A robot emergency self-rescue system

Abstract

The application discloses a robot emergency self-rescue system and relates to the technical field of robots, which comprises a perception acquisition module, a state recognition module, an energy evaluation module, an action scheduling module, an action execution module, a fault diagnosis module and a reconstruction execution module; the perception acquisition module is used for collecting multi-modal sensing data of a robot body and an environment, including inertial measurement data, force feedback data, visual images, current and voltage data, temperature data and environmental context data; the state recognition module is used for performing weighted fusion calculation on the data output by the perception acquisition module by adopting a multi-modal data fusion algorithm, combining historical state change trends and environmental context factors, and generating an emergency state index at a current time, which is used for judging whether the robot enters an emergency state; and the energy evaluation module is used for collecting energy state information of the robot, including battery voltage, residual capacity and power output capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, in particular to a robot emergency self-rescue system. BACKGROUND

[0002] Robot technology is a comprehensive discipline that covers multiple fields and technologies, including but not limited to mechanical engineering, electrical engineering, computer science, artificial intelligence, control theory, etc., and its goal is to design and manufacture mechanical devices that can operate autonomously or semi-autonomously, which can complete a series of tasks desired by humans, from simple repetitive work to complex exploration tasks, etc. Therefore, how to use advanced technical means to improve the intelligence level and safety of robots has become one of the problems to be solved at present.

[0003] In the field of robots, traditional robot systems are difficult to accurately and timely identify changes in their own state and the surrounding environment in complex or dynamic changing environments, especially in emergency situations such as mechanical failure, energy depletion or sudden environmental changes, and when faced with emergency situations, robots need to make quick decisions and execute appropriate self-rescue measures, but existing systems often lack efficient scheduling and priority sorting strategies. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a robot emergency self-rescue system to solve the problem that traditional robot systems are difficult to accurately and timely identify changes in their own state and the surrounding environment in complex or dynamic changing environments, especially in emergency situations such as mechanical failure, energy depletion or sudden environmental changes, and when faced with emergency situations, robots need to make quick decisions and execute appropriate self-rescue measures, but existing systems often lack efficient scheduling and priority sorting strategies.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a robot emergency self-rescue system, which comprises:

[0008] a perception acquisition module, a state recognition module, an energy evaluation module, an action scheduling module, an action execution module, a fault diagnosis module and a reconstruction execution module;

[0009] The perception acquisition module is configured to acquire multi-modal sensor data of the robot body and the environment, including inertial measurement data, force feedback data, visual images, current and voltage data, temperature data and environmental context data.

[0010] The state recognition module is configured to adopt a multi-modal data fusion algorithm to perform weighted fusion calculation on the data output by the perception acquisition module, combine historical state change trends and environmental context factors, and generate an emergency state index at the current time, which is used to determine whether the robot enters an emergency state.

[0011] The energy evaluation module is configured to collect energy state information of the robot, including battery voltage, residual capacity and power output capability, calculate energy consumption and execution success rate of each potential self-help action, and generate an energy utility value.

[0012] The action scheduling module is configured to prioritize a plurality of self-help actions based on the emergency state level output by the state recognition module and the energy utility value output by the energy evaluation module, and dynamically schedule an optimal self-help action sequence according to energy limitations and action deadlines.

[0013] The action execution module is configured to drive the robot actuator to execute the corresponding self-help action according to the action sequence output by the action scheduling module, and feed back the action execution state and energy consumption information.

[0014] The fault diagnosis module is configured to monitor the running state of each module during the action execution process, detect abnormal modules and evaluate their fault probability and system impact weight, and calculate the overall system reliability index based on a fault propagation model.

[0015] The reconfiguration execution module is configured to determine whether local system reconfiguration is needed according to the system reliability index output by the fault diagnosis module, and if needed, switch to redundant modules and complete function remapping without affecting the overall operation to restore system functions.

