Multi-sensor time sequence cooperation and mutual inhibition control method and system applied to robot inspection

Abstract

The application provides a multi-sensor timing coordination and mutual suppression control method and system applied to robot inspection, relates to the technical field of robot inspection, and first collects sensor parameters and equipment detection target point information, establishes mutual suppression trigger correlation and generates mutual suppression correlation data; timing peak-missing arrangement of sensor detection actions is carried out based on the mutual suppression correlation data; a hardware suppression structure is adaptively deployed in combination with electromagnetic interference data; linkage mutual suppression regulation is executed during sensor detection; and finally, comprehensive data is generated by integrating data. The application effectively solves the problems of sensor signal crosstalk, insufficient anti-interference capability, low detection precision and the like, and improves the efficiency, accuracy and reliability of robot inspection of high-risk areas of new energy stations.

Description

Technical Field

[0001] This invention relates to the field of robot inspection technology, and more specifically, to a multi-sensor timing coordination and mutual inhibition control method and system for robot inspection. Background Technology

[0002] With the booming development of new energy power plants, the safe and stable operation of power equipment is crucial to ensuring energy supply. However, areas such as wind farms and substations within new energy power plants involve a large number of high-frequency, high-risk inspection tasks. Traditional manual inspection methods are not only inefficient and unable to meet the needs of large-scale equipment inspections, but also pose numerous dangers to workers during inspections, such as electric shock and falls from heights, leaving their personal safety unprotected.

[0003] Currently, although some robotic inspection technologies have been applied to the field of power equipment inspection, there are still many shortcomings in practical applications. Existing robotic inspection systems struggle to achieve high-precision organic integration of multiple types of sensors (such as ultrasonic sensors, ultra-high frequency sensors, and transient ground voltage sensors). When different sensors are operating, signal transmission and reception are prone to mutual interference, leading to signal aliasing and affecting the accuracy and reliability of data acquisition. Furthermore, the lack of scientific planning in the timing of sensor operation often results in signal crosstalk, further reducing the quality of inspection data.

[0004] Furthermore, existing positioning technologies are prone to drift in complex new energy power plant environments due to strong electromagnetic interference and complex terrain, making it impossible to achieve high-precision positioning of power equipment inspection points. Moreover, the scheduling of inspection tasks lacks intelligence and flexibility, failing to allocate inspection resources rationally based on the actual condition of the equipment and inspection needs, resulting in low inspection efficiency. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, the present invention provides a multi-sensor timing coordination and mutual inhibition control method for robot inspection, the method comprising:

[0006] Collect the signal transmission frequency range, signal reception sensitivity parameters and detection duration of each sensor, and combine them with the spatial distribution information of equipment detection target points in the preset inspection task to establish a mutual inhibition triggering relationship between sensor detection actions and equipment detection target points, and generate mutual inhibition relationship data.

[0007] Based on the mutually inhibiting correlation data, the independent detection time period and action connection gap of each sensor are divided. The signal transmission time periods of different sensors are arranged in chronological order, and the overlap between each signal transmission time period is controlled to be non-overlapping. The timing staggered arrangement of sensor detection actions is executed to generate the timing staggered arrangement result.

[0008] Collect electromagnetic interference frequency and intensity data of the robot inspection area, combine the time-series staggered scheduling results, equip shielding and blocking devices, signal filtering channels and power supply stability lines that are adapted to the signal transmission frequency range and signal reception sensitivity parameters of each sensor, perform the adaptation and deployment of hardware suppression structure, and generate hardware suppression deployment parameters.

[0009] When the sensor enters an independent detection period to perform detection actions, it collects contact pressure data, contact angle data, and sensor signal transmission and reception data between the sensor and the detection target point. Combined with the timing staggered scheduling results and hardware suppression deployment parameters, it performs linkage and mutual suppression control between the robot's mechanical actions and sensor signals to generate dynamic mutual suppression control data.

[0010] Based on independent detection time periods and equipment detection targets, dynamic mutual inhibition and control data are integrated with the raw inspection data collected by various sensors to establish a one-to-one correspondence between control data and inspection data, thereby generating comprehensive robot inspection data.

[0011] Furthermore, the present invention also provides a multi-sensor timing coordination and mutual inhibition control system for robot inspection, comprising:

[0012] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described multi-sensor timing coordination and mutual inhibition control method for robot inspection by executing the machine-executable instructions.

[0013] In another aspect, the present invention also provides a computer program product, the computer program product including machine-executable instructions stored in a computer-readable storage medium, a processor of a multi-sensor timing coordination and mutual inhibition control system for robot inspection reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the multi-sensor timing coordination and mutual inhibition control system for robot inspection to execute the above-mentioned multi-sensor timing coordination and mutual inhibition control method for robot inspection.

[0014] Based on the above, by collecting the signal transmission frequency range, signal reception sensitivity parameters, and detection action duration of each sensor, and combining this with the spatial distribution information of the equipment's detection target points, a mutual inhibition triggering correlation between sensor detection actions and equipment detection target points was established. Based on this generated mutual inhibition correlation data, a reasonable division of the independent detection time periods and action transition gaps for each sensor was achieved. The signal transmission time periods of different sensors were precisely arranged in chronological order, ensuring no overlap between signal transmission time periods, effectively avoiding crosstalk between sensor signals and improving the accuracy and reliability of signal acquisition. Furthermore, by collecting electromagnetic interference frequency and intensity data of the robot's inspection area, and combining this with the timing staggered scheduling results, suitable shielding devices, signal filtering channels, and stable power supply lines were equipped for each sensor. This further enhanced the sensors' anti-interference capability in complex electromagnetic environments, ensuring normal operation of the sensors under various working conditions and improving the stability and reliability of the inspection system. When the sensor enters its independent detection period and performs detection actions, it collects contact pressure data, contact angle data, and sensor signal transmission and reception data between the sensor and the equipment detection target point. Combined with the timing-based staggered scheduling results and hardware suppression deployment parameters, it executes a coordinated and mutually inhibiting control of the robot's mechanical actions and sensor signals. This allows for real-time adjustments to the robot's mechanical actions and sensor signal transmission and reception based on actual conditions, ensuring the accuracy and stability of the detection process and improving the precision and efficiency of inspections. By classifying data according to independent detection periods and equipment detection targets, and integrating dynamic mutual inhibition control data with the raw inspection data collected by each sensor, a one-to-one correspondence between control data and inspection data is established, generating comprehensive robot inspection data. This helps achieve early warning and accurate diagnosis of defects and faults in power equipment, improving the safety and reliability of power equipment operation in new energy power plants. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the execution flow of the multi-sensor timing coordination and mutual inhibition control method for robot inspection provided in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of exemplary hardware and software components of a multi-sensor timing coordination and mutual inhibition control system for robot inspection provided in an embodiment of the present invention. Detailed Implementation

[0017] Figure 1 This is a flowchart illustrating a multi-sensor timing coordination and mutual inhibition control method for robot inspection provided by an embodiment of the present invention, which will be described in detail below.

[0018] Step S110: Collect the signal transmission frequency range, signal reception sensitivity parameters and detection action duration of each sensor, and combine them with the spatial distribution information of equipment detection target points in the preset inspection task to establish the mutual inhibition triggering association between sensor detection actions and equipment detection target points, and generate mutual inhibition association data.

[0019] This embodiment focuses on the ultrasonic sensor, ultra-high frequency sensor, and transient ground voltage sensor mounted on the quadrupedal inspection robot of new energy power stations. First, it collects the basic parameters and detection action duration of each sensor. Then, it combines the spatial information of transformer and GIS equipment detection target points in the preset inspection task to construct the mutual inhibition triggering association between sensor detection actions and target points, and finally generates association data containing multi-dimensional mutual inhibition rules.

[0020] Step S111: Obtain the technical specifications of each sensor, and extract the signal transmission frequency range and signal reception sensitivity parameters of the sensor from the technical specifications; obtain the duration of the detection action required for each sensor to complete a single detection action through the robot's motion controller; integrate the signal transmission frequency range, signal reception sensitivity parameters and the duration of the detection action to form the basic characteristic information of the sensor.

[0021] First, the corresponding parameters are extracted from the manufacturer's technical specifications of each sensor. The signal transmission frequency range for the ultrasonic sensor is [f1_min, f1_max], for the UHF sensor it is [f2_min, f2_max], and for the transient ground voltage sensor it is S_tev, representing the minimum identifiable signal strength threshold. Next, the motion controller on the robot triggers each of the three sensors to complete a single standard detection action. The complete duration from action initiation to data acquisition completion is recorded, resulting in the detection action durations T1 for the ultrasonic sensor, T2 for the UHF sensor, and T3 for the transient ground voltage sensor. Finally, the extracted signal transmission frequency range, signal reception sensitivity parameters, and recorded detection action durations are integrated according to sensor identifiers to form structured basic sensor characteristic information. Each sensor corresponds to an information entry containing all its basic parameters. f1_min and f1_max represent the lower and upper limits of the ultrasonic sensor's signal transmission frequency range, respectively. f2_min and f2_max represent the lower and upper limits of the UHF sensor's signal transmission frequency range, respectively.

[0022] Step S112: Read the task description file of the preset inspection task, parse out the list of equipment to be inspected and the three-dimensional coordinate information of the target points on each equipment from the task description file, and arrange the three-dimensional coordinate information according to the order of the equipment to form the spatial distribution information of the target points of the equipment.

[0023] First, the pre-defined inspection task description file stored in the robot task management module is read, and the list of equipment to be covered in this inspection is parsed out. The list includes M transformers and N sets of GIS equipment. Next, the pre-calibrated 3D coordinate information of the detection target points on each device is extracted from the file. This coordinate information is based on the robot's base coordinate system, and each target point corresponds to a unique 3D coordinate (X_i, Y_i, Z_i), where i represents the unique number of the target point. Finally, the 3D coordinate information of the detection target points of all devices is arranged according to the order of the device list to form the spatial distribution information of the device detection target points. Each entry in the information includes the device identifier, the target point identifier, and the corresponding 3D coordinates.

[0024] Step S113: Match the signal transmission frequency range in the sensor's basic characteristic information with the spatial distribution information of the device's detection target points, analyze different sensors that may be working simultaneously on the same device's detection target points, compare the signal transmission frequency ranges of these different sensors, identify sensor combinations with overlapping signal transmission frequency ranges, and form a list of potential interfering sensor combinations.

[0025] First, the system iterates through each target point in the spatial distribution information of the equipment detection targets, matching all sensors assigned to that target point in the preset inspection task to determine the multi-sensor combination corresponding to the same target point. Next, for each multi-sensor combination, the system retrieves the corresponding signal transmission frequency range from the sensor's basic characteristic information and compares whether there is any overlap in the frequency ranges of different sensors. For example, if a transformer target point is assigned both an ultrasonic sensor and a UHF sensor, the system compares [f1_min, f1_max] with [f2_min, f2_max]. If the two intervals overlap, the combination is determined to be a potential interference combination. Finally, all sensor combinations with overlapping frequency ranges are categorized and organized according to the target point identifier, forming a potential interference sensor combination list. Each entry in the list includes the target point identifier, sensor combination, and corresponding frequency overlap interval.

[0026] Step S114: Match the signal receiving sensitivity parameter in the sensor's basic characteristic information with the spatial distribution information of the device's detection target point, analyze the interference intensity that a sensor may be affected by when it performs a detection action at a set device detection target point, calculate the interference intensity value, and form a receiving interference intensity mapping table.

[0027] First, for each device detection target point, the signal receiving sensitivity parameters corresponding to all sensors assigned to that target point are extracted, where ultrasonic sensors correspond to S_ultra, ultra-high frequency sensors to S_uhf, and transient ground voltage sensors to S_tev. Next, when a sensor performs detection at that target point, the signal transmission power P_j and propagation loss L_j of other sensors that may be operating simultaneously are analyzed. Combined with the signal receiving sensitivity parameters of that sensor, the interference intensity value is calculated. The specific calculation process is as follows: First, the actual intensity of the signals transmitted by other sensors reaching the current sensor's receiver is calculated as P_j' = P_j - L_j. Then, P_j' is compared with the current sensor's signal receiving sensitivity parameter S_k. If P_j' is greater than S_k, the interference intensity value is the difference between P_j' and S_k; if P_j' is less than or equal to S_k, the interference intensity value is 0. Finally, the interference intensity values ​​of each sensor at the corresponding target point are organized according to the sensor-target point correspondence to form a receiving interference intensity mapping table. Each entry in the table includes the sensor identifier, target point identifier, interference source sensor identifier, and interference intensity value.

[0028] Step S115: Match the duration of the detection action in the sensor's basic characteristic information with the spatial distribution information of the device's detection target points, analyze the total time required for each sensor to traverse all its designated device detection target points, combine the potential interference sensor combination list, calculate the possible conflict time length if the detection periods of different sensors overlap, and form the detection period conflict prediction data.

[0029] First, for each sensor, count the number K of all target points it needs to detect. Then, combine this with the detection duration T_k of the sensor to calculate the total detection time T_total_k = K for the sensor to complete the detection of all target points. T_k is then used in conjunction with sensor movement time data (generated in subsequent steps) to calculate the total task time. Next, based on the list of potential interfering sensor combinations, the detection time periods of each combination are analyzed. If there is overlap in time periods, the duration of the overlap is calculated as the conflict time length. The specific calculation process is as follows: obtain the start and end times of detection by the two sensors at the same target point; if there is an intersection between the two time periods, the length of the intersection is the conflict time length. Finally, the conflict time lengths of all potential interfering combinations are categorized and organized to form detection time period conflict prediction data. Each entry in the data includes the sensor combination, target point identifier, and conflict time length.

[0030] Step S116: Integrate the potential interference sensor combination list, the received interference intensity mapping table, and the detection period conflict prediction data to determine the detection actions of the sensors that need to be suppressed at the set device detection target point, as well as the suppression triggering conditions, to form a set of mutual suppression triggering rules between sensors and target points.

[0031] First, for each device detection target point, extract the potential interfering sensor combinations, the interference intensity values ​​from the received interference intensity mapping table, and the conflict duration from the conflict prediction data of the detection period. Next, set an interference intensity threshold S_threshold and a conflict duration threshold T_threshold. If the interference intensity value of a sensor combination is greater than S_threshold and the conflict duration is greater than T_threshold, it is determined that the detection action of one of them needs to be suppressed. The suppression trigger condition is set as follows: when a higher-priority sensor enters the detection period of the target point, a suppression command is triggered for the lower-priority sensor, and the suppression duration is equal to the detection action duration of the higher-priority sensor. The priority determination rule is: ranked according to the importance of the sensors to the defect detection of the device, with UHF sensors having the highest priority for GIS equipment and ultrasonic sensors having the highest priority for transformers. Finally, organize the suppression rules corresponding to each target point to form a set of mutual suppression trigger rules between sensors and targets. Each entry in the set includes the target point identifier, the suppressed sensor identifier, the triggering sensor identifier, and the suppression duration.

