A data security monitoring method, device, intelligent gateway, system and storage medium for coal mine gas emission reduction

Abstract

The application provides a data security monitoring method, device, intelligent gateway, system and storage medium for coal mine gas emission reduction. The method comprises the following steps: acquiring first time series data collected by at least one sensor of a to-be-monitored device within a recent preset time period; generating a dynamic threshold according to the first time series data; acquiring second time series data collected by the at least one sensor of the to-be-monitored device; detecting whether the second time series data is within the dynamic threshold range; in the case that the second time series data is not within the dynamic threshold range, marking the second time series data as abnormal data; otherwise, marking the second time series data as normal data; and transmitting the second time series data marked as normal data to a national carbon market management platform. The embodiment of the application can transfer the task of abnormal data discrimination to the intelligent gateway side, greatly reducing the data transmission amount of the gateway and the national carbon market management platform, and avoiding waste of network bandwidth and storage resources.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data security monitoring method, device, smart gateway, system, and storage medium for coal mine gas emission reduction. Background Technology

[0002] With the development of the national carbon market, accurate measurement of emission reductions from projects such as Certified Emission Reductions (CCERs) is crucial. In January 2025, the Ministry of Ecology and Environment and other departments issued the "Methodology for Voluntary Greenhouse Gas Emission Reduction Projects: Utilization of Low-Concentration Methane and Ventilation Gas in Coal Mines with Methane Volume Concentration Below 8% (CCER-10-001-V01)". The methodology explicitly requires connection to the national carbon market management platform, and the monitoring data storage system should be connected to a smart gateway. The smart gateway should forward the monitoring data recorded by the monitoring data storage system to the national carbon market management platform. Furthermore, the data from the monitoring data storage system, the smart gateway, and the national carbon market management platform must be consistent with the sensor readings on the monitored equipment.

[0003] In traditional technologies, industrial smart gateways possess multiple protocol parsing capabilities, enabling them to collect data from field instruments and perform protocol conversion (such as MQTT conversion) before forwarding the data to a remote cloud platform or data center via the internet or 4G / 5G networks. The platform then deploys an independent data quality audit module and a data storage server to perform post-processing on the massive amounts of received data, completing operations such as anomaly screening, integrity and consistency verification, and storage. However, facing the massive amounts of data collected at a high frequency (seconds), the platform faces immense computational and storage pressure, leading to delayed anomaly detection results and hindering real-time intervention and alarms. Furthermore, the transmission and storage of a large amount of clearly abnormal or invalid data (such as zero values ​​during communication interruptions or out-of-range jumps) results in a significant waste of network bandwidth and storage resources. Therefore, there is a need to provide a data security monitoring method, device, smart gateway, system, and storage medium for coal mine gas emission reduction to address these issues. Summary of the Invention

[0004] This application is made in view of at least one of the aforementioned technical problems existing in the prior art. According to one aspect of this application, a data security monitoring method for coal mine gas emission reduction is provided, the method comprising: Acquire first time-series data collected by at least one sensor of the device to be monitored within the most recent preset time period; Cache the first time-series data into a first-in-first-out queue; Calculate the mean and standard deviation of all data in the first-in-first-out queue, and generate a dynamic threshold based on the mean and standard deviation; Acquire second time-series data collected by at least one sensor of the device to be monitored; Detect whether the second time-series data is within the range of the dynamic threshold; If the second time-series data is not within the dynamic threshold range, the trusted state of the second time-series data is marked as abnormal data; otherwise, the trusted state of the second time-series data is marked as normal data. The second time-series data, marked as normal data, is cached in the first-in-first-out queue and transmitted to the national carbon market management platform.

[0005] In some embodiments, calculating the mean and standard deviation of all data within the first-in-first-out queue to generate a dynamic threshold includes: Calculate the mean and standard deviation of all data in the first-in-first-out queue, and generate a temporary threshold based on the mean and standard deviation; The temporary threshold is smoothed using an exponentially weighted moving average algorithm to generate a dynamic threshold.

[0006] In some embodiments, the method of acquiring first time-series data collected by at least one sensor of the device to be monitored within a recent preset time period includes: Based on the Network Time Protocol service, standard time synchronization is performed on the device to be monitored; Acquire raw data from at least one sensor of the monitored device that has been synchronized with a standard time at a preset frequency.

[0007] In some embodiments, transmitting second-time data marked as normal data to the national carbon market management platform includes: The second time-series data, marked as normal data, the timestamp, and the device identifier are encapsulated into a data packet; A hash algorithm is used to calculate the hash value of the data packet. The data packet and its hash value are bound together to form a trusted data packet; The trusted data packet is encrypted using a preset encryption algorithm and then sent to the national carbon market management platform.

[0008] In some embodiments, the method further includes: When acquiring the second time-series data, the presence of a communication signal is detected; In the absence of a communication signal, the trusted state of the second time-series data is marked as abnormal.

[0009] In some embodiments, the method further includes: Obtain the status record of at least one sensor from the computer equipment management ledger of the device to be monitored, and determine whether the sensor used to collect the second time series data is in an uncalibrated state. If the sensor is not calibrated when acquiring the second time-series data, the second time-series data will be conservatively processed and marked as requiring conservative processing. The second time-series data marked as requiring conservative processing will be transmitted to the national carbon market management platform.

[0010] In some embodiments, the method further includes: Obtain the status records of at least one sensor from the computer equipment management ledger of the device to be monitored, and determine whether the data sensor used to collect the second time series data is in an uncalibrated state. If the data sensor is not calibrated when acquiring the second time-series data, then the second time-series data will be subjected to a logical check. If the second time series data passes the logical check, then the second time series data is marked as normal; otherwise, the second time series data is marked as abnormal.

[0011] In some embodiments, transmitting second-time data marked as normal data to the national carbon market management platform includes: The second time-series data is encrypted using a preset encryption algorithm; Based on a preset communication protocol, the encrypted second-series data is transmitted to the national carbon market management platform.

[0012] In some embodiments, before timestamping and device identifying the raw data, the method further includes: The raw data is cleaned and its format is standardized.

[0013] In some embodiments, the method further includes: Real-time detection of whether the network connection between the smart gateway and the national carbon market management platform is disconnected; In the event of a network connection failure, the smart gateway stores the second time-series data locally. Once the network connection is restored, the second time-series data will continue to be transmitted to the national carbon market management platform.

[0014] In some embodiments, the method further includes: The monitoring data is verified based on the verification request initiated by the national carbon market management platform. The verification of monitoring data based on the verification request initiated by the national carbon market management platform includes: Receive the second time-series data sent by the device under monitoring; Receive third time-series data sent by the national carbon market management platform; wherein the third time-series data is obtained by encrypting the second time-series data using a preset algorithm; The hash values ​​of the second time series data and the third time series data are compared to determine whether their hash values ​​are consistent. Under the condition of consistency, it is determined that the data stored by the monitoring device and the national carbon market management platform are complete and consistent; Otherwise, a notification indicating that the second timing data and the third timing data are inconsistent is sent and recorded.

