A product resource allocation system based on integration of production and teaching

Abstract

The application discloses a product resource allocation system based on production and teaching integration, relates to the technical field of information-based education, and comprises a behavior capturing module, a loss analysis module and a resource allocation module. The behavior capturing module is used for image recognition and behavior analysis on the operation behavior of students in an experiment process, identification of misoperation types and frequencies, and establishment of a capability profile. The loss analysis module is used for statistical analysis on the actual use of various materials in an experiment material bag, accurate recording of consumption data through RFID and a weighing mode, and intelligent adjustment of material proportioning based on a student capability file and an experiment task type, so that individualized customization and on-demand replenishment are realized, and resource waste is reduced. The behavior capturing module comprises an image acquisition unit, a behavior recognition module, a misoperation discrimination module, a loss material marking module, a material type distinguishing module and a capability profile establishment module. The application has the characteristics of accurate matching.

Description

Technical Field

[0001] This invention relates to the field of information technology in education, specifically to a product resource allocation system based on industry-education integration. Background Technology

[0002] In vocational and STEM education, experimental teaching is a crucial component in cultivating practical skills. To improve efficiency and standardize management, educational institutions typically use experimental material kits, distributing all necessary materials to students in a package for easy one-time use and centralized recycling. Currently, these kits are mostly uniformly distributed, with fixed types and quantities of materials determined based on the course content.

[0003] However, in actual teaching, due to differences in students' abilities and operating habits, material consumption varies greatly. Some students, due to unfamiliarity with the operation, frequently waste certain types of materials, while single-use materials may remain idle for extended periods or even be wasted. When a material runs out, students usually need to inform the teacher, and then the entire package is reissued. This not only results in the repeated distribution of a large amount of unused materials but also relies on manual judgment, lacking accurate data support, leading to resource waste and low replenishment efficiency.

[0004] Especially in high-consumption experiments such as welding, circuit debugging, structural assembly, and titration, the current method of providing all materials and replenishing them in bulk is insufficient to meet the needs of personalized and efficient management. Therefore, it is necessary to establish a mechanism for dynamically customizing and intelligently replenishing experimental material packages based on students' abilities and preferences, combined with actual consumption data, to improve teaching adaptability and conserve resources. Thus, designing a precisely matched product resource allocation system based on industry-education integration is essential. Summary of the Invention

[0005] The purpose of this invention is to provide a product resource allocation system based on industry-education integration to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a product resource allocation system based on industry-education integration, comprising a behavior capture module, a loss analysis module, and a resource allocation module. The behavior capture module is used to perform image recognition and behavior analysis on students' operational behaviors during experiments, identify the types and frequencies of misoperations, and establish a competency profile. The loss analysis module statistically analyzes the actual usage of various materials in the experimental material package, accurately recording consumption data through RFID and weighing methods. The resource allocation module intelligently adjusts the material ratio based on students' competency profiles and experimental task types, achieving personalized customization and on-demand replenishment, thereby reducing resource waste.

[0007] According to the above technical solution, the behavior capture module includes an image acquisition unit, a behavior recognition module, a misoperation discrimination module, a material loss marking module, a material type differentiation module, and a capability profile building module. The image acquisition unit is electrically connected to the behavior recognition module and the misoperation discrimination module in sequence, and the material loss marking module is electrically connected to the material type differentiation module. The image acquisition unit is used to acquire image information of students during experimental learning. The behavior recognition module is used to identify the behavior actions of each student during experimental learning. The misoperation discrimination module is used to compare the acquired behavior actions with the stored standard experimental actions and to identify erroneous actions that cause additional material loss. The material loss marking module is used to mark the material loss caused by various erroneous actions. The material type differentiation module is used to classify the types of material loss. The capability profile building module is used to build a unique capability profile for each student and to statistically analyze their error-prone actions.

[0008] The loss analysis module includes a pressure sensing unit, an RFID tag, an RFID reading unit, a weight change calculation module, and a loss recording module. The pressure sensing unit is electrically connected to the weight change calculation module, and the RFID tag is wirelessly connected to the RFID reading unit. The pressure sensing unit is used to detect the bottom pressure of the experimental material package. The RFID tag is used to establish RFID tags for repeatedly consumable materials. The RFID reading unit is used to read RFID tag information and identify materials. The weight change calculation module is used to identify the weight change caused by the bottom pressure of the experimental material package. The loss recording module is used to record the degree of loss of various materials during students' experimental learning.

[0009] The resource allocation module includes a material replenishment calculation module, a personalization module, and a rated material ratio module. The material replenishment calculation module is electrically connected to the loss recording module, and the personalization module is electrically connected to the ability profile establishment module and the loss recording module. The material replenishment calculation module is used to calculate the replenishment amount of various experimental materials. The personalization module is used to personalize experimental material packages and replenishment material packages for different students. The rated material ratio module is used to statistically analyze the material consumption of all students and optimize the ratio of various experimental materials.

