Environment perception based dust removal and oxygen supply robot adaptive regulation method

Abstract

The application relates to the technical field of robot control, and discloses a dust removal and oxygen supply robot adaptive regulation and control method based on environment perception, which acquires multi-source environment perception data inside a tunnel in real time to construct an environment state vector, carries out local environment risk assessment and divides risk grades, and generates a dust removal and oxygen supply coupling control index; target spraying flow and fan rotating speed are calculated in combination with environment coupling constraint conditions, secondary power reduction protection correction is carried out according to device bottom layer hardware resource constraints; operation modes are switched according to the risk grades and the index, and multi-target linkage coordination control is executed; in operation, closed loop feedback assessment is carried out based on environment improvement dynamic rate, device control parameters are corrected in real time until the environment reaches the standard and exits safely. The application can effectively balance the dust removal and oxygen supply output conflicts, prevent secondary dust raising and resource exhaustion out of control, and improve the safety and purification efficiency of autonomous operation in a closed space.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, specifically to an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception. Background Technology

[0002] Currently, in tunnel drilling and blasting construction, toxic and harmful gases are generated after blasting operations. Dust concentration inside the tunnel increases, exceeding safety limits. High concentrations of dust and harmful gases degrade the environmental quality of the enclosed space, affecting the health and safety of subsequent construction workers. Obstructed visibility increases waiting time for work sequence transitions. To ensure smooth construction operations, it is necessary to conduct dust settling and air purification at the tunnel face and surrounding areas after blasting, restoring local oxygen concentrations and reducing harmful gas concentrations.

[0003] Regarding the aforementioned issues, existing tunnel dust suppression equipment utilizes a mobile operating platform carrying a water tank and a spray system to perform dust reduction operations. Operators remotely control the system from an outside cab, propelling the chassis towards the tunnel face. As the equipment moves, the spray system activates, dispensing water mist to absorb airborne dust particles and cause them to settle. Once the dust concentration decreases, the spraying mechanism stops operating. Operators then monitor the tunnel face using onboard monitoring and multi-source sensors, assessing the ambient temperature, humidity, and gas parameters.

[0004] The existing control method treats dust suppression and air supply as independent operating processes. The equipment disperses harmful gases and replenishes oxygen by increasing the fan speed. The high wind speed disturbs the dust that has already settled on the ground. There is an output conflict between the equipment's air supply and the prevention of secondary dust re-entrainment, and the system lacks a dynamic linkage balancing mechanism; the consumption status of battery power and water tank level is not used as a hard control constraint during equipment operation. When water and electricity resources are depleted to a critical state, the equipment will directly shut down. The system lacks underlying power reduction protection logic, and the chassis cannot reserve enough power to complete the operation or leave the construction area; the current control logic relies on static open-loop commands. The fixed spray flow rate and air supply intensity cannot adapt to changes in the flow field within the confined space. The equipment triggers actions solely based on the gas concentration threshold, without considering the rate characteristics of dust decay and oxygen recovery. The system cannot adjust operating parameters in real time according to the environmental purification process, limiting the purification effect of multi-target collaborative operations.

[0005] Therefore, this invention provides an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception, in order to overcome the shortcomings of the prior art. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception. This method resolves the inherent physical conflict between high-volume ventilation dilution and secondary dust generation caused by airflow in confined spaces, as well as the problems of control lag and sudden shutdown and loss of control caused by the lack of multi-target dynamic linkage and underlying resource constraint protection in traditional dust removal equipment.

[0007] To achieve the above objectives, the present invention provides the following technical solution: An adaptive control method for a dust removal and oxygen delivery robot based on environmental perception includes the following steps: Initialize the task and collect multi-source environmental perception data of the tunnel in real time to construct an environmental state vector; Local environmental risk assessment is performed based on environmental state vectors, and the results of the local environmental risk assessment are divided into specific risk level states, and a dust removal and oxygen supply coupling control index is generated. The environmental coupling constraints are obtained. Based on the dust removal and oxygen supply coupling control index and the environmental coupling constraints, the target spray flow rate and the target fan speed are adaptively calculated, and secondary power reduction protection correction is performed in combination with the underlying hardware resource constraints. Based on the risk level and the dust removal and oxygen delivery coupling control index, the operation mode of the dust removal and oxygen delivery robot is matched and switched, and multi-target linkage and coordinated control is performed in combination with the corrected target spray flow rate and target fan speed. During the execution of multi-objective coordinated control, dynamic closed-loop feedback evaluation is performed based on the dynamic rate of environmental improvement, and control parameters are corrected in real time until the environmental state meets the standards and exits safely.

[0008] By adopting the above technical solution, by constructing an environmental state vector containing multi-source sensing data and generating a dust removal and oxygen supply coupling control index, and by combining environmental physical constraints to calculate target parameters and correct the power reduction protection of underlying hardware resources, dynamic closed-loop multi-objective linkage and coordinated control is executed under the risk level classification framework. Therefore, the effect of dynamically balancing the conflict between dust removal and oxygen supply output according to environmental changes, preventing secondary dust and resource waste, and improving the safety and purification efficiency of autonomous operation in enclosed spaces is achieved.

[0009] Preferably, the specific steps for initializing the task and constructing the environmental state vector by collecting multi-source environmental perception data of the tunnel in real time are as follows: Establish the initial operation task of the dust removal and oxygen delivery robot, and receive the coordinate information of the target operation area, the distance from the dust removal and oxygen delivery robot to the working face, the preset dust concentration safety threshold, the preset oxygen concentration safety threshold, and the preset harmful gas alarm threshold as parameter constraints. The driving mobile carrier module advances towards the target work area according to the initial path, and during each stage of the dust removal and oxygen delivery robot's movement and stop, the environmental perception module continuously collects multi-source environmental perception data inside the tunnel. The collected heterogeneous data is preprocessed and timestamp aligned to eliminate the timing deviation caused by different sampling frequencies of multiple devices. The aligned heterogeneous data is then combined to construct a continuous-time environmental state vector that represents the comprehensive multi-dimensional state characteristics of the local environment.

[0010] By adopting the above technical solution, the timing data misalignment caused by the difference in sampling frequency of different types of sensors is eliminated, a unified spatiotemporal information benchmark is established, and the accuracy of mapping the local multidimensional environmental state characteristics of the underlying sensing data and the reliability of subsequent control inputs are ensured.

[0011] Preferably, the specific steps for conducting local environmental risk assessment based on environmental state vectors and dividing the local environmental risk assessment results into specific risk level states are as follows: Key environmental features are extracted from the environmental state vector. The extracted key environmental features are then compared and analyzed in parallel with preset dust concentration safety thresholds, preset oxygen concentration safety thresholds, and preset harmful gas alarm thresholds stored in the internal memory. Based on the specific results of the comparative analysis, the environmental space risk classification logic is executed to divide the local environment of the current location of the dust removal and oxygen delivery robot into specific risk level states. The risk level states include at least one of the following: dust core area, dust diffusion area, oxygen deficiency risk area, harmful gas risk area, safety retesting area, and equipment confined area.

[0012] By adopting the above technical solutions, a parallel comparison mechanism of multi-dimensional thresholds is established to complete spatial risk classification, quantify the complex environmental hazards inside the tunnel, and provide accurate regional division and strategy reference for the system to execute targeted intervention actions.

[0013] Preferably, the specific steps for generating the dust removal and oxygen supply coupling control index are as follows: The deviations of the extracted key environmental features from the corresponding preset dust concentration safety threshold, preset oxygen concentration safety threshold, or preset hazardous gas alarm threshold are normalized and multiplied by the penalty weight coefficient corresponding to each key environmental feature. The environmental risk assessment values ​​of the processed deviations are linearly superimposed to generate the final dust removal and oxygen supply coupling control index. The dust removal and oxygen supply coupling control index is used to quantitatively define the priority and response magnitude of the external intervention actions performed by the subsequent dust removal and oxygen supply execution modules.

[0014] By adopting the above technical solution, and utilizing the normalization processing and linear superposition operation mechanism of penalty weight coefficients, the multi-dimensional environmental risk deviation characteristics are integrated into a single numerical control index, establishing the priority of dust removal and oxygen delivery physical actions, and logically avoiding system action coupling conflicts caused by two independent execution modules.

[0015] Preferably, the specific steps for obtaining environmental coupling constraints and adaptively calculating the target spray flow rate based on the dust removal and oxygen delivery coupling control index and environmental coupling constraints are as follows: The real-time dust concentration, real-time relative humidity, distance between the dust removal and oxygen delivery robot and the dust core area, and real-time dust attenuation rate during the environmental purification process are extracted from the environmental state vector as environmental coupling constraints. According to the preset weight allocation control strategy, the dust concentration exceeding the limit is used as the positive demand increment, the distance measurement is used as the long-distance attenuation compensation increment, the real-time ambient relative humidity is used as the negative suppression amount for water accumulation prevention, and the real-time dust attenuation rate is used as the negative suppression amount for excessive spraying prevention. Linear scaling and difference accumulation operations are performed on all the above input variables. The baseline correction of the overall control water volume is completed by introducing the basic spray flow rate, and the calculated temporary water volume assessment parameters are input into the limited treatment node for filtration verification calculation to obtain the target spray flow rate.

