LDAR compliance auditing method and system based on data traceability

Abstract

The invention relates to the technical field of industrial environment detection, in particular to an LDAR compliance auditing method and system based on data traceability, and the method comprises the steps: obtaining inspection equipment sampling data, environment background value monitoring data, equipment space topology data and path trajectory data in an LDAR detection process; extracting gas diffusion disturbance characteristics caused by the detection behavior of the inspection equipment, and constructing a detection behavior physical fingerprint representing the disturbance effect of the detection behavior on the gas concentration field; analyzing a detection action position and an action time sequence of the inspection equipment, and constructing a detection disturbance constraint sequence; and performing traceability verification on the physical fingerprint of the detection behavior through the detection disturbance constraint sequence, and generating a compliance auditing result of the LDAR detection behavior. According to the method, the detection behavior physical fingerprint and the detection disturbance constraint sequence are constructed and traceability verification is carried out, so that the reliability and the accuracy of LDAR compliance auditing are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of industrial environmental testing technology, and in particular to a LDAR compliance audit method and system based on data traceability. Background Technology

[0002] Leak Detection and Repair (LDAR) technology has been widely used in petrochemical enterprises as a primary technology for controlling dynamic and static seal leaks in equipment. Traditional LDAR compliance audits mainly rely on manual spot checks, paper record verification, and inspection of the completeness of test reports, lacking quantitative assessment methods for the authenticity and effectiveness of the testing itself.

[0003] In actual testing, when inspection equipment approaches the sealing point and performs testing operations, the movement, stopping, and detection activities of the equipment all cause observable disturbances to the local gas diffusion state, such as fluctuations in the local gas concentration field, changes in the gas concentration gradient, and environmental response phenomena such as the recovery process of the environmental background value. However, existing LDAR compliance audit methods typically use environmental monitoring data only as a basis for concentration determination, without using the environmental response of the detection activities to the gas diffusion state as a basis for verifying the authenticity of the detection, resulting in a lack of effective correlation between environmental data and detection activities.

[0004] Therefore, the existing technology has the following problems: (1) LDAR compliance audit is mainly based on recorded data rather than physical environment response, making it difficult to verify the authenticity of detection behavior; (2) There is a lack of analysis mechanism that can reflect the interaction between detection behavior and gas concentration field, making it impossible to establish a causal correspondence between detection behavior and environmental response; (3) Existing audit methods fail to use environmental background change information to trace and verify detection behavior, resulting in insufficient reliability of the authenticity evaluation of detection behavior. Summary of the Invention

[0005] To overcome the defects and shortcomings of existing technologies, this invention provides a data-based LDAR compliance audit method and system. By constructing a physical fingerprint of the detected behavior and a sequence of detected disturbance constraints and performing source verification, the reliability and accuracy of LDAR compliance audit are effectively improved.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a LDAR compliance audit method based on data traceability, including: Acquire sampling data from inspection equipment, environmental background monitoring data, equipment spatial topology data of the inspected equipment, and path trajectory data of the inspection equipment during the LDAR detection process; Based on environmental background monitoring data, gas diffusion disturbance characteristics caused by the detection behavior of inspection equipment are extracted, and a physical fingerprint of detection behavior is constructed to characterize the disturbance effect of detection behavior on the gas concentration field. Based on equipment spatial topology data and path trajectory data, the detection action location and timing of the inspection equipment are analyzed, and a detection disturbance constraint sequence corresponding to the detection path trajectory is constructed. By detecting perturbation constraint sequences, the physical fingerprint of the detection behavior is traced and verified to generate compliance audit results for the LDAR detection behavior.

[0007] Furthermore, the construction of the physical fingerprint of the detection behavior, which characterizes the perturbation effect of the detection behavior on the gas concentration field, includes: Historical environmental background monitoring data are obtained, and statistical learning is performed on the environmental background values ​​of the target detection area without the influence of the inspection equipment to generate a range of natural diffusion evolution base values ​​characterizing the natural diffusion process of gas concentration. The environmental background monitoring data during the LDAR detection process are compared with the range of natural diffusion evolution baseline values ​​to identify environmental response data that deviate from the range of natural diffusion evolution baseline values. The environmental response data are the environmental background monitoring data that deviate from the range of natural diffusion evolution baseline values. Environmental response features are extracted from environmental response data, and feature fusion encoding is performed on these features to generate a physical fingerprint of the detection behavior that reflects the response of the gas concentration field to the detection behavior.