[0010] Based on the above technical solution, the working method of this system is as follows:

[0011] S0. When producing experimental materials, the experimental materials are divided into multiple-consumption type and other types. Multiple-consumption type refers to materials that are consumed in each experiment. Other types refer to materials that can be reused and materials that are consumed only once. Weigh each type of material and record its weight. Establish RFID tags for multiple-consumption type materials.

[0012] S1. In the first batch of experimental learning, image recognition and behavior comparison were used to identify whether there were any errors in the students' operation actions or misuse of tools. Operations that caused material consumption were classified and marked to establish a student ability profile.

[0013] S2. Before students take out consumable materials, they should align the RFID tag with the RFID reading unit in the experimental material package to record the removal. Combined with the detection results of the weight change calculation module, the types and amounts of materials consumed in each experiment should be statistically analyzed. The loss situation should be compared with the average loss for verification, and the material loss caused by misoperation should be analyzed.

[0014] S3. Based on the student's ability profile and the types of misoperations and material losses caused by the misoperations in previous experimental learning, customize material replenishment packages for the student's current experimental project and personalize the experimental material packages for the student's other subsequent experiments.

[0015] S4. Compile statistics on the material consumption of all students in the current experimental project, and establish files for the experimental project to customize the rated material ratio of the experimental material package.

[0016] According to the above technical solution, S1 specifically refers to:

[0017] S1-1. When students begin their experimental learning, their hand movements are continuously collected, and the types of experimental materials on the experimental table are identified. When a student interacts with a certain experimental material, the action recognition algorithm is used to identify the student's hand movements, read the standard hand movement image data stored in the system, mark the operation steps involving material consumption, find the matching operation steps for comparison, and only when an operation step involves excessive material consumption is it necessary to judge whether there is an error.

[0018] S1-2, Break down the standard hand movement image data of each experimental learning session into several operational steps. ,in The quantity is determined by dividing each step into different operation step types. ,in The number of operation step types is defined as follows: one operation step necessarily corresponds to one type, and one type can correspond to multiple operation steps. Mark all operations involving material consumption. When it is determined that a student's action in a certain operation step differs from the standard operation step by more than a set value, record the type of operation step, and then record the number of times the student made an error in each operation step type. .

[0019] According to the above technical solution, S2 specifically refers to:

[0020] S2-1. When experimental materials are placed into the experimental material package at the production end, the system records the types, quantities, and weights of the materials in the package. Before each experiment, when students remove the materials, the system records the types and quantities of materials removed based on the weight difference before and after each removal. After each experiment, when the materials are returned, the RFID tag is aligned again with the RFID reader inside the experimental material package to calculate the weight difference for all repeatedly consumed materials. ,in For the types and quantities of reusable materials, then based on the total weight difference. The formula for recording single-use material loss is as follows: ,in For the first The weight of each single-use consumable material has been measured in S0. This is the first time during this experimental learning session. The quantity of consumable materials extracted per transaction. This refers to the number of different types of consumable materials in the current experimental material package, and , The amount of consumable material lost per transaction can be calculated using the formula.

[0021] S2-2. Calculate the average weight difference of each consumable material and the average quantity of each consumable material lost per transaction for all students who performed the correct operation in this experiment. This represents the normal loss caused by correct operation. Subtract the average weight difference and average quantity of loss from the weight difference and quantity in S2-1 to obtain the material loss increment caused by the current student's incorrect operation steps. Correspond the material loss increment to the type of incorrect operation steps involved to obtain the average increment of material loss per transaction caused by the current student in each type of incorrect operation step. The increment is defined as weight for consumable materials and quantity for consumable materials per transaction.

[0022] According to the above technical solution, the customized material replenishment package in S3 specifically refers to:

[0023] S3-1. Each time a material replenishment kit is customized, the average weight and quantity of materials lost by students who perform the correct operations in subsequent experiments are calculated based on the types, weights, and quantities of materials remaining in the current student experimental material kit. The types, weights, and quantities of materials that need to be replenished are statistically analyzed, and then material replenishment kits corresponding to the students are customized as needed.

[0024] According to the above technical solution, the personalized customization of the experimental material package in S3 is specifically as follows:

[0025] S3-2. After the previous experiment, statistically analyze the incorrect operations related to material consumption made by each student in this experiment, record the types of operational steps, and compare the number of incorrect operations with the number of correct operations to obtain the probability of error. ,in This refers to the number of operation steps for a certain type of operation step in this experimental learning. This represents the number of incorrect operation steps performed by a student in this operation step type.

[0026] S3-3. Before the next experimental learning session, first analyze the standard hand movement image data stored in the system, break it down into several operation steps, then merge them into various operation step types, record the number of operation steps in each operation step type, and determine the current error probability of students in each operation step type. By combining the average increase in material loss per instance caused by the student's various incorrect operation steps, the three factors are multiplied together to obtain the increase in material loss for the student in the next experiment. Based on this data, the weight and quantity of materials in the student's experimental material package are customized.

