large in error, the third-party checking cost is high, and the efficiency is low. Comprising the following steps: determining a specific encryption transmission mode of acquisition equipment according to software and hardware information of the acquisition equipment, and further transmitting carbon emission calculation data to an edge calculation node; the cloud server issues calculation formulas of a plurality of input data related to the current carbon emission analysis model to each edge calculation node; the edge calculation node substitutes the carbon emission calculation data into a corresponding calculation formula to obtain specific input data; and uploading the specific input data to a cloud server, and inputting the specific input data into a carbon emission analysis model to obtain carbon emission analysis data of the current carbon emission equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission analysis, and in particular to a carbon emission analysis method and system based on Internet of Things devices and a medium. BACKGROUND

[0002] At present, the acquisition of enterprise carbon emission data mainly relies on sampling monitoring or industry average emission factor estimation, which has problems such as data lag, low precision, and insufficient credibility. With the tightening of global carbon emission reduction policies (such as carbon tariff CBAM and carbon market expansion), enterprises urgently need more accurate, real-time, and verifiable carbon emission data to support carbon accounting, carbon trading, and green certification.

[0003] Traditional sensors can collect some data, but lack unified standards, resulting in prominent data island problems and difficulty in cross-system integration. In addition, the accounting method based on static emission factors cannot reflect the actual production fluctuations of enterprises, and the error is large, and third-party verification relies on periodic on-site audits, which is costly and inefficient. The existing technology faces challenges such as data lag, insufficient precision, and low credibility, and cannot meet the increasingly stringent carbon regulation and carbon market trading needs. SUMMARY

[0004] The present application provides a carbon emission analysis method and system based on Internet of Things devices and a medium to solve the problems of lack of unified standards, difficulty in cross-system integration, large error of static emission factor accounting method, and high cost and low efficiency of third-party verification in existing solutions.

[0005] In a first aspect, the present application provides a carbon emission analysis method based on Internet of Things devices, the method comprising: collecting carbon emission calculation data; wherein the collection device comprises: an internal monitoring component of the carbon emission device and an external sensor of the carbon emission device; determining the corresponding edge computing node according to the specific location of the collection device; determining the specific encryption transmission method of the collection device according to the software and hardware information of the collection device, and then transmitting the carbon emission calculation data to the edge computing node; the cloud server downloads the calculation formula of a plurality of input data involved in the current carbon emission analysis model to each edge computing node; the edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data; uploads the specific input data to the cloud server, and then inputs the carbon emission analysis model to obtain the carbon emission analysis data of the current carbon emission device.

[0006] In an implementation manner of the present application, the corresponding edge computing node is determined according to the specific location of the collection device, specifically comprising: obtain all edge computing nodes corresponding to the collection devices involved in the same carbon emission device; Taking the cumulative sum and minimum value of the distance between each collection device and the edge computing node as an objective function, an edge computing node corresponding to the minimum value of the objective function is determined from all edge computing nodes as the edge computing node corresponding to all collection devices related to the same carbon emission device.

[0007] In an implementation manner of the present application, before the edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data, the method further comprises: The edge computing node performs data preprocessing on the carbon emission calculation data; The preprocessed carbon emission calculation data is input into a preset data credibility evaluation model, an integrity evaluation model, and an accuracy evaluation model to obtain data evaluation data; wherein the data evaluation data includes credibility, integrity, and accuracy; The emission calculation data with credibility less than a preset credibility threshold, integrity less than a preset integrity threshold, or accuracy less than a preset accuracy threshold is deleted; All emission calculation data and data evaluation data are written into a preset data quality report, and the data quality report is shared to the data quality report.

[0008] In an implementation manner of the present application, after obtaining the carbon emission analysis data of the current carbon emission device, the method further comprises: The carbon emission analysis data and the data quality report are displayed through a preset interface.

[0009] In an implementation manner of the present application, after the cloud server distributes the calculation formula of several input data related to the current carbon emission analysis model to each edge computing node, the method further comprises: When it is detected that the carbon emission analysis model is updated, the input data of the updated carbon emission analysis model is obtained; When the current and the previous input data are consistent, it is continued to detect whether the carbon emission analysis model is updated; When the current and the previous input data are inconsistent, the port of the updated carbon emission analysis model is obtained, the calculation formula update requirement is distributed to the corresponding port, and the current calculation formula is updated to the feedback calculation formula.

