Gas density relay monitoring system based on edge computing

Abstract

This invention relates to the field of electrical equipment monitoring technology, specifically a gas density relay monitoring system based on edge computing. The system includes a physical deformation and environmental coupling sensing module, an edge logic threshold dynamic verification module, an edge gateway command mapping and binding module, a monitoring accuracy compensation and structural traceability module, and an edge node distribution and execution module. In this invention, multi-dimensional sensing based on physical deformation and environmental parameters, coupled with edge decoupling computation, eliminates nonlinear interference from temperature and humidity in real time, improving gas monitoring accuracy. By utilizing spatiotemporal coordinate binding and logic threshold verification, it strengthens the capture and synchronous verification of pressure jump points, solving the problem of malfunctions and failures to operate. It directly converts power grid regulations into edge execution code, achieving deep coupling between monitoring and compliance. Combined with calculus compensation and structural traceability mechanisms, it optimizes the sampling frequency and corrects mechanical fatigue, improving data reliability and ensuring compatible command output across different physical links, thus increasing distribution efficiency.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment monitoring technology, and in particular to a gas density relay monitoring system based on edge computing. Background Technology

[0002] The field of electrical equipment monitoring technology involves real-time tracking and evaluation of the operating status of various power equipment in substations and transmission and distribution lines. Its core aspects include sensor deployment, data acquisition, and status analysis. By continuously acquiring parameters such as current, voltage, partial discharge, temperature, and insulation medium status, a sensing network is constructed to ensure the stable operation of the power grid. Among these, the traditional gas density relay monitoring system refers to a device that detects the pressure and temperature of sulfur hexafluoride gas within high-voltage switchgear. It addresses the technical issue of reduced insulation strength caused by gas leakage. It uses a mechanical pressure gauge combined with a bimetallic strip compensation structure for on-site display, or acquires gas pressure and temperature values ​​through a pressure sensor and a platinum resistance thermometer. The pressure value is then converted to an equivalent pressure value at 20 degrees Celsius according to the ideal gas law, and the analog signal is transmitted to the acquisition server in the substation's main control room via wires.

[0003] Traditional gas density relay monitoring technology heavily relies on mechanical meters and offline sensing structures, resulting in physical response lag and difficulty in capturing minute leaks in real time. The compensation process relies solely on physical material deformation or ideal gas law calculations, leading to severe monitoring deviations due to nonlinear disturbances in metal bellows under extreme cold or heat environments. Signal transmission mainly adopts analog remote transmission mode, which is highly susceptible to electromagnetic interference, causing signal distortion and data loss. It lacks in-depth correlation analysis between relay mechanical fatigue and contact travel, making it impossible to form accurate feedback control for different equipment models. This results in blind spots in operation and maintenance decisions and greatly increases the operational risks of insulation breakdown in high-voltage switchgear. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a gas density relay monitoring system based on edge computing.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a gas density relay monitoring system based on edge computing includes: The physical deformation and environmental coupling sensing module acquires the displacement variables of the internal insulating gas pressure sensing diaphragm, the Young's modulus of the material and the coefficient of thermal expansion and contraction, captures the movement trend of the internal mechanical linkage of the relay, uses edge computing to offset the nonlinear disturbance of temperature and humidity on the metal bellows, identifies the correspondence between pressure compensation value and contact action stroke, and generates a correlation map between mechanical displacement and gas pressure state. The edge logic threshold dynamic verification module extracts the critical pressure jump point under different ambient temperatures based on the mechanical displacement and gas pressure state correlation map, and verifies the physical synchronization of relay action by combining the power grid operation procedures and the switchgear body manual, and constructs a gas monitoring closed-loop compliance judgment model. The edge gateway instruction mapping and binding module transforms the compliance path in the gas monitoring closed-loop compliance judgment model into the underlying control protocol, and uses the edge computing logic mapping mechanism to embed the mandatory provisions of the power regulations into the kernel execution sequence, forming a standard instruction set for edge-side gas monitoring. The monitoring accuracy compensation and structural traceability module performs calculus and integral compensation to correct signal glitches or sampling clock offsets during the execution of the edge-side gas monitoring standard instruction set, extracts mechanical fatigue deviation patterns, adjusts the triggering frequency and sampling depth, and constructs a gas monitoring information traceability enhancement structure set. The edge node distribution and execution module, based on the gas monitoring information traceability enhancement structure set, automatically generates an operation sequence that adapts to the target physical link according to the physical interface definition of the differentiated switchgear models on site and the load capacity of the edge node. It issues point-to-point instructions through the edge gateway, marks the execution weight and effective time stamp, and outputs a monitoring instruction distribution device binding table.

[0006] As a further aspect of the present invention, the mechanical displacement and gas pressure state correlation map includes a quantified displacement trend curve, a calibrated disturbance correction vector, and a correlated state data domain; the gas monitoring closed-loop compliance judgment model includes zone threshold parameters for adapting temperature, physical synchronization criteria for action verification, and a closed-loop compliance framework for the judgment model; the edge-side gas monitoring standard instruction set includes binding commands for protocol conversion, path control units for mandatory clauses, and computer-controlled logical instruction paths; the gas monitoring information traceability enhancement structure set includes signal compensation factors in the instruction process, structural adjustment parameters for trigger optimization, and an enhanced framework for information traceability; and the monitoring instruction distribution device binding table includes model interface adaptation parameters for field devices, weighted label for instruction execution, and binding records for time-stamped effectiveness.

