Safety protection method and system for feeding and discharging of visual guidance robot

Abstract

The invention relates to a safety protection method and system for feeding and discharging of a visual guidance robot, and the method comprises the steps: collecting sensor detection data in a preset time period in the operation process of the robot, judging the behavior state of a person in an operation region based on the sensor detection data, and obtaining a judgment result, the operation parameters of the robot are adjusted according to the judgment result; when an operation request that a person tries to open a fence door lock is detected, whether the robot is in a non-running state or not is judged, authority authentication is conducted on an operator, and door lock unlocking control is executed under the condition that a preset condition is met; triggering signals of the scram button are monitored, when any scram signal is detected, a global interruption control instruction is immediately sent out, and the current task of the robot is terminated; and recording various sensor trigger events and corresponding control responses, and generating a security event log. The method has the effect of improving the man-machine cooperation safety in the loading and unloading scene of the robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation and robot control, and in particular to a safety protection method and system for vision-guided robot feeding and discharging. BACKGROUND

[0002] Currently, industrial robots are widely used in manufacturing enterprises, especially in automatic feeding and discharging scenarios. With the increasing demand for flexible production and human-robot collaboration, robots are increasingly deployed in work environments that share space with human operators. In such co-domain operation mode, personnel safety protection is a key problem in system design and operation management.

[0003] Existing safety protection solutions usually use independent devices such as gratings, radars, physical fences, and emergency stop buttons to monitor and control the work area. For example, gratings are used to form a peripheral warning boundary, which triggers the robot to stop running when the light beam is blocked; radar sensors are used to detect personnel approaching and provide dynamic position monitoring; emergency stop buttons are used as an emergency means triggered by human to terminate robot operation; the fence entrance may be equipped with a lock to prevent personnel from mistakenly entering the dangerous area.

[0004] The existing technical solutions in the above have the following defects: the existing safety control mechanism is mostly single-point response, there is a lack of linkage strategy between sensors, the judgment logic is relatively static, and it often relies on fixed thresholds or manual operation, which is difficult to adapt to complex personnel behavior changes in real time, and therefore there is room for improvement. SUMMARY

[0005] In order to improve the safety of human-robot collaboration in robot feeding and discharging scenarios, the present application provides a safety protection method and system for vision-guided robot feeding and discharging.

[0006] The above invention purpose of the present application is realized by the following technical solutions: A safety protection method for vision-guided robot feeding and discharging, the safety protection method for vision-guided robot feeding and discharging comprising: constructing a digital space model of a robot work area, registering fence boundary, sensor deployment position information and safety area constraint conditions; during the operation of the robot, collecting sensor detection data within a preset time period, and based on the sensor detection data, judging the behavior state of personnel in the work area to obtain a judgment result, and adjusting the robot operation parameters according to the judgment result, the sensor detection data including grating sensor detection data and radar sensor detection data; When detecting an operation request of a person trying to open the fence door lock, it is judged whether the robot is in a non-running state, and the authority of the operator is authenticated, and the door lock opening control is executed under the condition that the preset condition is met; The trigger signal of the emergency stop button is listened to, and when any emergency stop signal is detected, a global interrupt control instruction is immediately issued to terminate the current task of the robot; Record various sensor trigger events and corresponding control responses, generate a safety event log, and dynamically adjust the robot operating parameters and risk assessment strategy based on the safety event log.

[0007] By adopting the above technical solutions, by constructing a digital space model of the robot working area, registering the fence boundary, sensor deployment position information and safety area constraint conditions, the spatial closed management and multi-source perception layout of the robot operating environment can be realized, thereby providing accurate and structured space basis for subsequent risk judgment and safety control strategy. By collecting sensor detection data during robot operation and judging the behavior state of the person, the robot operating parameters are dynamically adjusted according to the judgment result, which can realize the timely identification and response of the risk of personnel intrusion, thereby effectively reducing the collision risk caused by the approach or entry of personnel into the dangerous area. By performing state judgment and authority authentication when detecting an operation request of a person trying to open the fence door lock, it can be ensured that the robot is in a non-running state and the operator has legal authority, thereby preventing security risks caused by accidental operation or unauthorized access. By listening to the trigger signal of the emergency stop button and issuing a global interrupt instruction, the robot task can be quickly terminated in an emergency, thereby realizing timely control and safety protection of sudden risks. By recording various sensor trigger events and corresponding control responses, generating a safety event log, and dynamically adjusting the robot operating parameters and risk assessment strategy based on the safety event log, the adaptive optimization of the safety strategy can be realized, thereby enhancing the dynamic protection ability and continuous improvement ability of the system for different risk situations.

[0008] The present application can be further configured in an example as follows: Generate a spatial topology structure of the working area based on the working site layout map, and record the three-dimensional coordinate range of the fence boundary; Map the installation position information of the sensor group to the spatial topology structure to form a spatial distribution map of the perception device, the sensor group including a grating sensor and a radar sensor; Set the boundary conditions of the multi-level safety control area corresponding to the robot motion path, the safety control area including a dangerous area and a buffer area, for grading the risk of personnel approach and dynamically adjusting the robot operating strategy.

[0009] By adopting the technical scheme, the spatial model accurately expressing the work scene can be constructed by generating the spatial topology structure based on the work site layout and recording the three-dimensional coordinate range of the fence boundary, so that the accuracy of subsequent perception, path planning and safety area constraint judgment is improved; the unified association of sensor data and spatial coordinates can be realized by mapping the deployment information of the grating sensor and the radar sensor to the spatial topology to form a perception device distribution diagram, so that the integration and real-time analysis capability of multi-source perception information is improved; the hierarchical judgment of personnel approaching risk and the hierarchical execution of response strategy can be supported by setting the boundary conditions of the multi-level safety control area and dividing the danger area and the buffer area, so that the safety and collaboration of the robot work are ensured.

[0010] The application can be further configured in an example as follows: the judging personnel behavior state in the work area based on the sensor detection data, obtaining a judgment result, and adjusting the robot operation parameters according to the judgment result include: based on the grating sensor detection data, when it is detected that the blocking signal generated by the grating sensor continuously satisfies a preset time length, it is determined that personnel enter the danger area, and an emergency stop instruction is generated; when the radar sensor detects that the personnel are located in the buffer area, the motion trend features of the personnel are extracted, the risk level is calculated based on the motion trend features, and the motion trend features include the relative distance between the personnel and the robot, the motion direction and the approaching speed; switching the robot operation parameters according to the risk level, the robot operation parameters including the running speed, the joint activity range and the obstacle avoidance strategy.