[0032] Step S117: Based on the set of mutual inhibition triggering rules between sensors and targets, generate a list for each device to detect targets. The list specifies the sensor identifier that needs to be suppressed when performing detection at the target, the suppression start time condition, and the suppression end time condition, thus forming a target mutual inhibition triggering list.

[0033] First, each rule in the set of mutual inhibition triggering rules between sensors and targets is traversed. For each target detected by a device, all inhibition rules corresponding to that target are compiled. Next, based on the timing sequence of the detection actions of each sensor corresponding to that target, the suppression start time condition is determined as the start time of the triggering sensor's detection action, and the suppression end time condition is determined as the end time of the triggering sensor's detection action. For example, if the triggering sensor for a GIS target is a UHF sensor, with its detection action start time being t_start and end time being t_end, then the suppression start time for the suppressed ultrasonic sensor is t_start, and the end time is t_end. Finally, the inhibition information for each target is compiled into a structured list, forming a target mutual inhibition triggering list. Each entry in the list includes the target identifier, the suppressed sensor identifier, the suppression start time, and the suppression end time.

[0034] Step S118: Compile a list of mutual inhibition triggers for all device detection targets, sort them according to the execution path of the inspection task, connect the discrete mutual inhibition trigger relationships into a continuous temporal relationship chain, and form a global mutual inhibition trigger association chain.

[0035] First, the robot execution path in the preset inspection task is obtained. The path is arranged according to the detection order of the device targets, forming a target detection sequence. Next, according to the order of this target detection sequence, the suppression information in the target mutual inhibition trigger list of each target is summarized in turn, and the suppression end time of the previous target is connected with the suppression start time of the next target. If the suppression rules of adjacent targets involve the same sensor, the suppression time period is adjusted to form a continuous temporal chain to avoid time period breaks or overlaps. Finally, the suppression temporal information of all targets is connected in the order of the inspection path to form a global mutual inhibition trigger association chain. The chain contains the suppressed sensor identifier, suppression duration, and associated target information for each time period.

[0036] Step S119: Merge the sensor basic characteristic information, the spatial distribution information of the device detection target points, the potential interference sensor combination list, the received interference intensity mapping table, the detection period conflict prediction data, the set of mutual inhibition triggering rules between sensors and targets, the target mutual inhibition triggering list, and the global mutual inhibition triggering association chain to generate mutual inhibition association data.

[0037] First, the structured data generated in all the above steps are integrated in a unified format to ensure that each data module contains corresponding identification information for easy retrieval in subsequent steps. Next, a unified index is added to the integrated data, categorized by sensor identifier and target identifier for quick data location. Finally, the integrated data is stored as a structured file, forming mutually exclusive correlation data, which contains comprehensive information from basic sensor parameters to global mutually exclusive time series, providing a complete basis for subsequent time series staggered scheduling.

[0038] Step S120: Based on the cross-correlation data, divide the independent detection time period and action connection gap of each sensor, arrange the signal transmission time periods of different sensors in chronological order, and control the non-overlapping between each signal transmission time period, perform the timing staggered arrangement of sensor detection actions, and generate the timing staggered arrangement result.

[0039] In this embodiment, based on the mutually inhibiting and correlated data generated above, the independent detection time period is divided, the action connection gap is configured, and the timing node is determined for the three types of sensors carried by the quadrupedal inspection robot of the new energy power station. Finally, a timing staggered arrangement result without signal transmission overlap is generated.

[0040] Step S121: Extract the signal transmission frequency range, signal reception sensitivity parameters, detection action duration, and spatial distribution information of the detection target points of each sensor from the cross-inhibition correlation data, and summarize them to form the basic data of sensor detection actions.

[0041] First, the signal transmission frequency ranges of each sensor are extracted from the cross-correlation data, including [f1_min, f1_max] for the ultrasonic sensor, [f2_min, f2_max] for the ultra-high frequency sensor, and [f3_min, f3_max] for the transient ground voltage sensor. Next, the corresponding signal receiving sensitivity parameters S_ultra, S_uhf, and S_tev, as well as the detection action durations T1, T2, and T3, are extracted. Simultaneously, the three-dimensional coordinates (X_i, Y_i, Z_i) of all target points in the spatial distribution information of the detection target points are extracted, along with the corresponding device identifiers. Finally, the extracted parameters are categorized and summarized according to sensor identifiers to form basic data for sensor detection actions. Each entry in the data includes the sensor identifier, signal transmission frequency range, signal receiving sensitivity parameters, detection action duration, and a list of corresponding target points. f3_min and f3_max represent the lower and upper limits of the signal transmission frequency range of the transient ground voltage sensor, respectively.

[0042] Step S122: Compare the signal transmission frequency ranges of different sensors in the sensor detection action basic data, identify sensor combinations that may overlap in signal propagation, divide the sensors into groups according to the requirement that the signal transmission frequency ranges of different sensors do not overlap, and generate sensor group data.

[0043] First, all sensor combinations in the basic sensor detection action data are traversed, and the signal transmission frequency ranges of different sensors are compared to identify overlapping combinations, i.e., potential interfering sensor combinations. Next, based on the requirement of non-overlapping frequency ranges, the sensors are divided into multiple groups. The signal transmission frequency ranges of sensors within each group do not overlap, while overlap between different groups is allowed. For example, ultrasonic sensors and transient ground voltage sensors are grouped into the first group, and ultra-high frequency sensors are grouped into the second group, because the frequency ranges of the two sensors in the first group do not overlap, while the frequency ranges of the second group overlap with those of the first group. Finally, the divided sensor groups are organized by group number to form sensor group data. Each entry in the data includes a group number, a list of sensor identifiers, and the corresponding frequency range.

[0044] Step S123: Based on the duration of the detection action of each sensor group in the sensor group data, the entire inspection task cycle is broken down into multiple continuous time segments. Each time segment is assigned to a sensor as an independent detection period, generating an independent detection period division result.

[0045] First, for each sensor group, calculate the total time for all sensors within that group to complete the corresponding target detection, including the duration of the detection action and the movement time. Next, break down the entire inspection task cycle into multiple consecutive time segments based on the total time. The duration of each time segment equals the duration of the single target detection action for the corresponding sensor plus the movement time. For example, if the single target detection time for an ultrasonic sensor is T1 and the movement time is T_move1, then the duration of the independent detection period allocated to the ultrasonic sensor is T1 + T_move1. Finally, assign each time segment to the corresponding sensor, arranging them according to the inspection path sequence to form the independent detection period division results. Each entry in the result includes the sensor identifier, the start time of the period, the end time of the period, and the corresponding target identifier.

[0046] Step S124: Combining the spatial distribution information of the device detection target points, calculate the path length and time required for each sensor to move from the current device detection target point to the next device detection target point, and generate sensor movement time data.

[0047] Step S1241: Extract the device detection target sequence corresponding to each sensor from the cross-inhibition correlation data, determine the order of device detection target sequences that each sensor needs to detect in sequence, and generate sequence data.

[0048] In this embodiment, a list of target points assigned to ultrasonic, ultra-high frequency, and transient ground voltage sensors by preset inspection tasks is extracted from the spatial distribution information of device detection target points in the mutually exclusive data. These target points are arranged into a target point sequence according to the planned inspection path, with each sensor corresponding to a unique sequence number. For example, the target point sequence for the ultrasonic sensor is [target point 1, target point 3, target point 5], and the target point sequence for the ultra-high frequency sensor is [target point 2, target point 4, target point 6]. The identifier of each sensor and its corresponding target point sequence are organized in a unified format to generate sequence data. Each entry in the data includes the sensor identifier, target point sequence, sequence length, and device association identifier for each target point.

[0049] Step S1242: Extract the three-dimensional coordinate data of each device detection target from the spatial distribution information of the device detection target, arrange them according to the detection order in the sequence data, and generate target coordinate sequence data.

[0050] The three-dimensional coordinates (X_i, Y_i, Z_i) of each target point are extracted from the spatial distribution information of the target points detected by the equipment. These coordinates are then rearranged according to the target point detection order of each sensor in the sequence data to form a target point coordinate sequence. For example, the target point coordinate sequence for an ultrasonic sensor is [(X1, Y1, Z1), (X3, Y3, Z3), (X5, Y5, Z5)]. The sensor identifier, target point coordinate sequence, and corresponding target point identifiers for each sensor are then organized to generate target point coordinate sequence data. Each entry in this data contains a sensor identifier, a target point coordinate sequence, and a list of corresponding target point identifiers.

[0051] Step S1243: Calculate the three-dimensional coordinate difference between two adjacent target points detected by the device in the target point coordinate sequence data, calculate the straight-line distance between adjacent target points based on the spatial distance, and generate target point spacing data.

[0052] For each sensor's target point coordinate sequence, the 3D coordinate difference between adjacent target points is calculated sequentially: ΔX = X_next - X_curr, ΔY = Y_next - Y_curr, ΔZ = Z_next - Z_curr. The distance L is then calculated using the spatial straight-line distance formula: L = √(ΔX² + ΔY² + ΔZ²). For example, to calculate the distance between target point 1 and target point 3 of the ultrasonic sensor, L(1-3) is obtained. The sensor identifier, adjacent target point pairs, and corresponding straight-line distances are organized to generate target point distance data. Each entry in the data includes the sensor identifier, current target point identifier, next target point identifier, 3D coordinate difference, and straight-line distance. X_next, Y_next, Z_next: represent the 3D coordinate values ​​of the next device-detected target point in the sequence. X_curr, Y_curr, Z_curr: represent the 3D coordinate values ​​of the current device-detected target point in the sequence.

[0053] Step S1244: Based on the motion constraint parameters of the robot end effector, set the maximum moving speed parameter and turning radius parameter, perform path correction on the straight distance corresponding to the target point spacing data, and generate the actual moving path length data.

[0054] The motion constraint parameters of the robot's end effector are called, including the maximum moving speed V_max and the minimum turning radius R_min. For the spatial path corresponding to the straight-line distance, if there are obstacles or the line connecting adjacent target points is outside the robot's range of motion, then based on A... The path planning algorithm performs path correction, generating the actual movement path including turning nodes. For example, the straight path from target point 1 to target point 3 passes through the GIS equipment casing, and is corrected to a path around the outside of the equipment, with the path length corrected to L(1-3)'. The identifier of each sensor, adjacent target point pairs, corrected path length, and path node coordinates are organized to generate actual movement path length data. Each entry in the data includes sensor identifier, current target point identifier, next target point identifier, corrected path length, and a list of path nodes.

[0055] Step S1245: Based on the rated moving speed parameters of the robot's end effector and the actual moving path length data, calculate the theoretical moving time of the sensor from the current device detection target point to the next device detection target point, and generate theoretical moving time data.

[0056] The rated movement speed of the robot's end effector is V_rated. For each sensor's actual movement path length L_actual, the theoretical movement time T_theory = L_actual / V_rated is calculated. For example, the corrected path length from target point 1 to target point 3 of the ultrasonic sensor is L(1-3)', then the theoretical movement time T1-3 = L(1-3)' / V_rated. The identifier of each sensor, adjacent target point pairs, theoretical movement time, and rated movement speed are organized to generate theoretical movement time data. Each entry in the data includes the sensor identifier, current target point identifier, next target point identifier, theoretical movement time, and rated movement speed.

[0057] Step S1246: Refer to the action connection gap configuration data in the timing staggered scheduling results, analyze the detection action connection requirements of adjacent sensors, adjust the theoretical movement time data, reserve sensor preparation time when the device detection target point is switched, and generate the adjusted movement time data.

[0058] Extract the upper limit \(T_{gap\_max}\) of the connection gap duration between adjacent sensors from the action connection gap configuration data. For the theoretical movement time \(T_{theory}\) of each sensor, if \(T_{theory}<T_{gap\_max}\), then add the sensor preparation time \(T_{prepare}\) to the theoretical movement time. The preparation time is used for the pose calibration and signal preheating of the sensor. The adjusted movement time \(T_{adjust}=T_{theory}+T_{prepare}\); if \(T_{theory}\geq T_{gap\_max}\), then keep the theoretical movement time unchanged. For example, the theoretical movement time from the target point 1 to the target point 3 of the ultrasonic sensor is \(T_{1 - 3}\), and the upper limit of the connection gap is \(T_{gap\_max}\). If \(T_{1 - 3}<T_{gap\_max}\), the adjusted time is \(T_{1 - 3}+T_{prepare}\). Organize the identification of each sensor, adjacent target point pairs, adjusted movement time, and preparation time to generate the adjusted movement time-consuming data. Each entry in the data contains the sensor identification, the current target point identification, the next target point identification, the adjusted movement time, and the preparation time.

[0059] Step S1247: Record the target point coordinate sequence data, actual movement path length data, theoretical movement time-consuming data, and adjusted movement time-consuming data corresponding to each sensor, classify and organize them according to the sensor identification, and generate the basic sensor movement data.

[0060] Classify and summarize the target point coordinate sequence, actual movement path length, theoretical movement time, and adjusted movement time of each sensor according to the sensor identification, ensuring that all movement-related data of the same sensor corresponds to a unique identification. For example, the basic sensor movement data of the ultrasonic sensor includes its target point coordinate sequence, the path lengths of all adjacent target points, the theoretical time, and the adjusted time. Sort the summarized data according to the sensor identification to generate the basic sensor movement data. Each entry in the data contains the sensor identification, the target point coordinate sequence, the list of actual movement path lengths, the list of theoretical movement time consumptions, and the list of adjusted movement time consumptions.

[0061] Step S1248: Compare the adjusted movement time-consuming data with the upper limit of the duration in the action connection gap configuration data; if the adjusted movement time-consuming data is greater than the duration upper limit, then perform the next re-correction operation to generate the movement time-consuming verification data.

[0062] Extract the upper limit of duration T_gap_max from the action transition gap configuration data. Iterate through each entry in the adjusted movement time data, comparing the adjusted movement time T_adjust with T_gap_max. If T_adjust > T_gap_max, mark it as an entry that needs further correction; if T_adjust ≤ T_gap_max, mark it as an entry that has passed verification. Categorize and organize all entries according to the verification results to generate movement time verification data. Each entry in the data includes sensor identifier, adjacent target point pair, adjusted movement time, upper limit of duration, verification result, and correction mark.

[0063] Step S1249: Based on the movement time verification data, the adjusted movement time data that exceeds the time limit is corrected again, the actual movement path length data is optimized, the movement time is shortened, and the final movement time data is generated.

