Carbon emission monitoring method based on Internet of Things

Abstract

The invention discloses a carbon emission monitoring method based on the Internet of Things, and relates to the technical field of data monitoring. Comprising the steps of collecting multi-dimensional carbon emission data through sensor networks deployed on different carbon emission sources; an edge computing device is configured at each monitoring node, and primary analysis of real-time multi-dimensional carbon emission data is carried out; the multi-dimensional carbon emission data processing module is used for processing the multi-dimensional carbon emission data, uploading the processed data to a cloud end through the Internet of Things communication module and managing by adopting a distributed storage system, and a cloud end server adopts a distributed node storage mode and dynamically schedules storage resources; multi-dimensional carbon emission data are integrated at the cloud end, secondary analysis is performed on carbon emission based on primary analysis, and a monitoring result is obtained. Through cooperative work of the sensor network, the edge computing device and the cloud server, real-time acquisition, compression, uploading and deep analysis of carbon emission data are realized, and the problems of uneven computing resource distribution, insufficient data analysis and the like in the prior art are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a carbon emission monitoring method based on the Internet of Things. Background Art

[0002] In recent years, with the increasing severity of global environmental problems, the demand for carbon emission monitoring technology has gradually increased, especially in high-emission fields such as industry and energy. Real-time and accurate carbon emission monitoring has become an important means to address climate change. Traditional carbon emission monitoring mainly relies on centralized monitoring systems, which usually require the deployment of a large number of sensor devices and the collection of data through manual or semi-automatic methods. This model has certain limitations, especially in terms of the real-time nature of the data and the depth of analysis, which makes it difficult to meet modern environmental protection requirements.

[0003] Existing carbon emission monitoring technologies often lack intelligent and hierarchical processing capabilities during data processing. Specifically, (1) the amount of monitoring data is large, the real-time processing pressure is high, and the data processing capacity of edge devices is insufficient, resulting in data delays or loss. (2) Most systems are limited to one-time data collection and analysis, making it difficult to conduct multi-dimensional and multi-level in-depth analysis, and unable to timely detect potential carbon emission anomalies and trends. (3) In the process of cloud data storage and computing, due to the uneven allocation of storage resources, existing technologies are difficult to dynamically manage and schedule resources, thus affecting the overall efficiency of the system. In addition, the lack of effective data compression and segmented processing mechanisms also exacerbates the system's load pressure. Summary of the invention

[0004] In view of the problems existing in the data processing process of the existing carbon emission monitoring technology, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to perform intelligent carbon emission monitoring.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a carbon emission monitoring method based on the Internet of Things, which includes collecting multidimensional carbon emission data through a sensor network deployed on different carbon emission sources; configuring an edge computing device at each monitoring node to perform a primary analysis of the real-time multidimensional carbon emission data; processing the multidimensional carbon emission data, uploading the processed data to the cloud through an Internet of Things communication module, managing it with a distributed storage system, and the cloud server adopting a distributed node storage mode to dynamically schedule storage resources; integrating the multidimensional carbon emission data in the cloud, performing a secondary analysis of carbon emissions based on the primary analysis, and obtaining monitoring results.

[0008] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the primary analysis of real-time multi-dimensional carbon emission data includes the following steps: the edge computing device ensures the synchronous reception and integration of multi-source data through a multi-channel data interface; uses the moving median filtering method to detect and clean up outliers in the multi-dimensional data collected in real time; introduces a data compression algorithm in the edge computing device to compress the collected monitoring data in real time, and only uploads data that has significantly changed compared with the previous time point through a differential algorithm; at the same time, the edge computing device performs hierarchical management of the data according to the set threshold.

[0009] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the one-time analysis of real-time multi-dimensional carbon emission data also includes: the edge computing device performs hierarchical management of the data according to the set threshold value.

[0010] As a preferred solution of the carbon emission monitoring method based on the Internet of Things of the present invention, wherein: uploading the processed data to the cloud through the Internet of Things communication module includes: extracting the data information collected by the Internet of Things device, uploading the data in segments, and recording any data as D i , the time threshold corresponding to any data is recorded as T i , the size threshold is recorded as S i , define the data piecewise function DS(D i , T i S i ), where DS represents the data segmentation operation. The specific segmentation rules are as follows: If data D i The size is greater than S i , then press S i Segmentation; if data D i The time span is greater than T i , then press T i Segmentation is performed; otherwise, segmentation is not performed; the segmented data is uploaded to the cloud.

