Transformer explosion-proof intelligent monitoring and early warning system and device

Abstract

The invention relates to the technical field of transformer safety monitoring, and discloses a transformer explosion-proof intelligent monitoring and early warning system and device. The system comprises a monitoring main control unit, an environment sensing assembly, a data transmission module, a risk judgment module and an interaction warning unit. Before monitoring is started, the environment sensing assembly collects initial parameters through an oil temperature sensor, a pressure sensor and the like, the operation state and the signal transmission environment of the transformer are detected and evaluated, and a state signal is generated and transmitted to the interaction warning unit. And when the state is valid, the data transmission module activates the special link to realize data interaction, monitors data transmission reliability and generates a transmission signal. And when the transmission is qualified, the risk discrimination module deeply analyzes the operation data and generates a discrimination signal. The interaction warning unit triggers an early warning prompt when receiving the state failure signal, transmitting an abnormal signal or judging the abnormal signal. The system can comprehensively monitor the operation of the transformer, guarantee data transmission, accurately discriminate the risk and give an early warning in time, and effectively reduce the explosion risk of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer safety monitoring, and in particular to a transformer explosion-proof intelligent monitoring and early warning system and device. Background Art

[0002] Transformers are key equipment in power system operation, and their safe and stable operation directly affects the reliability of the power grid. While monitoring and early warning technologies for transformer explosion protection have made some progress, many practical issues remain to be addressed.

[0003] Traditional transformer explosion-proof monitoring devices have significant limitations in their monitoring dimensions. Most rely on a single or limited number of sensors to collect parameters, such as oil temperature or pressure sensors. This makes it difficult to fully reflect the transformer's actual operating status. For example, a transformer internal fault may be accompanied by multiple signals such as elevated oil temperature, abnormal pressure, fluid level changes, and increased noise. However, monitoring a single parameter often fails to capture complex fault symptoms in a timely manner, resulting in delayed early warning.

[0004] When it comes to data collection and status assessment, existing technologies lack systematic testing of initial sensor parameters and environmental adaptability assessments. Traditional systems typically fail to effectively verify the rationality of sensor placement and the environmental impact on signal transmission before starting monitoring. This can lead to distorted data due to sensor anomalies or environmental interference. Analyzing directly based on such unreliable data can easily lead to misjudgments or missed judgments, failing to provide accurate evidence for safe transformer operation.

[0005] The reliability assurance mechanisms for data transmission are also inadequate. Traditional data transmission modules often fail to monitor the connection strength, data latency, and information loss of communication links in real time. When communication link anomalies occur, they cannot be detected and addressed promptly, resulting in interrupted or distorted monitoring data transmission, which in turn affects subsequent risk assessment.

[0006] The risk identification module has limited analytical capabilities. Traditional technologies often use simple threshold comparisons to analyze transformer operating data, failing to deeply explore correlations and trends within the data. For example, analysis of the transformer's structural deformation and its rate of change is incomplete, making it difficult to identify potential explosion risks in advance.

[0007] Existing devices lack effective management of the monitoring system's operating status. Without a set management cycle to analyze the device's operating performance, problems such as inefficient monitoring, operational errors, and equipment anomalies cannot be promptly identified during the monitoring process. This is not conducive to device maintenance and optimization, and in turn affects the reliability and stability of the entire monitoring and early warning system. Summary of the Invention

[0008] The purpose of the present invention is to provide a transformer explosion-proof intelligent monitoring and early warning system and device to solve the problems raised in the above background technology.

[0009] To achieve the above-mentioned object, the present invention provides a transformer explosion-proof intelligent monitoring and early warning system, comprising: a monitoring main control unit, an environmental perception component, a data transmission module, a risk identification module and an interactive warning unit; Before starting monitoring, the oil temperature sensors, pressure sensors, liquid level sensors and noise sensors installed at key parts of the transformer are used to collect initial parameters. The environmental perception component detects and evaluates the operating status and signal transmission environment of the transformer, generates a status failure signal or a status valid signal through the evaluation, and transmits the status failure signal or status valid signal to the interactive warning unit through the monitoring main control unit; When a status valid signal is generated, the data transmission module activates a dedicated communication link, through which the sensor data and the monitoring main control unit can interact instantly. The data transmission module monitors the reliability of the data transmission process, generates a transmission qualified signal or a transmission abnormality signal through monitoring, and transmits the transmission qualified signal or the transmission abnormality signal to the interactive warning unit via the monitoring main control unit; When generating a qualified transmission signal, the risk identification module conducts an in-depth analysis of the transformer's operating data, generates an abnormality identification signal or a normal identification signal through the analysis, and transmits the abnormality identification signal or the normal identification signal to the interactive warning unit via the monitoring main control unit; the interactive warning unit triggers an early warning prompt when it receives a status failure signal, a transmission abnormality signal or a judgment abnormality signal.

[0010] Preferably, the environmental perception component obtains the operation reference identifier or the operation benchmark identifier through operation status detection, and obtains the environment reference identifier or the environment benchmark identifier through signal environment detection, and jointly evaluates the operation reference identifier and the environment reference identifier. If the combined result of the operation benchmark identifier and the environment benchmark identifier is obtained, a valid status signal is generated, and a status failure signal is generated in other cases.

[0011] Preferably, the specific evaluation process of the running status detection is as follows: Collect the actual temperature value of the oil temperature sensor, the actual pressure value of the pressure sensor, the actual level value of the liquid level sensor, and the actual noise value of the noise sensor, and mark the difference between the actual value of each sensor and the corresponding preset standard value as the parameter deviation; If any parameter deviation exceeds the preset deviation critical value, the sensor is marked as an abnormal monitoring point; If there is an abnormal monitoring point, an operation reference mark is assigned; if there is no abnormal monitoring point, the parameter deviations of all sensors are calculated and averaged to obtain the comprehensive deviation mean, and the single parameter deviation with the largest value is marked as the deviation extreme value; by comparing the comprehensive deviation mean with the deviation extreme value, if the comparison result exceeds the preset comparison critical value, an operation reference mark is assigned; if it does not exceed the preset comparison critical value, an operation benchmark mark is assigned.

