Machine tool electrical circuit troubleshooting examination teaching method and system

Abstract

This invention provides a teaching method and system for troubleshooting machine tool electrical circuits, relating to the field of practical training technology. The method includes: collecting state parameters of the machine tool electrical circuits; constructing a fault task feature matrix for the machine tool electrical circuits; obtaining standard fault configurations based on the fault task feature matrix using an improved adaptive genetic algorithm; obtaining fault configuration instructions for the machine tool electrical circuits based on the state parameters and standard fault configurations; sending the fault configuration instructions to students via the teacher's end; students performing troubleshooting operations according to the fault configuration instructions, obtaining operation logs and measurement data; calculating the total score for each student based on the operation logs and measurement data using a preset scoring model; and generating a learning report based on each student's total score. This invention breaks through the limitations of traditional practical training platforms that only adapt to a single machine model and have fixed fault scenarios, and can fully meet diverse practical training needs.

Description

Technical Field

[0001] This invention relates to the field of practical training and teaching technology, and in particular to a teaching method and system for troubleshooting machine tool electrical circuits. Background Technology

[0002] With the rapid upgrading of the intelligent manufacturing industry, machine tool operation and electrical circuit fault diagnosis have become core practical training and assessment content for vocational colleges' mechatronics majors. In the traditional practical training teaching model, troubleshooting machine tool electrical circuits relies on physical devices and manual control. However, the development needs of vocational education towards large-scale, standardized, and intelligent development have placed higher demands on the fault simulation capabilities of practical training devices, the fairness of assessment, and the efficiency of teaching data utilization.

[0003] Currently, the training devices for troubleshooting machine tool electrical circuits in the industry are in the stage of transitioning from manual control to preliminary intelligent control. In the early stages, these devices relied on physical relays, and teachers had to manually plug and unplug relays or short-circuit circuits to set fault points. Students could only use basic testing tools such as multimeters to troubleshoot. With the popularization of automation technology, some training systems have begun to introduce PLCs to realize automatic fault injection. At the same time, they are equipped with stand-alone HMIs to build basic operation interaction and result recording interfaces. Simple performance statistics can also be completed through local software. On this basis, a few systems have further attempted to connect to a simple cloud platform, which can realize basic teaching task distribution and data aggregation.

[0004] However, existing machine tool electrical troubleshooting training has significant shortcomings. Too much manual intervention in the fault simulation and assessment process restricts teaching efficiency and leads to inaccurate assessment. At the same time, the training platform has poor machine model compatibility and rigid functions, which cannot support accurate learning diagnosis and modern teaching management, and is difficult to meet the needs of intelligent vocational education construction. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a teaching method for troubleshooting machine tool electrical circuits, which can solve the problems of excessive manual intervention in fault simulation and assessment in the prior art, which restricts teaching efficiency and leads to inaccurate assessment; at the same time, the training platform has poor model compatibility and rigid functions, which cannot support accurate learning diagnosis and modern teaching management, and is difficult to meet the technical problems of the construction needs of intelligent vocational education.

[0006] A first aspect of this invention provides a teaching method for troubleshooting machine tool electrical circuits, comprising:

[0007] S1: Collects status parameters of the machine tool's electrical circuits;

[0008] S2: Construct the fault task feature matrix of the machine tool electrical circuit;

[0009] S3: Based on the fault task feature matrix, a standard fault configuration is obtained through an improved adaptive genetic algorithm;

[0010] S4: Based on the status parameters and standard fault configuration, obtain the fault configuration instructions for the machine tool electrical circuit;

[0011] S5: Send fault configuration commands to students via the teacher's end;

[0012] S6: According to the fault configuration instructions, students perform troubleshooting operations and obtain operation logs and measurement data;

[0013] S7: Calculate the total score for each student based on the operation log and measurement data using a preset scoring model;

[0014] S8: Generate a learning progress report based on each student's total score.