[0015] According to another aspect of this application, an edge computing device is also provided, the device comprising: The data acquisition module is used to acquire first time-series data collected by at least one sensor of the device under monitoring within the most recent preset time period; and to acquire second time-series data collected by at least one sensor of the device under monitoring. An anomaly detection module is configured to: cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue, and generate a dynamic threshold based on the mean and standard deviation; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the trust status of the second time-series data as anomalous data; otherwise, mark the trust status of the second time-series data as normal data; and cache the second time-series data in the FIFO queue. The rules engine module is used to perform logical checks on the second time-series data collected by uncalibrated sensors, and to mark the second time-series data as conservative or abnormal if the second time-series data fails the logical check. The storage and evidence module is used to calculate the hash values ​​of the second time-series data marked as conservatively processed and normal data using a hash algorithm. The encryption module is used to encrypt the second time-series data marked as conservatively processed and the normal data using a preset encryption algorithm to generate encrypted data. The communication module is used to transmit the encrypted data to the national carbon market management platform; The isolation module is used to unidirectionally transmit the second time-series data, which has been processed by the data acquisition module, the anomaly identification module, and the rule engine module and is marked as conservatively processed and normal, to the storage and evidence consolidation module, the encryption module, and the communication module for corresponding processing.

[0016] According to another aspect of this application, a smart gateway is also provided, the smart gateway including a memory and a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the data security monitoring method for coal mine gas emission reduction as described above; The smart gateway further includes: an internal network board, an isolation board, an external network board, a cooling fan, terminal blocks, and a power supply; wherein, the isolation board is provided with the terminal blocks, and the isolation board is connected to the internal network board and the external network board respectively through the terminal blocks; the internal network board is used to connect to a local area network; the external network board is used to connect to the Internet; the cooling fan is disposed inside the smart gateway to dissipate heat from the various power-consuming modules inside the smart gateway; the power supply is used to supply power to the various power-consuming modules inside the smart gateway.

[0017] According to another aspect of this application, a data processing system is also provided, the system comprising a monitoring data storage system, a smart gateway, and a national carbon market management platform; wherein, The monitoring data storage system is used to store monitoring data collected by at least one sensor on the device to be monitored; The intelligent gateway is configured to: acquire first time-series data collected by at least one sensor of the device under monitoring within a recent preset time period; cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue, and generate a dynamic threshold based on the mean and standard deviation; acquire second time-series data collected by at least one sensor of the device under monitoring; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the confidence status of the second time-series data as abnormal data; otherwise, mark the confidence status of the second time-series data as normal data; cache the second time-series data in the FIFO queue; and transmit the second time-series data marked as normal data to the national carbon market management platform. The national carbon market management platform is used to store second time-series data marked as normal data sent by the smart gateway based on a preset communication protocol.

[0018] According to another aspect of this application, a computer-readable storage medium is also provided, the computer-readable storage medium storing a computer-executable program, the computer-executable program being used to cause a computer to perform the data security monitoring method for coal mine gas emission reduction as described above.

[0019] This application provides a data security monitoring method, device, smart gateway, system, and storage medium for coal mine gas emission reduction. It generates a dynamic threshold using a dynamic adaptive threshold algorithm based on first time-series data collected within a recently preset time period. It then determines whether newly collected data points fall within the dynamic threshold range. If they do, the newly collected data points are considered normal data; otherwise, they are considered abnormal data. Abnormal data will not be transmitted to the national carbon market management platform. This shifts the task of abnormal data identification to the smart gateway, significantly reducing the data transmission volume between the gateway and the national carbon market management platform, and avoiding waste of network bandwidth and storage resources. Attached Figure Description

[0020] Figure 1 A schematic flowchart illustrating a data security monitoring method for coal mine gas emission reduction according to an embodiment of this application is shown. Figure 2 A schematic flowchart illustrating step S101 according to an embodiment of this application is shown; Figure 3 A schematic flowchart illustrating the process of cleaning and format standardizing raw data according to an embodiment of this application is shown. Figure 4 A schematic flowchart illustrating the gateway breakpoint resume process according to an embodiment of this application is shown. Figure 5 A schematic flowchart illustrating step 103 according to an embodiment of this application is shown; Figure 6 This diagram illustrates a schematic flowchart of marking second timing data as abnormal in the event of communication signal interruption or communication discontinuity, according to an embodiment of the present application. Figure 7 A schematic flowchart illustrating data acquired by an uncalibrated sensor according to an embodiment of this application, indicating data requiring conservative processing; Figure 8 A schematic flowchart illustrating a logic check of data acquired by an uncalibrated sensor according to an embodiment of this application is provided. Figure 9 A schematic flowchart illustrating step S108 according to one embodiment of this application is shown; Figure 10 This diagram illustrates a schematic flowchart of a verification request initiated by the national carbon market management platform according to an embodiment of this application, which verifies the integrity and consistency of monitoring data. Figure 11 A schematic flowchart illustrating step 1001 according to one embodiment of this application is shown; Figure 12This document illustrates a schematic flowchart of the integrity and consistency verification of monitoring data stored in the monitoring data storage system, smart gateway, and national carbon market management platform according to an embodiment of this application. Figure 13 A schematic block diagram of an edge computing device 1300 according to an embodiment of this application is shown; Figure 14 An exploded view of a smart gateway 1400 according to an embodiment of this application is shown; Figure 15 A schematic block diagram of a data processing system 1500 according to an embodiment of this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the embodiments of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] With the rapid development of the Internet of Things (IoT) and the Industrial Internet, edge computing, as a key link connecting terminal devices and the cloud, has been widely applied in smart factories, smart cities, telemedicine, and other scenarios. As the core device of edge nodes, the smart gateway is responsible for the collection, aggregation, preprocessing, and interaction with the cloud of data from terminal devices. Its data management efficiency and security directly affect the reliability and real-time performance of the entire system. Edge nodes are typically deployed in untrusted environments, and data faces risks such as eavesdropping, tampering, and leakage during storage. Existing encryption methods mostly employ static key mechanisms, lacking dynamic encryption strategies specifically for edge scenarios, making it difficult to cope with diverse security threats.

[0023] Traditional smart gateways upload all raw data without verification, with a central platform handling centralized anomaly detection. Faced with massive amounts of data collected at a high frequency (seconds per second), the central platform experiences immense computational and storage pressure, leading to delayed anomaly detection results and hindering real-time intervention and alerts. Furthermore, traditional smart gateways merely act as "transmission conduits," failing to validate data immediately upon its generation. The time lag between data source and platform means that data tampering risks cannot be perceived or proven during this period, reducing the credibility of the data received by the platform and making it impossible to verify consistency across all three ends (data endpoints, terminals, and the platform). Anomaly detection on the platform is often based on general statistical models, making it difficult to deeply integrate with specific business rules in carbon metering (e.g., invalid data for a given period if the instrument calibration status is invalid), resulting in complex and inflexible auditing logic. The complete transmission and storage of a large amount of clearly abnormal or invalid data (e.g., zero values ​​during communication interruptions, or out-of-range measurements) leads to a significant waste of network bandwidth and storage resources.