[0010] In a second aspect, the present application provides a carbon emission analysis system based on Internet of Things devices, which comprises: A collection module is configured to collect carbon emission calculation data; wherein the collection device comprises an internal monitoring component of the carbon emission device and an external sensor of the carbon emission device; A transmission module is configured to determine the corresponding edge computing node according to the specific location of the collection device, determine the specific encryption transmission mode of the collection device according to the software and hardware information of the collection device, and then transmit the carbon emission calculation data to the edge computing node. a cloud server configured to distribute a calculation formula of a plurality of input data involved in a current carbon emission analysis model to each edge computing node; an edge computing node configured to substitute carbon emission calculation data into the corresponding calculation formula to obtain specific input data, and upload the specific input data to the cloud server, and then input the carbon emission analysis model to obtain carbon emission analysis data of the current carbon emission device.

[0011] In an implementation form of the present application, the transmission module comprises a device determination unit, configured to obtain all edge computing nodes corresponding to the collection devices involved in the same carbon emission device; The objective function is the cumulative sum of the distances between each collection device and the edge computing node, and the edge computing node corresponding to the minimum value of the objective function is determined as the edge computing node corresponding to all collection devices involved in the same carbon emission device.

[0012] In an implementation form of the present application, the edge computing node comprises a data processing unit, configured to perform data preprocessing on the carbon emission calculation data by the edge computing node; The preprocessed carbon emission calculation data is input into a preset data credibility evaluation model, an integrity evaluation model and an accuracy evaluation model to obtain data evaluation data; wherein the data evaluation data comprises credibility, integrity and accuracy; The emission calculation data with credibility less than a preset credibility threshold, integrity less than a preset integrity threshold or accuracy less than a preset accuracy threshold is deleted; All emission calculation data and data evaluation data are written into a preset data quality report, and the data quality report is shared to the data quality report.

[0013] In an implementation form of the present application, the cloud server comprises a detection update unit, configured to obtain input data of the updated carbon emission analysis model when detecting that the carbon emission analysis model exists update; If the current and updated input data are consistent, continue to detect whether the carbon emission analysis model exists update behavior; If the current and updated input data are inconsistent, obtain the port of the updated carbon emission analysis model, distribute the calculation formula update demand to the corresponding port, and update the current calculation formula to the feedback calculation formula.

[0014] In a third aspect, the present application provides a non-volatile computer storage medium having computer instructions stored thereon, the computer instructions being executed to implement a carbon emission analysis method based on an Internet of Things device according to any one of the above.

[0015] From the above technical solution, the present application has the following advantages: The present application solves the problem of traditional sensor data island by unifying the data collection standard of Internet of Things equipment (internal monitoring components + external sensor cooperation). The edge computing node automatically matches the encryption transmission mode according to the device location and hardware and software characteristics, ensures the format standardization of heterogeneous data in the transmission layer, and provides a structured data basis for cloud integration. The cloud server uniformly issues calculation formulas to the edge computing node, forces all input data to follow the same calculation logic, and eliminates the differences between systems caused by inconsistent accounting rules. This "collection-transmission-computation" full-process standardization design enables carbon emission data from different manufacturer devices and different production systems to directly participate in model analysis, meeting the demand of real-time comparison of multi-source data in the carbon market.

[0016] Real-time dynamic data (such as device operating parameters, energy consumption fluctuations, etc.) is collected through Internet of Things equipment, replacing the static emission factor estimation method. The edge computing node performs formula calculation on site, dynamically substitutes actual working condition parameters (such as instantaneous load rate, fuel heat value, etc.) into the model, and improves the accuracy of carbon emission calculation to the device level and minute granularity. At the same time, the encryption transmission and automatic calculation process form an auditable data chain, and third-party verification can replace on-site audit by calling cloud historical calculation logs, shortening the traditional monthly / quarterly verification period to daily level, and reducing labor costs. The cloud model continuously receives actual data feedback from the edge node, and can also dynamically optimize the calculation formula parameters, forming a "monitoring-computation-calibration" closed loop, further reducing systematic errors. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a carbon emission analysis method flowchart provided by an embodiment of the present application based on Internet of Things equipment.

[0019] Figure 2 is an internal structure diagram of a carbon emission analysis system based on Internet of Things equipment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0021] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not represent that the present disclosure can only be implemented by the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative effort still fall within the protection scope of the present disclosure.