[0064] For the entries marked as needing correction in the mobile time verification data, call A again. The path planning algorithm optimizes the actual movement path by removing redundant path nodes, adjusting turning radii, and shortening the path length. For example, if the adjusted time from target point 1 to target point 3 of the ultrasonic sensor exceeds the upper limit, the optimized path length is shortened to L(1-3)''. The new movement time T_final = L(1-3)'' / V_rated is calculated, ensuring that T_final ≤ T_gap_max. The corrected final movement time and optimized path length are then combined to generate final movement time data. Each entry in the data includes the sensor identifier, adjacent target point pairs, final movement time, optimized path length, and verification result.

[0065] Where L(1-3) represents the straight-line distance from target point 1 to target point 3. L(1-3)' represents the corrected actual movement path length from target point 1 to target point 3. L(1-3)'' represents the further optimized path length from target point 1 to target point 3.

[0066] Step S12410: Integrate the sensor movement basic data, movement time verification data, and final movement time data, arrange them in the order of sensor identifier and target sequence, and generate sensor movement time data.

[0067] The sensor's basic movement data, movement time verification data, and final movement time data are linked and integrated according to the sensor identifier. Movement-related data of the same sensor are bound together and arranged in target point sequence order. For example, the movement time data of an ultrasonic sensor includes its target point coordinate sequence, the final movement time of all adjacent target points, path length, and verification results. The integrated data is sorted according to the sensor identifier to generate the final sensor movement time data, which can be directly used for configuring the action connection gaps in time-series staggered scheduling.

[0068] Step S125: Based on the independent detection period segmentation results and sensor movement time data, reserve an action connection gap between two adjacent independent detection periods. The duration of the action connection gap is set to the time length corresponding to the movement time data of the previous sensor, and generate action connection gap configuration data.

[0069] First, iterate through adjacent time periods in the independent detection time period segmentation results and extract the time length T_move_prev corresponding to the movement time data of the previous sensor. Next, reserve an action transition gap between the start time of the current time period and the end time of the previous time period, with the gap duration set to T_move_prev, ensuring that the current sensor starts its detection action only after the previous sensor has completed its movement. For example, if the end time of the previous sensor's time period is t_prev_end and the movement time is T_move_prev, then the start time of the current sensor's time period is t_prev_end + T_move_prev, and the duration of the action transition gap is T_move_prev. Finally, organize the action transition gap information of all adjacent time periods to form action transition gap configuration data. Each entry in the data includes the previous sensor identifier, the current sensor identifier, the gap start time, and the gap end time and duration.

[0070] Step S126: Based on the independent detection time period division results, action connection gap configuration data and the movement path length in the sensor movement time data, determine the detection action start time, detection action execution time period and detection action end time of each sensor, and generate sensor action timing node data.

[0071] First, for each sensor, based on the time period information in the independent detection time period segmentation results, the detection action start time is determined as the start time of the time period, the detection action execution time is the period from the start time to the start time of the time period plus the detection action duration T_k, and the detection action end time is the end time of the detection action execution time. Next, based on the movement path length in the sensor movement time data, the detection action start time is adjusted to ensure that detection is initiated only after the movement is complete. For example, if the movement time from the current target point to the next target point is T_move, then the detection action start time is the movement completion time, i.e., the end time of the previous detection action plus T_move. Finally, the detection action timing node information for each sensor is organized to form sensor action timing node data. Each entry in the data includes the sensor identifier, target point identifier, detection action start time, execution time period, and end time.

[0072] Step S127: Associate the sensor action timing node data with the signal reception sensitivity parameter in the sensor detection action basic data, mark the sensor's signal reception adaptation range in each independent detection period, and generate timing-sensitivity association data.

[0073] First, for each sensor's independent detection period, the time period information is extracted from the sensor action timing node data, along with the signal receiving sensitivity parameter S_k from the sensor detection action basic data. Next, the signal receiving adaptation range of the sensor within that time period is determined based on the signal receiving sensitivity parameter, i.e., the range of signal strengths that can be effectively identified [S_min_k, S_max_k], where S_min_k is the signal receiving sensitivity parameter S_k, and S_max_k is the maximum signal strength the sensor can withstand. Finally, the signal receiving adaptation range for each independent detection period is correlated with the time period information to form time-series-sensitivity correlation data. Each entry in the data includes the sensor identifier, the start time of the time period, the end time of the time period, the signal receiving adaptation range, and the corresponding sensitivity parameter. S_min_k, S_max_k: For any sensor k, S_min_k represents the lower limit of its signal receiving adaptation range, usually equal to its signal receiving sensitivity parameter; S_max_k represents the upper limit of its signal receiving adaptation range, i.e., the maximum signal strength that the sensor can normally receive without saturation.

[0074] Step S128: Based on the timing-sensitivity correlation data and the signal propagation attenuation parameter of the previous sensor, adjust the duration of the action connection gap to generate action connection gap adjustment data.

[0075] First, the signal propagation attenuation parameter α from the previous sensor is extracted. This parameter represents the signal strength attenuation rate during spatial propagation. Next, combining the signal reception adaptation range [S_min_curr, S_max_curr] of the current sensor from the time-sensitivity correlation data, the attenuated strength of the signal from the previous sensor at the current sensor's receiver is calculated. If the attenuated strength is still greater than the current sensor's signal reception sensitivity parameter S_min_curr, the duration of the action connection gap needs to be extended until the attenuated strength is less than S_min_curr. Specifically, the adjustment process involves calculating the time T_delay required for the signal to attenuate to S_min_curr and adjusting the duration of the action connection gap to the original duration plus T_delay. Finally, the adjusted action connection gap information is compiled into action connection gap adjustment data. Each entry in the data includes the previous sensor identifier, the current sensor identifier, the gap duration before adjustment, the gap duration after adjustment, and the reason for the duration adjustment. S_max_curr: represents the upper limit of the current sensor's signal reception adaptation range, i.e., the maximum signal strength the sensor can withstand. Signals exceeding this strength may cause saturation or damage to the sensor's receiving unit.

[0076] Step S129: Integrate the results of independent detection time period division, action connection gap adjustment data, sensor action timing node data and timing-sensitivity correlation data to generate the initial timing staggered arrangement result.

[0077] First, the time period information from the independent detection time period segmentation results is integrated with the gap information from the action connection gap adjustment data to ensure continuous and non-overlapping connection between time periods and gaps. Next, the detection action timing information from the sensor action timing node data is matched with the integrated time period-gap information to ensure that each detection action corresponds to the correct time period. Simultaneously, the signal reception adaptation range from the timing-sensitivity correlation data is associated with the corresponding time period to form complete timing information. Finally, all integrated data is arranged in chronological order to form the initial timing staggered arrangement result, which includes the sensor identifier, detection action timing, signal reception adaptation range, and action connection gap information for each time period.

[0078] Step S1210: Based on the duration of the detection action of each sensor, adjust the duration of its corresponding independent detection period, and supplement the driving command parameters for sensor action switching in the initial timing staggered arrangement result to generate the timing staggered arrangement result.

[0079] First, for each sensor's detection action duration T_k, compare it with the duration of its corresponding independent detection period. If the period is longer than T_k, adjust the period duration to T_k and simultaneously adjust the start time of subsequent periods to ensure no overlap in the overall timing. If the period is shorter than T_k, extend the period duration to T_k and adjust the start time of subsequent periods. Next, supplement the initial timing staggered arrangement result with driving instruction parameters for sensor action switching, including the trigger time, trigger signal type, and actuator instruction code. For example, at the end of the action transition interval, generate a driving instruction to trigger the current sensor to start its detection action. The instruction includes the sensor identifier, start time, and action code. Finally, integrate the adjusted period duration with the supplemented driving instruction parameters to generate the final timing staggered arrangement result, which can be directly used for the robot's sensor action control.

[0080] Step S130: Collect electromagnetic interference frequency and intensity data of the robot inspection area, combine them with the timing staggered scheduling results, assemble shielding and blocking devices, signal filtering channels and power supply stability lines that are adapted to the signal transmission frequency range and signal reception sensitivity parameters of each sensor, perform the adaptation and deployment of hardware suppression structure, and generate hardware suppression deployment parameters.

[0081] In this embodiment, based on the electromagnetic environment characteristics of the new energy power station inspection area and combined with the time-series staggered scheduling results generated above, corresponding hardware suppression structures are configured for the three types of sensors, and finally, hardware suppression deployment parameters that can be directly executed are generated.

[0082] Step S131: Using electromagnetic interference detection equipment, collect electromagnetic interference frequency and intensity data at different locations within the robot's inspection area, record the propagation direction information of electromagnetic interference, and generate electromagnetic interference data for the inspection area.

[0083] First, the electromagnetic interference (EMI) detection equipment mounted on the quadruped inspection robot is controlled to traverse key points within a preset inspection area, stopping at each point for a preset duration to collect data. The collected EMI frequency range covers [f_inter_min, f_inter_max], corresponding to common EMI frequency bands in new energy power plants; EMI intensity is represented by the power value per unit area, P_inter, and multiple sets of data are collected at each point and averaged. Simultaneously, the propagation direction vector (Dx, Dy, Dz) of EMI is recorded using the device's built-in direction sensor, based on the robot's base coordinate system. Finally, the collected data from all points is organized according to position coordinates to form EMI data for the inspection area. Each entry in the data includes position coordinates (X_p, Y_p, Z_p), EMI frequency range, average EMI intensity, and propagation direction vector. f_inter_min: represents the lower limit of the EMI frequency range collected by the EMI detection equipment. f_inter_max: represents the upper limit of the EMI frequency range collected by the EMI detection equipment.

[0084] Step S132: Extract the independent detection time period, signal transmission frequency range, and time-sensitivity correlation data of each sensor from the time-series staggered scheduling results to generate sensor time-series parameter data.

[0085] First, the independent detection time period information for each sensor is extracted from the time-series staggered scheduling results, including the start time t_start_m, the end time t_end_m, and the corresponding sensor identifier. Next, the signal transmission frequency range of each sensor is extracted: [f1_min, f1_max] for the ultrasonic sensor, [f2_min, f2_max] for the ultra-high frequency sensor, and [f3_min, f3_max] for the transient ground voltage sensor. Simultaneously, the signal reception adaptation range [S_min_k, S_max_k] and the corresponding sensitivity parameter S_k are extracted from the time-series-sensitivity correlation data. Finally, the above information is integrated according to the sensor identifier to form sensor time-series parameter data. Each entry in the data includes the sensor identifier, independent detection time period, signal transmission frequency range, signal reception adaptation range, and sensitivity parameter.

[0086] Step S133: Based on the signal transmission frequency range in the sensor timing parameter data and the electromagnetic interference frequency in the electromagnetic interference data of the inspection area, select a shielding material that covers both the sensor signal transmission frequency and the electromagnetic interference frequency, process and manufacture a shielding device, and generate basic data for the shielding device.

[0087] First, for each sensor's signal transmission frequency range [f_k_min, f_k_max], match the corresponding electromagnetic interference frequency interval [f_inter_k_min, f_inter_k_max] in the electromagnetic interference data of the inspection area to determine the total shielding frequency band [f_shield_min, f_shield_max] that needs to be covered. The minimum value of this total shielding frequency band is the smaller of the two minimum values, and the maximum value is the larger of the two maximum values. Next, select the corresponding shielding material based on the total shielding frequency band. The shielding effectiveness of the material must meet the requirement that the attenuation in this frequency band is greater than a preset threshold to ensure that the interference signal is effectively shielded. Subsequently, based on the sensor's installation dimensions at the robot's end effector, fabricate a suitable shielding device. The device has an arc-shaped shell to avoid obstructing the sensor's signal transmission path. Finally, organize the shielding material parameters, shielding frequency band, and device size information to form the basic data of the shielding device. Each entry in the data includes the sensor identifier, shielding frequency band, shielding material type, and device dimensions. f_k_min, f_k_max: For any sensor k, these represent the lower and upper limits of its own signal transmission frequency range, respectively. f_inter_k_min, f_inter_k_max: For the inspection area corresponding to sensor k, these represent the lower and upper limits of the electromagnetic interference frequency range of that area, respectively. f_shield_min, f_shield_max: These represent the lower and upper limits of the total shielding frequency band that the selected shielding material needs to cover. This range must cover both the sensor signal transmission frequency and the relevant electromagnetic interference frequency.

[0088] Step S134: Combining the installation coordinates of the sensor at the robot end with the signal transmission direction in the sensor timing parameter data, adjust the installation angle of the shielding device so that the shielding surface of the shielding device faces the electromagnetic interference propagation direction, while avoiding the sensor signal transmission path, and generate the installation parameters of the shielding device.

[0089] Step S1341: Using the robot's positioning system, obtain the three-dimensional coordinates of each sensor installed on the robot's end effector, record the fixed position of the sensor installation point, and generate sensor installation coordinate data.

[0090] The robot's laser-vision-force cooperative positioning system acquires the three-dimensional coordinates (X_s, Y_s, Z_s) of the ultrasonic, ultra-high frequency, and transient ground voltage sensors mounted on the end effector. These coordinates are based on the robot's base coordinate system, with the mounting point being the connection center between the sensor and the end effector. For example, the ultrasonic sensor's coordinates are (X_s1, Y_s1, Z_s1), and the ultra-high frequency sensor's coordinates are (X_s2, Y_s2, Z_s2). The sensor's identifier, coordinates, and mounting point type are then compiled to generate sensor mounting coordinate data. Each entry in this data includes the sensor identifier, the three-dimensional coordinates, the mounting point identifier, and the mounting direction vector.

[0091] Step S1342: Extract the signal emission direction data of each sensor from the sensor timing parameter data. The signal emission direction data is determined based on the sensor detection principle and the relative position of the detection target point of the device, and the sensor emission direction data is generated.

[0092] From the sensor timing parameter data, the signal emission direction vector (Ds_x, Ds_y, Ds_z) of each sensor is extracted. This signal emission direction vector is determined by the coordinates of the sensor installation coordinates pointing to the corresponding target point of the device. For example, the direction vector of the ultrasonic sensor pointing to target point 1 is (X1-X_s1, Y1-Y_s1, Z1-Z_s1). After normalization, a unit direction vector is obtained. The identifier of each sensor, the signal emission direction vector, and the corresponding target point identifier are organized to generate sensor emission direction data. Each entry in the data includes the sensor identifier, the signal emission direction vector, the corresponding target point identifier, and the vector normalization coefficient.

[0093] Step S1343: Extract the propagation direction information of electromagnetic interference from the electromagnetic interference data of the inspection area, sort the main interference propagation directions according to the interference intensity, and generate main interference direction data.

[0094] From the electromagnetic interference data of the inspection area, the electromagnetic interference propagation direction vector (Dx, Dy, Dz) and the corresponding interference intensity P_inter are extracted for each point. The vectors are sorted from highest to lowest interference intensity, and the average of the top N high-intensity interference direction vectors is taken to obtain the main interference propagation direction vector (D_main_x, D_main_y, D_main_z). For example, the main interference direction in the GIS equipment area of ​​a new energy power station is (D_main_x1, D_main_y1, D_main_z1). The main interference direction data for each inspection area, main interference direction vector, and average interference intensity is then generated. Each entry in the data includes the area identifier, main interference direction vector, average interference intensity, and the number of points involved in the calculation.