[0024] Based on at least one of the aforementioned technical problems, this application provides a data security monitoring method for coal mine gas emission reduction, applied to a smart gateway. The method includes: acquiring first time-series data collected by at least one sensor of the device to be monitored within a recent preset time period; caching the first time-series data in a first-in-first-out (FIFO) queue; calculating the mean and standard deviation of all data in the FIFO queue, and generating a dynamic threshold based on the mean and standard deviation; acquiring second time-series data collected by at least one sensor of the device to be monitored; detecting whether the second time-series data is within the range of the dynamic threshold; if the second time-series data is not within the range of the dynamic threshold, marking the credibility status of the second time-series data as abnormal data; otherwise, marking the credibility status of the second time-series data as normal data; caching the second time-series data marked as normal data in the FIFO queue, and transmitting it to the national carbon market management platform. This application provides a data security monitoring method for coal mine gas emission reduction. It generates a dynamic threshold using a dynamic adaptive threshold algorithm based on the first time-series data collected within the most recent preset time period, and determines whether newly collected data points are within the dynamic threshold range. If they are within the dynamic threshold range, the newly collected data points are considered normal data; otherwise, they are considered abnormal data. Abnormal data will not be transmitted to the national carbon market management platform. This transfers the task of abnormal data identification to the smart gateway side, greatly reducing the amount of data transmission between the gateway and the national carbon market management platform, and avoiding waste of network bandwidth and storage resources.

[0025] Figure 1 This diagram illustrates a schematic flowchart of a data security monitoring method for coal mine gas emission reduction according to an embodiment of this application; the data security monitoring method for coal mine gas emission reduction of this application is applied to a smart gateway. Figure 1 As shown, the data security monitoring method 100 for coal mine gas emission reduction according to an embodiment of this application may include the following steps S101, S102, S103, S104, S105, S106, S107, and S108: In step S101, first time-series data collected by at least one sensor of the device to be monitored within the most recent preset time period is obtained.

[0026] The devices to be monitored can be multiple devices located within the same local network domain, or multiple devices located in different network locations. At least one sensor can include sensors for temperature, pressure, flow rate, power, etc., installed in different devices to be monitored. Because these sensors collect data at a certain frequency (time point), the data collected is time-series data. For example, data is collected once every 1 second.

[0027] In this embodiment, the most recent preset time period is a preset time period backward from the current time. For example, if the preset time period is 1 hour and the current time is 2:10, then the most recent preset time period is the period from 1:10 to 2:10, which is based on the current time of 2:10. Therefore, the first time-series data collected within the most recent preset time period relative to the current time of 2:10 is the first time-series data collected within the most recent preset time period from 1:10 to 2:10.

[0028] In one embodiment of this application, such as Figure 2 As shown, step S101, acquiring first time-series data collected by at least one sensor of the device to be monitored within the most recent preset time period, includes: In step S201, standard time synchronization is performed on the device to be monitored based on the Network Time Protocol service; In step S202, raw data collected by at least one sensor of the device to be monitored, which has been synchronized with the standard time, at a preset frequency is obtained.

[0029] Network Time Protocol (NTP) is a standard Internet protocol used for high-precision time synchronization among distributed network devices. Its core function is to automatically calibrate and maintain the consistency of the clocks of all monitored devices in the network with Coordinated Universal Time (UTC), thereby providing a unified, reliable, and traceable time reference for various applications and effectively solving the problems of data chaos and logical errors caused by device clock drift or time differences in heterogeneous systems.

[0030] Among them, China Certified Emission Reductions (CCERs) are greenhouse gas emission reductions generated by renewable energy, forestry carbon sinks, methane utilization, and other projects within my country that have been officially quantified, verified, and registered. One CCER corresponds to one ton of carbon dioxide equivalent. Their core purpose is for carbon market compliance offsetting and carbon asset trading, serving as an important supplementary tool to the national carbon market. In this embodiment, the preset frequency can be set to once per second to meet the CCER requirements.

[0031] The data acquisition module of the smart gateway in this embodiment acquires raw data collected by the sensors of the monitored device at a preset frequency from the sensors or control systems (DCS / PLC) of the connected monitored device using various industrial protocols (e.g., Modbus protocol, OPC protocol, etc.). Simultaneously, it uses NTP service to provide standard time synchronization for different devices, ensuring consistency in data acquisition time.

[0032] In one embodiment of this application, such as Figure 3 As shown, before step S203, the method further includes step S301: In step S301, the raw data is cleaned and formatted.

[0033] Because raw data collected by sensors may contain missing data, noise, and other issues, and because different monitored devices or sensors may have different raw data formats, which is detrimental to subsequent data processing, it is necessary to perform preliminary cleaning and format standardization on the collected raw data. For example, missing data can be filled in using the mean or median. Another example is that raw data in different formats can be uniformly converted into standardized JSON format data.

[0034] In one embodiment of this application, such as Figure 4 The diagram shown is a schematic flowchart illustrating the gateway breakpoint resume process according to an embodiment of this application. The method further includes steps S401, S402, and S403: In step S401, it is detected in real time whether the network connection between the smart gateway and the national carbon market management platform is disconnected; In step S402, if the network connection is disconnected, the smart gateway stores the second time-series data in its local memory; In step S403, if the network connection is restored, the second time-series data continues to be transmitted to the national carbon market management platform.

[0035] The smart gateway involved in this application embodiment has an automatic breakpoint resume function. When the network is interrupted, the data is persistently stored locally and automatically resumed as soon as the network is detected to be restored.

[0036] In step S102, the first time-series data is cached in a first-in-first-out queue.

[0037] This application embodiment can call a dynamic adaptive threshold algorithm to generate a dynamic threshold. Specifically, a sliding window (FIFO) is generated based on data points (such as methane concentration) collected within the most recent preset time period to facilitate the generation of a dynamic threshold.

[0038] In step S103, the mean and standard deviation of all data in the first-in-first-out queue are calculated, and a dynamic threshold is generated based on the mean and standard deviation.

[0039] This application embodiment can call a dynamic adaptive threshold algorithm to generate dynamic thresholds. Specifically, a sliding window (FIFO) is generated based on data points (such as methane concentration) collected within the most recent preset time period. The mean and standard deviation of the data within the window are calculated to generate dynamic upper and lower thresholds. If newly collected data points exceed the threshold range, they are marked as "abnormal". If no communication signal is detected, they are marked as "communication interrupted" or directly marked as "abnormal". Otherwise, they are marked as "normal". Therefore, abnormal data can include data marked with "abnormal", i.e., data from times of abnormality or communication interruption.

[0040] Alternatively, in other embodiments, various lightweight anomaly detection algorithms can be employed to calculate the dynamic threshold. For example, a lightweight anomaly detection algorithm based on a "matrix profiling" technique can be used, which is also suitable for efficient operation on edge devices to detect anomalous segments in time series.

[0041] In one embodiment of this application, such as Figure 5 As shown, step S103, which calculates the mean and standard deviation of all data in the first-in-first-out queue and generates a dynamic threshold based on the mean and standard deviation, includes steps S501 and S502: In step S501, the mean and standard deviation of all data in the first-in-first-out queue are calculated, and a temporary threshold is generated based on the mean and standard deviation. In step S502, the temporary threshold is smoothed using an exponentially weighted moving average algorithm to generate a dynamic threshold.