[0011] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the cloud server adopts a distributed node storage mode, and dynamically schedules storage resources, including: when the amount of uploaded data continues to increase, the cloud server automatically schedules resources by monitoring the load of each storage node. If the storage capacity of a node is close to the upper limit, the system will dynamically expand new nodes; the cloud adopts a data redundancy mechanism to store copies of data on different physical nodes.

[0012] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the automatic resource scheduling includes: iThe load condition is monitored and the load monitoring function LM(n i )Evaluate the comprehensive load of the node:

[0013] LM(n i )=ω1×C(n i )+ω2×T(n i )+ω3×IO(n i )+ω4×R(n i );

[0014] Among them, ω1~ω4 are weight parameters of different load factors, C(n i ) is node n i Storage utilization rate, T(n i ) is node n i Data throughput, IO (n i ) is node n i I / O operation load, R(n i ) is node n i response time; set the load threshold T load , when LM(n i )>T load , triggers resource scheduling.

[0015] As a preferred solution of the carbon emission monitoring method based on the Internet of Things of the present invention, the automatic resource scheduling also includes: when the load of a certain node exceeds the load threshold T load When the overall load is unbalanced, the system relieves the pressure by adding new storage nodes. The expanded storage node set N' represents a new node n added to the existing storage node set N. k+1 The following collection:

[0016] N'=N∪{n k+1};

[0017] Where N′ represents the expanded storage node set. After expanding the new node, the system needs to migrate some data to the new node to achieve load balancing. The amount of data migrated D mi口 Determine based on the load difference of the nodes:

[0018] D mig =α1×(L(n i )-L avg );

[0019] Among them, α1 is the data migration coefficient, which is used to control the ratio of migration speed and data volume, L avg is the average load of all nodes.

[0020] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the secondary analysis of carbon emissions based on the primary analysis includes the following steps: multiple cloud nodes work together, using distributed computing to accelerate the prediction and analysis of equipment operation trends, and for the carbon emissions of large industrial equipment, adding a cluster prediction model, the future emission trend prediction of the equipment is expressed by the following formula:

[0021]

[0022] in, is the predicted future emissions, P(t) is the current emissions, β i is the weight of historical data, and E is the influence of environmental parameters.

[0023] As a preferred solution of the carbon emission monitoring method based on the Internet of Things described in the present invention, the secondary analysis of carbon emissions based on the primary analysis also includes a linkage and automatic response mechanism based on real-time and historical data and anomaly detection algorithms. When an abnormal carbon emission of the equipment is detected, the system not only triggers an alarm, but also performs intelligent analysis of the potential causes; wherein the standardized function of anomaly detection is expressed as:

[0024]

[0025] Among them, A(t) represents the standardized abnormal degree of carbon emissions, P(t) is the current emission, μp and σp are the mean and standard deviation of historical emission data respectively; when A(t) exceeds the set threshold, an abnormal alarm and emergency response are triggered.

[0026] The beneficial effects of the present invention are as follows: the present invention realizes the real-time collection, compression, uploading and in-depth analysis of carbon emission data through the collaborative work of sensor networks, edge computing devices and cloud servers, effectively solving the problems of uneven distribution of computing resources and insufficient data analysis in the prior art. At the same time, the present invention adopts a combination of primary analysis and secondary analysis, which can accurately predict future carbon emission trends and make intelligent responses to abnormal situations, thereby forming a complete monitoring closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 Flowchart of the carbon emission monitoring method based on the Internet of Things. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1

[0033] Reference Figure 1 , which is the first embodiment of the present invention, and provides a carbon emission monitoring method based on the Internet of Things, comprising the following steps:

[0034] S1: Through the sensor network deployed on different carbon emission sources such as industrial equipment, transportation facilities, energy facilities, etc., multi-dimensional carbon emission related data are collected, including but not limited to carbon dioxide concentration, equipment operating status, ambient temperature, humidity, meteorological conditions, etc.

[0035] Typical carbon emission sources include but are not limited to industrial equipment (such as boilers, generators, etc.), transportation facilities (such as vehicles, port facilities), and energy facilities (such as power plants, gas stations, etc.). The carbon emission characteristics of these facilities are defined according to different scenarios and emission characteristics.

