A vocational college big data driven practical training system
The big data-driven training system solves the problem of insufficient training data collection and analysis, improves the quality and efficiency of training, and provides personalized optimization and adjustment solutions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANXI HUAXING KERUAN CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-14
AI Technical Summary
In vocational school practical training, the low efficiency of practical training data collection and insufficient data analysis make it difficult to improve teaching quality and efficiency, and there is a lack of real-time optimization methods.
The training system, driven by big data, includes modules for data acquisition, governance, analysis, and optimization. It uses computer vision and motion capture technology to collect data, performs data standardization, cleaning, and quality assessment, and builds a device health prediction model by combining long short-term memory networks to provide personalized optimization suggestions.
It has enabled comprehensive collection and efficient analysis of practical training data, improved teaching quality, equipment management level and resource utilization efficiency, provided personalized optimization and adjustment solutions, and enhanced the effectiveness of practical training.
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Figure CN122390531A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of training data processing, and in particular to a big data-driven training system for vocational schools. Background Technology
[0002] In vocational education, with the continuous development of technology, practical training is becoming increasingly important for cultivating students' practical abilities and vocational skills. Practical training allows students to apply theoretical knowledge to real-world operations, improving their hands-on skills and problem-solving abilities, thus laying a solid foundation for their future career development. As vocational education becomes more widespread and in-depth, higher demands are being placed on the quality and efficiency of practical training.
[0003] Currently, in vocational school practical training, the processing and analysis of training data typically employs the following methods: Data collection relies heavily on manual recording of student operational information and training results, a method that is inefficient and prone to errors. Effective methods for collecting operating parameters of training equipment and environmental data from the training site are also lacking, making it impossible to obtain comprehensive and accurate data in real time. Data processing often involves simply organizing and storing data, failing to extract valuable information from massive amounts of data. Student and equipment evaluations during training largely depend on teachers' subjective experience and simple assessment methods, making it difficult to promptly identify problems during training and thus hindering timely optimization and adjustments to the training, ultimately affecting its quality and effectiveness. Summary of the Invention
[0004] To improve the quality of practical training, this application provides a big data-driven practical training system for vocational colleges, employing the following technical solution: A big data-driven practical training system for vocational schools includes: The data acquisition module is used to collect training data. The data governance module is used to preprocess the training data to obtain standard training data; The data analysis module is used to analyze the standard training data and determine the information to be optimized, including information on learning status, equipment, and teaching process. The optimization and adjustment module is used to optimize and adjust the information to be optimized.
[0005] By adopting the above technical solutions, the data acquisition module can collect training data, providing a foundation for subsequent analysis; the data governance module preprocesses the training data to obtain standard training data, which can improve data quality and usability; the data analysis module analyzes the standard training data to identify information that needs optimization in learning, equipment, teaching process, etc., providing direction for optimization; the optimization and adjustment module optimizes and adjusts this information, which can improve the teaching quality, equipment management level, and rationality of the teaching process in vocational schools.
[0006] Optionally, the data acquisition module includes: The behavior data acquisition submodule is used to collect student operation information based on computer vision and motion capture technology; The results data collection submodule is used to collect students' practical training results; The equipment data acquisition submodule is used to collect the equipment operating parameters of the training equipment. The environmental data acquisition submodule is used to collect environmental data from the training site. The consumables data acquisition submodule is used to collect consumables consumption information; The course information collection submodule is used to collect practical training teaching plans; The training site information collection submodule is used to collect information on the utilization of training sites.
[0007] By adopting the above technical solutions, it is possible to comprehensively collect the data required for practical training. Among them, computer vision and motion capture technology can accurately acquire student operation information, collect student practical training results to understand learning effectiveness, collect equipment operation parameters of practical training equipment to grasp equipment status, collect environmental data of practical training site to help analyze the impact of the environment on practical training, collect consumable consumption information to facilitate consumable management, collect practical training teaching plan to clarify teaching arrangements, and collect site utilization status of practical training site to rationally allocate site resources.
[0008] Optionally, the data governance module includes: The data standardization submodule is used to convert the training data into a format according to a preset standard. The data cleaning submodule is used to clean the training data after format conversion. The data cleaning includes outlier detection, missing value completion, and conflict resolution. The data quality assessment submodule is used to determine the data quality based on the cleaning results of the data cleaning submodule.
