Positive pressure explosion-proof robot leakage monitoring and emergency treatment method and system

Abstract

The application relates to a positive pressure explosion-proof robot leakage monitoring and emergency treatment method and system, which comprises the following steps: capturing operation state data in the operation process of a positive pressure explosion-proof robot through a multi-parameter linkage monitoring network composed of a gas sensor, a pressure sensor and a temperature sensor; then, performing feature processing on the data, and extracting leakage characteristic values such as a gas concentration change rate, a pressure fluctuation amplitude and a temperature gradient; determining a leakage risk level according to a preset risk assessment model; based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time sequence change law of the leakage characteristic values, performing positioning analysis on a leakage position; and according to the emergency treatment instructions generated according to the leakage risk level and the leakage position, starting emergency treatment measures in a targeted manner; the scheme can improve the safety and reliability of the operation of the positive pressure explosion-proof robot in a dangerous environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control, robots and information technology, in particular to a positive pressure explosion-proof robot leakage monitoring and emergency treatment method and system. BACKGROUND

[0002] In the field of industrial automation, positive pressure explosion-proof robots are widely used in dangerous environments such as flammable and explosive environments, and undertake important tasks such as inspection and maintenance. The positive pressure explosion-proof robot maintains the internal pressure higher than the external environment to prevent the entry of external flammable and explosive gas, thereby ensuring the safe operation of the robot in a dangerous environment. The monitoring of its operating state is crucial to ensuring safe production.

[0003] Currently, the operating state monitoring of the positive pressure explosion-proof robot mainly relies on a single sensor or a simple combination of a few sensors. This monitoring method can only obtain limited operating data and cannot fully and accurately reflect the actual operating conditions of the robot. When the robot leaks, due to the limitations of monitoring data, it is often difficult to timely and accurately detect leakage hazards, making it difficult to take effective measures.

[0004] The existing leakage monitoring method lacks comprehensive analysis and linkage processing of multiple parameters, resulting in inaccurate assessment of leakage risks and inability to develop reasonable emergency treatment strategies according to different leakage conditions, leading to low safety and reliability of the positive pressure explosion-proof robot in a dangerous environment. SUMMARY

[0005] The main purpose of the present application is to provide a positive pressure explosion-proof robot leakage monitoring and emergency treatment method, which can comprehensively and accurately monitor the operating state of the positive pressure explosion-proof robot, accurately assess the leakage risk level and locate the leakage position, and develop reasonable and effective emergency treatment strategies according to specific circumstances, thereby improving the safety and reliability of the positive pressure explosion-proof robot in a dangerous environment.

[0006] To achieve the above purpose, the embodiment of the present application provides a positive pressure explosion-proof robot leakage monitoring and emergency treatment method, which comprises:

[0007] The operating state data during the operation of the positive pressure explosion-proof robot is captured in real time by a multi-parameter linkage monitoring network, which is composed of gas sensors, pressure sensors and temperature sensors, and each sensor is arranged at a key position of the positive pressure explosion-proof robot according to a predetermined spatial distribution characteristic;

[0008] The captured operating state data is processed to extract leakage characteristic values, including gas concentration change rate, pressure fluctuation amplitude and temperature gradient;

[0009] According to the extracted leakage characteristic value, the leakage risk level of the positive pressure explosion-proof robot is determined through a preset risk assessment model, and the leakage risk level includes low risk, medium risk and high risk.

[0010] Based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time sequence change rule of the leakage characteristic value, the leakage position is analyzed to determine the leakage position of the positive pressure explosion-proof robot.

[0011] According to the leakage risk level and the leakage position, an emergency treatment instruction is generated, and the emergency treatment instruction is sent to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform corresponding emergency treatment operation, wherein the emergency treatment instruction includes starting a local sealing device, adjusting internal air pressure to a safe range, or issuing an alarm signal.

[0012] Correspondingly, the application also provides a positive pressure explosion-proof robot leakage monitoring and emergency treatment system, which comprises:

[0013] The acquisition module is configured to capture running state data in the running process of the positive pressure explosion-proof robot in real time through a multi-parameter linkage monitoring network, wherein the multi-parameter linkage monitoring network is composed of a gas sensor, a pressure sensor and a temperature sensor, and each sensor is arranged at a key position of the positive pressure explosion-proof robot according to a preset spatial distribution characteristic;

[0014] The feature processing module is configured to perform feature processing on the captured running state data to extract leakage characteristic values, wherein the leakage characteristic values include a gas concentration change rate, a pressure fluctuation amplitude and a temperature gradient.

[0015] The risk assessment module is configured to determine the leakage risk level of the positive pressure explosion-proof robot according to the extracted leakage characteristic value through a preset risk assessment model, wherein the leakage risk level includes low risk, medium risk and high risk.

[0016] The positioning analysis module is configured to analyze the leakage position based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time sequence change rule of the leakage characteristic value to determine the leakage position of the positive pressure explosion-proof robot.

[0017] The emergency treatment module is configured to generate an emergency treatment instruction according to the leakage risk level and the leakage position, and send the emergency treatment instruction to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform corresponding emergency treatment operation, wherein the emergency treatment instruction includes starting a local sealing device, adjusting internal air pressure to a safe range, or issuing an alarm signal.

[0018] In summary, by using the technical solution of the present application, through the multi-parameter linkage monitoring network composed of a gas sensor, a pressure sensor and a temperature sensor, the running state data in the running process of the positive pressure explosion-proof robot can be captured in real time and comprehensively. The data is processed by feature extraction, and the gas concentration change rate, pressure fluctuation amplitude and temperature gradient are extracted as leakage characteristic values, which provide rich and reliable basis for accurate evaluation of leakage risk. According to the preset risk evaluation model, the leakage risk level is determined, so that corresponding emergency treatment measures can be taken according to different risk levels. Based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time sequence change law of the leakage characteristic values, the leakage position is analyzed and positioned, and the positioning accuracy of the leakage position is improved. According to the emergency treatment instructions generated according to the leakage risk level and the leakage position, the local sealing device can be started, the internal air pressure can be adjusted to the safe range, or the alarm signal can be sent, so as to effectively deal with different degrees of leakage problems. At the same time, the real-time evaluation and adjustment method of the instruction execution effect ensures the effectiveness and adaptability of the emergency treatment instructions, and further improves the safety and reliability of the positive pressure explosion-proof robot in the dangerous environment. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a scene diagram of the positive pressure explosion-proof robot leakage monitoring and emergency treatment method in the embodiments of the present application;

[0021] Figure 2 is a flowchart of the positive pressure explosion-proof robot leakage monitoring and emergency treatment method provided in the embodiments of the present application;

[0022] Figure 3 is a flowchart of the running state data acquisition provided in the embodiments of the present application;

[0023] Figure 4 is a flowchart of the difference absolute value calculation provided in the embodiments of the present application;

[0024] Figure 5 is a flowchart of the leakage risk evaluation provided in the embodiments of the present application;

[0025] Figure 6 is a flowchart of the leakage position positioning analysis provided in the embodiments of the present application;

[0026] Figure 7This is a flowchart illustrating the generation of emergency handling instructions provided in an embodiment of this application.

[0027] Figure 8 A schematic diagram illustrating the process of optimizing spatial distribution characteristics provided in an embodiment of this application;

[0028] Figure 9 This is a schematic diagram of the structure of the positive pressure explosion-proof robot leakage monitoring and emergency handling system provided in the embodiments of this application;

[0029] Figure 10 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] This application provides a method and system for monitoring and handling leaks in a positive pressure explosion-proof robot, which will be described in detail below.

[0032] In this embodiment, the positive pressure explosion-proof robot leakage monitoring and emergency handling method is a comprehensive method for ensuring the safety of the positive pressure explosion-proof robot during operation.

[0033] A positive pressure explosion-proof robot is a robotic device specifically designed for safe operation in hazardous environments such as flammable and explosive environments. Based on the principle of positive pressure explosion protection, it maintains an internal air pressure higher than the external ambient air pressure, preventing flammable and explosive gases from entering the robot and thus avoiding explosions caused by electrical sparks from internal components. Positive pressure explosion-proof robots are typically equipped with a sophisticated air supply system that continuously delivers clean air or inert gas to maintain a positive pressure state. Simultaneously, the robot is equipped with pressure sensors to monitor the internal air pressure in real time. If the air pressure falls below a set safety value, the air supply system automatically increases the air flow to ensure a stable positive pressure state. Its outer shell is made of high-strength, well-sealed materials to prevent gas leakage. In hazardous environments, positive pressure explosion-proof robots can perform various tasks such as inspection, testing, and handling. Utilizing its onboard sensors and actuators, it monitors and operates the environment, effectively improving work efficiency and safety, and reducing the risk of personnel exposure to hazardous environments. For example, in the production workshops of petrochemical companies, positive pressure explosion-proof robots can regularly inspect pipelines, valves and other equipment to promptly detect potential leaks and equipment malfunctions, ensuring the normal operation of production.

[0034] Multi-parameter linkage means using multiple different types of sensors such as gas sensors, pressure sensors, and temperature sensors to work together to comprehensively monitor the operating status of the robot. Leakage monitoring is a process of real-time detection and judgment of whether the robot has gas leakage, and potential leakage situations are discovered by analyzing the data collected by the sensors. Emergency handling is to quickly take corresponding measures such as starting a local sealing device, adjusting the internal air pressure, or sending an alarm signal according to the risk level and location of the leakage after detecting the leakage, so as to reduce the harm caused by the leakage and ensure the safety of the robot and the surrounding environment.

[0035] As shown in Figure 1, a scenario of a leakage monitoring and emergency handling method for a positive-pressure explosion-proof robot is provided. Based on the explosion-proof control scenario of the robot, it mainly includes a positive-pressure explosion-proof machine, a multi-parameter linkage monitoring network, and a control center; among them, the positive-pressure explosion-proof machine, the multi-parameter linkage monitoring network, and the control center can be connected through wired or wireless networks.