[0036] The sensors used include but are not limited to gas sensors (used to monitor the concentration of carbon dioxide or other greenhouse gases in the air (such as carbon monoxide, methane, etc.)), temperature and humidity sensors (monitoring the temperature and humidity of equipment or environment, and analyzing the impact of temperature and humidity on carbon emissions), meteorological sensors (monitoring meteorological conditions such as wind speed, wind direction, atmospheric pressure, etc., and evaluating the impact of the environment on the diffusion and distribution of carbon emissions), and equipment operation status sensors (through sensors installed on industrial equipment, real-time collection of equipment operating parameters such as power, speed, fuel consumption, etc., to determine the carbon emissions of the equipment when it is working).

[0037] Furthermore, sensors should be placed as close as possible to the core locations of carbon emissions (such as exhaust ports and near combustion chambers) to ensure data accuracy; for transportation facilities, the main channels of vehicle exhaust emissions and the direction of air flow should be considered; for large-scale energy facilities, sensors should be distributed in different areas of emission sources to capture emission data from different equipment; and the distribution density of sensors can be optimized by simulating airflow and emission diffusion paths.

[0038] It should be noted that in order to ensure the consistency and accuracy of data from different sensors, unified calibration is required during the deployment process, and the sensor network collects carbon emission-related data in real time through wireless or wired means, and each sensor node uploads monitoring data in real time or at regular intervals.

[0039] S2: Configure edge computing devices at each monitoring node to perform a one-time analysis of real-time data.

[0040] Specifically, the edge computing device contained in each monitoring node receives data streams from multiple sensors in real time. These data streams include multi-dimensional carbon emission-related data such as CO2 concentration, temperature, humidity, air pressure, and equipment power.

[0041] The specific receiving process includes:

[0042] The CO2 concentration comes from the gas sensor, which indicates the carbon dioxide content per unit volume of air; the temperature and humidity sensor feeds back the temperature and humidity information of the environment to help analyze its impact on carbon emissions; the air pressure is provided by the meteorological sensor, combined with other meteorological data to evaluate the diffusion effect of the environment on carbon emissions; data such as equipment power and operating status come from the equipment status sensor to analyze the emission characteristics of equipment under different working conditions.

[0043] Edge computing devices ensure the synchronous reception and integration of multi-source data through efficient multi-channel data interfaces, preventing analysis errors caused by data delays or asynchrony.

[0044] Optionally, a moving median filtering method is used to detect and clean up outliers in multi-dimensional data collected in real time.

[0045] Furthermore, a data compression algorithm is introduced into the edge computing device to compress the collected monitoring data in real time, and only upload the data that has changed significantly compared with the previous time point through the differential algorithm. At the same time, the edge computing device manages the data in a hierarchical manner according to the set threshold, and uploads the regular data to the cloud for centralized storage and analysis, while the abnormal data is uploaded immediately for early warning.

[0046] It should be noted that when edge computing devices receive new data, they compare it with the data at the previous time point through a differential algorithm and only upload data that has changed significantly compared to the previous data. This method greatly reduces the redundancy and bandwidth occupancy of uploaded data and improves the transmission efficiency of the system.

[0047] According to the pre-set thresholds of different emission indicators, edge computing devices manage data in real time and in a hierarchical manner. For regular data below the threshold, edge devices upload it to the cloud at a lower frequency as basic data for long-term monitoring and trend analysis. If significant changes in data exceeding the threshold are detected (such as a sudden increase in CO2 concentration, abnormal equipment power, etc.), data will be uploaded immediately to trigger the abnormal warning system.

[0048] Furthermore, the edge computing device conducts preliminary trend analysis on the received monitoring data locally, such as the rate of change of CO2 concentration, the correlation between equipment operating status and carbon emissions, etc. By analyzing the data changes within a local time period, potential abnormal trends in carbon emissions can be identified at an early stage.

[0049] S3: Process the multi-dimensional carbon emission data, upload the processed data to the cloud through the Internet of Things communication module, and manage it using a distributed storage system. The cloud server adopts a distributed node storage mode and dynamically schedules storage resources.

[0050] S3.1: Upload the data.

[0051] In order to cope with network fluctuations or transmission failures, when uploading data, the data is segmented by time or size to ensure that even when the network is interrupted, the unuploaded part can continue to be uploaded after the network connection is restored.