[0011] By adopting the technical scheme, the emergency stop instruction is generated when the grating sensor blocking signal continuously satisfies the preset time length, so that the state of personnel entering the danger area can be accurately identified and immediately responded, thereby avoiding the physical collision risk caused by the robot continuing to run in the personnel intrusion state; by extracting the relative distance, the motion direction and the approaching speed of the personnel located in the buffer area, the dynamic perception of the personnel behavior intention can be realized, thereby providing data support for subsequent risk level judgment and robot strategy adjustment; by switching the robot running speed, the joint activity range and the obstacle avoidance strategy according to the risk level, the potential risk can be actively avoided, so that the dynamic balance between safety and running efficiency is realized.

[0012] The application can be further configured in an example as follows: the calculating risk level based on the motion trend features includes: extracting the historical position sequence and the current speed vector of the personnel from the motion trend features as model input data; inputting the model input data into a pre-trained trajectory prediction model, predicting a future motion trajectory of the person, and calculating a minimum spatial distance between the future motion trajectory and a current position and preset motion path of the robot, a predicted intersection time, and a trajectory overlap range; generating a corresponding risk level value by using hierarchical determination rules in combination with a current speed and acceleration of the person according to the minimum spatial distance, the predicted intersection time, and the trajectory overlap range.

[0013] By using the above technical solution, the historical position sequence and the current speed vector of the person are extracted from the motion trend feature and used as model input, which can provide a time sequence basis for trajectory prediction, thereby improving the continuity and reliability of the prediction result. By inputting into the pre-trained trajectory prediction model to calculate the future motion trajectory and the minimum spatial distance between the future motion trajectory and the current position and path of the robot, the predicted intersection time, and the trajectory overlap range, the potential contact risk between the person and the robot can be comprehensively evaluated, thereby realizing the early judgment and intervention of the future conflict scene. By combining the speed and acceleration of the person and using hierarchical determination rules to generate a risk level value, fine risk layering can be realized, thereby improving the agility and pertinence of the robot operation parameter adjustment.

[0014] In an example, the application can be further configured to: constructing a training data set based on the historical collected personnel position information and behavior sample data, the behavior sample data including a moving path, speed change, and obstacle avoidance behavior of the person in different work scenes; constructing a trajectory prediction model based on a long short-term memory network; performing normalization processing on the training data set and inputting the training data set into the trajectory prediction model for training, using a loss function of minimizing trajectory prediction error to optimize model weights, generating the pre-trained trajectory prediction model, and dynamically adjusting weight parameters and risk level determination thresholds of the pre-trained trajectory prediction model based on trigger frequency and response delay data recorded in the safety event log during operation.

[0015] By adopting the technical scheme, the training data set is constructed based on the historical collected position information and behavior samples, the dynamic behavior characteristics of the personnel in the real working scene can be reflected, and the generalization ability of the prediction model is enhanced; the trajectory prediction model is constructed based on the long short-term memory network, the time correlation of the personnel behavior can be effectively captured, and the trajectory prediction accuracy and the complex scene adaptability are improved; the training data is normalized, and the model is optimized by using the loss function of minimizing the prediction error, the generated model has stronger prediction stability, and the accuracy and real-time performance of the risk level evaluation are improved; the model weight and the threshold are adjusted online by combining the safety event log, the continuous adaptive evolution of the prediction mechanism can be realized, and the response ability of the robot system to the complex personnel behavior is improved.

[0016] In an example, the application can be further configured to: the permission authentication of the operator, the door lock opening control under the condition of meeting the preset condition includes: Obtain the identity information of the operator, and the identity information includes the ID of the work card, the fingerprint information and the face image data; Compare the identity information with the preset permission database, judge whether the operator has the authorized level of opening the fence door lock, and obtain the judgment result; When the judgment result is that the identity authentication is passed and the robot is in a non-running state, a door lock opening control instruction is generated to drive the execution mechanism to complete the fence door opening operation.

[0017] By adopting the technical scheme, the ID of the work card, the fingerprint information and the face image data of the operator are obtained, multi-factor identity recognition can be realized, and the security of the operator identity authentication is improved; by comparing with the permission database to judge whether the operator has the opening permission, the personnel without permission can be effectively prevented from operating the fence door lock, and the security risk of illegal intrusion of the robot working area is reduced; by generating the door lock control instruction only when the authentication is passed and the robot is in a non-running state, the operation condition can be strictly limited, and the safety accidents caused by state judgment error or permission bypassing can be prevented.

[0018] In an example, the application can be further configured to: the record of various sensor trigger events and corresponding control responses to generate a safety event log includes: Configure the corresponding rules of event trigger type and response action for each type of sensor, and record the event type, occurrence time, trigger sensor identifier, control response content and execution result when the trigger event is detected; The trigger event and the control response are stored in a time sequence to obtain storage data, and a multi-dimensional security event index table is constructed based on the storage data, which is used to support security analysis and backtracking query of specific sensors, specific time periods or specific response results.

[0019] By adopting the above technical scheme, the safety response events of various types in the operation can be systematically tracked by configuring the event trigger type and the control response rule for each type of sensor and recording the event type, time, sensor identifier and execution result when triggered, thereby enhancing the traceability of the device behavior; by storing the trigger and response data in a time sequence and constructing a multi-dimensional index table, efficient screening and analysis can be supported according to the sensor category, time period or response type, thereby providing detailed basis for subsequent safety policy optimization and fault backtracking.