[0007] As a further aspect of the present invention, the physical deformation and environment coupling sensing module includes: The physical state decoupling submodule uses edge computing to separate the pseudo-pressure fluctuations caused by temperature based on the mechanical thrust signal of the insulating gas pressure on the sensing diaphragm, identifies the current physical equilibrium stage of the relay, and generates a stage physical characteristic identifier. The spatiotemporal coordinate binding submodule calls the physical feature identifier of the stage and combines it with the precision clock source of the edge computing gateway to align the geospatial number of the switching equipment with the pressure mutation event at the nanosecond level, and obtain the spatiotemporal correlation monitoring record. The correlation map construction submodule calls the spatiotemporal correlation monitoring records, arranges the monitoring events according to the physical evolution logic, assigns weights according to the aging curve of the equipment material, and establishes a correlation map between mechanical displacement and air pressure status.

[0008] As a further aspect of the present invention, the edge logic threshold dynamic verification module includes: The action logic verification submodule calls the mechanical displacement and air pressure state correlation graph, analyzes the causal relationship between the pressure drop curve and the contact closure signal, calculates the logic threshold verification deviation value, and generates the link action verification matrix. The physical parameter matching submodule calls the link action verification matrix, compares the deviation between the actual measurement of the pressure sensor and the rated insulation strength index of the equipment, filters the monitoring path that meets the power safety regulations, and outputs the compliant path screening sequence. The compliance model solidification submodule calls the compliance path filtering sequence to determine the deviation range of key pressure control points, judges the hard boundaries of the edge computing gateway definition logic, and generates a gas monitoring closed-loop compliance judgment model.

[0009] As a further aspect of the present invention, the logic threshold verification deviation value refers to the calculation of the Euclidean distance between the instantaneous pressure value and the preset pressure alarm threshold by setting a preset pressure alarm threshold as a standard parameter, and converting the Euclidean distance into a percentage form of logic response accuracy to obtain the logic threshold verification deviation value.

[0010] As a further aspect of the present invention, the edge gateway instruction mapping and binding module includes: The control signal mapping submodule calls the gas monitoring closed-loop compliance judgment model, maps the preset pressure threshold in the model to the digital pulse frequency of the edge gateway, and generates a logical association response table for the physical mapping of the control signal and the pressure sensor execution unit. The procedure text embedding submodule calls the logical association response table to transform the power safety constraints into a combination triggering mechanism of logic gates, embedding the procedure, i.e., code, at the edge side to form a procedure logic index list; The binding instruction generation submodule calls the procedure logic index list and, based on the hardware trigger timing of the edge gateway, converts the static procedure into dynamic monitoring and control pulses, synchronizes the operating status of each monitoring node, and forms a standard instruction set for edge-side gas monitoring.

[0011] As a further aspect of the present invention, the physical mapping between the control signal and the pressure sensor execution unit refers to retrieving the hardware address code of the pressure sensor execution unit, assigning the frequency characteristic parameters to the corresponding general-purpose input / output ports, and establishing a correspondence between logic high and low levels and the on state of the pressure sensor execution unit through a level logic converter.

[0012] As a further aspect of the present invention, the monitoring accuracy compensation and structural traceability module includes: The deviation retrieval submodule is executed, which calls the edge-side gas monitoring standard instruction set to capture signal distortion or feedback delay during instruction execution in real time, matches the preset mechanical hysteresis compensation strategy, and forms an accuracy correction scheme library. The feature marking submodule calls the accuracy correction scheme library to identify the frequency characteristics of relay malfunction or failure to operate in the target environment, marks physical nodes that are prone to monitoring errors, and generates physical labels for repetitive deviations. The structure library enhancement submodule calls the repetitive deviation physical tag, reorganizes the execution sequence of the monitoring procedure through edge computing, automatically optimizes the sampling step frequency and trigger bandwidth of key control nodes, and constructs a gas monitoring information traceability enhancement structure set.

[0013] As a further aspect of the present invention, the signal distortion or feedback delay refers to the real-time acquisition of the waveform envelope of the control signal by the edge-side sampler, the calculation of the correlation coefficient between the waveform envelope and the preset waveform in the edge-side gas monitoring standard instruction set, and the determination of signal distortion when the correlation coefficient is lower than the preset correlation threshold. At the same time, the time difference between the instruction sending time and the feedback signal return time is recorded to identify signal distortion or feedback delay.