[0052] Specifically, extract the data information collected by the IoT devices, upload the data in segments, and record any data as D i , the time threshold corresponding to any data is recorded as T i , the size threshold is recorded as S i , define the data piecewise function DS(D i , T i S i ), where DS represents data segmentation operation, and the specific segmentation rules are as follows:

[0053] If the data D i The size is greater than S i , then press S i To segment;

[0054] If the data D i The time span is greater than T i , then press T i To segment;

[0055] Otherwise, no segmentation is performed.

[0056] Upload the segmented data to the cloud.

[0057] S3.2: Distributed architecture.

[0058] In order to ensure the reliability and scalability of cloud data storage, the cloud server adopts a distributed storage management form, stores data through multiple physical nodes, and each node undertakes a part of the storage task. It has high availability and load balancing functions and can cope with the upload and storage needs of large amounts of monitoring data.

[0059] The data uploaded to the cloud is partitioned and stored according to different dimensions, such as geographic location, device type, data type (such as CO2 concentration, temperature, humidity, etc.). This partitioning strategy facilitates subsequent data retrieval and analysis.

[0060] For example, data uploaded by traffic facilities may be assigned to the "traffic facility area", and data from industrial equipment to the "industrial equipment area", which are further subdivided according to time, equipment ID, etc. Each data area is also divided into the "real-time data area" (such as abnormal alarm data) and the "regular data area" (such as regular monitoring data) according to the importance of the data, and managed with different priorities.

[0061] S3.3: Monitor and regulate the load.

[0062] As the amount of uploaded data continues to increase, the cloud server automatically schedules resources by monitoring the load of each storage node. If the storage capacity of a node approaches the upper limit, the system will dynamically expand new nodes to ensure that data storage is not limited by space.

[0063] In order to prevent data loss caused by single point failure, the cloud uses a data redundancy mechanism to store copies of data on different physical nodes. For example, data can be stored on two or more nodes at the same time, ensuring that even if a node fails, the system can still quickly restore data through other nodes.

[0064] Specifically, the load monitoring and resource scheduling of the distributed storage system is carried out. The specific process is as follows:

[0065] For storage node n i The load condition is monitored and the load monitoring function LM(n i )Evaluate the comprehensive load of the node:

[0066] LM(n i )=ω1×C(n i )+ω2×T(n i )+ω3×IO(ni )+ω4×R(n i );

[0067] Among them, ω1~ω4 are weight parameters of different load factors, C(n i ) is node n i Storage utilization rate (such as the proportion of node capacity occupied), T(n i ) is node n i Data throughput (amount of data read and written per unit time), IO (n i ) is node n i I / O operation load (e.g., number of read and write requests per second), R (n i ) is node n i Response time (average time to process read and write requests).

[0068] Set the load threshold T load , when LM(n i )>T load When the load of a node exceeds the load threshold T load When the overall load is unbalanced, the system will relieve the pressure by adding new storage nodes. The expanded storage node set N' represents a new node n added to the existing storage node set N. k+1 The following collection:

[0069] N'=N∪{n k+1};

[0070] Wherein, N′ represents the expanded storage node set.

[0071] After expanding the new node, the system needs to migrate some data to the new node to achieve load balancing. The amount of data to be migrated is D mig It can be determined based on the load difference of the nodes:

[0072] D mig =α1×(L(n i )-L avg );

[0073] Among them, α1 is the data migration coefficient, which is used to control the ratio of migration speed and data volume, L avg is the average load of all nodes.

[0074] S3.4: Perform data security testing.

[0075] Optionally, the system conducts disaster recovery tests regularly to simulate node failure or data loss scenarios to ensure that data can be quickly restored in an emergency and reduce business interruption time. At the same time, the cloud supports automated fault detection and repair functions, which can automatically transfer and restore data when a node has a problem, reducing manual intervention.

[0076] Specifically, first, select some storage nodes for data security testing to ensure that the data storage and business operations of the production environment are not affected during the test. The same storage conditions and data traffic as the actual production environment can be simulated in the test environment; and before performing data security testing, ensure that the data in the storage system is fully backed up. In this way, even if data is damaged or lost during the test, it can still be restored through the backup to avoid actual data loss.

[0077] Artificially simulate hardware or software failures of storage nodes, such as power outages, disk damage, or network interruptions. The response speed of the disaster recovery system to detect node failures verifies whether the system can detect node failures in a timely manner and automatically trigger data recovery mechanisms.