[0036] Taking the production workshop in the petrochemical industry as an example, there are a large number of flammable and explosive chemical substances in the production workshop, such as petroleum, natural gas, etc. Once these substances leak and encounter a fire source, it may trigger an explosion or fire, posing a serious threat to personnel and equipment. The positive-pressure explosion-proof robot plays an important role in such a dangerous environment. For example, it conducts inspections on equipment such as pipelines and valves to timely detect potential leakage points; or it conducts material handling to improve production efficiency.

[0037] The positive-pressure explosion-proof robot is equipped with a multi-parameter linkage monitoring network composed of gas sensors, pressure sensors, and temperature sensors. The gas sensor can detect the concentration of flammable and explosive gases in the surrounding environment, such as detecting the content of gases such as methane and hydrogen. The pressure sensor is used to monitor the air pressure inside the robot in real time to ensure that the internal air pressure is always higher than the external environmental air pressure and prevent external gases from entering. The temperature sensor can monitor the temperature change inside the robot because leakage may cause a local temperature rise.

[0038] These sensors are arranged at key parts of the positive-pressure explosion-proof robot according to the preset spatial distribution characteristics. For example, gas sensors are arranged at the shell interfaces of the robot to detect whether there is gas leakage from the interfaces; temperature sensors are arranged inside the electrical control cabinet of the robot to monitor whether the heat generated by the electrical components during operation is abnormal; pressure sensors are arranged near the gas supply system of the robot to ensure the stability of the gas supply pressure.

[0039] During the operation of the robot, the multi-parameter linkage monitoring network continuously works to capture the operation status data in real time. When the robot approaches a pipeline where leakage may occur, the gas sensor detects that the surrounding gas concentration gradually increases, the pressure sensor may detect a slight fluctuation in the internal air pressure of the robot, and the temperature sensor may also find an upward trend in the local temperature. These data are transmitted to the control center in a timely manner, and the control center analyzes and processes the data to determine whether there is leakage and the severity of the leakage.

[0040] If the control center determines that there is a leakage risk, it will determine the leakage risk level according to the preset risk assessment model. If the risk level is low risk, an alarm signal may be issued to remind the operator to pay attention; if the risk level is medium risk, an instruction to adjust the internal air pressure to the safe range will be generated to relieve the leakage situation; if the risk level is high risk, the local sealing device will be immediately activated to isolate the leakage area and prevent the leakage from expanding further.

[0041] At the same time, the control center will perform a location analysis on the leakage location based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time series change law of the leakage characteristic values. By comparing the data differences detected by sensors at different positions, the specific location where the leakage occurs is determined. For example, if the gas concentration detected by the gas sensor at a certain position is significantly higher than that at other positions, and there are also abnormal changes in the pressure and temperature at this position, then it can be preliminarily judged that this position is the leakage point.

[0042] During the whole process, the execution effect of the instruction will also be evaluated and adjusted in real time. If the changes in the internal environment parameters (such as gas concentration, internal air pressure and temperature) do not reach the expected goal after executing the emergency treatment instruction, the instruction will be adjusted and resent to the positive pressure explosion-proof robot to ensure that the problem is effectively solved.

[0043] In addition, in order to improve the accuracy and effectiveness of monitoring, the spatial distribution characteristics of the multi-parameter linkage monitoring network will be optimized. According to the leakage risk distribution area of the key parts during the operation of the positive pressure explosion-proof robot, multiple monitoring zones are divided, the rationality of the sensor layout in each zone is evaluated, and adjustments are made according to the evaluation results so that the sensors can capture leakage information more accurately.

[0044] Reference Figure 2 , Figure 2 is a schematic flowchart of a method for leakage monitoring and emergency treatment of a positive pressure explosion-proof robot provided by an embodiment of the present application. The execution subject of this method can be a computer device (which can be used as a control center). This computer device can be a single computer device or a cluster composed of multiple computer devices. This computer device can be a terminal device or a server, etc. The method for leakage monitoring and emergency treatment of a positive pressure explosion-proof robot provided by an embodiment of the present application specifically includes:

[0045] S10: Real-time capture of operational status data during the operation of the positive pressure explosion-proof robot is achieved through a multi-parameter linkage monitoring network. The multi-parameter linkage monitoring network consists of gas sensors, pressure sensors, and temperature sensors, and each sensor is arranged in key parts of the positive pressure explosion-proof robot according to preset spatial distribution characteristics.

[0046] In this embodiment, the multi-parameter linkage monitoring network is a monitoring system composed of gas sensors, pressure sensors, and temperature sensors, used to comprehensively and in real-time acquire the operational status information of the positive pressure explosion-proof robot. A gas sensor is a device capable of detecting the concentration of a specific gas. For example, a catalytic combustion gas sensor can be used to detect the concentration of flammable and explosive gases. When the gas reacts with the catalyst on the sensor surface, heat is generated, causing a change in resistance. The gas concentration is determined by measuring this change in resistance. A pressure sensor is used to measure the internal air pressure of the positive pressure explosion-proof robot. Common pressure sensors include piezoresistive pressure sensors, which utilize the piezoresistive effect. When pressure is applied to the sensor's sensitive element, its resistance changes, and the pressure is calculated by measuring this change in resistance. A temperature sensor is used to monitor the internal temperature of the robot. For example, a thermocouple temperature sensor, based on the thermoelectric effect, generates a thermoelectric potential at the junction of two different metal materials when the temperature changes. The temperature is determined by measuring this thermoelectric potential. The preset spatial distribution characteristics refer to the pre-determined installation positions and layout of each sensor based on the structure of the positive pressure explosion-proof robot and potential leakage points. Critical parts include the robot's shell interface, air supply system, electrical components, and other areas prone to leakage or abnormalities.

[0047] In this embodiment, gas sensors, pressure sensors, and temperature sensors are arranged in key areas of the positive pressure explosion-proof robot according to a preset spatial distribution characteristic. These sensors can perceive changes in the surrounding environment in real time. For example, when there is a flammable or explosive gas leak around the robot, the gas sensor will detect changes in gas concentration; if the internal air pressure of the robot fluctuates, the pressure sensor will promptly detect the pressure anomaly; when electrical components overheat due to a fault, the temperature sensor will detect the temperature rise. By capturing these operational status data in real time, potential problems during the operation of the positive pressure explosion-proof robot can be detected in a timely manner. The technical advantage lies in providing a comprehensive and accurate data foundation for subsequent leak feature extraction and risk assessment, helping to identify potential leaks in advance and ensuring the safe operation of the positive pressure explosion-proof robot.

[0048] In one embodiment, an infrared gas sensor can be used as the gas sensor. Infrared gas sensors utilize the absorption characteristics of gases to infrared light of specific wavelengths to detect gas concentration. When gas molecules absorb infrared light of a specific wavelength, the intensity of the infrared light changes, and the gas concentration can be calculated by detecting the change in infrared light intensity. This type of sensor has advantages such as fast response speed, high accuracy, and good stability. For pressure sensors, capacitive pressure sensors can be selected. Capacitive pressure sensors are based on the principle of capacitance. When pressure is applied to the diaphragm of the sensor, the diaphragm deforms, causing a change in capacitance. The pressure is determined by measuring the change in capacitance. It features high sensitivity and good linearity. For temperature sensors, thermistor temperature sensors can be used. The resistance value of a thermistor temperature sensor changes with temperature, and the temperature value is obtained by measuring the change in resistance. This type of sensor has advantages such as small size and high accuracy. Installing these sensors according to a preset spatial distribution characteristic in key parts of the positive pressure explosion-proof robot enables real-time capture of operational status data by a multi-parameter linkage monitoring network.

[0049] S20: Perform feature processing on the captured operating status data to extract leakage feature values, including gas concentration change rate, pressure fluctuation amplitude, and temperature gradient.

[0050] In this embodiment, feature processing refers to the process of analyzing, transforming, and extracting the collected operational status data to obtain key features that reflect the leakage situation. The gas concentration change rate refers to the ratio of the change in gas concentration to the time interval within a certain time interval; it reflects the speed of gas concentration change. For example, if the gas concentration rises from 1% to 2% in 10 seconds, then the gas concentration change rate is (2% - 1%) / 10 seconds = 0.1% / second. The pressure fluctuation amplitude refers to the difference between the maximum and minimum pressure values ​​over a period of time; it reflects the degree of pressure instability. The temperature gradient refers to the rate of temperature change with distance in space, reflecting the non-uniformity of temperature distribution.

[0051] In this embodiment, when performing feature processing on the captured operational status data, the data is first preprocessed, such as by removing noise and filtering, to improve data quality. Then, based on the gas concentration, pressure, and temperature data, the gas concentration change rate, pressure fluctuation amplitude, and temperature gradient are calculated respectively. For example, the gas concentration change rate is obtained by performing differential calculations on continuously collected gas concentration data over a period of time; the pressure fluctuation amplitude is obtained by comparing the maximum and minimum pressure values ​​over a period of time; and the temperature gradient is obtained by measuring the temperature at different locations and calculating the ratio of the temperature difference to the distance. These leakage characteristic values ​​can more intuitively reflect whether a leak has occurred in the positive pressure explosion-proof robot and the severity of the leak. The technical effect is that it transforms the raw operational status data into more targeted leakage characteristic values, facilitating accurate subsequent leakage risk assessment.

[0052] In one embodiment, a sliding window algorithm can be used for feature processing. The sliding window algorithm sets a fixed-size window on the data sequence, and as time progresses, the window slides across the data sequence, processing the data within the window. For example, for gas concentration data, a sliding window of 20 data points can be set, and the average and standard deviation of the gas concentration can be calculated within each window. Then, the rate of change of gas concentration can be calculated based on the average and standard deviation. A similar method is used for pressure and temperature data. Through the sliding window algorithm, changes in data can be tracked in real time, and accurate leakage feature values ​​can be extracted.

[0053] S30: Based on the extracted leakage characteristic values, the leakage risk level of the positive pressure explosion-proof robot is determined through a preset risk assessment model. The leakage risk level includes low risk, medium risk, and high risk.

[0054] In this embodiment, the preset risk assessment model is a mathematical model based on a large amount of experimental data and actual operating experience, used to determine the leakage risk level of the positive pressure explosion-proof robot according to leakage characteristic values. Low risk indicates that the robot is unlikely to leak, or even if a leak occurs, its impact range and degree of harm are low; medium risk indicates that the robot has a certain leakage risk, which requires attention and corresponding measures; high risk indicates that the robot is very likely to have leaked, or the leakage situation is relatively serious, requiring immediate emergency treatment measures.