[0014] As a further aspect of the present invention, the edge node distribution execution module includes: The instruction version synchronization submodule calls the gas monitoring information traceability enhancement structure set, verifies the hash value and update time of the currently executed script on the edge side, compares the current version with the original version, selects the valid version file, and generates an instruction version verification table. The physical link distribution submodule, based on the instruction version verification table and combined with the port load and communication topology of the edge gateway, locks the access path of the target relay, records the communication cycle and protocol identifier, and generates a monitoring instruction distribution device binding table.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by combining multi-dimensional perception of physical deformation and environmental parameters with edge-side decoupling operations, the nonlinear interference of external temperature and humidity on the mechanical structure is eliminated in real time. The logic corresponding to the pressure compensation value and the contact stroke is accurately identified, significantly improving the gas monitoring accuracy under complex working conditions. By using spatiotemporal coordinate binding and dynamic verification of logic thresholds, the ability to capture critical pressure jump points is enhanced and physical synchronization is automatically verified, effectively solving the problems of false operation and failure to operate in traditional detection methods. The mandatory provisions of power grid regulations are directly converted into edge-side execution code, realizing deep coupling between the monitoring path and compliance standards. Combined with calculus compensation algorithm and structural traceability mechanism, the sampling frequency is automatically optimized and mechanical fatigue deviation is corrected, enhancing the reliability of monitoring data and ensuring the output of adaptable instructions for different physical links, greatly improving distribution efficiency. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the physical deformation and environment coupling sensing module in this invention; Figure 3 This is a flowchart of the dynamic verification module for edge logic thresholds in this invention; Figure 4 This is a flowchart of the edge gateway instruction mapping and binding module in this invention; Figure 5 This is a flowchart of the monitoring accuracy compensation and structural traceability module in this invention; Figure 6 This is a flowchart of the edge node distribution and execution module in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Please see Figure 1 The edge computing-based gas density relay monitoring system includes: The physical deformation and environmental coupling sensing module is based on the displacement variable of the pressure sensing diaphragm of the insulating gas inside the high-voltage switchgear. Combined with the Young's modulus and thermal expansion coefficient of the material, it captures the micron-level movement trend of the mechanical linkage inside the relay in real time. It also uses edge computing to offset the nonlinear disturbance of the ambient temperature and humidity to the metal bellows in real time, identifies the correspondence between the pressure compensation value and the contact stroke, and generates a correlation map between mechanical displacement and air pressure status. The edge logic threshold dynamic verification module extracts the critical pressure jump point under different ambient temperatures based on the correlation map between mechanical displacement and air pressure status. In accordance with the power grid operation procedures and the switchgear body manual, it establishes a dynamic logic preset threshold judgment matrix to verify the physical synchronization of relay closing and opening actions and construct a gas monitoring closed-loop compliance judgment model. The edge gateway instruction mapping and binding module is based on the gas monitoring closed-loop compliance judgment model. It transforms the compliance path in the model into the underlying control protocol of the edge gateway and uses the edge computing logic mapping mechanism to directly embed the mandatory provisions in the power regulations into the kernel execution sequence of the gateway, forming a standard instruction set for gas monitoring on the edge side. The monitoring accuracy compensation and structural traceability module, based on the standard instruction set for edge-side gas monitoring, performs edge-side calculus compensation correction for signal glitches or sampling clock offsets caused by electromagnetic interference during instruction execution, extracts the mechanical fatigue deviation law of relays during long-term operation, and structurally adjusts the trigger frequency and sampling depth in the control logic to construct a gas monitoring information traceability enhancement structure set. The edge node distribution and execution module is based on the gas monitoring information traceability enhancement structure set. According to the physical interface definition of the differentiated switchgear models on site and the load capacity of the edge node, it automatically generates the operation sequence adapted to the target physical link, issues point-to-point instructions through the edge gateway, marks the execution weight and effective time stamp, and outputs the monitoring instruction distribution device binding table.

[0020] The mechanical displacement and gas pressure state correlation map includes quantified displacement trend curves, calibrated disturbance correction vectors, and associated state data domains. The gas monitoring closed-loop compliance judgment model includes zone threshold parameters for adaptable temperatures, physical synchronization criteria for action verification, and a closed-loop compliance framework for the judgment model. The edge-side gas monitoring standard instruction set includes protocol-converted binding commands, path control units for mandatory clauses, and computerized logical instruction paths. The gas monitoring information traceability enhancement structure set includes signal compensation factors in the instruction process, structural adjustment parameters for trigger optimization, and an enhanced framework for information traceability. The monitoring instruction distribution device binding table includes model interface adaptation parameters for field devices, weighted label tags for instruction execution, and binding records for time-stamped effectiveness.