[0078] After detecting a node failure, check whether the system can automatically transfer the affected data from the failed node to the backup node to ensure that data availability is not affected. This process should include data synchronization and integrity verification to ensure that the transferred data is consistent with the original data.

[0079] After the simulated faulty node is repaired, verify whether its connection with the system can be automatically restored, and transmit the latest data copy back to the node through data synchronization to ensure the normal operation of the node after the repair. Perform integrity check on the data in each storage node, use hash algorithm (such as SHA-256) to generate data checksum, and regularly compare the hash value of the stored data to ensure that the data has not been tampered with or damaged.

[0080] S4: Integrate carbon emission data of different dimensions in the cloud, conduct secondary analysis on carbon emissions based on the primary analysis, and obtain monitoring results.

[0081] According to the emission characteristics and real-time conditions of different equipment, the data sampling frequency is intelligently adjusted. For example, for high-emission equipment or high-risk periods, the system automatically increases the data collection frequency; while in the stable operation stage, the sampling frequency can be reduced to reduce the pressure on data transmission and storage.

[0082] S4.1: Enhance the adaptive capabilities of machine learning algorithms based on one-time analysis, allowing automatic adjustment of the parameters of the prediction model based on updates to historical data and changes in real-time equipment status.

[0083] Specifically, multiple cloud nodes work together to use distributed computing to accelerate predictive analysis of device operation trends.

[0084] For carbon emissions from large industrial equipment, adding a cluster prediction model allows the system to mine interrelated information from data from different equipment, thereby improving overall prediction accuracy.

[0085] Among them, the future emission trend prediction of the equipment can be expressed by the following formula:

[0086]

[0087] in, is the predicted future emissions, P(t) is the current emissions, β i is the weight of historical data, and E is the influence of environmental parameters.

[0088] S4.2: Detect carbon emission anomalies.

[0089] Based on real-time and historical data, a new intelligent anomaly detection algorithm is added, and it is linked with the automatic response mechanism. When abnormal carbon emissions of equipment are detected, the system not only triggers an alarm, but also intelligently analyzes the potential causes and provides optimization solutions. For example, the system can adjust the operating parameters of the equipment to reduce energy consumption and carbon emissions.

[0090] Specifically, the standardized function of anomaly detection is expressed as:

[0091]

[0092] Among them, A(t) represents the standardized abnormal degree of carbon emissions, P(t) is the current emissions, μp and σp are the mean and standard deviation of historical emission data, respectively.

[0093] When A(t) exceeds the set threshold, an abnormal alarm and emergency response are triggered.

[0094] After each abnormal carbon emission incident, the system automatically analyzes the emergency response effect, learns and records historical processing experience. The next time a similar incident occurs, the system will prioritize solutions that have worked better in the past, thus forming a self-learning closed-loop feedback system.

[0095] S4.3: After the secondary analysis, the system will feed back the analysis results to the equipment management system to form an automated optimization closed loop. For example, when the system detects that the carbon emissions of a certain device are too high, it analyzes the reasons (such as equipment aging, excessive operating power, etc.) and feeds back to the equipment management module. The system automatically adjusts the operation strategy to reduce emissions, or prompts maintenance personnel to perform maintenance operations.

[0096] This embodiment also provides a computer device, which is suitable for the carbon emission monitoring method based on the Internet of Things, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the carbon emission monitoring method based on the Internet of Things proposed in the above embodiment.

[0097] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0098] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the carbon emission monitoring method based on the Internet of Things proposed in the above embodiment is implemented.

[0099] In summary, the present invention realizes the real-time collection, compression, uploading and in-depth analysis of carbon emission data through the collaborative work of sensor networks, edge computing devices and cloud servers, effectively solving the problems of uneven distribution of computing resources and insufficient data analysis in the prior art. At the same time, the present invention adopts a combination of primary analysis and secondary analysis, which can accurately predict future carbon emission trends and make intelligent responses to abnormal situations, thus forming a complete monitoring closed loop.