[0055] In this embodiment, a preset risk assessment model compares the extracted gas concentration change rate, pressure fluctuation amplitude, and temperature gradient with preset thresholds. For example, when the gas concentration change rate is lower than a first threshold, the pressure fluctuation amplitude is lower than a second threshold, and the temperature gradient is lower than a third threshold, it is determined to be low risk; when the gas concentration change rate is between the first and second thresholds, the pressure fluctuation amplitude is between the second and third thresholds, and the temperature gradient is between the third and fourth thresholds, it is determined to be medium risk; when the gas concentration change rate is higher than the second threshold, the pressure fluctuation amplitude is higher than the third threshold, and the temperature gradient is higher than the fourth threshold, it is determined to be high risk. In this way, the leakage risk level of the positive pressure explosion-proof robot can be accurately assessed based on different leakage characteristic values. Its technical effect is that it provides a basis for subsequently formulating reasonable emergency response strategies, helping to take timely measures to reduce the harm caused by leakage.

[0056] In one embodiment, the preset risk assessment model can employ a fuzzy comprehensive evaluation model. A fuzzy comprehensive evaluation model is an evaluation method based on fuzzy mathematics that considers the influence of multiple factors and performs a comprehensive evaluation based on the weights of each factor. In this application, the gas concentration change rate, pressure fluctuation amplitude, and temperature gradient are used as evaluation factors, and each factor is assigned a corresponding weight. The actual values ​​of these factors are converted into fuzzy membership degrees using fuzzy membership functions, and then a comprehensive evaluation is performed according to fuzzy inference rules to derive the leakage risk level of the positive pressure explosion-proof robot. This model can handle uncertain and fuzzy information, and more accurately assess leakage risk.

[0057] S40: Based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and combined with the time series variation law of leakage characteristic values, the leakage location is located and analyzed to determine the leakage location of the positive pressure explosion-proof robot.

[0058] In this embodiment, the spatial distribution characteristics of the multi-parameter linkage monitoring network refer to the installation positions and layout relationships of the gas sensors, pressure sensors, and temperature sensors on the positive pressure explosion-proof robot, reflecting the spatial distribution of the sensors. The time-series variation patterns of leakage characteristic values ​​refer to the trends in the gas concentration change rate, pressure fluctuation amplitude, and temperature gradient over time. By analyzing these variation patterns, the development process of the leakage can be understood. The leakage location refers to the specific location where a gas leak occurs on the positive pressure explosion-proof robot.

[0059] In this embodiment, since the leakage characteristic values ​​detected by sensors at different locations may differ, and these characteristics change over time, the location of a leak can be determined by analyzing the spatial distribution characteristics of a multi-parameter linkage monitoring network and the time-series variation patterns of the leakage characteristic values. For example, if the rate of change of gas concentration detected by a gas sensor at a certain location suddenly increases at a certain moment, and adjacent pressure and temperature sensors also detect corresponding abnormal changes, it can be preliminarily determined that a leak may exist near that location. By further analyzing the data from other sensors and the time-series variations of the leakage characteristic values, the leak location can be determined more accurately. The technical advantage lies in the ability to quickly and accurately locate the leak, providing a clear target for subsequent emergency response and improving the efficiency of handling leak problems.

[0060] In one embodiment, the centroid algorithm, a sensor network localization algorithm, can be used for leak location analysis. The basic idea of ​​the centroid algorithm is to use the locations of sensor nodes that detected abnormal data as reference points, calculate a centroid position based on the coordinates of these reference points, and use this centroid position as an estimate of the leak location. Specifically, first, sensor nodes that detected abnormalities in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient are identified. Then, their centroid coordinates are calculated based on the coordinates of these sensor nodes. To improve the accuracy of the location, the centroid position can be corrected by incorporating the time-series variation patterns of the leak characteristic values. For example, if a sensor node detects abnormal data for a longer period, it can be given a higher weight when calculating the centroid position.

[0061] S50: Generate an emergency handling instruction based on the leakage risk level and leakage location, and send the emergency handling instruction to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform the corresponding emergency handling operation. The emergency handling instruction includes activating a local sealing device, adjusting the internal air pressure to a safe range, or issuing an alarm signal.

[0062] In this embodiment, the emergency handling instructions are formulated based on the leakage risk level and leakage location, and are used to guide the positive pressure explosion-proof robot to take corresponding measures to deal with the leakage situation. Activating the local sealing device means that after a leak is detected, the local sealing device (such as a sealing valve, sealing cover, etc.) on the robot is activated to isolate the leakage area and prevent further diffusion of the leaked gas. Adjusting the internal air pressure to a safe range means adjusting the robot's air supply or exhaust system to maintain the internal air pressure within a safe range to prevent the entry of external flammable and explosive gases. Issuing an alarm signal means issuing an alarm through devices such as audible and visual alarms to alert surrounding personnel that there may be a leak in the positive pressure explosion-proof robot.

[0063] In this embodiment, once the leakage risk level and location are determined, corresponding emergency handling instructions are generated based on different situations. If the leakage risk level is high and the leakage location is identified, an instruction to activate a local sealing device will be generated first to quickly isolate the leakage source. If the leakage risk level is medium, an instruction to adjust the internal air pressure to a safe range will be generated to alleviate the leakage. If the leakage risk level is low, an instruction to issue an alarm signal will be generated to alert the operator. After these emergency handling instructions are sent to the positive pressure explosion-proof robot, the robot's control system will execute the corresponding operations according to the instructions. The technical advantage lies in its ability to take timely and effective measures based on different leakage situations, reducing the harm caused by the leakage and ensuring the safety of the positive pressure explosion-proof robot and the surrounding environment.

[0064] In one embodiment, a programmable logic controller (PLC) can be used to generate and send emergency handling instructions. A PLC is a computer specifically designed for industrial control, offering advantages such as high reliability and flexible programming. A program is written in the PLC to generate corresponding emergency handling instructions based on the judgment of the leakage risk level and leakage location. These instructions are then sent to the actuators of the positive pressure explosion-proof robot via a communication interface. For example, when an instruction to activate the partial sealing device is generated, the PLC controls the drive motor of the sealing device to start, causing the sealing device to close; when an instruction to adjust the internal air pressure to a safe range is generated, the PLC adjusts the opening of the air supply and exhaust valves to regulate the internal air pressure; when an instruction to issue an alarm signal is generated, the PLC controls the audible and visual alarm to sound.

[0065] In one embodiment, reference Figure 3 The method of this application may further include real-time evaluation and adjustment of the command execution effect. In this embodiment, the method for real-time evaluation and adjustment of the command execution effect refers to a method that monitors and evaluates the execution effect of the emergency handling command in real time after the positive pressure explosion-proof robot executes the command, and adjusts the command based on the evaluation results. Its purpose is to ensure that the emergency handling command achieves the expected effect and effectively solves the leakage problem. Specifically:

[0066] S61: Obtain data on changes in internal environmental parameters of the positive pressure explosion-proof robot after executing an emergency handling command. The internal environmental parameters include gas concentration, internal air pressure, and temperature.

[0067] In this embodiment, the data on changes in internal environmental parameters is a key set of information reflecting the changes in the internal state of the positive pressure explosion-proof robot after executing emergency handling commands. Gas concentration refers to the proportion of a specific flammable or explosive gas within a unit volume of the positive pressure explosion-proof robot; its changes directly reflect whether a gas leak exists or whether the leak has been controlled. For example, if the internal flammable gas concentration decreases after executing the command to activate the local sealing device, it indicates that the sealing device may have effectively prevented the entry of external gases. Internal pressure is the pressure generated by air or other gases inside the robot. Maintaining appropriate internal pressure is crucial for positive pressure explosion protection; changes in pressure reflect the working status of the gas supply system, exhaust system, and sealing structure. For example, when the command to adjust the internal pressure to a safe range is executed, and the internal pressure approaches the preset safe value, it indicates that the pressure regulation measures have been effective. Temperature reflects the thermal state inside the robot; the operation of electrical components and chemical reactions caused by gas leaks can all lead to temperature changes. For instance, a short circuit in an electrical component may cause a sharp increase in local temperature.

[0068] In one embodiment, to acquire data on changes in internal environmental parameters, high-precision gas sensors, pressure sensors, and temperature sensors can be strategically arranged inside the positive-pressure explosion-proof robot. The gas sensors can be electrochemical gas sensors, capable of highly sensitive detection of specific gases, determining gas concentration by detecting changes in current generated by the chemical reaction between the gas and electrodes. The pressure sensors are piezoresistive pressure sensors, utilizing the piezoresistive effect to convert pressure changes into resistance changes, thereby measuring the internal gas pressure. The temperature sensors are thermocouple temperature sensors, converting temperature changes into electrical signals based on the thermoelectric effect. These sensors transmit the collected data to a data acquisition module, which performs preliminary processing and storage, and then sends the data to the control center via a communication interface for subsequent analysis and evaluation.

[0069] S62: Based on the acquired data on changes in internal environmental parameters, calculate the actual change of each parameter and compare it with the preset theoretical change.

[0070] In this embodiment, the actual change refers to the actual change in internal environmental parameters (gas concentration, internal pressure, and temperature) of the positive pressure explosion-proof robot after executing an emergency handling command, relative to the value before the command was executed. For example, if the gas concentration was 5% before the command was executed and became 3% after execution, the actual change in gas concentration is a decrease of 2%. The preset theoretical change is the change in internal environmental parameters that should occur after executing the corresponding emergency handling command under ideal conditions, based on a large amount of experimental data and simulation analysis.

[0071] In this embodiment, comparing the actual change with a preset theoretical change allows for a determination of whether the execution effect of the emergency handling instruction meets expectations. If the actual change is close to the theoretical change, the instruction execution effect is good; if the difference is large, it indicates that the instruction execution may not have achieved the expected goal. The technical advantage lies in providing a quantitative basis for subsequent judgment on whether the instruction execution effect has met the target, helping to promptly identify problems and take adjustment measures.