[0015] A second aspect of this invention provides a machine tool electrical circuit troubleshooting examination and teaching system, comprising: a processor and a memory;

[0016] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the machine tool electrical circuit troubleshooting examination teaching method as described in the first aspect.

[0017] A third aspect of the present invention provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the machine tool electrical circuit troubleshooting examination teaching method as described in the first aspect.

[0018] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0019] In this embodiment of the invention, by generating standard fault configurations based on a fault task feature matrix and an improved adaptive genetic algorithm, and by calculating students' total scores using a preset scoring model based on operation logs and measurement data, manual intervention throughout the entire training process is effectively reduced, while significantly improving teaching organization efficiency and the fairness of assessment results. Simultaneously, the fault task feature matrix is ​​compatible with multiple models and types of fault cases, breaking the limitations of traditional training platforms that only adapt to a single model and have fixed fault scenarios, thus fully meeting diverse training needs. Furthermore, generating learning reports based on students' total scores enables accurate learning diagnosis, providing strong support for modern teaching management. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0021] Figure 1 This is a flowchart illustrating a teaching method for troubleshooting machine tool electrical circuits, provided in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of a machine tool electrical circuit troubleshooting examination and teaching system provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions 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, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] The following description, in conjunction with the accompanying drawings, details the teaching method for troubleshooting machine tool electrical circuits provided by the present invention through specific embodiments and application scenarios.

[0025] Reference manual attached Figure 1 The diagram shows a flowchart of a teaching method for troubleshooting machine tool electrical circuits according to an embodiment of the present invention.

[0026] This invention provides a teaching method for troubleshooting machine tool electrical circuits, which may include the following steps:

[0027] S1: Collect the status parameters of the machine tool's electrical circuits.

[0028] It should be noted that the control execution layer is based on a PLC-centric hardware deployment to collect the status parameters of the machine tool's electrical circuits.

[0029] The PLC hardware deployment consists of one or more industrial-grade devices with high-speed input / output interfaces and powerful computing capabilities (preferably Siemens S7-1200 or Mitsubishi FX5U series). Each PLC is connected to the machine tool's electrical circuit via a solid-state relay array and integrates multiple voltage and current sensors and switch signal acquisition modules. The PLC first completes hardware status verification through its built-in self-test program, and then uses the aforementioned sensors and signal acquisition modules to monitor and acquire parameters such as voltage, current, and on / off status of key nodes in the machine tool's electrical circuit in real time. The acquired standardized data is then uploaded to the host computer system, providing a data foundation for subsequent fault diagnosis, path matching degree calculation, and scoring model construction. At the same time, the PLC has a built-in emergency stop circuit. If abnormal voltage, overload, or illegal operation is detected during the acquisition process, the fault circuit will be immediately cut off and an emergency stop alarm will be triggered. All data acquisition is carried out within the teaching safety voltage range to ensure the safety of practical training operations.

[0030] S2: Construct the fault task feature matrix of the machine tool electrical circuit.

[0031] It should be noted that the basic data of the fault task feature matrix comes from the cloud management layer. Teachers create fault cases through the platform's graphical fault editor, set the fault type, topology information and standard troubleshooting approach. The cases are stored in the cloud database and transformed into the basic data of the feature matrix.

[0032] In one possible implementation, S2 specifically includes sub-steps S201 to S205:

[0033] S201: Treat the electrical circuit components of the machine tool as a set of nodes and the wire connection relationships as a set of edges.

[0034] The electrical circuit components of machine tools specifically include power supplies, contactors, buttons, and limit switches.

[0035] S202: Obtain the topology diagram of the machine tool electrical circuit based on the set of nodes and the set of edges.

[0036] It should be noted that each node in the machine tool electrical circuit topology diagram carries attribute information, including component type, address identifier, and measurement point number, which facilitates subsequent mapping and operation tracking.

[0037] S203: Define the set of faults in the machine tool electrical circuit based on the machine tool electrical circuit topology diagram.

