Carrying robot health detection method and system

Abstract

The invention discloses a health detection method and system for a transfer robot. The method comprises the following steps: acquiring a real-time health detection period of the transfer robot; judging whether the real-time health detection period is smaller than a preset period or not; if not, the battery health degree, the motion system health degree, the mechanical structure health degree, the electronic equipment health degree and the navigation health degree of the transfer robot are obtained; calculating a health value of the transfer robot according to the battery health degree, the motion system health degree, the mechanical structure health degree, the electronic equipment health degree and the navigation health degree; and obtaining a corresponding health degree grade according to the health value, and sending corresponding prompt information according to the health degree grade. According to the health detection method and system for the transfer robot, the health degree of the transfer robot is quantified, possible faults of the transfer robot can be predicted in advance, the operation reliability and safety of the transfer robot are improved, early warning or intervention is carried out in time before the faults occur, and the maintenance cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction, and in particular to a method and system for health monitoring of handling robots. Background Technology

[0002] In today's construction industry, due to the generally complex and harsh environments and difficult working conditions at construction sites, many sites have introduced robots to assist in material handling in order to improve efficiency. To ensure the efficient operation of these handling robots, their health status needs to be managed and monitored.

[0003] Currently, health status monitoring and management of handling robots mainly rely on regular manual inspections or simple fault alarm mechanisms based on preset thresholds (such as low battery alarms and mechanical jamming alarms). Their management scope only covers limited functions such as basic charging status monitoring. However, the construction environment has its own unique characteristics: robots need to travel on uneven roads for extended periods, carry massive amounts of building materials (often several tons), and are continuously exposed to harsh conditions including extreme temperature and humidity, high-intensity noise, and high-frequency vibration and impact. This complex environment significantly increases the risk of equipment failure, such as tires unexpectedly bursting due to long-term overload and road impacts, precision sensors becoming inaccurate due to collisions or vibrations, and mechanical structures experiencing fatigue fractures due to stress concentration.

[0004] To address the aforementioned issues, existing technologies have not yet proposed a systematic solution, resulting in handling robots still facing core pain points in practical applications, such as low reliability, high maintenance costs, and poor operational continuity, which seriously restricts the improvement of the level of logistics automation in the construction industry. Summary of the Invention

[0005] Therefore, it is necessary to address the problem of the inability to effectively monitor and manage the health status of the aforementioned handling robots, and to provide a health detection method and system suitable for construction scenarios that can quantify the health status of handling robots and improve their operational reliability and safety.

[0006] This invention provides a health detection method for a handling robot, comprising the following steps: Obtain the real-time health monitoring cycle of the handling robot; Determine if the real-time health monitoring cycle is shorter than the preset cycle; If not, obtain the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles, the motion system health is obtained based on the motor temperature and bearing wear of the handling robot, the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot, the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot, and the navigation health is obtained based on the SLAM positioning error rate of the handling robot; The health value of the handling robot is calculated based on the battery health, motion system health, mechanical structure health, electronic equipment health, and navigation health. Based on the health value, obtain the corresponding health level and issue corresponding prompts based on the health level.

[0007] In one embodiment, the step of calculating the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic device health, and navigation health includes: The weights of the battery health, motion system health, mechanical structure health, electronic device health, and navigation health are obtained; wherein the weight of the battery health is greater than the weight of the motion system health, and the weight of the motion system health is greater than the weight of the electronic device health. The health value is calculated based on the weights of the battery health, motion system health, mechanical structure health, electronic device health, and navigation health.

[0008] In one embodiment, the step of obtaining a corresponding health level based on the health value and issuing a corresponding prompt message based on the health level includes: When the health level is greater than or equal to 85, a normal operation prompt message will be issued; When the health level is greater than or equal to 80 and less than 85, a yellow warning message is issued; When the health level is greater than or equal to 70 and less than 80, an orange warning message is issued; A red alert is issued when the health level is less than 70.

[0009] In one embodiment, issuing a yellow warning includes generating a recommended maintenance list; issuing an orange warning includes initiating adaptive control of the handling robot; and issuing a red warning includes forcing the handling robot to stop and reporting the stoppage information.

[0010] In one embodiment, the adaptive control includes: replanning the path of the transport robot or reassigning the transport robot's transport tasks.

[0011] In one embodiment, the step of obtaining the real-time health monitoring cycle of the handling robot includes: Acquire path information, path impact information, temperature information, and baseline cycle information of the handling robot; The real-time health monitoring cycle is calculated based on the path information, path impact information, temperature information, and baseline cycle information.