[0042] Specifically, firstly, a first-in-first-out (FIFO) sliding window W is generated in memory, which continuously stores data from the most recent N time points [x]. t ,x t-1 ,...,x t-N+1 The window size N can be configured according to the data frequency and process characteristics. For each new data point x... t When a data point arrives, it is added to window W, and the oldest data point is removed (if the window is full). The mean (μ_w) and standard deviation (σ_w) of all data within the current window W are calculated in real time. These two values ​​represent the normal range of the data in the "recent" period, i.e., the dynamic threshold.

[0043] Then, the upper and lower limits of the dynamic threshold at the previous time step are defined as Upper and Lower, respectively. t-1 and Lower t-1 Calculate the temporary threshold for the current sliding window W: Temp_Upper_t = μ_w + k * σ_w; Temp_Lower_t=μ_w-k*σ_w; Where k represents a constant; μ_w represents the mean of all data in the current window W; σ_w represents the standard deviation of all data in the current window W; Temp_Upper_t represents the upper limit of the temporary threshold of the current sliding window W; and Temp_Lower_t represents the lower limit of the temporary threshold of the current sliding window W.

[0044] In a specific implementation, the value of k can be set according to needs and the scenario. For example, k can be set to 3.

[0045] In this embodiment, a smoothing factor (α) is introduced when calculating the dynamic threshold. An Exponentially Weighted Moving Average (EWMA) algorithm is used to smoothly update the temporary threshold, instead of directly using the temporary threshold, thus avoiding drastic jumps in the threshold due to single data fluctuations. The formula for calculating the dynamic threshold based on the temporary threshold is as follows: Upper_t=α*Temp_Upper_t+(1-α)*Upper_t-1; Lower_t=α*Temp_Lower_t+(1-α)*Lower_t-1; Where α represents the smoothing factor, 0 < α ≤ 1; Temp_Upper_t represents the upper limit of the temporary threshold of the current sliding window W; Temp_Lower_t represents the lower limit of the temporary threshold of the current sliding window W; Upper_t-1 represents the upper limit of the dynamic threshold at the previous time step; Lower_t-1 represents the lower limit of the dynamic threshold at the previous time step; Upper_t represents the upper limit of the dynamic threshold at the current time step; Lower_t represents the lower limit of the dynamic threshold at the current time step.

[0046] It is worth noting that the smoothing factor α (0 < α ≤ 1) is a key parameter here. The larger α is, the faster the dynamic threshold responds to new statistics, but it may be more sensitive; the smaller α is, the smoother the dynamic threshold changes and the stronger its resistance to fluctuations.

[0047] The embodiments of this application employ a dynamic adaptive threshold algorithm, which abandons the fixed threshold and uses a sliding window to calculate the mean (μ) and standard deviation (σ) of recent data in real time. It also uses techniques such as exponentially weighted moving average (EWMA) to smoothly update the threshold, so that the anomaly detection threshold can adapt to the slow drift of data distribution, significantly reducing false alarms and false negatives.

[0048] In step S104, second time-series data collected by at least one sensor of the device to be monitored is obtained.

[0049] In step S105, it is detected whether the second time-series data is within the dynamic threshold range. If it is, step S106 is executed; otherwise, step S107 is executed.

[0050] The second time series data consists of newly acquired data points.

[0051] For example, for a newly arrived data point x_t, it can be compared with a dynamic threshold. If Upper_t > x_t > Lower_t, then the data point x_t is normal data; if x_t is not within the range of the dynamic threshold, then x_t is abnormal data.

[0052] In step S106, the trusted state of the second time series data is marked as normal data, and then step S108 is executed.

[0053] Continuing from the previous example, if the data point x_t is normal data, the confidence status of x_t can be marked as normal.

[0054] In step S107, the trusted state of the second time series data is marked as abnormal data.

[0055] Continuing from the previous example, if data point x_t is abnormal data, the trust status of x_t can be marked as abnormal.

[0056] The inventors used 6 consecutive hours of minute-by-minute methane data (360 data points in total) provided by a coal mine as training data. First, experienced experts labeled methane data points that significantly deviated from normal concentrations. Eight of these were identified as outliers. To intuitively analyze the impact of constants on prediction accuracy, the F2-Score calculation formula was introduced, namely: Based on Precision = TP / (TP+FP) and Recall = TP / (TP+FN), the predicted anomalies and actual anomalies were compared under different values ​​of constants (k and α), as shown in Tables 1 and 2.

[0057] Table 1: K values

[0058] The results above show that when K=1.5, the temporary thresholds Temp_Upper_t and Temp_Lower_t have the highest scores, which can identify more anomalies and reduce false alarms.

[0059] Table 2: Cases involving α:

[0060] The results above show that when α=0.1 or α=0.2, the dynamic thresholds Upper_t and Lower_t have the highest scores, which can identify more anomalies and reduce false alarms.

[0061] In one embodiment of this application, such as Figure 6The diagram shown is a schematic flowchart illustrating how, in the event of a communication signal interruption or discontinuous communication, the second time-series data is marked as abnormal according to an embodiment of this application. The method further includes steps S601 and S502: In step S601, when acquiring the second timing data, the presence of a communication signal is detected; In step S602, if there is no communication signal, the trusted state of the second time-series data is marked as abnormal.

[0062] In this embodiment of the application, if the communication signal is interrupted or the communication is discontinuous when acquiring the second time series data, the acquired data will be zero, which is obviously not true. In this case, the second time series data should be marked as abnormal.

[0063] In one example, such as Figure 7 The diagram shown is a schematic flowchart illustrating the process of marking uncalibrated sensor-collected data as requiring conservative processing according to an embodiment of this application. The method further includes steps S701, S702, S703, and S704: In step S701, the status record of at least one sensor in the computer equipment management ledger of the device to be monitored is obtained; In step S702, it is detected whether the data sensor used to acquire the second time-series data is in an uncalibrated state; if so, the process ends; otherwise, step S703 is executed. In step S703, the second time series data is subjected to conservative processing and marked as requiring conservative processing; In step S704, the second time-series data marked as requiring conservative processing is transmitted to the national carbon market management platform.

[0064] In the embodiments of this application, conservative processing refers to proactively choosing a processing method that leans towards safety and avoids risks when data has uncertainties, errors, or risks. For example, if sensor calibration data has deviations, a "calibration error upper limit" is used to correct the measured value to ensure that the measurement results are compliant. As another example, when monitoring data is too low and it is suspected that the data is underestimated, conservative processing will appropriately amplify the value to ensure that true exceedances are not missed due to data errors. Furthermore, when data has random fluctuations, conservative processing will smooth the data and filter outliers to avoid misjudgments caused by a single fluctuation.

[0065] In one application scenario, conservative processing refers to a cautious data processing method used to avoid overestimating emission reductions when there is uncertainty in data quality (such as uncalibrated instruments). For specific requirements, refer to section 7.3.4, "Data Accuracy Control and Correction Requirements for Methodological Projects for Voluntary Greenhouse Gas Emission Reduction: Utilization of Low-Concentration Methane and Ventilation Gas in Coal Mines with Methane Volume Concentration Below 8% (CCER-10-001-V01)".