[0008] Furthermore, the environmental response features include environmental response amplitude features, environmental response gradient rate of change features, and environmental response time recovery features. The environmental response amplitude feature is the absolute value of the maximum difference between the environmental response data and the natural diffusion evolution baseline value. The environmental response gradient rate of change feature is the ratio of the difference between environmental response data at adjacent time nodes to the time interval. The environmental response time recovery feature is the time window during which the environmental response data recovers to the range of the natural diffusion evolution baseline value.

[0009] Furthermore, the construction of the detection perturbation constraint sequence corresponding to the detection path trajectory includes: A three-dimensional spatial topology structure of the inspected equipment is constructed based on the equipment spatial topology data, and the path trajectory of the inspected equipment is extracted through the path trajectory data. The path trajectory of the inspection equipment is mapped to the three-dimensional spatial topology of the equipment being inspected. The spatial position of the sealing point of the equipment being inspected and the effective detection radius of the corresponding inspection equipment are extracted from the three-dimensional spatial topology to determine the detection disturbance constraint of a single inspection behavior. The detection perturbation constraints are arranged in chronological order to generate a detection perturbation constraint sequence, which is used to limit the perturbation range and propagation time of the detection behavior in the gas concentration field.

[0010] Furthermore, the detection perturbation constraint for determining a single detection action includes: By performing time series analysis on the path trajectory of the inspection equipment, the spatial location of the inspection equipment within the dwell time interval is extracted. The detection action space position is determined based on the distance relationship between the spatial position of the inspection equipment and the spatial position of the sealing point. The difference between the spatial position of the detection action space position and the spatial position of the sealing point is less than the effective detection radius of the inspection equipment. The dwell time interval corresponding to the detection action spatial location is used as the detection action time window, and the detection action spatial location and the detection action time window are used as the detection disturbance constraints for a single detection action.

[0011] Furthermore, the compliance audit results for generating LDAR detection behavior include: The environmental response time recovery feature in the physical fingerprint of the detected behavior is time-series aligned with the detection action time window in the detection perturbation constraint sequence to obtain the time synchronization deviation value. The spatial location of the environmental response is determined based on the characteristics of the environmental response amplitude and the characteristics of the environmental response gradient change rate. The spatial location of the environmental response is then spatially matched with the spatial location of the detection action in the detection disturbance constraint sequence to obtain the spatial location deviation value. When the time synchronization deviation is less than the preset time synchronization deviation threshold and the spatial position deviation is less than the preset spatial deviation threshold, the sampling data of the inspection equipment in the corresponding LDAR detection process is used as reliable data, all reliable data are counted and LDAR compliance audit results are generated.

[0012] Secondly, this invention provides a data-based LDAR compliance audit system, including: The data acquisition module is used to acquire sampling data of inspection equipment, environmental background value monitoring data, equipment space topology data of the inspected equipment, and path trajectory data of the inspection equipment during the LDAR detection process. The detection behavior physical fingerprint construction module is used to extract gas diffusion disturbance characteristics caused by the detection behavior of inspection equipment based on environmental background monitoring data, and to construct a detection behavior physical fingerprint that characterizes the disturbance effect of detection behavior on the gas concentration field. The detection disturbance constraint sequence construction module is used to analyze the detection action position and timing of the inspection equipment based on the equipment spatial topology data and path trajectory data, and construct the detection disturbance constraint sequence corresponding to the detection path trajectory; The traceability and verification module is used to trace and verify the physical fingerprint of the detection behavior by detecting the perturbation constraint sequence, and generate the compliance audit result of the LDAR detection behavior.

[0013] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a data traceability-based LDAR compliance audit method by calling the computer program stored in the memory.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a data traceability-based LDAR compliance audit method.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. This invention uses the physical disturbance effect of the inspection behavior of the inspection equipment on the gas concentration field as the basis for LDAR compliance verification. By constructing a physical fingerprint of the inspection behavior, which includes environmental response amplitude characteristics, environmental response gradient change rate characteristics, and environmental response time recovery characteristics, the invention utilizes the disturbance effect of the inspection behavior on the objective physical environment to achieve reverse verification of the authenticity of the inspection behavior, effectively improving the reliability and accuracy of LDAR compliance audit.

