Positive pressure type explosion-proof robot intelligent control method

Abstract

The application relates to the technical field of safety control, and discloses a positive pressure type explosion-proof robot intelligent control method, which comprises the following steps: collecting multi-dimensional data through a sensor, calculating an external explosion approaching degree and a body disturbance degree after time synchronization and abnormal cleaning, and obtaining an explosion risk assessment index. According to the explosion risk assessment index, a target positive pressure is dynamically calculated and corrected by using leakage compensation; when it is detected that the pressure abnormally decreases and the external explosion approaching degree is high, a leakage warning is given. The control part adopts an adaptive proportional integral differential algorithm combining the external risk and the body disturbance, automatically adjusts the gain, realizes rapid and accurate regulation of the cabin pressure, and diagnoses multiple fault types. When a fault occurs, the safety positive pressure and emergency air supplement or pressure relief are automatically started, and the pressure is smoothly reduced according to a recovery coefficient in the recovery stage. The air supplement flow and power consumption are optimized according to the monitoring stability, energy efficiency and response time, so that accurate explosion prevention and high energy saving are ensured.

Description

Technical Field

[0001] This invention relates to the field of safety control technology, specifically to an intelligent control method for a positive pressure explosion-proof robot. Background Technology

[0002] In the application scenarios of explosion-proof robots, maintaining a positive pressure environment in the main control compartment is a crucial means to prevent the intrusion of external explosive gases and ensure the safety of internal electronic equipment and personnel. Currently, most positive pressure regulation systems still rely on factory-preset fixed pressure values ​​or manual settings by operators, and cannot automatically adjust the positive pressure supply according to real-time changes in the external environment. When the robot enters a complex and high-risk area with rapid fluctuations in temperature, humidity, flammable gas concentration, and oxygen concentration, this static or semi-static control method is very likely to lead to insufficient or excessive positive pressure inside the compartment. Insufficient positive pressure directly weakens the barrier against external hazardous gases, significantly increasing the risk of combustion and explosion; while excessive positive pressure accelerates gas consumption, shortens the equipment's endurance, and may cause excessive stress on the compartment's sealing structure, creating potential malfunctions.

[0003] To achieve dynamic adjustment, some studies have attempted to introduce external environmental sensors, but they generally lack systematic processing of multi-source heterogeneous data. In actual operation, due to sensor response delays, acquisition noise, and the influence of occasional interference pulses, the environmental data acquired by the control end is often distorted, leading to unexpected lags or misadjustments in positive pressure control. Furthermore, most existing methods assess the robot's motion state and environmental risks separately, failing to consider the dynamic weakening effect of body vibration and structural micro-deformation caused by high-mobility movements on the cabin's sealing performance. Therefore, they cannot accurately reflect the true positive pressure requirement under the dual coupling of "external explosion risk + internal motion disturbance." When facing emergencies such as leaks, gas supply failures, or cabin door malfunctions, existing systems also lack a complete closed-loop handling strategy from fault detection, emergency pressurization, overpressure protection to recovery transition, making it difficult to balance safety and system energy efficiency.

[0004] In summary, constructing an intelligent positive pressure system capable of cleaning and fusing multi-source sensor data, comprehensively assessing external environmental risks and body motion disturbances, predicting positive pressure requirements in advance, and adaptively adjusting and controlling them has become an important technical direction for improving the environmental adaptability and operational safety of explosion-proof robots. Summary of the Invention

[0005] This invention provides an intelligent control method for a positive pressure explosion-proof robot, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: an intelligent control method for a positive pressure explosion-proof robot, comprising:

[0007] Data from multiple sensors is collected and timestamps are unified. After gating and cleaning to remove anomalies, standardized control frames are generated.

[0008] The standardized control frames are made dimensionless and smoothed using a sliding window to generate smooth control frames.

[0009] The approach to an external explosion is obtained by standardizing the external environmental factors, and the body disturbance is obtained by calculating the standardized robot body state. These are then combined to construct an explosion risk assessment index.

[0010] The initial positive pressure requirement is obtained based on the explosion risk assessment indicators and the degree of airframe disturbance. The final positive pressure requirement is obtained after correction by the leakage amount. The pressure change in the main control room is monitored to determine the early signs of leakage and issue an early warning.

[0011] Calculate the changes and forecasts of the final positive pressure demand and environmental factors, obtain the risk forecast value, and determine the target positive pressure demand and the required make-up gas flow rate by combining the final positive pressure demand.

[0012] The positive pressure threshold is adjusted based on the target positive pressure demand and the proximity of the external explosion. The body disturbance is introduced for feedback adjustment, and then adaptive PID control is used to adjust the pressure in real time and detect pressure faults.

[0013] During the evaluation period, calculate the system stability assessment value, energy efficiency assessment value and system response time, and optimize the proportional gain, integral gain, derivative gain and adjust the gas supply flow and power consumption accordingly.

[0014] When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. When the pressure is over-pressured, the system initiates pressure relief. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient.

[0015] Optionally, the step of collecting data from multiple sensors, unifying timestamps, and generating standardized control frames after gating and filtering out anomalies includes:

[0016] Multiple sensors are deployed on the robot, and a uniform data collection cycle is set.

[0017] Each sensor collects data on external combustible gas concentration, external ambient temperature, main control cabin pressure, external relative humidity, and external ambient oxygen concentration in each cycle, and marks each data point with a timestamp.

[0018] The minimum time synchronization algorithm is adopted to take the earliest time among all sensor timestamps as the unified timestamp to achieve sampling time synchronization and ensure the consistency of data from all sensors.

[0019] Integrate sensor data under a unified timestamp into a single control frame;

[0020] Based on the operating range and accuracy of each sensor, set a threshold for the maximum allowable change within a single sampling period;

[0021] A gated cleaning algorithm is constructed to clean each sample value. If the absolute value of the difference between the current sample value and the previous sample value exceeds the maximum change threshold, it is determined to be abnormal and replaced by the previous sample value. Otherwise, the current sample value is retained to eliminate abnormal data caused by noise, jitter or external interference.

[0022] All gated cleaned sampled values ​​are arranged in timestamp order to generate a standardized control frame.

[0023] Optionally, the step of making the standardized control frame dimensionless and performing sliding window smoothing to generate a smooth control frame includes:

[0024] The sampled values ​​of each sensor are dimensionless, and the original sampled values ​​are mapped to a uniform numerical range using the minimum and maximum values ​​of the sensor, so that the data from different sensors are comparable.

[0025] A sliding window smoothing algorithm is used to smooth the dimensionless data, and the window radius is set according to the requirements.

[0026] Based on the smoothed data, a smoothed control frame is constructed according to the timestamp.

[0027] Optionally, the approach degree of the external explosion is obtained by standardizing the external environmental factors, and the body disturbance degree is obtained by calculating the standardized value of the robot's body state. These are then combined to construct an explosion risk assessment index, including:

[0028] The concentration of external combustible gas, external ambient temperature, external relative humidity, and external ambient oxygen concentration were standardized respectively.

[0029] The standardized values ​​are used to calculate the external explosion proximity to reflect the combined impact of various external environmental factors on the explosion risk.

[0030] Collect robot body state data within a fixed period, including robot movement speed, body vibration acceleration, and drive current;

[0031] Based on their respective maximum allowable values, the normalized values ​​of velocity, vibration acceleration, and driving current are calculated;

[0032] The robot body perturbation degree is calculated based on the standardized values ​​of the body state data to characterize the impact of the robot's own motion and vibration on the sealing performance.

[0033] Multiplying the external explosion proximity by the body disturbance yields an explosion risk assessment index, which is used to comprehensively evaluate the current overall explosion risk.

[0034] Optionally, the process of obtaining the initial positive pressure requirement based on explosion risk assessment indicators and airframe disturbance, correcting for leakage to obtain the final positive pressure requirement, and monitoring pressure changes in the main control compartment to determine early signs of leakage and issue warnings includes:

[0035] Using the aforementioned explosion risk assessment indicators and body disturbance degree, the preliminary positive pressure demand is calculated based on the basic positive pressure demand.

[0036] Based on the leakage amount and the preset adjustment coefficient, a positive pressure adjustment factor is generated in combination with the initial positive pressure demand to correct the initial positive pressure demand and obtain the final positive pressure demand, so as to maintain sufficient positive pressure in the event of leakage.

[0037] The system monitors changes in pressure in the main control compartment and generates a potential leak precursor signal when the absolute value of the pressure difference between adjacent moments exceeds the leak detection threshold.

[0038] If there are potential leak precursor signals, and the pressure difference is still not less than the leak detection threshold and the external explosion proximity exceeds the preset external explosion proximity threshold, then a leak precursor is determined to have occurred, triggering a leak precursor alarm and notifying the operator.

[0039] Optionally, the calculation of the final positive pressure demand and the changes and predicted values ​​of environmental factors, obtaining the risk prediction value, and determining the target positive pressure demand and the required make-up gas flow rate in conjunction with the final positive pressure demand, includes:

[0040] Calculate the changes in positive pressure demand, external combustible gas concentration, external ambient temperature, and external ambient humidity between the current time and the previous time.