[0066] In the embodiments of this application, if the sensor is not calibrated, the data collected by the sensor may not be accurate. In this case, the measured value can be corrected using a "calibration error upper limit" to ensure the measurement result is compliant. For example, when using a catalytic combustion sensor to measure methane concentration, the measured value is 2% CH4, and the calibration allowable error is ±0.1% CH4. Since the catalytic combustion sensor was not calibrated when collecting data, the measurement may be inaccurate. Therefore, the measured value can be increased by the calibration error, i.e., corrected to 2.1% CH4.

[0067] In another example, such as Figure 8 The diagram shown is a schematic flowchart illustrating a logic check performed on data collected by an uncalibrated sensor according to an embodiment of this application. The method further includes: In step S801, the status record of at least one sensor in the computer equipment management ledger of the device to be monitored is obtained; In step S802, it is detected whether the data sensor used to acquire the second time-series data is in an uncalibrated state; if so, the process ends; otherwise, step S803 is executed. In step S803, the second timing data is subjected to a logical check; In step S804, if the second timing data passes the logic check, the second timing data is marked as normal. In step S805, if the second timing data fails the logical check, the second timing data is marked as abnormal.

[0068] The logical check in this application refers to the process of verifying the inherent logical relationship between data, parameters, and events during data processing, system operation, and report generation to determine whether they conform to common sense, rules, or preset causal relationships, thereby identifying contradictions, errors, or anomalies. For example, the detected value of methane concentration cannot be negative, nor can it exceed 100% VOL (pure methane); if it is -0.2% or 150%, the logical check will directly determine that the methane concentration value is abnormal.

[0069] In other embodiments, instead of processing the uncalibrated data, all data collected by the sensor during this period can be forcibly marked as abnormal, and its credibility status can be set to untrustworthy.

[0070] In step S108, the second time-series data marked as normal data is cached in the first-in-first-out queue and transmitted to the national carbon market management platform.

[0071] In this embodiment, since abnormal data can interfere with the calculation of dynamic threshold, normal data only needs to be cached in a sliding window (first-in, first-out queue) for the calculation of the dynamic threshold at the next moment.

[0072] Furthermore, according to the requirements of the National Certified Emission Reductions (CCERs), the integrity and consistency of data stored on three terminals—the monitoring data storage system, the smart gateway, and the national carbon market management platform—need to be certified. Therefore, the second-time-series data needs to be transmitted to the national carbon market management platform. This embodiment of the application performs anomaly detection on the second-time-series data, sending only the second-time-series data marked as normal to the national carbon market management platform, while omitting the data marked as abnormal. This shifts the task of anomaly detection to the smart gateway, significantly reducing the amount of data transmitted between the gateway and the national carbon market management platform, and avoiding waste of network bandwidth and storage resources.

[0073] In one embodiment of this application, such as Figure 9 As shown, step S108, which involves transmitting the second time-series data marked as normal data to the national carbon market management platform, includes steps S901, S902, S903, and S904: In step S901, the second time-series data marked as normal data, the marked timestamp, and the device identifier are encapsulated into a data packet; In step S902, a hash algorithm is used to calculate the hash value of the data packet; In step S903, the data packet and its hash value are bound together to form a trusted data packet; In step S904, the trusted data packet is encrypted using a preset encryption algorithm and then sent to the national carbon market management platform.

[0074] The timestamp refers to the total number of seconds from 00:00:00 GMT on January 1, 1970 to the present. A timestamp is an encrypted credential document containing a file digest, the date and time the DTS received the file, and the DTS's digital signature. In this embodiment, data marked with a timestamp and device identifier is considered trusted data. Trusted data refers to data whose authenticity, integrity, and traceability can be ensured under the support of technology, processes, management, and verification mechanisms, ensuring its trustworthiness by users and meeting their application needs.

[0075] In this embodiment, the second time-series data marked as normal data, the marked timestamp, and the device identifier (the device's unique ID) are encapsulated into a data packet. A hash algorithm is then used to calculate the hash value corresponding to the data packet. The data packet and its corresponding hash value are bound together, thus forming a trusted data packet. This trusted and secure data packet is then transmitted to the national carbon market management platform.

[0076] In this embodiment of the application, when transmitting trusted data packets, the SM4 national cryptographic algorithm can be used for encryption, and the packets can be stably uploaded to the national carbon market management platform via the MQTT protocol.

[0077] Generally, trusted data refers to data whose authenticity, integrity, and traceability can be ensured with the support of technology, processes, management, and verification mechanisms, ensuring that it can be trusted by users and meet their application needs. Therefore, in this embodiment of the application, by storing and solidifying edge-side data, data marked as "valid" or "requiring conservative processing" is stored and solidified. This embodiment of the application binds the original data, timestamp, device ID, trusted status identifier, and corresponding hash value, forming data that is immutable and constitutes a trusted data packet.

[0078] In this application embodiment, a hash operation is performed on the data generated on the network edge device (such as a smart gateway) in real time to generate a digital fingerprint, and a trusted timestamp is bound to the data as soon as it is generated, thereby minimizing the risk of data being tampered with in subsequent transmission and storage.

[0079] Alternatively, a smart gateway can instantly upload the generated digital fingerprint to a blockchain network for storage, leveraging the immutability of the blockchain to replace centralized timestamp services and achieve distributed trust.

[0080] This application's embodiments ensure data quality from the source by implementing real-time anomaly identification and business rule judgment at the gateway edge, significantly reducing the time for problem discovery and handling, greatly alleviating the pressure on the back-end platform and the workload of manual review, and improving CCER data quality and review efficiency. Simultaneously, the "collection-as-proof" model ensures the originality and authenticity of the data, forming a strong electronic evidence chain, effectively preventing the possibility of later data tampering, fully meeting the CCER methodology's requirements for data anti-tampering and anti-repudiation, and enhancing data anti-repudiation and anti-tampering capabilities. Localized real-time processing avoids the delays caused by data uploads and waiting for central processing, enabling high-quality, reliable data to be immediately used for calculation and decision-making, providing a technical foundation for shortening the carbon trading cycle and ensuring data timeliness. By filtering and marking invalid data at the gateway side, network transmission traffic and remote storage space are significantly reduced, lowering the overall system operating cost, while also reducing the workload for third-party verification and improving verification efficiency.

[0081] In one embodiment of this application, such as Figure 10 The diagram shown is an illustrative flowchart illustrating the process of verifying the integrity and consistency of monitoring data based on a verification request initiated by the national carbon market management platform, according to an embodiment of this application. Figure 10 The method further includes step S1001: In step S1001, the monitoring data is verified for integrity and consistency based on the verification request initiated by the national carbon market management platform.

[0082] In one embodiment of this application, such as Figure 11 As shown, step S1001, based on the verification request initiated by the national carbon market management platform, performs integrity and consistency verification on the monitoring data, including steps S1101, S1102, S1103, S1104, and S1105: In step S1101, the second time-series data sent by the monitoring data storage system is received; In step S1102, the third time-series data sent by the national carbon market management platform is received; wherein the third time-series data is obtained by encrypting the second time-series data using a preset algorithm. In step S1103, the hash values ​​of the second time series data and the third time series data are compared to determine whether their hash values ​​are consistent; if they are, step S1104 is executed; otherwise, step S1105 is executed. In step S1104, it is determined that the data stored in the monitoring data storage system and the national carbon market management platform are complete and consistent; In step S1105, a prompt indicating that the second timing data and the third timing data are inconsistent is sent and recorded.