[0016] The perception acquisition module is configured to acquire multi-modal sensing data of the robot body and the environment, including:

[0017] Attitude angle, angular velocity and acceleration output by an inertial measurement unit (IMU);

[0018] Joint torque and contact force output by a force feedback sensor;

[0019] Image data collected by a vision sensor;

[0020] Power state output by a current-voltage sensor;

[0021] Module temperature output by a temperature sensor;

[0022] Environmental context factors, including terrain roughness, light intensity and communication signal strength;

[0023] The state recognition module includes the following steps:

[0024] Anomaly degree calculation is performed on the collected multi-modal data:

[0025] ;

[0026] wherein, is the mean value under normal state of the sensor, is the standard deviation of the sensor is the actual measurement value at the current time, represents the anomaly degree of the sensor;

[0027] According to the historical data, the weight of each sensor is dynamically adjusted:

[0028] ;

[0029] wherein, is the standard deviation of the sensor output, is a small constant to prevent division by zero, is the dynamic weight of the sensor at the current time;

[0030] The emergency index at the current time is calculated by combining the historical state change trend and the environmental context factor:

[0031] ;

[0032] wherein, represents the overall anomaly level of the current multi-source sensor data, is the change trend of the emergency index at the last time, is the trend influence coefficient, is the comprehensive environmental risk factor, is the environmental influence coefficient, is the sampling time, is the emergency index, is the change amount of the emergency index;

[0033] Set the emergency state threshold to determine whether to enter the emergency state:

[0034] If , enter the emergency state and output the emergency state level Level 1~3;

[0035] wherein, is the change trend of the emergency index at the last time, , is the trend and environmental influence coefficient;

[0036] Here, is the emergency index at the time before the last time, is the terrain roughness factor, Light intensity factor This refers to the strength of the communication signal.

[0037] As a preferred embodiment of the robot emergency self-rescue system of the present invention, the energy assessment module specifically includes the following steps:

[0038] Collect current battery voltage, remaining capacity, and power output capability;

[0039] Evaluate each potential self-rescue action and calculate its energy utility value:

[0040] ;

[0041] in, For action The utility value, For action The success rate For action The projected energy consumption The delay time required to perform this action, The time limit for the action to be completed. To prevent division by zero of small constants;

[0042] Output the energy utility value to the action scheduling module.

[0043] As a preferred embodiment of the robot emergency self-rescue system of the present invention, the action scheduling module specifically includes the following steps:

[0044] according to Sort the actions by value from highest to lowest;

[0045] If the current energy is insufficient to complete all actions, a downgrade self-rescue strategy will be activated, and only critical actions will be performed;

[0046] It supports a preemptive scheduling strategy, which interrupts the current low-priority action when a high-priority action occurs.

[0047] Output the action sequence to the action execution module.

[0048] As a preferred embodiment of the robot emergency self-rescue system of the present invention, the action execution module specifically includes the following steps:

[0049] Invoke the execution layer module to perform the scheduling action;

[0050] Continuous feedback on the execution status during the action execution process;

[0051] When an action fails to execute or the actuator malfunctions, the fault diagnosis module is entered.

[0052] During the action execution process, the environment and the energy state are continuously monitored, if the state is relieved, the self-help process can be terminated in advance;

[0053] After the action execution is completed, the state recognition module is returned to re-determine whether the emergency state is still in the emergency state.

[0054] As a preferred scheme of the robot emergency self-help system, wherein: the fault diagnosis module, the specific steps are:

[0055] Monitoring the running state of each module, detecting abnormal modules;

[0056] Assessing the fault probability of the module;

[0057] Calculating the overall reliability index of the system:

[0058] ;

[0059] Wherein, is the fault probability of the module , the module importance weight is , the current fault propagation degree is , and the propagation inhibition factor is ;

[0060] If the system reliability , enter the reconstruction execution module.

[0061] As a preferred scheme of the robot emergency self-help system, wherein: the reconstruction execution module, specifically includes:

[0062] Determine whether the reconstruction condition is met;

[0063] If it is met, switch to the redundant module;

[0064] Complete the function remapping and control logic adjustment;

[0065] After the reconstruction is completed, the state recognition process is re-entered.

[0066] Secondly, the application provides a computer device, including a memory and a processor, the memory stores a computer program, wherein: the computer program is executed by the processor to realize any step of the robot emergency self-help system according to the first aspect of the application.