[0016] 2. This invention generates a detection disturbance constraint sequence corresponding to the detection path trajectory by using equipment spatial topology data and path trajectory data. This constructs physical constraints on the spatial location and time window for the detection behavior of the inspection equipment, forming a causal closed-loop mechanism between the detection behavior and the environmental response. This can effectively identify abnormal or false detection behavior and reduce the cost of manual review and subjective error. Attached Figure Description

[0017] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the LDAR compliance audit method based on data traceability provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the LDAR compliance audit system based on data traceability provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0019] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the LDAR compliance audit method based on data traceability provided in this embodiment of the invention, which specifically includes the following steps: S110. Acquire sampling data from the inspection equipment, environmental background monitoring data, equipment spatial topology data of the inspected equipment, and path trajectory data of the inspection equipment during the LDAR detection process. The sampling data includes gas concentration data, sampling timestamp, detection equipment number, sampling flow rate, and detection duration, which can be obtained through the inspection equipment data interface. The environmental background monitoring data includes environmental gas concentration background values, monitoring timestamps, spatial location of monitoring points, and auxiliary environmental parameters (such as wind speed, wind direction, temperature, and humidity), which can be obtained from fixed environmental monitoring sensors deployed in the target detection area. The equipment spatial topology data includes the number of sealing points, spatial location of sealing points, and connection relationships of the inspected equipment, which can be obtained from the design drawings of the inspected equipment. The path trajectory data includes the GPS coordinates, timestamps, and movement speed of the inspection equipment during the detection period, which can be obtained through the inspection equipment's GPS positioning module. S120. Based on environmental background monitoring data, extract the gas diffusion disturbance characteristics caused by the detection behavior of the inspection equipment, and construct a physical fingerprint of the detection behavior to characterize the disturbance effect of the detection behavior on the gas concentration field. The core function of physical fingerprinting of detection behavior lies in utilizing the disturbance effect of detection behavior on the objective physical environment to achieve reverse verification of the authenticity of detection behavior. Specifically, when inspection equipment approaches the sealing point and performs detection operations, its movement, dwell time, and gas sampling process inevitably change the local gas diffusion state, causing a measurable physical disturbance to the gas concentration field that was originally in a natural diffusion process. By analyzing the environmental response characteristics induced by the detection behavior, the detection behavior is transformed into quantifiable physical evidence, i.e., physical fingerprinting of detection behavior. This establishes a causal correspondence between detection behavior and changes in the gas diffusion state, realizing the technical path of detection behavior changing the physical environment and using the environment to verify the detection behavior. Physical fingerprinting of detection behavior originates from the measurable disturbance of the gas diffusion state caused by the detection action, ensuring that any genuine detection behavior must leave a traceable physical trace in the environmental data. This provides objective evidence for subsequent source tracing verification based on spatiotemporal constraints. Constructing a physical fingerprinting of detection behavior that characterizes the disturbance effect of detection behavior on the gas concentration field includes: Historical environmental background monitoring data is acquired, and statistical learning is performed on the environmental background values ​​of the target detection area before the impact of inspection equipment detection activities. This generates a natural diffusion evolution baseline range characterizing the natural diffusion process of gas concentration. Specifically, environmental background monitoring data from the past 6-12 months are selected as historical environmental background monitoring data using fixed environmental monitoring sensors (such as infrared gas sensors and catalytic combustion gas sensors) deployed in the target detection area. The acquisition frequency is consistent with the monitoring frequency during LDAR detection (e.g., 1 second / time). The selection criteria are: no LDAR detection activities during the acquisition period, no... Equipment leakage incidents and the absence of extreme weather (such as heavy rain, strong winds, and high temperatures), avoiding environmental factors that could interfere with the natural diffusion of gases; calculating the mean and standard deviation of gas concentration at different time points (such as the same time every day) using historical environmental background monitoring data; using the mean gas concentration as the baseline value for natural diffusion evolution, and the difference between the mean gas concentration and the standard deviation of gas concentration as the lower limit of the baseline value range for natural diffusion evolution, and the sum of the mean gas concentration and the standard deviation of gas concentration as the upper limit of the baseline value range for natural diffusion evolution, the baseline value range for natural diffusion evolution is used to characterize the normal fluctuation range of gas concentration when there is no interference from detection activities; The environmental background monitoring data during the LDAR detection process is compared with the natural diffusion evolution baseline range to identify environmental response data that deviates from the natural diffusion evolution baseline range. Specifically, the environmental background monitoring data during the LDAR detection process is compared with the natural diffusion evolution baseline range at each time node. If the environmental background monitoring data is less than the lower limit of the natural diffusion evolution baseline range or greater than the upper limit of the natural diffusion evolution baseline range, it is considered as environmental response data. Environmental response features are extracted from environmental response data and then fused and encoded to generate a physical fingerprint of the detection behavior that reflects the response of the gas concentration field to the detection behavior. Specifically, the Min-Max normalization method is used to map the environmental response feature values ​​to the [0,1] interval. The normalized environmental response features are then encoded using the SHA-256 hash algorithm to generate a 256-bit fixed-length hash value, which is the physical fingerprint of the detection behavior that represents the perturbation effect of the detection behavior on the gas concentration field. The environmental response characteristics include environmental response amplitude characteristics, environmental response gradient rate of change characteristics, and environmental response time recovery characteristics. Among them, the environmental response amplitude characteristic is the absolute value of the maximum difference between the environmental response data and the natural diffusion evolution baseline value; the environmental response gradient rate of change characteristic is the ratio of the difference between environmental response data at adjacent time nodes to the time interval; and the environmental response time recovery characteristic is the time window during which the environmental response data recovers to the range of the natural diffusion evolution baseline value, including the response start time, response end time, and response duration.