[0083] In traditional technologies, smart gateways are edge computing devices with limited computing power. Therefore, under limited computing power, they cannot support a series of data processing behaviors such as data anomaly identification, encryption, forwarding, verification, and authentication. If smart gateways are forced to perform these data processing tasks, their operating efficiency will not meet the requirements. Therefore, it is necessary to balance the relationship between computing power and operating efficiency of smart gateways.

[0084] The anomaly identification algorithm adopted in this application, namely the sliding window statistical calculation method based on incremental updates, can significantly reduce computational complexity, making it highly suitable for edge computing devices. Secondly, this application also uses an exponentially weighted moving average algorithm, introducing a smoothing factor α and a scene coefficient (k) to make the acquisition of temporary thresholds smoother and the upper and lower limits of temporary thresholds more reasonable, avoiding thresholds that are too narrow or too wide, and threshold jumps caused by single data fluctuations. In some application scenarios, if computing power allows, the scene coefficient and smoothing factor can be subjected to localized supervised training. By combining specific scenarios to determine specific training strategies, dynamic scene coefficients (k) and dynamic smoothing factors (α) are obtained based on the training results. This application satisfies both data processing requirements and operational efficiency requirements without excessively consuming computing power.

[0085] This application embodiment can perform integrity and consistency checks on monitoring data stored in the monitoring data storage system, smart gateway, and national carbon market management platform. For example, integrity and consistency checks can be performed periodically on the data at both ends of the monitoring data storage system and the national carbon market management platform using timestamps and hash value comparisons. Under normal circumstances, the monitoring data is unmodified when the monitoring data storage system sends it to the smart gateway; therefore, the data at both ends of the monitoring data storage system and the smart gateway is consistent. For simplicity, simply performing integrity and consistency checks on the monitoring data in the monitoring data storage system or smart gateway and the monitoring data stored in the national carbon market management platform is sufficient to assume that the data at all three ends is consistent.

[0086] like Figure 12 The diagram illustrates a schematic flowchart of the integrity and consistency verification of monitoring data stored in the monitoring data storage system, smart gateway, and national carbon market management platform according to an embodiment of this application.

[0087] First, the data source (raw data) is obtained from the monitoring meters of the device to be monitored. Under NTP time synchronization, the raw data, timestamp, and device identifier are packaged into a data packet A. All data packets A are stored in the data storage system. During integrity and consistency verification, the data storage system transmits data packet A to the smart gateway. The smart gateway uses a hash algorithm to calculate a hash value Hash_A on data packet A. The purpose of generating the hash value Hash_A is to secure and verify data packet A. After securing and verifying data packet A, it can be confirmed that data packet A will not be modified within the smart gateway, facilitating subsequent integrity and consistency verification.

[0088] Simultaneously, the smart gateway encrypts data packet A using the SM4 national cryptographic algorithm and uploads it to the National Carbon Market Management Platform. The National Carbon Market Management Platform then generates data packet B based on data packet A. If the National Carbon Market Management Platform does not modify data packet A, data packet B will be completely identical to data packet A. However, the National Carbon Market Management Platform may alter data packet A, leading to inaccurate data. Therefore, it is necessary to verify the integrity and consistency of the monitoring data stored in the data storage system, the smart gateway, and the National Carbon Market Management Platform.

[0089] Regular integrity and consistency checks of monitoring data can be scheduled. For example, an integrity and consistency check could be performed annually in December. When the National Carbon Market Management Platform needs to verify the integrity and consistency of data at a certain point in time, it must send an integrity and consistency check request to the smart gateway. The National Carbon Market Management Platform sends data packet B to the smart gateway so that the smart gateway can compare the hash values ​​of data packets A and B. The smart gateway performs a hash calculation on data packet B, generating a hash value Hash_B for data packet B. Then, it compares the hash value Hash_A, which was pre-stored during the pre-certification and certification process, with Hash_B. If Hash_B = Hash_A, it indicates that the data at both ends of the National Carbon Market Management Platform and the smart gateway is complete and consistent. Since the data stored by the smart gateway and the monitoring data storage system is definitely consistent, the data stored by the National Carbon Market Management Platform and the monitoring data storage system is also consistent. If Hash_B ≠ Hash_A, it indicates that the data at both ends of the National Carbon Market Management Platform and the smart gateway is inconsistent. For example, a notification of inconsistency between the second time-series data and the third time-series data can be sent, and this can be recorded on the National Carbon Market Management Platform.

[0090] In this embodiment, the integrity and consistency verification is performed by the smart gateway to determine the consistency between the national carbon market management platform and the monitoring data stored by the smart gateway and the monitoring data storage system. In other embodiments, the integrity and consistency verification may also be performed by the national carbon market management platform.

[0091] The embodiments of this application can achieve the following beneficial effects: (1) Decentralize the anomaly identification task to the gateway edge to realize real-time and local judgment of data quality, identify invalid data from the source, and improve efficiency; (2) Store and solidify evidence within the smart gateway, generate hash values ​​and add "time stamps" to valid data to ensure that the data is trustworthy and tamper-proof from the source, and support self-certification of data consistency across the three ends; (3) Carbon metering refers to the process of quantitatively measuring, calculating, recording and reporting various activities related to greenhouse gas emissions and the carbon emissions they generate; the smart gateway in this application embodiment can execute preset carbon metering business rules to realize the automation and intelligence of data quality auditing; (4) Reduce invalid data transmission and optimize resource utilization; (5) Encrypt trusted data packets during transmission to reduce the risk of data leakage.

[0092] This application also provides an edge computing device. For example... Figure 13 The diagram shown is a schematic block diagram of an edge computing device 1300 according to an embodiment of this application. The edge computing device 1300 includes a data acquisition module 1301, an anomaly detection module 1302, a rule engine module 1303, a storage and evidence preservation module 1304, an encryption module 1305, a communication module 1306, and an isolation module 1307. The data acquisition module 1301 is used to acquire first time-series data collected by at least one sensor of the device under monitoring within the most recent preset time period; and to acquire second time-series data collected by at least one sensor of the device under monitoring. An anomaly detection module 1302 is configured to: cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue; generate a dynamic threshold based on the mean and standard deviation; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the trust status of the second time-series data as anomalous data; otherwise, mark the trust status of the second time-series data as normal data; and cache the second time-series data in the FIFO queue. The rule engine module 1303 is used to perform a logical check on the second time-series data collected by the uncalibrated sensor, and mark the second time-series data as conservative or abnormal if the second time-series data 1303 fails the logical check. The storage and evidence module 1304 is used to calculate the hash value of the second time-series data marked as conservatively processed and normal using a hash algorithm. Encryption module 1305 is used to encrypt the second time-series data marked as conservatively processed and normal data using a preset encryption algorithm to generate encrypted data; Communication module 1306 is used to transmit the encrypted data to the national carbon market management platform; The isolation module 1307 is used to unidirectionally transmit the second time-series data, which has been processed by the data acquisition module 1301, the anomaly identification module 1302, and the rule engine module 1303 and is marked as conservatively processed and normal, to the storage and evidence consolidation module 0304, the encryption module 0305, and the communication module 1306 for corresponding processing.