[0016] By adopting the above technical solution, physical variables such as distance compensation, humidity and dust attenuation rate are introduced to perform linear scaling and difference accumulation calculation, dynamically correct the basic water flow requirements, prevent the hidden danger of water accumulation in the work area and excessive consumption of water resources, and achieve precise water allocation.

[0017] Preferably, the specific steps for adaptively calculating the target fan speed based on the dust removal and oxygen supply coupling control index and environmental coupling constraints are as follows: Real-time oxygen volume concentration, real-time hazardous gas risk assessment parameters, and real-time dust secondary diffusion risk measurement parameters are extracted during the working period as environmental coupling constraints. By introducing the basic fan speed as a reference benchmark for ensuring the system maintains a minimum air circulation state, the system generates positive large-volume forced dilution and directional air supply demand based on the absolute deficit value of the ambient oxygen concentration deviating from the lower limit of the safety standard and the overall risk value of exceeding the limit of various harmful gases. By using the built-in dust disturbance suppression calculation item, strict numerical reduction and mandatory safety penalties are applied to the forced dilution of large forward air volume and the demand for directional air supply. The target fan speed is generated after the safety limit filtering and interception process of the upper and lower speed limits.

[0018] By adopting the above technical solution, while ensuring the basic air supply, the system addresses the combined air supply demand based on the deviation between oxygen deficiency and harmful gases, and forcibly introduces a dust disturbance suppression calculation item to perform numerical reduction, effectively resolving the technical contradiction between large-volume dilution gas and secondary dust generation caused by strong airflow.

[0019] The preferred method, combined with the underlying hardware resource constraints, involves the following specific steps for secondary power reduction protection correction: Extract the real-time remaining power ratio of the current power battery pack and the real-time remaining liquid level ratio of the current water tank as underlying hardware resource constraints; When the logic determines that the real-time remaining power ratio is lower than the preset low power tolerance warning threshold, or determines that the real-time remaining liquid level ratio is lower than the preset low water volume tolerance warning threshold, the resource scarcity degradation program is invoked. Based on the pre-set proportional load reduction coefficient, the generated target spray flow rate and target fan speed are numerically compressed and reduced to complete the secondary power reduction protection correction, and the corrected target spray flow rate and target fan speed commands are sent to the corresponding dust removal execution module and oxygen delivery execution module.

[0020] By adopting the above technical solution, when the power or water volume falls below the warning limit, the degradation operation program is invoked. The command parameters are numerically compressed using a proportional load reduction coefficient to prevent the system from suddenly crashing and losing control due to resource depletion, and to ensure that the chassis power can complete the remaining purification work or achieve safe evacuation.

[0021] Preferably, the specific steps for matching and switching the operating mode of the dust removal and oxygen delivery robot based on the risk level and the dust removal and oxygen delivery coupling control index, and combining the corrected target spray flow rate and target fan speed to perform multi-target linkage coordinated control are as follows: Based on the determined risk level and the calculated range of the dust removal and oxygen delivery coupling control index, the corresponding operation mode execution command is called from the preset operation mode library to match and switch the current operation mode of the dust removal and oxygen delivery robot. The dust removal execution module responds to the corrected target spray flow command and controls the spray host and nozzle group to output micron-level water mist. The oxygen delivery execution module responds to the corrected target fan speed command and, in conjunction with the guide hood, controls the fog cannon fan to deliver air directionally to the local area to be purified. The system sends speed adjustment commands to the mobile carrier module, controls the power drive components to distribute electrical energy, and drives the wheeled or tracked chassis and vehicle body to adjust the overall travel speed. This enables the dust removal and oxygen delivery robot to adaptively perform deceleration, stopping, or propulsion actions in different risk areas, achieving multi-target linkage and coordinated control.

[0022] By adopting the above technical solutions, the execution link from upper-level state decision-making to chassis mobility and working component output is opened up, enabling a control strategy that deeply links equipment travel speed, spray flow rate and air supply intensity, and accurately adapts to the actual physical needs of various risk areas.

[0023] Preferably, the specific steps for dynamic closed-loop feedback evaluation based on the dynamic rate of environmental improvement are as follows: Extract the dust concentration and oxygen concentration values ​​at two consecutive control cycles; Calculate the actual real-time dust decay rate in the environment. The real-time dust decay rate represents the rate at which the dust concentration in the local environment decreases over time. Calculate the real-time oxygen recovery rate at the current moment. The real-time oxygen recovery rate represents the rate at which the volume concentration of oxygen in the local environment increases over time. The real-time dust decay rate and real-time oxygen recovery rate are used as dynamic closed-loop feedback assessments to evaluate the dynamic rate of environmental improvement.

[0024] By adopting the above technical solution, the rate of difference in environmental parameters over time is extracted as a feedback metric, and a quantitative evaluation benchmark for the actual intervention effect of the system on the environment is established.

[0025] The preferred procedure, which involves real-time adjustment of control parameters until the environmental conditions meet the safety standards for safe exit, is as follows: When the dynamic closed-loop feedback evaluation result indicates that the real-time dust decay rate is lower than the preset concentration reduction expectation, the feedforward compensation adjustment mechanism is triggered to generate a correction instruction to increase the target spray flow or extend the stationary dwell time of the dust removal and oxygen delivery robot. When the real-time oxygen recovery rate is lower than the preset gas recovery expectation value, a correction command is generated to increase the target fan speed or fine-tune the driving direction of the wheeled or tracked chassis of the dust removal and oxygen delivery robot. When the real-time dust decay rate reaches the predetermined standard and the rate of liquid level drop inside the water tank of the dust removal and oxygen delivery robot exceeds the preset liquid level consumption threshold, a water-saving correction control command is triggered to reduce the target spray flow rate in the opposite direction. The above closed-loop parameter correction process is executed repeatedly until all key environmental characteristic parameters in the environmental state vector are restored to the corresponding preset dust concentration safety threshold, preset oxygen concentration safety threshold, and preset harmful gas alarm threshold. The system then outputs an environmental compliance detection signal and controls the dust removal and oxygen delivery robot to stop high-power operation and executes an automatic return command to safely exit the current work area.

[0026] By adopting the above technical solution, a result-oriented closed-loop correction mechanism based on the deviation between attenuation rate and recovery rate is established to overcome the feedback lag of static open-loop commands in complex flow field space, and to ensure that the equipment completes the thorough purification of the area and automatically returns and exits under the constraint of optimal resource consumption.

[0027] This invention provides an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception. It has the following beneficial effects: 1. This invention calculates the target fan speed by generating a dust removal and oxygen delivery coupling control index and combining it with environmental coupling constraints such as real-time dust secondary diffusion risk measurement parameters. During system operation, a built-in dust disturbance suppression calculation term is used to numerically weaken and impose safety penalties on the large-volume forced dilution demand caused by oxygen deficiency or excessive harmful gases. This resolves the physical contradiction between the large-volume oxygen delivery and dilution gas supply in enclosed spaces and the tendency of strong airflow to cause secondary dust re-entrainment from the ground. While ensuring basic air circulation, it achieves dynamic linkage and balance between the two independent execution modules of dust removal and directional oxygen delivery.

[0028] 2. This invention incorporates a secondary power reduction protection correction mechanism based on underlying hardware resource constraints. The system extracts the remaining battery pack charge percentage and water tank level percentage in real time. When these parameters are determined to be below the tolerance warning threshold, the system automatically invokes the resource scarcity degradation program and forcibly compresses and limits the generated target spray flow rate and target fan speed according to a proportional load reduction coefficient. This effectively prevents sudden loss of control and shutdown of robotic equipment in dangerous operating areas such as tunnels due to rapid water consumption or power depletion, ensuring that the chassis can utilize remaining power to maintain low-power basic purification operations or perform automatic return and evacuation maneuvers.

[0029] 3. This invention introduces a closed-loop feedback evaluation process based on the dynamic rate of environmental improvement. By continuously extracting dust and oxygen concentrations within the control cycle, the real-time dust decay rate and oxygen recovery rate within the local space are calculated. When the evaluation fails to meet the expected values, a feedforward compensation adjustment mechanism is triggered, issuing correction commands to increase spray flow, increase fan speed, or adjust chassis propulsion speed and orientation. This overcomes the feedback hysteresis defect of traditional static open-loop control in complex flow fields, enabling the robot's multi-objective linkage and coordinated control commands to correct deviations in real time according to the actual environmental purification process. The system only outputs detection signals and safely exits after all regional environmental indicators have completely met the standards. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall structure of the dust removal and oxygen delivery robot of the present invention; Figure 2 This is a block diagram of the dust removal and oxygen delivery robot control system of the present invention; Figure 3 This is a flowchart of the adaptive control method for a dust removal and oxygen delivery robot based on environmental perception, as described in this invention. Figure 4 This is a comparison curve of dust concentration changing over time under two different control methods of the present invention. Figure 5This is a comparison curve of the cumulative water consumption of the equipment over time under two different control methods of the present invention.

[0031] The components include: 1. Wheeled or tracked chassis; 2. Vehicle body; 3. Fog cannon; 4. Spray host; 5. Power battery pack; 6. Water tank; 7. Drain outlet; 8. Remote control camera; 9. Nozzle assembly; and 10. Deflector. Detailed Implementation

[0032] The technical solutions in 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.

[0033] See attached document Figure 1 The present invention provides an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception, including the following regarding the system platform architecture.