[0021] Please see Figure 2 The physical deformation and environment coupling sensing module includes: The physical state decoupling submodule uses edge computing to separate the pseudo-pressure fluctuations caused by temperature based on the mechanical thrust signal of the insulating gas pressure on the sensing diaphragm, identifies the current physical equilibrium stage of the relay, and generates a stage physical characteristic identifier. The electrical signal generated by the piezoresistive sensor inside the sensing cavity is retrieved. This signal is processed by an analog-to-digital converter into a 12-bit discrete value, representing the real-time mechanical thrust of the insulating gas pressure on the sensing diaphragm. Simultaneously, the millivolt-level voltage output from the platinum resistance temperature sensing element deployed inside the relay housing is acquired and converted into a real-time ambient temperature value, for example, a current ambient temperature of 45 degrees Celsius. Pre-stored sulfur hexafluoride gas state characteristic parameters are extracted, and a subtraction operation is performed to extract the net pressure fluctuation value. Specifically, based on the linear relationship between pressure and temperature under constant volume conditions, the pressure drift component caused by thermal expansion and contraction when the temperature rises from a standard 20 degrees Celsius to 45 degrees Celsius is calculated. For example, if the drift value is calculated to be 0.042 MPa, the 0.042 MPa interference term is subtracted from the original measured total pressure of 0.55 MPa to obtain the intrinsic pressure value reflecting the sealing state. Subsequently, by performing a first-order derivative operation on the intrinsic pressure value, the current physical equilibrium stage of the relay is identified. If the absolute value of the derivative is less than 0.001 MPa within 10 consecutive sampling periods, it is determined to be a static stable stage; if the absolute value of the derivative exceeds 0.05 MPa, it is determined to be a transient stage caused by contact action, thereby generating a physical characteristic identifier of the corresponding stage numbered 101 or 202.

[0022] The spatiotemporal coordinate binding submodule calls the stage physical feature identifier and combines the precision clock source of the edge computing gateway to align the geospatial number of the switching equipment with the pressure change event at the nanosecond level, and obtain spatiotemporal correlation monitoring records. Upon receiving the physical characteristic identifier of the stage, the high-precision hardware clock circuit inside the edge gateway is immediately accessed to extract the nanosecond-level pulse count as the reference time. The device ledger information stored in the non-volatile memory is retrieved to obtain the 16-bit hexadecimal geospatial number of the switchgear. This number uniquely maps to the specific geographical coordinates and physical interval of the substation. When the physical state decoupling submodule detects that the pressure change rate exceeds a preset abrupt change threshold, the nanosecond-level pulse value at that moment is encapsulated with the geospatial number in a data frame. During the encapsulation process, transmission delay compensation calculations are performed, dividing the communication time between the instruction issuance and feedback transmission into two equal compensation intervals. A fixed hardware circuit response time is deducted, for example, an inherent delay of 150 nanoseconds, to achieve atomic-level alignment between the pressure abrupt change event and the geospatial location. Finally, a spatiotemporal correlation monitoring record containing three-dimensional information of time, space, and state is obtained.

[0023] The correlation map construction submodule calls the spatiotemporal correlation monitoring records, arranges the monitoring events according to the physical evolution logic, assigns weights according to the aging curve of the equipment material, and establishes a correlation map between mechanical displacement and air pressure status. The system retrieves spatiotemporal correlation monitoring records and arranges pressure fluctuation events linearly along the physical time evolution axis. It then searches for preset aging fatigue curves of the sensing diaphragm material and assigns corresponding correction weights based on the cumulative operating time of the relays. For example, equipment with 5 years of operation is assigned an aging weight coefficient of 0.85, while equipment with less than 1 year of operation is assigned a weight coefficient of 1.0. The correction weights are multiplied by the measured travel data of the mechanical displacement sensor to obtain the equivalent displacement value. Subsequently, a two-dimensional vector matrix is ​​constructed in the logical memory space with the equivalent displacement value as the horizontal axis and the synchronously acquired air pressure state value as the vertical axis. The least squares fitting algorithm is used to depict the trajectory of the coordinate points within the matrix, and the slope correlation between displacement and pressure drop is calculated. When the slope falls within the range of 0.02 to 0.05 mm / MPa, a correlation map between mechanical displacement and air pressure state is established as the physical reference benchmark for subsequent logical judgments.

[0024] Please see Figure 3 The edge logic threshold dynamic verification module includes: The action logic verification submodule calls the correlation graph between mechanical displacement and air pressure state, analyzes the causal relationship between the pressure drop curve and the contact closure signal, calculates the logic threshold verification deviation value, and generates the link action verification matrix. The logic threshold verification deviation value refers to the Euclidean distance between the instantaneous pressure value and the preset pressure alarm threshold as a standard parameter, and the Euclidean distance is converted into a percentage form of logic response accuracy to obtain the logic threshold verification deviation value. The process involves calling the correlation graph between mechanical displacement and air pressure state, extracting the morphological characteristics of the pressure drop curve and the instantaneous current pulse at contact closure, executing causal relationship analysis logic, and calculating the time difference between the pressure drop to the warning threshold and the electrical signal transition. If this value falls within the reasonable mechanical action range of 3 to 8 milliseconds, the causal relationship is confirmed. Subsequently, the logic threshold verification deviation value is calculated. This process first sets a preset pressure alarm threshold of 0.38 MPa as the standard parameter coordinate, obtains the currently acquired instantaneous pressure value of 0.39 MPa, and calculates the absolute value of the subtraction between the instantaneous pressure value and the preset pressure alarm threshold, yielding an Euclidean distance of 0.01 MPa. To convert this distance into logic response accuracy, a division operation is performed, dividing 0.01 MPa by the standard parameter 0.38 MPa to obtain the deviation ratio. Subtracting this deviation ratio from the number 1 yields a coefficient of 0.9737, which is then converted into a percentage form of 97.37% as the logic response accuracy. Finally, this is combined with the environmental compensation coefficient to generate the process action verification matrix.