[0020] S130. Based on the equipment spatial topology data and path trajectory data, analyze the detection action position and action sequence of the inspection equipment, and construct the detection disturbance constraint sequence corresponding to the detection path trajectory. By constructing a detection disturbance constraint sequence, the spatial location and temporal sequence of the inspection equipment during the inspection process are described, thereby defining and limiting the disturbance range and propagation time of the gas concentration field caused by the inspection behavior. The detection disturbance constraint sequence helps to correlate the movement and stationary actions of the inspection equipment with changes in gas diffusion in the actual environment, accurately defining the spatial impact area of ​​each inspection action and its temporal persistence. This effectively traces the physical impact of the inspection behavior and ensures that the disturbance effect conforms to the spatial layout and temporal progression of the actual inspection operation. It effectively eliminates external interference factors, providing a scientific basis for subsequent compliance audits. The constructed detection disturbance constraint sequence includes: Based on the equipment spatial topology data, a three-dimensional spatial topology structure of the inspected equipment is constructed, and the path trajectory of the inspection equipment is extracted through path trajectory data. Specifically, the equipment spatial topology data includes the number of sealing points, the spatial location of the sealing points, and their connection relationships of the inspected equipment. Using three-dimensional modeling tools (such as AutoCAD and SolidWorks), the equipment spatial topology data is transformed into a visualized three-dimensional spatial topology structure, clarifying the specific coordinate positions of all sealing points in three-dimensional space. The path trajectory data includes the GPS coordinates, timestamps, and movement speed of the inspection equipment during the inspection period. The path trajectory of the inspection equipment is mapped onto the three-dimensional spatial topology of the equipment being inspected. The spatial positions of the sealing points of the equipment being inspected and the corresponding effective detection radius of the inspection equipment are extracted from the three-dimensional spatial topology. The detection disturbance constraints for a single inspection are determined. Specifically, a coordinate mapping relationship between the path trajectory data and the three-dimensional spatial topology is established. Each GPS coordinate in the path trajectory of the inspection equipment is transformed (e.g., spatial coordinate translation and rotation) to the coordinate system of the three-dimensional spatial topology of the equipment being inspected, achieving precise positioning of the path trajectory in the three-dimensional spatial topology. By extracting the spatial positions of the sealing points of all the equipment being inspected in the three-dimensional spatial topology, the three-dimensional coordinate information of each sealing point is clarified. The effective detection radius of the inspection equipment is obtained from the equipment parameter manual of the inspection equipment. Combining the mapped path trajectory of the inspection equipment, the spatial positions of the sealing points, and the effective detection radius of the inspection equipment, the positions and times that meet the inspection conditions during the inspection process are selected, and the detection disturbance constraints for a single inspection are determined. The detection perturbation constraints are arranged in chronological order to generate a detection perturbation constraint sequence, which is used to limit the perturbation range and propagation time of the detection behavior in the gas concentration field. Determine the detection perturbation constraints for a single detection action, including: By performing time series analysis on the path trajectory of the inspection equipment, the spatial location of the inspection equipment within the dwell time interval is extracted. Specifically, time series analysis is performed on the path trajectory data of the inspection equipment, and the sliding window method (e.g., the window size is set to 10 seconds) is used to analyze the changes in the movement speed of the inspection equipment. A speed threshold (e.g., 0.1 m / s) is set. When the movement speed of the inspection equipment is continuously lower than the speed threshold and the duration exceeds the preset duration (e.g., 10 seconds), this period is determined to be the dwell time interval of the inspection equipment. The spatial coordinates of the inspection equipment within each dwell time interval are extracted, and the average value of all coordinates within the dwell time interval is calculated. The average value is used as the spatial location of the inspection equipment within that dwell time interval. Based on the distance relationship between the spatial location of the inspection equipment and the spatial location of the sealing point, the detection action spatial location is determined. Specifically, the difference between the detection action spatial location and the sealing point spatial location must be less than the effective detection radius of the inspection equipment. In particular, combining the spatial locations of the sealing points of all inspected equipment and the effective detection radius of the inspection equipment, the three-dimensional Euclidean distance between the spatial location of the inspection equipment and each sealing point spatial location within each dwell time interval is calculated. The spatial locations of the inspection equipment whose distance difference from the sealing point spatial location is less than the effective detection radius are selected and used as the detection action spatial location for the corresponding dwell time interval. The dwell time interval corresponding to the detection action spatial location is used as the detection action time window, and the detection action spatial location and detection action time window are used as the detection disturbance constraint for a single detection action. The detection disturbance constraint for a single detection action needs to be labeled with the corresponding inspection equipment number, sealing point number and detection period, so as to provide a basis for the sorting and verification of the subsequent detection disturbance constraint sequence.