[0022] It should be further noted that the terms "comprising", "containing" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0023] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0024] The embodiments provide a carbon emission analysis method based on an Internet of Things device, as shown in Figure 1 The method provided by the embodiments of the present application mainly includes the following steps: Step 110, collecting carbon emission calculation data.

[0025] The collecting device includes: an internal monitoring component of the carbon emission device and an external sensor of the carbon emission device.

[0026] In some embodiments, the internal monitoring component collects examples: Taking a coal-fired power plant as an example, the internal monitoring component can directly obtain real-time data from the boiler control system, including: Fuel consumption (such as coal input per minute, unit: tons / min); Combustion temperature (Celsius temperature value monitored by the furnace thermocouple); Steam flow (flow meter data at the turbine inlet, unit: m³ / h).

[0027] These data are transmitted directly to the edge computing node through the built-in OPC-UA protocol of the device.

[0028] External sensor supplementary collection example: In the same scenario, the external sensor is responsible for making up for the dimensions not covered by the internal system: The NDIR infrared sensor is installed at the flue gas outlet to monitor the CO2 concentration in real time (unit: ppm); The flue gas flowmeter records the exhaust volume flow (unit: m³ / s); The environmental temperature and humidity sensor corrects the gas volume conversion parameters.

[0029] Step 120, according to the specific location of the collection device, determine the corresponding edge computing node; according to the software and hardware information of the collection device, determine the specific encryption transmission mode of the collection device, and then transmit the carbon emission calculation data to the edge computing node.

[0030] Among them, according to the specific location of the collection device, determine the corresponding edge computing node, specifically including: Obtain all edge computing nodes corresponding to the collection devices involved in the same carbon emission device; Taking the cumulative sum of the distance between each collection device and the edge computing node as the objective function, determine the edge computing node corresponding to the minimum value of the objective function from all edge computing nodes as the edge computing node corresponding to all collection devices involved in the same carbon emission device.

[0031] Based on the above description, this step reduces transmission delay and network load by dynamically matching edge computing nodes. Specifically, according to the location of the collection device, the edge node with the minimum cumulative distance is selected to ensure that the data of the associated sensors of the same carbon emission device (such as boiler temperature probes and flue gas analyzers) are gathered to the same computing node, reducing the time overhead caused by cross-node communication. For example, when there are three edge nodes around a coal-fired unit, the system automatically selects the node that minimizes the sum of (sensor A to node distance + sensor B to node distance), avoiding the timing error caused by data fragmentation transmission. In view of the heterogeneity of devices, by identifying the sensor hardware model (such as RS485 interface flowmeter) and software protocol (such as Modbus-TCP), the AES-256 or national SM4 encryption strategy is dynamically adapted, which not only meets the high security requirement scene (such as chemical enterprise explosion-proof area data), but also considers the energy efficiency balance of low-power devices (such as LoRa temperature and humidity sensor). This position-aware and security policy adaptive design ensures that the transmission path from the source to the edge node is always in the optimal state, providing low-latency and high-integrity data foundation for subsequent real-time carbon accounting, while avoiding the single-point failure risk commonly seen in traditional centralized transmission.

[0032] Step 130, the cloud server distributes the calculation formula of the current carbon emission analysis model involving several input data to each edge computing node.

[0033] After the cloud server distributes the calculation formula of the current carbon emission analysis model involving several input data to each edge computing node, the method further comprises: When it is detected that the carbon emission analysis model is updated, the input data of the updated carbon emission analysis model is obtained; When the current and the latter input data are consistent, it is continued to detect whether the carbon emission analysis model is updated; When the current and the latter input data are inconsistent, the port of the updated carbon emission analysis model is obtained, the calculation formula update demand is distributed to the corresponding port, and the current calculation formula is updated to the feedback calculation formula.