[0025] The physical parameter matching submodule calls the link action verification matrix, compares the deviation between the actual measurement of the pressure sensor and the rated insulation strength index of the equipment, filters the monitoring path that meets the power safety regulations, and outputs the compliant path screening sequence. The system retrieves the action verification matrix and extracts the real-time measurement values ​​of the pressure sensors. Simultaneously, it accesses the equipment's rated insulation strength index table to obtain the lower limit of the rated pressure for maintaining arc-extinguishing performance, which is 0.35 MPa. The deviation between the real-time measurement value and the lower limit of the rated pressure is calculated by subtraction, followed by division by the lower limit. For example, when the measured value is 0.33 MPa, the calculated deviation is 5.71%. This deviation is compared to the 5% compliance limit defined in power safety regulations. If the deviation exceeds the limit, the monitoring path is deemed to have an insulation risk. By iterating and comparing the deviations of multiple sensor channels, redundant paths with deviations greater than 5% are eliminated. Monitoring channels that meet the safety margin requirements are selected and sorted in descending order of signal quality, outputting a compliant path selection sequence to ensure that the monitoring results comply with power industry safety standards.

[0026] The compliance model solidification submodule calls the compliance path filtering sequence, determines the deviation range of key pressure control points, judges the hard boundary of the edge computing gateway definition logic, and generates a gas monitoring closed-loop compliance judgment model. The compliance path screening sequence is invoked to identify the operational fluctuation range of key pressure control points within the sequence, determining the deviation range to be 0.375 MPa to 0.385 MPa. The microcontroller register configuration of the edge computing gateway is retrieved to enforce the definition of logical hard boundaries. The hard boundary judgment logic stipulates that when the pressure value touches the lower limit of the deviation range, the microcontroller must directly trigger a protection command via a hardware interrupt. The numerical boundary of the deviation range is converted into a comparator reference level, and a Boolean logic judgment expression is embedded in the logic judgment unit. If the measured pressure is less than or equal to 0.375 MPa and the duration exceeds two sampling cycles, the compliance judgment result is output as false. All the above hard judgment logic is encapsulated and solidified to generate a gas monitoring closed-loop compliance judgment model. This model no longer relies on the instruction loop of external application software but forms a self-sufficient closed-loop judgment mechanism in the underlying logic unit, ensuring deterministic and compliant responses.

[0027] Please see Figure 4 The edge gateway command mapping and binding module includes: The control signal mapping submodule calls the gas monitoring closed-loop compliance judgment model, maps the pressure preset threshold in the model to the digital pulse frequency of the edge gateway, and generates a logical association response table for the physical mapping of control signals and pressure sensor execution units. The physical mapping between the control signal and the pressure sensor execution unit refers to retrieving the hardware address code of the pressure sensor execution unit, assigning the frequency characteristic parameters to the corresponding general-purpose input / output ports, and establishing the correspondence between logic high and low levels and the on state of the pressure sensor execution unit through a level logic converter. The system retrieves a preset pressure threshold of 0.38 MPa from the gas monitoring closed-loop compliance judgment model and performs a proportional mapping operation to convert the pressure threshold into a digital pulse frequency of the pulse width modulation generator inside the edge gateway. The mapping logic uses a fixed proportional coefficient; for example, setting each 0.01 MPa to a 1 Hz frequency increment, 0.38 MPa is mapped to a 38 Hz reference frequency. Subsequently, a physical mapping is performed between the control signal and the pressure sensor execution unit. The physical address code of the pressure sensor execution unit is retrieved from the hardware resource description file, for example, address 0xFE01. The generated 38 Hz frequency parameter is assigned to the corresponding general-purpose input / output port pin of the processor, controlling the level-to-logic converter to establish a mapping between the logic high level and the execution unit's on state. If the logic output is 1, the pin outputs a 3.3V high level, which is amplified to a 24V drive voltage by the level converter; if the logic output is 0, the pin remains at a 0V low level. This process eliminates the address deviation between the logic signal and the physical hardware, ultimately generating a logic association response table recording the address, frequency, and pin mapping relationship.

[0028] The procedure text embedding submodule calls the logical association response table to transform power safety constraints into a combination triggering mechanism of logic gates. The procedure, i.e. code, is embedded at the edge side to form a procedure logic index list. The logic-related response table is retrieved, and the power safety constraints are transformed into combination trigger logic of low-level logic gates. For example, the clause in the procedure stating "trigger enhanced monitoring when the pressure is below 0.38 MPa and the ambient temperature is above 40 degrees Celsius" is transformed into a logic AND gate, whose inputs are the outputs of a pressure comparator and a temperature comparator, respectively. At the edge, the procedure (i.e., code) is embedded, directly compiling this logical relationship into a logic control unit that can run in the gateway kernel. This operation avoids scheduling intervention from the upper-level operating system, making the procedure clause a hardware-level logical constraint. By performing this transformation on all compliance constraints, the complex natural language procedures are converted into a set of Boolean algebras composed of logical AND, logical OR, and logical NOT operations, ultimately forming a procedure logic index list. This list assigns a clear procedure traceability number and logical trigger condition to each monitoring action.