[0034] The dust removal and oxygen delivery robot's carrier includes a wheeled or tracked chassis 1 and a vehicle body 2. The wheeled or tracked chassis 1 is used to travel on slippery tunnel surfaces and undulating ground containing gravel. The vehicle body 2 is fixedly mounted on the top surface of the wheeled or tracked chassis 1.

[0035] A power battery pack 5 and a water tank 6 are fixedly installed inside or outside the vehicle body 2. The power battery pack 5 is electrically connected to the chassis drive motor, control motherboard, and various environmental sensors via a power supply harness, and provides DC power to the entire robot system. The water tank 6 is used to store water or dust suppressant liquid required for operation. A drain outlet 7 is provided at the bottom of the side wall of the water tank 6, which is used to drain the remaining liquid inside the water tank 6.

[0036] The vehicle body 2 is equipped with dust removal and air supply components. The dust removal component includes a spray host 4 and nozzle assembly 9. Multiple nozzle assemblies 9 are arranged around the outer edge of the air outlet of the spray host 4. The nozzle assembly 9 is connected to the water tank 6 via an internal rigid water pipe or flexible hose. A flow guide shroud 10 is fixedly installed on the lower outer end of the spray host 4. The flow guide shroud 10 is used to constrain and guide the atomized droplets sprayed by the spray host 4 to flow forward. The air supply component includes a fog cannon 3. The fog cannon 3 is fixedly installed above the bracket on the top of the vehicle body 2.

[0037] A remote control camera 8 is installed on the top front area of ​​vehicle body 2. The remote control camera 8 is connected to the core control module via a high-frequency data cable and is used to capture visual image information in front of the tunnel.

[0038] See attached document Figure 2The adaptive control system for the dust removal and oxygen delivery robot includes a mobile support module, an environmental sensing module, a dust removal execution module, an oxygen delivery execution module, a filtration and dust discharge module, and a core control module. These modules interact with each other via a controller area network (CLAN) bus or an industrial Ethernet bus to achieve high-speed transmission of sensing data and execution commands.

[0039] The mobile carrier module includes a chassis motor control circuit and a power distribution management circuit. The chassis motor control circuit receives travel speed control commands from the core control module and outputs corresponding drive current to the walking drive device of the wheeled or tracked chassis 1, thereby controlling the robot's travel speed and stopping action. The power distribution management circuit monitors the remaining percentage of power in the power battery pack 5 in real time.

[0040] The environmental sensing module includes a dust concentration sensor, an oxygen concentration sensor, a hazardous gas sensor, a temperature and humidity sensor, a differential pressure sensor, and a remote-controlled camera. The dust concentration sensor collects data on the mass concentration of suspended particulate matter in the work area air. The oxygen concentration sensor measures the volume percentage of oxygen in the local air medium. The hazardous gas sensor detects the concentrations of carbon monoxide and methane. The temperature and humidity sensor collects the Celsius temperature and relative humidity of the ambient atmosphere. The environmental sensing module continuously transmits the periodically collected real-time heterogeneous environmental characteristic data to the input port of the core control module.

[0041] The dust removal execution module includes a water circuit electrically controlled valve, a flow regulation circuit, and a spray host 4. The core control module sends a quantitative spray flow control command to the flow regulation circuit. The flow regulation circuit changes the opening degree of the water circuit electrically controlled valve according to the received command, thereby precisely regulating the liquid volume flow rate input to the nozzle assembly 9. The drive device inside the spray host 4 disperses and atomizes the sprayed liquid and continuously blows it into the space in front.

[0042] The oxygen delivery module includes a fan speed control circuit, a wind direction adjustment mechanism, and a fog cannon 3. The core control module sends specific fog cannon fan speed control commands to the fan speed control circuit based on the degree of oxygen deficiency. The fan speed control circuit adjusts the voltage frequency output to the fog cannon 3 drive motor, thereby changing the airflow volume of the fog cannon 3. The wind direction adjustment mechanism receives position control signals to adjust the pitch and yaw angles of the fog cannon 3's air outlet.

[0043] The filtration and dust discharge module includes a high-strength filter, replaceable filter media, and a dust discharge port. The high-strength filter is located at the side of the air inlet of both the fog cannon 3 and the spray unit 4, and is used to intercept large-diameter dust particles and gravel debris in the airflow. Differential pressure sensors are connected to pressure measurement holes on the windward and leeward sides of the high-strength filter, respectively, to collect real-time air pressure difference data across the filter. When this air pressure difference exceeds a preset air pressure difference alarm threshold pre-written into the memory, the core control module determines that the high-strength filter is experiencing excessive resistance. The dust discharge port is located at the bottom of the filtration chamber, used to collect and discharge solid dust that falls after being intercepted by the high-strength filter, maintaining unobstructed airflow.

[0044] The core control module includes a microprocessor chip and an edge computing logic unit. It receives multi-source sensing data aggregated by the environmental sensing module and performs preprocessing and timestamp alignment. The core control module executes a multi-objective collaborative decision-making algorithm to generate corresponding dust removal and oxygen supply coupled control commands, which are then synchronously issued to the mobile carrier module, dust removal execution module, and oxygen supply execution module, thus forming a closed-loop adaptive control architecture.

[0045] See attached document Figure 3 This invention provides an adaptive control method for a dust removal and oxygen delivery robot based on environmental perception. This adaptive control method operates in the core control module of the dust removal and oxygen delivery robot and may include: Step S100: Initialize the operation task and collect multi-source environmental perception data of the tunnel in real time to construct an environmental state vector. Specifically, establish the initial operation task of the dust removal and oxygen delivery robot, and receive the coordinate information of the target operation area, the distance from the dust removal and oxygen delivery robot to the tunnel face, the preset dust concentration safety threshold, the preset oxygen concentration safety threshold, and the preset harmful gas alarm threshold as parameter constraints.

[0046] The core control module drives the mobile carrier module to advance towards the target work area along the initial path. During each stage of the dust removal and oxygen delivery robot's movement and rest, various sensor components in the environmental perception module and the remote-controlled camera 8 continuously collect multi-source environmental perception data inside the tunnel at set sampling time intervals.

[0047] After acquiring multi-source environmental perception data, the core control module preprocesses and aligns the collected heterogeneous data with timestamps to eliminate timing deviations caused by different sampling frequencies of multiple devices. The aligned heterogeneous data is then combined to construct a continuous-time environmental state vector that represents the comprehensive multi-dimensional state characteristics of the local environment.

[0048] The preset safety threshold for dust concentration is greater than 0 mg / m³.3 And less than or equal to 4 mg / m 3 The preset numerical limits are: the preset safe threshold for oxygen concentration is a lower limit of the set volume concentration percentage greater than or equal to 19.5%, and the preset alarm threshold for harmful gases is a set value standard of carbon monoxide concentration greater than or equal to 24 ppm or methane concentration greater than or equal to 0.5%.

[0049] Step S200 involves conducting a local environmental risk assessment based on the environmental state vector and generating a dust removal and oxygen supply coupling control index. Specifically, various environmental feature parameters are extracted based on the constructed environmental state vector, and these extracted environmental feature parameters are compared and analyzed with pre-set safety thresholds.

[0050] Based on the comparative analysis results, a local environmental risk assessment is conducted on the current location of the dust removal and oxygen delivery robot. The local environmental risk assessment results are divided into specific risk levels, including at least one of the following: dust core area, dust diffusion area, oxygen deficiency risk area, harmful gas risk area, safety retesting area, and equipment restricted area.

[0051] The core control module performs a composite calculation based on the degree of deviation of dust concentration from the limit, the degree of oxygen volume concentration deficiency, the degree of danger of harmful gas accumulation, and the state of limited visual visibility in the current environment, and generates a dust removal and oxygen supply coupling control index. This dust removal and oxygen supply coupling control index is used as the dust removal and oxygen supply coupling control index to quantitatively define the priority level and response range of the external intervention actions to be performed by the subsequent dust removal execution module and the oxygen supply execution module.

[0052] Step S300: Based on the environmental coupling constraints, adaptively calculate the target spray flow rate and the target fan speed. Specifically, after obtaining the dust removal and oxygen delivery coupling control index, adaptively calculate the target spray flow rate and the target fan speed of the dust removal and oxygen delivery robot based on the preset environmental coupling constraints.

[0053] When calculating the target spray flow rate, the real-time dust concentration, relative humidity, distance from the dust removal and oxygen delivery robot to the working face or dust core area, and real-time dust attenuation rate are extracted from the environmental state vector. The target water volume value that matches the actual purification needs of the current environment is calculated to prevent the system from controlling the spray host 4 and nozzle group 9 to spray excessive water mist under the drive of a single concentration index, thereby avoiding water accumulation on the tunnel surface or secondary mud formation of dust.

[0054] When calculating the target fan speed, the oxygen supply demand of fresh oxygen-containing air is forcibly coupled with the risk of secondary diffusion caused by unsettled dust and the dilution requirements of carbon monoxide and methane gas to obtain the target fan speed that the fog cannon 3 needs to respond to, thus limiting the occurrence of high-concentration dust spreading and expanding over a large area due to direct large-volume output.

[0055] After the calculation is completed, the core control module uses the remaining liquid level ratio of water tank 6 and the remaining power ratio of power battery pack 5 as equipment resource constraints, performs secondary power reduction protection correction on the initially obtained target spray flow rate and target fan speed, and sends the corrected target spray flow rate and target fan speed commands to the corresponding dust removal execution module and oxygen delivery execution module.