[0034] Based on the above description, this step dynamically distributes the calculation formula to the edge computing node through the cloud server, and establishes a model update detection mechanism, realizing the flexibility and consistency guarantee of the carbon emission analysis system. The core lies in: through centralized management and differential update of the calculation formula, the data accuracy and model timeliness in the distributed computing environment are ensured. When the model is updated, the system intelligently judges whether the formula update process needs to be triggered by comparing the consistency of the input data before and after, avoiding invalid repeated calculation and resource waste. For example, if the carbon emission model of a coal-fired power plant adds a sulfur oxide conversion rate parameter, the cloud will identify the change of the input data (such as adding a flue gas desulfurization efficiency monitoring item), and only push the updated calculation formula to the associated edge node (such as the node equipped with a desulfurization tower sensor), while the unaffected node (such as the node only monitoring CO2 concentration) maintains the original calculation logic. This differential update strategy not only reduces the network transmission overhead, but also ensures that each node always performs calculation methods matching the latest model. At the same time, through the port directional update mechanism, the formula change range can be accurately controlled, preventing system instability risks caused by global forced update. This design is suitable for industrial scenes with complex production processes and scattered monitoring points, can maintain the continuous comparability of carbon accounting data in the model iteration process, and significantly reduces the calculation resource occupancy rate of the edge.

[0035] Step 140, the edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data; uploads the specific input data to the cloud server, and then inputs the carbon emission analysis model to obtain the carbon emission analysis data of the current carbon emission device.

[0036] In some embodiments, before the edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data, the method further comprises: The edge computing node performs data preprocessing on the carbon emission calculation data; The pre-processed carbon emission calculation data is input into a preset data credibility evaluation model, a completeness evaluation model, and an accuracy evaluation model to obtain data evaluation data; the data evaluation data includes credibility, completeness, and accuracy; The emission calculation data with credibility less than a preset credibility threshold, completeness less than a preset completeness threshold, or accuracy less than a preset accuracy threshold is deleted. All the emission calculation data and the data evaluation data are written into a preset data quality report, and the data quality report is shared to the data quality report.

[0037] Based on the above description, this step performs data preprocessing and quality evaluation through an edge computing node, improving the reliability and calculation efficiency of carbon emission analysis results. The core technical advantages are reflected in three aspects: first, completing data preprocessing (such as time alignment of sensor data and unit standardization) on the edge side can reduce cloud computing load; second, through the three evaluation models of credibility, completeness, and accuracy (for example, using Markov chain to detect data continuity to judge completeness, and verifying accuracy based on historical error range of equipment), abnormal data caused by sensor failure or communication interference (such as sudden drift data of a thermocouple) can be actively filtered; finally, a two-dimensional report containing original data and quality evaluation results is generated, which not only retains data traceability ability (such as tracing the sensor ID and timestamp of a batch of excluded data), but also provides quantitative basis for subsequent model optimization (such as triggering calibration warning when the average credibility of a certain area sensor group is less than 0.7). This design ensures that the input data uploaded to the cloud meets the preset quality threshold (such as completeness > 90%), ensuring the stability of the carbon emission model input layer, while through the edge-cloud collaborative computing architecture, the data transmission volume of typical industrial scenarios is reduced under the premise of maintaining analysis accuracy.

[0038] In addition, after obtaining the carbon emission analysis data of the current carbon emission device, the method can further include: The carbon emission analysis data and the data quality report are displayed through a preset interface.

[0039] Based on the foregoing description, it can be known that the embodiment realizes the improvement of real-time and accuracy of carbon accounting in an industrial scene by constructing a complete carbon emission data collection, transmission, calculation and analysis closed loop. In the data collection stage, the internal monitoring component and the external sensor are used to work together to ensure the comprehensiveness and complementarity of the data source. The internal component directly obtains the core parameters of the combustion process through the device native interface (such as OPC-UA), and the sampling frequency can reach seconds, and the data delay is controlled within milliseconds; the external sensor is used to supplement the monitoring of the exhaust port and the environmental parameters. This dual-source collection mode effectively solves the monitoring blind area problem that may exist in the traditional single data source. In the data transmission link, the edge node dynamic selection algorithm based on the geographical position optimizes the data transmission time delay in the typical scene. At the same time, the encryption strategy adapted to the device type prolongs the battery life of the low-power devices such as LoRa under the premise of ensuring data security.