[0020] The above-mentioned second invention object of the application is achieved by the following technical scheme: A safety protection system for visual guidance robot feeding and discharging, the safety protection system for visual guidance robot feeding and discharging comprises: A space modeling module is configured to construct a digital space model of a robot operation area, register a fence boundary, a sensor deployment position information and a safety area constraint condition; A personnel behavior judgment module is configured to acquire sensor detection data in a preset time period during robot operation, judge a personnel behavior state in the operation area based on the sensor detection data to obtain a judgment result, and adjust a robot operation parameter according to the judgment result, wherein the sensor detection data includes grating sensor detection data and radar sensor detection data; A door lock control module is configured to judge whether the robot is in a non-operation state when detecting an operation request of a person trying to open a fence door lock, and perform authority authentication on the operator, and execute a door lock unlocking control under the condition that a preset condition is met; An interrupt control module is configured to listen to a trigger signal of an emergency stop button, and immediately issue a global interrupt control instruction to terminate a current task of the robot when detecting any emergency stop signal; An event recording and adjustment module is configured to record various sensor trigger events and corresponding control responses, generate a safety event log, and dynamically adjust the robot operation parameter and the risk assessment strategy based on the safety event log.

[0021] By adopting the technical scheme, the digital space model of the robot operation area is constructed, the fence boundary, sensor deployment position information and safety area constraint condition are registered, the spatial closed management and multi-source perception layout of the robot operation environment can be realized, and an accurate and structured space basis is provided for subsequent risk judgment and safety control strategy; by collecting sensor detection data in the robot operation process and judging the personnel behavior state, the robot operation parameters are dynamically adjusted according to the judgment result, the personnel intrusion risk can be identified and responded in time, and the collision risk caused by the personnel approaching or entering the dangerous area can be effectively reduced; by performing state judgment and permission authentication when the operation request of the personnel trying to open the fence door lock is detected, it can be ensured that the robot is in a non-operation state and the operator has a legal permission, so as to prevent the safety hidden danger caused by accidental operation or unauthorized entry; by listening to the trigger signal of the emergency stop button and issuing a global interrupt instruction, the robot task can be quickly terminated in an emergency, and the sudden risk can be timely controlled and safely guaranteed; by recording various sensor trigger events and corresponding control responses, a safety event log is generated, and the robot operation parameters and risk evaluation strategy are dynamically adjusted, the adaptive optimization of the safety strategy can be realized, and the dynamic protection ability and continuous improvement ability of the system to different risk situations can be enhanced.

[0022] In summary, the present application includes the following beneficial technical effects: 1. By constructing a digital space model of a robot operation area, registering fence boundaries, sensor deployment position information and safety area constraint conditions, spatial closed management and multi-source perception layout of the robot operation environment can be realized, thereby providing accurate and structured space basis for subsequent risk judgment and safety control strategy; by collecting sensor detection data in the robot operation process and judging the personnel behavior state, the robot operation parameters are dynamically adjusted according to the judgment result, the personnel intrusion risk can be identified and responded in time, and the collision risk caused by the personnel approaching or entering the dangerous area can be effectively reduced; 2. By performing state judgment and permission authentication when the operation request of the personnel trying to open the fence door lock is detected, it can be ensured that the robot is in a non-operation state and the operator has a legal permission, so as to prevent the safety hidden danger caused by accidental operation or unauthorized entry; by listening to the trigger signal of the emergency stop button and issuing a global interrupt instruction, the robot task can be quickly terminated in an emergency, and the sudden risk can be timely controlled and safely guaranteed; 3. By recording various sensor trigger events and corresponding control responses, a safety event log is generated, and the robot operation parameters and risk evaluation strategy are dynamically adjusted, the adaptive optimization of the safety strategy can be realized, and the dynamic protection ability and continuous improvement ability of the system to different risk situations can be enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of a safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 2 is an implementation flowchart of step S10 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 3 is an implementation flowchart of step S20 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 4 is an implementation flowchart of step S22 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 5 is an implementation flowchart of step S222 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 6 is an implementation flowchart of step S30 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application; Figure 7 is an implementation flowchart of step S50 in the safety protection method for visual guidance robot feeding and discharging in an embodiment of the present application Figure 8 is a principle block diagram of a safety protection system for visual guidance robot feeding and discharging in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] In an embodiment, as shown in Figure 1 , the present application discloses a safety protection method for visual guidance robot feeding and discharging, which specifically comprises the following steps: S10: constructing a digital space model of the robot working area, registering the fence boundary, sensor deployment position information and safety area constraint conditions.

[0026] Specifically, based on the input robot working area layout and field device configuration data, the boundary coordinates of the fence structure in the working area, the material attributes and fixed point positions are extracted, the deployment point positions and direction information of various sensor devices for sensing in the working space are identified, and a three-dimensional point cloud space model is established to restore the real working environment. In the modeling process, the boundary line and buffer level area for representing the safe working range are set, such as setting the area close to the robot arm length range as a first-level high-risk area, and expanding to twice the arm length as a second-level buffer area. By configuring boundary lines and virtual warning bands of different colors, the initialization construction and safety element registration of the digital space model are completed.

[0027] S20: In the process of robot operation, sensor detection data is collected within a preset time period, and the behavior state of personnel in the work area is judged based on the sensor detection data to obtain a judgment result, and the robot operation parameters are adjusted according to the judgment result. The sensor detection data includes grating sensor detection data and radar sensor detection data.

[0028] Specifically, based on the set timing trigger mechanism, the data collection task is periodically started, the grating sensor data interface is called to obtain the blocking state and judge whether there is a continuous blocking signal, the millimeter wave radar device is called to obtain the two-dimensional or three-dimensional coordinates of the target point in the work space, the speed vector and the orientation angle of the target body are analyzed, the above data is compared with the data of the last time period to calculate the relative displacement and acceleration value of the personnel, and according to whether the personnel enter the defined high-risk area or the moving trend of the personnel is towards the core work area of the robot, it is judged whether the behavior state of the personnel in the work area exists high-risk behaviors such as approaching, lingering, crossing, etc., and according to this, the parameters of the current motion speed, path priority or operation mode of the robot are adjusted, for example, when the personnel quickly approach the core work area, the robot action mode is adjusted to low-speed obstacle avoidance or pause waiting mode.

[0029] S30: When detecting that the personnel attempts to open the operation request of the fence door lock, it is judged whether the robot is in a non-running state, and the authority of the operator is authenticated, and the door lock unlocking control is executed under the condition that the preset condition is met.