[0095] Step S1344: Based on the sensor installation coordinate data and sensor transmission direction data, draw a schematic diagram of the sensor's signal transmission path, determine the spatial range of signal transmission, and generate signal transmission path data.

[0096] Based on the sensor installation coordinates (X_s, Y_s, Z_s) and the signal transmission direction vector (Ds_x, Ds_y, Ds_z), a spatial ray tracing method is used to draw the signal transmission path. The starting point of the path is the fixed installation point, and the ending point is the surface center point of the corresponding detection target point. The spatial range of the path is a cone centered on the ray and with the sensor beam angle as the diffusion angle. For example, if the beam angle of an ultrasonic sensor is α_ultra, the spatial range of the transmission path is the region of the cone angle α_ultra. The identifier of each sensor, the starting coordinates of the transmission path, the ending coordinates of the transmission path, the beam angle, and the spatial range boundary are organized to generate signal transmission path data. Each entry in the data contains a list of sensor identifier, starting point of the transmission path, ending point of the transmission path, beam angle, and spatial range boundary coordinates.

[0097] Step S1345: Based on the sensor installation coordinate data and the main interference direction data, set the installation reference position of the shielding device. The installation reference position is located on one side of the sensor's signal receiving unit and does not affect the signal transmission path. Generate installation reference position data.

[0098] The sensor's signal receiving unit is located opposite the transmitting unit. Based on the installation coordinates (X_s, Y_s, Z_s) and the main interference direction vectors (D_main_x, D_main_y, D_main_z), a mounting reference position (X_base, Y_base, Z_base) is determined on one side of the sensor receiving unit at a preset distance d from the fixed point. This ensures that the vertical distance from the reference position to the transmission path is greater than half the width of the sensor beam, thus not obstructing signal transmission. For example, the mounting reference position of an ultrasonic sensor is (X_s1+D_main_x1d, Y_s1+D_main_y1d, Z_s1+D_main_z1). d). Organize the identification, installation reference position, and vertical distance from the transmission path of each sensor to generate installation reference position data. Each entry in the data includes the sensor identification, installation reference position, vertical distance from the transmission path, and preset distance d.

[0099] Step S1346: Calculate the angle between the main interference direction data and the normal direction of the sensor signal receiving unit, adjust the shielding surface angle of the shielding device so that the shielding surface is perpendicular to the main interference propagation direction, and generate shielding surface angle data.

[0100] The normal direction vector of the sensor signal receiving unit is (D_rec_x, D_rec_y, D_rec_z), and the main interference direction vector is (D_main_x, D_main_y, D_main_z). Calculate the angle θ_inter between them: θ_inter = arccos[(D_rec_x, D_main_y, D_main_z). D_main) / (|D_rec| |D_main|)]. Adjust the normal direction of the shielding surface of the shielding device to be opposite to the main interference direction vector, i.e., (-D_main_x, -D_main_y, -D_main_z), ensuring that the shielding surface is perpendicular to the interference propagation path. For example, if the main interference direction is (D_main_x1, D_main_y1, D_main_z1), then the normal direction of the shielding surface is (-D_main_x1, -D_main_y1, -D_main_z1). Organize the identifier of each sensor, the normal direction of the shielding surface, the included angle θ_inter, and the adjustment angle value to generate shielding surface angle data. Each entry in the data includes the sensor identifier, the normal direction of the shielding surface, the interference receiving angle, and the adjustment angle value.

[0101] Step S1347: Combining the structural dimension data of the shielding device with the sensor installation coordinate data, set the installation height and horizontal offset of the shielding device so that the shielding surface completely covers the sensor's signal receiving unit, and generate installation position offset data.

[0102] The shielding device has structural dimensions of length W, width H, and thickness T. Based on the sensor mounting coordinates (X_s, Y_s, Z_s) and mounting reference position (X_base, Y_base, Z_base), the mounting height offset ΔZ and horizontal offsets ΔX and ΔY are calculated to ensure that the projected area of ​​the shielding surface completely covers the surface of the signal receiving unit. For example, the signal receiving unit of an ultrasonic sensor has a size of w_ultra. h_ultra is used to set the horizontal offset ΔX=(W-w_ultra) / 2, ΔY=(H-h_ultra) / 2, and the installation height offset ΔZ=0. The identification, installation height offset, horizontal offset, and device structural dimensions of each sensor are compiled to generate installation position offset data. Each entry in the data includes the sensor identification, height offset, horizontal X offset, horizontal Y offset, and device structural dimensions.

[0103] Step S1348: Through simulated installation testing, verify that the shielding device, under the installation state corresponding to the installation reference position data, shielding surface angle data, and installation position offset data, blocks electromagnetic interference in the main interference direction without blocking the signal transmission path of the sensor, and generates installation verification data.

[0104] Electromagnetic simulation software was used for simulated installation testing. The sensor installation coordinates, shielding device installation parameters, and main interference directions were input. The simulation calculated the attenuation of the interference signal and the transmission rate of the effective signal. If both the interference signal attenuation and the effective signal transmission rate exceeded a preset threshold, the verification passed. If it failed, the installation position offset or the angle of the shielding surface was fine-tuned, and the simulation was repeated. For example, in the simulation test of the ultrasonic sensor, the interference signal attenuation was 30dB (greater than the preset 25dB), and the effective signal transmission rate was 98% (greater than the preset 95%), thus the verification passed. The identification, simulation parameters, interference attenuation, signal transmission rate, and verification results for each sensor were compiled to generate installation verification data. Each entry in the data included the sensor identification, simulated interference attenuation, effective signal transmission rate, verification result, and fine-tuning record.

[0105] Step S1349: Based on the installation verification data, fine-tune the shielding surface angle data and the installation position offset data to optimize the shielding effect and signal transmission path avoidance effect, and generate the adjusted installation parameter data.

[0106] For items that failed the installation verification data, the shielding surface angle was fine-tuned in 1° increments, and the installation position offset was fine-tuned in 5mm increments. Simulation tests were then repeated until verification was successful. For example, if a UHF sensor failed the initial test, adjusting the shielding surface angle by 2° and increasing the horizontal X-offset by 5mm resulted in an interference attenuation of 28dB and a signal transmission rate of 97%, thus passing the verification. The sensor's identifier, the fine-tuned shielding surface angle, the fine-tuned offset, and the final simulation parameters were compiled to generate adjusted installation parameter data. Each item in the data includes the sensor identifier, the fine-tuned shielding surface normal direction, the fine-tuned height offset, the fine-tuned horizontal offset, and the final simulation result.

[0107] Step S13410: Integrate sensor installation coordinate data, sensor transmission direction data, main interference direction data, signal transmission path data, installation reference position data, shielding surface angle data, installation position offset data, installation verification data, and adjusted installation parameter data to generate shielding device installation parameters.

[0108] The parameters generated from all the above steps are linked and integrated according to the sensor identifier. Installation-related data for the same sensor are bound together as a group, containing full-dimensional information from basic coordinates to final adjustment parameters. For example, the installation parameters for an UHF sensor include its installation coordinates, signal transmission direction, main interference direction, reference position of the shielding device, and fine-tuned angle and offset. The integrated data is sorted according to the sensor identifier to generate the final shielding device installation parameters, which can be directly used for on-site deployment of robot hardware.

[0109] Step S135: For the signal receiving sensitivity parameter in the sensor timing parameter data, set the frequency interception range of the signal filtering channel so that the signal filtering channel only allows signals within the sensor signal receiving adaptation range to pass through, and generate the signal filtering channel frequency parameter.

[0110] First, for each sensor's signal receiving sensitivity parameter S_k, the corresponding signal receiving adaptation range [S_min_k, S_max_k] is extracted. Combined with the sensor's signal receiving frequency range [f_rec_k_min, f_rec_k_max], the allowable frequency range [f_pass_min, f_pass_max] of the signal filtering channel is determined. This allowable frequency range perfectly matches the signal receiving frequency range. Next, the frequency interception range is set to all frequency bands outside the allowable range; that is, frequency bands below f_pass_min or above f_pass_max are intercepted. Simultaneously, the attenuation threshold of the filtering channel is adjusted according to the signal receiving sensitivity parameter to ensure that interference signals with strength lower than S_k are completely intercepted, while valid signals with strength higher than S_k pass normally. Finally, the allowable frequency range, interception range, and attenuation threshold information are compiled to form the signal filtering channel frequency parameters. Each entry in the data includes the sensor identifier, allowable frequency range, interception frequency range, and attenuation threshold. f_rec_k_min, f_rec_k_max: For any sensor k, these represent the lower and upper limits of the frequency range of signals that it is designed to receive, respectively. f_pass_min, f_pass_max: These represent the lower and upper limits of the frequency range of signals allowed to pass through the signal filtering channel configured for a specific sensor. This range is usually matched with f_rec_k_min, f_rec_k_max.

[0111] Step S136: Connect the signal filtering channel in series to the signal receiving link of the sensor, fix the installation position of the signal filtering channel, so that the signal collected by the sensor is processed by the signal filtering channel before being transmitted to the data processing unit, and generate signal filtering channel deployment data.

[0112] First, based on the sensor's installation location on the robot's end effector, determine the installation location of the signal filtering channel. This location should be close to the sensor's signal output port to reduce additional interference during signal transmission. Next, connect the signal filtering channel's input port to the sensor's signal output port using a shielded cable. Connect the output port to the robot's data processing unit's input port to ensure the shielding of the signal transmission path. Then, use an insulated mounting bracket to fix the signal filtering channel at the designated location on the robot's end effector to prevent loosening due to robot movement. Finally, compile the installation location, cable parameters, and fixing method information to form the signal filtering channel deployment data. Each entry in the data includes the sensor identifier, installation 3D coordinates, cable type, and mounting bracket parameters.

[0113] Step S137: Analyze the power supply energy requirements of the sensor during detection action execution, action connection gap and standby state, adjust the voltage output value and current output value of the power supply stabilization line, and generate the voltage parameters of the power supply stabilization line.

[0114] First, for each sensor, the power supply voltage requirements V_op, V_wait, and V_standby, as well as the corresponding current requirements I_op, I_wait, and I_standby, are collected in the detection action execution state, action transition gap state, and standby state. Next, combining the independent detection period and action transition gap information from the timing staggered scheduling results, the power supply state of each sensor in different time periods is determined. For example, the power supply parameters for the detection action execution state are used in the independent detection period, and the power supply parameters for the action transition gap state are used in the action transition gap state. Subsequently, the voltage output range of the power supply stabilization line is adjusted to cover the voltage requirements in all states, while the upper limit of the current output is set to 1.2 times the maximum current requirement to ensure power supply stability. Finally, the voltage output values, current output values, and power supply period information in different states are compiled to form the voltage parameters of the power supply stabilization line. Each entry in the data includes the sensor identifier, detection state voltage, standby state voltage, transition state voltage, and corresponding upper limit of current. V_op and I_op represent the power supply voltage requirement and power supply current requirement of the sensor in the detection action execution state, respectively. V_wait, I_wait: These represent the required power supply voltage and current of the sensor during the intermittent operation gap, respectively. V_standby, I_standby: These represent the required power supply voltage and current of the sensor during standby, respectively.

[0115] Step S138: Connect voltage filter components and current regulator components in series in the power supply stabilization line to suppress voltage and current fluctuations in the power supply line and generate power supply stabilization line configuration data.

[0116] First, based on the voltage output range in the power supply stabilization line voltage parameters, the filtering frequency band of the voltage filter element is selected. This ensures that the frequency band covers the common fluctuation frequencies in the power supply system of new energy power plants, and that the insertion loss of the filter element is less than a preset threshold to avoid affecting the normal power supply to the sensor. Next, the voltage regulation range of the current regulator element is selected to match the upper limit of the current output, and the response time of the regulator element is less than a preset value to ensure rapid adjustment during current fluctuations. Then, the voltage filter element is connected in series at the input terminal of the power supply line, and the current regulator element is connected in series after the voltage filter element, close to the power supply input terminal of the sensor. Finally, the component model, installation location, and connection method information are compiled to form the power supply stabilization line configuration data. Each entry in the data includes the sensor identifier, voltage filter element model, current regulator element model, component installation location, and connection cable parameters.

[0117] Step S139: Integrate the installation parameters of the shielding and blocking device, the frequency parameters of the signal filtering channel, the deployment data of the signal filtering channel, the voltage parameters of the power supply stabilization line, and the configuration data of the power supply stabilization line to generate a draft of the hardware suppression deployment.

[0118] First, the hardware parameters generated in the above steps are categorized and integrated according to sensor identifiers, ensuring that all hardware suppression parameters for each sensor correspond to a unique identifier. Next, the logical consistency between parameters is checked; for example, whether the installation angle of the shielding / blocking device conflicts with the signal transmission path, and whether the frequency range of the signal filtering channel matches the sensor's receiving range. Then, the integrated parameters are sorted by hardware type: shielding / blocking device, signal filtering channel, and power supply stability line, forming a structured initial draft of the hardware suppression deployment. Each entry in the draft includes the sensor identifier and all parameter information corresponding to the three types of hardware.

[0119] Step S1310: Based on the start time of the independent detection period in the sensor timing parameter data, set the trigger timing of each control module in the hardware suppression structure and generate hardware suppression deployment parameters.

[0120] First, the start time t_start_m of each sensor's independent detection period is extracted from the sensor timing parameter data. For each hardware suppression structure's control module, a trigger sequence is set. For example, the shielding / blocking device's control module starts a preset time before t_start_m, adjusting the blocking surface angle to a preset position; the signal filtering channel's control module switches to the corresponding frequency range at t_start_m; and the power supply stabilization line's control module adjusts to the detection state's power supply parameters a preset time before t_start_m. Next, the shutdown sequence of each control module is set. For example, after the end time t_end_m of the period, the shielding / blocking device resets to the standby position, the signal filtering channel switches to the default range, and the power supply line adjusts to standby mode. Finally, the trigger sequence, shutdown sequence, and hardware parameters are integrated to generate the final hardware suppression deployment parameters. These parameters include control commands and time nodes that can directly drive the robot hardware.

[0121] Step S140: When the sensor enters the independent detection period and performs the detection action, the contact pressure data, contact angle data and sensor signal transmission and reception data of the sensor and the detection target point are collected. Combined with the timing staggered scheduling results and hardware suppression deployment parameters, the linkage and mutual suppression control of the robot's mechanical action and the sensor signal are executed to generate dynamic mutual suppression control data.

[0122] In this embodiment, based on the time-series staggered scheduling result, the time-series trigger signal is used to collect contact status and signal transmission and reception data for the sensor entering the independent detection time period. Combined with hardware suppression parameters, multi-dimensional regulation is performed to finally generate dynamic mutual inhibition regulation data that includes the entire regulation process.