[0100] Example 2

[0101] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a carbon emission monitoring method based on the Internet of Things. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0102] In order to verify the effectiveness of the carbon emission monitoring method based on the Internet of Things of the present invention, a medium-sized industrial park was selected as a test scene. The park includes multiple carbon emission sources, such as industrial equipment such as boilers and generators, and transportation facilities such as forklifts and trucks. The sensor network is deployed on these typical carbon emission sources, mainly monitoring the carbon dioxide concentration, ambient temperature, humidity, equipment operation status, meteorological conditions, etc. of the equipment. The deployed sensors include: gas sensors, temperature and humidity sensors, meteorological sensors, and equipment operation status sensors, ensuring the full collection of carbon emission-related data.

[0103] When installing sensors, we focus on core areas close to emission sources, such as boiler flues and vehicle exhaust outlets, to ensure the accuracy of monitoring data. On industrial equipment, the frequency of data collection is set to once every 10 seconds, and is transmitted to edge computing devices in real time via wireless networks. The sensor network is uniformly calibrated to ensure data consistency among sensors.

[0104] Edge computing devices are deployed at each monitoring node to process the collected multi-dimensional carbon emission data in real time. The moving median filter method is used to clean up data outliers, and only the data with significant changes is uploaded through the differential algorithm, thereby reducing data redundancy and improving system efficiency. For example, when the CO2 concentration of the boiler suddenly increases from 400ppm (parts per million) to 500ppm, the edge computing device immediately uploads the data and triggers an abnormal warning. For conventional carbon emission data, the equipment is classified according to the preset threshold, and only abnormal data is uploaded immediately.

[0105] Through the IoT communication module, the processed data is uploaded to the cloud and managed using a distributed storage system. Segmented data upload avoids the impact of network fluctuations and ensures that even when the network is interrupted, the unuploaded data can continue to be uploaded after recovery. In addition, the cloud server dynamically monitors the load of each storage node and schedules resources to prevent transmission delays or failures caused by data overload.

[0106] During the experiment, a preliminary analysis was conducted on the correlation between the equipment operating status and CO2 emissions, such as the rate of increase in CO2 emission concentration when the boiler power increases, and the impact of weather changes on emission diffusion. Through preliminary trend analysis of edge devices, possible carbon emission anomalies can be effectively identified. For example, in vehicle exhaust emissions, when the ambient temperature changes significantly, the volatility of CO2 emission concentration increases significantly.

[0107] Table 1 Part of the experimental data

[0108]

[0109] It can be seen from the table that there is an obvious correlation between the CO2 emission concentration and other parameters of various carbon emission sources. For example, the CO2 concentrations of boiler 1 and boiler 2 are 450ppm and 600ppm respectively, and their powers are 500kW and 600kW respectively, indicating that the increase in boiler power significantly increases carbon emissions. In addition, the emission data of forklifts and trucks also show the emission characteristics of transportation facilities under different load conditions. Among them, the CO2 concentration of truck 1 reaches 800ppm when the power is 300kW.

[0110] In the changes in temperature and humidity, it can be observed that the CO2 concentration emitted by equipment with higher humidity (such as boiler 2 and truck 1) is also correspondingly higher.

[0111] Temperature changes have a significant impact on equipment emissions, especially in the state of generator 1, where CO2 concentration changes significantly due to fluctuations in ambient temperature, indicating that the effects of temperature and humidity on carbon emissions are obvious. Abnormal equipment status (such as boiler 2 and generator 1) further increases CO2 emissions, as shown by a more significant increase in emission concentration when power fluctuates and equipment is abnormal.

[0112] It can be seen that compared with the existing technology, the present invention can efficiently manage and provide real-time warning for multi-dimensional data of carbon emission sources, reduce the burden of data transmission and storage, and dynamically adjust the monitoring strategy according to the environment and equipment status.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A carbon emission monitoring method based on the Internet of Things, characterized in that: include, Collect multi-dimensional carbon emission data through a sensor network deployed on different carbon emission sources; Configure edge computing devices at each monitoring node to conduct a one-time analysis of real-time multi-dimensional carbon emission data; Process the multi-dimensional carbon emission data, upload the processed data to the cloud through the Internet of Things communication module, and manage it using a distributed storage system. The cloud server adopts a distributed node storage mode and dynamically schedules storage resources; Integrate multi-dimensional carbon emission data in the cloud, conduct secondary analysis of carbon emissions based on primary analysis, and obtain monitoring results.