[0038] Among them, the set of faults in machine tool electrical circuits Specifically, this includes the set of affected components. Affected set of connections Fault type (e.g., open circuit, short circuit, poor contact, etc.), typical phenomenon description symp kAnd standard troubleshooting approach (solu) k .

[0039] S204: Encode the fault tasks in the fault set to obtain feature vectors.

[0040] Among them, feature vector Specifically, this includes task score, assessment type, number of operation steps, estimated time, difficulty level, knowledge point tags, applicable models, and assessment tags.

[0041] S205: Integrate the feature vectors to form a fault task feature matrix of the machine tool electrical circuit.

[0042] It should be noted that the fault task feature matrix serves as the data foundation for subsequent test paper generation algorithms, supporting optimization search under multi-dimensional constraints.

[0043] In this embodiment of the invention, by constructing a standardized, multi-dimensional fault task feature matrix, structured and quantifiable data support is provided for subsequent intelligent test paper generation and accurate assessment. On the one hand, it relies on the graphical fault editor of the cloud management layer to realize the unified creation and storage of fault cases, which not only ensures the standardization and reusability of fault data, but also realizes the accurate mapping between faults and line physical structures through the node attributes (component type, address identifier, etc.) of the topology graph, which facilitates subsequent operation tracking and path matching. On the other hand, by decomposing faults into vectors containing 8-dimensional features such as task score, difficulty coefficient, and knowledge point tags and integrating them into a matrix, not only is the digital and standardized description of fault tasks realized, but it also provides clear optimization dimensions and constraints for improving the adaptive genetic algorithm, ensuring that the algorithm can accurately select the optimal fault configuration scheme that takes into account the teaching needs such as knowledge point coverage, difficulty gradient, and assessment time.

[0044] S3: Based on the fault task feature matrix, the standard fault configuration is obtained through an improved adaptive genetic algorithm.

[0045] Among them, the adaptive genetic algorithm is an intelligent optimization algorithm that optimizes and improves the traditional genetic algorithm. It can abandon the fixed crossover probability and mutation probability mode of the traditional algorithm, and instead dynamically adjust these two key parameters according to the population evolution state and individual fitness. In this way, it balances the global search capability and local convergence capability of the algorithm, and solves the problems of "premature convergence" (getting stuck in local optimum) or "low search efficiency" that are prone to occur in the traditional algorithm.

[0046] It should be noted that the test paper generation engine built into the cloud management layer, combined with the improved adaptive genetic algorithm (SAGA), can automatically generate test papers that meet the constraints of difficulty distribution and knowledge point coverage based on teaching objectives.

[0047] In one possible implementation, S3 specifically includes sub-steps S301 to S308:

[0048] S301: Number the fault tasks in the fault task feature matrix.

[0049] S302: Sort the fault tasks after numbering according to the preset length to obtain multiple chromosomes, perform population initialization, and form the initial population Q1.

[0050] Here, chromosomes are represented as task number sequences. .

[0051] Those skilled in the art can set the preset length according to actual needs, and the present invention does not limit this.

[0052] S303: Based on the fault task corresponding to each chromosome, calculate the fitness value of each chromosome using the fitness function.

[0053] The fitness function is the core evaluation function in genetic algorithms (including improved adaptive genetic algorithms) used to assess the quality of each "individual" (i.e., a feasible solution to the optimization problem) in the population. Its role is to transform the optimization objective and constraints into quantifiable fitness values, providing the core decision-making basis for the selection, crossover, and mutation operations of the algorithm.

[0054] Optionally, the fitness function is calculated using the following formula:

[0055] ;

[0056] ;

[0057] Where x represents a chromosome, f represents the fitness of the current chromosome, n represents the number of constraints, i represents the index of the constraint, and R... i (x) represents the deviation value of the current chromosome x under the i-th constraint, E i Let A represent the expected value under the i-th constraint. i (x) represents the actual value of the current chromosome x under the i-th constraint, T i This represents the weight coefficient corresponding to the i-th constraint.