[0123] Step S141: Using the pressure sensing element mounted on the end of the robot, collect in real time the contact pressure data and contact angle data between the sensor currently performing the detection action and the detection target point of the device, and summarize them to form contact state acquisition data.

[0124] First, before the sensor enters its independent detection period, the pressure sensing element at the robot's end effector is activated. This pressure sensing element is a six-dimensional force sensor capable of acquiring pressure components (Fx, Fy, Fz) and torque components (Mx, My, Mz) in three-dimensional space. Once the sensor contacts the target point, it collects contact pressure data in real time. The pressure data is represented by the resultant force value F_total, obtained by calculating the vector magnitude of the three-dimensional pressure components. Simultaneously, the sensor's built-in angle sensor collects contact angle data. The angle data is represented by the angle θ between the normal to the sensor's contact surface and the normal to the target surface, ranging from 0 to a preset angle threshold. Subsequently, the average of multiple sets of pressure and angle data collected per second is taken and recorded chronologically to form contact state acquisition data. Each entry in the data includes a timestamp, the resultant contact pressure value, the contact angle value, and the pressure and torque components.

[0125] Step S142: Collect signal transmission power data and signal transmission frequency data through the signal transmission monitoring element of the sensor currently performing the detection action, and collect signal reception strength data and signal reception frequency data through the signal reception monitoring element, and summarize them to form signal transmission and reception data.

[0126] First, the signal transmission monitoring element of the current sensor is triggered to collect the signal transmission power P_trans in real time, which is expressed as the energy value per unit time; at the same time, the signal transmission frequency f_trans is collected to ensure that it is within the preset range of [f_k_min, f_k_max]. Next, the signal reception monitoring element is triggered to collect the signal reception strength P_rec in real time, which is the actual signal power at the sensor's receiving end; at the same time, the signal reception frequency f_rec is collected to identify the effective signal frequency and interference signal frequency. Subsequently, the average of multiple sets of transmission and reception data collected per second is taken and recorded in chronological order to form signal transmission and reception acquisition data. Each entry in the data includes the acquisition timestamp, signal transmission power, signal transmission frequency, signal reception strength, effective signal frequency range, and interference signal frequency range.

[0127] Step S143: Extract the sensor identifier, detection action start time, detection action end time, and time-sensitivity correlation data corresponding to the current independent detection period from the time-series staggered scheduling results to generate the time-series data for the current period.

[0128] First, the robot's task management module obtains the information of the currently triggered independent detection period, extracts the corresponding sensor identifier, and identifies it as one of an ultrasonic, ultra-high frequency, or transient ground voltage sensor. Next, the start time (t_start_curr) and end time (t_end_curr) of the detection action for this period are extracted to determine the control time range. Simultaneously, the corresponding signal reception adaptation range [S_min_curr, S_max_curr] and sensitivity parameter S_curr are extracted from the time-series-sensitivity correlation data as the basis for signal control. Finally, the above information is integrated to form the time-series data for the current period, which includes the current sensor identifier, the start and end times of the period, the signal reception adaptation range, and the sensitivity parameter.

[0129] Step S144: Retrieve the shielding and blocking device installation parameters, signal filtering channel frequency parameters, and power supply stability line voltage parameters corresponding to the current sensor identifier from the hardware suppression deployment parameters, and generate the current hardware suppression parameters.

[0130] First, based on the sensor identifier in the current time-series data, the corresponding hardware parameters are matched in the hardware suppression deployment parameters. This involves retrieving the shielding device installation parameters, including the current sensor's installation coordinates (X_s_curr, Y_s_curr, Z_s_curr), the normal direction of the shielding surface, and the installation angle; retrieving the signal filtering channel frequency parameters, including the allowed pass frequency range [f_pass_min_curr, f_pass_max_curr] and the attenuation threshold; and retrieving the power supply stability line voltage parameters, including the voltage V_op_curr and the current limit I_op_max_curr under detection conditions. Finally, these parameters are integrated to form the current hardware suppression parameters, which can be directly used for hardware control of the current sensor.

[0131] Step S145: Compare the contact pressure data in the contact state acquisition data with the pressure adaptation value corresponding to the time-sensitivity correlation data in the time series data of the current time period, generate the pressure adjustment drive signal of the robot arm, drive the pressure actuator of the robot arm to adjust the contact pressure between the sensor and the target point of the device, and generate pressure adjustment execution data.

[0132] Step S1451: Extract the pressure adaptation value corresponding to the current sensor identifier from the time-series data of the current time period. The pressure adaptation value is determined based on the sensor's signal receiving sensitivity parameter and the surface material data of the device's detection target.

[0133] From the time-sensitivity correlation data of the current period's time-series data, extract the pressure adaptation value F_target of the current sensor. This pressure adaptation value is jointly determined by the sensor signal reception sensitivity parameter S_k and the elastic modulus E_m of the target surface material, that is, F_target = kS_k / E_m, where k is the material correction coefficient. For example, if the sensitivity parameter of the ultrasonic sensor is S_ultra and the target surface material is epoxy resin (elastic modulus E1), then the pressure adaptation value F_target1 = k1S_ultra / E1. Organize the current sensor identification, pressure adaptation value, sensitivity parameter, material elastic modulus, and correction coefficient to generate the basic data of the pressure adaptation value. Each entry in the data includes the sensor identification, pressure adaptation value, signal reception sensitivity parameter, material elastic modulus, and correction coefficient.

[0134] Step S1452: Extract the contact pressure data from the contact state acquisition data, compare the contact pressure data with the pressure adaptation value, obtain the difference data between the two, and generate the pressure difference data.

[0135] Extract the real-time contact pressure resultant force value F_total from the contact state acquisition data, and calculate the difference ΔF = F_total - F_target with the pressure adaptation value F_target. If F_total > F_target, then ΔF is positive, indicating that the contact pressure is too high; if F_total < F_target, then ΔF is negative, indicating that the contact pressure is too low; if F_total = F_target, then ΔF is 0. Organize the current sensor identification, real-time contact pressure value, pressure adaptation value, difference ΔF, and difference sign to generate the pressure difference data. Each entry in the data includes the sensor identification, timestamp, real-time contact pressure value, pressure adaptation value, pressure difference, and difference sign.

[0136] Step S1453: Based on the drive parameters of the pressure actuator of the robot manipulator, set the response rate parameter for pressure adjustment. This response rate parameter corresponds to the pressure difference data. Set the response rate parameter for pressure adjustment based on the pressure difference data to generate the pressure response rate data.

[0137] The driving parameters of the pressure actuator of the robotic arm include the maximum adjustment rate V_max_reg and the minimum adjustment rate V_min_reg. For the absolute value of the pressure difference ΔF |ΔF|, the response rate V_reg = V_min_reg + (V_max_reg - V_min_reg)(|ΔF| / F_target) is set; that is, the larger the difference, the faster the adjustment rate. For example, if the absolute value of the pressure difference of the ultrasonic sensor is |ΔF1|, then the response rate V_reg1 = V_min_reg + (V_max_reg - V_min_reg)(|ΔF1| / F_target1). The current sensor identifier, absolute value of the pressure difference, response rate, and maximum / minimum adjustment rate are organized to generate pressure response rate data. Each entry in the data includes the sensor identifier, timestamp, absolute value of the pressure difference, adjustment response rate, maximum adjustment rate, and minimum adjustment rate.

[0138] Step S1454: Based on the pressure difference data and pressure response rate data, calculate the change in drive current and the change in drive voltage of the robot arm pressure actuator, and generate pressure drive parameter data.

[0139] The driving current I of the pressure actuator in a robotic arm is linearly related to the contact pressure F, i.e., I = k_fF + b_f, where k_f is the pressure-current coefficient and b_f is the no-load current. Based on the pressure difference ΔF, the change in driving current ΔI = k_fΔF and the change in driving voltage ΔU = R_mΔI are calculated, where R_m is the internal resistance of the actuator motor. For example, if the pressure difference of the ultrasonic sensor is ΔF1, then the change in current ΔI1 = k_fΔF1 and the change in voltage ΔU1 = R_m. ΔI1. The current sensor identifier, pressure difference, change in drive current, change in drive voltage, pressure-current coefficient, and motor internal resistance are processed to generate pressure drive parameter data. Each entry in the data includes sensor identifier, timestamp, pressure difference, change in drive current, change in drive voltage, pressure-current coefficient, and motor internal resistance.

[0140] Step S1455: Combining the start and end times of the detection action in the current time series data, set the start and end times of pressure regulation so that pressure regulation is completed before the detection action is executed, and generate pressure regulation time series data.

[0141] The start time of the current detection action is t_start_curr, and the pressure regulation start time is set to t_start_curr - t_pre_reg, where t_pre_reg is the preset adjustment preparation time. The adjustment completion time is t_start_curr - t_delay_reg, where t_delay_reg is the preset delay time, ensuring sufficient time for stable contact pressure after adjustment. For example, if the detection start time of the ultrasonic sensor is t_start1 and the adjustment preparation time is t_pre_reg1, then the adjustment start time is t_start1 - t_pre_reg1, and the completion time is t_start1 - t_delayreg1. The current sensor identifier, adjustment start time, adjustment completion time, detection action start time, preparation time, and delay time are organized to generate pressure regulation timing data. Each entry in the data includes the sensor identifier, adjustment start time, adjustment completion time, detection start time, preparation time, and delay time.

[0142] Step S1456: Encode the pressure drive parameter data, pressure response rate data, and pressure regulation timing data according to the control protocol of the robot arm to generate a standardized pressure regulation drive signal and generate pressure drive signal data.

[0143] The robotic arm uses the CANopen control protocol, encoding the drive current change ΔI, adjustment response rate V_reg, and adjustment start time t_reg_start according to the protocol frame format. The frame header contains the sensor identifier and command type, the frame data segment contains the parameter values, and the frame tail contains the checksum. For example, the drive signal frame header of an ultrasonic sensor is 0x01 (ultrasonic sensor identifier) ​​+ 0x0A (pressure adjustment command), the frame data segment contains the hexadecimal values ​​of ΔI1, V_reg1, and t_reg_start1, and the frame tail contains the CRC checksum. The current sensor identifier, protocol frame content, parameter encoding values, and checksum are organized to generate pressure drive signal data. Each entry in the data includes the sensor identifier, protocol frame header, frame data segment, frame tail checksum, and parameter correspondence.

[0144] Step S1457: Send the pressure drive signal data to the pressure actuator controller of the robot arm to drive the motor of the pressure actuator to run, move the sensor closer to or away from the detection target of the device, adjust the contact pressure, and generate pressure mechanism action data.

[0145] The pressure drive signal is sent to the pressure actuator controller of the robotic arm via the robot's CAN bus. After decoding, the controller outputs the corresponding current and voltage to drive the servo motor. If ΔF is positive (excessive pressure), the motor moves the sensor away from the target point to reduce the contact pressure; if ΔF is negative (insufficient pressure), the motor moves the sensor closer to the target point to increase the contact pressure; if ΔF is 0, the motor remains stationary. For example, if ΔF1 of an ultrasonic sensor is positive, the motor moves the sensor along the negative Z-axis to reduce the contact pressure. The current sensor identifier, signal transmission time, motor rotation direction, displacement, and drive current value are processed to generate pressure mechanism action data. Each entry in the data includes sensor identifier, timestamp, motor rotation direction, displacement, real-time drive current, and execution status.

[0146] Step S1458: Collect contact pressure data in real time during the adjustment process through pressure sensing elements, record the entire process data of pressure data changing from the initial value to the pressure adaptation value, and generate pressure adjustment trajectory data.

[0147] The pressure sensing element collects 10 sets of contact pressure data per second, recording the pressure change trajectory from adjustment initiation to adjustment completion, including the initial pressure value F_init, the pressure value sequence during the process [F1, F2, ..., Fn], and the final stable pressure value F_stable. For example, the initial pressure value of an ultrasonic sensor is F_init1, the pressure value sequence during the process is [F1a, F1b, ..., F1n], and the final stable pressure value is F_stable1 (close to F_target1). The current sensor identifier, pressure change trajectory, initial pressure, final stable pressure, and acquisition frequency are then processed to generate pressure adjustment trajectory data. Each entry in the data includes the sensor identifier, timestamp sequence, pressure value sequence, initial pressure, final stable pressure, and acquisition frequency.

[0148] Step S1459: Extract the stable phase pressure data from the pressure adjustment trajectory data, ensure that the stable phase pressure data is consistent with the pressure adaptation value, and generate pressure adjustment verification data.

[0149] The pressure data within 10 seconds after adjustment is extracted from the pressure adjustment trajectory data, and the average value F_avg is calculated. If |F_avg - F_target| ≤ the preset error threshold ΔF_err, the verification is successful; if |F_avg - F_target| > ΔF_err, a secondary adjustment is triggered. For example, if the average pressure during the stable phase of an ultrasonic sensor is F_avg1 and the error threshold is ΔF_err1, the verification is successful if |F_avg1 - F_target1| ≤ ΔF_err1. The current sensor identifier, the average pressure during the stable phase, the pressure adaptation value, the error threshold, the verification result, and the secondary adjustment marker are organized to generate pressure adjustment verification data. Each entry in the data includes the sensor identifier, the average stable pressure, the pressure adaptation value, the error threshold, the verification result, and the secondary adjustment marker.

[0150] Step S14510: Integrate pressure drive signal data, pressure mechanism action data, pressure adjustment trajectory data, and pressure adjustment verification data to generate pressure regulation execution data.

[0151] The pressure regulation data generated in all the above steps are linked and integrated according to sensor identifiers and timestamps, with signals, actions, trajectories, and verification data for the same regulation cycle grouped together. For example, the pressure regulation execution data of an ultrasonic sensor includes its drive signal, motor action, pressure trajectory, and verification results. The integrated data is arranged in chronological order to generate the final pressure regulation execution data, which can be directly used for data recording and feedback of dynamic mutual inhibition control.

[0152] Step S146: Refer to the angle adaptation value corresponding to the contact angle data in the contact state acquisition data and the spatial distribution information of the detection target point of the device, generate the angle adjustment drive signal of the robot arm, drive the angle actuator of the robot arm to adjust the contact angle between the sensor and the detection target point of the device, and generate angle adjustment execution data.