[0012] In one embodiment, the step of determining that the real-time health monitoring period is less than a preset period includes: If so, a maintenance reminder will be issued in advance based on the real-time detection cycle.

[0013] In one embodiment, the path impact information includes path wear coefficient, path smoothness coefficient, impact coefficient, and the number of times the transport robot brakes suddenly while traveling on the path. The path wear coefficient and path smoothness coefficient are obtained by LiDAR scanning, while the impact coefficient and the number of times the robot brakes suddenly are obtained by sensors.

[0014] In one embodiment, the transport robot is equipped with an acoustic emission sensor, which is located at a key node of the transport robot's frame, and is used to obtain the health status of the mechanical structure.

[0015] The present invention also provides a health detection system for a handling robot, comprising: The acquisition module is used to acquire the real-time health monitoring cycle of the handling robot; A judgment module, connected to the acquisition module, is used to determine whether the real-time health detection cycle is less than a preset cycle; if not, it acquires the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles, the motion system health is obtained based on the motor temperature and bearing wear of the handling robot, the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot, the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot, and the navigation health is obtained based on the SLAM positioning error rate of the handling robot; The calculation module, connected to the judgment module, is used to calculate the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic device health, and navigation health. An information prompting module, connected to the calculation module, is used to obtain the corresponding health level based on the health value and issue corresponding prompt information based on the health level.

[0016] The aforementioned health detection method and system for handling robots monitors the robot's structure, motion, and electronic systems from all dimensions. It integrates five core indicators—battery, motion, structure, electronics, and navigation—to calculate and quantify the robot's health status. This helps to predict potential malfunctions in advance, improves the robot's operational reliability and safety, and enables timely warnings or interventions before malfunctions occur, thereby reducing maintenance costs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a health detection method for a handling robot according to one embodiment; Figure 2 This is a block diagram of a health monitoring system for a handling robot according to one embodiment. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The health detection method for the handling robot of the present invention quantifies the health status of the handling robot by calculating its structure and operating condition. This allows for the prediction of robot malfunctions, which helps improve the operational reliability and safety of the handling robot while reducing maintenance costs. The health detection method for the handling robot of this application will be further described below with reference to the accompanying drawings.

[0020] The handling robot of this application integrates multiple sensors, including temperature sensors, humidity sensors, vibration sensors, and load sensors, to monitor the robot's operating status in real time. The robot also has a wireless communication module to connect with a server, transmitting the sensor data to the server for analysis and processing using data processing and machine learning algorithms. Based on the analysis results, potential robot malfunctions can be predicted and timely alarms issued, allowing maintenance personnel to take appropriate maintenance measures. Furthermore, the robot itself possesses data analysis capabilities, enabling it to adaptively adjust its operating parameters based on analysis results to prevent malfunctions. Simultaneously, the robot also features a user interface, allowing operators to monitor the robot's status in real time and intervene when necessary, thereby reducing the likelihood of robot malfunctions.

[0021] like Figure 1 As shown, a health detection method for a handling robot in one embodiment is performed by the server and includes the following steps: Step S10: Obtain the real-time health monitoring cycle of the handling robot.

[0022] The real-time health check cycle refers to the time a handling robot can perform tasks normally. After reaching the end of the health check cycle, the robot needs to undergo maintenance. For example, if a handling robot's real-time health check cycle is 72 hours, it means that the robot should undergo maintenance after 72 hours of operation. The handling robot calculates its health check cycle after each handling task to assess whether it can continue to perform the next task and determine whether it needs to pause the handling task for maintenance. The real-time health check cycle can be automatically adjusted based on parameters such as the robot's path impact and sudden braking frequency.

[0023] Before conducting real-time health checks, the health check points for the handling robot should be set up, and each health check point should be ensured to be in normal operating condition. These health check points include: Battery Status: Monitoring parameters such as voltage, current, and temperature of the handling robot's battery to assess battery health and remaining lifespan. Voltage, current, and temperature can be monitored in real-time using sensors. Once these parameters are acquired, a pre-trained model can be used to predict battery health and remaining lifespan. Methods commonly used in the field can be employed for assessing battery health and predicting remaining lifespan. For example, RC circuits can be used to simulate battery dynamics, and voltage-current curves can be fitted to estimate internal resistance and capacity decay. Remaining lifespan can be predicted using machine learning, for instance, using cycle count, temperature fluctuations, and average charge / discharge depth as input features, and employing a random forest classification aging model to predict remaining lifespan. The detection of battery status points can be performed by either the handling robot or a server.