[0058] Those skilled in the art can set the expected values ​​of each constraint according to the actual situation, and this invention does not impose any limitations.

[0059] The constraints specifically include constraints on exam difficulty, fault type coverage, knowledge point coverage, number of tasks, scoring structure, fault safety and feasibility, conflict / mutual exclusion between tasks, and task execution time.

[0060] It should be noted that the fitness function takes into account multiple constraints, including total score deviation, difficulty deviation, and knowledge point coverage.

[0061] Furthermore, the fitness function is used to calculate the reciprocal form of the test paper's fitness, reflecting the degree of matching between the test paper and the constraints. The higher the fitness, the closer the test paper is to the ideal goal.

[0062] S304: Based on the fitness values ​​of each chromosome, an elite selection strategy is adopted for the initial population Q1 to perform selection operations and form population Q2.

[0063] S305: Generate a random number r1 and combine the random number r1 with the adaptive crossover probability p c Compare the sizes; if the random number r1 is less than the adaptive crossover probability p... c Then, crossover operations are performed on population Q2 to form population Q3.

[0064] Optionally, the adaptive crossover probability is calculated as follows:

[0065] ;

[0066] Among them, P c This represents the adaptive crossover probability, where k1 represents the upper limit of the crossover probability, and f... max f represents the maximum fitness of the current population, and f represents the fitness of the current chromosome. avg k represents the average fitness of the current population, and k3 represents the fixed crossover probability of individuals with low fitness.

[0067] The adaptive crossover probability dynamically adjusts the crossover strength based on individual fitness, maintaining a high probability of crossover in the early stages of population evolution or when there are many low-fitness individuals. This enhances the population's gene recombination ability, increases the coverage of the search space, and prevents the population from rapidly converging to local optima. Conversely, for high-fitness individuals close to the optimal solution, the crossover probability automatically decreases, reducing the risk of disrupting superior gene structures and enabling the algorithm to achieve stable convergence while maintaining diversity. Therefore, the adaptive crossover probability achieves a more reasonable balance between "exploration" and "utilization," significantly improving the convergence speed and generalization ability of the genetic algorithm.

[0068] It should be noted that the adaptive crossover probability is based on the Sigmoid function to achieve nonlinear dynamic adjustment of the crossover probability. The crossover probability is reduced for high-quality chromosomes with fitness higher than the population average to avoid the destruction of their excellent genes, while a fixed high crossover probability is maintained for low-fitness chromosomes to enhance the population's gene recombination and global search capabilities, thus balancing the convergence and diversity of the algorithm.

[0069] S306: Perform a mutation operation on population Q3 to form population Q4.

[0070] In one possible implementation, the calculation method for the mutation operation on population Q3 is as follows:

[0071] ;

[0072] Among them, P m f represents the adaptive mutation probability, k2 represents the upper limit of the mutation probability, and f max f represents the maximum fitness of the current population, and f represents the fitness of the current chromosome. avg k represents the average fitness of the current population, and k4 represents the fixed mutation probability of individuals with low fitness.

[0073] The adaptive mutation probability automatically adjusts the mutation intensity based on the individual's fitness, ensuring that low-fitness individuals maintain a higher mutation probability to generate more random perturbations, thereby improving their ability to escape local optima and enhancing overall population diversity. Conversely, for high-fitness individuals, the mutation probability decreases to a smaller value to avoid disrupting the established dominant gene structure and ensure the stability of the evolutionary process. Through this fitness-driven dynamic adjustment mechanism, the adaptive mutation probability can establish an effective dynamic balance between global search and local refinement, enabling the genetic algorithm to achieve stronger robustness and faster convergence performance.

[0074] It should be noted that the adaptive mutation probability and crossover probability adjustment logic are matched. The mutation probability is reduced for high-fitness chromosomes to retain dominant genes, while the mutation probability is increased for low-fitness chromosomes. By breaking local optima through random gene mutations, new genetic information is introduced into the population.