[0067] Thirdly, the application provides a computer readable storage medium, which stores a computer program, wherein: the computer program is executed by the processor to realize any step of the robot emergency self-help system according to the first aspect of the application.

[0068] The application has the beneficial effects that: by integrating the perception acquisition module, the state identification module, the energy evaluation module, the action scheduling module, the action execution module, the fault diagnosis module and the reconstruction execution module, a complete technical closed loop with multi-modal emergency state identification, energy priority scheduling and local system reconstruction capability is constructed, the system can accurately identify the state of the robot under complex environment or sudden failure, dynamically evaluate energy consumption and action utility, preferentially execute high-value self-help actions, and realize system-level fault tolerance recovery through redundant module switching and function remapping when part of the modules fail, which significantly improves the survival ability, task continuity and autonomous decision level of the robot in extreme environment. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0070] Figure 1 The schematic diagram of the robot emergency self-help system in embodiment 1. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0072] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0073] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0074] Embodiment, refer to Figure 1 For the embodiments of the present application, the embodiment provides a robot emergency self-help system, comprising:

[0075] The perception acquisition module, the state identification module, the energy evaluation module, the action scheduling module, the action execution module, the fault diagnosis module and the reconstruction execution module;

[0076] a perception acquisition module, configured to acquire multi-modal sensing data of the robot body and the environment, including inertial measurement data, force feedback data, visual image data, current voltage data, temperature data, and environmental context data;

[0077] further, the inertial measurement unit (IMU) outputs attitude angle, angular velocity, and acceleration;

[0078] the force feedback sensor outputs joint torque and contact force;

[0079] the visual sensor acquires image data;

[0080] the current voltage sensor outputs power status;

[0081] the temperature sensor outputs module temperature;

[0082] the environmental context factor includes terrain roughness, light intensity, and communication signal strength;

[0083] It should be noted that in the perception acquisition module, the inertial measurement unit (IMU) acquires robot attitude angle, angular velocity, and linear acceleration in real time to determine whether the robot has abnormal states such as tipping, sliding, or losing balance; the force feedback sensor detects joint torque and external contact force to identify whether there is external collision or actuator jamming; the visual sensor acquires image data for environment recognition and obstacle detection; the current voltage sensor evaluates battery status and energy reserve; the temperature sensor monitors the temperature of key modules to prevent functional failure due to overheating; and the environmental context factor is obtained through external sensors to improve the environmental adaptability and judgment accuracy of emergency state recognition.

[0084] a state recognition module, configured to use a multi-modal data fusion algorithm to perform weighted fusion calculation on the data output by the perception acquisition module, combine historical state change trends and environmental context factors, and generate an emergency state index at the current time to determine whether the robot enters an emergency state;

[0085] Further, the multi-modal data collected is calculated for abnormality degree:

[0086]

[0087] wherein, is the mean value under normal state of the sensor, is the mean value under normal state of the sensor, is the actual measurement value at the current time, represents the abnormality degree of the sensor;

[0088] According to historical data, the weights of each sensor are dynamically adjusted:

[0089] ​ ;

[0090] wherein, is the standard deviation of sensor output, is a small constant to prevent division by zero, is the sensor dynamic weight at the current time;

[0091] Combine the historical state change trend and environmental context factors to calculate the emergency state index at the current time:

[0092] ;

[0093] wherein, represents the overall abnormal level of the current multi-source sensor data, is the change trend of the emergency index at the last time, is the trend influence coefficient, is the comprehensive environmental risk factor, is the environmental influence coefficient, is the sampling time, is the emergency state index, is the change amount of the emergency state index;

[0094] Set the emergency state threshold to determine whether to enter the emergency state:

[0095] If , enter the emergency state and output the emergency state level Level 1~3; wherein, is the change trend of the emergency index at the last time, , is the trend and environmental influence coefficient;

[0096] Here, is the emergency index at the time before the last time, is the terrain roughness factor, is the light intensity factor, is the communication signal strength.