[0056] In step S400, based on the risk level and coupling control index, the operating mode of the dust removal and oxygen delivery robot is intelligently switched and linkage control is executed. Specifically, the core control module matches and switches the current operating mode of the dust removal and oxygen delivery robot according to the environmental risk level determined in step S200 and the calculated range of the dust removal and oxygen delivery coupling control index.

[0057] The core control module calls the corresponding operation mode execution instructions from the preset operation mode library. The operation modes include water mist barrier mode, near-face strong dust suppression mode, oxygen delivery and dilution mode, collaborative purification mode, low-disturbance inspection mode, and safe exit mode.

[0058] Once the corresponding operating mode is matched and switched, the dust removal module responds to the target spray flow command and controls the spray host 4 and nozzle group 9 to output micron-level water mist. The oxygen delivery module responds to the target fan speed command and, in conjunction with the guide shroud 10, controls the fog cannon 3 to deliver air directionally to the localized area to be purified.

[0059] At the same time, the core control module sends speed adjustment commands to the mobile carrier module, controls the power drive components to distribute electrical energy, and drives the wheeled or tracked chassis 1 and vehicle body 2 to adjust the overall travel speed. This enables the dust removal and oxygen delivery robot to adaptively perform deceleration, stopping or advancing actions in different risk areas, realizing multi-objective linkage and coordinated control of dust removal and oxygen delivery functions.

[0060] Step S500 involves performing a dynamic closed-loop feedback evaluation based on the environmental purification rate and adjusting the control parameters in real time until the environment meets the standards and the system safely exits. Specifically, during the execution of multi-objective linkage control, a dynamic closed-loop feedback evaluation of the environmental purification rate is performed based on the set control sampling period.

[0061] The core control module extracts the dust concentration and oxygen concentration values ​​at two consecutive control cycles and calculates the actual dust concentration decay rate and oxygen volume recovery rate in the environment.

[0062] When the assessment results indicate that the dust concentration decay rate is lower than the preset expected concentration decrease, the core control module triggers the feedforward compensation adjustment mechanism to generate correction instructions to increase the target spray flow rate or extend the stationary dwell time of the dust removal and oxygen delivery robot. When the oxygen volume recovery rate is lower than the preset expected gas recovery value, correction instructions are generated to increase the target fan speed or fine-tune the travel orientation of the wheeled or tracked chassis 1.

[0063] When the dust concentration decay rate reaches the predetermined standard and the rate of liquid level drop inside water tank 6 exceeds the preset liquid level consumption threshold, a water-saving correction control command is triggered to reversely reduce the target spray flow rate.

[0064] By repeatedly executing the above perception, evaluation, and parameter closed-loop correction process until all key environmental feature parameters in the environmental state vector are restored to the set safety threshold range, the system outputs an environmental compliance detection signal and an auxiliary judgment signal that allows construction personnel to enter the tunnel. It also controls the dust removal and oxygen delivery robot to stop high-power operation and switch to low-power standby mode or execute an automatic return command to safely exit the current work area.

[0065] Step S100: Initialize the task and collect multi-source environmental perception data of the tunnel in real time to construct an environmental state vector. This may include the following sub-steps: Sub-step S101: Establish the initial operation task of the dust removal and oxygen delivery robot, and receive the coordinate information of the target operation area, the distance from the dust removal and oxygen delivery robot to the working face, the preset dust concentration safety threshold, the preset oxygen concentration safety threshold, and the preset harmful gas alarm threshold as parameter constraints.

[0066] The control module receives the operation start command from the remote control terminal via the communication module, responds to the command, and establishes the initial operation task for the dust removal and oxygen delivery robot. The control module allocates a task register area in its internal storage space and loads the relevant constraint parameters of the initial operation task into this register area for subsequent control algorithms to use.

[0067] The received parameter constraints specifically cover the three-dimensional coordinate information of the target working area and the straight-line distance between the initial position of the dust removal and oxygen delivery robot and the working face. The control module plans the initial travel path of the dust removal and oxygen delivery robot based on the three-dimensional coordinate information of the target working area, and uses the planned initial travel path as the navigation basis for the mobile carrier module.

[0068] The received parameter constraints also cover various environmental safety standard limits. The preset dust concentration safety threshold is set to be greater than 0 mg / m³. 3 And less than or equal to 4 mg / m 3The specific values ​​are used to determine whether the dust concentration poses a respiratory hazard to personnel. The preset safe threshold for oxygen concentration is set at a volume percentage lower limit of 19.5% or higher, used to determine whether the local environment has the basic oxygen supply conditions to maintain normal breathing for personnel. The preset alarm threshold for harmful gases is set at specific numerical standards for carbon monoxide concentration greater than or equal to 24 ppm or methane concentration greater than or equal to 0.5%, used to determine whether a dangerous situation has occurred.

[0069] In sub-step S102, the mobile carrier module is driven to advance along the initial path, and during the movement and stopping process, the environmental sensing module periodically collects on-site parameters and the status of the equipment itself according to the set sampling time interval.

[0070] The control module generates chassis drive signals and sends them to the power drive assembly. The power drive assembly draws electrical energy from the power battery pack 5 and distributes it to the travel motor of the wheeled or tracked chassis 1, driving the wheeled or tracked chassis 1 to steadily advance the vehicle body 2 and auxiliary components along the planned initial travel path towards the target work area. During the advancement process, the mobile carrier module feeds back its chassis position coordinates and motor speed information to the control module in real time.

[0071] Throughout the entire operation cycle, including the continuous advancement of the dust removal and oxygen delivery robot and its stationary position in a specific area, the control module triggers the working status of the environmental perception module. The dust concentration sensor, oxygen concentration sensor, hazardous gas sensor, and temperature and humidity sensor within the environmental perception module synchronously initiate data acquisition operations based on preset sampling time intervals, continuously acquiring multi-source environmental perception data within the local space of the tunnel.

[0072] In addition to external environmental data, the control module synchronously collects the device's own status information via the internal status monitoring bus. The control module extracts the remaining power of the power battery pack 5, the remaining water level information from the liquid level sensor inside the water tank 6, and the air pressure difference detection data before and after the filter. Simultaneously, the control module acquires forward image data from the remote-controlled camera 8 and distance measurement data from the distance sensing device to the obstruction in front of the dust removal and oxygen delivery robot.

[0073] In sub-step S103, the control module preprocesses and aligns the collected heterogeneous data with timestamps, and combines them to generate a continuous-time environmental state vector.

[0074] Due to differences in the working principles and hardware sampling frequencies of various sensors in the environmental perception module, the acquired multi-source environmental perception data exhibits heterogeneous characteristics and time series deviations. The control module first performs filtering preprocessing on the acquired raw heterogeneous data to remove invalid data caused by abnormal mutations due to electromagnetic interference or momentary sensor failures, ensuring that the data sequence used in subsequent calculations is smooth and effective.

[0075] After completing the filtering preprocessing, the control module uses an external timing signal or an internal high-precision real-time clock as a reference to perform timestamp alignment on the processed heterogeneous data. The control module employs linear interpolation or a zero-order hold algorithm to uniformly map sensor data at different sampling frequencies to the same discrete time point of the system control cycle, eliminating timing deviations caused by different sampling frequencies of multiple devices.

[0076] The control module extracts various heterogeneous data after timestamp alignment and performs combination and splicing operations according to a pre-defined data structure order. Through data combination operations, a continuous-time environment state vector representing the comprehensive multi-dimensional state characteristics of the current local environment is constructed, and this environment state vector is stored in a cache queue as the data basis for subsequent assessment of the local environment's hazard level. The calculation formula for the continuous-time environment state vector is as follows: ; in: for The environmental state vector at time t is a comprehensive multidimensional state feature of the local environment in which the dust removal and oxygen delivery robot is located at the current time. for The real-time dust concentration value at any given moment represents the current mass concentration of suspended particulate matter in the space, expressed in mg / m³. 3 ; for The real-time oxygen volume concentration value at any given time represents the percentage of oxygen content in the local air, expressed in % (%). for The real-time carbon monoxide concentration value at any given moment represents the concentration of the harmful gas carbon monoxide after the explosion, and the unit is ppm. for The real-time methane concentration value at a given time, representing the concentration of methane gas, is expressed in % (%). for The real-time ambient temperature value at any given moment, which represents the degree of hotness or coldness of the ambient air, is expressed in °C. for The real-time relative humidity value at any given moment represents the degree to which the water vapor in the ambient air is close to saturation, expressed in % (%). for The real-time image clarity at any given moment, which represents a visual visibility index, with a value ranging from 0 to 1; for The distance measured from the current dust removal and oxygen delivery robot to the working face or dust core area at any given time represents the spatial distance between the dust removal and oxygen delivery robot and the target working area, in meters. for The real-time rotation speed of the fog cannon 3 at any given moment represents the current operating speed of the fog cannon 3, in rpm. for The real-time water flow rate of the spray host 4 at any given moment represents the actual amount of dust suppression water output by the system at present, in L / min; for The current real-time remaining liquid level percentage of water tank 6 at any given moment represents the percentage of remaining water in water tank 6 relative to its total capacity, expressed in % (%). for The real-time remaining charge percentage of the current power battery pack 5 at any given time represents the percentage of available remaining electrical energy in the system, expressed as a percentage (%). for The real-time air pressure difference before and after the filter at any given moment represents the degree of resistance change caused by the interception of dust in the filter component, and is measured in Pa.