[0012] Preferably, the specific evaluation process of signal environment detection is as follows: Collect the real-time connection strength value, data delay value and information loss rate of the dedicated communication link, mark the difference between the connection strength value and the preset strength reference value as the strength difference, and similarly obtain the delay difference and loss difference; The comprehensive environmental difference value is obtained by weighting the intensity difference, delay difference and loss difference. If the comprehensive environmental difference value exceeds the preset environmental difference critical value, the environmental reference mark is assigned; if it does not exceed the preset environmental difference critical value, the environmental benchmark mark is assigned.

[0013] Preferably, the specific operation process of the data transmission module includes: Obtain the actual transmission time and final transmission data volume during the data transmission process, match and verify the actual transmission time with the preset transmission time interval, and the final transmission data volume with the preset data volume interval respectively. If the actual transmission time or the final transmission data volume is not within the corresponding preset interval, a transmission abnormality signal is generated.

[0014] Preferably, if the actual transmission time and the final transmission data volume are both within the corresponding preset ranges, the flow change trajectory during the data transmission process is collected and placed in a coordinate system with time as the horizontal axis and flow as the vertical axis; a number of monitoring nodes with equal time intervals are selected on the flow change trajectory; Calculate the flow change value between adjacent monitoring nodes and mark it as flow fluctuation. Calculate the dispersion of all flow fluctuations to obtain the flow fluctuation discrete value. If the flow fluctuation discrete value exceeds the preset fluctuation discrete critical value, a transmission abnormality signal is generated. If the flow fluctuation discrete value does not exceed the preset fluctuation discrete critical value, each flow fluctuation amount is matched and verified with the preset fluctuation amount interval. If the flow fluctuation amount is not within the preset fluctuation amount interval, the flow fluctuation amount is marked as abnormal fluctuation amount, and the degree of deviation between the abnormal fluctuation amount and the preset fluctuation amount interval is recorded; The number of abnormal fluctuations is counted and marked as the abnormal fluctuation number, the abnormal fluctuation with the largest deviation is marked as the extreme fluctuation value, and the average deviation of all abnormal fluctuations is marked as the fluctuation deviation mean; by comprehensively calculating the abnormal fluctuation number, the extreme fluctuation value and the fluctuation deviation mean, if the calculation result exceeds the preset comprehensive calculation critical value, a transmission abnormality signal is generated; if it does not exceed the preset comprehensive calculation critical value, a transmission qualified signal is generated.

[0015] Preferably, the specific parsing process of the risk identification module is as follows: The final structural deformation of the transformer is collected and marked as the deformation detection value. At the same time, the deformation change trajectory of the transformer during the monitoring period is collected, and several deformation acceleration nodes are identified on the deformation change trajectory. The ratio of the deformation growth value to the time interval between adjacent deformation acceleration nodes is calculated and marked as the deformation growth rate. The average value of all deformation growth rates is calculated to obtain the deformation growth mean, and each deformation growth rate is compared with a preset growth rate critical value. If the deformation growth rate exceeds the preset growth rate critical value, the deformation growth rate is marked as an abnormal growth rate, and the number of abnormal growth rates is counted as a deformation variation constant. By comprehensively evaluating the deformation detection value, deformation growth mean and deformation variation constant, if the evaluation result exceeds the preset evaluation critical value, an abnormality judgment signal is generated; if it does not exceed the preset evaluation critical value, a normal judgment signal is generated.

[0016] Preferably, the monitoring main control unit is communicatively connected to the operation management module, the operation management module is used to set a management cycle, analyze the operation performance of the transformer monitoring system within the management cycle, generate a management qualification signal or a management failure signal through the analysis, and transmit the management qualification signal or the management failure signal to the interactive warning unit via the monitoring main control unit. When the interactive warning unit receives the management failure signal, it triggers an early warning prompt.

[0017] Preferably, the specific analysis process of the operation management module is as follows: The total number of transformer monitoring times within the management cycle is counted and marked as the monitoring frequency. The ratio of the number of monitoring times in which the single monitoring duration exceeds the preset critical value to the monitoring frequency within the management cycle is marked as the inefficient monitoring ratio. At the same time, the average of all single monitoring durations within the management cycle is calculated to obtain the mean monitoring duration. The total number of operational errors made by operators during the monitoring process within the management cycle is counted, and the ratio of the total number of operational errors to the monitoring frequency is marked as the operational error rate; the management evaluation value is obtained by comprehensively calculating the proportion of inefficient monitoring, the average monitoring time and the operational error rate. If the management evaluation value exceeds the preset management evaluation critical value, a management failure signal is generated; if it does not exceed the preset management evaluation critical value, a management qualification signal is generated.

[0018] Preferably, the specific analysis process of the operation management module also includes: counting the number of abnormal shutdowns of the monitoring equipment during the management cycle and marking it as the number of equipment shutdowns, and marking the ratio of the number of equipment shutdowns to the monitoring frequency as the equipment abnormality rate; by comprehensively evaluating the proportion of inefficient monitoring, the average monitoring time, the operation error rate and the equipment abnormality rate, if the evaluation result exceeds the preset management evaluation critical value, a management unqualified signal is generated; if it does not exceed the preset management evaluation critical value, a management qualified signal is generated.

[0019] The present invention also discloses a transformer explosion-proof intelligent monitoring and early warning device, comprising a meter, an oil temperature sensor, a pressure sensor, a liquid level sensor, and a noise sensor. The meter is electrically connected to the oil temperature sensor, the pressure sensor, the liquid level sensor, and the noise sensor via a wire. The meter is used to receive data from the oil temperature sensor, the pressure sensor, the liquid level sensor, and the noise sensor and to display and output the data. The meter is also electrically connected to a data collection terminal via a wire to output the collected data. The external rotating sleeve at the bottom end of the meter is provided with a screw cover for screwing onto the transformer oil tank port, and a sealing ring is provided at the connection between the screw cover and the meter. The liquid level sensor is fixedly connected to the bottom end of the meter. The outside of the bottom end of the meter is also threadedly connected to a mounting tube. The front and rear sides of the mounting tube are both opened to pass oil. The bottom end part of the opening of the mounting tube is also provided with an elastic sheet for inserting and engaging an oil temperature sensor and a pressure sensor. One side of the meter is connected to a pressure relief valve through a pressure relief pipe, and an oil channel connecting the bottom end and the pressure relief pipe is reserved in the meter.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The transformer explosion-proof intelligent monitoring and early warning system realizes comprehensive and accurate monitoring and early warning of the transformer operating status through the collaborative work of multiple modules, effectively improving the reliability and effectiveness of transformer explosion-proof monitoring.