[0021] S140. By detecting the perturbation constraint sequence, the physical fingerprint of the detection behavior is traced and verified to generate the compliance audit result of the LDAR detection behavior; By verifying the physical fingerprint of detection behavior through the detection disturbance constraint sequence, the spatiotemporal consistency of the inspection equipment's detection behavior is matched with the actual environmental response characteristics generated in the gas concentration field. This enables the determination of the causal relationship between the detection behavior and the environmental physical response. Specifically, by aligning the environmental response time recovery characteristics with the detection action time window and spatially matching the environmental response spatial location with the detection action spatial location, it is determined whether the gas concentration field disturbance is caused by the detection behavior within a reasonable spatiotemporal range. This transforms environmental monitoring data from a simple basis for concentration determination into a physical verification basis for the authenticity of detection behavior, establishing a closed-loop traceability relationship between detection behavior, gas diffusion disturbance, and environmental response. By identifying false and abnormal detection behaviors, the credibility of detection data and the accuracy and reliability of audit results are effectively improved, generating LDAR compliance audit results, including: The environmental response time recovery feature in the physical fingerprint of the detection behavior is time-aligned with the detection action time window in the detection perturbation constraint sequence to obtain the time synchronization deviation value. Specifically, the environmental response time recovery feature (i.e., the time window in which the environmental response data recovers to the natural diffusion evolution baseline, including the response start time, response end time, and response duration) is extracted from the physical fingerprint of the detection behavior. This time window is used as the environmental response time window. Simultaneously, the detection action time window (including the detection start time, detection end time, and detection duration) for the corresponding single detection behavior in the detection perturbation constraint sequence is extracted. Using a time axis alignment method, the same... The environmental response time window and the detection action time window corresponding to the detection behavior are mapped to the same time axis. Taking the detection start time of the detection action time window as the benchmark, the difference between the corresponding start times of the environmental response time window and the detection action time window is calculated, and the difference between the corresponding end times of the two time windows is also calculated. The maximum absolute value of the two differences is taken as the time synchronization deviation value of this detection behavior. If the environmental response time window falls completely within the detection action time window, the smaller difference is taken as the time synchronization deviation value. The timing alignment and time synchronization deviation value calculation of a single detection are completed. The above operation is repeated for all single detection behaviors in the detection disturbance constraint sequence to obtain the time synchronization deviation value of each detection. The spatial location of the environmental response is determined based on the amplitude and gradient rate characteristics of the environmental response. This spatial location is then spatially matched with the detection action spatial location in the detection perturbation constraint sequence to obtain the spatial location deviation value. Specifically, the amplitude characteristics of the environmental response (the absolute value of the maximum difference between the environmental response data and the natural diffusion evolution baseline value) and the gradient rate characteristics of the environmental response (the ratio of the difference in environmental response data between adjacent time nodes to the time interval) are extracted from the physical fingerprint of the detection behavior. Simultaneously, environmental background monitoring data during the LDAR detection process is acquired. Based on the extracted amplitude and gradient rate characteristics of the environmental response, spatial inversion analysis is performed on the environmental background monitoring data. The following steps are employed: A spatial interpolation algorithm (such as Kriging interpolation) is used in conjunction with environmental background monitoring data from each monitoring point to construct a spatial distribution model of the gas concentration field within the detection area. The peak region of gas concentration disturbance is determined based on the environmental response amplitude characteristics. The diffusion trend of the disturbance region is analyzed by combining the environmental response gradient change rate characteristics. The center coordinates of the most significant gas concentration disturbance region are used as the spatial location of the environmental response. The spatial location of the detection action corresponding to a single detection behavior is extracted from the detection disturbance constraint sequence. The straight-line distance between two spatial locations is calculated using a three-dimensional spatial distance formula; this distance is the spatial location deviation value for this detection. The above steps are repeated to complete the spatial matching and spatial location deviation value calculation for all single detection behaviors. When the time synchronization deviation is less than the preset time synchronization deviation threshold and the spatial position deviation is less than the preset spatial deviation threshold, the sampling data of the inspection equipment in the corresponding LDAR detection process is taken as reliable data. All reliable data are counted and LDAR compliance audit results are generated. The percentage of reliable data to the total amount of sampling data is used as the detection compliance rate, and the detection compliance rate is used as the LDAR compliance audit result. The preset time synchronization deviation threshold is the response time of the fixed environment monitoring sensor, and the preset spatial deviation threshold is the horizontal positioning error of the GPS positioning module of the inspection equipment.