[0153] First, the surface normal vector (Nx, Ny, Nz) of the current target point is extracted from the spatial distribution information of the target points detected by the device. Combined with the normal direction of the current sensor contact surface, an angle adaptation value θ_target is set. This angle adaptation value is the target angle between the normal directions of the two, typically 0, to ensure complete contact between the sensor contact surface and the target surface. Next, the contact angle value θ from the contact state acquisition data is compared with θ_target, and the angle difference Δθ = θ - θ_target is calculated. If Δθ is greater than a preset threshold, an angle adjustment drive signal is generated, driving the robot arm's angle actuator to adjust the sensor's pose, making θ approach θ_target. During the adjustment process, the rotation angles of each joint of the robot arm are calculated using the robot's kinematic model to ensure the adjustment path avoids surrounding equipment. Simultaneously, feedback data after angle adjustment is collected in real time, including the adjusted contact angle value, joint rotation angle, and adjustment time. Finally, the drive signal parameters, joint rotation commands, and feedback data are organized to form angle adjustment execution data. Each entry in the data includes an adjustment timestamp, target angle, actual feedback angle, joint rotation angle, and adjustment time.

[0154] Step S147: Based on the signal reception strength data in the signal transmission and reception acquisition data and the signal filtering channel frequency parameter in the current hardware suppression parameters, adjust the real-time filtering threshold of the signal filtering channel and generate filtering parameter adjustment data.

[0155] First, the received signal strength P_rec is extracted from the signal transmission and reception data. The effective signal strength P_rec_valid and the interference signal strength P_rec_inter are separated. The effective signal frequency is within the range [f_pass_min_curr, f_pass_max_curr], and the rest is interference. Next, the ratio of interference signal strength to effective signal strength R_inter = P_rec_inter / P_rec_valid is calculated. If R_inter is greater than a preset threshold, the real-time filtering threshold of the signal filtering channel needs to be adjusted. The adjustment method is to narrow the allowed frequency range, making the new range closer to the actual frequency range of the current effective signal, while increasing the attenuation threshold to enhance the filtering capability of interference signals. If R_inter is less than the preset threshold, the current filtering parameters are maintained. Subsequently, the adjusted frequency range and attenuation threshold are converted into control signals for the signal filtering channel and sent to the channel's control module. Simultaneously, the adjusted signal reception feedback data, including the new effective signal strength and interference signal strength, is collected in real time. Finally, the parameters before adjustment, the parameters after adjustment, the control signals, and the feedback data are organized to form the filter parameter adjustment data. Each entry in the data includes the adjustment timestamp, the frequency range before adjustment, the frequency range after adjustment, the attenuation threshold before adjustment, the attenuation threshold after adjustment, the effective feedback signal strength, and the feedback interference signal strength.

[0156] Step S148: Based on the signal transmission frequency data in the signal transmission and acquisition data and the sensor identifier in the current time period sequence data, send a signal transmission pause command to other non-currently detected sensors on the robot to stop the active signal transmission action of the non-currently detected sensors and generate sensor pause control data.

[0157] For example, step S1481: Extract the sensor identifier currently performing the detection action from the time series data of the current time period, determine the identity of the sensor currently in an independent detection period, and generate the current detection sensor identifier data.

[0158] Extract the current sensor identifier from the time series data of the current time period. For example, if the current time period is an independent detection period for an ultrasonic sensor, the sensor identifier is 0x01. Organize the current sensor identifier, time period number, detection start time, and end time to generate the current detection sensor identifier data. Each entry in the data includes the sensor identifier, time period number, detection start time, detection end time, and sensor type (ultrasonic / ultra-high frequency / transient ground voltage).

[0159] Step S1482: Retrieve the identification list of all sensors on the robot, remove the sensors corresponding to the currently detected sensor identification data, and generate a list of non-currently detected sensors.

[0160] The robot's sensor identifier list is [0x01 (ultrasound), 0x02 (ultra-high frequency), 0x03 (transient ground voltage)]. After removing the currently detected sensor identifier, a list of non-currently detected sensors is generated. For example, if the currently detected sensor is 0x01, the list of non-currently detected sensors would be [0x02, 0x03]. The current sensor identifier, the list of non-currently detected sensor identifiers, and the list of sensor types are then organized to generate a list of non-currently detected sensors. Each entry in this list contains the current sensor identifier, the list of non-currently detected sensor identifiers, the list of non-currently detected sensor types, and the total number of sensors.

[0161] Step S1483: Extract the signal transmission frequency data of the current detection sensor from the signal transmission and reception acquisition data; traverse the signal transmission frequency range of each non-current detection sensor in the sensor timing parameter data, filter out the sensors whose signal transmission frequency range intersects with the signal transmission frequency data of the current detection sensor, and generate potential interference sensor data.

[0162] Extract the real-time signal transmission frequency f_trans of the currently detected sensor from the signal transmission and reception data. Iterate through the signal transmission frequency range [f_k_min, f_k_max] of non-currently detected sensors. If f_trans ∈ [f_k_min, f_k_max], it is identified as a potential interfering sensor. For example, the current ultrasonic sensor's transmission frequency f_trans1 = 50kHz, while the frequency range of the ultra-high frequency sensor is [30kHz, 60kHz], showing an overlap, thus identifying it as potential interference. The transient ground voltage sensor's frequency range is [100kHz, 200kHz], showing no overlap, and is therefore excluded. Organize the current sensor identifier, real-time transmission frequency, list of potential interfering sensor identifiers, and corresponding frequency ranges to generate potential interfering sensor data. Each entry in the data includes the current sensor identifier, timestamp, real-time transmission frequency, list of potential interfering sensor identifiers, and list of corresponding frequency ranges.

[0163] Step S1484: For each sensor in the potential interference sensor data, generate a signal transmission pause command. The signal transmission pause command includes the sensor identifier, pause start time, pause duration, and pause duration equal to the duration of the independent detection period of the currently detected sensor, thus generating the basic data for the pause command.

[0164] The duration of the current independent detection period is T_curr = t_end_curr - t_start_curr, the pause / start time is t_start_curr, and the pause duration is T_curr. For example, if the current ultrasonic sensor's period duration is T_curr1 = t_end1 - t_start1, then the pause command for the UHF sensor includes identifier 0x02, start time t_start1, and duration T_curr1. The current sensor identifier, potential interfering sensor identifier, pause / start time, pause duration, and period duration are organized to generate basic pause command data. Each entry in this data includes the current sensor identifier, potential interfering sensor identifier, pause / start time, pause duration, and period duration.

[0165] Step S1485: According to the communication protocol between the robot and the sensor, the basic data of the pause instruction is formatted and encoded into a digital signal instruction that the sensor can recognize, thereby generating the encoded pause instruction data.

[0166] The robot communicates with the sensors using the RS485 protocol. The command frame format is: frame header (0xAA) + sensor identifier + command type (0x0B for pause command) + pause start time (4-byte timestamp) + pause duration (4-byte duration value) + frame tail (0x55). For example, the pause command frame for a UHF sensor is 0xAA + 0x02 + 0x0B + 4-byte encoding of t_start1 + 4-byte encoding of T_curr1 + 0x55. The current sensor identifier, potential interfering sensor identifier, protocol frame content, encoding parameters, and checksum are organized to generate encoded pause command data. Each entry in the data includes the current sensor identifier, potential interfering sensor identifier, protocol frame content, start time encoding, duration encoding, and checksum.

[0167] Step S1486: Through the robot's sensor communication bus, send the encoded pause command data one by one to each sensor in the potential interference sensor data, record the command sending time and sending status, and generate command sending record data.

[0168] The robot uses its RS485 communication bus to send encoded pause commands one by one to potential interference sensors, recording the command transmission time (t_send), transmission status (success / failure), and transmission count. For example, for a UHF sensor, the command transmission time is t_send1, the transmission status is successful, and the transmission count is 1. The current sensor identifier, potential interference sensor identifier, command transmission time, transmission status, transmission count, and bus number are then organized to generate command transmission record data. Each entry in this data includes the current sensor identifier, potential interference sensor identifier, timestamp, transmission status, transmission count, and bus number.

[0169] Step S1487: Receive the command response signal returned by the potential interference sensor, confirm whether each potential interference sensor has successfully received and executed the signal transmission pause command, and generate command response feedback data.

[0170] After receiving the command, the potential interference sensor returns a response signal. The response frame format is: frame header (0xBB) + sensor identifier + command type + execution status (0x00 for success, 0x01 for failure) + frame tail (0x55). For example, the response frame returned by the UHF sensor is 0xBB+0x02+0x0B+0x00+0x55, indicating successful execution of the pause command. The current sensor identifier, potential interference sensor identifier, response signal frame, execution status, and response time are then processed to generate command response feedback data. Each entry in this data includes the current sensor identifier, potential interference sensor identifier, timestamp, response frame content, execution status, and response time.

[0171] Step S1488: For potential interference sensors that have not successfully received the instruction, resend the encoded paused instruction data, increase the transmission power, and shorten the transmission interval to generate instruction retransmission data.

[0172] For entries in the instruction response feedback data with a failure execution status, increase the transmission power to 1.2 times the original power, shorten the transmission interval to 0.5 times the original interval, resend the encoded instruction, and record the retransmission count, retransmission power, and retransmission interval. For example, if the UHF sensor fails to respond for the first time, the retransmission power is 1.2 times the original power, the interval is 0.5 times the original interval, and the retransmission count is 1. Organize the current sensor identifier, potential interference sensor identifier, retransmission count, retransmission power, retransmission interval, and retransmission time to generate instruction retransmission data. Each entry in the data contains the current sensor identifier, potential interference sensor identifier, timestamp, retransmission count, retransmission power, retransmission interval, and retransmission time.

[0173] Step S1489: Continuously monitor the signal emission status of potential interference sensors, collect the signal transmission power data of each potential interference sensor through a signal monitoring device, and confirm whether active signal emission has stopped to generate emission status monitoring data.

[0174] Continuously collect the signal transmission power P_k_trans of potential interference sensors through the signal monitoring device carried by the robot. If P_k_trans < the preset power threshold P_stop_thr, it is determined that the transmission has stopped; if P_k_trans ≥ P_stop_thr, it is determined that the transmission has not stopped. For example, the monitored power of the UHF sensor P_trans2 = 1 dBm < P_stop_thr1 (5 dBm), and it is determined that the transmission has stopped. Organize the current sensor identifier, potential interference sensor identifier, monitoring time, transmission power, stop determination result, and power threshold to generate emission status monitoring data. Each entry in the data contains the current sensor identifier, potential interference sensor identifier, timestamp, transmission power, stop determination result, and power threshold.

[0175] Step S14810: Integrate the current detection sensor identifier data, non-current detection sensor list data, potential interference sensor data, paused instruction basic data, encoded paused instruction data, instruction transmission record data, instruction response feedback data, instruction retransmission data, and emission status monitoring data to generate sensor pause control data.

[0176] The sensor pause control data generated in all the above steps are linked and integrated according to the current sensor identifier and timestamp. Identifiers, lists, commands, transmissions, responses, retransmissions, and monitoring data within the same pause control cycle are grouped together. For example, the current pause control data for an ultrasonic sensor includes its current identifier, non-current list, list of potential interfering sensors, pause command, transmission record, response feedback, retransmission record, and transmission status monitoring data. The integrated data is arranged in chronological order to generate the final sensor pause control data, which can be directly used for dynamic mutual inhibition control data recording and feedback.

[0177] Step S149: Continuously collect contact pressure feedback data corresponding to pressure regulation execution data, contact angle feedback data corresponding to angle regulation execution data, signal reception feedback data corresponding to filter parameter adjustment data, and status feedback data corresponding to sensor pause control data. Record the operation content, adjustment time, and feedback results of each adjustment, and generate control process record data.

[0178] First, a data acquisition thread is initiated to continuously collect feedback data on pressure adjustment execution, including the contact pressure value and adjustment completion time after each adjustment; feedback data on angle adjustment execution, including the contact angle value and joint rotation angle after each adjustment; feedback data on filter parameter adjustment, including the adjusted signal reception strength and interference signal strength; and status feedback data on sensor pause control, including the target sensor's signal transmission status and whether pause was successful. Next, the content of each control operation is recorded, such as the direction and amplitude of pressure adjustment, the joint rotation amount of angle adjustment, the adjustment range of filter parameters, and the reception status of the pause command. Simultaneously, the timestamp of each adjustment and the corresponding feedback result are recorded, forming a complete control trajectory. Finally, all collected feedback data and operation records are integrated chronologically to form control process record data. Each entry in the data includes a timestamp, control type, operation content, feedback result, and status parameters.

[0179] Step S1410: Integrate pressure regulation execution data, angle regulation execution data, filter parameter adjustment data, sensor pause control data, and regulation process recording data, and arrange them in chronological order to form dynamic mutual inhibition regulation data.

[0180] First, the control execution data generated in the above steps are sorted by timestamp to ensure data temporal consistency. Next, different types of control data with the same timestamp are associated; for example, pressure adjustment data, angle adjustment data, and filter parameter adjustment data at the same time point correspond to the same control cycle. Then, a global timeline identifier is added to map all data onto the global timeline of the inspection task, facilitating subsequent association with inspection data. Finally, the integrated data is stored as a structured file, forming dynamic mutual-restraint control data, which includes all control operations and feedback information of the sensor within independent detection periods.

[0181] Step S150: According to the independent detection period and equipment detection target, integrate the dynamic mutual inhibition control data and the inspection raw data collected by each sensor, establish a one-to-one correspondence between the control data and the inspection data, and generate comprehensive robot inspection data.

[0182] In this embodiment, based on the dynamic mutual inhibition and control data generated above and the original inspection data collected by the sensors, the data is classified and integrated according to time period and target point to establish a precise one-to-one correspondence, and finally form comprehensive robot inspection data that can be directly used for defect diagnosis.

[0183] For example, step S151: Extract the raw inspection data collected by each sensor, classify them according to sensor identifier and equipment detection target identifier, and generate inspection data for each sensor corresponding to a set of raw inspection data containing all detection targets.

[0184] First, the raw data collected by each sensor during the inspection process is extracted from the robot's data storage unit. This includes echo signal data from the ultrasonic sensor, discharge signal data from the UHF sensor, and surface voltage data from the transient ground voltage sensor. Each type of raw data contains a timestamp, sensor identifier, target identifier, signal waveform data, and corresponding environmental parameters. Next, the data is initially categorized by sensor identifier, with each sensor corresponding to a set of data. Then, within each set of data, a secondary categorization is performed based on the device's detection target identifier, with each target corresponding to all raw data collected by that sensor at that target point. Finally, the categorized data is arranged in the order of sensor identifier and target identifier to generate inspection data. Each entry in this data set includes a sensor identifier, target identifier, timestamp, raw signal data, and environmental parameters.

[0185] Step S152: Extract pressure regulation execution data, angle regulation execution data, filter parameter adjustment data, sensor pause control data, and regulation process recording data from the dynamic mutual inhibition regulation data. Divide the data into independent detection periods, with each independent detection period corresponding to a set of regulation data, and generate period-regulation data.

[0186] First, all entries in the dynamic mutual inhibition control data are traversed and split according to the time range of independent detection periods, with each period corresponding to a set of control data. The splitting is based on the start time t_start_m and end time t_end_m of the period, and all control data with timestamps within this range are assigned to the corresponding period. Next, within each group of period data, it is classified according to control type: pressure regulation, angle regulation, filter parameter adjustment, sensor pause control, and control process recording, ensuring that data of each control type is grouped independently. Finally, the split period data is arranged in period order to generate period-control data. Each entry in the data includes a period identifier, sensor identifier, start and end times of the period, and the corresponding type of control data group.