[0021] Before the monitoring starts, the device collects initial parameters through oil temperature, pressure, liquid level and noise sensors placed at key parts of the transformer, and the environmental perception component detects and evaluates the transformer's operating status and signal transmission environment. This multi-sensor collaborative collection and environmental assessment mechanism ensures the validity of the initial data. Specifically, the operating status detection comprehensively determines whether the sensor is abnormal by comparing the deviation between the actual measured value of each sensor and the preset standard value, combining the comparison of the comprehensive deviation mean and the deviation extreme value; the signal environment detection evaluates the reliability of the signal transmission environment by weighting the connection strength, data delay and information loss rate of the dedicated communication link. In this way, the data transmission link is only activated when the operating status and signal environment meet the requirements, ensuring the basic conditions for data collection and transmission from the source and avoiding subsequent misjudgments due to unreliable initial data.

[0022] After activating the dedicated communication link, the data transmission module not only verifies the actual transmission time and final data volume, but also conducts in-depth analysis of the dispersion and fluctuation of traffic flow changes. This multi-level reliability monitoring can promptly detect anomalies in the data transmission process, such as excessive transmission time, abnormal data volume, and severe traffic fluctuations. It then generates a transmission anomaly signal to ensure the accuracy and integrity of the transmitted data, providing reliable data support for subsequent risk assessment.

[0023] The risk identification module conducts in-depth analysis of transformer operating data, not only collecting final structural deformation but also monitoring deformation changes during the monitoring period. By identifying deformation acceleration nodes and calculating deformation growth rates, it comprehensively analyzes transformer deformation. This comprehensive assessment, combining deformation detection values, mean deformation growth values, and deformation variation constants, can keenly detect potential abnormal changes within the transformer, generate preemptive abnormality signals, and provide early warning of explosion-proof risks, effectively safeguarding the transformer's safe operation.

[0024] The operation management module, which monitors the communication connection with the main control unit, conducts a multi-dimensional analysis of the device's operational performance by setting management cycles. It compiles statistics on indicators such as monitoring frequency, the proportion of inefficient monitoring, average monitoring duration, operational error rate, and equipment anomaly rate, and performs comprehensive calculations and assessments. This operation management mechanism promptly identifies inefficiencies, operational errors, and equipment failures during the monitoring process, generates management failure signals, and triggers early warnings, facilitating timely maintenance and optimization of the device by operators. This improves the efficiency and reliability of the entire monitoring system and ensures the device continues to perform its monitoring and early warning functions in a stable and consistent manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a diagram showing the working principle of the system of the present invention; Figure 2 Flowchart for signal environment detection and assessment; Figure 3 Flowchart for risk identification module analysis; Figure 4 A flowchart for the primary analysis of the operation management module; Figure 5 A schematic diagram of the structure of the device.

[0026] In the figure: 1-meter, 2-oil temperature sensor, 3-pressure sensor, 4-liquid level sensor, 5-noise sensor, 6-collection terminal, 7-screw cover, 8-pressure relief valve, 9-mounting pipe, 91-elastic sheet. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] See also Figure 1-Figure 4 The present invention provides a transformer explosion-proof intelligent monitoring and early warning system, including: a monitoring main control unit, an environmental perception component, a data transmission module, a risk identification module and an interactive warning unit. The specific implementation steps are as follows: Before starting monitoring, the initial parameters are collected by the oil temperature sensor 2, pressure sensor 3, liquid level sensor 4 and noise sensor 5 arranged at the key parts of the transformer. The environmental perception component detects and evaluates the operating status and signal transmission environment of the transformer, generates a status failure signal or a status valid signal, and transmits the signal to the interactive warning unit through the monitoring main control unit. When the status valid signal is generated, the data transmission module activates the dedicated communication link to realize the instant interaction between the sensor data and the monitoring main control unit. At the same time, it monitors the reliability of the data transmission process, generates a transmission qualified signal or a transmission abnormality signal, and transmits it to the interactive warning unit through the monitoring main control unit. If a transmission qualified signal is generated, the risk identification module conducts an in-depth analysis of the transformer's operating data, generates a judgment abnormality signal or a judgment normal signal, and transmits it to the interactive warning unit through the monitoring main control unit. When the interactive warning unit receives the status failure signal, the transmission abnormality signal or the judgment abnormality signal, it triggers an early warning prompt. Example

[0029] This embodiment describes in detail the specific implementation of the environmental perception component. The role of the environmental perception component is to obtain an operation reference identifier or an operation benchmark identifier through operation status detection, and to obtain an environment reference identifier or an environment benchmark identifier through signal environment detection, and to jointly evaluate the operation reference identifier and the environment reference identifier to generate a state failure signal or a state validity signal.

[0030] For operational status monitoring, it is necessary to collect the actual temperature values measured by oil temperature sensor 2, the actual pressure values measured by pressure sensor 3, the actual liquid level values measured by liquid level sensor 4, and the actual noise values measured by noise sensor 5. Here, oil temperature sensor 2 is deployed in key locations such as the transformer's oil tank to monitor the oil temperature in real time during operation. Pressure sensor 3 is installed in locations such as the transformer's internal cavity to obtain internal pressure data. Liquid level sensor 4 is installed in a corresponding location in the oil tank to monitor the oil level. Noise sensor 5 is arranged around the transformer's casing to collect noise signals during operation.

[0031] After obtaining the actual measured value of each sensor, the difference between each sensor's measured value and the corresponding preset standard value is marked as the parameter deviation. The preset standard value is pre-set based on the parameter range during normal operation of the transformer. For example, the preset standard value of oil temperature may be the middle value of the normal temperature range under certain operating conditions or a certain reference value.

[0032] Determine whether any parameter deviation exceeds a preset critical deviation value. This threshold serves as a measure of whether a parameter deviation is abnormal. If a parameter deviation exceeds this threshold, the corresponding sensor monitoring point may be abnormal. If such an abnormal monitoring point exists, the sensor is marked as an abnormal monitoring point and assigned an operational reference flag.