[0075] S307: Determine if the maximum number of iterations has been reached. If yes, output the optimal chromosome with the highest fitness value. Otherwise, return to continue iterating.

[0076] S308: Use the set of faulty tasks corresponding to the optimal chromosome as the standard fault configuration.

[0077] In this embodiment of the invention, an improved adaptive genetic algorithm is used to optimize the fault task feature matrix to generate standard fault configurations. Compared with traditional genetic algorithms, the algorithm achieves nonlinear correction of the difficulty coefficient through dynamically adjustable adaptive crossover and mutation rates. This avoids premature convergence and getting stuck in local optima, while balancing global search capability and local convergence accuracy, significantly improving the stability and solution efficiency of the test paper generation algorithm. Based on the fitness function that comprehensively considers core teaching needs such as total score deviation, difficulty deviation, and knowledge point coverage, the abstract test paper generation constraints are transformed into quantifiable fitness values, ensuring that the generated standard fault configurations accurately match the teaching and assessment objectives, taking into account the balance of test paper difficulty, the completeness of knowledge point coverage, and the rationality of scores. By using chromosome encoding to transform fault tasks into ordered sequences, and by using multiple generations of iteration to select the optimal chromosome and map it to the fault configuration, the test paper generation process is automated and intelligent, replacing the inefficient traditional manual test paper generation mode. At the same time, the integration of the cloud-based test paper generation engine can support remote batch test paper generation, adapting to the needs of large-scale practical training and teaching, and laying a scientific foundation for subsequent standardized fault injection and accurate assessment.

[0078] S4: Based on the status parameters and standard fault configuration, obtain the fault configuration instructions for the machine tool electrical circuit.

[0079] S5: Send fault configuration commands to students via the teacher's end.

[0080] It should be noted that the intelligent interaction layer consists of an industrial-grade touchscreen HMI installed on each training workstation. It runs a custom-developed operating system and supports separate permissions for teacher and student modes. In teacher mode, users can log in via encrypted login to access the management interface and perform functions such as course package distribution, fault settings (including random generation, sequence setting, and fixed-point setting), and assessment timing control.

[0081] For example, teachers can select "M7475B spindle not starting fault group" on the interface, and after clicking "one-click distribution", the system will simultaneously send fault configuration instructions to the PLC of the specified workstation, so that multiple training stations enter the same fault state at the same time, realizing standardized and large-scale teaching organization.

[0082] It should be noted that in student mode, the HMI provides clear circuit diagram displays, troubleshooting step prompts, and fault phenomenon simulations. Students can follow the virtual interface guidance to complete the selection of measurement points, recording of instrument readings, and fault diagnosis. The system supports two learning modes: one is "guided learning," which provides appropriate prompts during student operation to help them understand the troubleshooting logic; the other is "independent assessment mode," which only displays the fault phenomenon and operation interface, requiring students to complete the entire troubleshooting process independently. In addition, the HMI also has local intelligent guidance capabilities, which can judge in real time whether the student's operation behavior deviates from the standard path and provide corrective suggestions in teaching mode.

[0083] S6: According to the fault configuration instructions, the student performs troubleshooting operations and obtains operation logs and measurement data.

[0084] In this embodiment of the invention, students are asked to troubleshoot problems by following the fault configuration instructions and collect operation logs and measurement data. This ensures the relevance and standardization of the training tasks and allows for complete traceability of the entire troubleshooting process. It provides objective and verifiable core data for subsequent accurate scoring and learning diagnosis, solving the problem of traditional training emphasizing results over process and lacking sufficient assessment criteria.

[0085] S7: Based on the operation log and measurement data, calculate the total score for each student using a preset scoring model.

[0086] In one possible implementation, S7 specifically includes sub-steps S701 to S703:

[0087] S701: Records operation logs and measurement data throughout their entire lifecycle to obtain behavior sequences and joint time series.

[0088] It should be noted that the operational actions specifically include actions such as measuring point selection, instrument switching, and component replacement. Each action is accompanied by a timestamp and object identifier, forming a sequence of actions. .