[0072] S63: If the deviation between the actual change and the theoretical change exceeds the first deviation threshold, it is determined that the execution effect of the current emergency handling instruction has not achieved the expected goal.

[0073] In this embodiment, the first deviation threshold is a pre-set standard value used to measure the allowable deviation range between the actual change and the preset theoretical change. When the deviation exceeds this threshold, it indicates that the actual situation differs significantly from the expectation, and the execution effect of the current emergency handling instruction is not ideal.

[0074] In this embodiment, by setting a first deviation threshold, a clear standard can be established to determine whether the instruction execution effect meets the target. Once the deviation between the actual change and the theoretical change exceeds this threshold, it can be promptly determined that the instruction execution has not achieved the expected goal, so that further adjustment measures can be taken. Its technical advantage lies in providing a clear judgment criterion, ensuring that problems in the instruction execution process can be detected in a timely manner, and preventing leakage problems from remaining unresolved.

[0075] In one embodiment, the first deviation threshold can be set according to different internal environmental parameters and emergency handling command types. For example, for gas concentration, the first deviation threshold can be set to 1%; for internal air pressure, the first deviation threshold can be set to 5 kPa; and for temperature, the first deviation threshold can be set to 5 °C. In practical applications, the calculated deviation is compared with the corresponding first deviation threshold based on specific parameters and commands to make a judgment.

[0076] S64: In case the expected goal is not achieved, the emergency handling command is adjusted. The adjusted command instructs to adjust the starting sequence of the local sealing device or readjust the internal air pressure to a new safe range according to the direction of deviation.

[0077] In this embodiment, the deviation direction refers to the direction of the difference between the actual change and the theoretical change. For example, if the actual decrease in gas concentration is less than the theoretical decrease, the deviation direction is "insufficient reduction." Adjusting the activation sequence of the local sealing device refers to changing the activation order or method of the local sealing device to better isolate the leakage area. Readjusting the internal gas pressure to a new safe range involves determining a new suitable gas pressure range based on the actual situation and achieving this by adjusting the gas supply or exhaust system.

[0078] In this embodiment, when it is determined that the current emergency response command's execution effect has not achieved the expected goal, the command is adjusted according to the direction of the deviation. If the deviation is due to poor isolation effect of the local sealing device, the adjusted command can instruct a change in the activation sequence of the local sealing devices, prioritizing the activation of sealing devices in more critical locations; if the deviation is due to improper internal air pressure regulation, the adjusted command can instruct a readjustment of the internal air pressure to a new safe range. Through this adjustment, the emergency response command can be made more consistent with the actual situation, improving the response effect. Its technical effect lies in enhancing the adaptability and effectiveness of the emergency response command, enabling better handling of leakage problems.

[0079] In one embodiment, to adjust the activation sequence of local sealing devices, a priority list can be established, determining the priority of each sealing device based on the leak location and the function of different sealing devices. When adjustment is required, the sealing devices are activated according to the new priority order. To readjust the internal air pressure to a new safe range, the opening of the air supply valve and exhaust valve can be adjusted using a PID controller based on the actual internal environmental parameters and deviations, so that the internal air pressure reaches the new safe range.

[0080] S65: Resend the adjusted instructions to the positive pressure explosion-proof robot.

[0081] In this embodiment of the application, the adjusted instructions are resent to the positive pressure explosion-proof robot so that the robot can perform emergency handling operations again according to the new instructions, in order to improve the situation where the expected goal was not achieved before.

[0082] In this embodiment, by resending the adjusted instructions to the positive pressure explosion-proof robot, the robot's control system will control the corresponding actuators, such as local sealing devices, air supply and exhaust valves, to perform emergency handling again. The technical effect is to ensure that problems in the previous instruction execution process can be corrected in a timely manner, enabling the positive pressure explosion-proof robot to more effectively deal with leakage situations and ensure its safe operation.

[0083] In one embodiment, the adjusted command can be sent to the control system of the positive pressure explosion-proof robot through the same communication interface as the original command, such as serial communication or Ethernet communication. Upon receiving the new command, the control system parses the command content and controls the actuators to move according to the command requirements.

[0084] In one embodiment, reference Figure 4 Step S10 may include S101-S103, which will be described in detail below:

[0085] S101: Obtain the sampling frequency of the gas sensor, pressure sensor, and temperature sensor per unit time, and set the data acquisition cycle of the multi-parameter linkage monitoring network according to the sampling frequency.

[0086] In this embodiment, the sampling frequency refers to the number of times the gas sensor, pressure sensor, and temperature sensor sample the physical quantity per unit time (usually seconds). It reflects the density of data acquisition by the sensor; the higher the sampling frequency, the more data points are acquired in the same time period, and the more detailed the capture of changes in the physical quantity. The data acquisition cycle is the time interval required for the multi-parameter linkage monitoring network to complete one complete data acquisition. Setting the data acquisition cycle reasonably can ensure the acquisition of effective data while avoiding excessive processing burden due to excessive data volume.

[0087] In this embodiment, different types of sensors may have different sampling frequencies. For example, gas sensors may have a higher sampling frequency to capture rapid changes in gas concentration in a timely manner; while pressure and temperature sensors change relatively slowly, and their sampling frequencies may be relatively lower. Setting the data acquisition cycle according to the sampling frequencies of these sensors ensures that the data acquisition of each sensor in the multi-parameter linkage monitoring network is synchronized and coordinated. The technical effect is to ensure that the data acquired by each sensor has temporal consistency, providing an accurate and reliable foundation for subsequent data processing and analysis.

[0088] In one embodiment, the sampling frequency information of the sensor can be obtained by reading the sensor's technical manual or by communicating with the sensor. Then, a suitable data acquisition period is selected based on these sampling frequencies. For example, if the gas sensor's sampling frequency is 10 times / second, the pressure sensor's sampling frequency is 5 times / second, and the temperature sensor's sampling frequency is 3 times / second, the data acquisition period can be set to 1 second to ensure that data from all sensors is acquired. Within each data acquisition period, gas concentration, pressure, and temperature data are acquired sequentially according to the sensor's sampling frequency.

[0089] S102: During each data acquisition cycle, record the gas concentration value detected by the gas sensor, the pressure value detected by the pressure sensor, and the temperature value detected by the temperature sensor.

[0090] In this embodiment, the gas concentration value is the concentration of a specific gas in the surrounding environment detected by the gas sensor in each data acquisition cycle, typically expressed as a percentage or ppm (parts per million). The pressure value is the internal air pressure of the positive pressure explosion-proof robot measured by the pressure sensor in each data acquisition cycle, typically expressed in Pascals (Pa) or kilopascals (kPa). The temperature value is the internal temperature of the robot acquired by the temperature sensor in each data acquisition cycle, commonly expressed in degrees Celsius (°C).

[0091] In this embodiment, accurately recording these values ​​within each data acquisition cycle forms a continuous dataset reflecting changes in gas concentration, pressure, and temperature during the operation of the positive pressure explosion-proof robot. This data serves as a crucial basis for subsequent leak feature extraction and risk assessment. The technical advantage lies in accumulating a large amount of operational status data, providing data support for a comprehensive analysis of the positive pressure explosion-proof robot's operational status and timely detection of potential leaks.

[0092] In one embodiment, a data acquisition card can be used to record these values. The data acquisition card has multiple input channels, which are connected to a gas sensor, a pressure sensor, and a temperature sensor, respectively. At the beginning of each data acquisition cycle, the data acquisition card reads the output signals of each sensor in a preset order and converts them into digital signals for storage. The stored data can be stored in local memory or transmitted over a network to a remote server for further processing and analysis.

[0093] S103: Store the gas concentration, pressure, and temperature values ​​according to a preset data format, and mark the spatial location information corresponding to each data point.

[0094] In this embodiment, the preset data format refers to a predefined file format or database structure used to store gas concentration, pressure, and temperature values. Common data formats include CSV (comma-separated values) and JSON (JavaScript Object Notation). Spatial location information refers to the installation position of each sensor on the positive pressure explosion-proof robot. By marking the spatial location information, the specific location corresponding to each data point can be clearly identified.

[0095] In this embodiment, the collected gas concentration, pressure, and temperature values ​​are stored according to a preset data format for easy subsequent data management and analysis. Simultaneously, the spatial location information corresponding to each data point is marked, enabling more accurate determination of the leak location and assessment of the leak situation by combining the spatial distribution characteristics of the sensors during data analysis. The technical advantage lies in improving data manageability and analytical accuracy, facilitating more effective utilization of the collected data.

[0096] In one embodiment, if CSV data format is used for storage, a table containing gas concentration values, pressure values, temperature values, and spatial location information can be created. Each row represents the data collected within a data acquisition cycle, and each column corresponds to the gas concentration value, pressure value, temperature value, and spatial location information, respectively. For example, the first column stores the gas concentration value, the second column stores the pressure value, the third column stores the temperature value, and the fourth column stores the spatial location information. When storing the data, the data collected in each data acquisition cycle is written to the CSV file sequentially according to the format of this table. For spatial location information, coordinates or numbers can be used to mark it for easy identification and processing.

[0097] In one embodiment, reference Figure 5 Step S30 may include S301-S304, which will be described in detail below:

[0098] S301: Obtain the gas concentration change rate, pressure fluctuation amplitude, and temperature gradient in the leakage characteristic values ​​through a preset risk assessment model, and compare them with the preset first threshold, second threshold, and third threshold respectively.

[0099] In this embodiment, the preset risk assessment model is a mathematical model established based on a large amount of experimental data and actual operating experience. It is used to assess the leakage risk level of the positive pressure explosion-proof robot based on leakage characteristic values. The gas concentration change rate, pressure fluctuation amplitude, and temperature gradient are important characteristic values ​​reflecting whether the positive pressure explosion-proof robot has leaked and the severity of the leak. The preset first threshold, second threshold, and third threshold are pre-set standard values, corresponding to the critical values ​​of the gas concentration change rate, pressure fluctuation amplitude, and temperature gradient, respectively, and are used to determine the level of leakage risk.