[0009] By adopting the above technical solutions, the data standardization submodule converts the training data into a format according to preset standards, which can unify the data format and facilitate subsequent processing; the data cleaning submodule performs cleaning operations such as outlier detection, missing value completion, and conflict resolution on the format-converted training data, which can improve data quality and accuracy; the data quality assessment submodule determines the data quality based on the cleaning results, which helps to understand the data status and provides a reliable data foundation for subsequent data analysis.
[0010] Optionally, the data analysis module includes a student analysis submodule, which is used for: The student's practical training results are matched with the student's operation information to determine the association between the operation results; The qualification status of the student's operational information is determined based on the correlation of the operational results and the student's practical training results. If the qualified condition is deemed unqualified, then the operation level is determined to be the third level; If the qualified status is qualified, the student operation information is analyzed to determine the operation matching degree and operation time of each operation step; The proficiency of an operator is determined based on the operation matching degree, the operation duration, and the consumable consumption information. The operation level is determined based on the aforementioned operational proficiency. Based on the aforementioned operational level, information regarding the learning status that needs optimization is determined.
[0011] By adopting the above technical solutions, matching students' practical training results with their operational information to determine the correlation between the results can clarify the correspondence between the two. By determining the pass / fail status of students' operational information, the operational level can be determined, and the students' operational level can be quantified. If the pass / fail status is determined, the operational matching degree and operation time can be further analyzed, and the operational proficiency can be determined in combination with consumable consumption information, which can more comprehensively evaluate students' operational abilities. Determining the operational level based on operational proficiency and identifying information on learning progress that needs optimization can help to improve students' learning in a targeted manner.
[0012] Optionally, the data analysis module includes a device analysis submodule, which is used for: Acquire historical operating data of the equipment, which includes historical equipment operating parameters and historical environmental data; A device health prediction model is constructed using the device's historical operating data and long short-term memory network. The health status of the equipment is determined based on the equipment operating parameters, the environmental data, and the equipment health prediction model. The consumption information of the consumables is analyzed to determine the consumption rate of the consumables; The available duration of consumables is determined based on the consumption rate of the consumables and the current consumables inventory. Based on the device's health status and the available time of the consumables, information on the device that needs optimization is determined.
[0013] By adopting the above technical solutions, the system can accurately acquire historical operating data of the equipment, construct a high-precision equipment health prediction model using long short-term memory networks, and thus accurately determine the health status of the equipment; it can analyze consumable consumption information to determine the consumption rate, and combine the current consumable inventory to determine the available time of consumables; finally, by comprehensively considering the equipment health status and the available time of consumables, it can determine the equipment optimization information, providing a scientific basis for the maintenance and management of training equipment.
[0014] Optionally, the data analysis module further includes a teaching resource analysis submodule, which is used for: Based on the aforementioned practical training plan, determine the teaching categories, number of students, and planned teaching venues; The equipment category is determined based on the teaching category; Obtain historical device usage information; The probability of equipment failure is determined based on the historical equipment usage data. The required number of devices is determined based on the probability of device failure and the number of students. Determine whether the planned teaching space is reasonable based on the required number of equipment. If the planned teaching venue is unreasonable, then the teaching process optimization information is determined based on the venue utilization, the practical training plan, and the required number of equipment.
[0015] By adopting the above technical solution, the probability of equipment damage is taken into account when determining the required number of equipment, which improves the rationality of the required number of equipment and thus improves the accuracy of site rationality judgment.
[0016] Optionally, the optimization adjustment module includes a learning optimization submodule, which is used for: Based on the learning situation and the information to be optimized, the first training category to be optimized is determined; Obtain students' performance in history practical training; The students' historical training performance was analyzed to determine the second training category to be optimized; A third training category to be optimized is determined based on the first training category to be optimized, the second training category to be optimized, and the association between training categories. Obtain practical training course resources; Recommended course resources are determined based on the first training category to be optimized, the second training category to be optimized, the third training category to be optimized, and the training course resources.
[0017] By adopting the above technical solution, the learning optimization submodule determines various training categories to be optimized based on the learning situation information to be optimized, students' historical training performance, and the correlation of training categories. It analyzes training course resources to determine recommended course resources, which can accurately recommend suitable training course resources to students and improve their training learning effect.