[0041] Based on the changes and the preset prediction time step, the predicted values ​​of positive pressure demand, external combustible gas concentration, external ambient temperature and external ambient humidity are obtained by linear extrapolation.

[0042] Each predicted value is standardized, and the risk prediction value is calculated in a manner similar to the external explosion proximity, to represent the overall future risk;

[0043] Calculate the target positive pressure demand based on the current final positive pressure demand and the aforementioned risk forecast value;

[0044] Then, based on the ratio of the target positive pressure requirement to the current main control cabin pressure, and the current replenishment air flow rate, calculate the required replenishment air flow rate to maintain the target positive pressure.

[0045] Optionally, the step of adjusting the positive pressure threshold based on the target positive pressure demand and the proximity of an external explosion, introducing body disturbance for feedback adjustment, and then using adaptive PID control to adjust the pressure in real time and detect pressure faults includes:

[0046] Based on the target positive pressure demand and the proximity of external explosions, the positive pressure threshold is dynamically set so that the positive pressure threshold increases as the risk of external explosions increases.

[0047] The positive pressure regulation value is calculated by using the external explosion proximity and the body disturbance, so that the pressure regulation amount reflects both external risks and its own disturbances.

[0048] The PID control algorithm is used to correct the positive pressure adjustment value by using the deviation between the target positive pressure demand and the current main control chamber pressure, as well as its integral and derivative, to achieve real-time positive pressure control.

[0049] An adaptive mechanism is designed to adjust the proportional gain, integral gain, and derivative gain of the PID controller in real time based on the machine body disturbance degree and the external explosion proximity degree, respectively, in order to improve the adjustment accuracy and response speed.

[0050] A pressure fault detection mechanism is set up so that when the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the maximum allowable pressure error threshold, it is judged as a fault, and staff are notified to carry out maintenance.

[0051] Optionally, during the evaluation period, the system stability assessment value, energy efficiency assessment value, and system response time are calculated, and the proportional gain, integral gain, and derivative gain are optimized accordingly, and the gas supply flow and power consumption are adjusted, including:

[0052] Set an evaluation period, and calculate the average deviation between the main control cabin pressure and the target positive pressure requirement within the evaluation period. This average value is used as the system stability evaluation value, and the smaller the value, the more stable the system.

[0053] The energy efficiency assessment value is calculated by dividing the product of the make-up air flow rate and the target positive pressure demand by the electrical energy consumption used for positive pressure adjustment. The higher the value, the better the energy efficiency.

[0054] Record the time of occurrence of external disturbance and the time of start of positive pressure adjustment, and calculate the system response time;

[0055] Based on system stability assessment values, energy efficiency assessment values, and system response time, the proportional gain, integral gain, and derivative gain of the PID controller are optimized and adjusted to improve response speed and stability.

[0056] Energy efficiency assessment values ​​are used to optimize and adjust the gas supply flow and power consumption to reduce energy consumption.

[0057] Optionally, the step of determining a fault and distinguishing the fault type when the pressure deviation exceeds the limit, switching to the safe positive pressure demand and generating an emergency air supply flow, initiating pressure relief when overpressure occurs, and gradually restoring to the target positive pressure demand according to the recovery coefficient after the fault is cleared includes:

[0058] When the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the allowable error threshold, a fault is determined to exist.

[0059] In case of a malfunction, the type of malfunction should be identified by combining the current actual pressure inside the cabin, the target replenishment air flow rate, the actual intake air flow rate of the replenishment air pipeline, and the cabin door status signal.

[0060] If the actual pressure is lower than the target pressure and the actual intake flow rate is not less than the target make-up air flow rate, then it is a structural leakage fault.

[0061] If the actual pressure is lower than the target pressure and the actual intake flow is less than the target replenishment flow, then it is a gas supply insufficiency fault.

[0062] If the hatch is open, it is a hatch opening malfunction; if any sensor sample value exceeds the allowable range, it is a sensor sampling malfunction.

[0063] After a failure occurs, the target positive pressure requirement and the safety redundancy pressure are used as the safety positive pressure requirement.

[0064] For structural leakage or insufficient air supply, the maximum allowable replenishment flow rate is used for emergency replenishment; for abnormal doors or sensor malfunctions, the target replenishment flow rate is maintained.

[0065] During emergency gas replenishment, if the pressure in the main control cabin exceeds the maximum allowable pressure limit, an emergency depressurization process will be initiated to release the excess pressure and prevent overpressure.

[0066] After the fault is cleared, a gradual recovery method is adopted, gradually reducing the pressure from the safe positive pressure to the target positive pressure requirement based on the recovery coefficient, so as to avoid pressure jumps.

[0067] The present invention has the following beneficial effects:

[0068] 1. This intelligent control method for a positive-pressure explosion-proof robot involves deploying multiple sensors on the robot to synchronously collect data on external combustible gas concentration, ambient temperature, main control compartment pressure, relative humidity, and oxygen concentration at fixed intervals. A minimum time synchronization algorithm unifies the timestamps of each sensor to generate a control frame. A gating cleaning algorithm then replaces abnormal sampled values ​​exceeding the maximum allowable variation with the previous time value, forming a standardized control frame. All sampled values ​​are then dimensionless and mapped to the same numerical range. Finally, a sliding window smoothing algorithm eliminates residual fluctuations, resulting in a smoothed control frame. This eliminates information misalignment caused by inconsistent sampling times, ensuring precise correspondence between environmental conditions and robot compartment pressure on a unified timeline, laying a strict synchronization foundation for any subsequent calculations. Furthermore, the gating cleaning mechanism intelligently identifies and removes outliers that may occur due to sensor noise, jitter, or transient interference, preventing the risk of a single outlier contaminating the entire control chain and ensuring data authenticity from the source. Next, dimensionless processing transforms the values ​​of different physical quantities such as gas concentration, temperature, pressure, and humidity to the same scale, completely breaking down the barriers between multi-source heterogeneous data and enabling previously incomparable physical quantities to be jointly calculated. Finally, sliding window smoothing further suppresses minor high-frequency fluctuations, making the data curves smoother and preventing the adjustment system from malfunctioning due to data jitter.

[0069] 2. This intelligent control method for a positive-pressure explosion-proof robot standardizes the concentrations of external combustible gases, oxygen, ambient temperature, and relative humidity. Based on the standardized values, it calculates the external explosion proximity, reflecting the contribution of the external environment to the explosion risk. Simultaneously, it collects and standardizes the robot's movement speed, body vibration acceleration, and drive current to calculate the body disturbance degree, which characterizes the intensity of the robot's movement and its impact on sealing. Finally, it combines the external explosion proximity and body disturbance degree to derive a comprehensive explosion risk assessment index. This method, for the first time, incorporates the external hazardous environment and the robot's dynamic behavior into a unified risk assessment framework, demonstrating a progressive innovation from isolated perception to coupled analysis. Traditional explosion-proof positive-pressure control often focuses only on a single gas concentration or pressure threshold, neglecting the synergistic effects of ambient temperature, humidity, and oxygen concentration, and further ignoring the instantaneous decrease in cabin sealing caused by the robot's high-speed movement and severe vibration. This solution deeply integrates multi-dimensional environmental hazard factors with the robot's working state by separately calculating the external explosion proximity and the robot's disturbance degree. This allows the risk assessment indicators to not only reflect the explosive tendency of external gases but also capture potential leakage risks caused by robot movements in real time. This dual-dimensional fusion risk value has extremely strong foresight and sensitivity: when the robot changes from stationary to high-speed movement, or when uneven ground causes strong vibrations, even if the external gas concentration has not yet reached the alarm value, the comprehensive risk indicator will significantly increase due to the increased disturbance degree, providing a direct basis for raising the positive pressure in advance. The entire risk assessment is no longer a passive comparison of a single threshold but a dynamic, three-dimensional, and intelligent quantitative process that closely follows the robot's interaction with the environment. This provides a scientific decision-making basis for the dynamic generation and trend prediction of subsequent positive pressure demand, representing a crucial step in the shift of positive pressure regulation from static to dynamic, and from fixed thresholds to cognitive calculation.