[0030] Specifically, after receiving the unlocking attempt event issued by the door lock detection module, the current state checking logic of the robot is triggered to determine whether it is in an idle, standby or emergency stop non-running state, the identification record corresponding to the operator such as card swiping record, fingerprint scanning result or face recognition image is called in the non-running state, an authentication request is sent to the identity database through an interface and a verification status code is returned, an unlocking authorization command is generated under the premise that the verification is passed and there is no robot action execution, the lock controller is driven to execute the electromagnetic release action, and the action log and the personnel operation time point are recorded for audit and traceback during the whole operation process.

[0031] S40: Listen to the trigger signal of the emergency stop button, and when detecting any emergency stop signal, immediately issue a global interrupt control instruction to terminate the current task of the robot.

[0032] Specifically, by accessing the multi-channel emergency stop button signal bus, all emergency stop trigger ports are monitored in real time. When any channel receives a level mutation or open circuit signal, the interrupt process logic is immediately executed. First, the execution state of the current instruction queue is forcibly terminated, the robot driving instructions are emptied and switched to zero power state, then the interrupt event is broadcast to all associated control nodes such as the above and below clamping jaw controllers, conveyor belt linkage units, etc. to ensure that all actuators enter a stationary or locked state synchronously, while generating an interrupt response record and writing it into the central control event log file for subsequent tracking.

[0033] S50: Record various sensor trigger events and corresponding control responses, generate a safety event log, and dynamically adjust robot operating parameters and risk assessment strategies based on the safety event log.

[0034] Specifically, during operation, set the corresponding trigger conditions and response action logic for each type of sensor. For example, when the optical grating sensor detects an obstruction, it automatically marks the event as an "intrusion alert". When the radar senses a dynamic obstacle approaching, it marks it as an "approach alert" and defines response actions such as deceleration, obstacle avoidance, emergency stop, etc. Through the event-driven model, automatically record the event occurrence time, sensor type, identification number, and executed control action after each trigger event, and record the action response status code and execution result such as "execution success" and "no response". Append all events to the safety log file in chronological order, and generate a multi-dimensional index table based on the log content for subsequent screening and analysis by time period, sensor type, or response category. For example, if the emergency stop button is frequently triggered within a certain period of time, it can be used to identify abnormal operation trends or personnel misoperation risks, thereby dynamically adjusting robot behavior parameters and related safety strategy thresholds.

[0035] By adopting the above technical solutions, and constructing a digital spatial model of the robot's operating area, registering fence boundaries, sensor deployment location information, and safety zone constraints, it is possible to achieve spatial closed management and multi-source perception layout of the robot's operating environment. This provides a precise and structured spatial foundation for subsequent risk assessment and safety control strategies. By collecting sensor detection data and judging human behavior during robot operation, and dynamically adjusting robot operating parameters based on the judgment results, it is possible to promptly identify and respond to human intrusion risks, thereby effectively reducing the collision risk caused by personnel approaching or entering dangerous areas. By performing status judgment and authorization authentication when a human attempts to open the fence lock, it is possible to ensure that the robot is in a non-operating state and the operator has legitimate permissions, thereby preventing safety hazards caused by accidental operation or unauthorized entry. By listening to the trigger signal of the emergency stop button and issuing a global interrupt command, it is possible to quickly terminate the robot's task in an emergency, thereby achieving timely control and safety assurance against sudden risks. By recording various sensor trigger events and corresponding control responses, generating a safety event log, and dynamically adjusting robot operating parameters and risk assessment strategies accordingly, it is possible to achieve adaptive optimization of safety strategies, thereby enhancing the system's dynamic protection capabilities and continuous improvement capabilities for different risk scenarios.

[0036] In one embodiment, such as Figure 2 As shown, in step S10, a digital spatial model of the robot's working area is constructed, and fence boundaries, sensor deployment location information, and safety area constraints are registered. Specifically, this includes: S11: Generate the spatial topology of the work area based on the work site layout diagram, and record the three-dimensional coordinate range of the fence boundary.

[0037] Specifically, by loading a two-dimensional CAD drawing or a three-dimensional point cloud map representing the work site, the key elements in the image, such as the fence structure, work platform, and equipment body, are extracted and spatially located. The coordinates of the drawing are converted into the actual physical space coordinate system using affine transformation and scaling methods. The support points and vertex information of the fence boundary are extracted to generate polygonal boundary curves, and the boundary height is marked to form a three-dimensional coordinate range. The structural closure and void area attributes are recorded, which are used as the boundary judgment benchmark for subsequent robot path planning and personnel approach detection.

[0038] S12: Map the installation location information of the sensor group to the spatial topology to form a spatial distribution map of the sensing devices. The sensor group includes grating sensors and radar sensors.

[0039] Specifically, after completing the spatial topology construction, the sensor deployment configuration file is called to obtain the specific installation point and orientation angle of each grating sensor and radar sensor. The physical coordinates of the sensors are matched with the scene model coordinate system through a spatial mapping algorithm to generate a spatial distribution map of sensing devices that corresponds one-to-one with the spatial topology. The coverage area, effective area number and device ID of each sensor are marked on the distribution map to support the rapid location of trigger sources and identification of the intersection relationship between the sensed target and the path in actual operation. For example, the radar numbered S1 is mapped to the top of the robot forearm, covering a 90° fan-shaped area on the front of the workbench for personnel approach detection.

[0040] S13: Set the boundary conditions of the multi-level safety control area corresponding to the robot's movement path. The safety control area includes the danger zone and the buffer zone, which are used to classify the risk of personnel approaching and dynamically adjust the robot's operation strategy.

[0041] Specifically, based on the robot's normal operating path planning results, the spatial areas that the robot may traverse during task execution are calculated, and layered safety control zones are set outward from its action radius. The first layer is defined as the danger zone, covering all possible locations that the robot may reach, and is used to trigger high-priority control responses such as emergency stop. The second layer is a buffer zone, set at a certain distance outside the danger zone, used to identify the approach trend of personnel in advance and initiate preventive measures such as deceleration and warnings. By configuring different judgment rules and response levels for each layer, the robot can dynamically switch action strategies according to the current position of the personnel and the zone level. For example, when a person enters the buffer zone but does not enter the danger zone, the robot speed is reduced and a voice warning is issued.