[0033] If there are no abnormal monitoring points, further processing is required. Calculate the average values of the parameter deviations of all sensors separately to obtain the comprehensive deviation mean. At the same time, mark the single parameter deviation with the largest value as the deviation extreme value. Then, perform a numerical comparison between the comprehensive deviation mean and the deviation extreme value. The comparison here can be the difference, ratio or other forms of comparison between the two. If the comparison result exceeds the preset comparison critical value, it means that although the deviation of each parameter does not exceed the preset deviation critical value individually, the overall view or the relationship between the maximum deviation and the comprehensive deviation is abnormal. At this time, an operation reference mark is assigned; if it does not exceed the preset comparison critical value, an operation benchmark mark is assigned.

[0034] Signal environment monitoring requires collecting real-time connection strength, data delay, and information loss rate of the dedicated communication link. The dedicated communication link is activated by the data transmission module to enable real-time interaction between sensor data and the monitoring control unit. The connection strength reflects the link's stability, the data delay indicates the time delay in data transmission, and the information loss rate reflects any information lost during the data transmission process.

[0035] The difference between the connection strength value and the preset strength reference value is labeled as the strength difference. Similarly, the difference between the data delay value and the preset delay reference value is labeled as the delay difference, and the difference between the information loss rate and the preset loss rate reference value is labeled as the loss difference. The preset strength reference value, preset delay reference value, and preset loss rate reference value are standard values set based on the parameters of the normal operation of the communication link.

[0036] The comprehensive environmental difference value is calculated by weighting the intensity difference, delay difference, and loss difference. Weighting is performed by assigning different weight coefficients to each difference based on its importance to the signal environment. If the comprehensive environmental difference value exceeds the preset environmental difference threshold, it indicates an abnormality in the signal transmission environment and is assigned an environmental reference designation. If it does not exceed the preset environmental difference threshold, it is assigned an environmental baseline designation.

[0037] The operational reference identifier and the environmental reference identifier are jointly evaluated. If the combined result of the operational reference identifier and the environmental reference identifier is obtained, that is, the operational status and the signal environment are both normal, a valid status signal is generated; otherwise, if the operational status or the signal environment is abnormal, or both are abnormal, a failed status signal is generated.

[0038] Through this series of detection, calculation, and assessment processes, the environmental sensing component accurately determines the transformer's operating status and the normality of the signal transmission environment, providing a reliable basis for subsequent data transmission and risk assessment. The entire process strictly adheres to pre-set standards and procedures, ensuring the accuracy and reliability of the assessment results. Example

[0039] This embodiment describes in detail the specific implementation of the data transmission module. The function of the data transmission module is to activate a dedicated communication link when a status valid signal is generated, enabling real-time interaction between sensor data and the monitoring main control unit, and to monitor the reliability of the data transmission process, generating a transmission qualified signal or a transmission abnormality signal.

[0040] During the data transmission process, the data transmission module first obtains the actual transmission time and the final data volume of the data transmission process. The actual transmission time refers to the time from the start of data transmission to the completion of the transmission, and the final data volume refers to the total amount of data actually successfully transmitted.

[0041] Next, the actual transfer time is checked against the preset transfer time interval, and the final data volume is checked against the preset data volume interval. The preset transfer time interval is a reasonable range based on the time required for normal data transmission, and the preset data volume interval is based on the normal range of data volume per transmission. If the actual transfer time is not within the preset transfer time interval, or the final data volume is not within the preset data volume interval, it indicates that the data transfer process is abnormal, and a transfer anomaly signal is generated.

[0042] If both the actual transfer duration and the final data volume are within the corresponding preset ranges, the data transfer is initially considered normal, but further monitoring of traffic flow during the data transfer process is required. In this case, the traffic flow trajectory during the data transfer process is collected and placed in a coordinate system with time as the horizontal axis and traffic flow as the vertical axis. This provides a visual representation of how data flow changes over time.

[0043] Select several monitoring nodes at equal intervals along the flow trajectory. Setting equal intervals ensures regularity and consistency in monitoring, facilitating subsequent analysis of flow changes. For example, you can select a monitoring node at regular intervals (e.g., 1 second, 5 seconds, etc.).

[0044] The flow rate change between adjacent monitoring nodes is calculated and labeled as flow fluctuation. The flow fluctuation reflects the magnitude of the change in data flow between two adjacent monitoring time points. All flow fluctuations are then subjected to a dispersion calculation to obtain a flow fluctuation discrete value. This dispersion calculation measures the dispersion of flow fluctuations and reflects the stability of flow changes. If the flow fluctuation discrete value exceeds the preset fluctuation discrete threshold, it indicates a high degree of dispersion and unstable data transmission, and a transmission anomaly signal is generated.

[0045] If the traffic fluctuation dispersion value does not exceed the preset fluctuation dispersion threshold, the dispersion of the traffic fluctuation is within the normal range. However, further verification is required to ensure that each traffic fluctuation is within a reasonable range. Each traffic fluctuation is checked against the preset fluctuation range, which is set based on the range of normal data traffic fluctuation. If a traffic fluctuation does not fall within the preset fluctuation range, it is marked as abnormal, and the degree of deviation from the preset fluctuation range is recorded. The degree of deviation can be determined by calculating the difference between the abnormal fluctuation and the upper and lower limits of the preset fluctuation range.

[0046] The number of abnormal fluctuations is counted and marked as the fluctuation anomaly number. At the same time, the abnormal fluctuation with the largest deviation is marked as the fluctuation extreme value, and the deviation degree of all abnormal fluctuations is calculated as the average value, which is marked as the fluctuation deviation mean. Then, a comprehensive result is obtained by comprehensively calculating the fluctuation anomaly number, fluctuation extreme value and fluctuation deviation mean. The comprehensive calculation here can be a combination calculation of these parameters according to certain rules, such as weighted summation. If the comprehensive calculation result exceeds the preset comprehensive calculation critical value, it means that although the discrete value of the flow fluctuation is normal, there are more abnormal fluctuations or a larger degree of deviation, and there is still an abnormal risk in the data transmission process. At this time, a transmission abnormality signal is generated; if it does not exceed the preset comprehensive calculation critical value, a transmission qualified signal is generated.