[0070] 3. This intelligent control method for a positive-pressure explosion-proof robot calculates the initial positive pressure requirement based on explosion risk assessment indicators and robot body disturbance. It then incorporates a leakage rate to calculate a positive pressure adjustment factor to obtain the final positive pressure requirement. Simultaneously, it uses changes in the main control compartment pressure and the proximity of an external explosion to jointly determine leak precursors and issue warnings. Furthermore, it calculates changes in the positive pressure requirement, external combustible gas concentration, ambient temperature, and humidity, predicting corresponding values ​​for future moments. After standardization, it generates a risk prediction value, which is used to correct the target positive pressure requirement, ultimately calculating the required replenishment gas flow rate. This achieves a leap from real-time response to proactive prediction. Firstly, when determining the positive pressure requirement, it no longer uses a simple fixed base but fully considers the robot's own disturbance and existing leaks. An adjustment factor is used to instantly compensate for pressure losses caused by leaks, and pressure change trends are monitored simultaneously. Once a dangerous sign of abnormal pressure drop and simultaneous increase in external explosion risk is detected, a leak precursor warning is triggered, eliminating the accident in its early stages. More importantly, this solution goes beyond simply responding precisely to the current state. It further utilizes changes in positive pressure demand and various environmental parameters to extrapolate trends into the short-term future, generating risk prediction values. This addition of prediction values ​​means that the target positive pressure demand is no longer an instantaneous value at the current moment, but rather a forward-looking measure incorporating impending risk changes. When predictions indicate a rapid increase in external gas concentration or temperature, the system proactively increases the target positive pressure demand and correspondingly increases the make-up gas flow rate, establishing a stronger pressure barrier before the actual danger arrives, transforming the adjustment action from "lagging compensation" to "proactive defense." Furthermore, the linked calculation of target positive pressure and make-up gas flow rate ensures that the pressure regulation execution end receives precise flow instructions matching the demand. This layered approach of "sensing the present—early warning of leaks—predicting the future—proactively applying pressure" completely changes the traditional passive waiting-for-environment mode of positive pressure regulation, giving the positive pressure system proactive defense capabilities in the time dimension, significantly improving explosion-proof safety margins, and avoiding brief negative pressure risk windows caused by regulatory lag.

[0071] 4. This intelligent control method for a positive-pressure explosion-proof robot dynamically adjusts the positive pressure threshold based on the target positive pressure demand and the proximity of an external explosion. It incorporates a positive pressure feedback adjustment algorithm designed to address the robot's perturbation level, employing proportional-integral-derivative (PI-DI) control and adaptively adjusting the proportional, integral, and derivative coefficients in real-time based on external risks and disturbances. Pressure fault detection is also implemented. Furthermore, the system's stability, energy efficiency, and response time are periodically evaluated. Based on the evaluation results, control parameters and supplementary gas flow are further optimized, and energy-saving optimizations are implemented to reduce power consumption. This series of technical features embodies a progressive innovation in both control performance and operational energy efficiency. After obtaining the predicted target positive pressure, the system does not use a fixed set of parameters for forced execution. Instead, the positive pressure threshold itself dynamically fluctuates with the degree of environmental hazard and the robot's perturbation level. When the external explosion risk increases or the robot's movement intensifies, the threshold automatically rises, employing a more proactive pressure maintenance strategy to address potential threats. The introduction of a proportional-integral-derivative (PID) controller enables the pressure regulation process to possess characteristics of rapid response, steady-state error-free operation, and dynamic damping. The adaptive tuning of the three coefficients is a significant breakthrough: the system dynamically adjusts the strength of the coefficients by sensing disturbance levels and external risks in real time. This results in smoother and more energy-efficient control during stable robot movement, and a more decisive and powerful response during severe disturbances, avoiding the predicament of fixed parameters failing to address all aspects under complex and variable conditions. More profoundly, the system begins to self-evaluate its performance—objectively measuring the current control quality through three quantitative indicators: stability assessment value, energy efficiency assessment value, and response time. These evaluation results are then used to further automatically optimize control parameters and air supply flow, forming a closed-loop evolutionary capability of "evaluation—optimization—re-evaluation." Air supply flow and power consumption are also incorporated into the energy-saving optimization algorithm, striving to reduce unnecessary energy waste while ensuring safe positive pressure. In this way, the positive pressure regulation system can not only accurately track the target in the short term, but also continuously learn its own characteristics and automatically tend to the optimal state of high efficiency and low consumption in long-term operation, which reflects a profound progression in the electrical control architecture from manual tuning to self-adaptation, and then to self-evaluation and self-optimization.

[0072] 5. This intelligent control method for a positive-pressure explosion-proof robot establishes a fault judgment mechanism. When the deviation between the main control compartment pressure and the target value exceeds the allowable error threshold, a fault flag is triggered. Combining the current actual pressure inside the compartment, the target replenishment air flow rate, the actual intake air flow rate, the hatch status signal, and the sensor sampling range, faults are classified into structural leakage faults, insufficient air supply faults, abnormal hatch opening faults, and sensor malfunction faults. In case of a fault, the target positive pressure is automatically switched to the safe positive pressure requirement with safety redundancy. Emergency replenishment at maximum flow rate is initiated for structural leakage or insufficient air supply. If the pressure exceeds the safety upper limit during an emergency, pressure relief protection is implemented. After the fault is cleared, the system does not directly jump back to the target positive pressure but gradually and smoothly decreases to the target value according to the set recovery coefficient. This part adds a rigorous fault diagnosis and emergency protection layer to the existing intelligent adjustment, representing an inherently safe progression towards worst-case scenarios. Even the most sophisticated conventional control loops cannot completely eliminate extreme anomalies such as hardware failure, pipeline damage, or improperly closed hatches. This solution does not avoid these problems but incorporates them into the top-level design of the system. By comparing multi-source information such as expected pressure, actual pressure, and intake flow rate, the system can automatically determine the type of fault, rather than issuing a general alarm. This provides crucial guidance for maintenance personnel to quickly locate the root cause of the problem, greatly shortening troubleshooting time. More importantly, at the same moment the fault is diagnosed, the system immediately switches the control target from the normal optimized value to a higher pressure value with safety redundancy, and initiates emergency air replenishment at maximum flow rate depending on the nature of the fault, preventing pressure drop in the most decisive way and buying valuable time for emergency response. At the same time, overpressure relief control ensures the structural safety of the cabin during emergency air replenishment, preventing new dangers caused by blind pressurization. After the fault disappears, the recovery process does not adopt a simple step jump, but achieves a slow and gradual decrease through the pressure recovery coefficient, avoiding the instantaneous negative pressure and system shock that may be caused by a sudden drop from high redundant pressure. This end-to-end design, encompassing fault identification, classification, emergency pressure maintenance, overpressure protection, and smooth recovery, ensures that the positive pressure system remains robust in the face of unforeseen failures. It extends safety protection from fine-tuning during normal operation to extreme survival under fault conditions, forming a complete and well-defined safety progression loop. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the process of the present invention.

[0074] Figure 2 This is a diagram illustrating risk assessment.

[0075] Figure 3 This is a diagram illustrating the dynamic changes in explosion risk assessment indicators.

[0076] Figure 4 This is a schematic diagram showing the system stability and response time distribution. Detailed Implementation

[0077] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Example 1, see Figures 1 to 4 A method for intelligent control of a positive-pressure explosion-proof robot, comprising:

[0079] Data from multiple sensors is collected and timestamps are unified. After gating and cleaning to remove anomalies, standardized control frames are generated.

[0080] The standardized control frames are made dimensionless and smoothed using a sliding window to generate smooth control frames.

[0081] The approach to an external explosion is obtained by standardizing the external environmental factors, and the body disturbance is obtained by calculating the standardized robot body state. These are then combined to construct an explosion risk assessment index.

[0082] The initial positive pressure requirement is obtained based on the explosion risk assessment indicators and the degree of airframe disturbance. The final positive pressure requirement is obtained after correction by the leakage amount. The pressure change in the main control room is monitored to determine the early signs of leakage and issue an early warning.

[0083] Calculate the changes and forecasts of the final positive pressure demand and environmental factors, obtain the risk forecast value, and determine the target positive pressure demand and the required make-up gas flow rate by combining the final positive pressure demand.

[0084] The positive pressure threshold is adjusted based on the target positive pressure demand and the proximity of the external explosion. The body disturbance is introduced for feedback adjustment, and then adaptive PID control is used to adjust the pressure in real time and detect pressure faults.

[0085] During the evaluation period, calculate the system stability assessment value, energy efficiency assessment value and system response time, and optimize the proportional gain, integral gain, derivative gain and adjust the gas supply flow and power consumption accordingly.

[0086] When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. When the pressure is over-pressured, the system initiates pressure relief. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient.

[0087] The process involves collecting data from multiple sensors, unifying timestamps, and generating standardized control frames after gating and filtering out anomalies. These frames include:

[0088] Multiple sensors are deployed on the robot, and a uniform data collection cycle is set.

[0089] Each sensor collects data on external combustible gas concentration, external ambient temperature, main control cabin pressure, external relative humidity, and external ambient oxygen concentration in each cycle, and marks each data point with a timestamp.

[0090] The minimum time synchronization algorithm is adopted to take the earliest time among all sensor timestamps as the unified timestamp to achieve sampling time synchronization and ensure the consistency of data from all sensors.

[0091] Integrate sensor data under a unified timestamp into a single control frame;

[0092] Based on the operating range and accuracy of each sensor, set a threshold for the maximum allowable change within a single sampling period;

[0093] A gated cleaning algorithm is constructed to clean each sample value. If the absolute value of the difference between the current sample value and the previous sample value exceeds the maximum change threshold, it is determined to be abnormal and replaced by the previous sample value. Otherwise, the current sample value is retained to eliminate abnormal data caused by noise, jitter or external interference.

[0094] All gated cleaned sampled values ​​are arranged in timestamp order to generate a standardized control frame.