[0100] In this embodiment, these leakage characteristic values ​​are obtained through a preset risk assessment model and compared with corresponding thresholds to preliminarily determine the leakage risk of the positive pressure explosion-proof robot. If a certain characteristic value exceeds the corresponding threshold, it indicates that the leakage risk in that aspect may be high. The technical effect is that it provides a quantitative basis for accurately assessing the leakage risk level subsequently.

[0101] In one embodiment, the preset risk assessment model can be a rule-based expert system. This system makes judgments based on the input gas concentration change rate, pressure fluctuation amplitude, and temperature gradient, according to preset rules. For example, when the gas concentration change rate exceeds a first threshold, the system marks a high risk in terms of gas concentration; when the pressure fluctuation amplitude exceeds a second threshold, it marks a high risk in terms of pressure; and when the temperature gradient exceeds a third threshold, it marks a high risk in terms of temperature.

[0102] S302: If the rate of change of gas concentration is lower than the first threshold, the pressure fluctuation amplitude is lower than the second threshold, and the temperature gradient is lower than the third threshold, then the leakage risk level is determined to be low risk.

[0103] In this embodiment of the application, when the gas concentration change rate, pressure fluctuation amplitude and temperature gradient are all lower than their respective thresholds, it indicates that the positive pressure explosion-proof robot is relatively stable in these three aspects and the possibility of leakage is small. Therefore, the leakage risk level is determined to be low risk.

[0104] In this embodiment, this comprehensive judgment method fully considers the impact of multiple factors on leakage risk. Only when all key leakage characteristic values ​​are within safe ranges is the risk deemed low, avoiding the limitations of single-factor judgment. Its technical advantage lies in accurately identifying situations with low leakage risk, providing operators with clear risk information, and facilitating the rational arrangement of monitoring and maintenance work.

[0105] In one embodiment, when the risk is determined to be low, the data collection cycle can be appropriately extended to reduce the workload of data processing and analysis, while maintaining a certain monitoring frequency to ensure that potential risk changes can be detected in a timely manner.

[0106] S303: If the rate of change of gas concentration is between the first threshold and the second threshold, the pressure fluctuation amplitude is between the second threshold and the third threshold, and the temperature gradient is between the third threshold and the fourth threshold, then the leakage risk level is determined to be medium risk.

[0107] In this embodiment of the application, when the gas concentration change rate, pressure fluctuation amplitude and temperature gradient are respectively in the corresponding intermediate threshold range, it indicates that the operating status of the positive pressure explosion-proof robot is abnormal, but has not yet reached the level of high risk. Therefore, the leakage risk level is determined to be medium risk.

[0108] In this embodiment, setting an intermediate threshold range and making a comprehensive judgment allows for a more detailed classification of leakage risk levels, providing a more accurate assessment of situations in the intermediate state. Once determined to be of medium risk, operators can be alerted to closely monitor the operating status of the positive pressure explosion-proof robot and take preventative measures. The technical effect is to promptly detect potential leakage hazards and prevent further escalation of risks.

[0109] In one embodiment, when the risk level is determined to be medium, the data acquisition frequency can be increased, the monitoring of the positive pressure explosion-proof robot can be strengthened, and the relevant equipment can be inspected and maintained to reduce the risk of leakage.

[0110] S304: If the rate of change of gas concentration is higher than the second threshold, the pressure fluctuation amplitude is higher than the third threshold, and the temperature gradient is higher than the fourth threshold, then the leakage risk level is determined to be high risk.

[0111] In this embodiment of the application, when the rate of change of gas concentration, the amplitude of pressure fluctuation, and the temperature gradient all exceed a high threshold, it indicates that the operating status of the positive pressure explosion-proof robot has become seriously abnormal, and a leak may have already occurred or is about to occur. Therefore, the leakage risk level is determined to be high risk.

[0112] In this embodiment, the stringent judgment criteria enable timely identification of high-risk situations, facilitating immediate emergency response and minimizing the harm caused by leaks. Once a high-risk situation is identified, the corresponding emergency plan must be activated rapidly to ensure the safety of the positive-pressure explosion-proof robot and its surrounding environment. The technical advantage lies in ensuring a timely response in high-risk situations, minimizing losses to the greatest extent possible.

[0113] In one embodiment, when a high risk is determined, the local sealing device should be activated immediately to adjust the internal air pressure to a safe range and issue an alarm signal to notify relevant personnel to carry out emergency handling.

[0114] In one embodiment, reference Figure 6 Step S40 may include S401-S405, which will be described in detail below:

[0115] S401: Obtain the spatial coordinate information of each sensor in the multi-parameter linkage monitoring network and construct the spatial distribution matrix of the sensors.

[0116] In this embodiment, spatial coordinate information refers to the specific position coordinates of the gas sensor, pressure sensor, and temperature sensor in the three-dimensional space where the positive pressure explosion-proof robot is located. By obtaining this coordinate information, the relative spatial positions of each sensor can be determined. The spatial distribution matrix of the sensors is a mathematical matrix that represents the spatial distribution of each sensor in matrix form. The elements in the matrix can represent the coordinates or other relevant information of the sensors.

[0117] In this embodiment, constructing a spatial distribution matrix of the sensors quantifies and structures their spatial distribution information, facilitating subsequent data analysis and processing. Through matrix operations and other methods, the relationships and mutual influences between different sensors can be analyzed more easily. The technical advantage lies in providing a clear spatial structural basis for leak location analysis, thus helping to improve the accuracy of the location.

[0118] In one embodiment, a 3D modeling software can be used to model the positive pressure explosion-proof robot, and the installation positions of each sensor can be marked in the model. The spatial coordinate information of each sensor is obtained through the software's measurement function. Then, a spatial distribution matrix of the sensors is constructed based on this coordinate information. For example, for a multi-parameter linkage monitoring network containing n sensors, an n×3 matrix can be constructed, where each row of the matrix corresponds to one sensor, and each column corresponds to the x, y, and z coordinates of the sensor, respectively.

[0119] S402: Calculate the signal difference between adjacent sensors based on the time series variation of leakage characteristic values. The signal difference includes differences in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient.

[0120] In this embodiment, the time-series variation pattern of leakage characteristic values ​​refers to the changing trends of gas concentration change rate, pressure fluctuation amplitude, and temperature gradient over time. Adjacent sensors refer to sensors that are spatially close to each other. Signal difference values ​​refer to the differences in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient between adjacent sensors at the same point in time or over a period of time.

[0121] In this embodiment, calculating the signal difference between adjacent sensors reflects the inconsistency of physical quantity changes at the sensor locations. If a leak exists in a certain area, the signal difference values ​​of sensors near that area may be large. By analyzing these differences, the area where the leak may occur can be preliminarily determined. The technical advantage is that it provides important clues for locating the leak and narrows the search range for the leak location.

[0122] In one embodiment, for differences in the rate of change of gas concentration, the difference in the rate of change of gas concentration between adjacent sensors can be calculated within each data acquisition cycle. A similar method is used to calculate differences in pressure fluctuation amplitude and temperature gradient. Then, statistical analysis is performed on the signal difference values ​​over a period of time, such as calculating the average value and standard deviation, to more accurately determine the magnitude and stability of the signal differences.

[0123] S403: Match the signal difference values ​​with the spatial distribution matrix to determine the sensor pair with the largest signal difference values.

[0124] In this embodiment, the signal difference value is calculated based on the difference between leakage characteristic values ​​(such as gas concentration change rate, pressure fluctuation amplitude, and temperature gradient) detected by adjacent sensors, reflecting the degree of inconsistency in the changes of physical quantities at adjacent locations. The spatial distribution matrix is ​​a structured representation of the spatial location information of each sensor in the multi-parameter linkage monitoring network, presenting the specific coordinates of each sensor on the positive pressure explosion-proof robot in matrix form. Matching the signal difference value with the spatial distribution matrix means associating the signal difference value of each pair of adjacent sensors with the spatial location of that pair of sensors.

[0125] In this embodiment, identifying the sensor pair with the largest signal difference value allows for the discovery of the region with the most inconsistent changes in physical quantities, which is likely the location of the leak. By matching the signal difference value with a spatial distribution matrix, the spatial location information of the sensors can be combined to more accurately pinpoint the leak location. The technical advantage lies in further narrowing down the range of the leak location and improving the accuracy of the location.

[0126] In one embodiment, matching the signal difference values ​​with the spatial distribution matrix and determining the sensor pair with the largest signal difference value can be accomplished using a computer program. First, the calculated signal difference values ​​of all adjacent sensor pairs are stored in an array, with the array index corresponding to different sensor pairs. Simultaneously, the spatial distribution matrix stores the coordinate information of each sensor in the form of a two-dimensional array. The program iterates through the signal difference value array, finds the maximum value, and records its corresponding index. Based on this index, the specific coordinates of the two sensors in that sensor pair can be obtained from the spatial distribution matrix, thereby determining the spatial location of the sensor pair with the largest signal difference value.

[0127] S404: Calculate the estimated coordinates of the leak location based on the spatial coordinate information of the sensor pair with the largest signal difference.

[0128] In this embodiment, the estimated coordinates of the leak location are obtained by calculating the spatial coordinates of the sensor pair with the largest signal difference using a specific method. Since the sensor pair with the largest signal difference reflects the area of ​​most drastic change in physical quantities, the leak location can be estimated based on the positional information of these two sensors.

[0129] In this embodiment, calculating the estimated coordinates of the leak location is a process of further refining the leak location based on the already determined approximate area where a leak might occur (i.e., the area where the sensor pair with the largest signal difference value is located). Mathematical models and algorithms can be used for calculation, such as inferring the location of the leak point based on the degree and trend of changes in the physical quantities detected by the sensors, combined with the spatial relationship of the sensors. The technical advantage is that it refines the leak location from an approximate area to a specific coordinate point, providing a more accurate target for subsequent emergency response.