[0047] Through monitoring and analysis of the above multiple links, the data transmission module can comprehensively and meticulously evaluate the reliability of the data transmission process, promptly detect potential abnormalities, ensure the accuracy and stability of data transmission, and provide reliable data support for the subsequent risk identification module. Example

[0048] This embodiment describes in detail the specific implementation of the risk identification module. The function of the risk identification module is to perform in-depth analysis on the transformer operation data when generating the transmission qualified signal, and generate an abnormality identification signal or a normal identification signal.

[0049] During the analysis process, the risk identification module collects the final structural deformation of the transformer and marks it as the deformation detection value. The final structural deformation here refers to the deformation of the transformer structure after a complete monitoring cycle. This value is obtained by corresponding deformation detection sensors. The sensors are placed in key structural parts of the transformer, such as the casing and windings, to accurately monitor the transformer's structural deformation.

[0050] The transformer's deformation trajectory is collected during the monitoring period. This trajectory is a curve showing the time-varying deformation of the transformer structure over the entire monitoring period, reflecting the dynamic process of the transformer's structural deformation. Several deformation acceleration nodes are identified along the deformation trajectory. These nodes are defined as points in time where the rate of deformation change significantly accelerates. These nodes are identified by analyzing the slope of the deformation trajectory.

[0051] Calculate the ratio of the deformation growth value to the time interval between adjacent deformation acceleration nodes and mark it as the deformation growth rate. Assume that the two adjacent deformation acceleration nodes are the i-th and i+1-th nodes respectively, and the deformation of the i-th node is , time is , the deformation variable of the i+1th node is , time is , then the deformation growth rate The calculation formula is:

[0052] in, represents the deformation of the i-th deformation acceleration node, in millimeters; represents the deformation of the i+1th deformation acceleration node, in millimeters; Indicates the time of the i-th deformation acceleration node, in seconds; Indicates the time of the i+1th deformation acceleration node, in seconds; Indicates the deformation growth rate between two adjacent deformation acceleration nodes, in mm / s.

[0053] The average of all deformation growth rates is calculated to obtain the mean deformation growth value. The mean deformation growth value reflects the average level of transformer structural deformation growth rate during the entire monitoring period. The calculation method is to add up all deformation growth rates and divide them by the number of deformation growth rates.

[0054] Each deformation growth rate is compared with a preset growth rate threshold. This threshold is a pre-set threshold based on the deformation growth rate range during normal transformer operation and is used to determine whether the deformation growth rate is abnormal. If a deformation growth rate exceeds the preset growth rate threshold, it is marked as abnormal. The number of abnormal growth rates is counted and marked as the deformation variation constant.

[0055] By comprehensively evaluating the deformation detection value, deformation growth mean, and deformation variation constant, an abnormality discrimination signal or a normality discrimination signal is generated. The comprehensive evaluation process combines and evaluates these three parameters according to certain rules. For example, by setting a weight for each parameter, calculating the weighted comprehensive value, and then comparing it with a preset assessment threshold. The preset assessment threshold is set according to the standards for safe operation of the transformer and is used to determine the operating risk of the transformer. If the comprehensive evaluation result exceeds the preset assessment threshold, it indicates that the structural deformation of the transformer is abnormal and there is an explosion risk. In this case, an abnormality discrimination signal is generated. If it does not exceed the preset assessment threshold, it indicates that the structural deformation of the transformer is within the normal range, and a normality discrimination signal is generated.

[0056] Through this comprehensive monitoring and in-depth analysis of transformer structural deformation, the risk identification module can accurately determine the transformer's operational risk status, promptly identify potential explosion-proof hazards, and provide a reliable basis for triggering early warnings in the interactive warning unit. The entire analysis process, through the collection, calculation, and comprehensive assessment of multiple deformation-related parameters, ensures the accuracy and reliability of transformer operational risk assessments, thereby enabling intelligent monitoring and early warning capabilities for transformer explosion protection. Example

[0057] This embodiment describes in detail the interaction between the monitoring main control unit and the operation management module, as well as the specific analysis process of the operation management module. The monitoring main control unit is communicatively connected to the operation management module, which is responsible for setting a management cycle, analyzing the operational performance of the transformer monitoring system within the management cycle, generating a management pass signal or a management fail signal, and transmitting this signal via the monitoring main control unit to the interactive warning unit. Upon receiving the management fail signal, the interactive warning unit triggers an early warning prompt.

[0058] For example, let's assume a 30-day management cycle. The operation management module collects and processes operational data from the transformer monitoring system within that cycle. First, the total number of transformer monitoring operations within the management cycle is counted, which is labeled the monitoring frequency. Assuming the system monitors the transformer 100 times during those 30 days, the monitoring frequency is 100.

[0059] Next, the ratio of the number of monitoring times exceeding the preset threshold to the monitoring frequency within the management cycle is marked as the inefficient monitoring percentage. The preset threshold is set based on the normal monitoring time, for example, 30 minutes. If 15 out of 100 monitoring times exceeded 30 minutes, the inefficient monitoring percentage is 15 ÷ 100 × 100% = 15%.

[0060] At the same time, the average duration of all single monitoring times within the management period is calculated to obtain the mean monitoring duration. For example, if the duration of 100 monitoring times is different, these values are added together and divided by 100. Assuming that the mean monitoring duration is 25 minutes.

[0061] Next, count the total number of operator errors during monitoring within the management cycle and calculate the ratio of total errors to monitoring frequency as the error rate. Assuming the operator made 5 errors during 100 monitoring sessions, the error rate is 5 ÷ 100 × 100% = 5%.

[0062] The management evaluation value is then calculated by comprehensively calculating the percentage of inefficient monitoring, the average monitoring duration, and the operational error rate. This comprehensive calculation can be performed by assigning different weights to each parameter based on its importance and then adding them together. For example, a weight of 0.4 is assigned to the percentage of inefficient monitoring, 0.3 to the average monitoring duration, and 0.3 to the operational error rate. Assuming the percentage of inefficient monitoring is 15%, this translates to a value of 15. The average monitoring duration is 25 minutes, the normal average is 20 minutes, the excess is 5, and the operational error rate is 5%, this translates to a value of 5. The management evaluation value might be 15 × 0.4 + 5 × 0.3 + 5 × 0.3 = 6 + 1.5 + 1.5 = 9.

