Modular design method and system for full-performance automatic detection robot

Abstract

The invention discloses a modular design method and system for a full-performance automatic detection robot, and relates to the technical field of automatic detection, and the method comprises the steps: dividing a robot system into a plurality of modules with independent functions, obtaining state parameters and working parameters of the modules, and generating module output data based on the state parameters and the working parameters; in the task execution process, the robot adjusts module configuration in real time by dynamically adjusting combination and weight of the modules, optimizing output of a task stage and module selection based on comprehensive evaluation of error minimization and a synergistic effect among the modules, and the optimal module performs selection according to real-time tasks and environment changes. The problem that the overall efficiency and stability of a robot system are affected due to the fact that the robot system in the prior art generally adopts a fixed structural design, flexibility and adjustability between modules are lacked, and different tasks cannot be rapidly switched and adjusted can be solved.

Description

Technical Field

[0001] The present invention relates to the field of automated detection technology, and in particular to a modular design method and system for a full-performance automated detection robot. Background Art

[0002] As the requirements for automated inspection accuracy and efficiency in industrial production and service fields continue to increase, existing inspection robot systems have exposed some shortcomings when dealing with complex and changing task requirements. Traditional automated inspection robot systems are usually based on fixed structure designs, and their functional modules lack sufficient flexibility and adjustability. This structural design often results in the system being unable to quickly adapt to new inspection tasks in different environments, making it difficult to meet personalized and diversified work needs. Therefore, how to improve the adaptability, flexibility and efficiency of the robot system has become an urgent need for technological development.

[0003] At the same time, with the continuous development of intelligent technology, higher requirements are placed on robot systems. Modern detection tasks often involve a variety of complex environmental factors (such as power, gravity, temperature, humidity, light intensity, etc.), and the task objectives may also change dynamically at different stages. Therefore, the robot system not only needs to have powerful data processing and detection capabilities, but also be able to adjust its own working state and parameters in time according to changes in the external environment to ensure that each task can be completed under optimal conditions. In addition, with the gradual development of robot technology, the collaborative work of multiple modules has become an important direction for improving robot performance. In order to effectively solve these problems, the robot system is required to have not only highly integrated capabilities in hardware, but also sufficient intelligence in software and control systems, and be able to self-adjust and optimize according to real-time task requirements.

[0004] There are at least the following technical problems in the prior art: the robot system in the prior art usually adopts a fixed structural design, lacks flexibility and adjustability between modules, and cannot quickly switch and adjust between different tasks, which affects the overall efficiency and stability of the robot system; the modules in the prior art are difficult to effectively adjust according to the real-time working environment or task objectives, resulting in low efficiency of the system when processing multiple tasks, and even failure to complete tasks; the modules in the prior art have poor collaboration between modules and lack of intelligent collaborative scheduling mechanism, which leads to poor information transmission between different modules or uneven distribution of computing resources, thereby affecting the overall performance. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention provides a modular design method and system for a full-performance automated inspection robot, which can solve the problems mentioned in the background technology.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: A modular design method for a full-performance automated inspection robot, comprising: dividing a robot system into a plurality of functionally independent modules, obtaining state parameters and working parameters of the modules, and generating module output data based on the state parameters and the working parameters;

[0008] During the task execution process, the robot optimizes the output of the task stage by dynamically adjusting the combination and weight of modules. Module selection is based on a comprehensive evaluation of error minimization and synergy between modules. The optimal module is selected according to real-time tasks and environmental changes, and the module configuration is adjusted in real time.

[0009] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of constructing a modular system includes:

[0010] Obtain environmental factor parameters and module efficiency coefficient;

[0011] Calculate the initial module output data, the initial module output data satisfies:

[0012] D i (0) = η i ·(S i (0)·P i (0))+ξ i ·E i ;

[0013] Among them, η i is the efficiency coefficient of the i-th module, which reflects the efficiency of the module in completing the task in the initial state and is obtained through experimental measurement; ξ i It is the influence coefficient of external environmental factors.