[0022] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of the LDAR compliance audit system based on data traceability provided in this embodiment of the invention, including: Data acquisition module 210 is used to acquire sampling data of inspection equipment, environmental background value monitoring data, equipment space topology data of the inspected equipment, and path trajectory data of inspection equipment during the LDAR detection process; The detection behavior physical fingerprint construction module 220 is used to extract gas diffusion disturbance characteristics caused by the detection behavior of the inspection equipment based on environmental background monitoring data, and to construct a detection behavior physical fingerprint that characterizes the disturbance effect of the detection behavior on the gas concentration field. The detection disturbance constraint sequence construction module 230 is used to analyze the detection action position and action timing of the inspection equipment based on the equipment spatial topology data and path trajectory data, and construct the detection disturbance constraint sequence corresponding to the detection path trajectory; The traceability verification module 240 is used to trace and verify the physical fingerprint of the detection behavior by detecting the disturbance constraint sequence, and generate the compliance audit result of the LDAR detection behavior.

[0023] In this embodiment of the invention, the detection behavior physical fingerprint construction module 220 is used to extract gas diffusion disturbance characteristics caused by the detection behavior of the inspection equipment based on environmental background monitoring data, and to construct a detection behavior physical fingerprint characterizing the disturbance effect of the detection behavior on the gas concentration field, including: Historical environmental background monitoring data are obtained, and statistical learning is performed on the environmental background values ​​of the target detection area without the influence of the inspection equipment to generate a range of natural diffusion evolution base values ​​characterizing the natural diffusion process of gas concentration. The environmental background monitoring data during the LDAR detection process are compared with the range of natural diffusion evolution baseline values ​​to identify environmental response data that deviate from the range of natural diffusion evolution baseline values. The environmental response data are the environmental background monitoring data that deviate from the range of natural diffusion evolution baseline values. Environmental response features are extracted from environmental response data and then fused and encoded to generate a physical fingerprint of the detection behavior that reflects the response of the gas concentration field to the detection behavior. Specifically, the environmental response features include environmental response amplitude features, environmental response gradient change rate features, and environmental response time recovery features. Among them, the environmental response amplitude feature is the absolute value of the maximum difference between the environmental response data and the natural diffusion evolution baseline value, the environmental response gradient change rate feature is the ratio of the difference between environmental response data at adjacent time nodes to the time interval, and the environmental response time recovery feature is the time window in which the environmental response data recovers to the range of the natural diffusion evolution baseline value.