[0093] like Figure 14 The image shown is an exploded view of a smart gateway 1400 according to an embodiment of this application. The smart gateway 1400 includes a memory and a processor, as well as a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the data security monitoring method for coal mine gas emission reduction as described above. The smart gateway 1400 further includes: an internal network board 1401, an isolation card 1402 (isolation module), an external network board 1403, a cooling fan 1404, a terminal block 1405, and a power supply 1406; wherein, the isolation card is provided with the terminal block, and the isolation card is connected to the internal network board and the external network board respectively through the terminal block; the internal network board is used to connect to a local area network; the external network board is used to connect to the Internet; the cooling fan is disposed inside the smart gateway to dissipate heat from the various power-consuming modules inside the smart gateway; the power supply is used to supply power to the various power-consuming modules inside the smart gateway.

[0094] The smart gateway in this embodiment further includes a main body, a display mechanism, a data acquisition and calculation mechanism, a trusted authentication mechanism, and a communication mechanism (not shown in the figure). The main body consists of a front panel, a handle, a top cover, a vent, a power switch, etc. The internal network board 1401, the isolation card 1402, the external network board 1403, the terminal block 1405, the power supply 1406, and the cooling fan 1404 constitute the data acquisition and calculation mechanism, the evidence storage and preservation mechanism, and the communication mechanism of the smart gateway. In one example, the internal network board 1401 can be configured with 2 network ports and 4 USB ports; the external network board 1403 can be configured with 2 network ports and 1 USB port.

[0095] For example, the display mechanism may include a 7-inch LCD touchscreen located on the front panel of the main body and connected to the intranet board 1401 via a serial port. The data acquisition and calculation mechanism (including the intranet board 1401, which acquires external input data via a network port, a USB port without terminal blocks, and RS485) communicates with the isolation card 1402 via PCIe to achieve unidirectional data transmission.

[0096] For example, a trusted authentication authority (including external network board 1403) can communicate with the external network via network port, Wi-Fi, 4G, etc., and use a USB key connected via USB port to achieve unique identity authentication. External network board 1403 can obtain one-way data communication from board 1 through the PCIe serial port of isolation card 1402, and realize trusted data signing and verification functions through trusted authentication module.

[0097] For example, the communication mechanism supports multiple communication methods such as Wi-Fi, 4G / 5G and Ethernet through the network card, and forwards the data after it has been encrypted with SM4.

[0098] Furthermore, according to embodiments of this application, a data processing system is also provided. For example... Figure 15 The diagram shown is a schematic block diagram of a data processing system 1500 according to an embodiment of this application. The data processing system of this embodiment includes three parts: a data source 1501, a smart gateway 1502, and an application platform 1503.

[0099] The monitoring data storage system 1501 is used to store monitoring data collected by at least one sensor on the device to be monitored. The intelligent gateway 1502 is configured to: acquire first time-series data collected by at least one sensor of the device under monitoring within a recent preset time period; cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue, and generate a dynamic threshold based on the mean and standard deviation; acquire second time-series data collected by at least one sensor of the device under monitoring; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the confidence status of the second time-series data as abnormal data; otherwise, mark the confidence status of the second time-series data as normal data; cache the second time-series data in the FIFO queue; and transmit the second time-series data marked as normal data to the national carbon market management platform. Application platform 1503 includes a national carbon market management platform, which is used to store second time-series data marked as normal data sent by the smart gateway based on a preset communication protocol, and monitoring data collected by at least one sensor on the device to be monitored.

[0100] On the data source side, sensors of the devices to be monitored can be installed at RTO (Regenerative Thermal Oxidizer) devices, pump outlets, pipeline inlets, power plants, and DCS / OPC / PLCs. The collected data can be stored in the monitoring data storage system on the data source side.

[0101] Combination Figure 12 and Figure 15 The smart gateway may include a data acquisition module 1301, an anomaly identification module 1302, a rule engine module 1303, a storage and evidence preservation module 1304, an encryption module 1305, a communication module 1306, and an isolation module 1307.

[0102] In the data processing system of this application embodiment, the intelligent gateway sends methane emission reduction data to the monitoring and data storage system. This enables full-process traceability, compliant documentation, and source tracking of emission reduction data, meeting the stringent requirements of the CCER (China Certified Emission Reduction) project for emission reduction data. This achieves the following effects: (1) Achieve traceability of monitoring data for CCER projects. The emission reductions of CCER projects need to be fully recorded in terms of the generation process, data source, and calculation basis in order to pass third-party audits. For example, the smart gateway stores the calculated methane emission reductions back to the monitoring data storage system to establish a correspondence between the emission reductions and the original data (e.g., RTO devices). In subsequent third-party audits, the monitoring data storage system can be used to trace which original data the emission reductions were based on and what rules were used to calculate them, thus proving the authenticity of the emission reductions.

[0103] (2) Realize the correlation and evidence storage of raw data and emission reduction. The monitoring data storage system is the data base of the entire process. The data source provides the raw data of equipment operation (such as methane concentration and flow rate). The smart gateway will calculate the methane emission reduction based on this raw data and store the methane emission reduction back to the monitoring data storage system, so that the raw data and the derived emission reduction are stored and managed in a unified manner, avoiding the disconnect between emission reduction and raw data, and ensuring the integrity of the data chain.

[0104] (3) Supporting subsequent anomaly review and compliance audit. The smart gateway performs logical checks on the monitoring data (such as whether the emission reduction matches the original data), while the methane emission reduction and original data stored in the storage system are the basis for anomaly review and problem tracing. If anomalies in the emission reduction data are subsequently found, the corresponding original data can be retrieved through the storage system to reverse verify whether the calculation process is compliant, and it can also provide complete data support for the audit by regulatory authorities.

[0105] Furthermore, according to embodiments of this application, a storage medium is also provided, on which program instructions are stored. When executed by a computer or processor, these program instructions are used to perform corresponding steps of the data security monitoring method for coal mine gas emission reduction according to embodiments of this application. The storage medium may, for example, include a personal computer hard drive, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media.

[0106] Furthermore, according to embodiments of this application, a computer program product is also provided, which, when executed by a processor, implements the steps of the method described above.

[0107] The edge computing device, smart gateway, data processing system, computer program product, and storage medium of this application embodiment have the same advantages as the aforementioned data security monitoring method for coal mine gas emission reduction because they can implement the aforementioned data security monitoring method for coal mine gas emission reduction.

[0108] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0109] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0111] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0112] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0113] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature serving the same, equivalent, or similar purpose.