[0130] In one embodiment, a weighted average method can be used to calculate the estimated coordinates of the leak location. Assume that the sensor pair with the largest signal difference is sensor A and sensor B, with spatial coordinates (x1, y1, z1) and (x2, y2, z2), respectively. Based on the degree of difference in physical quantities detected by these two sensors, such as the rate of change of gas concentration, pressure fluctuation amplitude, and temperature gradient, different weights w1 and w2 (the sum of the weights is 1) are assigned to sensor A and sensor B, respectively. The estimated coordinates (x, y, z) of the leak location can then be calculated using the following formula:

[0131] x = w1 × x1 + w2 × x2

[0132] y = w1 × y1 + w2 × y2

[0133] z = w1 × z1 + w2 × z2

[0134] The weights can be determined based on the magnitude of the signal difference; sensors with larger signal difference values ​​are assigned higher weights.

[0135] S405: Compare the estimated coordinates with the preset spatial error range. If the estimated coordinates are within the spatial error range, the estimated coordinates are confirmed as the leakage location. Otherwise, the signal difference value is recalculated and the estimated coordinates are updated.

[0136] In this embodiment, the preset spatial error range is a pre-defined allowable error range. Considering factors such as sensor measurement errors, limitations of calculation methods, and the complexity of the actual environment, the estimated coordinates of the leak location may have certain errors. The spatial error range is used to determine the accuracy of the estimated coordinates. If the estimated coordinates are within this range, it indicates that the estimated coordinates have high reliability and can be confirmed as the leak location; if they exceed this range, it indicates that the estimation is inaccurate and needs to be recalculated.

[0137] In one embodiment, the preset spatial error range can be determined based on factors such as sensor accuracy and the requirements of the actual application scenario. For example, a three-dimensional sphere centered on the actual leak location can be set, and the radius of the sphere is the spatial error range. When the distance from the calculated estimated coordinates to the center of the sphere is less than the radius of the sphere, it indicates that the estimated coordinates are within the spatial error range. If the estimated coordinates are not within the spatial error range, sensor data can be re-acquired, the signal difference value can be calculated, different calculation methods can be used, or the calculation parameters can be adjusted, and the estimated coordinates of the leak location can be recalculated until the requirements are met.

[0138] In one embodiment, reference Figure 7 Step S50 may include S501-S503, which will be described in detail below:

[0139] S501: If the leakage risk level is high, a first emergency handling instruction to activate the local sealing device will be generated first. The first emergency handling instruction indicates that the local sealing device should be activated first and the leakage area should be isolated.

[0140] In this embodiment, when the leakage risk level is determined to be high, it means that the positive pressure explosion-proof robot faces a serious leakage hazard, which may cause great harm to the equipment and the surrounding environment. The local sealing device is a device installed on the positive pressure explosion-proof robot to isolate the leakage area. It can prevent the further diffusion of leaked gas through a sealing structure. The first emergency handling instruction is an instruction generated under high-risk conditions to guide the robot to perform emergency handling operations. Its core is to prioritize the activation of the local sealing device to quickly isolate the leakage area and prevent the leakage from expanding.

[0141] In this embodiment, the instruction to activate the local sealing device is prioritized under high-risk conditions because timely isolation of the leak area is the most effective means of controlling the leak's hazards. Once a high-risk leak occurs, if it is not isolated promptly, the leaked gas may rapidly spread into the surrounding environment, increasing the probability of safety accidents such as explosions and fires. By activating the local sealing device, the leaked gas can be confined to a smaller area, buying time for subsequent handling. Its technical advantage lies in its ability to control the spread of the leak in the first instance, reducing safety risks and protecting the positive-pressure explosion-proof robot and the surrounding environment.

[0142] In one embodiment, the first emergency handling command can be sent to the drive module of the local sealing device via the control system of the positive pressure explosion-proof robot. Upon receiving the command, the drive module immediately starts the motor or actuator, causing the sealing components of the local sealing device (such as sealing valves, sealing covers, etc.) to actuate and seal the leaking area. Simultaneously, sensors can be installed on the local sealing device to monitor the sealing effect in real time, ensuring that the leaking area is effectively isolated.

[0143] In one embodiment, the first emergency handling command further instructs to dynamically adjust the coverage of the local sealing device according to the location of the leak, ensuring that the leak area is completely isolated.

[0144] In this embodiment, since the leak locations may vary, and their sizes and shapes may also differ, a local sealing device with a fixed coverage area may not be able to fully adapt to all leak situations. Dynamically adjusting the coverage area of ​​the local sealing device refers to, after determining the leak location, changing the size, shape, or position of the local sealing device in real time according to the actual size and shape of the leak area, so that it can accurately cover the leak area.

[0145] In this embodiment, dynamically adjusting the coverage area of ​​the local sealing device according to the leak location can improve the sealing effect. If the coverage area of ​​the local sealing device is too small, it cannot completely isolate the leak area, and the leaked gas may still leak into the surrounding environment; if the coverage area is too large, it will waste resources and may affect the normal operation of other components of the positive pressure explosion-proof robot. Through dynamic adjustment, the local sealing device can be perfectly matched with the leak area, minimizing leakage. Its technical effect is to enhance the adaptability and effectiveness of the local sealing device, better ensuring the safety of the positive pressure explosion-proof robot and the surrounding environment.

[0146] In one embodiment, a retractable and deformable local sealing device can be used. This sealing device consists of multiple adjustable components, whose size and shape can be changed via an electric or hydraulic drive system. Once the leak location is determined, a first emergency response command can control the drive system to adjust the coverage area of ​​the local sealing device according to the size and shape of the leak area. Simultaneously, devices such as cameras or laser sensors can be used to monitor the leak area and the coverage of the sealing device in real time, making dynamic adjustments based on the monitoring results to ensure the leak area is completely isolated.

[0147] S502: If the leakage risk level is medium risk, a second emergency handling instruction is generated to adjust the internal air pressure to a safe range.

[0148] In this embodiment, when the risk level is determined, it indicates that the positive pressure explosion-proof robot has a certain leakage risk, but it has not yet reached the severity of a high risk. The internal air pressure is the pressure value inside the positive pressure explosion-proof robot, and the safe range is a pre-set air pressure range within which the robot can operate normally and the leakage risk is relatively low. The second emergency handling instruction is generated for medium-risk situations, and its purpose is to alleviate the leakage situation and reduce the leakage risk by adjusting the internal air pressure to the safe range.

[0149] In this embodiment, adjusting the internal air pressure to a safe range can alter the pressure difference between the robot's interior and external environment, thereby affecting the speed and extent of leakage. If the internal air pressure is too high, it may exacerbate the leakage; if the internal air pressure is too low, the positive pressure explosion-proof effect may not be guaranteed. Therefore, adjusting the internal air pressure to a safe range can balance these two factors to some extent, reducing the possibility of leakage. The technical effect is that by reasonably adjusting the internal air pressure, leakage risk can be effectively controlled, preventing further escalation of the risk.

[0150] In one embodiment, the second emergency handling command can adjust the internal air pressure by controlling the air supply and exhaust systems of the positive pressure explosion-proof robot. For example, when the internal air pressure is higher than the safe range, the command can control the exhaust valve to open, releasing some gas and reducing the internal air pressure; when the internal air pressure is lower than the safe range, the command can control the air supply valve to open, increasing the air supply and raising the internal air pressure. Simultaneously, a pressure sensor can be installed to monitor the internal air pressure in real time, and the valve opening can be dynamically adjusted based on the monitoring results to ensure that the internal air pressure remains stable within the safe range.

[0151] S503: If the leakage risk level is low, a third emergency handling instruction is generated to issue an alarm signal.

[0152] In this embodiment, when the leakage risk level is low, it indicates that although the positive pressure explosion-proof robot has a certain potential leakage risk, the current situation is relatively stable and has not yet posed a serious threat to the equipment and the surrounding environment. An alarm signal is a signal used to alert operators to potential risks; it can take the form of audible and visual signals, SMS notifications, etc. The third emergency handling instruction is generated for low-risk situations. Its function is to attract the operator's attention by issuing an alarm signal, allowing them to understand the robot's operating status in a timely manner and make appropriate preparations.

[0153] In one embodiment, the third emergency response command can control the audible and visual alarm to sound an alarm and flash lights. Simultaneously, it can send an SMS notification to the mobile phones of relevant operators via an SMS gateway, informing them that the positive-pressure explosion-proof robot has a low-risk leakage situation. Upon receiving the alarm signal, the operators can conduct further inspections and monitoring of the robot as needed.

[0154] In one embodiment, reference Figure 8 The method for optimizing the spatial distribution characteristics of the multi-parameter linkage monitoring network may include S71-S74, which will be described in detail below:

[0155] S71: Determine the leakage risk distribution area of ​​key parts during the operation of the positive pressure explosion-proof robot, and divide the leakage risk distribution area into multiple monitoring zones.

[0156] In this embodiment, critical components refer to those parts of the positive pressure explosion-proof robot where a leak could significantly impact the robot's normal operation and the safety of the surrounding environment, such as the robot's air supply pipe interfaces and the sealing points of the electrical control cabinet. The leakage risk distribution area refers to the spatial distribution of the probability of leakage at different parts during the operation of the positive pressure explosion-proof robot. By analyzing the robot's structure, working principle, and past leakage accident data, the leakage risk distribution of these critical components can be determined. Monitoring zones divide the positive pressure explosion-proof robot into multiple relatively independent areas based on the leakage risk distribution area. Each zone has similar leakage risk characteristics, facilitating targeted monitoring.

[0157] In this embodiment, identifying the leakage risk distribution area of ​​key components and dividing it into monitoring zones allows for more targeted sensor deployment, improving monitoring efficiency. Different monitoring zones can be configured with different numbers and types of sensors based on their leakage risk levels, enabling precise monitoring of the positive pressure explosion-proof robot. The technical advantage lies in optimizing the sensor placement scheme, reducing monitoring costs, and simultaneously improving the accuracy of leakage risk monitoring.

[0158] In one embodiment, finite element analysis software can be used to simulate and analyze the positive pressure explosion-proof robot. Combined with the robot's actual operating conditions, the pressure distribution and gas flow at various key locations can be determined, thereby inferring the leakage risk distribution areas. Then, based on the spatial location and leakage risk characteristics of these areas, the robot is divided into multiple monitoring zones. For example, the gas supply pipeline interface area, which has a high leakage risk, can be divided into a separate monitoring zone, and the density of gas sensors can be increased.