[0024] In this embodiment of the invention, the detection disturbance constraint sequence construction module 230 is used to analyze the detection action position and timing of the inspection equipment based on the equipment spatial topology data and path trajectory data, and construct a detection disturbance constraint sequence corresponding to the detection path trajectory, including: A three-dimensional spatial topology structure of the inspected equipment is constructed based on the equipment spatial topology data, and the path trajectory of the inspected equipment is extracted through the path trajectory data. The path trajectory of the inspection equipment is mapped onto the three-dimensional spatial topology of the equipment being inspected. The spatial location of the sealing point of the equipment being inspected and the effective detection radius of the corresponding inspection equipment are extracted from the three-dimensional spatial topology. The detection disturbance constraints for a single inspection action are determined, including: By performing time series analysis on the path trajectory of the inspection equipment, the spatial location of the inspection equipment within the dwell time interval is extracted. The detection action space position is determined based on the distance relationship between the spatial position of the inspection equipment and the spatial position of the sealing point. The difference between the spatial position of the detection action space position and the spatial position of the sealing point is less than the effective detection radius of the inspection equipment. The dwell time interval corresponding to the detection action spatial location is used as the detection action time window, and the detection action spatial location and the detection action time window are used as the detection disturbance constraints for a single detection action. The detection perturbation constraints are arranged in chronological order to generate a detection perturbation constraint sequence, which is used to limit the perturbation range and propagation time of the detection behavior in the gas concentration field.

[0025] In this embodiment of the invention, the traceability verification module 240 is used to trace and verify the physical fingerprint of the detection behavior by detecting the perturbation constraint sequence, and generate a compliance audit result of the LDAR detection behavior, including: The environmental response time recovery feature in the physical fingerprint of the detected behavior is time-series aligned with the detection action time window in the detection perturbation constraint sequence to obtain the time synchronization deviation value. The spatial location of the environmental response is determined based on the characteristics of the environmental response amplitude and the characteristics of the environmental response gradient change rate. The spatial location of the environmental response is then spatially matched with the spatial location of the detection action in the detection disturbance constraint sequence to obtain the spatial location deviation value. When the time synchronization deviation is less than the preset time synchronization deviation threshold and the spatial position deviation is less than the preset spatial deviation threshold, the sampling data of the inspection equipment in the corresponding LDAR detection process is used as reliable data, all reliable data are counted and LDAR compliance audit results are generated.

[0026] The parameters and steps for implementing the corresponding functions of each unit module in the data traceability-based LDAR compliance audit system of the present invention described above can be referred to the parameters and steps in the embodiments of the data traceability-based LDAR compliance audit method described above, and will not be repeated here.

[0027] Please refer to Figure 3 The present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a data traceability-based LDAR compliance audit method that can be loaded by the processor 320 and executed as provided in the above embodiments.

[0028] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the data traceability-based LDAR compliance audit method provided in the above embodiments. The data storage area may store data involved in the data traceability-based LDAR compliance audit method provided in the above embodiments.

[0029] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data according to the present invention. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and the embodiments of the present invention do not specifically limit this.

[0030] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0031] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for the LDAR compliance audit method based on data tracing.

[0032] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), lectern random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0033] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0034] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

Claims

1. A data-based LDAR compliance audit method, characterized in that, include: Acquire sampling data from inspection equipment, environmental background monitoring data, equipment spatial topology data of the inspected equipment, and path trajectory data of the inspection equipment during the LDAR detection process; Based on environmental background monitoring data, gas diffusion disturbance characteristics caused by the detection behavior of inspection equipment are extracted, and a physical fingerprint of detection behavior is constructed to characterize the disturbance effect of detection behavior on the gas concentration field. Based on equipment spatial topology data and path trajectory data, the detection action location and timing of the inspection equipment are analyzed, and a detection disturbance constraint sequence corresponding to the detection path trajectory is constructed. By detecting perturbation constraint sequences, the physical fingerprint of the detection behavior is traced and verified to generate compliance audit results for the LDAR detection behavior.

2. The LDAR compliance audit method based on data traceability according to claim 1, characterized in that, The construction of the physical fingerprint of the detection behavior, which characterizes the perturbation effect of the detection behavior on the gas concentration field, includes: Historical environmental background monitoring data are obtained, and statistical learning is performed on the environmental background values ​​of the target detection area without the influence of the inspection equipment to generate a range of natural diffusion evolution base values ​​characterizing the natural diffusion process of gas concentration. The environmental background monitoring data during the LDAR detection process are compared with the range of natural diffusion evolution baseline values ​​to identify environmental response data that deviate from the range of natural diffusion evolution baseline values. The environmental response data are the environmental background monitoring data that deviate from the range of natural diffusion evolution baseline values. Environmental response features are extracted from environmental response data, and feature fusion encoding is performed on these features to generate a physical fingerprint of the detection behavior that reflects the response of the gas concentration field to the detection behavior.