[0042] In one embodiment, such as Figure 3 As shown, in step S20, the behavior status of personnel within the work area is determined based on sensor detection data, a judgment result is obtained, and the robot's operating parameters are adjusted according to the judgment result. Specifically, this includes: S21: Based on the detection data of the grating sensor, when the interruption signal generated by the grating sensor is continuously satisfied for a preset time length, it is determined that personnel have entered the danger zone and an emergency stop command is generated.

[0043] Specifically, by periodically sampling the grating sensor output signal, it is analyzed whether the blocking state continuously occurs, when the blocking signal is detected in a plurality of consecutive sampling periods and the cumulative duration exceeds a set safety threshold time value, for example, reaches 1.2 seconds, it is determined that a substantial object has entered the grating monitoring area based on the stability of the light beam obstruction, and on this basis, combined with the spatial position of the grating corresponding area and the current motion trajectory of the robot, it is determined that the shielding behavior may cause direct contact risk between personnel and the robot, thereby immediately generating an emergency stop command, triggering the robot to stop all motion axes and turn off the actuator power supply, for example, when the operator accidentally stretches his hand into the working range causing continuous shielding, the system can trigger the stop after the shielding duration exceeds 1 second to ensure personal safety.

[0044] S22: When the radar sensor detects that the personnel is located in the buffer area, the motion trend characteristics of the personnel are extracted, and the risk level is calculated based on the motion trend characteristics. The motion trend characteristics include the relative distance between the personnel and the robot, the motion direction and the approaching speed.

[0045] Specifically, by performing spatial reconstruction and target identification operations on the echo data collected by the radar sensor, moving objects in the working area are extracted in real time and it is determined whether they are personnel targets. When the identification result points to personnel and their position is within the pre-set buffer area range, the relative distance between the personnel target and the current position of the robot is calculated from the radar data, and the motion direction and approaching speed of the personnel are further extracted from the detection results of a plurality of consecutive frames to form a complete motion trend characteristic vector. On this basis, the trend analysis logic is executed to determine whether the personnel has a trend of approaching the robot, for example, in a certain detection period, the personnel is identified to move from the periphery to the workbench direction, the direction is towards the robot, and the speed is 1.5 meters per second. Then, combined with the buffer zone boundary, it is determined that the personnel has a medium risk level and needs early warning processing.

[0046] S23: Switching robot running parameters according to the risk level, the robot running parameters including running speed, joint activity range and obstacle avoidance strategy.

[0047] Specifically, according to the current calculated risk level value, it is mapped to the corresponding safety strategy level, and the running parameter adjustment operation is performed according to the level. If the current risk level is low, the default running speed and standard action range are maintained, if the risk level is medium, the robot running speed is reduced to 70% of the rated value and the joint activity range is reduced, and if the risk level is high, the obstacle avoidance strategy is immediately activated. By real-time path correction, the action direction is deviated from the personnel direction to avoid collision risk, for example, when the personnel is identified to approach the left side of the robot at a faster speed, the control logic automatically folds the left arm action range and turns to the right side to complete the grabbing task, thereby reducing the possibility of potential interference and ensuring task continuity.

[0048] In one embodiment, such as Figure 4 As shown, in step S22, which calculates the risk level based on movement trend characteristics, the specific steps include: S221: Extract the historical location sequence and current velocity vector of personnel from the motion trend features, and use them as input data for the model.

[0049] Specifically, the radar or visual detection results over multiple consecutive time periods are processed into a time series, from which the spatial coordinates of personnel within the work area are extracted to construct a historical position sequence of personnel. For example, data is collected every 100ms to form a data sequence of 50 position points within 5 seconds. At the same time, the velocity vector of the personnel relative to the robot coordinate system in the current frame is obtained. This velocity vector includes horizontal and vertical velocity components as well as the resultant velocity value. By combining the historical trajectory with the current velocity, a complete input vector is formed, which is used to provide subsequent prediction models to characterize the dynamic movement characteristics and trend changes of personnel.

[0050] S222: Input the model input data into the pre-trained trajectory prediction model to predict the future movement trajectory of the person, and calculate the minimum spatial distance, predicted intersection time, and trajectory overlap range between the future movement trajectory and the robot's current position and preset movement path.

[0051] Specifically, the extracted historical position sequences and velocity vectors are uniformly transformed into a standardized coordinate system and then used as input variables into a trajectory prediction model that has been trained in advance based on a typical working environment. This model can be a recurrent neural network or a spatiotemporal attention mechanism network. The model outputs a set of predicted trajectory points for personnel positions in several future time periods. Combined with the current precise position of the robot and the planned path, the minimum spatial distance between the pair of points closest to the robot's path in the predicted trajectory is calculated based on the Euclidean distance algorithm. The future intersection time of the pair of points is calculated by combining the time axis. Then, the range of overlapping segments between the predicted trajectory and the robot's path in space is statistically analyzed. For example, if the predicted trajectory enters the working range of the robot's right arm in 2 seconds and there is a 40cm path overlap distance, this situation is recorded as a potential intersection event.

[0052] S223: Based on the minimum spatial distance, predicted intersection time, and trajectory overlap range, combined with the current speed and acceleration of personnel, a corresponding risk level value is generated using a tiered judgment rule.

[0053] Specifically, numerical comprehensive judgment is made on the calculated minimum spatial distance, predicted intersection time and trajectory overlap range, the speed size and direction change trend of the extracted personnel in the current frame are combined to judge the acceleration index, when the personnel is in an accelerating approach state, the minimum spatial distance is less than a set safety threshold such as 1 meter, the intersection time is less than 1.5 seconds, and the trajectory overlap range exceeds 30 cm, it is determined as a high-risk level event, and the risk level will be assigned a value of "3" in the grading judgment rule, which is used to forcibly trigger the emergency avoidance action in the control logic. On the contrary, if the trajectories intersect but the speed is in the direction away from each other and the acceleration is negative, the low risk level "1" is comprehensively assigned, which is used to maintain the current running state and continue to monitor the trend change.

[0054] In an embodiment, as shown in Figure 5 The safety protection method for visual guidance robot loading and unloading further comprises: S22201: Construct a training data set based on historical personnel position information and behavior sample data, and the behavior sample data includes the moving path, speed change and obstacle avoidance behavior of personnel in different work scenes.