[0014] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of constructing a modular system includes:

[0015] Collect the output data of the function module at the previous moment;

[0016] Obtain target output data and module coordination coefficient;

[0017] Calculate the update amount of the state parameter and update the state parameter, wherein:

[0018] The update of the state parameters satisfies:

[0019] S i (t) = S i (t-1)+ΔS i (t);

[0020] The update amount of the state parameter satisfies:

[0021]

[0022] Among them, S i (t) represents the state of the i-th module at time t, based on the state S at the previous moment i (t-1) and the current state change ΔS i (t) Update; state change ΔS i (t) consists of two parts: one is the adjustment term α based on the output error i ·(D i (t-1)-D target (t-1)), and the other is an adjustment term based on the collaboration between modules Among them, D i (t-1) and D target (t-1) are the previous output and target output of the module respectively; α i is the sensitivity coefficient of the i-th module to its own error, which determines the output error D i (t-1)-D target (t-1) the degree of influence on the state update; w ik (t) is the synergy coefficient between the i-th module and the k-th module at the current moment; β i represents the sensitivity of the i-th module to the collaborative information feedback; n is the number of modules; D k (t-1) is the output of the kth module at the previous moment.

[0023] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of executing module optimization includes:

[0024] Obtain activation sensitivity coefficients and co-regulatory factors;

[0025] Calculate module weight value;

[0026] A system output result is generated based on the module weight value, and the system output result satisfies:

[0027]

[0028] Among them, O j represents the output of the j-th task stage, which is obtained by weighted summation of the outputs of each module; the output of the i-th module in the j-th task stage is determined by the weight w i And the output of the activation function; is a co-regulatory factor; C ik(t) is the synergy measure between the i-th module and the k-th module at time t.

[0029] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of executing module optimization includes:

[0030] Get error threshold parameters;

[0031] Calculate the module error value of the functional module, the module error value satisfies:

[0032] E i (t)=|D i (t)-D target (t)|≤∈ i ;

[0033] Among them, E i (t) is the output error of the module at time t, ∈ i is the error threshold, which indicates the maximum error allowed by the system; when the error E i (t) is less than or equal to the set threshold ∈ j When , it means that the module output is close to the target value and performs well; on the contrary, the module with an error greater than the set threshold will be suppressed in the optimal selection process.

[0034] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of executing module optimization includes:

[0035] Get the output data of the functional module;

[0036] Calculate the output mean of the functional module;

[0037] Calculate the coordination parameters between the functional modules, and the coordination parameters satisfy:

[0038]

[0039] Among them, C ik (t) represents the synergy measure between the i-th module and the k-th module at time t; and are the output means of the i-th module and the k-th module respectively.

[0040] As a preferred solution of the modular design method of the full-performance automated inspection robot described in the present invention, the step of executing module optimization includes:

[0041] Obtain error threshold parameters and coordination threshold parameters;

[0042] Calculate module error values ​​and coordination parameters of functional modules;

[0043] Determine the magnitude relationship between the module error value and the error threshold parameter;

[0044] Determining a magnitude relationship between the coordination parameter and the coordination threshold parameter;

[0045] The module weight value is adjusted based on the judgment result.

[0046] To further solve the above technical problems, the present invention provides the following technical solutions: A modular design system for a full-performance automated inspection robot, comprising: a parameter configuration module, for dividing the robot system into a plurality of functionally independent modules, and obtaining state parameters and working parameters of the functionally independent modules;

[0047] A data generation module, configured to generate module output data based on the state parameter and the working parameter;

[0048] The optimization control module is used to calculate the module error value and the coordination parameter according to the module output data, determine the module weight value based on the module error value and the coordination parameter, and generate the system output result.

[0049] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the modular design method of the full-performance automated inspection robot as described above are implemented.

[0050] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the modular design method of the full-performance automated inspection robot as described above are implemented.