[0024] Drive System: The drive system of the handling robot includes LiDAR, cameras, ultrasonic sensors, etc. The proper functioning of the sensors is ensured by detecting the accuracy and response time of the drive system. The accuracy and response time of the drive system are detected using commonly used methods in this field, and are not limited here.

[0025] Structural integrity: This involves inspecting the tightness, cracks, and deformation of the robot's body structure to assess its safety. For example, acoustic emission sensors can be used to detect cracks, and 3D laser scanning can be used for comparative analysis to detect structural deformation.

[0026] Navigation System: The navigation system of the handling robot includes GPS, IMU, encoders, etc. The positioning accuracy and navigation stability of the navigation system are tested. Commonly used testing methods in this field can be used to test the positioning accuracy and navigation stability of the navigation system; no specific methods are limited here.

[0027] When the detection results of the above-mentioned health monitoring points are all in a normal state, the real-time health monitoring cycle is then obtained. That is, this application first detects whether the handling robot is in a normal state, and then quantitatively evaluates its normal state. In this embodiment, the steps for obtaining the real-time health monitoring cycle of the handling robot include: Acquire path information, path impact information, temperature information, and baseline cycle information of the handling robot.

[0028] The real-time health monitoring cycle is calculated based on path information, path impact information, temperature information, and baseline cycle information.

[0029] The path information refers to the total distance the robot travels on a specific path. This total distance can be obtained by the robot itself. Path impact information includes the path wear coefficient, path smoothness coefficient, impact coefficient, and the number of times the robot brakes suddenly while traveling on the path. Path information reflects road conditions; poor road conditions, such as numerous gravel sections or potholes, increase wear on the robot and affect its health. The path wear coefficient and path smoothness coefficient are obtained through LiDAR scanning, while the impact coefficient and number of sudden braking events are obtained through sensors. Temperature information can be obtained through a temperature sensor; the temperature information here refers to the ambient temperature. High ambient temperatures can affect the robot's battery, leading to lower health. The baseline cycle information is the manufacturer's baseline cycle, which defaults to 72 hours.

[0030] The formula for calculating the real-time health monitoring cycle P is as follows:

[0031] Where D: The total distance traveled by the transport robot on a specific path (unit: kilometers). W: Path wear coefficient, reflecting the wear and tear on the robot's frame caused by road surface friction, is calculated using LiDAR scanning. LiDAR scans the road surface to obtain its three-dimensional coordinates and reflection intensity. Based on the known relationship between the reflection intensity of the material and the coefficient of friction, the path wear coefficient can be obtained. Furthermore, the path wear coefficient can be customized to reflect the frictional forces of different road surfaces.

[0032] S: Path smoothness coefficient, which reflects the impact of potholes on the chassis of the handling robot, and is obtained by LiDAR scanning calculation; I: Impact coefficient, which reflects the impact of the acceleration generated by the sudden braking / stopping of the handling robot on the handling robot. It is recorded by the IMU sensor and sent to the server for calculation. B stop The number of times the transport robot brakes suddenly while running on the path is recorded by the IMU sensor, and 1 point is awarded for each sudden braking. T: Ambient temperature. High temperatures will accelerate battery aging, which can be obtained through a temperature sensor. C: Manufacturer's baseline cycle, defaults to 72 hours.

[0033] After each task is completed, the handling robot calculates the real-time health check cycle according to the above formula and proceeds to the next step.

[0034] Step S20: Determine whether the real-time health monitoring cycle is less than the preset cycle.

[0035] During the handling process, the path information of the transport robot may change. For example, the sudden appearance of a gravel section will increase the path wear coefficient and impact coefficient, resulting in a shorter real-time health monitoring cycle, which will differ from the preset cycle. If the real-time health monitoring cycle is shorter than the preset cycle, it indicates that the health status of the transport robot has changed during task execution, requiring early maintenance and intervention.

[0036] Therefore, in this embodiment, when the real-time health monitoring cycle is shorter than a preset cycle, a maintenance reminder is issued in advance based on the real-time health monitoring cycle. Specifically, the server can send the maintenance reminder to the handling robot, which will then display it on the robot's user interface to remind staff to check.

[0037] The above-mentioned real-time health monitoring cycle will be explained below with specific examples.