[0055] Specifically, the personnel trajectory data recorded in the robot work area for a long period is discretely sampled, the collected personnel position information is arranged in time sequence as a continuous trajectory sequence, and the moving speed change, direction deviation, whether to actively avoid the robot path and other behavior characteristics at the corresponding time point are combined to construct structured sample data. Different work scenes include standard material handling area, closed fence operation area and temporary access passage area. By collecting diversified personnel behavior paths in each type of scene, sample diversity coverage is achieved. For example, typical behaviors collected in the passage area include looking back at the robot while walking, bypassing the robot direction of travel, and sudden acceleration crossing, thereby forming complete sample pairs in the training data set including moving path, speed curve and obstacle avoidance action label.

[0056] S22202: Construct a trajectory prediction model based on a long short-term memory network.

[0057] Specifically, the long short-term memory network (LSTM) is used as the main structure of the trajectory prediction model, the input layer accepts fixed-length historical trajectory and speed sequence vectors, the hidden layer contains multiple gate units to capture long-term dependencies in time series, and the output layer generates future multi-frame position coordinate prediction values. In order to improve the prediction accuracy, an attention mechanism module can be introduced into the model structure to assign dynamic weights to input features at different time periods, for example, recent acceleration behavior is assigned a higher weight to reflect sudden trends. This network structure has good time series memory ability and can effectively learn the space-time evolution pattern of personnel behavior.

[0058] S22203: Normalize the training dataset and input it into the trajectory prediction model for training. Optimize the model weights using a loss function that minimizes the trajectory prediction error to generate a pre-trained trajectory prediction model. During operation, dynamically adjust the weight parameters and risk level judgment threshold of the pre-trained trajectory prediction model based on the trigger frequency and response delay data recorded in the security event log.

[0059] Specifically, the processed training dataset is normalized, including scaling all position coordinates to a uniform range, converting velocity vectors to unit vectors, and encoding obstacle avoidance behavior labels as standard categorical variables. This data is then input into the constructed trajectory prediction model for supervised training. During training, the average Euclidean distance between the predicted trajectory and the actual trajectory is used as the loss function. The network weight parameters are continuously iterated and optimized until the prediction error on the validation set converges to a preset threshold range. Simultaneously, during the model deployment and operation phase, the recorded data in the safety event log is continuously monitored, and the trigger frequency of various sensors and the robot's response delay are extracted as feedback indicators. When a rise in the high-risk trigger frequency or an excessive response time delay is detected, the weights of some layers in the model are dynamically adjusted or the risk level judgment threshold is reset. For example, the intersection time judged as "high risk" is reduced from 1.5 seconds to 1.2 seconds to improve the ability to predict and respond to emergencies.

[0060] In one embodiment, such as Figure 6 As shown, in step S30, the operator's authorization is authenticated, and the door lock unlocking control is executed if preset conditions are met. This specifically includes: S31: Obtain the operator's identification information, including employee ID, fingerprint information, and facial image data.

[0061] Specifically, the system acquires the identity information actively submitted or passively collected by the operator when attempting to open the fence gate lock. The employee ID can be read by the employee ID card recognition device via card swiping. The fingerprint information is collected in real time by a capacitive fingerprint sensor embedded in the gate lock control terminal. The facial image data is captured by a high-definition camera installed above the gate lock to capture the current operator's frontal image and verify the image clarity and angle. For example, if the operator's facial angle deviation exceeds 30 degrees or the image is blurry due to insufficient lighting, a re-collection will be prompted. All collected data is packaged in a structured format to form an identity recognition request, which serves as the input basis for subsequent authentication judgments.

[0062] S32: Compare the identity information with the preset permission database to determine whether the operator has the authorization level to open the fence gate lock, and obtain the judgment result.

[0063] Specifically, the collected employee ID, fingerprint feature value, and facial image vector are matched and compared with the authorized personnel data stored in the permission database. The employee ID is directly compared using a unique code. The fingerprint information is similar to the hash value generated by matching fingerprint feature points. The facial image data is compared by calling the embedded facial recognition engine to generate a confidence score. For example, if the employee ID is successfully matched but the fingerprint similarity is lower than a set threshold or the facial recognition confidence score is lower than 85%, the authentication is deemed to have failed. The accuracy and security of authentication are improved through a multi-factor joint comparison mechanism, and the final result is the judgment of whether the permission is granted.

[0064] S33: When the judgment result is that the identity authentication is successful and the robot is in a non-operating state, generate a door lock unlocking control command to drive the actuator to complete the unlocking operation of the fence door.

[0065] Specifically, when the identity authentication result is successful and the current robot task status is in a non-running state, a door lock unlocking control command is immediately generated. This control command includes the target door lock number, unlocking action type, and execution time limit parameters. The control command is then transmitted to the execution control unit connected to the door lock, and the door lock is unlocked by a low-voltage electric drive electromagnetic mechanism. For example, the effective time for a single unlock is set to 5 seconds. If the door is not opened within the time limit, it will automatically reset to the locked state. In addition, the control command will be recorded in the operation log for subsequent security audits and personnel behavior tracing.

[0066] In one embodiment, such as Figure 7 As shown, in step S50, various sensor trigger events and corresponding control responses are recorded to generate a safety event log, which specifically includes: S51: Configure the corresponding rules for event triggering type and response action for each type of sensor, and record the event type, occurrence time, triggering sensor identifier, control response content and execution result when a triggering event is detected.

[0067] Specifically, each type of sensor such as a light barrier sensor, a radar sensor, and an emergency stop button is configured with a corresponding event trigger type and a response action rule, for example, the light barrier sensor is configured with "blockage signal lasting more than 2 seconds" as the trigger type, and the corresponding response action is "issue a stop command and record the state", the radar sensor is configured with "detecting a moving object entering the buffer zone" as the trigger type, and the corresponding response is "reduce the speed of the robot and enter the observation mode", and the emergency stop button is configured with "manual pressing" as the trigger type, and the corresponding response is "immediately interrupt all actions and maintain the original posture". After detecting any sensor trigger event, an event record is automatically generated and written into a log, and the record content includes the event type (such as personnel approaching or button pressing), the event occurrence time (in milliseconds), the unique identification number of the triggering sensor, the control response instruction content (such as speed limiting or stopping) executed, and the actual execution result (such as successful execution or abnormal interruption), and an execution time delay index is attached for evaluating the response efficiency of the control chain.