[0077] Step S200 involves performing a local environmental risk assessment based on the environmental state vector and generating a dust removal and oxygen supply coupling control index. This may include the following sub-steps: In sub-step S201, the control module extracts the feature quantities from the environmental state vector and compares and analyzes them with the set safety threshold.

[0078] After acquiring the environmental state vector over continuous time, the control module initiates an internal local environmental risk assessment program. The control module extracts key environmental features from the environmental state vector, including real-time dust concentration, real-time oxygen volume concentration, real-time carbon monoxide concentration, real-time methane concentration, and real-time foreground image clarity. The control module then performs parallel comparative analysis of these extracted key environmental features with corresponding safety thresholds pre-stored in its internal memory.

[0079] During the comparative analysis, the control module first evaluates dust and oxygen indicators. It calculates the difference between the real-time dust concentration value and the safe dust concentration threshold to determine whether the current dust concentration exceeds the limit and the specific extent of the exceedance. Simultaneously, the control module compares the real-time oxygen volume concentration value with the set oxygen concentration threshold to determine if there is an oxygen deficiency in the local space. The safe dust concentration threshold is set to be greater than 0 mg / m³. 3 And less than or equal to 4 mg / m 3The specific value is specified. The oxygen concentration threshold is set to a volume percentage value that is greater than or equal to 19.5% and less than or equal to 23.5%.

[0080] In addition to dust and oxygen levels, the control module simultaneously performs safety checks on harmful gases and visual visibility. The module compares real-time carbon monoxide and methane concentrations to ensure they do not exceed their respective alarm thresholds. The carbon monoxide alarm threshold is set at 24 ppm, and the methane alarm threshold is set at 0.5%. Furthermore, the control module compares the real-time image clarity of the foreground with a safety threshold set to a value greater than or equal to 0.3 and less than or equal to 0.5. This safety threshold is used to confirm whether the current visual perception conditions meet the requirements for the safe continued operation of the dust removal and oxygen delivery robot.

[0081] Sub-step S202: Based on the comparison results, classify the current location of the dust removal and oxygen delivery robot into at least one risk level state.

[0082] Based on the specific results of the above comparative analysis, the control module executes environmental spatial risk classification logic, dividing the local environment of the current location of the dust removal and oxygen delivery robot into at least one of several risk levels. When the comparison results show that the real-time dust concentration value is greater than the dust concentration safety threshold and the dust concentration decay rate is lower than the preset decay rate threshold, the control module classifies the current area as a Class A dust core area. When the real-time dust concentration value is greater than the dust concentration safety threshold but the dust concentration decay rate is greater than or equal to the preset decay rate threshold, it is classified as a Class B dust diffusion area.

[0083] Based on the gas concentration comparison results, the control module classifies the corresponding gas risk level. When the real-time oxygen volume concentration is less than the set oxygen concentration threshold, the control module classifies the current area as a Class C oxygen deficiency risk zone. When the real-time carbon monoxide concentration is greater than or equal to the carbon monoxide alarm threshold, or the real-time methane concentration is greater than or equal to the methane alarm threshold, the control module classifies the current area as a Class D hazardous gas risk zone. The determination of hazardous gas risk zone status has higher priority than other environmental risk statuses; once this status is triggered, the control module will immediately block the switching of other normal operating modes.

[0084] The control module also classifies specific states based on the equipment's own status and environmental improvement. When the real-time dust concentration is less than or equal to the dust concentration safety threshold and the concentrations of all gases are within safe ranges, the control module determines it to be a Class E safety retesting zone. When the control module extracts the real-time remaining liquid level ratio of the current water tank 6 from the environmental state vector to be less than 10%, or the real-time remaining charge ratio of the current power battery pack 5 to be less than 15%, or the real-time air pressure difference before and after the current filter to be greater than or equal to 200Pa, the control module determines the current area to be a Class F equipment confined zone.

[0085] In sub-step S203, based on the current environmental conditions of dust exceeding limits, oxygen deficiency, harmful gases, and visibility, the control algorithm generates a dust removal and oxygen supply coupling control index to guide the intensity of subsequent execution.

[0086] After classifying the risk levels, the algorithm unit within the control module generates a dust removal and oxygen supply coupling control index to guide the operation intensity of subsequent dust removal and oxygen supply actuators, based on the current environmental conditions, including dust exceedance, oxygen deficiency, harmful gas accumulation, and obstructed visibility. The index is generated using a multi-objective weighted function, quantifying multi-dimensional environmental risks into a single scalar value to facilitate global control decisions.

[0087] To accurately calculate the aforementioned dust removal and oxygen delivery coupling control index, the control module normalizes the deviations between the extracted feature quantities and each set threshold, and then multiplies them by their respective penalty weight coefficients. The control module assigns different weights to penalties for excessive dust, oxygen deficiency, excessive harmful gases, and reduced visibility, ensuring that in complex tunnel environments with multiple intertwined risks, the dust removal and oxygen delivery robot prioritizes responses to environmental factors that pose the greatest threat to the lives of construction workers.

[0088] The control module linearly superimposes the various environmental risk assessment values ​​after the above processing to generate the final dust removal and oxygen supply coupling control index. The calculation formula for the continuous-time dust removal and oxygen supply coupling control index is as follows: ; in: for The dust removal and oxygen supply coupling control index at any given time represents the overall urgency of environmental improvement through external intervention in the current local environment; for The real-time dust concentration value at any given moment represents the current mass concentration of suspended particulate matter in the space, expressed in mg / m³. 3 ; The dust concentration safety threshold represents the highest dust concentration standard that allows personnel to work safely. Its value ranges from greater than 0 to less than or equal to 4, and the unit is mg / m³. 3 ; for The real-time oxygen volume concentration value at any given time represents the percentage of oxygen content in the local air, expressed in % (%). A threshold value is set for oxygen concentration, representing the lower limit of safe oxygen concentration for maintaining normal breathing in personnel. The value ranges from greater than or equal to 19.5% to less than or equal to 23.5%, and the unit is %. for The real-time hazardous gas risk coefficient at any given moment represents a combined penalty term for deviations of carbon monoxide and methane concentrations from safety standards, with a value ranging from 0 to 1. for The real-time visibility risk coefficient represents the assessment value of the operational hazards caused by obstructed vision due to smoke and dust, and the value ranges from 0 to 1. The dust exceeding limit penalty weighting coefficient represents the proportion of the importance of dust reduction demand in the overall control decision, and its value ranges from 0 to 1. The hypoxia penalty weighting coefficient represents the proportion of the importance of oxygen delivery demand in the overall control decision, and its value ranges from 0 to 1. The penalty weighting coefficient for hazardous gases represents the absolute priority of gas and explosion prevention in control decisions, with a value ranging from 0 to 1. The visibility penalty weighting coefficient represents the weight of visual clarity in regulatory decisions, and its value ranges from 0 to 1.

[0089] The sum of the weighting coefficients for dust exceeding limits, oxygen deficiency, harmful gas, and visibility is equal to one.

[0090] Step S300: Adaptively calculate the target spray flow rate and target fan speed based on environmental coupling constraints. This may include the following sub-steps: Step S301 calculates the target spray flow rate based on comprehensive environmental factors. The control module continuously acquires the multi-dimensional environmental state vector data generated in the aforementioned environmental perception process, as well as various preset baseline control parameters. The control module extracts in real-time dust concentration, relative humidity, distance between the robot and the dust core area, and real-time dust attenuation rate during the environmental purification process from the environmental state vector. The control module aggregates the above heterogeneous data to comprehensively assess the degree of dust pollution diffusion and the environmental moisture saturation state in the current local space. Based on this, the control module breaks through the traditional rigid control limitation of passively adjusting water output solely based on the degree of dust exceeding the limit, and instead calculates the precisely matched spray flow rate by comprehensively considering the current dust concentration, environmental humidity, distance, and actual purification effect.

[0091] The control module calculates the target spray flow rate based on multi-dimensional environmental measurement parameters, following a built-in mathematical analytical relationship. The control module assigns a control strategy according to preset weights, using excessive dust concentration as a positive demand increment, spatial distance as a long-distance attenuation compensation increment, ambient relative humidity as a negative suppression factor for water accumulation prevention, and the dust concentration decrease rate as a negative suppression factor for overspray prevention. Linear scaling and difference accumulation operations are performed on all the above input variables. After multi-parameter superposition processing, the control module completes the benchmark correction of the overall control water volume by introducing the equipment's basic spray flow rate. The calculated temporary water volume assessment parameters are then input to the limiting processing node for filtering and verification to ensure that the final spray control command issued to the execution port is strictly limited to the flow rate boundary for safe operation of the water pump equipment.