[0095] The step of making the standardized control frame dimensionless and performing sliding window smoothing to generate a smooth control frame includes:

[0096] The sampled values ​​of each sensor are dimensionless, and the original sampled values ​​are mapped to a uniform numerical range using the minimum and maximum values ​​of the sensor, so that the data from different sensors are comparable.

[0097] A sliding window smoothing algorithm is used to smooth the dimensionless data, and the window radius is set according to the requirements.

[0098] Based on the smoothed data, a smoothed control frame is constructed according to the timestamp.

[0099] The approach to an external explosion is obtained by standardizing external environmental factors, and the body disturbance is obtained by calculating the standardized robot state. These are combined to construct an explosion risk assessment index, including:

[0100] The concentration of external combustible gas, external ambient temperature, external relative humidity, and external ambient oxygen concentration were standardized respectively.

[0101] The standardized values ​​are used to calculate the external explosion proximity to reflect the combined impact of various external environmental factors on the explosion risk.

[0102] Collect robot body state data within a fixed period, including robot movement speed, body vibration acceleration, and drive current;

[0103] Based on their respective maximum allowable values, the normalized values ​​of velocity, vibration acceleration, and driving current are calculated;

[0104] The robot body perturbation degree is calculated based on the standardized values ​​of the body state data to characterize the impact of the robot's own motion and vibration on the sealing performance.

[0105] Multiplying the external explosion proximity by the body disturbance yields an explosion risk assessment index, which is used to comprehensively evaluate the current overall explosion risk.

[0106] The initial positive pressure requirement is obtained based on explosion risk assessment indicators and airframe disturbance. This is then corrected for by the leakage rate to obtain the final positive pressure requirement. Monitoring pressure changes in the main control cabin is used to identify and issue early warnings of potential leaks. This includes:

[0107] Using the aforementioned explosion risk assessment indicators and body disturbance degree, the preliminary positive pressure demand is calculated based on the basic positive pressure demand.

[0108] Based on the leakage amount and the preset adjustment coefficient, a positive pressure adjustment factor is generated in combination with the initial positive pressure demand to correct the initial positive pressure demand and obtain the final positive pressure demand, so as to maintain sufficient positive pressure in the event of leakage.

[0109] The system monitors changes in pressure in the main control compartment and generates a potential leak precursor signal when the absolute value of the pressure difference between adjacent moments exceeds the leak detection threshold.

[0110] If there are potential leak precursor signals, and the pressure difference is still not less than the leak detection threshold and the external explosion proximity exceeds the preset external explosion proximity threshold, then a leak precursor is determined to have occurred, triggering a leak precursor alarm and notifying the operator.

[0111] The calculation of the final positive pressure demand and the changes and predictions of environmental factors, obtaining the risk prediction value, and combining the final positive pressure demand to determine the target positive pressure demand and the required make-up gas flow rate, includes:

[0112] Calculate the changes in positive pressure demand, external combustible gas concentration, external ambient temperature, and external ambient humidity between the current time and the previous time.

[0113] Based on the changes and the preset prediction time step, the predicted values ​​of positive pressure demand, external combustible gas concentration, external ambient temperature and external ambient humidity are obtained by linear extrapolation.

[0114] Each predicted value is standardized, and the risk prediction value is calculated in a manner similar to the external explosion proximity, to represent the overall future risk;

[0115] Calculate the target positive pressure demand based on the current final positive pressure demand and the aforementioned risk forecast value;

[0116] Then, based on the ratio of the target positive pressure requirement to the current main control cabin pressure, and the current replenishment air flow rate, calculate the required replenishment air flow rate to maintain the target positive pressure.

[0117] The process of adjusting the positive pressure threshold based on the target positive pressure demand and the proximity of an external explosion, incorporating body disturbance for feedback adjustment, and then using adaptive PID control to adjust the pressure in real time and detect pressure faults includes:

[0118] Based on the target positive pressure demand and the proximity of external explosions, the positive pressure threshold is dynamically set so that the positive pressure threshold increases as the risk of external explosions increases.

[0119] The positive pressure regulation value is calculated by using the external explosion proximity and the body disturbance, so that the pressure regulation amount reflects both external risks and its own disturbances.

[0120] The PID control algorithm is used to correct the positive pressure adjustment value by using the deviation between the target positive pressure demand and the current main control chamber pressure, as well as its integral and derivative, to achieve real-time positive pressure control.

[0121] An adaptive mechanism is designed to adjust the proportional gain, integral gain, and derivative gain of the PID controller in real time based on the machine body disturbance degree and the external explosion proximity degree, respectively, in order to improve the adjustment accuracy and response speed.

[0122] A pressure fault detection mechanism is set up so that when the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the maximum allowable pressure error threshold, it is judged as a fault, and staff are notified to carry out maintenance.

[0123] During the evaluation period, the system stability assessment value, energy efficiency assessment value, and system response time are calculated. Based on these, the proportional gain, integral gain, and derivative gain are optimized, and the gas supply flow and power consumption are adjusted, including:

[0124] Set an evaluation period, and calculate the average deviation between the main control cabin pressure and the target positive pressure requirement within the evaluation period. This average value is used as the system stability evaluation value, and the smaller the value, the more stable the system.

[0125] The energy efficiency assessment value is calculated by dividing the product of the make-up air flow rate and the target positive pressure demand by the electrical energy consumption used for positive pressure adjustment. The higher the value, the better the energy efficiency.

[0126] Record the time of occurrence of external disturbance and the time of start of positive pressure adjustment, and calculate the system response time;

[0127] Based on system stability assessment values, energy efficiency assessment values, and system response time, the proportional gain, integral gain, and derivative gain of the PID controller are optimized and adjusted to improve response speed and stability.

[0128] Energy efficiency assessment values ​​are used to optimize and adjust the gas supply flow and power consumption to reduce energy consumption.

[0129] When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. In case of overpressure, pressure relief is initiated. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient. This includes:

[0130] When the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the allowable error threshold, a fault is determined to exist.

[0131] In case of a malfunction, the type of malfunction should be identified by combining the current actual pressure inside the cabin, the target replenishment air flow rate, the actual intake air flow rate of the replenishment air pipeline, and the cabin door status signal.

[0132] If the actual pressure is lower than the target pressure and the actual intake flow rate is not less than the target make-up air flow rate, then it is a structural leakage fault.

[0133] If the actual pressure is lower than the target pressure and the actual intake flow is less than the target replenishment flow, then it is a gas supply insufficiency fault.

[0134] If the hatch is open, it is a hatch opening malfunction; if any sensor sample value exceeds the allowable range, it is a sensor sampling malfunction.

[0135] After a failure occurs, the target positive pressure requirement and the safety redundancy pressure are used as the safety positive pressure requirement.

[0136] For structural leakage or insufficient air supply, the maximum allowable replenishment flow rate is used for emergency replenishment; for abnormal doors or sensor malfunctions, the target replenishment flow rate is maintained.

[0137] During emergency gas replenishment, if the pressure in the main control cabin exceeds the maximum allowable pressure limit, an emergency depressurization process will be initiated to release the excess pressure and prevent overpressure.

[0138] After the fault is cleared, a gradual recovery method is adopted, gradually reducing the pressure from the safe positive pressure to the target positive pressure requirement based on the recovery coefficient, so as to avoid pressure jumps.

[0139] Example 2: A method for intelligent control of a positive-pressure explosion-proof robot, comprising:

[0140] Data from multiple sensors is collected and timestamps are unified. After gating and cleaning to remove anomalies, standardized control frames are generated.

[0141] The standardized control frames are made dimensionless and smoothed using a sliding window to generate smooth control frames.

[0142] The approach to an external explosion is obtained by standardizing the external environmental factors, and the body disturbance is obtained by calculating the standardized robot body state. These are then combined to construct an explosion risk assessment index.

[0143] The initial positive pressure requirement is obtained based on the explosion risk assessment indicators and the degree of airframe disturbance. The final positive pressure requirement is obtained after correction by the leakage amount. The pressure change in the main control room is monitored to determine the early signs of leakage and issue an early warning.

[0144] Calculate the changes and forecasts of the final positive pressure demand and environmental factors, obtain the risk forecast value, and determine the target positive pressure demand and the required make-up gas flow rate by combining the final positive pressure demand.

[0145] The positive pressure threshold is adjusted based on the target positive pressure demand and the proximity of the external explosion. The body disturbance is introduced for feedback adjustment, and then adaptive PID control is used to adjust the pressure in real time and detect pressure faults.

[0146] During the evaluation period, calculate the system stability assessment value, energy efficiency assessment value and system response time, and optimize the proportional gain, integral gain, derivative gain and adjust the gas supply flow and power consumption accordingly.

[0147] When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. When the pressure is over-pressured, the system initiates pressure relief. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient.