[0187] Step S153: Associate the original inspection data corresponding to each detection target point in the inspection data with the time period-control data corresponding to the collection period of the original inspection data, so that each piece of original inspection data corresponds to a set of control data in the same period, and generate inspection-control associated data.

[0188] First, for each raw data point in the inspection data, its collection timestamp is extracted. This timestamp is then matched with the corresponding time period in the control data to determine the corresponding time period identifier and control data group. Next, the sensor identifier and target identifier of the raw data are compared with the sensor identifier in the control data group to ensure consistency and avoid association errors. Then, the raw data is bound to the corresponding control data group. Each raw data point carries all control information from the same period, including pressure adjustment records, angle adjustment records, filter parameter adjustment records, and sensor pause records. Finally, all bound data is arranged chronologically to generate inspection-control associated data. Each entry in this data includes the raw data, the corresponding time period identifier, the corresponding control data group, and the association verification status.

[0189] Step S154: Filter the raw inspection data in the inspection-control correlation data, retain the raw inspection data after pressure adjustment, angle adjustment, filter parameter adjustment and sensor pause control, and remove the raw inspection data that has not undergone a complete control process to generate valid inspection data.

[0190] First, the criteria for determining a complete control process are established: within the time period corresponding to the raw data, at least one pressure adjustment, one angle adjustment, and one filter parameter adjustment must be completed, and the sensor pause control command must be successfully executed. Next, each data point in the inspection-control correlation data is traversed, checking whether the corresponding control data group meets the criteria. If any control type record is missing from the control data group of a raw data point, or if the sensor pause control was not successfully executed, the raw data is determined to have not undergone a complete control process and is discarded. If all criteria are met, the raw data is retained. Finally, the retained raw data is categorized and organized according to sensor identifier and target identifier to generate valid inspection data. Each entry in the data includes the raw data, the corresponding complete control data group, and the validity determination result.

[0191] Step S155: Bind the effective inspection data with the pressure adjustment execution data, angle adjustment execution data, and filter parameter adjustment data in the corresponding control data to generate a comprehensive data unit containing detection data, pressure control information, angle control information, and filter control information.

[0192] First, for each piece of raw data in the valid inspection data, the final pressure value, number of adjustments, and adjustment trajectory are extracted from the corresponding pressure adjustment execution data; the final angle value, number of adjustments, and joint rotation trajectory are extracted from the angle adjustment execution data; and the final frequency range, final attenuation threshold, and number of adjustments are extracted from the filter parameter adjustment data. Next, the signal waveform data and environmental parameters of the raw data are bound to the above-mentioned control information to form an independent integrated data unit, which contains the detection data and corresponding key control information. Then, a unique unit identifier is added to each integrated data unit to facilitate subsequent retrieval and analysis. Finally, all integrated data units are arranged according to sensor identifier and target identifier to generate an initial integrated dataset.

[0193] Step S156: Group all integrated data units according to the device detection target point identifier. Each device detection target point corresponds to a group of integrated data units from different sensors, generating target point-integrated data.

[0194] First, all integrated data units are traversed, and the device detection target point identifier for each unit is extracted. Next, the units are grouped according to the target point identifier, with each target point identifier corresponding to a group of integrated data units. Each group contains fully regulated detection data and control information collected by three sensors at that target point. If a target point only has data collected by one or two sensors, the group only contains the integrated data units for that corresponding sensor. Then, within each data group, the data is sorted by sensor identifier to ensure that data from different sensors at the same target point is arranged in a fixed order. Finally, basic information such as the target point identifier, target point 3D coordinates, and device identifier is added to each data group to generate target-integrated data. Each entry in this data contains the target point identifier, device identifier, target point 3D coordinates, and a list of integrated data units for the corresponding sensor.

[0195] Step S157: Extract the regulation process record data from the target-integrated data, organize the regulation events, regulation time, regulation parameters and regulation results of each device's detection target in chronological order, and generate target regulation record data.

[0196] First, for each target-integrated data set, the control process records corresponding to all integrated data units are extracted. Next, the control process records are sorted by timestamp, and the control event sequence for each target is organized. Events include pressure adjustment, angle adjustment, filter parameter adjustment, and sensor pause control. For each control event, the event occurrence time, control parameters (such as the target value for pressure adjustment, the rotation amount for angle adjustment, and the adjustment range for filter parameters), and control results (such as the adjusted pressure value, the adjusted angle value, and the adjusted signal strength) are recorded. Subsequently, a control record list is generated for each target, containing detailed information on all control events. Finally, the control record lists for all targets are organized by target identifier to generate target control record data. Each entry in the data includes the target identifier, a list of control events, the event time sequence, and the parameters and results for each event.

[0197] Step S158: Summarize the valid inspection data in the target-integrated data according to the sensor identifier, generate the inspection data summary of each sensor for all device detection target points, and generate sensor inspection summary data.

[0198] First, all integrated data units in the target-integrated data are traversed and categorized by sensor identifier. Each sensor corresponds to a set of integrated data units, containing valid inspection data and control information collected by that sensor at all target points. Next, each set of sensor data is sorted by target identifier, ensuring that data from the same sensor is arranged in the target point order of the inspection path. Then, basic sensor parameter information is added to each set of sensor data, including signal transmission frequency range, signal reception sensitivity parameters, and detection duration. Finally, the summary data from all sensors is arranged by sensor identifier to generate sensor inspection summary data. Each entry in this data includes the sensor identifier, basic sensor parameters, and a list of integrated data units corresponding to all target points.

[0199] Step S159: Integrate target point-comprehensive data, target point control record data, and sensor inspection summary data, supplement the basic information of inspection task execution time and inspection area location, and generate initial comprehensive inspection data.

[0200] First, target point-comprehensive data, target point control record data, and sensor inspection summary data are integrated according to device identification. Each device corresponds to all target point data, control records, and sensor data for that device. Next, basic information for the inspection task is added, including the start and end times of the inspection task, the location range of the inspection area, and the robot's inspection path information. Then, version information, generation time, and checksums are added to ensure data integrity and traceability. Finally, all integrated data is stored in a unified format to generate initial comprehensive inspection data, which includes full-dimensional information from raw detection data to control records.

[0201] Step S1510: Sort the data units in the initial inspection data in ascending order according to the start time of their corresponding independent inspection period to generate robot inspection data.

[0202] First, extract the start time t_start_m of the independent detection period corresponding to all integrated data units in the initial inspection integrated data. Next, sort all integrated data units in ascending order of t_start_m to ensure the data is arranged according to the inspection time sequence. During sorting, if multiple data units correspond to the same time period, they are arranged according to the fixed order of sensor identifiers. Subsequently, adjust the order of target point-integrated data, target point control record data, and sensor inspection summary data to match the sorting of the integrated data units. Finally, integrate all sorted data to generate the final robot inspection integrated data, which can be directly used for defect diagnosis and condition assessment of power equipment in new energy power plants.

[0203] In one exemplary embodiment, a multi-sensor timing coordination and mutual inhibition control system for robot inspection is provided. This control system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2 As shown, this multi-sensor timing coordination and mutual inhibition control system for robot inspection includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements a multi-sensor timing coordination and mutual inhibition control method for robot inspection. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the shell of the multi-sensor timing coordination and mutual inhibition control system for robot inspection, or an external keyboard, touchpad, or mouse, etc.

[0204] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A multi-sensor time-series cooperative and mutual inhibition control method for robot inspection, characterized in that, The method includes: Collect the signal transmission frequency range, signal reception sensitivity parameters and detection duration of each sensor, and combine them with the spatial distribution information of equipment detection target points in the preset inspection task to establish a mutual inhibition triggering relationship between sensor detection actions and equipment detection target points, and generate mutual inhibition relationship data. Based on the mutually inhibiting correlation data, the independent detection time period and action connection gap of each sensor are divided. The signal transmission time periods of different sensors are arranged in chronological order, and the overlap between each signal transmission time period is controlled to be non-overlapping. The timing staggered arrangement of sensor detection actions is executed to generate the timing staggered arrangement result. Collect electromagnetic interference frequency and intensity data of the robot inspection area, combine the time-series staggered scheduling results, equip shielding and blocking devices, signal filtering channels and power supply stability lines that are adapted to the signal transmission frequency range and signal reception sensitivity parameters of each sensor, perform the adaptation and deployment of hardware suppression structure, and generate hardware suppression deployment parameters. When the sensor enters an independent detection period to perform detection actions, it collects contact pressure data, contact angle data, and sensor signal transmission and reception data between the sensor and the detection target point. Combined with the timing staggered scheduling results and hardware suppression deployment parameters, it performs linkage and mutual suppression control between the robot's mechanical actions and sensor signals to generate dynamic mutual suppression control data. Classified by independent detection time period and equipment detection target point, the dynamic mutual inhibition control data and the inspection raw data collected by each sensor are integrated to establish a one-to-one correspondence between control data and inspection data, and generate comprehensive robot inspection data. The process involves dividing the independent detection time periods and action transition gaps of each sensor based on mutually inhibiting correlation data, arranging the signal transmission time periods of different sensors in chronological order, controlling the absence of overlap between signal transmission time periods, performing time-series staggered scheduling of sensor detection actions, and generating time-series staggered scheduling results, including: Extract the signal transmission frequency range, signal reception sensitivity parameters, detection duration, and spatial distribution information of the detection target points of each sensor from the cross-inhibition correlation data, and summarize them to form basic data of sensor detection actions. By comparing the signal transmission frequency ranges of different sensors in the basic data of sensor detection actions, the sensor combinations that cause overlapping signal propagation are identified, and the sensors are grouped according to the requirement that the signal transmission frequency ranges of different sensors do not overlap, thus generating sensor group data. Based on the duration of the detection action of each sensor group in the sensor group data, the entire inspection task cycle is broken down into multiple consecutive time segments, and each time segment is assigned to a sensor as an independent detection period, generating independent detection period division results. By combining the spatial distribution information of the equipment detection target points, the path length and time required for each sensor to move from the current equipment detection target point to the next equipment detection target point are calculated, and sensor movement time data is generated. Based on the independent detection period segmentation results and sensor movement time data, an action connection gap is reserved between two adjacent independent detection periods. The duration of the action connection gap is set to the time length corresponding to the movement time data of the previous sensor, and action connection gap configuration data is generated. Based on the independent detection period division results, action connection gap configuration data, and movement path length in the sensor movement time data, the detection action start time, detection action execution period, and detection action end time of each sensor are determined, and sensor action timing node data are generated. The sensor action time sequence node data is associated with the signal reception sensitivity parameter in the sensor detection action basic data, and the sensor signal reception adaptation range in each independent detection period is marked to generate time sequence-sensitivity association data. Based on the time-sensitivity correlation data, and according to the signal propagation attenuation parameter of the previous sensor, the duration of the action connection gap is adjusted to generate action connection gap adjustment data; By integrating the results of independent detection time period division, action connection gap adjustment data, sensor action timing node data, and timing-sensitivity correlation data, an initial timing staggered arrangement result is generated. Based on the duration of each sensor's detection action, the duration of its corresponding independent detection period is adjusted, and the driving command parameters for sensor action switching are added to the initial timing staggered arrangement result to generate the timing staggered arrangement result.

2. The multi-sensor timing coordination and mutual inhibition control method for robot inspection according to claim 1, characterized in that, The system collects the signal transmission frequency range, signal reception sensitivity parameters, and detection duration of each sensor. Combined with the spatial distribution information of equipment detection targets in the pre-defined inspection task, it establishes a mutual inhibition triggering correlation between sensor detection actions and equipment detection targets, generating mutual inhibition correlation data, including: The technical specifications of each sensor are obtained, and the signal transmission frequency range and signal reception sensitivity parameters of the sensor are extracted from the technical specifications. The duration of the detection action required for each sensor to complete a single detection action is obtained through the robot's motion controller. The signal transmission frequency range, signal reception sensitivity parameters and the duration of the detection action are integrated to form the basic characteristic information of the sensor. Read the task description file of the preset inspection task, parse out the list of equipment to be inspected and the three-dimensional coordinate information of the target points on each equipment from the task description file, and arrange the three-dimensional coordinate information according to the order of the equipment to form the spatial distribution information of the target points of the equipment. Match the signal transmission frequency range in the basic characteristic information of the sensor with the spatial distribution information of the detection target of the device, analyze different sensors working simultaneously on the same detection target of the device, compare the signal transmission frequency ranges of these different sensors, identify sensor combinations with overlapping signal transmission frequency ranges, and form a list of potential interference sensor combinations. The signal receiving sensitivity parameter in the basic characteristic information of the sensor is matched with the spatial distribution information of the detection target of the device. When a certain sensor performs a detection action at the set detection target of the device, the interference intensity of its signal receiving process is affected by the signal emission from other sensors. The interference intensity value is calculated and a receiving interference intensity mapping table is formed. The detection duration in the sensor's basic characteristic information is matched with the spatial distribution information of the device's detection target points. The total time required for each sensor to traverse all its designated device detection target points is analyzed. Combined with the list of potential interfering sensor combinations, the conflict time length generated when the detection periods of different sensors overlap is calculated to form the detection period conflict prediction data. By integrating a potential interference sensor combination list, a received interference intensity mapping table, and conflict prediction data during the detection period, the detection actions of the sensors that need to be suppressed at the set device detection target point are determined, as well as the suppression triggering conditions, forming a set of mutual suppression triggering rules between sensors and target points. Based on the set of mutual inhibition triggering rules between sensors and targets, a list is generated for each device to detect target points. The list specifies the sensor identifier that needs to be suppressed when detection is performed at the target point, the suppression start time condition, and the suppression end time condition, thus forming a target point mutual inhibition triggering list. Summarize the list of mutual inhibition triggers of all equipment detection targets, sort them according to the execution path of the inspection task, connect the discrete mutual inhibition trigger relationships into a continuous temporal relationship chain, and form a global mutual inhibition trigger association chain; The basic characteristic information of sensors, spatial distribution information of equipment detection targets, list of potential interference sensor combinations, received interference intensity mapping table, conflict prediction data during detection period, set of mutual inhibition triggering rules between sensors and targets, list of mutual inhibition triggering targets, and global mutual inhibition triggering association chain are merged to generate mutual inhibition association data.