[0097] It should be noted that in the state recognition module, the multi-modal data fusion algorithm is based on a dynamic weighting mechanism, which combines historical state trends and environmental context factors to effectively improve the robustness and accuracy of emergency state recognition. Among them, the sensor abnormality calculation is used to quantify the degree of deviation of the current data from the normal state; the dynamic weight mechanism is adaptively adjusted according to the stability of the sensor output, enhancing the system's tolerance to sensor abnormalities or failures; the introduction of environmental context factors enables the system to maintain stable emergency state recognition performance under different terrain, light and communication conditions, and is suitable for complex and variable application scenarios.

[0098] The energy assessment module is used to collect the robot's energy status information, including battery voltage, remaining capacity and power output capability, calculate the energy consumption and success rate of each potential self-rescue action, and generate energy utility value;

[0099] Furthermore, it collects current battery voltage, remaining capacity, and power output capability;

[0100] Evaluate each potential self-rescue action and calculate its energy utility value:

[0101] ;

[0102] in, For action The utility value, For action The success rate For action The projected energy consumption The delay time required to perform this action, The time limit for the action to be completed. To prevent division by zero of small constants;

[0103] Output the energy utility value to the action scheduling module;

[0104] It should be noted that in the energy assessment module, the energy utility value comprehensively considers the utility of the action, success rate, energy consumption, execution delay, and time constraints, thereby realizing the quantitative assessment and priority ranking of self-rescue actions. This module ensures that high-value self-rescue actions are executed first under limited energy conditions by dynamically calculating the energy utility value of each action, avoiding mission interruption due to energy depletion. In addition, this module supports a degraded self-rescue strategy, automatically selecting key actions to be executed when energy is insufficient, ensuring the maintenance of the system's core functions.

[0105] The action scheduling module is used to prioritize multiple self-rescue actions based on the emergency status level output by the status recognition module and the energy utility value output by the energy assessment module, and dynamically schedule the optimal self-rescue action sequence according to energy limits and action deadlines.

[0106] Furthermore, according to Sort the actions by value from highest to lowest;

[0107] If the current energy is insufficient to complete all actions, a downgrade self-rescue strategy will be activated, and only critical actions will be performed;

[0108] It supports a preemptive scheduling strategy, which interrupts the current low-priority action when a high-priority action occurs.

[0109] Output the action sequence to the action execution module;

[0110] It should be noted that in the action scheduling module, the multiple self-help actions are prioritized based on the emergency state level and the energy utility value, ensuring that the system can quickly respond and execute the optimal action sequence in an emergency state; the module supports preemptive scheduling strategy, which can interrupt the currently executed low-priority action when a high-priority action occurs, improving the system response speed and flexibility; after the action sequence is output to the execution module, the system will dynamically adjust the scheduling strategy according to the actual execution situation, ensuring efficient execution of self-help actions and rational use of resources.

[0111] The action execution module is configured to drive the robot executor to execute corresponding self-help actions according to the action sequence output by the action scheduling module, and feedback the action execution state and energy consumption information;

[0112] Further, the execution layer module is called to execute the scheduled action;

[0113] The execution state is continuously fed back during the action execution process;

[0114] When an action fails to execute or the executor is abnormal, the fault diagnosis module is entered;

[0115] During the action execution process, the environment and energy state are continuously monitored, and if the state is relieved, the self-help process can be terminated in advance;

[0116] After the action execution is completed, the state recognition module is returned to re-determine whether it is still in an emergency state;

[0117] It should be noted that in the action execution module, the execution layer module includes but is not limited to key execution units such as leg drive, mechanical arm control, and track drive, which are used to drive the robot to complete self-help actions such as standing, obstacle avoidance, turning over, and escaping obstacles; during the action execution process, the system continuously feeds back execution state information, including position, speed, current, and other parameters, for real-time monitoring of action execution effect; if the action fails to execute or the executor is abnormal, the system will automatically switch to the fault diagnosis module for abnormal handling to prevent further deterioration of the task; at the same time, the system continuously monitors the environment and energy state during the execution process to ensure that unnecessary self-help processes are terminated in a timely manner when the state is relieved, saving energy resources.