[0038] Background: At a construction site, a transport robot needs to transport 20 tons of building materials daily, with the route including 10% gravel sections.

[0039] During the movement of the transport robot, LiDAR scanning revealed that the presence of gravel on the path increased friction, causing the path wear coefficient W to rise from 5 to 8 and the impact coefficient I to rise from 3 to 10 (due to the increased impact coefficient caused by sudden braking to avoid gravel).

[0040] Substituting the values ​​into the formula for the real-time health monitoring cycle, the original cycle P1 is calculated as (72 × 50) / (5 + 4 + 3 + 1 + 25) = 92 hours. After encountering a gravel section on the path, the real-time health monitoring cycle is recalculated as P2 = (72 × 50) / (8 + 4 + 3 + 1 + 25) = 73 hours. Compared to P1, P2 is shortened by 20%, meaning the real-time health monitoring cycle is shortened by 20%. When the server detects that the real-time health monitoring cycle of the transport robot has shortened by 20%, it issues a maintenance reminder in advance so that maintenance personnel can be arranged to inspect the frame and tires.

[0041] Similarly, if the road conditions on a route that the transport robot regularly follows become smoother, the real-time health check cycle will change, for example, become longer. In this case, the server will update the maintenance information of the transport robot with the relevant personnel.

[0042] In addition, if the real-time health monitoring cycle is less than the preset cycle, in this embodiment, the step further includes: further determining whether the real-time health monitoring cycle has reached the maintenance time. If the maintenance time has been reached, the handling robot is scheduled for maintenance in advance. If the real-time health monitoring cycle is less than the preset cycle but has not reached the maintenance time, the handling task continues and the health status is calculated.

[0043] Step S30: If not, obtain the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles; the motion system health is obtained based on the motor temperature and bearing wear of the handling robot; the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot; the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot; and the navigation health is obtained based on the SLAM positioning error rate of the handling robot.

[0044] The handling robot is equipped with five types of sensors to achieve comprehensive monitoring of structure, motion, and electronics. The following are the types of sensors, their locations, monitored parameters, corresponding functions, and accuracy requirements: The battery health B of the handling robot is obtained based on the remaining SoC capacity and the number of charge-discharge cycles of the handling robot's battery. The calculation formula is: B = SoC remaining capacity × 0.6 + cycle life health × 0.4, cycle life health = ((1000 - number of charge cycles) × 0.1) + hel(number of charge cycles, 1000, k)), hel(number of charge cycles, 1000, k) = 6.18 × k × ((2 × 1000 - number of charge cycles) / 1000), where 1000 is the manufacturer's recommended number of cycles, and k is the balancing coefficient, which is obtained by weighted averaging of the individual cell voltage differences read by the BMS, and the value range is 2 ≤ k ≤ 20.

[0045] The health rating M of the motion system is obtained based on the motor temperature and bearing wear of the handling robot. The calculation formula is M = motor temperature × 0.4 + bearing wear × 0.6. The bearing wear can be obtained through an acoustic emission sensor, calculated by acquiring the elastic waves released by the bearing wear. The calculation method commonly used in this field is adopted and is not limited here. For example, a temperature sensor (PT100 platinum resistance type) can be embedded in the stator winding slot of the motor and connected to the edge computing unit through a twisted-pair shielded cable to estimate the bearing wear based on the running time.

[0046] The structural health score S is obtained based on the crack length and deformation of the robot's chassis. The calculation formula is S = crack length × 0.7 + deformation × 0.3. The crack length and deformation of the chassis can be obtained and calculated using acoustic emission sensors.

[0047] The health rating E of the electronic equipment is obtained based on the sensor failure rate and the number of controller errors of the handling robot. The calculation formula is E = sensor failure rate × 0.5 + number of controller errors × 0.5. The sensor failure rate and the number of controller errors of the handling robot can be obtained from the historical operating data of the handling robot.

[0048] The navigation health score N is obtained based on the SLAM (Simultaneous Localization and Mapping) positioning error rate of the handling robot, and the calculation formula is N = SLAM positioning error rate × 1.0. The SLAM positioning error rate can be obtained from the historical operation data of the handling robot.

[0049] Step S40: Calculate the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic equipment health, and navigation health.

[0050] Specifically, the weights of battery health, motion system health, mechanical structure health, electronic device health, and navigation health are obtained; wherein, the weight of battery health is greater than the weight of motion system health, and the weight of motion system health is greater than the weight of electronic device health. The health value is calculated based on the weights of battery health, motion system health, mechanical structure health, electronic device health, and navigation health.