[0148] The process involves collecting data from multiple sensors, unifying timestamps, and generating standardized control frames after gating and filtering out anomalies. These frames include:

[0149] Multiple sensors are deployed on the robot, and the sensor acquisition period is set to [value missing]. Ensure that the data sampling frequency is sufficient to respond to environmental changes;

[0150] Each sensor operates at a fixed cycle. Internal data acquisition, data acquired by sensors include:

[0151] ;

[0152] in, Indicates the concentration of external combustible gases. Indicates the external ambient temperature. Indicates the pressure in the main control compartment. Indicates external relative humidity. Indicates the oxygen concentration in the external environment;

[0153] The sampling data from each sensor is timestamped to record the sampling time of each data point. ;

[0154] The minimum time synchronization algorithm is used to select the earliest timestamp as the unified timestamp based on the timestamp of each sensor. To ensure all data is processed according to a unified time standard, the minimum time synchronization algorithm is as follows:

[0155] ;

[0156] This method synchronizes the sampling times of different sensors, ensuring the consistency of data from each sensor.

[0157] All sensors at a unified timestamp Data collected in real time Integrated into a single control frame :

[0158] ;

[0159] Set the maximum change threshold based on the operating range and accuracy of each sensor. This refers to the maximum change of each sensor within a sampling period, a value determined by the sensor's technical specifications and field usage conditions.

[0160] Record the data collected by each sensor as a sample value;

[0161] Construct a gated cleaning algorithm to perform gated cleaning on each sampled value:

[0162] If the difference between the current sampled value and the value at the previous time exceeds the preset maximum change threshold... If the current sampled value is an outlier, it will be replaced with the sampled value from the previous time step, as follows:

[0163] ;

[0164] in, Indicates the current time The sensor sample value, Indicates the previous moment The sampled values, This indicates the maximum allowable change of the sensor, which is typically set by sensor calibration and environmental conditions. : Sampled values ​​after gating and cleaning;

[0165] Eliminate abnormal data caused by noise, vibration, or external interference to ensure the accuracy of data in the control system;

[0166] All sampled data after gating According to timestamp Arrange the frames to generate the final standardized control frames. Specifically, it is expressed as:

[0167] .

[0168] The step of making the standardized control frame dimensionless and performing sliding window smoothing to generate a smooth control frame includes:

[0169] The sampled data from each sensor is dimensionless, as follows:

[0170] ;

[0171] in, This represents the original sensor sample value. This represents the minimum value of the sensor's sampled data. This indicates the maximum value of the sensor's sampled data. This represents the dimensionless sensor sample value, ranging from... ;

[0172] All collected sensor data are standardized to ensure that all data fall within the same unit of measurement, as follows:

[0173] ;

[0174] in, Indicates sensor The sensor sample value, Indicates sensor The sensor sample value, Indicates sensor The sensor sample value, Indicates sensor Dimensionless sensor sample values;

[0175] By standardizing the data to a uniform range, the comparability of different types of data is ensured;

[0176] A sliding window smoothing algorithm is used to smooth the dimensionless sensor sample values, as follows:

[0177] ;

[0178] in, Indicates the window radius. Indicates the smoothed result ;

[0179] Construct a smoothed control frame Specifically, it is expressed as:

[0180] ;

[0181] in, This represents the nth time.

[0182] By deploying multiple sensors on the robot, external combustible gas concentration, ambient temperature, main control chamber pressure, relative humidity, and oxygen concentration are collected synchronously at fixed intervals. A minimum time synchronization algorithm unifies the timestamps of each sensor to generate a control frame. Then, a gating cleaning algorithm replaces abnormal sampled values ​​exceeding the maximum allowable variation with the previous time value, forming a standardized control frame. All sampled values ​​are then dimensionless and mapped to the same numerical range. Finally, a sliding window smoothing algorithm eliminates residual fluctuations, resulting in a smoothed control frame. This eliminates information misalignment caused by inconsistent sampling times, ensuring precise correspondence between environmental conditions and robot chamber pressure on a unified timeline, laying a strict synchronization foundation for any subsequent calculations. Subsequently, the gating cleaning mechanism intelligently identifies and removes outliers that may occur due to sensor noise, jitter, or transient interference, preventing the risk of a single outlier contaminating the entire control chain and ensuring data authenticity from the source. Next, dimensionless processing transforms the values ​​of different physical quantities such as gas concentration, temperature, pressure, and humidity to the same scale, completely breaking down the barriers between multi-source heterogeneous data and enabling previously incomparable physical quantities to be jointly calculated. Finally, sliding window smoothing further suppresses minor high-frequency fluctuations, making the data curves smoother and preventing the adjustment system from malfunctioning due to data jitter.

[0183] The approach to an external explosion is obtained by standardizing external environmental factors, and the body disturbance is obtained by calculating the standardized robot state. These are combined to construct an explosion risk assessment index, including:

[0184] Calculate the external environmental factors separately, namely the concentration of external combustible gases. external ambient temperature External relative humidity and the oxygen concentration in the external environment The standardized values ​​are as follows:

[0185] , , , ;

[0186] in, This represents the standardized value of the external combustible gas concentration. This represents the standardized value of the external oxygen concentration. This represents the standardized value of the external ambient temperature. This represents the standardized value of external humidity. This represents the maximum value of the flammable gas concentration. , This represents the minimum and maximum values ​​of oxygen concentration in the external environment. , Indicates the minimum and maximum values ​​of the external ambient temperature;

[0187] In the above formula , , , and It was obtained through safety standards and historical data statistics;

[0188] The external explosion proximity is calculated using standardized values ​​of external environmental factors to reflect the degree of influence of each external environmental factor on the explosion risk, as detailed below:

[0189] ;

[0190] in, Indicates the proximity of the external explosion, with a value range of [value missing]. The higher the value, the greater the risk of explosion in the external environment. The standardized values ​​representing external environmental factors are as follows:

[0191] ;

[0192] In a fixed period Internally collects robot body state data, specifically including: robot movement speed. Robot body vibration acceleration and robot drive current ;

[0193] The standardized values ​​of the robot's body state data are calculated as follows:

[0194] , , ;

[0195] in, This represents the standardized value of the robot's movement speed. This represents the standardized value of the robot's body vibration acceleration. This represents the standardized value of the robot's drive current. , , These represent the maximum allowable values ​​for robot movement speed, body vibration acceleration, and drive current, respectively.

[0196] The robot body perturbation degree is calculated based on the standardized values ​​of the robot body state data. This is used to reflect the robot's current disturbance level:

[0197] ;

[0198] in, Indicates the degree of body disturbance, with a value range of [value missing]. A higher value indicates more intense robot movement and vibration, and a greater impact on the robot's sealing performance. The standardized values ​​representing the robot's body state data are as follows:

[0199] ;

[0200] Approaching the external explosion and body disturbance Calculate explosion risk assessment indicators The details are as follows:

[0201] ;

[0202] in, This represents an explosion risk assessment indicator, with a value range of [value range missing]. ;

[0203] This explosion risk assessment indicator Taking into account both external explosion risks and the robot's own disturbances, it can reflect the overall risk level under the current conditions.

[0204] By standardizing the concentrations of external combustible gases, oxygen, ambient temperature, and relative humidity, the external explosion proximity is calculated based on the standardized values, reflecting the contribution of the external environment to the explosion risk. Simultaneously, robot movement speed, body vibration acceleration, and drive current are collected and standardized to calculate body disturbance, characterizing the intensity of the robot's movement and its impact on sealing. Finally, the external explosion proximity and body disturbance are combined to derive a comprehensive explosion risk assessment index. This approach, which for the first time incorporates the external hazardous environment and the robot's dynamic behavior into a unified risk assessment framework, represents a progressive innovation from isolated perception to coupled analysis. Traditional explosion-proof positive pressure control often focuses only on a single gas concentration or pressure threshold, neglecting the synergistic effects of ambient temperature, humidity, and oxygen concentration, and even more so, the instantaneous decrease in cabin sealing caused by the robot's high-speed movement and severe vibration. This solution, by separately calculating the external explosion proximity and body disturbance, deeply integrates multi-dimensional environmental hazard factors with the robot's working state, enabling the risk assessment index to not only reflect the explosive tendency of external gases but also to capture the potential leakage risk caused by the robot's movements in real time. This dual-dimensional risk assessment possesses exceptional foresight and sensitivity: when the robot transitions from a stationary position to high-speed movement, or when uneven ground causes strong vibrations, even if the external gas concentration has not yet reached the alarm threshold, the comprehensive risk index will significantly increase due to the heightened disturbance, providing a direct basis for proactively raising the positive pressure. The entire risk assessment is no longer a passive comparison of a single threshold, but rather a dynamic, three-dimensional, and intelligently quantified process that closely follows the robot's interaction with the environment. This provides a scientific decision-making basis for the dynamic generation and trend prediction of subsequent positive pressure demands, representing a crucial step in shifting positive pressure regulation from static to dynamic, and from fixed thresholds to cognitive computation.

[0205] The initial positive pressure requirement is obtained based on explosion risk assessment indicators and airframe disturbance. This is then corrected for by the leakage rate to obtain the final positive pressure requirement. Monitoring pressure changes in the main control cabin is used to identify and issue early warnings of potential leaks. This includes:

[0206] According to explosion risk assessment indicators and body disturbance Determine the initial positive pressure demand at the current moment:

[0207] ;

[0208] in, The initial positive pressure demand at the current moment. This represents the basic positive pressure requirement, which is determined by the initial settings of the system design.