[0118] The fault diagnosis module is configured to monitor the running state of each module during the action execution process, detect abnormal modules, and evaluate their fault probability and system impact weight, and calculate the overall reliability index of the system in combination with the fault propagation model;

[0119] Further, the running state of each module is monitored to detect abnormal modules;

[0120] The fault probability of the module is evaluated;

[0121] The overall system reliability index is:

[0122]

[0123] Wherein, is the failure probability of the module, is the module importance weight, is the current fault propagation degree, is the propagation inhibition factor; If the system reliability

[0124] , then enter the reconstruction execution module;

[0125] It should be pointed out that in the fault diagnosis module, by real-time monitoring of the running state of each module, the system can quickly identify abnormal modules and evaluate their failure probability; the module importance weight is used to measure the influence degree of each module on the overall system function, combined with the current fault propagation degree and the propagation inhibition factor, the system can calculate the overall reliability index; this index comprehensively reflects the health status and potential risk of the current system; when the system reliability is lower than the set threshold, the system will automatically trigger the reconstruction process and enter the reconstruction execution module to ensure that the system can still maintain basic function operation in the case of partial module failure.

[0126] The reconstruction execution module is used to judge whether local system reconstruction is needed according to the system reliability index output by the fault diagnosis module, and if needed, switch to the redundant module and complete function remapping without affecting the overall operation to restore system function;

[0127] Further, it is judged whether the reconstruction condition is met;

[0128] If so, switch to the redundant module;

[0129] Complete function remapping and control logic adjustment;

[0130] After the reconstruction is completed, re-enter the state recognition process;

[0131] It should be pointed out that in the reconstruction execution module, the system determines whether to start the reconstruction process by judging whether the current system reliability meets the set threshold; if the reconstruction condition is met, the system will switch to the redundant module without affecting the overall operation, complete function remapping and control logic adjustment; function remapping includes key steps such as control signal redirection, communication channel update, configuration parameter migration, etc., to ensure that the redundant module can seamlessly replace the faulty module; after the reconstruction is completed, the system will re-enter the state recognition process to re-evaluate the current state, ensuring that the system returns to normal operation state; this module supports hot plug and remote log recording functions, facilitating subsequent system maintenance and optimization.

[0132] ​​The embodiment also provides a computer device suitable for the robot emergency self-rescue system, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the robot emergency self-rescue system proposed in the above embodiment.