[0018] Optionally, the optimization and adjustment module includes an equipment optimization submodule and a teaching optimization submodule, wherein the equipment optimization submodule is used for: Based on the equipment optimization information, determine the equipment to be optimized; A maintenance plan is determined based on the health status of the equipment to be optimized. The consumable procurement plan is determined based on the available duration of the consumables. The teaching optimization submodule is used for: The utilization of the site was analyzed to determine the site utilization rate, site equipment information, and site idle time of each training site. The teaching time will be determined based on the aforementioned practical training plan; The teaching venue corresponding to the practical training plan is determined based on the venue utilization rate, the venue equipment information, the venue idle time, the teaching time, the equipment category, and the required number of equipment.
[0019] By adopting the above technical solutions, the equipment optimization submodule can identify the equipment to be optimized based on the equipment optimization information, formulate a maintenance plan based on the equipment health status, and determine a procurement plan based on the available time of consumables, thereby ensuring the normal operation of the equipment and the supply of consumables; the teaching optimization submodule can determine the teaching venues corresponding to the practical training teaching plan and rationally arrange teaching resources.
[0020] Optionally, the system also includes a cross-platform linkage module for pushing the training data to a preset professional platform.
[0021] By adopting the above technical solutions, practical training data can be pushed to a pre-set vocational platform, realizing cross-platform data linkage, facilitating data sharing and exchange between different platforms, and providing more extensive resources and support for practical training in vocational colleges.
[0022] Optionally, the system further includes a security module, which includes a data security submodule and an access control submodule. The data security submodule is used for data desensitization and transmission encryption; the access control submodule is used for configuring access permissions for data according to preset rules.
[0023] By adopting the above technical solutions, the data security submodule can protect the privacy and transmission security of training data by performing data anonymization and transmission encryption, while the access control submodule can ensure the security and standardization of data access by configuring data access permissions according to preset rules. Attached Figure Description
[0024] Figure 1 This is a structural block diagram of a big data-driven training system for vocational colleges provided in an embodiment of this application.
[0025] Figure 2 This is a structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation
[0026] The present application will be further described in detail below with reference to the accompanying drawings.
[0027] This application provides a big data-driven training system for vocational colleges. The logical methods of each module in this system can be executed by electronic devices, which can be servers or terminal devices. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.
[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0029] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.
[0030] Figure 1 This is a structural block diagram of a big data-driven training system 100 for vocational schools, provided as an embodiment of this application.
[0031] like Figure 1As shown, a big data-driven training system 100 for vocational colleges mainly includes a data acquisition module 101, a data governance module 102, a data analysis module 103, an optimization and adjustment module 104, a cross-platform linkage module 105, and a security module 106.
[0032] Data acquisition module 101 is used to collect training data.
[0033] Specifically, the data acquisition module 101 includes a behavior data acquisition submodule 1011, an outcome data acquisition submodule 1012, an equipment data acquisition submodule 1013, an environmental data acquisition submodule 1014, a consumables data acquisition submodule 1015, a course information acquisition submodule 1016, and a practical training site information acquisition submodule 1017.
[0034] The behavior data acquisition submodule 1011 is used to collect student operation information based on computer vision and motion capture technology.
[0035] The behavior data acquisition submodule 1011 can consist of high-definition cameras and motion capture sensors. The high-definition cameras are installed in multiple locations within the training area, clearly capturing students' operational movements. For example, in machining training, the cameras can record students' machine tool operations from different angles and identify student operational information based on computer vision technology. The motion capture sensors can be worn by students to acquire real-time movement data of their limbs.
[0036] The results data collection submodule 1012 is used to collect students' practical training results.
[0037] The results data acquisition submodule 1012 can consist of a scanner and a data entry terminal. The scanner can quickly scan students' practical training works, such as design drawings and handicrafts, and convert the scanned content into electronic data. The data entry terminal allows teachers to manually input evaluations of students' practical training results, such as grades and comments. Alternatively, a camera can be used instead of a scanner for convenient and quick acquisition of image information of practical training results.
[0038] The equipment data acquisition submodule 1013 is used to collect the equipment operating parameters of the training equipment.