[0089] It should be noted that joint timing This is used for subsequent scoring and learning analysis.

[0090] S702: Based on the behavioral sequence and joint timing, calculate the score for each fault task using a pre-defined scoring model.

[0091] ;

[0092] Among them, S k b represents the final score of the k-th faulty task. k This indicates whether the k-th fault task was successfully resolved. Let α represent the maximum possible score for the k-th faulty task. kLet β represent the k-th time factor. k This represents the matching degree coefficient of the k-th path. This represents the penalty for the k-th misoperation.

[0093] The scoring model is built based on indicators such as task success or failure, troubleshooting time, path matching degree, and number of erroneous operations.

[0094] It should be noted that by combining the troubleshooting results, the task's full score, the troubleshooting time factor, the matching degree of the operation path, and the penalty for misoperation, a multi-dimensional quantitative score for the troubleshooting process is achieved, which not only focuses on the results but also takes into account the standardization and efficiency of the operation.

[0095] S703: Add up the scores of each fault task to get the total score for each student.

[0096] Optionally, the student's total score is calculated as follows:

[0097] ;

[0098] Among them, S total This represents the student's total score. S represents the set of selected fault tasks. k This represents the final score of the k-th faulty task.

[0099] It should be noted that the scores of all the fault-related tasks completed by the students are accumulated to form the final assessment score, providing intuitive quantitative data support for subsequent learning progress diagnosis.

[0100] In this embodiment of the invention, based on operation logs and measurement data, a multi-dimensional scoring model is used to calculate the student's total score, breaking through the traditional single evaluation mode that only determines the grade based on "whether the fault has been resolved". First, the troubleshooting behavior sequence and joint time sequence are recorded throughout the entire cycle. Then, the score of each task is calculated by weighting multiple indicators such as task completion, troubleshooting time, path matching degree, and number of misoperations, and the total score is summarized. This not only achieves a refined and quantitative assessment of the troubleshooting process, ensuring the objectivity and fairness of the scoring, but also accurately identifies students' skill gaps through stratified indicators, providing a quantifiable scoring basis for subsequent learning analysis and teaching optimization.

[0101] S8: Generate a learning progress report based on each student's total score.

[0102] It should be noted that the cloud-based management system generates learning reports in the form of data dashboards based on the total score, supporting teachers in remote learning analysis.

[0103] Reference manual attached Figure 2 The diagram shows a structural schematic of a machine tool electrical circuit troubleshooting examination and teaching system provided by an embodiment of the present invention.

[0104] This invention provides a machine tool electrical circuit troubleshooting examination and teaching system 20, including: a processor 201 and a memory 202;

[0105] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-mentioned machine tool electrical circuit troubleshooting examination teaching method and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0106] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0107] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0108] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0109] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0110] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0112] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0115] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described machine tool electrical circuit troubleshooting examination teaching method and achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A teaching method for troubleshooting machine tool electrical circuits, characterized in that, include: S1: Collects status parameters of the machine tool's electrical circuits; S2: Construct the fault task feature matrix of the machine tool electrical circuit; S3: Based on the fault task feature matrix, a standard fault configuration is obtained through an improved adaptive genetic algorithm; S4: Based on the status parameters and the standard fault configuration, obtain the fault configuration instruction for the machine tool electrical circuit; S5: Send the fault configuration command to the student terminal via the teacher's terminal; S6: According to the fault configuration instruction, the student performs troubleshooting operations and obtains operation logs and measurement data; S7: Based on the operation log and the measurement data, calculate the total score for each student using a preset scoring model; S8: Generate a learning progress report based on the total score of each student.

2. The teaching method for troubleshooting machine tool electrical circuits according to claim 1, characterized in that, S2 specifically includes: S201: Treat the electrical circuit components of the machine tool as a set of nodes and the wire connection relationships as a set of edges; S202: Based on the set of nodes and the set of edges, obtain the topology diagram of the machine tool electrical circuit; S203: Define the fault set of the machine tool electrical circuit according to the machine tool electrical circuit topology diagram; S204: Encode the fault tasks in the fault set to obtain feature vectors; S205: Integrate the feature vectors to form the fault task feature matrix of the machine tool electrical circuit.