[0114] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0115] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0116] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0117] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A data security monitoring method for coal mine gas emission reduction, characterized in that, The method includes: Acquire first time-series data collected by at least one sensor of the device to be monitored within the most recent preset time period; Cache the first time-series data into a first-in-first-out queue; Calculate the mean and standard deviation of all data in the first-in-first-out queue, and generate a dynamic threshold based on the mean and standard deviation; Acquire second time-series data collected by at least one sensor of the device to be monitored; Detect whether the second time-series data is within the range of the dynamic threshold; If the second time-series data is not within the dynamic threshold range, the trusted state of the second time-series data is marked as abnormal data; otherwise, the trusted state of the second time-series data is marked as normal data. The second time-series data, marked as normal data, is cached in the first-in-first-out queue and transmitted to the national carbon market management platform.

2. The method according to claim 1, characterized in that, Calculate the mean and standard deviation of all data within the first-in-first-out queue to generate a dynamic threshold, including: Calculate the mean and standard deviation of all data in the first-in-first-out queue, and generate a temporary threshold based on the mean and standard deviation; The temporary threshold is smoothed using an exponentially weighted moving average algorithm to generate a dynamic threshold.

3. The method according to claim 1, characterized in that, The method of acquiring first time-series data collected by at least one sensor of the device to be monitored within a recent preset time period includes: Based on the Network Time Protocol service, standard time synchronization is performed on the device to be monitored; Acquire raw data from at least one sensor of the monitored device that has been synchronized with a standard time at a preset frequency.

4. The method according to claim 1, characterized in that, The second time-series data, marked as normal, will be transmitted to the national carbon market management platform, including: The second time-series data, marked as normal data, the timestamp, and the device identifier are encapsulated into a data packet; A hash algorithm is used to calculate the hash value of the data packet. The data packet and its hash value are bound together to form a trusted data packet; The trusted data packet is encrypted using a preset encryption algorithm and then sent to the national carbon market management platform.

5. The method according to claim 1, characterized in that, The method further includes: When acquiring the second time-series data, the presence of a communication signal is detected; In the absence of a communication signal, the trusted state of the second time-series data is marked as abnormal.

6. The method according to claim 1, characterized in that, The method further includes: Obtain the status record of at least one sensor from the computer equipment management ledger of the device to be monitored, and determine whether the sensor used to collect the second time series data is in an uncalibrated state. If the sensor is not calibrated when acquiring the second time-series data, the second time-series data will be conservatively processed and marked as requiring conservative processing. The second time-series data marked as requiring conservative processing will be transmitted to the national carbon market management platform.

7. The method according to claim 1, characterized in that, The method further includes: Obtain the status record of at least one sensor from the computer equipment management ledger of the device to be monitored, and determine whether the data sensor used to collect the second time series data is in an uncalibrated state. If the data sensor is not calibrated when acquiring the second time-series data, then the second time-series data will be subjected to a logical check. If the second time series data passes the logical check, then the second time series data is marked as normal; otherwise, the second time series data is marked as abnormal.

8. The method according to claim 1, characterized in that, The second time-series data, marked as normal, will be transmitted to the national carbon market management platform, including: The second time-series data is encrypted using a preset encryption algorithm; Based on a preset communication protocol, the encrypted second-series data is transmitted to the national carbon market management platform.

9. The method according to claim 8, characterized in that, Before tagging the raw data with a timestamp and device identifier, the method further includes: The raw data is cleaned and its format is standardized.

10. The method according to claim 1, characterized in that, The method further includes: Real-time detection of whether the network connection between the smart gateway and the national carbon market management platform is disconnected; In the event of a network connection failure, the smart gateway stores the second time-series data locally. Once the network connection is restored, the second time-series data will continue to be transmitted to the national carbon market management platform.

11. The method according to claim 1, characterized in that, The method further includes: The monitoring data is verified based on the verification request initiated by the national carbon market management platform. The verification of monitoring data based on the verification request initiated by the national carbon market management platform includes: Receive second time-series data sent by the monitoring data storage system; Receive third time-series data sent by the national carbon market management platform; wherein the third time-series data is obtained by encrypting the second time-series data using a preset algorithm; The hash values ​​of the second time series data and the third time series data are compared to determine whether their hash values ​​are consistent. Under the condition of consistency, it is determined that the data stored in the monitoring data storage system and the national carbon market management platform are complete and consistent; Otherwise, a notification indicating that the second timing data and the third timing data are inconsistent is sent and recorded.

12. An edge computing device, characterized in that, The device includes: The data acquisition module is used to acquire first time-series data collected by at least one sensor of the device under monitoring within the most recent preset time period; and to acquire second time-series data collected by at least one sensor of the device under monitoring. An anomaly detection module is configured to: cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue, and generate a dynamic threshold based on the mean and standard deviation; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the trust status of the second time-series data as anomalous data; otherwise, mark the trust status of the second time-series data as normal data; and cache the second time-series data in the FIFO queue. The rules engine module is used to perform logical checks on the second time-series data collected by uncalibrated sensors, and to mark the second time-series data as conservative or abnormal if the second time-series data fails the logical check. The storage and evidence module is used to calculate the hash values ​​of the second time-series data marked as conservatively processed and normal data using a hash algorithm. The encryption module is used to encrypt the second time-series data marked as conservatively processed and the normal data using a preset encryption algorithm to generate encrypted data. The communication module is used to transmit the encrypted data to the national carbon market management platform; The isolation module is used to unidirectionally transmit the second time-series data, which has been processed by the data acquisition module, the anomaly identification module, and the rule engine module and is marked as conservatively processed and normal, to the storage and evidence consolidation module, the encryption module, and the communication module for corresponding processing.

13. A smart gateway, characterized in that, The smart gateway includes a memory and a processor, as well as a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the data security monitoring method for coal mine gas emission reduction as described in any one of claims 1 to 11. The smart gateway further includes: an internal network board, an isolation board, an external network board, a cooling fan, terminal blocks, and a power supply; wherein, the isolation board is provided with the terminal blocks, and the isolation board is connected to the internal network board and the external network board respectively through the terminal blocks; the internal network board is used to connect to a local area network; the external network board is used to connect to the Internet; the cooling fan is disposed inside the smart gateway to dissipate heat from the various power-consuming modules inside the smart gateway; the power supply is used to supply power to the various power-consuming modules inside the smart gateway.

14. A data processing system, characterized in that, The system includes a monitoring and data storage system, a smart gateway, and a national carbon market management platform; wherein... The monitoring data storage system is used to store monitoring data collected by at least one sensor on the device to be monitored; The intelligent gateway is configured to: acquire first time-series data collected by at least one sensor of the device under monitoring within a recent preset time period; cache the first time-series data in a first-in-first-out (FIFO) queue; calculate the mean and standard deviation of all data in the FIFO queue, and generate a dynamic threshold based on the mean and standard deviation; acquire second time-series data collected by at least one sensor of the device under monitoring; detect whether the second time-series data is within the dynamic threshold range; if the second time-series data is not within the dynamic threshold range, mark the confidence status of the second time-series data as abnormal data; otherwise, mark the confidence status of the second time-series data as normal data; cache the second time-series data in the FIFO queue; and transmit the second time-series data marked as normal data to the national carbon market management platform. The national carbon market management platform is used to store second time-series data marked as normal data sent by the smart gateway based on a preset communication protocol.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer-executable program for causing a computer to perform the data security monitoring method for coal mine gas emission reduction as described in any one of claims 1 to 11.

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