[0039] The device data acquisition submodule 1013 may include various sensors and data acquisition units, such as temperature sensors to monitor the device's temperature and current sensors to detect the device's current. The data acquisition unit is responsible for collecting data from these sensors and transmitting the data to the system.
[0040] The environmental data acquisition submodule 1014 is used to collect environmental data from the training site.
[0041] The environmental data acquisition submodule 1014 can be composed of temperature and humidity sensors, air quality sensors, etc. The temperature and humidity sensors are installed in the training site to monitor the temperature and humidity of the site in real time, and the air quality sensor can detect the concentration of harmful gases in the site.
[0042] The consumables data acquisition submodule 1015 is used to collect consumables consumption information.
[0043] The consumables data acquisition submodule 1015 can consist of an electronic scale and a barcode scanner. The electronic scale is used to weigh the remaining weight of the consumables to calculate the consumption status; the barcode scanner can scan the barcode of the consumables to record the usage status, thereby completing the collection of consumables consumption information.
[0044] The course information collection submodule 1016 is used to collect practical training teaching plans.
[0045] The course information collection submodule 1016 can collect practical training teaching plans from the teaching system or teachers.
[0046] The training site information collection submodule 1017 is used to collect information on the utilization of training sites.
[0047] Before using a training site, it needs to be registered. The training site information collection submodule 1017 can obtain the site utilization status from the registration point (registration system or registration personnel).
[0048] The data governance module 102 is used to preprocess the training data to obtain standard training data.
[0049] Specifically, the data governance module 102 includes a data standardization submodule 1021, a data cleaning submodule 1022, and a data quality assessment submodule 1023.
[0050] The data standardization submodule 1021 is used to convert the training data into a format according to a preset standard.
[0051] Professional data standardization software can be used to convert training data according to preset standards (including the format to be converted, conversion templates, etc.), thereby improving the efficiency and accuracy of data conversion.
[0052] The data cleaning submodule 1022 is used to clean the training data after format conversion. Data cleaning includes outlier detection, missing value completion, and conflict resolution.
[0053] Outlier detection can be performed using statistical methods (such as the 3σ principle), missing data points can be intelligently filled using time-series data interpolation algorithms (such as linear interpolation and hot-card imputation), and conflicts can be resolved using preset conflict resolution rules (e.g., if there is a conflict between data from two sources, the data from the higher confidence source is used).
[0054] The data quality assessment submodule 1023 is used to determine data quality based on the cleaning results of the data cleaning submodule.
[0055] The data cleaning results include various data anomaly probabilities, missing data probabilities, and conflict probabilities. The database stores the correlation between anomaly probabilities, missing data probabilities, conflict probabilities, and data quality. Data quality is matched from the database using anomaly probabilities, missing data probabilities, and conflict probabilities (this can be represented by levels or scores). If the data quality is poor, staff can be reminded to adjust the data acquisition method.
[0056] The data analysis module 103 is used to analyze standard training data and determine information to be optimized. The information to be optimized includes information on learning progress, equipment, and teaching process.
[0057] The data analysis module 103 includes a student analysis submodule 1031, an equipment analysis submodule 1032, and a teaching resource analysis submodule 1033.
[0058] The student analysis submodule 1031 is used to: match student training results with student operation information to determine the correlation of operation results; determine the pass / fail status of student operation information based on the correlation of operation results and student training results; if the pass / fail status is unqualified, the operation level is determined to be the third level; if the pass / fail status is qualified, the student operation information is analyzed to determine the operation matching degree and operation time of each operation step; determine the operation proficiency based on the operation matching degree, operation time and consumable consumption information; determine the operation level based on the operation proficiency; and determine the learning situation to be optimized based on the operation level.
[0059] In this embodiment, the operation result association refers to the correspondence between student training results and student operation information, that is, the student training results corresponding to each student operation information. Student training results include pass / fail status. Based on the operation result association, the student training results corresponding to the student operation information are determined, thereby determining the pass / fail status of the student operation information. If the pass / fail status is unqualified, the operation level is directly determined to be level three; if the pass / fail status is qualified, the operation matching degree between each operation step in the student operation information and the standard operation steps is determined through preset matching rules (e.g., calculating behavioral similarity), and the operation time of each operation step is statistically analyzed. The database stores the correspondence between operation matching degree, operation time, consumable consumption information and operation proficiency, as well as the correspondence between operation proficiency and operation level. Operation proficiency is matched from the database based on operation matching degree, operation time, and consumable consumption information to determine the operation level. If the operation level is lower than the preset operation level, learning status optimization information is generated, which includes the training category to be optimized. If the operation level is not lower than the preset operation level, learning status optimization information is not generated.