3. The teaching method for troubleshooting machine tool electrical circuits according to claim 1, characterized in that, S3 specifically includes: S301: Number the fault tasks in the fault task feature matrix; S302: Sort the fault tasks after numbering according to the preset length to obtain multiple chromosomes, perform population initialization, and form the initial population Q1; S303: Based on the fault task corresponding to each chromosome, calculate the fitness value of each chromosome using a fitness function; S304: Based on the fitness values ​​of each chromosome, an elite selection strategy is adopted for the initial population Q1 to perform selection operations and form population Q2; S305: Generate a random number r1 and combine the random number r1 with the adaptive crossover probability p c Compare the sizes; if the random number r1 is less than the adaptive crossover probability p... c A crossover operation is performed on the population Q2 to form population Q3; S306: Perform a mutation operation on the population Q3 to form population Q4; S307: Determine if the maximum number of iterations has been reached; if yes, output the optimal chromosome with the highest fitness value; otherwise, return to continue iterating. S308: Use the set of faulty tasks corresponding to the optimal chromosome as the standard fault configuration.

4. The machine tool electrical circuit troubleshooting examination teaching method according to claim 3, characterized in that, The specific formula for calculating the fitness function is as follows: ； ； Where x represents chromosome, f represents fitness value, n represents the number of constraints, i represents the index of constraint, and R i (x) represents the deviation value of the current chromosome x under the i-th constraint, E i Let A represent the expected value under the i-th constraint. i (x) represents the actual value of the current chromosome x under the i-th constraint, T i This represents the weight coefficient corresponding to the i-th constraint.

5. The machine tool electrical circuit troubleshooting examination teaching method according to claim 3, characterized in that, The adaptive crossover probability is calculated as follows: ； Among them, P c This represents the adaptive crossover probability, where k1 represents the upper limit of the crossover probability, and f... max f represents the maximum fitness of the current population, and f represents the fitness of the current chromosome. avg k represents the average fitness of the current population, and k3 represents the fixed crossover probability of individuals with low fitness.

6. The machine tool electrical circuit troubleshooting examination teaching method according to claim 3, characterized in that, The specific calculation method for performing mutation operations on the population Q3 is as follows: ； Among them, P m f represents the adaptive mutation probability, k2 represents the upper limit of the mutation probability, and f max f represents the maximum fitness of the current population, and f represents the fitness of the current chromosome. avg k represents the average fitness of the current population, and k4 represents the fixed mutation probability of individuals with low fitness.

7. The teaching method for troubleshooting machine tool electrical circuits according to claim 1, characterized in that, Specifically, S7 includes: S701: Record the operation log and the measurement data throughout their entire lifecycle to obtain the behavior sequence and joint time series; S702: Based on the behavioral sequence and the joint timing, calculate the score for each fault task using the preset scoring model: ； Among them, S k b represents the final score of the k-th faulty task. k This indicates whether the k-th fault task was successfully resolved. Let α represent the maximum possible score for the k-th faulty task. k Let β represent the k-th time factor. k This represents the matching degree coefficient of the k-th path. This represents the penalty for the k-th misoperation. S703: Add up the scores of each of the fault tasks to obtain the total score of each student.

8. The machine tool electrical circuit troubleshooting examination teaching method according to claim 7, characterized in that, The specific method for calculating the student's total score is as follows: ； Among them, S total This represents the student's total score. S represents the set of selected fault tasks. k This represents the final score of the k-th faulty task.

9. A machine tool electrical circuit troubleshooting examination and teaching system, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the machine tool electrical circuit troubleshooting examination teaching method as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the machine tool electrical circuit troubleshooting examination teaching method as described in any one of claims 1 to 8.

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