[0063] The management evaluation value is compared with a preset management evaluation threshold. The preset management evaluation threshold is set based on the management standard for normal operation of the device, for example, 8. If the management evaluation value exceeds the preset management evaluation threshold, such as 9 obtained from the above calculation is greater than 8, a management failure signal is generated; if the management evaluation value does not exceed the preset management evaluation threshold, such as 7 less than 8, a management pass signal is generated.

[0064] The generated management qualified signal or management unqualified signal is transmitted to the interactive warning unit through the monitoring main control unit. When the interactive warning unit receives the management unqualified signal, it triggers an early warning prompt to remind relevant personnel that the operation management of the device is abnormal and needs to be checked and adjusted.

[0065] For example, suppose another management cycle is set at 60 days, the monitoring frequency is 200 times, and the preset duration threshold is still 30 minutes. If 20 of the monitoring times exceed 30 minutes, the proportion of inefficient monitoring is 20 ÷ 200 × 100% = 10%. The average monitoring time is calculated to be 28 minutes, which is 8 minutes longer than the normal average of 20 minutes. The total number of operational errors is 8, and the operational error rate is 8 ÷ 200 × 100% = 4%. Similarly, according to the above weight calculation, the management evaluation value is 10 × 0.4 + 8 × 0.3 + 4 × 0.3 = 4 + 2.4 + 1.2 = 7.6. If the preset management evaluation threshold is 8, and 7.6 is less than 8, a management qualification signal is generated, and the interactive warning unit does not trigger the early warning prompt.

[0066] In this way, the operation management module collects statistics and calculates parameters such as monitoring frequency, the proportion of inefficient monitoring, the average monitoring duration, and the operational error rate within the set management cycle. This comprehensively evaluates the transformer monitoring system's operational performance, generates corresponding signals, and transmits them to the interactive warning unit, enabling monitoring and early warning of the device's operational management status. Through detailed numerical statistics and calculations, the entire process objectively reflects the device's operational management status, providing a basis for promptly identifying and resolving operational management issues and ensuring the normal operation of the transformer explosion-proof intelligent monitoring and early warning system. Example

[0067] This embodiment details the specific analysis process of the operation management module when including the equipment anomaly rate indicator. In addition to calculating the monitoring frequency, the proportion of inefficient monitoring, the average monitoring duration, and the operational error rate within the management cycle, the operation management module also counts the number of abnormal shutdowns of monitored equipment within the management cycle and marks them as the number of equipment downtimes. The ratio of the number of equipment downtimes to the monitoring frequency is marked as the equipment anomaly rate. By comprehensively evaluating these parameters, a management pass or failure signal is generated.

[0068] Taking a 30-day management cycle as an example, assume that the system monitors the transformer 100 times during this cycle, meaning the monitoring frequency is 100. The preset duration threshold is set at 30 minutes. If 15 of these 100 monitoring times exceed 30 minutes, the proportion of inefficient monitoring is 15 ÷ 100 × 100% = 15%. Record the duration of each of the 100 monitoring times, for example, if each monitoring time is 20 minutes, 25 minutes, 35 minutes, and so on. Add all the durations and divide by 100. Assume the average monitoring duration is 28 minutes. At the same time, count the number of operator errors during the monitoring process. If there are 5 errors, the error rate is 5 ÷ 100 × 100% = 5%.

[0069] Additionally, count the number of abnormal downtimes of monitoring equipment during the management cycle. Abnormal downtime refers to unplanned equipment downtime, such as downtime caused by sensor failure or abnormal interruption of the data transmission module. Assuming that a monitoring device experiences four abnormal downtimes within 30 days, the total number of equipment downtimes is 4. Therefore, the equipment abnormality rate is 4 ÷ 100 × 100% = 4%.

[0070] Next, a comprehensive assessment is conducted on the percentage of inefficient monitoring, average monitoring duration, operational error rate, and equipment abnormality rate. This comprehensive assessment combines these parameters according to specific rules. For example, assume that the weight of each parameter is 0.3 for the percentage of inefficient monitoring, 0.2 for the average monitoring duration, 0.2 for the operational error rate, and 0.3 for the equipment abnormality rate. Each parameter is converted into a numerical value. For example, a 15% percentage of inefficient monitoring corresponds to a value of 15, a difference of 8 between the average monitoring duration of 28 minutes and the normal average of 20 minutes, a 5% operational error rate corresponds to a value of 5, and a 4% equipment abnormality rate corresponds to a value of 4. The comprehensive assessment value is then calculated as: 15 × 0.3 + 8 × 0.2 + 5 × 0.2 + 4 × 0.3 = 4.5 + 1.6 + 1 + 1.2 = 8.3.

[0071] The comprehensive assessment value is compared with a preset management assessment threshold, set at 8 based on the device's operating standards. If the comprehensive assessment value exceeds this threshold, for example, 8.3 > 8, a management failure signal is generated; if it does not exceed this threshold, for example, 7.5 < 8, a management success signal is generated. This generated signal is transmitted via the monitoring main control unit to the interactive alarm unit. Upon receiving the management failure signal, the interactive alarm unit triggers an early warning.

[0072] Taking another 60-day management cycle as an example, with a monitoring frequency of 200 and a preset duration threshold of 30 minutes, if 20 monitoring sessions exceed 30 minutes, the proportion of inefficient monitoring is 20 ÷ 200 × 100% = 10%. The average monitoring duration is calculated to be 26 minutes, which is 6 minutes longer than the normal average of 20 minutes. There are 6 operational errors, with an operational error rate of 6 ÷ 200 × 100% = 3%. There are 5 equipment downtimes, with an equipment abnormality rate of 5 ÷ 200 × 100% = 2.5%. Using the above weights, the comprehensive assessment value is calculated as: 10 × 0.3 + 6 × 0.2 + 3 × 0.2 + 2.5 × 0.3 = 3 + 1.2 + 0.6 + 0.75 = 5.55. If the preset threshold is 8, and 5.55 is less than 8, a management pass signal is generated, and the interactive alert unit does not trigger an alert.