[0060] The equipment analysis submodule 1032 is used for: obtaining historical equipment operation data from the database, including historical equipment operation parameters and historical environmental data; constructing an equipment health prediction model using historical equipment operation data and a long short-term memory network; determining the equipment health status based on equipment operation parameters, environmental data, and the equipment health prediction model; analyzing consumable consumption information to determine the consumable consumption rate; determining the consumable availability time based on the consumable consumption rate and current consumable inventory; and determining equipment optimization information based on equipment health status and consumable availability time.
[0061] In this embodiment, a long short-term memory network is trained using historical equipment operating data to construct an equipment health prediction model. Equipment operating parameters and environmental data are input into the equipment health prediction model to obtain the equipment health status. The equipment health status may include the equipment failure probability. If the equipment failure probability in the equipment health status exceeds a first preset failure probability, equipment optimization information is generated, including the equipment to be optimized and the failure probability.
[0062] Data analysis tools (such as Python, Excel, etc.) are used to analyze consumable consumption information to obtain consumable consumption rate. The consumable available time = current consumable inventory / consumable consumption rate. If the consumable available time is less than the preset safe usage time, equipment optimization information is generated, including consumables to be purchased, consumable consumption rate, consumable available time, and current consumable inventory.
[0063] If the probability of equipment failure in the equipment health status does not exceed the first preset failure probability and the available time of consumables is greater than or equal to the preset safe usage time, then no equipment optimization information will be generated.
[0064] The teaching resource analysis submodule 1033 is used to: determine the teaching category, number of students, and planned teaching venue based on the practical training teaching plan; determine the equipment category based on the teaching category; obtain historical equipment usage data; determine the probability of equipment failure based on historical equipment usage data; determine the required number of equipment based on the probability of equipment failure and the number of students; determine whether the planned teaching venue is reasonable based on the required number of equipment; if the planned teaching venue is unreasonable, determine the information to be optimized in the teaching process based on the venue utilization, the practical training teaching plan, and the required number of equipment.
[0065] In this embodiment, different teaching venues correspond to different equipment categories and quantities. The teaching category, number of students, and planned teaching venue are determined from the practical training plan. Based on the teaching category, the required equipment category is matched from the database. Historical equipment usage data is retrieved from the database and analyzed using data analysis tools to determine the probability of equipment damage. The actual required number of equipment = number of students / number of students sharing the equipment (e.g., 3 people sharing one piece of equipment for training), and the required number of equipment = actual required number of equipment / (1 - probability of equipment damage). If the number of equipment in the planned teaching venue is less than the required number of equipment, the planned teaching venue is considered unreasonable, and information for optimization of the teaching process is generated, including venue utilization, practical training plan, and required equipment quantity. If the number of equipment in the planned teaching venue is greater than or equal to the required number of equipment, the planned teaching venue is considered reasonable, and no information for optimization of the teaching process is generated.
[0066] The optimization and adjustment module 104 is used to optimize and adjust the information to be optimized.
[0067] Specifically, the optimization and adjustment module 104 includes a learning optimization submodule 1041, an equipment optimization submodule 1042, and a teaching optimization submodule 1043.
[0068] The learning optimization submodule 1041 is used for: determining the first training category to be optimized based on the learning situation and optimization information; obtaining students' historical training performance; analyzing students' historical training performance to determine the second training category to be optimized; determining the third training category to be optimized based on the first training category to be optimized, the second training category to be optimized, and the relationship between training categories; obtaining training course resources; and determining recommended course resources based on the first training category to be optimized, the second training category to be optimized, the third training category to be optimized, and the training course resources.
[0069] In this embodiment, a first training category to be optimized is determined from the learning situation optimization information; students' historical training performance is obtained from the database; the students' historical training performance is analyzed using data analysis tools to determine a second training category to be optimized (i.e., training categories with poor past performance); the training category association includes the mastery association of various training categories, for example: training category A and training category B have a training category association, which means that when the mastery of training category A is poor, the mastery of training category B is often also poor; a third training category to be optimized is matched from the training category association that has an association with the first training category to be optimized and / or the second training category to be optimized.