[0029] The binding instruction generation submodule calls the procedure logic index list and, based on the hardware trigger timing of the edge gateway, transforms the static procedure into dynamic monitoring and control pulses, synchronizes the operating status of each monitoring node, and forms a standard instruction set for edge-side gas monitoring. The procedure logic index list is invoked and dynamically transformed using the edge gateway's hardware trigger timing controller. Based on the gateway's base clock cycle, the static procedure logic index is converted into monitoring and control pulses with a temporal sequence. For example, a pressure status polling pulse is initiated every 10 milliseconds, and verification codes from the procedure logic index list are embedded in the pulse's data bits. This synchronizes the operating status of each monitoring node under the gateway, ensuring strict alignment of pressure acquisition pulses and temperature compensation pulses on the time axis, avoiding logical misjudgments due to timing misalignment. The pulse sequences with time stamps, physical address mappings, and procedure constraints are assembled to form a standard instruction set for edge-side gas monitoring. This instruction set specifies the binary message format sent by the gateway to each actuator. Each message includes a header, logic instruction code, physical execution address, and cyclic redundancy check code, achieving standardized distribution of monitoring logic.

[0030] Please see Figure 5 The monitoring accuracy compensation and structure traceability module includes: The deviation retrieval submodule calls the standard instruction set for edge-side gas monitoring, captures signal distortion or feedback delay during instruction execution in real time, matches the preset mechanical hysteresis compensation strategy, and forms an accuracy correction scheme library. Signal distortion or feedback delay refers to the real-time acquisition of the waveform envelope of the control signal by the edge-side sampler, the calculation of the correlation coefficient between the waveform envelope and the preset waveform in the edge-side gas monitoring standard instruction set, and the determination of signal distortion when the correlation coefficient is lower than the preset correlation threshold. At the same time, the time difference between the instruction sending time and the feedback signal return time is recorded to identify signal distortion or feedback delay. The system monitors the execution feedback of the standard instruction set for edge-side gas monitoring in real time, retrieves the control signal waveform envelope captured by the edge-side sampler, and calculates the waveform correlation coefficient. The calculation process involves cross-correlation between the real-time acquired voltage waveform sequence and the preset standard square wave sequence in the instruction set. The calculated correlation coefficient is 0.75, which is compared to the preset correlation threshold of 0.85. Since 0.75 is less than 0.85, the submodule determines that the current signal is distorted. Simultaneously, the feedback delay is measured, recording the rising edge of the instruction pulse and the rising edge of the sensor feedback signal pulse. The difference between the two is calculated to be 12 milliseconds, while the standard response time is set to 5 milliseconds. This 10-millisecond deviation is used as a search condition to retrieve the corresponding phase correction parameter from the mechanical hysteresis compensation strategy library. The correction parameter instructs the system to execute the trigger action earlier in subsequent loops, thereby offsetting the delay caused by mechanical inertia, ultimately generating a precision correction scheme library containing phase offset and amplitude correction coefficients.

[0031] The feature marking submodule calls the accuracy correction scheme library to identify the frequency characteristics of relay malfunction or failure to operate in the target environment, marks physical nodes that are prone to monitoring errors, and generates physical labels for repetitive deviations. The accuracy correction scheme library is invoked to extract statistical features from the deviation data generated during long-term operation, identifying the frequency characteristics of relay malfunctions or failures to operate under the target environment. The target environment refers to the current real-time environmental parameters falling within harsh ranges such as high humidity or high salt spray. The number of signal distortions occurring at a specific physical node in the past 72 hours is counted; if the number exceeds 5, the node is determined to be an unstable node prone to monitoring errors. A unique repeatability deviation physical tag is assigned to this node, containing its physical port number, electromagnetic sensitivity level, and typical delay time constant. For example, monitoring point 3, which is severely affected by solenoid valve interference, is marked with a physical tag coded "DEV-003." The tag information is stored in non-volatile storage space on the edge side to guide subsequent monitoring sequence optimization.

[0032] The structure library enhancement submodule calls the physical tag of repetitive deviation, reorganizes the execution sequence of the monitoring procedure through edge computing, automatically optimizes the sampling step frequency and trigger bandwidth of key control nodes, and builds a gas monitoring information traceability enhancement structure set; The physical tags for repetitive deviations are invoked, and the execution sequence of the monitoring procedure is reorganized and optimized through an edge computing engine. For nodes tagged "DEV-003," the sampling step frequency of key control points is automatically adjusted. This process increases the original default sampling rate of 100 Hz to 1000 Hz to capture higher frequency signal details. Simultaneously, dynamic adjustment logic for trigger bandwidth is executed, expanding the fixed level trigger point to a trigger envelope interval with a bandwidth of 0.01 MPa, enhancing the fault tolerance for mechanical oscillation signals. The optimized frequency parameters, bandwidth parameters, and reorganized logic decision sequence are integrated to construct a gas monitoring information traceability enhancement structure set. This structure set not only includes the corrected operating parameters but also records the logical evolution process of each adjustment, enabling the monitoring system to automatically evolve its monitoring structure based on the historical performance of physical nodes, achieving adaptive traceability and enhancement for complex operating conditions.