[0209] Initial positive pressure demand Based on this, the positive pressure adjustment factor is calculated. The positive pressure demand has been further revised as follows:

[0210] ;

[0211] in, This represents the positive pressure adjustment factor, used to correct for positive pressure demand. This represents the adjustment factor, set based on historical data and experimental results, and is a constant. The amount of leakage is indicated and is based on historical detection results;

[0212] Adjust the positive pressure requirement based on the leakage amount to ensure that sufficient positive pressure can be maintained to cope with the pressure drop caused by the leakage when a leak occurs;

[0213] The final positive pressure requirement is determined using the positive pressure adjustment factor and the initial positive pressure requirement, as follows:

[0214] ;

[0215] in, This indicates the final positive pressure demand.

[0216] When the pressure change in the main control compartment exceeds a set threshold, a leak detection signal is generated and marked as a potential leak precursor, as follows:

[0217] ;

[0218] in, This represents the difference in pressure between the current moment and the previous moment in the main control cabin, expressed as follows: , This indicates the pressure in the main control cabin at the previous moment. Indicates the leak detection threshold;

[0219] This indicates a potential leak precursor signal; a value of 1 indicates the presence of a potential leak precursor, while a value of 0 indicates the absence of a potential leak precursor.

[0220] when When the value is 1, it is based on the pressure difference in the main control cabin. Proximity to external explosion To determine if there are any signs of an impending leak, the specific criteria are as follows:

[0221] ;

[0222] in, This indicates a leak precursor warning sign. A value of 1 indicates the presence of leak precursors, while a value of 0 indicates the absence of leak precursors. Indicates the threshold for external explosion proximity;

[0223] Early signs of leakage When triggered, the system automatically activates an early warning mechanism to notify the operator.

[0224] The calculation of the final positive pressure demand and the changes and predictions of environmental factors, obtaining the risk prediction value, and combining the final positive pressure demand to determine the target positive pressure demand and the required make-up gas flow rate, includes:

[0225] The changes in positive pressure demand, external combustible gas concentration, external ambient temperature, and external ambient humidity are calculated separately, as follows:

[0226] ;

[0227] ;

[0228] ;

[0229] ;

[0230] in, This represents the change in positive pressure demand at the current moment. This indicates the change in the concentration of external combustible gases. It represents the change in external ambient temperature. Indicates the change in external environmental humidity;

[0231] Based on the calculated changes, the corresponding predicted values ​​are calculated as follows:

[0232] ;

[0233] ;

[0234] ;

[0235] ;

[0236] in, This represents the predicted value of positive pressure demand. This represents the predicted value of external combustible gas concentration. This represents the predicted value of the external ambient temperature. This represents the predicted value of external environmental humidity. The predicted time step;

[0237] The predicted values ​​obtained from the above calculations are standardized, and the risk prediction values ​​are calculated as follows:

[0238] ;

[0239] in, This represents the predicted value after standardization, specifically:

[0240] ;

[0241] in, This represents the predicted risk value, with a range of values. , This represents the maximum positive pressure demand, obtained through safety standards and historical data statistics.

[0242] Based on risk forecast values and positive pressure demand Calculate the target positive pressure demand The details are as follows:

[0243] ;

[0244] Based on risk predictions, the target positive pressure demand is dynamically adjusted to ensure that the robot can cope with upcoming environmental changes.

[0245] Based on the target positive pressure demand Based on the current pressure status in the main control compartment, calculate the required makeup air flow rate. To maintain the target positive pressure, the specific measures are as follows:

[0246] ;

[0247] in, This indicates the current gas supply flow rate;

[0248] Calculate the required air supply flow rate based on the target positive pressure demand to ensure the stability of positive pressure under different environments.

[0249] Based on the explosion risk assessment indicators and the degree of disturbance of the robot, the initial positive pressure requirement is calculated, and a positive pressure adjustment factor is introduced to obtain the final positive pressure requirement. Simultaneously, leak precursors are determined and warnings are issued by jointly analyzing the pressure changes in the main control compartment and the proximity of external explosions. Furthermore, the changes in positive pressure requirement, external combustible gas concentration, ambient temperature, and humidity are calculated to predict the corresponding values ​​at future times. After standardization, a risk prediction value is generated, and the target positive pressure requirement is corrected based on this prediction value, ultimately calculating the required replenishment gas flow rate. This achieves a leap from real-time response to proactive prediction. First, when determining the positive pressure requirement, it is no longer a simple fixed base, but fully considers the robot's own disturbances and the amount of leakage that has already occurred. Pressure losses caused by leakage are compensated in real time through adjustment factors, and pressure change trends are monitored simultaneously. Once a dangerous sign of abnormal pressure drop and simultaneous increase in external explosion risk is detected, a leak precursor warning is triggered, eliminating the accident in its infancy. More importantly, this solution goes beyond simply responding precisely to the current state. It further utilizes changes in positive pressure demand and various environmental parameters to extrapolate trends into the short-term future, generating risk prediction values. This addition of prediction values ​​means that the target positive pressure demand is no longer an instantaneous value at the current moment, but rather a forward-looking measure incorporating impending risk changes. When predictions indicate a rapid increase in external gas concentration or temperature, the system proactively increases the target positive pressure demand and correspondingly increases the make-up gas flow rate, establishing a stronger pressure barrier before the actual danger arrives, transforming the adjustment action from "lagging compensation" to "proactive defense." Furthermore, the linked calculation of target positive pressure and make-up gas flow rate ensures that the pressure regulation execution end receives precise flow instructions matching the demand. This layered approach of "sensing the present—early warning of leaks—predicting the future—proactively applying pressure" completely changes the traditional passive waiting-for-environment mode of positive pressure regulation, giving the positive pressure system proactive defense capabilities in the time dimension, significantly improving explosion-proof safety margins, and avoiding brief negative pressure risk windows caused by regulatory lag.

[0250] The process of adjusting the positive pressure threshold based on the target positive pressure demand and the proximity of an external explosion, incorporating body disturbance for feedback adjustment, and then using adaptive PID control to adjust the pressure in real time and detect pressure faults includes:

[0251] The positive pressure threshold is dynamically adjusted based on the target positive pressure demand and the proximity of an external explosion. The details are as follows:

[0252] ;

[0253] in, This represents the adjustment coefficient, which is set according to safety requirements and is a constant.

[0254] Dynamically adjust the positive pressure threshold to ensure that the positive pressure system can respond quickly to potential risk changes based on the degree of danger in the external environment;

[0255] Based on external explosion proximity and body disturbance A positive pressure feedback regulation algorithm is designed, as follows:

[0256] ;

[0257] in, Indicates the positive pressure adjustment value. Indicates the disturbance coefficient;

[0258] Through the positive pressure feedback regulation algorithm, the robot can dynamically adjust the pressure inside the cabin according to its own movement and changes in external risks, so as to maintain system stability;

[0259] Based on the current main control cabin pressure and the target positive pressure requirement and positive pressure threshold Design a PID control algorithm for Further revisions are made, as follows:

[0260] ;

[0261] in, This indicates the positive pressure regulation value at the current moment. Indicates proportional gain, which adjusts the system response speed. Indicates integral gain, used to adjust the long-term error of the system. It represents the differential gain, which adjusts the dynamic response of the system;

[0262] Real-time positive pressure regulation is achieved through PID control algorithm to ensure that the cabin pressure is always kept near the target positive pressure value, adapting to rapidly changing environments;

[0263] Design adaptive algorithms to automatically adjust , and To achieve more precise positive pressure regulation, the details are as follows:

[0264] ;

[0265] ;

[0266] ;

[0267] in, Indicates the real-time adjustment , and , This represents the adaptive adjustment coefficient, which is adjusted based on real-time feedback from the system.

[0268] Adaptive adjustment based on changes in robot movement and the external environment. , and This improves the accuracy and response speed of positive pressure regulation;

[0269] Configure a pressure fault detection mechanism as follows:

[0270] ;

[0271] in, This indicates the maximum allowable pressure error threshold. This indicates a fault detection flag; if a fault occurs, the value is 1.

[0272] when If the fault occurs, staff will be notified to troubleshoot it.

[0273] During the evaluation period, the system stability assessment value, energy efficiency assessment value, and system response time are calculated. Based on these, the proportional gain, integral gain, and derivative gain are optimized, and the gas supply flow and power consumption are adjusted, including:

[0274] Set an evaluation cycle by monitoring the pressure in the main control cabin and The deviation between them is used to calculate the system stability assessment value:

[0275] ;

[0276] in, This represents the system stability assessment value; the smaller the value, the more stable the system. Indicates the number of samples taken within the evaluation period;

[0277] Design an energy efficiency assessment algorithm to calculate the system's energy efficiency assessment value. The details are as follows:

[0278] ;

[0279] in, This indicates the electrical energy consumption used for positive pressure regulation. This represents the energy efficiency assessment value; a higher value indicates better energy efficiency.