[0159] S72: Obtain the initial spatial coordinate information of the sensors in each monitoring zone, and construct the sensor distribution matrix within the zone based on the initial spatial coordinate information.

[0160] In this embodiment, the initial spatial coordinate information refers to the starting position coordinates of the gas sensor, pressure sensor, and temperature sensor within each monitoring zone in the three-dimensional space where the positive pressure explosion-proof robot is located. The sensor distribution matrix is ​​a mathematical matrix used to describe the spatial distribution of sensors within a zone; the elements in the matrix can represent the coordinates of the sensors or other information related to their positions.

[0161] In one embodiment, three-dimensional laser scanning technology can be used to acquire the initial spatial coordinate information of the sensors within each monitoring zone. The three-dimensional laser scanner can quickly and accurately measure the position of the sensors in three-dimensional space. Then, a sensor distribution matrix is ​​constructed based on this coordinate information. For example, for a monitoring zone containing n sensors, an n×3 matrix can be constructed, where each row corresponds to one sensor, and each column corresponds to the x, y, and z coordinates of the sensor, respectively.

[0162] S73: Based on the sensor distribution matrix within the zone, calculate the signal difference value between adjacent sensors within each monitoring zone. The signal difference value includes differences in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient.

[0163] In this embodiment, adjacent sensors refer to sensors that are spatially close to each other within the monitoring zone. Signal difference values ​​refer to the differences in the rate of change of gas concentration, pressure fluctuation amplitude, and temperature gradient between adjacent sensors at the same point in time or over a period of time. By calculating these signal difference values, the inconsistencies in the changes of physical quantities at different locations within the zone can be understood.

[0164] In one embodiment, for differences in gas concentration change rates, the difference in gas concentration change rates between adjacent sensors can be calculated within each data acquisition cycle. Similar methods are used to calculate differences in pressure fluctuation amplitude and temperature gradient. Then, statistical analysis is performed on the signal difference values ​​over a period of time, such as calculating the average and standard deviation, to more accurately assess the signal differences between sensors within the partition.

[0165] S74: Based on the statistical results of signal difference values, evaluate the rationality of sensor layout in each monitoring zone, and adjust the spatial distribution characteristics of the sensors according to the evaluation results.

[0166] In this embodiment, the statistical results of signal difference values ​​refer to the results obtained after statistically analyzing the differences in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient between adjacent sensors within each monitoring zone, such as average value and standard deviation. Sensor placement rationality refers to whether the arrangement of sensors within the monitoring zone can effectively monitor leaks and accurately reflect changes in physical quantities. Evaluating the rationality of sensor placement based on the statistical results of signal difference values ​​means determining whether the current sensor arrangement keeps the signal difference between adjacent sensors within a reasonable range. If the signal difference is too large, it indicates that the sensor arrangement may be unreasonable and needs adjustment.

[0167] In the embodiments of the present application, evaluating the rationality of sensor arrangement and adjusting the spatial distribution characteristics of sensors can improve the monitoring effect of sensors in the monitoring area. By reasonably adjusting the position and quantity of sensors, the sensors can be more evenly distributed in the monitoring area, reducing signal differences and improving the monitoring accuracy of leakage situations. The technical effect is that the arrangement scheme of sensors is optimized, the performance of the multi-parameter linkage monitoring network is enhanced, and the safe operation of the positive pressure explosion-proof robot is better ensured.

[0168] In one embodiment, if the standard deviation of the difference in the gas concentration change rate between adjacent sensors in a certain monitoring area is large, it indicates that the arrangement of sensors in this area may lead to uneven gas concentration monitoring. According to the specific situation, some sensors can be moved to the area with a large gas concentration change, or the number of sensors in this area can be increased to reduce signal differences. At the same time, reconstruct the sensor distribution matrix in the area, recalculate the signal difference value, and evaluate the rationality of the adjusted sensor arrangement until a satisfactory effect is achieved.

[0169] In one embodiment, step S74 can be implemented in the following manner:

[0170] A1: Obtain the signal difference values between adjacent sensors in each monitoring area, and calculate the standard deviation of the signal difference values in each monitoring area.

[0171] In the embodiments of the present application, the standard deviation of the signal difference value is a statistic used to measure the dispersion degree of the signal difference values between adjacent sensors in each monitoring area. The larger the standard deviation, the greater the fluctuation of the signal difference value, and the more uneven the arrangement of sensors may be; the smaller the standard deviation, the more stable the signal difference value, and the more reasonable the arrangement of sensors may be. Obtaining the signal difference values between adjacent sensors in each monitoring area and calculating its standard deviation can quantitatively evaluate the distribution uniformity of sensors in the area.

[0172] In the embodiments of the present application, calculating the standard deviation of the signal difference value provides an objective quantitative index for evaluating the rationality of sensor arrangement. By comparing the magnitudes of the standard deviations of different monitoring areas, it can be quickly determined which areas need to adjust the arrangement of sensors. The technical effect is that it provides a clear direction for the optimization of sensor arrangement and helps to improve the performance of the multi-parameter linkage monitoring network.

[0173] In one embodiment, signal difference values, such as differences in gas concentration change rate, pressure fluctuation amplitude, and temperature gradient between adjacent sensors within each monitoring zone, are first acquired from the data acquisition system. Then, statistical analysis software (such as Excel or Python statistical libraries) is used to calculate the standard deviation of these signal difference values. For example, for gas concentration change rate difference data within a monitoring zone, it is input into the statistical analysis software, and the standard deviation calculation function can be used to obtain the standard deviation of the gas concentration change rate difference within that zone.

[0174] A2: Evaluate the spatial distribution uniformity of sensors within each monitoring zone based on the standard deviation of signal difference values. The smaller the standard deviation, the more uniform the spatial distribution of sensors.

[0175] In this embodiment, the spatial distribution uniformity of the sensors refers to whether the sensor positions are evenly distributed within the monitoring zone, and whether they can evenly cover the entire monitoring area to accurately monitor changes in physical quantities. Since the standard deviation of the signal difference value reflects the dispersion of signal differences between adjacent sensors, the smaller the standard deviation, the smaller the difference in changes in physical quantities detected by adjacent sensors, which means that the spatial distribution of sensors is more uniform, enabling more comprehensive and accurate monitoring of the situation within the monitoring zone.

[0176] In one embodiment, a standard deviation threshold is set. When the standard deviation of the signal difference values ​​within a monitoring zone is less than the threshold, the spatial distribution of sensors within that zone is considered uniform; when the standard deviation is greater than the threshold, the spatial distribution of sensors is considered to need adjustment. For example, the standard deviation threshold for the difference in gas concentration change rate is set to 0.5. If the standard deviation of the difference in the gas concentration change rate of a monitoring zone is 0.3, it indicates that the spatial distribution of gas sensors within that zone is relatively uniform; if the standard deviation is 0.8, the positions of the gas sensors need to be adjusted.

[0177] A3: Evaluate the rationality of sensor placement within each monitoring zone based on the aforementioned spatial distribution uniformity.

[0178] In this embodiment, the rationality of sensor arrangement depends not only on the number of sensors but also on the uniformity of their spatial distribution. Uneven spatial distribution of sensors within the monitoring zone may lead to inadequate monitoring in some areas and over-monitoring in others, thus affecting the accurate assessment of leakage in the positive pressure explosion-proof robot. Therefore, evaluating the rationality of sensor arrangement based on spatial distribution uniformity means determining whether the current sensor arrangement meets monitoring requirements and effectively covers the entire monitoring zone.

[0179] In this embodiment, the spatial distribution uniformity is used to assess the rationality of sensor placement, enabling a comprehensive and accurate evaluation of the sensor placement effect within each monitoring zone. Uneven spatial distribution indicates potential problems with the sensor placement, requiring adjustments to improve monitoring accuracy and reliability. The technical advantage lies in ensuring that the multi-parameter linkage monitoring network can efficiently and accurately monitor the operational status of the positive pressure explosion-proof robot, promptly identifying potential leakage hazards.

[0180] In one embodiment, if the spatial distribution of sensors within a monitoring zone is uneven, adjustments are made according to the specific circumstances. If the unevenness is caused by an insufficient number of sensors, the number of sensors can be increased; if the unevenness is caused by unreasonable sensor placement, the sensor positions can be rearranged. After adjustment, the standard deviation of the signal difference values ​​is recalculated to evaluate the uniformity of spatial distribution and the rationality of sensor placement until a satisfactory result is achieved.

[0181] Accordingly, to better implement the above methods, this application also provides a positive pressure explosion-proof robot leakage monitoring and emergency handling system. For example... Figure 9 As shown, the positive pressure explosion-proof robot leakage monitoring and emergency response system 80 includes:

[0182] The acquisition module 801 is used to capture the operating status data of the positive pressure explosion-proof robot in real time through a multi-parameter linkage monitoring network. The multi-parameter linkage monitoring network consists of a gas sensor, a pressure sensor and a temperature sensor. Each sensor is arranged in key parts of the positive pressure explosion-proof robot according to a preset spatial distribution characteristic.

[0183] The feature processing module 802 is used to perform feature processing on the captured operating status data to extract leakage feature values, including gas concentration change rate, pressure fluctuation amplitude, and temperature gradient.

[0184] The risk assessment module 803 is used to determine the leakage risk level of the positive pressure explosion-proof robot based on the extracted leakage characteristic values ​​and a preset risk assessment model. The leakage risk level includes low risk, medium risk and high risk.

[0185] The location analysis module 804 is used to perform location analysis on the leakage location based on the spatial distribution characteristics of the multi-parameter linkage monitoring network and the time series variation law of leakage characteristic values, so as to determine the leakage location of the positive pressure explosion-proof robot.

[0186] The emergency handling module 805 is used to generate emergency handling instructions based on the leakage risk level and leakage location, and send the emergency handling instructions to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform corresponding emergency handling operations. The emergency handling instructions include activating a local sealing device, adjusting the internal air pressure to a safe range, or issuing an alarm signal.

[0187] The implementation details of each module are provided in the preceding method embodiments and will not be repeated here. The technical effects achieved by each module and device are described in the foregoing method embodiments.