3. The LDAR compliance audit method based on data traceability according to claim 2, characterized in that, The environmental response features include environmental response amplitude features, environmental response gradient change rate features, and environmental response time recovery features. The environmental response amplitude feature is the absolute value of the maximum difference between the environmental response data and the natural diffusion evolution baseline value. The environmental response gradient change rate feature is the ratio of the difference between environmental response data at adjacent time nodes to the time interval. The environmental response time recovery feature is the time window during which the environmental response data recovers to the range of the natural diffusion evolution baseline value.

4. The LDAR compliance audit method based on data traceability according to claim 1, characterized in that, The constructed detection perturbation constraint sequence corresponding to the detection path trajectory includes: A three-dimensional spatial topology structure of the inspected equipment is constructed based on the equipment spatial topology data, and the path trajectory of the inspected equipment is extracted through the path trajectory data. The path trajectory of the inspection equipment is mapped to the three-dimensional spatial topology of the equipment being inspected. The spatial position of the sealing point of the equipment being inspected and the effective detection radius of the corresponding inspection equipment are extracted from the three-dimensional spatial topology to determine the detection disturbance constraint of a single inspection behavior. The detection perturbation constraints are arranged in chronological order to generate a detection perturbation constraint sequence, which is used to limit the perturbation range and propagation time of the detection behavior in the gas concentration field.

5. The LDAR compliance audit method based on data traceability according to claim 4, characterized in that, The detection perturbation constraints for determining a single detection action include: By performing time series analysis on the path trajectory of the inspection equipment, the spatial location of the inspection equipment corresponding to the dwell time interval is extracted. The detection action space position is determined based on the distance relationship between the spatial position of the inspection equipment and the spatial position of the sealing point. The difference between the spatial position of the detection action space position and the spatial position of the sealing point is less than the effective detection radius of the inspection equipment. The dwell time interval corresponding to the detection action spatial location is used as the detection action time window, and the detection action spatial location and the detection action time window are used as the detection disturbance constraints for a single detection action.

6. The LDAR compliance audit method based on data traceability according to claim 1, characterized in that, The compliance audit results for generating LDAR detection behavior include: The environmental response time recovery feature in the physical fingerprint of the detected behavior is time-series aligned with the detection action time window in the detection perturbation constraint sequence to obtain the time synchronization deviation value. The spatial location of the environmental response is determined based on the characteristics of the environmental response amplitude and the characteristics of the environmental response gradient change rate. The spatial location of the environmental response is then spatially matched with the spatial location of the detection action in the detection disturbance constraint sequence to obtain the spatial location deviation value. When the time synchronization deviation is less than the preset time synchronization deviation threshold and the spatial position deviation is less than the preset spatial deviation threshold, the sampling data of the inspection equipment in the corresponding LDAR detection process is used as reliable data, all reliable data are counted and LDAR compliance audit results are generated.

7. A data-based LDAR compliance audit system, used to implement the data-based LDAR compliance audit method according to any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire sampling data of inspection equipment, environmental background value monitoring data, equipment space topology data of the inspected equipment, and path trajectory data of the inspection equipment during the LDAR detection process. The detection behavior physical fingerprint construction module is used to extract gas diffusion disturbance characteristics caused by the detection behavior of inspection equipment based on environmental background monitoring data, and to construct a detection behavior physical fingerprint that characterizes the disturbance effect of detection behavior on the gas concentration field. The detection disturbance constraint sequence construction module is used to analyze the detection action location and timing of the inspection equipment based on the equipment spatial topology data and path trajectory data, and construct the detection disturbance constraint sequence corresponding to the detection path trajectory; The traceability and verification module is used to trace and verify the physical fingerprint of the detection behavior by detecting the perturbation constraint sequence, and generate the compliance audit result of the LDAR detection behavior.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the LDAR compliance audit method based on data traceability as described in any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the LDAR compliance audit method based on data traceability as described in any one of claims 1-6.

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