[0033] Please see Figure 6 The edge node distribution execution module includes: The instruction version synchronization submodule calls the gas monitoring information traceability enhancement structure set, verifies the hash value and update time of the currently executed script on the edge side, compares the current version with the original version, selects the valid version file, and generates an instruction version verification table. The system invokes the gas monitoring information traceability enhancement structure set, executes firmware version verification and synchronization logic, performs hash calculations on the currently running script on the edge side, and generates a fixed-length 256-bit digital digest. It then extracts the script's last modification timestamp (e.g., 16:30 on April 23, 2026) and compares the generated digest value bit-by-bit with the latest valid version digest stored in the enhancement structure set. If the comparison results are inconsistent, a differential analysis is performed to determine the updated content of the latest version according to the logical judgment criteria. Subsequently, a valid version file matching the current hardware architecture is selected from the version repository, and the successfully verified version number, digest value, and update source are recorded, generating a detailed instruction version verification table. This table serves as the basis for update distribution, ensuring that all edge nodes participating in monitoring run on logic scripts with the latest traceability enhancement features, eliminating algorithmic differences between different nodes.

[0034] The physical link distribution submodule, based on the instruction version verification table and combined with the port load and communication topology of the edge gateway, locks the access path of the target relay, records the communication cycle and protocol identifier, and generates a monitoring instruction distribution device binding table. Based on the command version verification table and combined with the port communication status of the edge gateway, the system executes the delivery action and monitors the load rate of each communication port of the edge gateway in real time. For example, if the bandwidth utilization of the main communication channel is detected to be 80%, while the utilization rate of the redundant backup channel is only 15%, the current communication topology mapping map is retrieved to pinpoint the physical access path of the target relay. Path optimization selection logic is executed to avoid high-load congested links, and update command packets are delivered through the redundant backup channel. During the distribution process, the communication cycle of each command is recorded in real time. For example, if the round-trip time of a single delivery transaction is measured to be 40 milliseconds, the communication protocol identifier is identified and locked, and the 16-bit hardware identifier code of the target relay is strongly bound to the selected physical port and protocol type. This process generates a monitoring command distribution device binding table, which details the physical transmission path of each set of enhanced commands from the edge gateway to the end relay, ensuring accurate delivery and reliable execution of monitoring commands.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A gas density relay monitoring system based on edge computing, characterized in that, The system includes: The physical deformation and environmental coupling sensing module acquires the displacement variables of the internal insulating gas pressure sensing diaphragm, the Young's modulus of the material and the coefficient of thermal expansion and contraction, captures the movement trend of the internal mechanical linkage of the relay, uses edge computing to offset the nonlinear disturbance of temperature and humidity on the metal bellows, identifies the correspondence between pressure compensation value and contact action stroke, and generates a correlation map between mechanical displacement and gas pressure state. The edge logic threshold dynamic verification module extracts the critical pressure jump point under different ambient temperatures based on the mechanical displacement and gas pressure state correlation map, and verifies the physical synchronization of relay action by combining the power grid operation procedures and the switchgear body manual, and constructs a gas monitoring closed-loop compliance judgment model. The edge gateway instruction mapping and binding module transforms the compliance path in the gas monitoring closed-loop compliance judgment model into the underlying control protocol, and uses the edge computing logic mapping mechanism to embed the mandatory provisions of the power regulations into the kernel execution sequence, forming a standard instruction set for edge-side gas monitoring. The monitoring accuracy compensation and structural traceability module performs calculus and integral compensation to correct signal glitches or sampling clock offsets during the execution of the edge-side gas monitoring standard instruction set, extracts mechanical fatigue deviation patterns, adjusts the triggering frequency and sampling depth, and constructs a gas monitoring information traceability enhancement structure set. The edge node distribution and execution module, based on the gas monitoring information traceability enhancement structure set, automatically generates an operation sequence that adapts to the target physical link according to the physical interface definition of the differentiated switchgear models on site and the load capacity of the edge node. It issues point-to-point instructions through the edge gateway, marks the execution weight and effective time stamp, and outputs a monitoring instruction distribution device binding table.

2. The gas density relay monitoring system based on edge computing according to claim 1, characterized in that, The mechanical displacement and gas pressure state correlation map includes a quantified displacement trend curve, a calibrated disturbance correction vector, and a correlated state data domain. The gas monitoring closed-loop compliance judgment model includes zone threshold parameters for adaptable temperature, physical synchronization criteria for action verification, and a closed-loop compliance framework for the judgment model. The edge-side gas monitoring standard instruction set includes protocol-converted binding commands, path control units for mandatory clauses, and computer-mechanical logical instruction paths. The gas monitoring information traceability enhancement structure set includes signal compensation factors in the instruction process, structural adjustment parameters for trigger optimization, and an enhanced framework for information traceability. The monitoring instruction distribution device binding table includes model interface adaptation parameters for field devices, weighted label tags for instruction execution, and binding records for time-stamped effectiveness.