[0051] Different weights are assigned to battery health, motion system health, mechanical structure health, electronic equipment health, and navigation health. Battery health has a significant impact on the handling robot; a battery malfunction will cause the robot to stop. Assigning different weights to these four categories according to their importance makes the health values ​​more reliable.

[0052] In this embodiment, the formula for calculating the health value is as follows: H = 0.35 × B + 0.25 × M + 0.2 × S + 0.1 × E + 0.1 × N.

[0053] The health value reflects the health status of the handling robot. The higher the health value, the better the robot's health; conversely, the lower the health value, the lower the robot's health and the higher the risk of malfunction. After obtaining the robot's health value, the next step is to apply different classifications based on the different health values.

[0054] Step S50: Obtain the corresponding health level based on the health value, and issue corresponding prompt information based on the health level.

[0055] The system uses a tiered response based on health values. In this embodiment, a normal operation alert is issued when the health value is 85 or higher; a yellow alert is issued when the health value is 80 or higher but less than 85; an orange alert is issued when the health value is 70 or higher but less than 80; and a red alert is issued when the health value is less than 70. A lower health value indicates a higher probability of the handling robot malfunctioning, requiring early intervention and appropriate handling measures.

[0056] Specifically, issuing a yellow warning includes generating a recommended maintenance list. During the health value calculation process, the server identifies and records the points of failure, then generates a corresponding recommended maintenance list. Issuing an orange warning includes initiating adaptive control of the transport robot. Furthermore, in this embodiment, adaptive control includes replanning the transport robot's path or reassigning its transport tasks. An orange warning indicates that the current transport robot is no longer suitable to continue working on its current path. For example, when the health of a transport robot is between 70 and 80, the server determines that the current robot is not suitable to perform the current task and can assign the task to another transport robot. Issuing a red warning includes forcibly stopping the transport robot and reporting the shutdown information.

[0057] The following is a hierarchical response triggered by the health value H, which realizes a closed loop of "fault warning - path optimization - task reallocation". The following detailed explanation, with reference to specific embodiments, illustrates the above-mentioned different grading processes based on different health values.

[0058] Example 1: Battery Aging Warning and Task Reassignment Background: A handling robot's battery has been used 1200 cycles (the manufacturer recommends 1000 cycles as the replacement threshold), the cycle life health is 27, and the calculated health value is 78, so an orange warning message is issued.

[0059] The specific process is as follows: the temperature sensor detects that the battery temperature is continuously higher than 50℃ (the normal temperature is less than 45℃), and calculates the battery health score B=60.

[0060] The health value of the handling robot is H = 0.35×70 + 0.25×80 + 0.2×80 + 0.1×87 + 0.1×88 = 78. A health value between 70 and 80 triggers an orange alert. The system automatically: 1. Generates a "Recommended Battery Replacement" list; 2. Transfers the day's heavy-load tasks to other handling robots. Maintenance personnel are notified to replace the battery, and the handling robot returns to normal operation.

[0061] Example 2: Structural Damage Detection Caused by Sudden Braking Background: A transport robot braked suddenly (acceleration 8 m / s²) to avoid a pedestrian; the IMU sensor recorded B. stop The coefficient (number of emergency braking times) is 3.

[0062] The specific process is as follows: LiDAR scanning detected a 0.3mm dent in the chassis of the handling robot; acoustic emission sensors detected high-frequency sound waves (crack signals) emanating from the frame welds; the impact coefficient I was calculated to be 8, and the number of emergency braking cycles B was calculated to be... stop The value is 3. The calculated health value H is 0.35×80+0.25×40+0.2×50+0.1×80+0.1×90=65. The health value is less than 70, triggering a red alert, forcing a shutdown and reporting to the maintenance center.

[0063] like Figure 2 As shown, a health detection system for a handling robot in one embodiment includes an acquisition module, a judgment module, a calculation module, and an information prompting module.