[0073] In another scenario, with a 30-day management cycle and 80 monitoring sessions, inefficient monitoring accounted for 20% (16 timeouts), an average monitoring duration of 32 minutes (12 minutes longer than the normal average), an 8% operational error rate (6 errors), 6 equipment downtimes, and an equipment abnormality rate of 6 ÷ 80 × 100% = 7.5%. The overall assessment value is 20 × 0.3 + 12 × 0.2 + 8 × 0.2 + 7.5 × 0.3 = 6 + 2.4 + 1.6 + 2.25 = 12.25, far exceeding the preset threshold of 8. This generates a management failure signal, triggering an alert in the interactive alarm unit, indicating operational management anomalies. Investigations are needed to address issues such as inefficient monitoring and equipment downtime, including frequent sensor failures, communication link stability, and operator training to ensure proper operation.

[0074] By incorporating the equipment abnormality rate indicator, the operation management module can more comprehensively assess the operating status of the transformer monitoring system. Data statistics and comprehensive assessments are conducted across multiple dimensions, including monitoring efficiency, operational compliance, and equipment stability. This allows for timely identification of potential operational issues, providing maintenance personnel with clear guidance on abnormalities and preventing the impact of accumulated problems such as abnormal equipment downtime or operational errors on the accuracy of monitoring and early warnings, thereby ensuring the reliable operation of the intelligent transformer explosion-proof monitoring and early warning system. Through detailed numerical statistics and comprehensive calculations, the entire process objectively reflects the operational status of the device, enabling intelligent identification and early warning of management status.

[0075] See Figure 5 The present invention also discloses a transformer explosion-proof intelligent monitoring and early warning device, including a meter 1, an oil temperature sensor 2, a pressure sensor 3, a liquid level sensor 4, and a noise sensor 5. The meter 1 is electrically connected to the oil temperature sensor 2, the pressure sensor 3, the liquid level sensor 4, and the noise sensor 5 through a wire. The meter 1 is used to receive data from the oil temperature sensor 2, the pressure sensor 3, the liquid level sensor 4, and the noise sensor 5 and display and output it. The meter 1 is also electrically connected to a collection terminal 6 through a wire for outputting the collected data. The external rotating sleeve at the bottom end of the meter 1 is provided with a screw cover 7 for screwing onto the transformer oil tank port, and a sealing ring is provided at the connection between the screw cover and the meter 1. The liquid level sensor 4 is fixedly connected to the bottom end of the meter 1. The outside of the bottom end of the meter 1 is also threadedly connected to a mounting tube 9. Openings are set on both the front and rear sides of the mounting tube 9 to pass the oil. The bottom end part of the opening of the mounting tube 9 is also provided with an elastic sheet 91 for inserting and locking the oil temperature sensor 2 and the pressure sensor 3. The elastic locking method facilitates installation and replacement. The elastic sheet 91 is opened to the side with two transverse grooves, and an elastic sheet 91 is formed between the transverse grooves. One side of the meter 1 is connected to a pressure relief valve 8 through a pressure relief pipe, and an oil channel connecting the bottom end and the pressure relief pipe is reserved in the meter 1. The above sensors are all existing products.

[0076] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0077] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A transformer explosion-proof intelligent monitoring and early warning system, characterized in that: It includes a monitoring main control unit, an environmental perception component, a data transmission module, a risk identification module, and an interactive warning unit; Before starting monitoring, the oil temperature sensors, pressure sensors, liquid level sensors and noise sensors installed at key parts of the transformer collect initial parameters. The environmental perception component detects and evaluates the operating status and signal transmission environment of the transformer, generates a status failure signal or a status valid signal through the evaluation, and transmits the status failure signal or status valid signal to the interactive warning unit through the monitoring main control unit; When a status valid signal is generated, the data transmission module activates a dedicated communication link, through which the sensor data and the monitoring main control unit can interact instantly. The data transmission module monitors the reliability of the data transmission process, generates a transmission qualified signal or a transmission abnormality signal through monitoring, and transmits the transmission qualified signal or the transmission abnormality signal to the interactive warning unit via the monitoring main control unit; When generating a qualified transmission signal, the risk identification module conducts an in-depth analysis of the transformer's operating data, generates an abnormality identification signal or a normal identification signal through the analysis, and transmits the abnormality identification signal or the normal identification signal to the interactive warning unit via the monitoring main control unit; The interactive warning unit triggers an early warning prompt when it receives a status failure signal, transmits an abnormal signal, or identifies an abnormal signal.

2. The transformer explosion-proof intelligent monitoring and early warning system according to claim 1 is characterized in that: The environmental perception component obtains the operation reference identifier or operation benchmark identifier through operation status detection, and obtains the environment reference identifier or environment benchmark identifier through signal environment detection, and jointly evaluates the operation reference identifier and the environment reference identifier. If the combined result of the operation benchmark identifier and the environment benchmark identifier is obtained, a valid status signal is generated, and a status failure signal is generated in other cases.

3. The transformer explosion-proof intelligent monitoring and early warning system according to claim 2 is characterized in that: The specific evaluation process of the operating status detection is as follows: Collect the actual temperature value of the oil temperature sensor, the actual pressure value of the pressure sensor, the actual level value of the liquid level sensor, and the actual noise value of the noise sensor, and mark the difference between the actual value of each sensor and the corresponding preset standard value as the parameter deviation; If any parameter deviation exceeds the preset deviation critical value, the sensor is marked as an abnormal monitoring point; If there is an abnormal monitoring point, an operation reference mark will be assigned; If there are no abnormal monitoring points, the parameter deviations of all sensors are calculated and averaged to obtain the comprehensive deviation mean, and the single parameter deviation with the largest value is marked as the deviation extreme value; by comparing the comprehensive deviation mean with the deviation extreme value, if the comparison result exceeds the preset comparison critical value, an operation reference mark is assigned; If the preset comparison threshold is not exceeded, an operating benchmark flag is assigned.

4. The transformer explosion-proof intelligent monitoring and early warning system according to claim 2 is characterized in that: The specific evaluation process of signal environment detection is as follows: Collect the real-time connection strength value, data delay value and information loss rate of the dedicated communication link, mark the difference between the connection strength value and the preset strength reference value as the strength difference, and similarly obtain the delay difference and loss difference; The comprehensive environmental difference value is obtained by weighting the intensity difference, delay difference and loss difference. If the comprehensive environmental difference value exceeds the preset environmental difference critical value, the environmental reference mark is assigned; if it does not exceed the preset environmental difference critical value, the environmental benchmark mark is assigned.