[0068] S52: structurally store the trigger events and control responses in time sequence to obtain storage data, and construct a multi-dimensional safety event index table based on the storage data, for supporting safety analysis and backtracking query of specific sensors, specific time periods, or specific response results.

[0069] Specifically, the complete record of the above sensor trigger events and corresponding control responses is organized in time sequence as a structured data format, for example, using a timestamp as the main index to form a two-dimensional table data containing fields such as event type, sensor ID, response action, and execution result, and stored in the database in daily units, and a multi-dimensional safety event index table is constructed based on the recorded data, the index dimensions including sensor type, event trigger time range, control response type, and execution success rate, etc., so as to facilitate the operation and maintenance personnel to quickly retrieve event data under specific conditions when needed, for example, all unresponsive events triggered by a specified radar sensor in the past 72 hours or all emergency stop response records with failed execution results can be retrieved, thereby realizing safety analysis, cause tracing, and response strategy optimization of key events.

[0070] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] In an embodiment, a safety protection system for visual guidance robot loading and unloading is provided, which corresponds one-to-one to the safety protection method for visual guidance robot loading and unloading in the above embodiment. As Figure 8As shown, the safety protection system for the vision-guided robot loading and unloading includes a space modeling module, a personnel behavior judgment module, a door lock control module, an interruption control module, and an event record and adjustment module. The detailed description of each functional module is as follows: The space modeling module is used to construct a digital space model of the robot operation area, register the fence boundary, sensor deployment position information, and safety area constraint conditions; The personnel behavior judgment module is used to collect sensor detection data within a preset time period during the robot operation, judge the personnel behavior state in the operation area based on the sensor detection data, obtain a judgment result, and adjust the robot operation parameters according to the judgment result. The sensor detection data includes grating sensor detection data and radar sensor detection data; The door lock control module is used to judge whether the robot is in a non-operation state when detecting an operation request of the operator trying to open the fence door lock, and to perform permission authentication on the operator. The door lock unlocking control is executed under the condition that the preset condition is met; The interruption control module is used to listen to the trigger signal of the emergency stop button, and immediately issue a global interruption control instruction to terminate the current task of the robot when any emergency stop signal is detected; The event record and adjustment module is used to record various sensor trigger events and corresponding control responses, generate a safety event log, and dynamically adjust the robot operation parameters and risk assessment strategies based on the safety event log.

[0072] Optionally, the space modeling module includes: The space topology modeling submodule is used to generate a space topology structure of the operation area based on the operation site layout diagram, and record the three-dimensional coordinate range of the fence boundary; The sensor mapping submodule is used to map the installation position information of the sensor group to the space topology structure to form a space distribution diagram of the perception device. The sensor group includes grating sensors and radar sensors; The safety area configuration submodule is used to set the boundary conditions of multiple safety control areas corresponding to the robot motion path. The safety control area includes a danger area and a buffer area, which are used to judge the personnel approach to the risk and dynamically adjust the robot operation strategy.

[0073] Optionally, the personnel behavior judgment module includes: The blocking detection submodule is used to determine that the personnel enters the danger area and generate an emergency stop instruction based on the grating sensor detection data when the blocking signal generated by the grating sensor continuously meets the preset time length; a motion feature extraction submodule, configured to extract a motion trend feature of the person when the radar sensor detects that the person is located in the buffer area, and calculate a risk level based on the motion trend feature, the motion trend feature including a relative distance between the person and the robot, a motion direction, and an approaching speed; a parameter adjustment submodule, configured to switch robot operation parameters according to the risk level, the robot operation parameters including an operation speed, a joint range of motion, and an obstacle avoidance strategy.

[0074] Optionally, the motion feature extraction submodule includes: a trajectory modeling input unit, configured to extract a historical position sequence and a current speed vector of the person from the motion trend feature as model input data; a future trajectory prediction unit, configured to input the model input data into a pre-trained trajectory prediction model, predict a future motion trajectory of the person, and calculate a minimum spatial distance between the future motion trajectory and a current position of the robot and a preset motion path, a predicted intersection time, and a trajectory overlap range; a risk level evaluation unit, configured to generate a corresponding risk level value according to the minimum spatial distance, the predicted intersection time, and the trajectory overlap range, in combination with a current speed and an acceleration of the person, and using a hierarchical decision rule.

[0075] Optionally, the safety protection system for the vision-guided robot loading and unloading further includes: a sample data construction module, configured to construct a training data set based on historical collected person position information and behavior sample data, the behavior sample data including a moving path, a speed change, and an obstacle avoidance behavior of the person in different work scenarios; a trajectory model construction module, configured to construct a trajectory prediction model based on a long short-term memory network; a model training and optimization module, configured to perform normalization processing on the training data set, and input the training data set into the trajectory prediction model for training, perform model weight optimization using a loss function that minimizes trajectory prediction error, generate a pre-trained trajectory prediction model, and adjust weight parameters and decision thresholds of the pre-trained trajectory prediction model in real time based on a safety event log during operation.

[0076] Optionally, the door lock control module includes: an identity information acquisition submodule, configured to acquire identity recognition information of an operator, the identity recognition information including a badge ID, fingerprint information, and face image data; a permission comparison submodule, configured to compare the identity recognition information with a preset permission database, determine whether the operator has an authorized level to open the fence door lock, and obtain a determination result; The unlocking execution submodule is configured to generate a door lock unlocking control instruction to drive the execution mechanism to complete the unlocking operation of the fence door when the judgment result is that the identity authentication is passed and the robot is in a non-running state.

[0077] Optionally, the event recording and adjustment module comprises: The trigger rule configuration module is configured to configure a corresponding rule of an event trigger type and a response action for each type of sensor, and record the event type, occurrence time, trigger sensor identifier, control response content and execution result when a trigger event is detected. The log structuring module is configured to store the trigger event and the control response in a time sequence to obtain storage data, and construct a multi-dimensional security event index table based on the storage data, to support security analysis and backtracking query of a specific sensor, a specific time period or a specific response result.