[0064] The acquisition module is used to acquire the real-time health monitoring cycle of the handling robot. The judgment module is connected to the acquisition module and is used to determine whether the real-time health detection cycle is less than a preset cycle; if so, it acquires the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles, the motion system health is obtained based on the motor temperature and bearing wear of the handling robot, the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot, the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot, and the navigation health is obtained based on the SLAM positioning error rate of the handling robot; The calculation module is connected to the judgment module and is used to calculate the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic device health, and navigation health. The information prompting module is connected to the calculation module and is used to obtain the corresponding health level based on the health value, and issue corresponding prompt information based on the health level.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A health detection method for a handling robot, characterized in that, Includes the following steps: Obtain the real-time health monitoring cycle of the handling robot; Determine if the real-time health monitoring cycle is shorter than the preset cycle; If not, obtain the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles, the motion system health is obtained based on the motor temperature and bearing wear of the handling robot, the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot, the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot, and the navigation health is obtained based on the SLAM positioning error rate of the handling robot; The health value of the handling robot is calculated based on the battery health, motion system health, mechanical structure health, electronic equipment health, and navigation health. Based on the health value, obtain the corresponding health level and issue corresponding prompts based on the health level.

2. The health detection method for a handling robot according to claim 1, characterized in that, The steps for calculating the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic equipment health, and navigation health include: The weights of the battery health, motion system health, mechanical structure health, electronic device health, and navigation health are obtained; wherein the weight of the battery health is greater than the weight of the motion system health, and the weight of the motion system health is greater than the weight of the electronic device health. The health value is calculated based on the weights of the battery health, motion system health, mechanical structure health, electronic device health, and navigation health.

3. The health detection method for a handling robot according to claim 1 or 2, characterized in that, The steps of obtaining the corresponding health level based on the health value and issuing corresponding prompts based on the health level include: When the health level is greater than or equal to 85, a normal operation prompt message will be issued; When the health level is greater than or equal to 80 and less than 85, a yellow warning message is issued; When the health level is greater than or equal to 70 and less than 80, an orange warning message is issued; A red alert is issued when the health level is less than 70.

4. The health detection method for a handling robot according to claim 1 or 2, characterized in that, The issuance of a yellow warning message includes: generating a recommended maintenance list; the issuance of an orange warning message includes: initiating adaptive control of the handling robot; the issuance of a red warning message includes: forcing the handling robot to stop and reporting the stoppage information.

5. The health detection method for a handling robot according to claim 4, characterized in that, The adaptive control includes: replanning the path of the transport robot or reallocating the transport robot's transport tasks.

6. The health detection method for a handling robot according to claim 1 or 2, characterized in that, The steps for obtaining the real-time health monitoring cycle of the handling robot include: Acquire path information, path impact information, temperature information, and baseline cycle information of the handling robot; The real-time health monitoring cycle is calculated based on the path information, path impact information, temperature information, and baseline cycle information.

7. The health detection method for a handling robot according to claim 6, characterized in that, The step of determining whether the real-time health monitoring period is less than a preset period includes: If so, a maintenance reminder will be issued in advance based on the real-time detection cycle.

8. The health detection method for a handling robot according to claim 6, characterized in that, The path impact information includes path wear coefficient, path smoothness coefficient, impact coefficient, and the number of times the transport robot brakes suddenly while traveling on the path. The path wear coefficient and path smoothness coefficient are obtained by LiDAR scanning, while the impact coefficient and the number of times the robot brakes suddenly are obtained by sensors.

9. The health detection method for a handling robot according to claim 1 or 2, characterized in that, The transport robot is equipped with an acoustic emission sensor, which is located at a key node of the transport robot's frame. The acoustic emission sensor is used to obtain the health status of the mechanical structure.

10. A health detection system for a handling robot, characterized in that, include: The acquisition module is used to acquire the real-time health monitoring cycle of the handling robot; A judgment module, connected to the acquisition module, is used to determine whether the real-time health detection cycle is less than a preset cycle; if not, it acquires the battery health, motion system health, mechanical structure health, electronic device health, and navigation health of the handling robot; wherein, the battery health is obtained based on the remaining power of the handling robot's battery SoC and the number of charge-discharge cycles, the motion system health is obtained based on the motor temperature and bearing wear of the handling robot, the mechanical structure health is obtained based on the frame crack length and frame deformation of the handling robot, the electronic device health is obtained based on the sensor failure rate and controller error count of the handling robot, and the navigation health is obtained based on the SLAM positioning error rate of the handling robot; The calculation module, connected to the judgment module, is used to calculate the health value of the handling robot based on the battery health, motion system health, mechanical structure health, electronic device health, and navigation health. An information prompting module, connected to the calculation module, is used to obtain the corresponding health level based on the health value and issue corresponding prompt information based on the health level.

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