5. The transformer explosion-proof intelligent monitoring and early warning system according to claim 1 is characterized in that: The specific operation process of the data transmission module includes: Obtain the actual transmission time and final transmission data volume during the data transmission process, match and verify the actual transmission time with the preset transmission time interval, and the final transmission data volume with the preset data volume interval respectively. If the actual transmission time or the final transmission data volume is not within the corresponding preset interval, a transmission abnormality signal is generated.

6. The transformer explosion-proof intelligent monitoring and early warning system according to claim 5 is characterized in that: If the actual transmission time and the final data volume are both within the corresponding preset range, the flow change trajectory during the data transmission process is collected and placed in a coordinate system with time as the horizontal axis and flow as the vertical axis; several monitoring nodes with equal time intervals are selected on the flow change trajectory; Calculate the flow change value between adjacent monitoring nodes and mark it as flow fluctuation. Calculate the dispersion of all flow fluctuations to obtain the flow fluctuation discrete value. If the flow fluctuation discrete value exceeds the preset fluctuation discrete critical value, a transmission abnormality signal is generated. If the flow fluctuation discrete value does not exceed the preset fluctuation discrete critical value, each flow fluctuation amount is matched and verified with the preset fluctuation amount interval. If the flow fluctuation amount is not within the preset fluctuation amount interval, the flow fluctuation amount is marked as abnormal fluctuation amount, and the degree of deviation between the abnormal fluctuation amount and the preset fluctuation amount interval is recorded; Count the number of abnormal fluctuations and mark them as the abnormal fluctuation number, mark the abnormal fluctuation with the largest deviation as the extreme fluctuation value, and mark the average deviation of all abnormal fluctuations as the fluctuation deviation mean; By comprehensively calculating the number of fluctuation anomalies, fluctuation extreme values and fluctuation deviation from the mean, if the calculation result exceeds the preset comprehensive calculation critical value, a transmission abnormality signal is generated; If the preset comprehensive calculation threshold is not exceeded, a pass signal is generated.

7. The transformer explosion-proof intelligent monitoring and early warning system according to claim 1 is characterized in that: The specific parsing process of the risk identification module is as follows: The final structural deformation of the transformer is collected and marked as the deformation detection value. At the same time, the deformation change trajectory of the transformer during the monitoring period is collected, and several deformation acceleration nodes are identified on the deformation change trajectory. The ratio of the deformation growth value to the time interval between adjacent deformation acceleration nodes is calculated and marked as the deformation growth rate. The average value of all deformation growth rates is calculated to obtain the deformation growth mean, and each deformation growth rate is compared with a preset growth rate critical value. If the deformation growth rate exceeds the preset growth rate critical value, the deformation growth rate is marked as an abnormal growth rate, and the number of abnormal growth rates is counted as a deformation variation constant. By comprehensively evaluating the deformation detection value, deformation growth mean and deformation variation constant, if the evaluation result exceeds the preset evaluation critical value, an abnormality judgment signal is generated; If the preset evaluation threshold is not exceeded, a normal judgment signal is generated.

8. The transformer explosion-proof intelligent monitoring and early warning system according to claim 1 is characterized in that: The monitoring main control unit is communicated with the operation management module. The operation management module is used to set a management cycle, analyze the operating performance of the transformer monitoring system within the management cycle, generate a management qualification signal or a management failure signal through the analysis, and transmit the management qualification signal or management failure signal to the interactive warning unit via the monitoring main control unit. When the interactive warning unit receives the management failure signal, it triggers an early warning prompt; The specific analysis process of the operation management module is as follows: The total number of transformer monitoring times within the management cycle is counted and marked as the monitoring frequency. The ratio of the number of monitoring times in which the single monitoring duration exceeds the preset critical value to the monitoring frequency within the management cycle is marked as the inefficient monitoring ratio. At the same time, the average of all single monitoring durations within the management cycle is calculated to obtain the mean monitoring duration. The total number of operational errors made by operators during the monitoring process within the management cycle is counted, and the ratio of the total number of operational errors to the monitoring frequency is marked as the operational error rate. The management evaluation value is obtained by comprehensively calculating the proportion of inefficient monitoring, the average monitoring time, and the operational error rate. If the management evaluation value exceeds the preset management evaluation critical value, a management failure signal is generated. If the preset management assessment threshold is not exceeded, a management pass signal is generated.

9. The transformer explosion-proof intelligent monitoring and early warning system according to claim 8 is characterized in that: The specific analysis process of the operation management module also includes: counting the number of abnormal shutdowns of the monitoring equipment during the management cycle and marking it as the number of equipment shutdowns, and marking the ratio of the number of equipment shutdowns to the monitoring frequency as the equipment abnormality rate; by comprehensively evaluating the proportion of inefficient monitoring, the average monitoring time, the operation error rate and the equipment abnormality rate, if the evaluation result exceeds the preset management evaluation critical value, a management unqualified signal is generated; if it does not exceed the preset management evaluation critical value, a management qualified signal is generated.

10. A transformer explosion-proof intelligent monitoring and early warning device, used for collecting parameters of the transformer explosion-proof intelligent monitoring and early warning system according to any one of claims 1 to 9, characterized in that: The device comprises a meter, an oil temperature sensor, a pressure sensor, a liquid level sensor and a noise sensor. The meter is electrically connected to the oil temperature sensor, the pressure sensor, the liquid level sensor and the noise sensor via wires. The meter is used to receive data from the oil temperature sensor, the pressure sensor, the liquid level sensor and the noise sensor and display and output the data. The meter is also electrically connected to a data collection terminal via wires for outputting the collected data. The external rotating sleeve at the bottom end of the meter is provided with a screw cover for screwing onto the transformer oil tank port, and a sealing ring is provided at the connection between the screw cover and the meter. The liquid level sensor is fixedly connected to the bottom end of the meter. The outside of the bottom end of the meter is also threadedly connected to a mounting tube. The front and rear sides of the mounting tube are both opened to pass oil. The bottom end part of the opening of the mounting tube is also provided with an elastic sheet for inserting and engaging an oil temperature sensor and a pressure sensor. One side of the meter is connected to a pressure relief valve through a pressure relief pipe, and an oil channel connecting the bottom end and the pressure relief pipe is reserved in the meter.

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