[0040] In addition, resource optimization is realized through a layered calculation architecture. The data preprocessing work completed by the edge node includes local processing such as cumulative calculation of the pulse signal of the coal flow meter and conversion of the millivolt signal of the thermocouple into a standard temperature value, which makes the cloud server only need to process lightweight data. The model updating mechanism adopts a differentiated "on-demand push" strategy, which reduces the network bandwidth occupation during model version iteration in the application of a certain steel enterprise, and no data interruption event occurs due to formula update. The data quality evaluation system sets multiple threshold values (such as setting the integrity threshold of the flue gas flow data to 95%). The carbon emission analysis data and quality report generated finally are presented through a visual interface, supporting multi-dimensional data extraction and analysis, for example, a user of a certain chemical plant can compare the carbon emission intensity difference of different teams through the time axis, or count the data qualified rate according to the equipment type.

[0041] In addition, the present application Figure 2 A carbon emission analysis system based on Internet of Things devices is provided for the embodiment of the present application. As shown in Figure 2 The system provided by the embodiment of the present application mainly comprises: The acquisition module 210 is used to acquire carbon emission calculation data; wherein the acquisition device comprises: an internal monitoring component of a carbon emission device and an external sensor of the carbon emission device.

[0042] The transmission module 220 is used to determine the corresponding edge calculation node according to the specific position of the acquisition device; determine the specific encryption transmission mode of the acquisition device according to the software and hardware information of the acquisition device, and then transmit the carbon emission calculation data to the edge calculation node.

[0043] The transmission module 220 comprises a device determination unit, used to obtain all edge calculation nodes corresponding to the acquisition devices related to the same carbon emission device; Determine the edge computing node corresponding to the minimum value of the objective function from all edge computing nodes as the objective function of the cumulative sum of the distance between each collection device and the edge computing node.

[0044] The cloud server 230 is configured to distribute the calculation formula of the input data involved in the current carbon emission analysis model to each edge computing node.

[0045] The cloud server 230 includes a detection updating unit, for detecting when the carbon emission analysis model is updated, obtaining the input data of the updated carbon emission analysis model; When the current and previous input data are consistent, continue to detect whether the carbon emission analysis model is updated; When the current and previous input data are inconsistent, obtain the port of the updated carbon emission analysis model, distribute the calculation formula update requirement to the corresponding port, and update the current calculation formula to the feedback calculation formula.

[0046] The edge computing node 240 is configured to substitute the carbon emission calculation data into the corresponding calculation formula to obtain specific input data, upload the specific input data to the cloud server, and then input the carbon emission analysis model to obtain the carbon emission analysis data of the current carbon emission device.

[0047] The edge computing node 240 includes a data processing unit, for data preprocessing of the carbon emission calculation data by the edge computing node; input the preprocessed carbon emission calculation data into a preset data credibility evaluation model, an integrity evaluation model, and an accuracy evaluation model to obtain data evaluation data; wherein the data evaluation data includes credibility, integrity, and accuracy; delete the emission calculation data with credibility less than a preset credibility threshold, integrity less than a preset integrity threshold, or accuracy less than a preset accuracy threshold; write all emission calculation data and data evaluation data into a preset data quality report, and share the data quality report to the data quality report.

[0048] In addition, the embodiment of the present application also provides a non-volatile computer storage medium having executable instructions stored thereon, which, when executed, implement a carbon emission analysis method based on an Internet of Things device as described above.

[0049] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the appended claims are intended to cover all such modifications that do not depart from the true spirit and scope of the application. Therefore, the application is not limited to the embodiments shown but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A carbon emission analysis method based on Internet of Things devices, characterized in that: The method comprises: Collecting carbon emission calculation data; wherein the collection equipment includes: internal monitoring components of the carbon emission equipment and external sensors of the carbon emission equipment; Determine the corresponding edge computing node based on the specific location of the collection device; determine the specific encryption transmission method of the collection device based on the software and hardware information of the collection device, and then transmit the carbon emission calculation data to the edge computing node; The cloud server sends the calculation formulas of several input data involved in the current carbon emission analysis model to each edge computing node; The edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data; the specific input data is uploaded to the cloud server, and then input into the carbon emission analysis model to obtain the carbon emission analysis data of the current carbon emission equipment.

2. The carbon emission analysis method based on Internet of Things devices according to claim 1 is characterized in that: Determine the corresponding edge computing node based on the specific location of the acquisition device, including: Obtain all edge computing nodes corresponding to the collection devices involved in the same carbon emission device; The minimum value of the cumulative sum of the distances between each collection device and the edge computing node is used as the objective function, and the edge computing node corresponding to the minimum value of the objective function is determined from all edge computing nodes as the edge computing node corresponding to all collection devices involved in the same carbon emission device.