[0280] Calculate the system response time: ;

[0281] in, Indicates system response time. Indicates the start time of positive pressure adjustment. Indicates the time when the external disturbance occurs, i.e. The timestamp corresponding to the first occurrence of a change exceeding the fluctuation threshold between adjacent time points; the fluctuation threshold can be set according to the working precision.

[0282] Based on system stability assessment values, energy efficiency assessment values, and system response time, adjust the proportional gain. Integral gain and differential gain To further optimize the system's response speed and stability, the specific adjustments are as follows:

[0283] ;

[0284] ;

[0285] ;

[0286] in, These are the adjusted proportional gains. Integral gain and differential gain , These are the adjustment coefficients;

[0287] An energy-saving optimization algorithm was designed to adjust the gas supply flow and positive pressure demand, thereby reducing power consumption, as detailed below:

[0288] ;

[0289] ;

[0290] in, This indicates the optimized air supply flow rate. This indicates the optimized energy consumption. This represents the energy-saving optimization coefficient, which is adjusted based on the system's operating status.

[0291] The positive pressure threshold is dynamically adjusted based on the target positive pressure demand and the proximity of an external explosion. A positive pressure feedback regulation algorithm is designed in conjunction with the robot's disturbance level, employing proportional-integral-derivative (PI-DE) control with real-time adaptive adjustments to the proportional, integral, and derivative coefficients based on external risks and disturbances. Pressure fault detection is also implemented. Furthermore, system stability, energy efficiency, and response time are periodically evaluated. Based on the evaluation results, control parameters and supplementary gas flow are further optimized, and energy-saving optimizations are implemented to reduce power consumption. This series of technical features embodies a progressive innovation in the dual optimization of control performance and operational energy efficiency. After obtaining the predictive target positive pressure, the system does not use a fixed set of parameters for forced execution. Instead, the positive pressure threshold itself dynamically fluctuates with the degree of environmental hazard and the robot's disturbance level. When the risk of an external explosion increases or the robot's movement intensifies, the threshold automatically rises, employing a more proactive pressure maintenance strategy to address potential threats. The introduction of a proportional-integral-derivative (PID) controller enables the pressure regulation process to possess characteristics of rapid response, steady-state error-free operation, and dynamic damping. The adaptive tuning of the three coefficients is a significant breakthrough: the system dynamically adjusts the strength of the coefficients by sensing disturbance levels and external risks in real time. This results in smoother and more energy-efficient control during stable robot movement, and a more decisive and powerful response during severe disturbances, avoiding the predicament of fixed parameters failing to address all aspects under complex and variable conditions. More profoundly, the system begins to self-evaluate its performance—objectively measuring the current control quality through three quantitative indicators: stability assessment value, energy efficiency assessment value, and response time. These evaluation results are then used to further automatically optimize control parameters and air supply flow, forming a closed-loop evolutionary capability of "evaluation—optimization—re-evaluation." Air supply flow and power consumption are also incorporated into the energy-saving optimization algorithm, striving to reduce unnecessary energy waste while ensuring safe positive pressure. In this way, the positive pressure regulation system can not only accurately track the target in the short term, but also continuously learn its own characteristics and automatically tend to the optimal state of high efficiency and low consumption in long-term operation, which reflects a profound progression in the electrical control architecture from manual tuning to self-adaptation, and then to self-evaluation and self-optimization.

[0292] When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. In case of overpressure, pressure relief is initiated. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient. This includes:

[0293] The fault determination mechanism is designed as follows:

[0294] ;

[0295] in, This represents the pressure deviation value of the main control cabin at the current moment, and the specific expression is as follows: , This indicates the maximum permissible pressure error threshold. This indicates a fault determination flag; a value of 1 indicates the presence of a fault, and a value of 0 indicates the absence of a fault.

[0296] when At that time, combined with the actual pressure inside the cabin Target gas replenishment flow rate Actual intake flow rate of the air supply line hatch status signal The fault type is determined as follows:

[0297] ;

[0298] in, Indicates the fault type number. This indicates the actual air intake flow rate of the current air supply line. This indicates the status signal of the hatch; a value of 0 indicates it is closed, and a value of 1 indicates it is open. This represents the current sampled value of any sensor. This indicates the minimum allowable sampling value for the corresponding sensor. This indicates the maximum allowed sampling value for the corresponding sensor;

[0299] like If the air supply is normal, it is marked as a structural leakage fault, indicating that the air supply is normal but the pressure is still lower than the target value;

[0300] like If the gas supply is insufficient, it is marked as a gas supply failure, indicating that the gas supply flow rate cannot reach the target gas supply flow rate.

[0301] like If so, it is marked as an abnormal opening fault of the hatch;

[0302] like Sensor sampling malfunction;

[0303] when At that time, the normal target positive pressure demand is switched to a safe positive pressure. In addition, a safety redundancy pressure is added based on the target positive pressure demand, as detailed below:

[0304] ;

[0305] in, This indicates the required positive pressure for safety. Indicates safety redundancy pressure;

[0306] In the event of a structural leak or insufficient gas supply, an emergency gas replenishment flow rate is generated. Priority should be given to preventing the cabin pressure from continuing to drop, as follows:

[0307] ;

[0308] in, Indicates emergency gas replenishment flow rate. Indicates the maximum allowable gas injection flow rate;

[0309] If the current main control compartment pressure during the fault response process Exceed Then, emergency pressure relief control will be activated:

[0310] ;

[0311] ;

[0312] in, This indicates the pressure relief control flag; a value of 1 indicates pressure relief is initiated, and a value of 0 indicates no pressure relief. This indicates the pressure difference that needs to be released;

[0313] Prevent overpressure during emergency gas replenishment;

[0314] When the fault condition is cleared, the system does not directly switch from... Jump to Instead, it is based on the coefficient of recovery. Gradually reduce positive pressure to generate positive pressure during the recovery phase. The details are as follows:

[0315] ;

[0316] in, Indicates positive pressure during the recovery phase. This represents the pressure recovery coefficient, with a value range of [value missing]. .

[0317] A fault diagnosis mechanism is established, triggering a fault flag when the main control compartment pressure deviates from the target value beyond the allowable error threshold. Combining the current actual compartment pressure, target replenishment air flow, actual intake air flow, hatch status signals, and sensor sampling range, faults are categorized into structural leakage faults, insufficient air supply faults, abnormal hatch opening faults, and sensor malfunction faults. In the event of a fault, the target positive pressure is automatically switched to a safe positive pressure requirement with safety redundancy. Emergency replenishment at maximum flow rate is initiated for structural leakage or insufficient air supply. If the pressure exceeds the safety limit during an emergency, pressure relief protection is implemented. After the fault is cleared, the pressure does not immediately jump back to the target positive pressure but gradually and smoothly decreases to the target value according to the set recovery coefficient. This part adds a robust fault diagnosis and emergency protection layer to the existing intelligent regulation, representing an inherently safe progression towards worst-case scenarios. Even the most sophisticated conventional control loops cannot completely eliminate extreme anomalies such as hardware failure, pipeline damage, or improperly closed hatches. This solution does not shy away from these issues but incorporates them into the system's top-level design. By comparing multi-source information such as expected pressure, actual pressure, and intake flow rate, the system can automatically determine the type of fault, rather than issuing a general alarm. This provides crucial guidance for maintenance personnel to quickly locate the root cause of the problem, greatly shortening troubleshooting time. More importantly, at the same moment the fault is diagnosed, the system immediately switches the control target from the normal optimized value to a higher pressure value with safety redundancy, and initiates emergency air replenishment at maximum flow rate depending on the nature of the fault, preventing pressure drop in the most decisive way and buying valuable time for emergency response. At the same time, overpressure relief control ensures the structural safety of the cabin during emergency air replenishment, preventing new dangers caused by blind pressurization. After the fault disappears, the recovery process does not adopt a simple step jump, but achieves a slow and gradual decrease through the pressure recovery coefficient, avoiding the instantaneous negative pressure and system shock that may be caused by a sudden drop from high redundant pressure. This end-to-end design, encompassing fault identification, classification, emergency pressure maintenance, overpressure protection, and smooth recovery, ensures that the positive pressure system remains robust in the face of unforeseen failures. It extends safety protection from fine-tuning during normal operation to extreme survival under fault conditions, forming a complete and well-defined safety progression loop.