[0051] The beneficial effects of the present invention are as follows: 1. By dividing the robot system into multiple functionally independent modules, each module focuses on a specific task, so that the system can be flexibly combined and configured according to actual needs. This modular structure greatly enhances the scalability of the system, and can quickly adjust and adapt the module combination according to different detection tasks, thereby achieving the best detection effect. The independence between modules enables the system to be easily adjusted when encountering new tasks or changing requirements, without having to significantly modify the overall structure, reducing the coupling of hardware and software, and enhancing the maintainability and upgradeability of the system;

[0052] 2. The present invention allows each module to dynamically adjust its initial state and working parameters according to the current environmental changes and task requirements; the working state and output of the module not only depend on its inherent characteristics, but are also closely related to environmental factors (such as power, voltage, gravity, temperature, humidity, etc.) and task objectives; by accurately configuring the initial conditions, states and working parameters of the module, and monitoring the performance and feedback information of the module in real time, the present invention ensures that the system can adapt to different working environments, thereby maximizing the accuracy and efficiency of the detection task;

[0053] 3. In the present invention, modules can not only work independently, but also work together according to task requirements; by establishing collaborative metrics between modules, modules can be dynamically adjusted according to each other's output information when performing tasks; this collaborative mechanism not only enhances the accuracy of task completion, but also optimizes the task execution results according to the interaction between modules; when multiple modules work together, they can better allocate task resources, reduce system errors, and ultimately improve overall performance;

[0054] 4. During the task execution process, the present invention ensures that the most suitable module is selected at each task stage by optimizing the configuration and combination of modules; through error minimization and inter-module coordination measurement, it can ensure that the optimal combination of modules is used at each stage, thereby minimizing the task execution error; the contribution of each module is evaluated through weighted fusion, and combined with its synergy with other modules, finally the optimal module is selected for task execution; this method of dynamically adjusting and optimizing module selection enables the robot to always maintain high efficiency and accuracy in a changing task environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 This is a schematic diagram of the overall process of a modular design method for a full-performance automated inspection robot proposed by the present invention;

[0057] Figure 2 This is a diagram of computer equipment in a modular design method for a full-performance automated inspection robot proposed in the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a modular design method for a full-performance automated inspection robot.

[0061] S1. Divide the robot system into modules with independent functions. Each module is dynamically adjusted according to specific initial conditions, states and working parameters to adapt to different task requirements and environmental conditions. By adjusting the collaboration and feedback between modules, the module status and output are dynamically updated during the task.

[0062] The robot system is divided into modules with independent functions. Each module is responsible for specific tasks, such as data acquisition, signal processing, detection algorithm, decision-making system, etc. The modules can be recombined and configured according to actual needs without making major changes to the overall structure of the robot. The modular design not only improves the scalability and flexibility of the system, but also allows for the rapid adaptation of different functional module combinations according to different detection tasks to achieve the best detection effect.

[0063] To ensure that each module can effectively collaborate in a changing working environment, each module needs to be precisely configured according to specific initial conditions, states, and working parameters. Each module is not static at startup, but is dynamically adjusted based on the overall needs of the system and the actual conditions of the current environment.

[0064] The initial conditions, states and working parameters of each module are defined according to the functional requirements, environmental characteristics and preset mission objectives of the module. Specifically, the initial conditions refer to the external environmental factors and mission objectives set by the system when the module is started, such as power, gravity, voltage, temperature, humidity, light intensity, network delay and other variables, which directly affect the working performance of the module; the state refers to the operating status of the module at each moment, including the working status of the module (such as on / off, normal / faulty, etc.), feedback information during the execution process, the operation results of the previous moment, etc.; the working parameters include all adjustable variables that determine the behavior of the module, such as processing speed, data acquisition frequency, signal amplitude, drive current, etc., which are dynamically adjusted through external control signals to ensure that the module can operate stably under the ever-changing environment and mission requirements.

[0065] At task startup, each module sets its state S according to specific initial conditions. i (0) and operating parameters P i (0), and calculate the initial output D i (0); The initial output is not only affected by the inherent characteristics of the module, but also by environmental factors; by analyzing the initial state, initial parameters and external environment of the module, the preliminary task output can be obtained. The initial output is calculated by the following formula:

[0066] D i (0) = η i ·(S i (0)·P i (0))+ξ i ·E i ;

[0067] Among them, η i is the efficiency coefficient of the i-th module, which reflects the efficiency of the module in completing the task in the initial state and is obtained through experimental measurement; ξ i is the influence coefficient of external environmental factors, which reflects the influence of external environment on module performance and is obtained through experiments; the external environmental factor E of the i-th module i Variables such as power, gravity, voltage, temperature, and humidity directly affect the working status of the module. In the initial stage, the initial output results are used to predict the performance of each module at the beginning of the mission, thereby providing basic data for subsequent adjustments and optimizations.