3. The carbon emission analysis method based on Internet of Things devices according to claim 1 is characterized in that: Before the edge computing node substitutes the carbon emission calculation data into the corresponding calculation formula to obtain specific input data, the method further includes: The edge computing nodes pre-process the carbon emission calculation data; Input the pre-processed carbon emission calculation data into a preset data credibility assessment model, integrity assessment model, and accuracy assessment model to obtain data assessment data; wherein the data assessment data includes: credibility, integrity, and accuracy; Delete emission calculation data whose credibility is less than a preset credibility threshold, whose completeness is less than a preset completeness threshold, or whose accuracy is less than a preset accuracy threshold; Write all emission calculation data and data assessment data into the preset data quality report, and share the data quality report to the data quality report.

4. The carbon emission analysis method based on Internet of Things devices according to claim 3 is characterized in that: After obtaining the carbon emission analysis data of the current carbon emission equipment, the method further includes: Carbon emission analysis data and data quality reports are displayed through a preset interface.

5. The carbon emission analysis method based on Internet of Things devices according to claim 1 is characterized in that: After the cloud server sends the calculation formulas for the input data involved in the current carbon emission analysis model to each edge computing node, the method further includes: When it is detected that the carbon emission analysis model has been updated, the input data of the updated carbon emission analysis model is obtained; When the current and subsequent input data are consistent, continue to check whether the carbon emission analysis model has updated behavior; When the current and subsequent input data are inconsistent, obtain the port for updating the carbon emission analysis model, send the calculation formula update request to the corresponding port, and update the current calculation formula to the feedback calculation formula.

6. A carbon emission analysis system based on Internet of Things devices, characterized in that: The system comprises: A collection module is used to collect carbon emission calculation data; wherein the collection equipment includes: an internal monitoring component of the carbon emission equipment and an external sensor of the carbon emission equipment; The transmission module is used to determine the corresponding edge computing node based on the specific location of the collection device; determine the specific encryption transmission method of the collection device based on the software and hardware information of the collection device, and then transmit the carbon emission calculation data to the edge computing node; The cloud server is used to send the calculation formulas of several input data involved in the current carbon emission analysis model to each edge computing node; The edge computing node is used to substitute the carbon emission calculation data into the corresponding calculation formula to obtain specific input data; the specific input data is uploaded to the cloud server, and then input into the carbon emission analysis model to obtain the carbon emission analysis data of the current carbon emission equipment.

7. The carbon emission analysis system based on Internet of Things devices according to claim 6 is characterized in that: The transmission module includes a device determination unit, Used to obtain all edge computing nodes corresponding to the collection devices involved in the same carbon emission device; The minimum value of the cumulative sum of the distances between each collection device and the edge computing node is used as the objective function, and the edge computing node corresponding to the minimum value of the objective function is determined from all edge computing nodes as the edge computing node corresponding to all collection devices involved in the same carbon emission device.

8. The carbon emission analysis system based on Internet of Things devices according to claim 6 is characterized in that: The edge computing node includes a data processing unit, Used to pre-process carbon emission calculation data through edge computing nodes; Input the pre-processed carbon emission calculation data into a preset data credibility assessment model, integrity assessment model, and accuracy assessment model to obtain data assessment data; wherein the data assessment data includes: credibility, integrity, and accuracy; Delete emission calculation data whose credibility is less than a preset credibility threshold, whose completeness is less than a preset completeness threshold, or whose accuracy is less than a preset accuracy threshold; Write all emission calculation data and data assessment data into the preset data quality report, and share the data quality report to the data quality report.

9. The carbon emission analysis system based on Internet of Things devices according to claim 6 is characterized in that: The cloud server includes a detection and update unit, When an update of the carbon emission analysis model is detected, the input data of the updated carbon emission analysis model is obtained; When the current and subsequent input data are consistent, continue to check whether the carbon emission analysis model has updated behavior; When the current and subsequent input data are inconsistent, obtain the port for updating the carbon emission analysis model, send the calculation formula update request to the corresponding port, and update the current calculation formula to the feedback calculation formula.

10. A non-volatile computer storage medium, characterized in that Computer instructions are stored thereon, and when the computer instructions are executed, they implement a carbon emission analysis method based on an Internet of Things device as described in any one of claims 1 to 5.

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