3. The multi-sensor temporal coordination and mutual inhibition control method for robot inspection as described in claim 1, characterized in that, The method combines the spatial distribution information of the detection target points of the equipment to calculate the path length and time required for each sensor to move from the current detection target point to the next detection target point, generating sensor movement time data, including: Extract the device detection target sequence corresponding to each sensor from the cross-inhibition correlation data, determine the order of device detection target sequences that each sensor needs to detect sequentially, and generate sequence data; Extract the three-dimensional coordinate data of each device detection target point from the spatial distribution information of the device detection target points, arrange them according to the detection order in the sequence data, and generate target point coordinate sequence data; Calculate the three-dimensional coordinate difference between two adjacent target points detected by the device in the target point coordinate sequence data, calculate the straight-line distance between adjacent target points based on the spatial distance, and generate target point spacing data. Based on the motion constraint parameters of the robot's end effector, the maximum moving speed parameter and turning radius parameter are set, and the straight distance corresponding to the target point spacing data is corrected to generate the actual moving path length data. Based on the rated moving speed parameters of the robot's end effector and combined with the actual moving path length data, the theoretical moving time of the sensor from the current device detection target point to the next device detection target point is calculated, and theoretical moving time data is generated. Referring to the action connection gap configuration data in the timing staggered scheduling results, the detection action connection requirements of adjacent sensors are analyzed, the theoretical movement time data is adjusted, and the sensor preparation time is reserved when the equipment detection target point is switched, and the adjusted movement time data is generated. Record the target point coordinate sequence data, actual movement path length data, theoretical movement time data, and adjusted movement time data for each sensor, classify and organize them according to sensor identification, and generate basic sensor movement data. The adjusted movement time data is compared with the upper limit of the duration of the action connection gap configuration data; if the adjusted movement time data is greater than the upper limit of duration, the next step of correction is performed to generate movement time verification data. Based on the verification data of movement time, the adjusted movement time data that exceeded the time limit was revised again, the actual movement path length data was optimized, the movement time was shortened, and the final movement time data was generated. By integrating basic sensor movement data, movement time verification data, and final movement time data, and arranging them in the order of sensor identifier and target sequence, sensor movement time data is generated.

4. The multi-sensor timing coordination and mutual inhibition control method for robot inspection as described in claim 1, characterized in that, The electromagnetic interference frequency and intensity data of the area inspected by the collection robot are combined with the timing staggered scheduling results. Shielding and blocking devices, signal filtering channels, and stable power supply lines adapted to the signal transmission frequency range and signal reception sensitivity parameters of each sensor are installed. The hardware suppression structure is then adapted and deployed, generating hardware suppression deployment parameters, including: Electromagnetic interference detection equipment is used to collect data on electromagnetic interference frequency and intensity at different locations within the robot's inspection area, record the propagation direction of electromagnetic interference, and generate electromagnetic interference data for the inspection area. Extract the independent detection periods, signal transmission frequency ranges, and time-sensitivity correlation data of each sensor from the time-series staggered scheduling results to generate sensor time-series parameter data; Based on the signal transmission frequency range in the sensor timing parameter data and the electromagnetic interference frequency in the electromagnetic interference data of the inspection area, a shielding material that covers both the sensor signal transmission frequency and the electromagnetic interference frequency is selected, a shielding device is fabricated, and the basic data of the shielding device is generated. By combining the sensor's installation coordinates at the robot's end effector with the signal transmission direction in the sensor's timing parameter data, the installation angle of the shielding device is adjusted so that the normal direction of its shielding surface is opposite to the electromagnetic interference propagation direction, and the shielding surface is not on the sensor signal transmission path, thus generating the shielding device installation parameters. For the signal receiving sensitivity parameter in the sensor timing parameter data, the frequency interception range of the signal filtering channel is set so that the signal filtering channel only allows signals within the sensor signal receiving adaptation range to pass through, and the signal filtering channel frequency parameter is generated. The signal filtering channel is connected in series to the signal receiving link of the sensor, and the installation position of the signal filtering channel is fixed so that the signal collected by the sensor is processed by the signal filtering channel before being transmitted to the data processing unit, generating signal filtering channel deployment data. Analyze the power supply energy requirements of the sensor during detection action execution, action connection gap, and standby state, adjust the voltage and current output values ​​of the power supply stabilization circuit, and generate the voltage parameters of the power supply stabilization circuit. In a stable power supply line, voltage filtering components and current regulating components are connected in series to suppress voltage and current fluctuations in the power supply line and generate stable power supply line configuration data. Integrate the installation parameters of the shielding and blocking device, the frequency parameters of the signal filtering channel, the deployment data of the signal filtering channel, the voltage parameters of the power supply stabilization line, and the configuration data of the power supply stabilization line to generate a draft of the hardware suppression deployment. Based on the start time of the independent detection period in the sensor timing parameter data, the trigger timing of each control module in the hardware suppression structure is set, and hardware suppression deployment parameters are generated.

5. The multi-sensor timing coordination and mutual inhibition control method for robot inspection according to claim 4, characterized in that, The installation angle of the shielding device is adjusted by combining the sensor's installation coordinates at the robot's end effector with the signal transmission direction in the sensor's timing parameter data. This ensures that the shielding surface of the shielding device faces the direction of electromagnetic interference propagation while avoiding the sensor signal transmission path. The installation parameters for the shielding device are then generated, including: The robot's positioning system acquires the three-dimensional coordinates of each sensor on the robot's end effector, records the fixed points of the sensor installation, and generates sensor installation coordinate data. The signal emission direction data of each sensor is extracted from the sensor timing parameter data. This signal emission direction data is determined based on the sensor's detection principle and the relative position of the device's detection target point, thus generating sensor emission direction data. Extract the propagation direction information of electromagnetic interference from the electromagnetic interference data of the inspection area, sort the main interference propagation directions according to the interference intensity, and generate main interference direction data. Based on the sensor installation coordinate data and sensor transmission direction data, a schematic diagram of the sensor's signal transmission path is drawn to determine the spatial range of signal transmission and generate signal transmission path data. Based on the sensor installation coordinate data and the main interference direction data, the installation reference position of the shielding and blocking device is set. This installation reference position is located on one side of the sensor's signal receiving unit and does not affect the signal transmission path, thus generating installation reference position data. Calculate the angle between the main interference direction data and the normal direction of the sensor signal receiving unit, adjust the shielding surface angle of the shielding device so that the shielding surface is perpendicular to the main interference propagation direction, and generate shielding surface angle data. By combining the structural dimensions of the shielding device with the sensor installation coordinates, the installation height and horizontal offset of the shielding device are set so that the shielding surface completely covers the sensor's signal receiving unit, and installation position offset data is generated. Through simulated installation tests, the shielding device was verified to block electromagnetic interference in the main interference direction without blocking the signal transmission path of the sensor under the installation state corresponding to the installation reference position data, shielding surface angle data and installation position offset data, and installation verification data was generated. Based on the installation verification data, the shielding surface angle data and installation position offset data are finely adjusted to optimize the shielding effect and signal transmission path avoidance effect, and the adjusted installation parameter data is generated. By integrating sensor installation coordinate data, sensor emission direction data, main interference direction data, signal transmission path data, installation reference position data, shielding surface angle data, installation position offset data, installation verification data, and adjusted installation parameter data, the installation parameters of the shielding device are generated.

6. The multi-sensor timing coordination and mutual inhibition control method for robot inspection according to claim 1, characterized in that, When the sensor enters an independent detection period and performs a detection action, it collects contact pressure data, contact angle data, and sensor signal transmission and reception data between the sensor and the detection target point. Combining this with the timing staggered scheduling results and hardware suppression deployment parameters, it executes the linkage and mutual inhibition control between the robot's mechanical actions and the sensor signals, generating dynamic mutual inhibition control data, including: The robot uses pressure sensing elements mounted on its end effector to collect real-time contact pressure data between the sensor currently performing the detection action and the target point of the device. It also uses angle sensing elements to collect contact angle data between the two, and then summarizes the data to form contact state data. The signal transmission power data and signal transmission frequency data are collected by the signal transmission monitoring element of the sensor that is currently performing the detection action, and the signal reception strength data and signal reception frequency data are collected by the signal reception monitoring element. The data are then combined to form signal transmission and reception data. Extract the sensor identifier, detection action start time, detection action end time, and time-sensitivity correlation data corresponding to the current independent detection period from the time-series staggered scheduling results to generate the time-series data for the current period. Retrieve the installation parameters of the shielding and blocking device, the frequency parameters of the signal filtering channel, and the voltage parameters of the power supply stability line corresponding to the current sensor identifier from the hardware suppression deployment parameters, and generate the current hardware suppression parameters. By comparing the contact pressure data in the contact state acquisition data with the pressure adaptation value corresponding to the time-sensitivity correlation data in the time series data of the current time period, a pressure adjustment drive signal for the robot arm is generated, which drives the pressure actuator of the robot arm to adjust the contact pressure between the sensor and the target point detected by the device, and generates pressure adjustment execution data. By referring to the contact angle data in the contact state acquisition data and the angle adaptation value corresponding to the spatial distribution information of the device detection target, the angle adjustment drive signal of the robot arm is generated, which drives the angle actuator of the robot arm to adjust the contact angle between the sensor and the device detection target, and generates angle adjustment execution data. Based on the signal reception strength data in the signal transmission and reception acquisition data and the signal filtering channel frequency parameter in the current hardware suppression parameters, adjust the real-time filtering threshold of the signal filtering channel to generate filtering parameter adjustment data. Based on the signal transmission frequency data in the signal transmission and acquisition data and the sensor identifier in the current time period sequence data, a signal transmission pause command is sent to other non-currently detected sensors on the robot to stop the active signal transmission action of the non-currently detected sensors and generate sensor pause control data. Continuously collect contact pressure feedback data corresponding to pressure regulation execution data, contact angle feedback data corresponding to angle regulation execution data, signal reception feedback data corresponding to filter parameter adjustment data, and status feedback data corresponding to sensor pause control data. Record the operation content, adjustment time, and feedback results of each adjustment, and generate control process record data. The data from pressure regulation execution, angle regulation execution, filter parameter adjustment, sensor pause control, and regulation process recording are integrated and arranged in chronological order to form dynamic mutual inhibition regulation data.

7. The multi-sensor timing coordination and mutual inhibition control method for robot inspection according to claim 6, characterized in that, The pressure adaptation value corresponding to the contact pressure data in the compared contact state acquisition data and the time-sensitivity correlation data in the current time period data is used to generate a pressure adjustment drive signal for the robot arm. This signal drives the pressure actuator of the robot arm to adjust the contact pressure between the sensor and the target point detected by the device, generating pressure adjustment execution data, including: Extract the pressure adaptation value corresponding to the current sensor identifier from the time-series data of the current time period time-series data. The pressure adaptation value is determined based on the sensor’s signal receiving sensitivity parameter and the surface material data of the device’s detection target point. Extract the contact pressure data from the contact state acquisition data, compare the contact pressure data with the pressure adaptation value, obtain the difference data between the two, and generate pressure difference data. Based on the driving parameters of the pressure actuator of the robotic arm, the response rate parameter of pressure regulation is set. This response rate parameter corresponds to the pressure difference data. Based on the pressure difference data, the response rate parameter of pressure regulation is set, and pressure response rate data is generated. Based on the pressure difference data and pressure response rate data, the changes in drive current and drive voltage of the robot arm's pressure actuator are calculated to generate pressure drive parameter data. By combining the start and end times of the detection actions in the current time series data, the start and end times of pressure regulation are set so that the pressure regulation is completed before the detection actions are executed, and pressure regulation time series data is generated. The pressure drive parameter data, pressure response rate data, and pressure regulation timing data are encoded according to the control protocol of the robot arm to generate standardized pressure regulation drive signals and pressure drive signal data. The pressure drive signal data is sent to the pressure actuator controller of the robot arm, which drives the motor of the pressure actuator to rotate, moves the sensor closer or further away from the detection target of the device, adjusts the contact pressure, and generates pressure mechanism action data. The contact pressure data during the adjustment process is collected in real time by pressure sensing elements, and the entire process data of pressure data changing from the initial value to the pressure adaptation value is recorded to generate pressure adjustment trajectory data. Extract the stable phase pressure data from the pressure adjustment trajectory data, ensure that the stable phase pressure data is consistent with the pressure adaptation value, and generate pressure adjustment verification data; Integrate pressure drive signal data, pressure mechanism action data, pressure adjustment trajectory data, and pressure adjustment verification data to generate pressure regulation execution data.

8. The multi-sensor timing coordination and mutual inhibition control method for robot inspection according to claim 6, characterized in that, The step of adjusting the real-time filtering threshold of the signal filtering channel based on the signal received strength data in the signal transmission and reception acquisition data and the signal filtering channel frequency parameter in the current hardware suppression parameters, and generating filtering parameter adjustment data, includes: Signal strength data is extracted from the signal transmission and reception data, and the effective signal strength data that conforms to the current sensor signal reception frequency range is separated from the interference signal strength data that exceeds the current sensor signal reception frequency range to generate signal strength classification data. Retrieve the signal filtering channel frequency parameter from the current hardware suppression parameters, determine the current allowed frequency range of the signal filtering channel, and generate the current filtering range data. Calculate the ratio of interfering signal strength data to valid signal strength data in the signal strength classification data to generate signal interference ratio data; The signal interference ratio data is compared with a preset signal interference ratio threshold. If the signal interference ratio data is greater than or equal to the signal interference ratio threshold, an instruction to narrow the allowed frequency range is generated. If the signal interference ratio data is less than the signal interference ratio threshold, an instruction to maintain the allowed frequency range is generated. The instructions are integrated to generate filter adjustment direction data. Based on the current filtering range data and filtering adjustment direction data, calculate the new upper and lower limits of the allowed passing frequency for the signal filtering channel. The range defined by the new upper and lower limits of the allowed passing frequency overlaps less with the frequency region corresponding to the interference signal strength data and more with the frequency region corresponding to the effective signal strength data compared to the current allowed passing frequency range, and generate filtering threshold adjustment data. The filter threshold adjustment data is converted into a control voltage signal for the signal filtering channel. This control voltage signal corresponds to the frequency parameter in the filter threshold adjustment data, thereby generating filter control signal data. The filter control signal data is sent to the control module of the signal filter channel to drive the filter element of the signal filter channel to adjust its working state, realize the change of the allowed frequency range, and generate filter channel action data. The signal reception strength data after adjustment is collected by the signal receiving monitoring element, and the effective signal strength data and interference signal strength data are separated again to generate the adjusted signal classification data. Calculate the ratio of interference signal strength data to effective signal strength data in the adjusted signal classification data, and generate the adjusted signal interference ratio data. Integrate signal strength classification data, current filtering range data, signal interference ratio data, filtering adjustment direction data, filtering threshold adjustment data, filtering control signal data, filtering channel action data, adjusted signal classification data, and adjusted signal interference ratio data to generate filtering parameter adjustment data.

9. A multi-sensor timing coordination and mutual inhibition control system for robot inspection, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the multi-sensor timing coordination and mutual inhibition control method for robot inspection as described in any one of claims 1 to 8 by executing the machine-executable instructions.

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