[0068] As the task progresses, the module status and output will change, so the status of each module must be constantly adjusted to adapt to the current working environment and task requirements; based on the error between the output at the previous moment and the target output, combined with the feedback of the collaborative module, the module status is dynamically adjusted. The formula for updating the module status at each moment is:

[0069] S i (t) = S i (t-1)+ΔS i (t);

[0070]

[0071] Among them, S i (t) represents the state of the i-th module at time t, based on the state S at the previous moment i (t-1) and the current state change ΔS i (t) Update; state change ΔS i (t) consists of two parts: one is the adjustment term α based on the output error i ·(D i (t-1)-D target (t-1)), and the other is an adjustment term based on the collaboration between modules Among them, D i (t-1) and D target (t-1) are the previous output and target output of the module respectively; α i is the sensitivity coefficient of the i-th module to its own error, which determines the output error D i (t-1)-D target (t-1) The degree of impact on the status update is determined by the engineer’s experience; w ik (t) is the synergy coefficient between the i-th module and the k-th module at the current moment, which determines the degree of mutual influence between modules and is obtained through experimental adjustment; β i represents the sensitivity of the i-th module to collaborative information feedback, obtained through experiments or design optimization; n is the number of modules; D k (t-1) is the output of the kth module at the previous moment. The module state update ensures that the module state can be adjusted in time according to the current error and the output of other modules.

[0072] S2. During the task execution, the robot optimizes the output of the task stage by dynamically adjusting the combination and weight of modules. Module selection is based on a comprehensive evaluation of error minimization and synergy between modules. The optimal module is selected according to real-time tasks and environmental changes, and the module configuration is adjusted in real time.

[0073] At each task stage, the configuration and combination of modules are dynamically adjusted to select the optimal module based on the specific task requirements and the stage goals of the current task. The optimization goal of task division and module configuration is to minimize the execution error of the task. To this end, the combination and weight of the modules are optimized and calculated using the following formula:

[0074]

[0075] Among them, O j represents the output of the j-th task stage, which is obtained by weighted summation of the outputs of each module; the output of the i-th module in the j-th task stage is determined by the weight w i And the output of the activation function; the activation function uses the Sigmoid function, through γ i Adjust the sensitivity to module output errors and activate the sensitivity coefficient γ i Adjust through practical testing; is the synergistic regulatory factor, which controls the influence of the synergistic effect on the final output and is obtained through experiments; C ik (t) is the synergy measure between the i-th module and the k-th module at time t. The output error of each module is mapped to an interval of [0,1]. The smaller the error, the closer the output is to 1, indicating that the module contributes more to the task completion. Therefore, the optimal module is selected in the following two ways:

[0076] 1. Minimize error output: In order to measure the performance of each module in the task stage, an error measurement formula is used to quantify the difference between the module output and the target output, so as to select the module with the greatest contribution. The error formula is defined as:

[0077] E i (t)=|D i (t)-D target (t)|≤∈ i ;

[0078] Among them, E i (t) is the output error of the module at time t, ∈ i is the error threshold, which indicates the maximum error allowed by the system. i (t) is less than or equal to the set threshold ∈ j When , it means that the module output is close to the target value and performs well; on the contrary, the module whose error is greater than the set threshold will be suppressed in the optimal selection process. The process of minimizing the error is achieved through the activation function, which adjusts the contribution of each module so that the module whose error is less than or equal to the set threshold has a higher weight in the final weighted sum.

[0079] 2. Module synergy: A synergy metric is introduced to describe the synergy between a module and other modules in the task stage, which ultimately affects the module selection.

[0080]

[0081] Among them, C ik (t) represents the synergy measure between the i-th module and the k-th module at time t; and are the output means of the i-th module and the k-th module respectively. The synergy metric evaluates the synergy effect between modules by calculating the output correlation between them. Modules with strong synergy mean that they can jointly optimize the completion of tasks, thereby improving the overall performance of the system. The larger the value of the synergy metric, the stronger the synergy between modules, and the corresponding module will get a higher weight in the final output.