[0092] The specific formula for calculating the target spray flow rate is: ; in: For a moment The calculated target spray flow rate represents the precise dust-suppressing water volume that the system expects to output, in units of L / min; The base spray flow rate represents the default water volume used to maintain the most basic water mist barrier, and is measured in L / min. This is the concentration adjustment coefficient, which represents the weighting effect of the dust concentration difference on the calculation of the water spray volume. For a moment The real-time dust concentration value represents the current mass concentration of suspended particulate matter in the space, with units of mg / m³. 3 ; The dust concentration safety threshold represents the highest dust concentration standard that allows personnel to work safely, and is expressed in mg / m³.3 The dust concentration safety threshold is a pre-set fixed constraint value; This is the distance adjustment coefficient, which represents the weighted influence coefficient of spatial distance measurement data on the increase or decrease in water volume; The distance measured from the robot's current position to the working face or dust core area is in meters (m). This is the humidity suppression coefficient, which represents the reverse suppression weighting coefficient of the environmental humidity parameter on the output water volume; For a moment The real-time relative humidity value represents the percentage of water vapor in the current air, expressed in % (%). The attenuation rate suppression coefficient represents the negative feedback suppression weighting coefficient of the dust purification descent rate on the increased water spray demand. For a moment The real-time dust decay rate represents the rate at which dust concentration decreases over time, expressed in mg / (m³). 3 ·s); The minimum allowable effective spray flow rate of the equipment, expressed in L / min; The maximum flow rate limit is set in L / min to avoid water accumulation and pump overheating. This is a limiting function, which means that the internal linear calculation results are forcibly constrained within a safe operating range between the minimum effective spray flow rate and the maximum limiting flow rate.

[0093] Step S302 calculates the target fan speed based on gas composition and dust disturbance prevention constraints. The control module synchronously retrieves environmental measurement parameters directly related to gas safety attributes from the multi-source environmental state vector and establishes a deep mathematical coupling constraint relationship between the tunnel's local directional oxygen supply volume and the risk of secondary dust diffusion. The control module continuously extracts real-time oxygen volume concentration values, real-time hazardous gas risk assessment parameters, and real-time dust secondary diffusion risk measurement parameters during the working period. The control module comprehensively assesses the severity of oxygen deficiency and the degree of danger of excessive accumulation of toxic and hazardous gases within the current confined working space, and uses real-time dust secondary diffusion risk measurement parameters to numerically assess and quantify the environmental degradation hazard of excessive air supply causing bottom-sedimentary dust to re-entrain.

[0094] The control module uses a baseline fan speed as a safety net to ensure minimal airflow. Building upon this, the module generates a demand for forced dilution and directional airflow based on the absolute deficit of ambient oxygen concentration (deviating from the lower limit of safety standards) and the overall risk of exceeding limits for various harmful gases. To strictly prevent the excessive increase in airflow from causing large-scale secondary diffusion of fine dust particles that haven't yet been fully enveloped and settled by water mist, the control module employs a built-in dust disturbance suppression calculation to rigorously weaken and penalize the aforementioned cumulative positive airflow demand. The initial fan speed calculated internally by the control module is filtered and intercepted by safety limits at the upper and lower speed limits, resulting in a smooth and controllable speed drive command.

[0095] The specific formula for calculating the target fan speed is: ; in: For a moment The calculated target fan speed represents the control target speed that the fog cannon 3 needs to respond to, and the unit is rpm; The base fan speed represents the reference speed for maintaining basic local airflow in the tunnel, and its unit is rpm; This is the oxygen deficit adjustment coefficient, which represents the weighting factor of the degree of oxygen deficiency deterioration on the demand for air volume replenishment. A threshold is set for oxygen concentration, which represents the lower limit of safe oxygen concentration for maintaining normal breathing operations for personnel entering the site. The unit is %, and this oxygen concentration threshold is a preset fixed reference value. For a moment The real-time oxygen volume concentration value represents the percentage of absolute oxygen content in the local air, expressed in % %. The hazardous gas dilution adjustment coefficient represents the weighting factor of the accumulation level of toxic and explosive gases on the demand for ventilation, dilution, and hazard removal. For a moment The real-time hazardous gas risk coefficient represents the combined penalty term for the deviation of carbon monoxide and methane concentrations in the blasting space from safety standards, with a value range of 0 to 1. The dust disturbance suppression coefficient represents the control weight coefficient for forcibly suppressing the output of large air volume when the overall dust concentration is extremely high and the dust has not settled sufficiently. For a moment The real-time dust secondary diffusion risk coefficient represents the probability of dust risk calculated based on the current proportion of dust that has not settled and the spatial wind direction distribution model. Its value ranges from 0 to 1. The minimum safe operating speed for the fan to maintain anti-surge and anti-stall operation, measured in rpm; The maximum full-load speed that the motor hardware allows for continuous long-term operation, expressed in rpm; For limiting functions, it means that the calculated speed command value after comprehensive superposition and reduction is forcibly constrained within the safe fluid operation hardware range between the minimum safe speed and the maximum full-load speed.

[0096] Step S303, considering the robot's own underlying hardware resource constraints, performs power reduction protection and secondary compliance verification on the initial system calculation control commands. The control module collects real-time information on the equipment's main energy inventory status and consumable material reserves from the chassis power management node via the vehicle communication data bus. The control module extracts the real-time remaining power percentage of the power battery pack 5, calculated from the power supply voltage monitoring. Simultaneously, it acquires the real-time remaining liquid level ratio of the current water tank 6 as measured by the liquid level sensor array. The control module incorporates the device's current absolute power and water capacity as independent factors into the upper-level core environmental control closed-loop control logic matrix. It performs an anti-overdraft check on the target spray flow rate output in step S301 and the target fan speed output in step S302 before the actual hardware command execution. This represents the current real-time remaining charge percentage of the power battery pack 5, indicating the availability of power reserves in the vehicle's power system, expressed as a percentage. This represents the current real-time remaining liquid level ratio of water tank 6, indicating the remaining level of purified water resources in the internal water storage container of the equipment, expressed in units of _____.

[0097] During the initial system setup phase, the control module sets preset low power tolerance and low water tolerance warning thresholds as boundary parameters for determining resource insufficiency. In each decision calculation cycle, the control module compares the extracted real-time resource inventory monitoring data with the aforementioned safety thresholds. When the logic determines that the real-time remaining power ratio is lower than the low power tolerance warning threshold, or that the real-time remaining liquid level ratio is lower than the low water tolerance warning threshold, the control module immediately invokes the resource scarcity degradation program. Based on a pre-set proportional load reduction coefficient, it numerically compresses and reduces the generated target spray flow rate and target fan speed. This resource constraint hardware load reduction mechanism triggered by the control module ensures that the hardware execution intensity of the equipment performing continuous environmental intervention operations is always strictly matched with its remaining continuous power support capacity, preventing unexpected power outages and system shutdowns in high-dust, high-risk areas due to rapid depletion of critical operational resources.

[0098] After all numerical analysis calculations, cross-module parameter constraints, and hardware resource constraint verification and protection processes have been successfully completed and compared, the control module converts the final corrected and confirmed absolute control values ​​of the water pump flow rate and the absolute control values ​​of the fog cannon fan speed into standard level pulse width modulation digital drive signals. The control module stably transmits the electronic control drive digital signals to the bottom water circuit execution electronic control valve node and the fan speed control frequency converter drive node via the internal hard wiring matrix. The spray host 4 strictly adjusts the outlet pressure drop and output flow rate of the internal micro high-pressure water pump according to the received final corrected water volume command, thereby achieving a precise matching of the mobile water volume release to meet the environmental dust suppression requirements. At the same time, the fog cannon 3 precisely and smoothly adjusts the angular velocity of the internal fan drive rotor according to the final issued speed command to stably maintain the target guiding ventilation volume required by the system assessment. At this point, the control module has completely completed the automated adaptive drive control process from the perception and recognition of multi-dimensional state variables of the local environment in the tunnel to the closed-loop execution of the final control action at the bottom level of the main equipment.

[0099] Step S400: Based on the environmental risk level and the dust removal and oxygen delivery coupling control index, automatically switch the operating mode of the dust removal and oxygen delivery robot and execute the linkage control. This may include the following sub-steps: In sub-step S401, the core control module matches the corresponding operation mode library based on the environmental risk level status determined in the previous steps and the preset value range of the dust removal and oxygen supply coupling control index.

[0100] Sub-step S402 triggers a dust removal-oriented control strategy for high-concentration dust environments. This control strategy includes a water mist barrier mode and a near-face dust suppression mode. In water mist barrier mode, the core control module controls the spray host 4 and nozzle group 9 to perform lateral dust interception operations during the initial stage of blasting. In near-face dust suppression mode, the core control module controls the spray host 4 and nozzle group 9 to increase the spray flow rate to cover the core high-concentration dust area, and controls the wheeled or tracked chassis 1 to reduce its travel speed.

[0101] Sub-step S403 triggers a coupled control strategy for dust removal and oxygen supply in response to the accumulation of harmful gases and the complex environment. This coupled control strategy includes an oxygen supply dilution mode, a collaborative purification mode, a low-disturbance inspection mode, and a safety exit mode. In the oxygen supply dilution mode, the core control module controls the mist cannon 3 and the deflector 10 to provide an oxygen flow that limits the fan speed to prevent dust re-entrainment. In the collaborative purification mode, the core control module first controls the spray host 4 and the nozzle group 9 to spray water mist to establish a dust collection zone, and then controls the mist cannon 3 and the deflector 10 to deliver air to form a directional penetrating airflow. In the low-disturbance inspection mode, the core control module reduces the spray flow rate of the spray host 4 and the fan speed of the mist cannon 3, and activates the remote camera 8 to collect images of the scene ahead. In the safety exit mode, in conjunction with abnormal situations such as the real-time air pressure difference before and after the filter exceeding a preset pressure difference threshold or the gas concentration exceeding a preset alarm threshold, the core control module controls the wheeled or tracked chassis 1 to stop advancing or automatically return.