[0070] Retrieve practical training course resources from the resource library; firstly, identify practical training course resources that match all three categories of practical training to be optimized as recommended course resources; if no such resources exist, identify practical training course resources that match any two of the three categories as recommended course resources, and simultaneously find practical training course resources corresponding to the other unmatched category for recommendation; if no practical training course resources match either of the above two categories, then match practical training course resources corresponding to the three categories as recommended course resources.
[0071] The equipment optimization submodule 1042 is used for: determining the equipment to be optimized based on the equipment optimization information; determining the maintenance plan based on the equipment health status of the equipment to be optimized; and determining the consumable procurement plan based on the available time of consumables.
[0072] In this embodiment, the device to be optimized is determined from the device optimization information; the maintenance plan includes: if the failure probability exceeds the second preset failure probability (higher than the first preset failure probability), then the device maintenance is performed immediately; if the failure probability does not exceed the second preset failure probability, but is higher than the first preset failure probability, then the maintenance is performed after the end of this training.
[0073] The consumables procurement plan includes purchasing consumables during the procurement period, where the procurement period = current time + consumable availability time - preset advance procurement time.
[0074] The teaching optimization submodule 1043 is used to: analyze the site utilization, determine the site utilization rate, site equipment information, and site idle time of each training site; determine the teaching time based on the training teaching plan; and determine the teaching site corresponding to the training teaching plan based on the site utilization rate, site equipment information, site idle time, teaching time, equipment type, and required equipment quantity.
[0075] In this embodiment, the utilization of the training site is analyzed using data analysis tools to determine the site utilization rate, equipment information (including equipment type and quantity), and idle time of each training site; the teaching time is determined from the training teaching plan; the site with the highest site utilization rate that meets the requirements of the training teaching plan in terms of equipment type, quantity, and idle time is determined as the teaching site corresponding to the training teaching plan.
[0076] The cross-platform linkage module 105 is used to push training data to a preset professional platform.
[0077] This can be achieved through data interfaces and data transmission protocols. The data interface is responsible for data interaction with the pre-defined professional platform, while the data transmission protocol ensures secure data transmission.
[0078] The security module 106 includes a data security submodule 1061 and an access control submodule 1062. The data security submodule 1061 is used for data desensitization and transmission encryption, which can be implemented using data encryption algorithms and desensitization tools. The access control submodule 1062 is used to configure access permissions for data according to preset rules.
[0079] Figure 2 This is a structural block diagram of an electronic device 200 provided in an embodiment of this application.
[0080] like Figure 2 As shown, the electronic device 200 includes a processor 201 and a memory 202, and may further include one or more of an information input / output (I / O) interface 203, a communication component 204, and a communication bus 205.
[0081] The processor 201 controls the overall operation of the electronic device 200 to complete all or part of the logical steps of each module in the aforementioned vocational college big data-driven training system. The memory 202 stores various types of data to support the operation of the electronic device 200. This data may include, for example, instructions for any application or method operating on the electronic device 200, as well as application-related data. The memory 202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of the following: Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0082] I / O interface 203 provides an interface between processor 201 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 204 is used for wired or wireless communication between electronic device 200 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 204 may include a Wi-Fi component, a Bluetooth component, and an NFC component.
[0083] The electronic device 200 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the logical methods of each module in the big data-driven training system for vocational colleges given in the above embodiments.
[0084] The communication bus 205 may include a path for transmitting information between the aforementioned components. The communication bus 205 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 205 may be divided into an address bus, a data bus, a control bus, etc.
[0085] Electronic device 200 may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.
[0086] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the logical methods of each module in the above-mentioned vocational college big data-driven training system.
[0087] The computer-readable storage medium may include 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.
[0088] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0089] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.
Claims
1. A big data-driven practical training system for vocational schools, characterized in that, include: The data acquisition module is used to collect training data. The data governance module is used to preprocess the training data to obtain standard training data; The data analysis module is used to analyze the standard training data and determine the information to be optimized, including information on learning status, equipment, and teaching process. The optimization and adjustment module is used to optimize and adjust the information to be optimized.