[0082] The selection process of the optimal module is based on a comprehensive evaluation of the module output error and the synergy between modules. The module with small error and strong synergy will be given the highest weight as the optimal module, and finally the optimal task output is formed through weighted fusion. j .

[0083] The selected optimal module will be connected to other modules in a networked manner, and each module will exchange data through a standardized communication interface to ensure the stability and efficiency of data transmission. The selection of the optimal module depends not only on the output and synergy of the current mission stage, but also on the dynamic changes of the mission. For example, environmental changes may cause the working efficiency of some modules to decline, or new mission requirements may require the introduction of different types of modules. Therefore, during the execution of the mission, the progress of the mission and changes in the environment will be continuously monitored, and the module configuration will be adjusted in real time to ensure that the most suitable module combination can be used at each stage.

[0084] In summary, the beneficial effects of the present invention are as follows: 1. By dividing the robot system into multiple functionally independent modules, each module focuses on a specific task, so that the system can be flexibly combined and configured according to actual needs. This modular structure greatly enhances the scalability of the system, and can quickly adjust and adapt the module combination according to different detection tasks, thereby achieving the best detection effect. The independence between modules enables the system to be easily adjusted when encountering new tasks or changing requirements, without having to significantly modify the overall structure, reducing the coupling of hardware and software, and enhancing the maintainability and upgradeability of the system;

[0085] 2. The present invention allows each module to dynamically adjust its initial state and working parameters according to the current environmental changes and task requirements; the working state and output of the module not only depend on its inherent characteristics, but are also closely related to environmental factors (such as power, voltage, gravity, temperature, humidity, etc.) and task objectives; by accurately configuring the initial conditions, states and working parameters of the module, and monitoring the performance and feedback information of the module in real time, the present invention ensures that the system can adapt to different working environments, thereby maximizing the accuracy and efficiency of the detection task;

[0086] 3. In the present invention, modules can not only work independently, but also work together according to task requirements; by establishing collaborative metrics between modules, modules can be dynamically adjusted according to each other's output information when performing tasks; this collaborative mechanism not only enhances the accuracy of task completion, but also optimizes the task execution results according to the interaction between modules; when multiple modules work together, they can better allocate task resources, reduce system errors, and ultimately improve overall performance;

[0087] 4. During the task execution process, the present invention ensures that the most suitable module is selected at each task stage by optimizing the configuration and combination of modules; through error minimization and inter-module coordination measurement, it can ensure that the optimal combination of modules is used at each stage, thereby minimizing the task execution error; the contribution of each module is evaluated through weighted fusion, and combined with its synergy with other modules, finally the optimal module is selected for task execution; this method of dynamically adjusting and optimizing module selection enables the robot to always maintain high efficiency and accuracy in a changing task environment.

[0088] Embodiment 2 is an embodiment of the present invention, which provides a modular design system for a full-performance automated inspection robot, including:

[0089] A parameter configuration module is used to divide the robot system into multiple functionally independent modules and obtain state parameters and working parameters of the functionally independent modules;

[0090] A data generation module, used for generating module output data based on state parameters and working parameters;

[0091] The optimization control module is used to calculate the module error value and coordination parameters according to the module output data, determine the module weight value based on the module error value and coordination parameters, and generate the system output result.

[0092] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0094] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0095] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A modular design method for a full-performance automated inspection robot, characterized in that: include: Dividing the robot system into a plurality of modules with independent functions, obtaining state parameters and working parameters of the modules, and generating module output data based on the state parameters and the working parameters; During the task execution process, the robot optimizes the output of the task stage by dynamically adjusting the combination and weight of modules. Module selection is based on a comprehensive evaluation of error minimization and synergy between modules. The optimal module is selected according to real-time tasks and environmental changes, and the module configuration is adjusted in real time.