[0133] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0134] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the robot emergency self-rescue system proposed in the above embodiment. The storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0135] To sum up, the application integrates the perception acquisition module, the state identification module, the energy evaluation module, the action scheduling module, the action execution module, the fault diagnosis module and the reconstruction execution module, constructs a complete technical closed loop with multi-modal emergency state identification, energy priority scheduling and local system reconstruction capability, the system can accurately identify the state of the robot in complex environment or sudden failure, dynamically evaluate the energy consumption and action utility, preferentially execute high-value self-help action, and realize system-level fault-tolerant recovery through redundant module switching and function remapping when part of the module fails, which significantly improves the survival ability, task continuity and autonomous decision level of the robot in extreme environment.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A robot emergency self-rescue system, characterized in that: include: The module includes a sensing and acquisition module, a status recognition module, an energy assessment module, an action scheduling module, an action execution module, a fault diagnosis module, and a reconfiguration and execution module. The perception and acquisition module is used to collect multimodal sensing data of the robot body and the environment, including inertial measurement data, force feedback data, visual images, current and voltage data, temperature data and environmental context data. The state recognition module is used to perform weighted fusion calculation on the data output by the perception and acquisition module using a multimodal data fusion algorithm, and combine historical state change trends and environmental context factors to generate an emergency state index at the current moment, which is used to determine whether the robot has entered an emergency state. The energy assessment module is used to collect the robot's energy status information, including battery voltage, remaining capacity and power output capability, calculate the energy consumption and execution success rate of each potential self-rescue action, and generate energy utility value. The action scheduling module is used to prioritize multiple self-rescue actions based on the emergency status level output by the status recognition module and the energy utility value output by the energy assessment module, and to dynamically schedule the optimal self-rescue action sequence according to energy limits and action deadlines. The action execution module is used to drive the robot actuator to perform corresponding self-rescue actions according to the action sequence output by the action scheduling module, and to provide feedback on the action execution status and energy consumption information. The fault diagnosis module is used to monitor the operating status of each module during the execution of the action, detect abnormal modules and evaluate their failure probability and system impact weight, and calculate the overall system reliability index in combination with the fault propagation model. The reconfiguration execution module is used to determine whether a partial system reconfiguration is needed based on the system reliability indicators output by the fault diagnosis module. If so, it switches to the redundant module and completes the function remapping to restore the system function without affecting the overall operation. The sensing and acquisition module is used to collect multimodal sensing data of the robot body and the environment, including: The attitude angle, angular velocity, and acceleration output by the inertial measurement unit (IMU); The joint torque and contact force output by the force feedback sensor; Image data acquired by a vision sensor; Power status output by the current and voltage sensors; The module temperature output by the temperature sensor; Environmental context factors, including terrain roughness, light intensity, and communication signal strength; The status recognition module includes the following steps: Anomaly calculation is performed on the collected multimodal data: ； in, This is the average value under normal sensor conditions. For sensors The actual measurement at the current time, Indicates the anomaly degree of the sensor; The weights of each sensor are dynamically adjusted based on historical data: ; in, The standard deviation of the sensor output. To prevent small constants from being divided by zero, For sensors The dynamic weight at the current moment; By combining historical trends in state changes with environmental context factors, the emergency index for the current moment is calculated: ; in, This indicates the overall anomaly level of the current multi-source sensor data. This represents the trend of the emergency index at the previous moment. This is the trend influence coefficient. To comprehensively consider environmental risk factors, This is the environmental impact factor. Sampling time, This is an emergency index. This represents the change in the emergency index; Set emergency threshold Determine whether to enter a state of emergency: like If so, an emergency state is entered, and an emergency state level of Level 1~3 is output; in, This represents the trend of the emergency index at the previous moment. , The coefficient represents the influence of trends and the environment. Here, This represents the emergency index at the moment immediately preceding it. For terrain roughness factor, Light intensity factor This refers to the strength of the communication signal.

2. The robot emergency self-rescue system as described in claim 1, characterized in that: The energy assessment module specifically includes the following steps: Collect current battery voltage, remaining capacity, and power output capability; Evaluate each potential self-rescue action and calculate its energy utility value: ; in, For action The utility value, For action The success rate For action The projected energy consumption The delay time required to perform this action, The time limit for the action to be completed. To prevent division by zero of small constants; Output the energy utility value to the action scheduling module.

3. The robot emergency self-rescue system as described in claim 2, characterized in that: The action scheduling module specifically includes the following steps: according to Sort the actions by value from highest to lowest; If the current energy is insufficient to complete all actions, a downgrade self-rescue strategy will be activated, and only critical actions will be performed; It supports a preemptive scheduling strategy, which interrupts the current low-priority action when a high-priority action occurs. Output the action sequence to the action execution module.

4. The robot emergency self-rescue system as described in claim 3, characterized in that: The action execution module specifically includes the following steps: Invoke the execution layer module to perform the scheduling action; Continuous feedback on the execution status during the action execution process; When an action fails to execute or the actuator malfunctions, the fault diagnosis module is entered. During the execution of the action, the environmental and energy status is continuously monitored. If the situation improves, the self-rescue process can be terminated in advance. After the action is completed, the status recognition module returns to determine whether the emergency status is still in effect.

5. The robot emergency self-rescue system as described in claim 4, characterized in that: The specific steps of the fault diagnosis module are as follows: Monitor the operating status of each module and detect abnormal modules; Assess the probability of module failure; Overall reliability metrics of the computing system: ; in, For module The probability of failure, As the importance weight of the module, As the current degree of fault propagation, It is a transmission inhibitor; If system reliability If so, then proceed to the refactoring execution module.

6. The robot emergency self-rescue system as described in claim 5, characterized in that: The reconstruction execution module specifically includes: Determine whether the reconstruction conditions are met; If the conditions are met, switch to the redundant module; Complete the function remapping and control logic adjustment; After reconstruction is complete, the state recognition process is restarted.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the robot emergency self-rescue system according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the robot emergency self-rescue system according to any one of claims 1 to 6.

Images