3. The gas density relay monitoring system based on edge computing according to claim 1, characterized in that, The physical deformation and environment coupling sensing module includes: The physical state decoupling submodule uses edge computing to separate the pseudo-pressure fluctuations caused by temperature based on the mechanical thrust signal of the insulating gas pressure on the sensing diaphragm, identifies the current physical equilibrium stage of the relay, and generates a stage physical characteristic identifier. The spatiotemporal coordinate binding submodule calls the physical feature identifier of the stage and combines it with the precision clock source of the edge computing gateway to align the geospatial number of the switching equipment with the pressure mutation event at the nanosecond level, and obtain the spatiotemporal correlation monitoring record. The correlation map construction submodule calls the spatiotemporal correlation monitoring records, arranges the monitoring events according to the physical evolution logic, assigns weights according to the aging curve of the equipment material, and establishes a correlation map between mechanical displacement and air pressure status.

4. The gas density relay monitoring system based on edge computing according to claim 3, characterized in that, The edge logic threshold dynamic verification module includes: The action logic verification submodule calls the mechanical displacement and air pressure state correlation graph, analyzes the causal relationship between the pressure drop curve and the contact closure signal, calculates the logic threshold verification deviation value, and generates the link action verification matrix. The physical parameter matching submodule calls the link action verification matrix, compares the deviation between the actual measurement of the pressure sensor and the rated insulation strength index of the equipment, filters the monitoring path that meets the power safety regulations, and outputs the compliant path screening sequence. The compliance model solidification submodule calls the compliance path filtering sequence to determine the deviation range of key pressure control points, judges the hard boundaries of the edge computing gateway definition logic, and generates a gas monitoring closed-loop compliance judgment model.

5. The gas density relay monitoring system based on edge computing according to claim 4, characterized in that, The logic threshold verification deviation value refers to the Euclidean distance between the instantaneous pressure value and the preset pressure alarm threshold, which is set as a standard parameter. The Euclidean distance is then converted into a percentage of the logic response accuracy to obtain the logic threshold verification deviation value.

6. The gas density relay monitoring system based on edge computing according to claim 4, characterized in that, The edge gateway command mapping and binding module includes: The control signal mapping submodule calls the gas monitoring closed-loop compliance judgment model, maps the preset pressure threshold in the model to the digital pulse frequency of the edge gateway, and generates a logical association response table for the physical mapping of the control signal and the pressure sensor execution unit. The procedure text embedding submodule calls the logical association response table to transform the power safety constraints into a combination triggering mechanism of logic gates, embedding the procedure, i.e., code, at the edge side to form a procedure logic index list; The binding instruction generation submodule calls the procedure logic index list and, based on the hardware trigger timing of the edge gateway, converts the static procedure into dynamic monitoring and control pulses, synchronizes the operating status of each monitoring node, and forms a standard instruction set for edge-side gas monitoring.

7. The gas density relay monitoring system based on edge computing according to claim 6, characterized in that, The physical mapping between the control signal and the pressure sensor execution unit refers to retrieving the hardware address code of the pressure sensor execution unit, assigning the frequency characteristic parameters to the corresponding general-purpose input / output ports, and establishing the correspondence between logic high and low levels and the on state of the pressure sensor execution unit through a level logic converter.

8. The gas density relay monitoring system based on edge computing according to claim 6, characterized in that, The monitoring accuracy compensation and structural traceability module includes: The deviation retrieval submodule is executed, which calls the edge-side gas monitoring standard instruction set to capture signal distortion or feedback delay during instruction execution in real time, and matches the preset mechanical hysteresis compensation strategy to form an accuracy correction scheme library. The feature marking submodule calls the accuracy correction scheme library to identify the frequency characteristics of relay malfunction or failure to operate in the target environment, marks physical nodes that are prone to monitoring errors, and generates physical labels for repetitive deviations. The structure library enhancement submodule calls the repetitive deviation physical tag, reorganizes the execution sequence of the monitoring procedure through edge computing, automatically optimizes the sampling step frequency and trigger bandwidth of key control nodes, and constructs a gas monitoring information traceability enhancement structure set.

9. The gas density relay monitoring system based on edge computing according to claim 8, characterized in that, The aforementioned signal distortion or feedback delay refers to the process of acquiring the waveform envelope of the control signal in real time through the edge-side sampler, calculating the correlation coefficient between the waveform envelope and the preset waveform in the edge-side gas monitoring standard instruction set, and determining signal distortion when the correlation coefficient is lower than the preset correlation threshold. At the same time, the time difference between the instruction sending time and the feedback signal return time is recorded to identify signal distortion or feedback delay.

10. The gas density relay monitoring system based on edge computing according to claim 8, characterized in that, The edge node distribution execution module includes: The instruction version synchronization submodule calls the gas monitoring information traceability enhancement structure set, verifies the hash value and update time of the currently executed script on the edge side, compares the current version with the original version, selects the valid version file, and generates an instruction version verification table. The physical link distribution submodule, based on the instruction version verification table and combined with the port load and communication topology of the edge gateway, locks the access path of the target relay, records the communication cycle and protocol identifier, and generates a monitoring instruction distribution device binding table.

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