2. The modular design method of the full-performance automated inspection robot according to claim 1, characterized in that: The steps to building a modular system include: Obtain environmental factor parameters and module efficiency coefficient; Calculate the initial module output data, the initial module output data satisfies: D i (0)=h i ·(S i (0)·P i (0))+ξ i ·E i ; Among them, η i is the efficiency coefficient of the i-th module, which reflects the efficiency of the module in completing the task in the initial state and is obtained through experimental measurement; i It is the influence coefficient of external environmental factors.

3. The modular design method of the full-performance automated inspection robot according to claim 2, characterized in that: The steps to building a modular system also include: Collect the output data of the function module at the previous moment; Obtain target output data and module coordination coefficient; Calculate the update amount of the state parameter and update the state parameter, wherein: The update of the state parameters satisfies: S i (t)=S i (t-1)+ΔS i (t); The update amount of the state parameter satisfies: Among them, S i (t) represents the state of the i-th module at time t, based on the state S at the previous moment i (t-1) and the current state change ΔS i (t) Update; state change ΔS i (t) consists of two parts: one is the adjustment term α based on the output error i ·(D i (t-1)-D target (t-1)), and the other is an adjustment term based on the collaboration between modules Among them, D i (t-1) and D target (t-1) are the previous output and target output of the module respectively; α i is the sensitivity coefficient of the i-th module to its own error, which determines the output error D i (t-1)-D target (t-1) the degree of influence on the state update; w ik (t) is the synergy coefficient between the i-th module and the k-th module at the current moment; β i represents the sensitivity of the i-th module to the collaborative information feedback; n is the number of modules; D k (t-1) is the output of the kth module at the previous moment.

4. The modular design method of the full-performance automated inspection robot according to claim 3, characterized in that: The steps to perform module optimization include: Obtain activation sensitivity coefficients and co-regulatory factors; Calculate module weight value; A system output result is generated based on the module weight value, and the system output result satisfies: Among them, O j represents the output of the j-th task stage, which is obtained by weighted summation of the outputs of each module; the output of the i-th module in the j-th task stage is determined by the weight w i And the output of the activation function; is a co-regulatory factor; C ik (t) is the synergy measure between the i-th module and the k-th module at time t.

5. The modular design method of the full-performance automated inspection robot according to claim 4, characterized in that: The steps of performing module optimization also include: Get error threshold parameters; Calculate the module error value of the functional module, the module error value satisfies: E i (t)=|D i (t)-D target (t)|≤∈ i ; Among them, E i (t) is the output error of the module at time t, ∈ i is the error threshold, which indicates the maximum error allowed by the system; when the error E i (t) is less than or equal to the set threshold ∈ j When , it means that the module output is close to the target value and performs well; on the contrary, the module with an error greater than the set threshold will be suppressed in the optimal selection process.

6. The modular design method of a full-performance automated inspection robot according to claim 5, characterized in that: The steps of performing module optimization also include: Get the output data of the functional module; Calculate the output mean of the functional module; Calculate the coordination parameters between the functional modules, and the coordination parameters satisfy: Among them, C ik (t) represents the synergy measure between the i-th module and the k-th module at time t; and are the output means of the i-th module and the k-th module respectively.

7. The modular design method of a full-performance automated inspection robot according to claim 6, characterized in that: The steps of performing module optimization also include: Obtain error threshold parameters and coordination threshold parameters; Calculate module error values ​​and coordination parameters of functional modules; Determine the magnitude relationship between the module error value and the error threshold parameter; Determining a magnitude relationship between the coordination parameter and the coordination threshold parameter; The module weight value is adjusted based on the judgment result.

8. A modular design system for a full-performance automated inspection robot, based on the modular design method for a full-performance automated inspection robot according to any one of claims 1 to 7, characterized in that: include, A parameter configuration module, used to divide the robot system into a plurality of functionally independent modules, and obtain state parameters and working parameters of the functionally independent modules; A data generation module, configured to generate module output data based on the state parameter and the working parameter; The optimization control module is used to calculate the module error value and the coordination parameter according to the module output data, determine the module weight value based on the module error value and the coordination parameter, and generate the system output result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the modular design method of the full-performance automated inspection robot described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the modular design method of a full-performance automated inspection robot according to any one of claims 1 to 7 are implemented.

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