[0188] like Figure 10 As shown, this application embodiment also provides a computer device 90, which includes a processor 901 and a memory 902, wherein the memory 902 stores a computer program, and when the computer program is executed by the processor 901, the processor 901 performs the steps of any of the methods described above.

[0189] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A positive pressure explosion-proof robot leak monitoring and emergency handling method, characterized in that, The method comprises the following steps: Real-time capture operation state data in the running process of the positive pressure explosion-proof robot through a multi-parameter linkage monitoring network composed of gas sensors, pressure sensors and temperature sensors, each sensor being arranged at a key position of the positive pressure explosion-proof robot according to a preset spatial distribution characteristic; Characteristic processing of the captured operation state data is performed to extract leakage characteristic values, the leakage characteristic values including a gas concentration change rate, a pressure fluctuation amplitude and a temperature gradient; According to the extracted leakage characteristic values, a preset risk assessment model is used to determine a leakage risk level of the positive pressure explosion-proof robot, the leakage risk level including low risk, medium risk and high risk; A leakage risk distribution area of the key position in the running process of the positive pressure explosion-proof robot is determined, and a plurality of monitoring sub-zones are divided according to the leakage risk distribution area; initial spatial coordinate information of the sensors in each monitoring sub-zone is obtained, and a sensor distribution matrix in the sub-zone is constructed according to the initial spatial coordinate information; a signal difference value between adjacent sensors in each monitoring sub-zone is calculated according to the sensor distribution matrix in the sub-zone, the signal difference value including a gas concentration change rate difference, a pressure fluctuation amplitude difference and a temperature gradient difference; the sensor arrangement rationality in each monitoring sub-zone is evaluated according to the statistical result of the signal difference value, and the spatial distribution characteristic of the sensor is adjusted according to the evaluation result; Based on the spatial distribution characteristic of the multi-parameter linkage monitoring network and in combination with the time sequence change rule of the leakage characteristic values, a leakage position is positioned and analyzed to determine the leakage position of the positive pressure explosion-proof robot; specifically, spatial coordinate information of each sensor in the multi-parameter linkage monitoring network is obtained, and a spatial distribution matrix of the sensors is constructed; a signal difference value between adjacent sensors is calculated according to the time sequence change rule of the leakage characteristic values, the signal difference value including a gas concentration change rate difference, a pressure fluctuation amplitude difference and a temperature gradient difference; the signal difference value is matched with the spatial distribution matrix to determine a sensor pair with the maximum signal difference value; estimated coordinates of the leakage position are calculated according to the spatial coordinate information of the sensor pair with the maximum signal difference value; the estimated coordinates are compared with a preset spatial error range, and if the estimated coordinates are within the spatial error range, the estimated coordinates are confirmed as the leakage position; According to the leakage risk level and the leakage position, an emergency treatment instruction is generated and sent to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform a corresponding emergency treatment operation, wherein the emergency treatment instruction includes starting a local sealing device, adjusting internal air pressure to a safe range or sending an alarm signal. Obtain the internal environment parameter change data of the positive pressure explosion-proof robot after executing the emergency processing instruction, the internal environment parameters include gas concentration, internal gas pressure and temperature; according to the obtained internal environment parameter change data, the actual change amount of each parameter is calculated and compared with the preset theoretical change amount; if the deviation between the actual change amount and the theoretical change amount exceeds the first deviation threshold, it is determined that the execution effect of the current emergency processing instruction does not reach the expected target; for the case that the expected target is not reached, the emergency processing instruction is adjusted, the adjusted instruction indicates that the starting order of the local sealing device is adjusted or the internal gas pressure is re-adjusted to a new safe range; the adjusted instruction is re-sent to the positive pressure explosion-proof robot.

2. The method of claim 1, wherein, The running state data of the positive pressure explosion-proof robot during operation is captured in real time through the multi-parameter linkage monitoring network, including: Obtain the sampling frequency of the gas sensor, pressure sensor and temperature sensor in unit time, and set the data acquisition period of the multi-parameter linkage monitoring network according to the sampling frequency; In each data acquisition period, record the gas concentration value detected by the gas sensor, the pressure value detected by the pressure sensor and the temperature value detected by the temperature sensor respectively; The gas concentration value, pressure value and temperature value are stored in a preset data format, and the spatial position information corresponding to each data point is marked.

3. The method of claim 1, wherein, According to the extracted leakage characteristic value, the leakage risk level of the positive pressure explosion-proof robot is determined through a preset risk assessment model, including: Obtain the gas concentration change rate, pressure fluctuation amplitude and temperature gradient in the leakage characteristic value through the preset risk assessment model, and compare them with the preset first threshold, second threshold and third threshold respectively; If the gas concentration change rate is lower than the first threshold, the pressure fluctuation amplitude is lower than the second threshold, and the temperature gradient is lower than the third threshold, it is determined that the leakage risk level is low risk; If the gas concentration change rate is between the first threshold and the second threshold, the pressure fluctuation amplitude is between the second threshold and the third threshold, and the temperature gradient is between the third threshold and the fourth threshold, it is determined that the leakage risk level is medium risk; If the gas concentration change rate is higher than the second threshold, the pressure fluctuation amplitude is higher than the third threshold, and the temperature gradient is higher than the fourth threshold, it is determined that the leakage risk level is high risk.

4. The method of claim 1, wherein, The emergency processing instruction is generated according to the leakage risk level and the leakage position, including: If the leakage risk level belongs to high risk, the first emergency processing instruction of starting the local sealing device is generated in priority, the first emergency processing instruction indicates that the local sealing device is started in priority and the leakage area is isolated; If the leakage risk level is medium risk, the second emergency processing instruction of adjusting the internal gas pressure to a safe range is generated; If the leakage risk level is low risk, the third emergency processing instruction of issuing an alarm signal is generated.

5. The method of claim 4, wherein, The first emergency processing instruction also indicates that the coverage range of the local sealing device is dynamically adjusted according to the leakage position to ensure that the leakage area is completely isolated.

6. The method according to any one of claims 1 to 5, characterized in that, According to the statistical result of the signal difference value, the rationality of the sensor arrangement in each monitoring partition is evaluated, including: acquiring a signal difference value between adjacent sensors in each monitoring partition, and calculating a standard deviation of the signal difference value in each monitoring partition; evaluating spatial distribution uniformity of the sensors in each monitoring partition according to the standard deviation of the signal difference value, wherein the smaller the standard deviation is, the more uniform the spatial distribution of the sensors is; evaluating rationality of the sensor arrangement in each monitoring partition according to the spatial distribution uniformity.

7. A positive pressure explosion-proof robot leak monitoring and emergency handling system, characterized in that, The system comprises: an acquisition module, configured to capture running state data in a running process of the positive pressure explosion-proof robot in real time through a multi-parameter linkage monitoring network, the multi-parameter linkage monitoring network being composed of a gas sensor, a pressure sensor and a temperature sensor, and each sensor being arranged at a key position of the positive pressure explosion-proof robot according to a preset spatial distribution characteristic; a feature processing module, configured to perform feature processing on the captured running state data, so as to extract a leakage characteristic value, the leakage characteristic value including a gas concentration change rate, a pressure fluctuation amplitude and a temperature gradient; a risk assessment module, configured to determine a leakage risk level of the positive pressure explosion-proof robot according to the extracted leakage characteristic value and through a preset risk assessment model, the leakage risk level including a low risk, a medium risk and a high risk; a positioning analysis module, configured to determine a leakage risk distribution area of the key position of the positive pressure explosion-proof robot in the running process, and divide a plurality of monitoring partitions according to the leakage risk distribution area; acquire initial spatial coordinate information of the sensors in each monitoring partition, and construct a sensor distribution matrix in the partition according to the initial spatial coordinate information; calculate a signal difference value between adjacent sensors in each monitoring partition according to the sensor distribution matrix in the partition, the signal difference value including a gas concentration change rate difference, a pressure fluctuation amplitude difference and a temperature gradient difference; evaluate rationality of the sensor arrangement in each monitoring partition according to a statistical result of the signal difference value, and adjust the spatial distribution characteristic of the sensors according to an evaluation result; and based on the spatial distribution characteristic of the multi-parameter linkage monitoring network, in combination with a time sequence change rule of the leakage characteristic value, perform positioning analysis on a leakage position, so as to determine the leakage position of the positive pressure explosion-proof robot; specifically configured to acquire spatial coordinate information of each sensor in the multi-parameter linkage monitoring network, and construct a spatial distribution matrix of the sensors; calculate a signal difference value between adjacent sensors according to a time sequence change rule of the leakage characteristic value, the signal difference value including a gas concentration change rate difference, a pressure fluctuation amplitude difference and a temperature gradient difference; match the signal difference value with the spatial distribution matrix, so as to determine a sensor pair with the maximum signal difference value; calculate an estimated coordinate of the leakage position according to spatial coordinate information of the sensor pair with the maximum signal difference value; compare the estimated coordinate with a preset spatial error range, and if the estimated coordinate is located in the spatial error range, confirm that the estimated coordinate is the leakage position. The emergency treatment module is configured to generate an emergency treatment instruction according to the leakage risk level and the leakage position, and send the emergency treatment instruction to the positive pressure explosion-proof robot to instruct the positive pressure explosion-proof robot to perform a corresponding emergency treatment operation. The emergency treatment instruction includes starting a local sealing device, adjusting the internal air pressure to a safe range, or sending an alarm signal. The internal environment parameter change data of the positive pressure explosion-proof robot after executing the emergency treatment instruction is obtained, and the internal environment parameters include gas concentration, internal air pressure, and temperature. According to the obtained internal environment parameter change data, the actual change amount of each parameter is calculated and compared with the preset theoretical change amount. If the deviation between the actual change amount and the theoretical change amount exceeds a first deviation threshold, it is determined that the execution effect of the current emergency treatment instruction does not achieve the expected target. In the case that the expected target is not achieved, the emergency treatment instruction is adjusted. The adjusted instruction instructs to adjust the starting sequence of the local sealing device or readjust the internal air pressure to a new safe range according to the deviation direction. The adjusted instruction is sent to the positive pressure explosion-proof robot again.

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