2. The carbon emission monitoring method based on the Internet of Things as claimed in claim 1, characterized in that: The analysis of real-time multi-dimensional carbon emission data includes the following steps: Edge computing devices use multi-channel data interfaces to ensure the synchronous reception and integration of multi-source data; Use the moving median filter method to detect and clean up outliers in multi-dimensional data collected in real time; A data compression algorithm is introduced into the edge computing device to compress the collected monitoring data in real time. Through the differential algorithm, only the data with significant changes compared with the previous time point is uploaded.

3. The carbon emission monitoring method based on the Internet of Things as claimed in claim 2, characterized in that: The analysis of real-time multi-dimensional carbon emission data also includes: Edge computing devices manage data in different levels according to set thresholds.

4. The carbon emission monitoring method based on the Internet of Things as claimed in claim 3, characterized in that: Uploading the processed data to the cloud via the Internet of Things communication module includes: Extract the data information collected by IoT devices, upload the data in segments, and record any data as D i , the time threshold corresponding to any data is recorded as T i , the size threshold is recorded as S i , define the data piecewise function DS(D i ,T i S i ), where DS represents data segmentation operation, and the specific segmentation rules are as follows: If the data D i The size is greater than S i , then press S i To segment; If the data D i The time span is greater than T i , then press T i To segment; Otherwise, no segmentation is performed; Upload the segmented data to the cloud.

5. The carbon emission monitoring method based on the Internet of Things as claimed in claim 4, characterized in that: The cloud server adopts a distributed node storage mode, and the dynamic scheduling of storage resources includes: When the amount of uploaded data continues to increase, the cloud server automatically schedules resources by monitoring the load of each storage node. If the storage capacity of a node approaches the upper limit, the system will dynamically expand new nodes; The cloud uses a data redundancy mechanism to store copies of data on different physical nodes.

6. The carbon emission monitoring method based on the Internet of Things as claimed in claim 5, characterized in that: The automatic resource scheduling includes: For storage node n i The load condition is monitored and the load monitoring function LM(n i )Evaluate the comprehensive load of the node: LM(n i )=ω1×C(n i )+ω2×T(n i )+ω3×IO(n i )+ω4×R(n i ): Among them, ω1~ω4 are weight parameters of different load factors, C(n i ) is node n i Storage utilization rate, T(n i ) is node n i Data throughput, IO (n i ) is node n i I / O operation load, R(n i ) is node n i Response time; Set the load threshold T load , when LM(n i )>T load , triggers resource scheduling.

7. The carbon emission monitoring method based on the Internet of Things according to claim 6, characterized in that: The automatic resource scheduling also includes: When the load of a node exceeds the load threshold T load When the overall load is unbalanced, the system relieves the pressure by adding new storage nodes. The expanded storage node set N' represents a new node n added to the existing storage node set N. k+1 The following collection: N‘=N∪{n k+1 }; Wherein, N' represents the expanded storage node set; After expanding the new node, the system needs to migrate some data to the new node to achieve load balancing; Amount of data migrated mig Determine based on the load difference of the nodes: D mig =α1×(L(n i )-L avg ); Among them, α1 is the data migration coefficient, which is used to control the ratio of migration speed and data volume, L avg is the average load of all nodes.

8. The carbon emission monitoring method based on the Internet of Things according to claim 7, characterized in that: The secondary analysis of carbon emissions based on the primary analysis comprises the following steps: Multiple cloud nodes work together, using distributed computing to accelerate the predictive analysis of equipment operation trends, and add cluster prediction models for carbon emissions from large industrial equipment.

9. The carbon emission monitoring method based on the Internet of Things as claimed in claim 8, characterized in that: The predictive analysis of the running trend of the distributed computing acceleration device is expressed by the following formula: in, is the predicted future emissions, P(t) is the current emissions, β i is the weight of historical data, and E is the influence of environmental parameters.

10. The carbon emission monitoring method based on the Internet of Things according to claim 9, characterized in that: The secondary analysis of carbon emissions based on the primary analysis also includes: Based on real-time and historical data and anomaly detection algorithms, linkage and automatic response mechanisms, when abnormal carbon emissions from equipment are detected, the system not only triggers an alarm, but also conducts intelligent analysis of potential causes; Among them, the standardized function of anomaly detection is expressed as: Among them, A(t) represents the standardized abnormal degree of carbon emissions, P(t) is the current emissions, μp and σp are the mean and standard deviation of the historical emission data respectively; When A(t) exceeds the set threshold, an abnormal alarm and emergency response are triggered.

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