[0102] Step S500 involves performing a dynamic closed-loop feedback evaluation based on the dynamic rate of environmental improvement, and adjusting control parameters in real time until the environmental state meets the standards and exiting the process. Step S500 may specifically include the following sub-steps: In sub-step S501, at the end of each control cycle, the core control module extracts feedback indicators and calculates the dynamic rate of environmental improvement based on the changes in dust concentration and oxygen concentration between the two consecutive time points. The specific calculation formula is as follows: ; ; in: The real-time dust decay rate represents the rate at which the dust concentration in the local environment decreases over time. A positive real-time dust decay rate indicates that the dust concentration is decreasing, while a negative rate indicates that the dust concentration is increasing and deteriorating. The unit is mg / (m³). 3 ·s); This is the real-time dust concentration value at the previous moment, representing the mass concentration of suspended particulate matter in the space at the previous moment, with units of mg / m³. 3 ; This is the real-time dust concentration value at the current moment, representing the mass concentration of suspended particulate matter in the space at that moment, with units of mg / m³. 3 ; The control cycle time interval step size represents the time span between two sampling and control command issuance by the control system, measured in seconds. The real-time oxygen recovery rate is the rate at which the oxygen volume concentration in the local environment increases over time. A positive real-time oxygen recovery rate indicates that the ambient oxygen is in an improved and replenished state, and the unit is % / s. This is the real-time oxygen volume concentration value at the current moment, representing the percentage of oxygen content in the local air at that moment, expressed as a percentage (%). The real-time oxygen volume concentration value at the previous moment represents the percentage of oxygen content in the local air at that moment, expressed in units of .

[0103] In sub-step S502, the core control module performs comparison, evaluation, and feedback actions based on the calculated dynamic rate. When the real-time dust decay rate is lower than the preset expected threshold for the decay rate, it is determined that the water mist envelopment effect has not met the requirements. The core control module triggers a feedforward compensation control command, controlling the spray host 4 to increase the spray flow rate or controlling the wheeled or tracked chassis 1 to extend its residence time in the current area. When the real-time oxygen recovery rate is lower than the preset expected threshold for the recovery rate, the core control module corrects the fan speed of the fog cannon 3 and guides the wheeled or tracked chassis 1 to fine-tune the orientation of the vehicle body 2, so that the air delivery direction is closer to the low oxygen concentration area. When the real-time dust decay rate reaches the preset dust decay threshold but the water consumption rate of the water tank 6 exceeds the preset consumption threshold, the core control module triggers a water-saving correction control command, reversing the spray flow rate of the spray host 4.

[0104] In sub-step S503, the core control module performs compliance judgment and safe exit operation. When the real-time dust concentration value, real-time oxygen volume concentration value, and harmful gas concentration value in the environmental state vector all reach the corresponding preset safety threshold range, the core control module outputs an environmental compliance signal or an auxiliary passage signal allowing personnel to enter the work area, and controls the wheeled or tracked chassis 1 to drive the vehicle body 2 to the standby point or perform an automatic return operation.

[0105] Specific application examples: This embodiment uses the tunnel face (working face) after blasting in a high-speed railway tunnel as the target working area. During the system initialization phase (time... The control module receives and sets various parameter constraints, including the dust concentration safety threshold. =4mg / m 3Oxygen concentration setting threshold =19.5%, and the carbon monoxide alarm threshold is set to 24 ppm. Simultaneously, the preset control weights and coefficient parameters are: dust exceeding limit penalty weight coefficient. =0.4, hypoxia penalty weighting coefficient =0.3, harmful gas penalty weighting coefficient =0.2, visibility penalty weighting coefficient =0.1; Basic spray flow rate =15L / min, concentration adjustment coefficient =0.5, distance adjustment coefficient =0.2, humidity suppression coefficient =0.1, attenuation rate suppression coefficient =5; Basic fan speed =1000rpm, oxygen deficit adjustment coefficient =200, harmful gas dilution adjustment coefficient =500, dust disturbance suppression coefficient =300.

[0106] When the dust removal and oxygen delivery robot advances to a distance of 20 meters from the working face (at a certain time) When the environmental perception module collects and constructs the current environmental state vector, the following key data is extracted: real-time dust concentration value. =44mg / m 3 Real-time oxygen volume concentration value =17.5%, distance measured =20m, real-time ambient relative humidity value =70%. The real-time hazardous gas risk coefficient is calculated as follows: =0.5 (at this time, the carbon monoxide concentration is approximately 30 ppm), real-time visibility risk factor. =0.8, and since it has just arrived in the area, the real-time dust decay rate is... =0mg / (m 3 ·s), system assessment of real-time dust secondary diffusion risk coefficient =0.6.

[0107] The core control module first calculates the time based on the above data. Dust removal and oxygen supply coupling control index Through the formula Perform composite calculations to obtain ≈4.211. Because The value is much higher than the set high-risk trigger threshold, and the dust concentration is seriously exceeded and accompanied by oxygen deficiency. The system determines that the current state is an overlap of the hazardous gas risk zone and the dust core zone, and automatically switches to the collaborative purification mode.

[0108] Subsequently, the control module adaptively calculates the target execution parameters based on environmental coupling constraints. For the target spray flow rate, the parameters are substituted into the formula. Calculations yielded =32L / min. Substituting this into the formula for the target fan speed... Calculations yielded =1470rpm. Based on underlying hardware resource constraints, the current remaining water volume in the tank is... =90%, battery capacity =85%, all within the safety margin; the system does not need to reduce its frequency, and can proceed directly. =32L / min and =1470rpm output drive command.

[0109] After controlling the time interval step size =After 10 seconds of high-intensity collaborative purification, the time is reached At this time, the sensor data showed that the real-time dust concentration value had decreased to =24mg / m 3 The real-time oxygen volume concentration value rebounded to =18.5%, humidity rises to =75%. The system performs dynamic closed-loop feedback evaluation to calculate the current real-time dust decay rate. =2mg / (m 3 ·s). At this point, the control module updates the target spray flow rate again: =11.5 L / min. This calculation result intuitively demonstrates the negative feedback suppression mechanism of the present invention: due to the dust decay rate in the previous cycle... With a temperature as high as 2 and increased ambient humidity, the system adaptively reduced the water spray rate from 32L / min to 11.5L / min to prevent secondary mud formation of dust and water accumulation on the tunnel floor, thus achieving an optimal balance between dust suppression and resource conservation.

[0110] Experimental verification and effect comparison: Comparative experiments were conducted on a real tunnel construction simulation platform. The control group used a traditional fixed threshold control method (i.e., as long as dust or oxygen deficiency exceeded the standard, the equipment continuously outputs water mist and airflow at its maximum fixed power until the standard is met and then shuts down). (Refer to...) Figure 4 , Figure 4 These are comparative curves of dust concentration changes over time under two different control methods extracted in this experiment; (Refer to...) Figure 5 , Figure 5 This is a comparison curve of the cumulative water consumption of the equipment over time under two different control methods extracted in this experiment.

[0111] Combination Figure 4Analysis shows that in the initial stage after blasting (within 0 to 10 minutes), both methods resulted in a concentration of approximately 46 mg / m³. 3 Dust settling begins at a high concentration starting point. However, the adaptive control curve of this invention, marked with a triangle, exhibits an extremely high real-time dust decay rate, rapidly decreasing to approximately 12 mg / m³. 3 The decrease in concentration was steeper than that of the conventional method with circular markers. When the operation was progressing to approximately 25 to 30 minutes, the control curve of this invention smoothly transitioned and successfully decreased to 4 mg / m³. 3 The dust concentration remained below the dashed (dotted) safety threshold line and remained stable within the safe range for the subsequent time. Conversely, traditional fixed threshold control methods, lacking deep coupling and matching with multi-dimensional environmental characteristic parameters, exhibited a relatively slow dust decrease rate and a significant tailing effect; after 60 minutes of continuous operation, the dust concentration remained at 5 mg / m³. 3 The above results failed to exceed the dust safety threshold. This demonstrates the advantages of this invention in dust removal efficiency and speed of achieving the required standards.

[0112] Simultaneously combined Figure 5 Based on the data, the adaptive control method of this invention demonstrates superior economic efficiency in operational resource scheduling. The traditional method, marked with a star, exhibits a completely linear and rapid increase in cumulative water consumption due to its indiscriminate, fixed high-power output. By the end of a 60-minute operation, the total water consumption reaches as high as 1800L. This extensive method easily leads to severe water accumulation on-site and premature emptying of the equipment's water tank. In contrast, the curve of this invention, marked with a square, outputs a larger volume of water in the early stages (approximately the first 10 minutes) to quickly suppress high-concentration dust. However, as the dust concentration rapidly decreases, the negative suppression calculation and feedback mechanism within the system begins to function, and the spray flow adaptively weakens, resulting in a significant slowdown in the slope of the cumulative water consumption curve in the later stages. At the end of the 60-minute operation cycle, the cumulative water consumption of this invention is only slightly over 900L. Experimental data clearly show that the method of the present invention reduces the total water consumption by about 50% (from 1800L to about 900L) while achieving environmental compliance earlier, effectively preventing the risk of secondary mud formation of dust, and significantly extending the single-cycle unreplenished operation capability of the dust removal and oxygen delivery robot.