[0078] For specific limitations of the safety protection system for the vision-guided robot feeding and discharging, refer to the limitations of the safety protection method for the vision-guided robot feeding and discharging in the above, which will not be repeated here. Each module in the safety protection system for the vision-guided robot feeding and discharging can be realized by software, hardware and combinations thereof, in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0080] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A safety protection method for visual guidance of robot loading and unloading, characterized in that, The safety protection method for visual guidance of robot loading and unloading comprises the following steps: A digital space model of a robot working area is constructed, the fence boundary, sensor deployment position information and safety area constraint conditions are registered; During the robot operation, sensor detection data is collected within a preset time period, and the personnel behavior state in the working area is judged based on the sensor detection data to obtain a judgment result, and the robot operation parameters are adjusted according to the judgment result, wherein the sensor detection data includes grating sensor detection data and radar sensor detection data; When an operation request of trying to open the fence door lock of a personnel is detected, it is judged whether the robot is in a non-operation state, and the authority of the operator is authenticated, and the door lock opening control is executed under the condition that a preset condition is met; The trigger signals of the emergency stop button are listened to, and when any emergency stop signal is detected, a global interruption control instruction is immediately issued to terminate the current task of the robot; Various sensor trigger events and corresponding control responses are recorded to generate a safety event log, and the robot operation parameters and risk assessment strategies are dynamically adjusted based on the safety event log. 2.The safety protection method for vision-guided robot pick-and-place according to claim 1, wherein, The construction of the digital space model of the robot working area, the registration of the fence boundary, the sensor deployment position information and the safety area constraint conditions comprises: A spatial topology structure of the working area is generated based on a working site layout map, and the three-dimensional coordinate range of the fence boundary is recorded; The installation position information of a sensor group is mapped into the spatial topology structure to form a spatial distribution map of the perception device, wherein the sensor group includes grating sensors and radar sensors; Multi-level safety control area boundary conditions corresponding to the robot motion path are set, and the safety control area includes a danger area and a buffer area, which are used to judge the personnel's approach to risk and dynamically adjust the robot operation strategy. 3.The safety protection method of visual guidance robot pick-and-place according to claim 2, wherein, The judgment of the personnel behavior state in the working area based on the sensor detection data to obtain a judgment result, and the adjustment of the robot operation parameters according to the judgment result comprises: Based on the grating sensor detection data, when the blocking signal generated by the grating sensor is detected to continuously satisfy a preset time length, it is determined that the personnel enters the danger area, and an emergency stop instruction is generated; When the radar sensor detects that the personnel is located in the buffer area, the motion trend characteristics of the personnel are extracted, the risk level is calculated based on the motion trend characteristics, and the motion trend characteristics include the relative distance between the personnel and the robot, the motion direction and the approach speed; The robot operation parameters including the running speed, the joint activity range and the obstacle avoidance strategy are switched according to the risk level.

4. The safety protection method for vision-guided robot pick-and-place according to claim 3, wherein, The calculation of the risk level based on the motion trend characteristics comprises: The historical position sequence and the current speed vector of the personnel are extracted from the motion trend characteristics as model input data; The model input data is input into a pre-trained trajectory prediction model to predict the future motion trajectory of the personnel, and the minimum spatial distance between the future motion trajectory and the current position of the robot and the preset motion path, the predicted intersection time and the trajectory overlap range are calculated; According to the minimum space distance, the predicted intersection time and the trajectory overlap range, in combination with the current speed and acceleration of the personnel, a hierarchical determination rule is used to generate a corresponding risk level value.

5. The safety protection method for vision-guided robot pick-and-place according to claim 4, wherein, The safety protection method of the vision-guided robot feeding and discharging further includes: Based on the historical personnel position information and behavior sample data collected, a training data set is constructed, and the behavior sample data includes the moving path, speed change and obstacle avoidance behavior of the personnel in different work scenes; A trajectory prediction model is constructed based on a long short-term memory network; The training data set is normalized and input into the trajectory prediction model for training, a loss function minimizing the trajectory prediction error is used for model weight optimization, the pre-trained trajectory prediction model is generated, and in the running process, based on the trigger frequency and response delay data recorded in the safety event log, the weight parameters and risk level determination threshold of the pre-trained trajectory prediction model are dynamically adjusted.

6. The safety protection method for vision-guided robot pick-and-place according to claim 1, wherein, The permission authentication of the operator, and the door lock unlocking control under the condition that the preset condition is met include: Obtaining the identity information of the operator, the identity information including the ID of the work card, the fingerprint information and the face image data; The identity information is compared with the preset permission database to determine whether the operator has the authorized level to open the fence door lock, and a judgment result is obtained; When the judgment result is that the identity authentication is passed and the robot is in a non-running state, a door lock unlocking control instruction is generated to drive the execution mechanism to complete the unlocking operation of the fence door.

7. The safety protection method for vision-guided robot pick-and-place according to claim 1, wherein, The recording of various sensor trigger events and corresponding control responses to generate a safety event log includes: For each type of sensor, the corresponding rules of event trigger type and response action are configured, and when a trigger event is detected, the event type, occurrence time, trigger sensor identifier, control response content and execution result are recorded; The trigger events and control responses are stored in a structured manner according to time sequence to obtain storage data, and a multi-dimensional safety event index table is constructed based on the storage data to support safety analysis and backtracking query of specific sensors, specific time periods or specific response results.

8. A safety guard system for vision guided robot pick and place, characterized in that, The safety protection system of the vision-guided robot feeding and discharging includes: A space modeling module is configured to construct a digital space model of a robot work area, register fence boundary, sensor deployment position information and safety area constraint conditions; A personnel behavior judgment module is configured to collect sensor detection data within a preset time period during robot operation, judge the behavior state of personnel in the work area based on the sensor detection data, obtain a judgment result, and adjust the robot operation parameters according to the judgment result, the sensor detection data including grating sensor detection data and radar sensor detection data; A door lock control module is configured to detect an operation request of the personnel attempting to open the fence door lock, judge whether the robot is in a non-running state, and perform permission authentication of the operator to execute door lock unlocking control under the condition that the preset condition is met. An interrupt control module is configured to listen to the trigger signals of the emergency stop buttons, and immediately issue a global interrupt control instruction to terminate the current task of the robot when any emergency stop signal is detected. An event recording and adjustment module is configured to record various sensor trigger events and corresponding control responses, generate a safety event log, and dynamically adjust the robot operation parameters and risk assessment strategies based on the safety event log.

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