[0318] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0319] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent control of a positive-pressure explosion-proof robot, characterized in that, include: Data from multiple sensors is collected and timestamps are unified. After gating and cleaning to remove anomalies, standardized control frames are generated. The standardized control frames are made dimensionless and smoothed using a sliding window to generate smooth control frames. The approach to an external explosion is obtained by standardizing the external environmental factors, and the body disturbance is obtained by calculating the standardized robot body state. These are then combined to construct an explosion risk assessment index. The initial positive pressure requirement is obtained based on the explosion risk assessment indicators and the degree of airframe disturbance. The final positive pressure requirement is obtained after correction by the leakage amount. The pressure change in the main control room is monitored to determine the early signs of leakage and issue an early warning. Calculate the changes and forecasts of the final positive pressure demand and environmental factors, obtain the risk forecast value, and determine the target positive pressure demand and the required make-up gas flow rate by combining the final positive pressure demand. The positive pressure threshold is adjusted based on the target positive pressure demand and the proximity of the external explosion. The body disturbance is introduced for feedback adjustment, and then adaptive PID control is used to adjust the pressure in real time and detect pressure faults. During the evaluation period, calculate the system stability assessment value, energy efficiency assessment value and system response time, and optimize the proportional gain, integral gain, derivative gain and adjust the gas supply flow and power consumption accordingly. When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. When the pressure is over-pressured, the system initiates pressure relief. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient.

2. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The process involves collecting data from multiple sensors, unifying timestamps, and generating standardized control frames after gating and filtering out anomalies. These frames include: Multiple sensors are deployed on the robot, and a uniform data collection cycle is set. Each sensor collects data on external combustible gas concentration, external ambient temperature, main control cabin pressure, external relative humidity, and external ambient oxygen concentration in each cycle, and marks each data point with a timestamp. The minimum time synchronization algorithm is adopted to take the earliest time among all sensor timestamps as the unified timestamp to achieve sampling time synchronization and ensure the consistency of data from all sensors. Integrate sensor data under a unified timestamp into a single control frame; Based on the operating range and accuracy of each sensor, set a threshold for the maximum allowable change within a single sampling period; A gated cleaning algorithm is constructed to clean each sample value. If the absolute value of the difference between the current sample value and the previous sample value exceeds the maximum change threshold, it is determined to be abnormal and replaced by the previous sample value. Otherwise, the current sample value is retained to eliminate abnormal data caused by noise, jitter or external interference. All gated cleaned sampled values ​​are arranged in timestamp order to generate a standardized control frame.

3. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The step of making the standardized control frame dimensionless and performing sliding window smoothing to generate a smooth control frame includes: The sampled values ​​of each sensor are dimensionless, and the original sampled values ​​are mapped to a uniform numerical range using the minimum and maximum values ​​of the sensor, so that the data from different sensors are comparable. A sliding window smoothing algorithm is used to smooth the dimensionless data, and the window radius is set according to the requirements. Based on the smoothed data, a smoothed control frame is constructed according to the timestamp.

4. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The approach to an external explosion is obtained by standardizing external environmental factors, and the body disturbance is obtained by calculating the standardized robot state. These are combined to construct an explosion risk assessment index, including: The concentration of external combustible gas, external ambient temperature, external relative humidity, and external ambient oxygen concentration were standardized respectively. The standardized values ​​are used to calculate the external explosion proximity to reflect the combined impact of various external environmental factors on the explosion risk. Collect robot body state data within a fixed period, including robot movement speed, body vibration acceleration, and drive current; Based on their respective maximum allowable values, the normalized values ​​of velocity, vibration acceleration, and driving current are calculated; The robot body perturbation degree is calculated based on the standardized values ​​of the body state data to characterize the impact of the robot's own motion and vibration on the sealing performance. Multiplying the external explosion proximity by the body disturbance yields an explosion risk assessment index, which is used to comprehensively evaluate the current overall explosion risk.

5. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The preliminary positive pressure requirement is obtained based on explosion risk assessment indicators and airframe disturbance. This is then corrected for by the leakage rate to obtain the final positive pressure requirement. Monitoring pressure changes in the main control cabin is used to identify and issue early warnings of potential leaks. This includes: Using the aforementioned explosion risk assessment indicators and body disturbance degree, the preliminary positive pressure demand is calculated based on the basic positive pressure demand. Based on the leakage amount and the preset adjustment coefficient, a positive pressure adjustment factor is generated in combination with the initial positive pressure demand to correct the initial positive pressure demand and obtain the final positive pressure demand, so as to maintain sufficient positive pressure in the event of leakage. The system monitors changes in pressure in the main control compartment and generates a potential leak precursor signal when the absolute value of the pressure difference between adjacent moments exceeds the leak detection threshold. If there are potential leak precursor signals, and the pressure difference is still not less than the leak detection threshold and the external explosion proximity exceeds the preset external explosion proximity threshold, then a leak precursor is determined to have occurred, triggering a leak precursor alarm and notifying the operator.

6. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The calculation of the final positive pressure demand and the changes and predictions of environmental factors, obtaining the risk prediction value, and combining the final positive pressure demand to determine the target positive pressure demand and the required make-up gas flow rate, includes: Calculate the changes in positive pressure demand, external combustible gas concentration, external ambient temperature, and external ambient humidity between the current time and the previous time. Based on the changes and the preset prediction time step, the predicted values ​​of positive pressure demand, external combustible gas concentration, external ambient temperature and external ambient humidity are obtained by linear extrapolation. Each predicted value is standardized, and the risk prediction value is calculated in a manner similar to the external explosion proximity, to represent the overall future risk; Calculate the target positive pressure demand based on the current final positive pressure demand and the aforementioned risk forecast value; Then, based on the ratio of the target positive pressure requirement to the current main control cabin pressure, and the current replenishment air flow rate, calculate the required replenishment air flow rate to maintain the target positive pressure.

7. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: The process of adjusting the positive pressure threshold based on the target positive pressure demand and the proximity of an external explosion, incorporating body disturbance for feedback adjustment, and then using adaptive PID control to adjust the pressure in real time and detect pressure faults includes: Based on the target positive pressure demand and the proximity of external explosions, the positive pressure threshold is dynamically set so that the positive pressure threshold increases as the risk of external explosions increases. The positive pressure regulation value is calculated by using the external explosion proximity and the body disturbance, so that the pressure regulation amount reflects both external risks and its own disturbances. The PID control algorithm is used to correct the positive pressure adjustment value by using the deviation between the target positive pressure demand and the current main control chamber pressure, as well as its integral and derivative, to achieve real-time positive pressure control. An adaptive mechanism is designed to adjust the proportional gain, integral gain, and derivative gain of the PID controller in real time based on the machine body disturbance degree and the external explosion proximity degree, respectively, in order to improve the adjustment accuracy and response speed. A pressure fault detection mechanism is set up so that when the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the maximum allowable pressure error threshold, it is judged as a fault, and staff are notified to carry out maintenance.

8. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: During the evaluation period, the system stability evaluation value, energy efficiency evaluation value, and system response time are calculated. Based on these, the proportional gain, integral gain, and derivative gain are optimized, and the gas supply flow and power consumption are adjusted, including: Set an evaluation period, and calculate the average deviation between the main control cabin pressure and the target positive pressure requirement within the evaluation period. This average value is used as the system stability evaluation value, and the smaller the value, the more stable the system. The energy efficiency assessment value is calculated by dividing the product of the make-up air flow rate and the target positive pressure demand by the electrical energy consumption used for positive pressure adjustment. The higher the value, the better the energy efficiency. Record the time of occurrence of external disturbance and the time of start of positive pressure adjustment, and calculate the system response time; Based on system stability assessment values, energy efficiency assessment values, and system response time, the proportional gain, integral gain, and derivative gain of the PID controller are optimized and adjusted to improve response speed and stability. Energy efficiency assessment values ​​are used to optimize and adjust the gas supply flow and power consumption to reduce energy consumption.

9. The intelligent control method for a positive pressure explosion-proof robot according to claim 1, characterized in that: When the pressure deviation exceeds the limit, a fault is identified and the fault type is distinguished. The system switches to the safe positive pressure requirement and generates an emergency air supply flow. In case of overpressure, pressure relief is initiated. After the fault is cleared, the system gradually restores the pressure to the target positive pressure requirement according to the recovery coefficient. This includes: When the deviation between the main control cabin pressure and the target positive pressure requirement exceeds the allowable error threshold, a fault is determined to exist. In case of a malfunction, the type of malfunction should be identified by combining the current actual pressure inside the cabin, the target replenishment air flow rate, the actual intake air flow rate of the replenishment air pipeline, and the cabin door status signal. If the actual pressure is lower than the target pressure and the actual intake flow rate is not less than the target make-up air flow rate, then it is a structural leakage fault. If the actual pressure is lower than the target pressure and the actual intake flow is less than the target replenishment flow, then it is a gas supply insufficiency fault. If the hatch is open, it is a hatch opening malfunction; if any sensor sample value exceeds the allowable range, it is a sensor sampling malfunction. After a failure occurs, the target positive pressure requirement and the safety redundancy pressure are used as the safety positive pressure requirement. For structural leakage or insufficient air supply, the maximum allowable replenishment flow rate is used for emergency replenishment; for abnormal doors or sensor malfunctions, the target replenishment flow rate is maintained. During emergency gas replenishment, if the pressure in the main control cabin exceeds the maximum allowable pressure limit, an emergency depressurization process will be initiated to release the excess pressure and prevent overpressure. After the fault is cleared, a gradual recovery method is adopted, gradually reducing the pressure from the safe positive pressure to the target positive pressure requirement based on the recovery coefficient, so as to avoid pressure jumps.

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