2. The system according to claim 1, characterized in that, The data acquisition module includes: The behavior data acquisition submodule is used to collect student operation information based on computer vision and motion capture technology; The results data collection submodule is used to collect students' practical training results; The equipment data acquisition submodule is used to collect the equipment operating parameters of the training equipment. The environmental data acquisition submodule is used to collect environmental data from the training site. The consumables data acquisition submodule is used to collect consumables consumption information; The course information collection submodule is used to collect practical training teaching plans; The training site information collection submodule is used to collect information on the utilization of training sites.
3. The system according to claim 1, characterized in that, The data governance module includes: The data standardization submodule is used to convert the training data into a format according to a preset standard. The data cleaning submodule is used to clean the training data after format conversion. The data cleaning includes outlier detection, missing value completion, and conflict resolution. The data quality assessment submodule is used to determine the data quality based on the cleaning results of the data cleaning submodule.
4. The system according to claim 2, characterized in that, The data analysis module includes a student analysis submodule, which is used for: The student's practical training results are matched with the student's operation information to determine the association between the operation results; The qualification status of the student's operational information is determined based on the correlation of the operational results and the student's practical training results. If the qualified condition is deemed unqualified, then the operation level is determined to be the third level; If the qualified status is qualified, the student operation information is analyzed to determine the operation matching degree and operation time of each operation step; The proficiency of an operator is determined based on the operation matching degree, the operation duration, and the consumable consumption information. The operation level is determined based on the level of operational proficiency. Based on the operational level, information to be optimized regarding the learning situation is determined.
5. The system according to claim 2, characterized in that, The data analysis module includes a device analysis submodule, which is used for: Acquire historical operating data of the equipment, which includes historical equipment operating parameters and historical environmental data; A device health prediction model is constructed using the device's historical operating data and long short-term memory network. The health status of the equipment is determined based on the equipment operating parameters, the environmental data, and the equipment health prediction model. The consumption information of the consumables is analyzed to determine the consumption rate of the consumables; The available duration of consumables is determined based on the consumption rate of the consumables and the current consumables inventory. Based on the device's health status and the available time of the consumables, information on the device that needs optimization is determined.
6. The system according to claim 5, characterized in that, The data analysis module further includes a teaching resource analysis submodule, which is used for: Based on the aforementioned practical training plan, determine the teaching categories, number of students, and planned teaching venues; The equipment category is determined based on the teaching category; Obtain historical device usage information; The probability of equipment failure is determined based on the historical equipment usage data. The required number of devices is determined based on the probability of device failure and the number of students. Determine whether the planned teaching space is reasonable based on the required number of equipment. If the planned teaching venue is unreasonable, then the information to be optimized in the teaching process is determined based on the venue utilization, the practical training plan, and the required number of equipment.
7. The system according to claim 4, characterized in that, The optimization and adjustment module includes a learning optimization submodule, which is used for: Based on the learning situation and the information to be optimized, the first training category to be optimized is determined; Obtain students' performance in history practical training; The students' historical training performance was analyzed to determine the second training category to be optimized; A third training category to be optimized is determined based on the first training category to be optimized, the second training category to be optimized, and the association between training categories. Obtain practical training course resources; Recommended course resources are determined based on the first training category to be optimized, the second training category to be optimized, the third training category to be optimized, and the training course resources.
8. The system according to claim 6, characterized in that, The optimization and adjustment module includes an equipment optimization submodule and a teaching optimization submodule. The equipment optimization submodule is used for: Based on the equipment optimization information, determine the equipment to be optimized; A maintenance plan is determined based on the health status of the equipment to be optimized. The consumable procurement plan is determined based on the available duration of the consumables. The teaching optimization submodule is used for: The utilization of the site was analyzed to determine the site utilization rate, site equipment information, and site idle time of each training site. The teaching time will be determined based on the aforementioned practical training plan; The teaching venue corresponding to the practical training plan is determined based on the venue utilization rate, the venue equipment information, the venue idle time, the teaching time, the equipment category, and the required number of equipment.
9. The system according to claim 1, characterized in that, The system also includes a cross-platform linkage module, which is used to push the training data to a preset professional platform.
10. The system according to claim 1, characterized in that, The system also includes a security module, which comprises a data security submodule and an access control submodule. The data security submodule is used for data desensitization and transmission encryption; the access control submodule is used for configuring access permissions for data according to preset rules.