Claims

1. An adaptive control method for a dust removal and oxygen delivery robot based on environmental perception, characterized in that, Includes the following steps: Initialize the task and collect multi-source environmental perception data of the tunnel in real time to construct an environmental state vector; Based on the environmental state vector, a local environmental risk assessment is performed, the results of the local environmental risk assessment are divided into specific risk level states, and a dust removal and oxygen supply coupling control index is generated. The environmental coupling constraints are obtained. Based on the dust removal and oxygen supply coupling control index and the environmental coupling constraints, the target spray flow rate and the target fan speed are adaptively calculated, and a secondary power reduction protection correction is performed in combination with the underlying hardware resource constraints. Based on the risk level and the dust removal and oxygen delivery coupling control index, the operation mode of the dust removal and oxygen delivery robot is matched and switched, and multi-target linkage and coordination control is performed in combination with the corrected target spray flow rate and the target fan speed. During the execution of the multi-objective linkage coordinated control, dynamic closed-loop feedback evaluation is performed based on the dynamic rate of environmental improvement, and control parameters are corrected in real time until the environmental state meets the standard and exits safely.

2. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, The specific steps for initializing the task and constructing the environmental state vector by collecting multi-source environmental perception data of the tunnel in real time are as follows: The initial operation task of the dust removal and oxygen delivery robot is established, and the coordinate information of the target operation area, the distance from the dust removal and oxygen delivery robot to the working face, the preset dust concentration safety threshold, the preset oxygen concentration safety threshold, and the preset harmful gas alarm threshold are received as parameter constraints. The driving mobile carrier module advances towards the target work area according to the initial path, and during each stage of the dust removal and oxygen delivery robot's movement and stop, the environmental perception module continuously collects the multi-source environmental perception data inside the tunnel. The collected heterogeneous data is preprocessed and timestamp aligned to eliminate the timing deviation caused by different sampling frequencies of multiple devices. The aligned heterogeneous data is then combined to construct a continuous-time environmental state vector that represents the comprehensive multi-dimensional state characteristics of the local environment.

3. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, The specific steps for conducting a local environmental risk assessment based on the aforementioned environmental state vector and classifying the local environmental risk assessment results into specific risk level states are as follows: Key environmental features are extracted from the environmental state vector, and the extracted key environmental features are compared and analyzed in parallel with preset dust concentration safety threshold, preset oxygen concentration safety threshold and preset harmful gas alarm threshold stored in the internal memory. Based on the specific results of the comparative analysis, the environmental space risk classification logic is executed to divide the local environment of the current location of the dust removal and oxygen delivery robot into specific risk level states. The risk level states include at least one of the following: dust core area, dust diffusion area, oxygen deficiency risk area, harmful gas risk area, safety retesting area, and equipment confined area.

4. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 3, characterized in that, The specific steps for generating the dust removal and oxygen supply coupling control index are as follows: The deviations of the extracted key environmental features from the corresponding preset dust concentration safety threshold, preset oxygen concentration safety threshold, or preset hazardous gas alarm threshold are normalized and multiplied by the penalty weight coefficient corresponding to each key environmental feature. The environmental risk assessment values ​​of the various deviations after processing are linearly superimposed to generate the final dust removal and oxygen supply coupling control index. The dust removal and oxygen supply coupling control index is used to quantitatively define the priority and response magnitude of the external intervention actions performed by the subsequent dust removal and oxygen supply execution modules.

5. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, The specific steps for obtaining the environmental coupling constraints and adaptively calculating the target spray flow rate based on the dust removal and oxygen delivery coupling control index and the environmental coupling constraints are as follows: The real-time dust concentration value, real-time relative humidity value, distance between the dust removal and oxygen delivery robot and the dust core area, and real-time dust attenuation rate during the environmental purification process are extracted from the environmental state vector as the environmental coupling constraint conditions. According to the preset weight allocation control strategy, the dust concentration exceeding the limit is used as the positive demand increment, the ranging distance is used as the long-distance attenuation compensation increment, the real-time ambient relative humidity value is used as the negative suppression amount for water accumulation prevention, and the real-time dust attenuation rate is used as the negative suppression amount for excessive spraying prevention. Linear scaling and difference accumulation operations are performed on all the above input variables. The baseline correction of the overall control water volume is completed by introducing a basic spray flow rate, and the calculated temporary water volume assessment parameters are input into the limiting treatment node for filtration verification calculation to obtain the target spray flow rate.

6. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, Based on the dust removal and oxygen supply coupling control index and the environmental coupling constraints, the specific steps for adaptively calculating the target fan speed are as follows: The real-time oxygen volume concentration, real-time hazardous gas risk assessment parameters, and real-time dust secondary diffusion risk measurement parameters during the working period are extracted as the environmental coupling constraints. By introducing the basic fan speed as a reference benchmark for ensuring the system maintains a minimum air circulation state, the system generates positive large-volume forced dilution and directional air supply demand based on the absolute deficit value of the ambient oxygen concentration deviating from the lower limit of the safety standard and the overall risk value of exceeding the limit of various harmful gases. The built-in dust disturbance suppression calculation item performs strict numerical reduction and mandatory safety penalty on the positive large air volume forced dilution and directional air supply requirements, and generates the target fan speed after the safety limit filtering and interception process of the upper and lower speed limits.

7. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, The specific steps for secondary power reduction protection correction, combined with underlying hardware resource constraints, are as follows: Extract the real-time remaining power ratio of the current power battery pack and the real-time remaining liquid level ratio of the current water tank as the underlying hardware resource constraints; When the logic determines that the real-time remaining power ratio is lower than the preset low power tolerance warning threshold, or determines that the real-time remaining liquid level ratio is lower than the preset low water volume tolerance warning threshold, the resource scarcity degradation operation program is invoked. The target spray flow rate and target fan speed are numerically compressed and reduced based on a pre-set proportional load reduction coefficient to complete the secondary power reduction protection correction. The corrected target spray flow rate and target fan speed commands are then sent to the corresponding dust removal execution module and oxygen delivery execution module.

8. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 4, characterized in that, Based on the aforementioned risk level and the aforementioned dust removal and oxygen delivery coupling control index, the specific steps for matching and switching the operating mode of the dust removal and oxygen delivery robot, and combining the corrected target spray flow rate and target fan speed to perform multi-target linkage coordinated control are as follows: Based on the determined risk level and the calculated range of the dust removal and oxygen delivery coupling control index, the corresponding operation mode execution instruction is called from the preset operation mode library to match and switch the current operation mode of the dust removal and oxygen delivery robot. The dust removal execution module responds to the corrected target spray flow command and controls the spray host and nozzle group to output micron-level water mist. The oxygen delivery execution module responds to the corrected target fan speed command and controls the fog cannon fan to deliver air to the local area to be purified in a directional manner in conjunction with the guide hood. A speed adjustment command is sent to the mobile carrier module to control the power drive component to distribute electrical energy, drive the wheeled or tracked chassis and vehicle body to adjust the overall travel speed, so that the dust removal and oxygen delivery robot can adaptively perform deceleration, stop or advance actions in different risk areas, and realize the multi-target linkage and coordination control.

9. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 1, characterized in that, The specific steps for dynamic closed-loop feedback evaluation based on the dynamic rate of environmental improvement are as follows: Extract the dust concentration and oxygen concentration values ​​at two consecutive control cycles; Calculate the actual real-time dust decay rate in the environment, where the real-time dust decay rate represents the rate at which the dust concentration in the local environment decreases over time. Calculate the real-time oxygen recovery rate at the current moment, where the real-time oxygen recovery rate represents the rate at which the oxygen volume concentration in the local environment increases over time; The real-time dust decay rate and the real-time oxygen recovery rate are used as the dynamic rate of environmental improvement for dynamic closed-loop feedback evaluation.

10. The adaptive control method for a dust removal and oxygen delivery robot based on environmental perception according to claim 9, characterized in that, The specific steps for continuously correcting control parameters until the environmental conditions meet the standards for safe exit are as follows: When the dynamic closed-loop feedback evaluation result indicates that the real-time dust decay rate is lower than the preset concentration decrease expectation value, the feedforward compensation adjustment mechanism is triggered to generate a correction instruction to increase the target spray flow or extend the stationary dwell time of the dust removal and oxygen delivery robot. When the real-time oxygen recovery rate is lower than the preset gas recovery expectation value, a correction command is generated to increase the target fan speed or to fine-tune the driving direction of the wheeled or tracked chassis of the dust removal and oxygen delivery robot. When the real-time dust decay rate reaches the predetermined standard and the rate of liquid level drop inside the water tank of the dust removal and oxygen delivery robot exceeds the preset liquid level consumption threshold, a water-saving correction control command is triggered to reverse and reduce the target spray flow rate. The above parameter closed-loop correction process is executed repeatedly until all key environmental feature parameters in the environmental state vector are restored to the corresponding preset dust concentration safety threshold, preset oxygen concentration safety threshold, and preset harmful gas alarm threshold range. The system outputs an environmental compliance detection signal and controls the dust removal and oxygen delivery robot to stop high-power operation and execute an automatic return command to safely exit the current work area.

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