[0027] According to the above technical solution, S4 specifically refers to:

[0028] S4-1. For students who are doing an experiment for the first time and have not collected experimental learning data, it is necessary to customize the experimental material package according to the rated material ratio. First, count all types of experimental materials consumed by all students in a certain experimental learning, then count the average weight of consumable materials lost, and the average number of consumable materials lost per time. Based on these two indicators, customize the experimental material package for this experimental learning.

[0029] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention captures and analyzes the images of students' operational behaviors in experimental learning, marks the types and frequency of behaviors that lead to material loss, thereby determining which type of material is more likely to be lost in experimental learning, establishes files for experimental projects to optimize the rated material ratio of experimental material packages, and establishes files for individual students to personalize the experimental material packages for their subsequent experiments, making the initial material ratio more reasonable.

[0030] By establishing RFID tags and weighing reusable materials in the experimental material kits, and weighing single-use materials, the material loss after students' experimental learning is statistically analyzed. Based on the subsequent material needs of the experimental project, material replenishment kits are customized for each student. There is no need for students to manually provide feedback to the teacher. With accurate data support, all kinds of materials can be used to the fullest extent and no waste is caused. Attached Figure Description

[0031] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0032] Figure 1 This is a schematic diagram of the overall modular structure of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 This invention provides a technical solution: a product resource allocation system based on industry-education integration, including a behavior capture module, a loss analysis module, and a resource allocation module. The behavior capture module is used to perform image recognition and behavior analysis on students' operational behavior during the experiment, identify the type and frequency of misoperation, and establish a capability profile. The loss analysis module performs statistical analysis on the actual usage of various materials in the experimental material package, and accurately records consumption data through RFID and weighing methods. The resource allocation module intelligently adjusts the material ratio based on the student's capability profile and the type of experimental task, realizing personalized customization and on-demand replenishment, and reducing resource waste.

[0035] The behavior capture module includes an image acquisition unit, a behavior recognition module, a misoperation discrimination module, a material loss marking module, a material type differentiation module, and a competency profile building module. The image acquisition unit is electrically connected to the behavior recognition module and the misoperation discrimination module in sequence, and the material loss marking module is electrically connected to the material type differentiation module. The image acquisition unit is used to collect image information of students during experimental learning. The behavior recognition module is used to identify the behavior actions of each student during experimental learning. The misoperation discrimination module is used to compare the collected behavior actions with the stored standard experimental actions to identify erroneous actions that cause additional material loss. The material loss marking module is used to mark the material loss caused by various erroneous actions. The material type differentiation module is used to classify the types of material loss. The competency profile building module is used to build a unique competency profile for each student and to statistically analyze their error-prone actions.

[0036] The loss analysis module includes a pressure sensing unit, an RFID tag, an RFID reading unit, a weight change calculation module, and a loss recording module. The pressure sensing unit and the weight change calculation module are electrically connected, and the RFID tag and the RFID reading unit are wirelessly connected. The pressure sensing unit is used to detect the bottom pressure of the experimental material package, the RFID tag is used to create RFID tags for repeatedly consumable materials, the RFID reading unit is used to read RFID tag information and identify materials, the weight change calculation module is used to identify the weight change caused by the bottom pressure of the experimental material package, and the loss recording module is used to record the degree of loss of various materials by students during experimental learning.

[0037] The resource allocation module includes a material replenishment calculation module, a personalized customization module, and a rated material ratio module. The material replenishment calculation module is electrically connected to the loss recording module, and the personalized customization module is electrically connected to the ability profile establishment module and the loss recording module. The material replenishment calculation module is used to calculate the replenishment amount of various experimental materials. The personalized customization module is used to personalize experimental material packages and replenishment material packages for different students. The rated material ratio module is used to statistically analyze the material consumption of all students and optimize the ratio of various experimental materials.

[0038] The system works as follows:

[0039] S0. When producing experimental materials, the experimental materials are divided into multiple-consumption type and other types. Multiple-consumption type refers to materials that are consumed in each experiment. Other types refer to materials that can be reused and materials that are consumed only once. Weigh each type of material and record its weight. Establish RFID tags for multiple-consumption type materials.

[0040] S1. In the first batch of experimental learning, image recognition and behavior comparison were used to identify whether there were any errors in the students' operation actions or misuse of tools. Operations that caused material consumption were classified and marked to establish a student ability profile.

[0041] S2. Before students take out consumable materials, they should align the RFID tag with the RFID reading unit in the experimental material package to record the removal. Combined with the detection results of the weight change calculation module, the types and amounts of materials consumed in each experiment should be statistically analyzed. The loss situation should be compared with the average loss for verification, and the material loss caused by misoperation should be analyzed.

[0042] S3. Based on the student's ability profile and the types of misoperations and material losses caused by the misoperations in previous experimental learning, customize material replenishment packages for the student's current experimental project and personalize the experimental material packages for the student's other subsequent experiments.

[0043] S4. Statistically analyze the material consumption of all students in the current experimental project, and establish a file for the experimental project to customize the rated material ratio of the experimental material package;

[0044] S1 specifically refers to:

[0045] S1-1. When students begin their experimental learning, their hand movements are continuously collected, and the types of experimental materials on the experimental table are identified. When a student interacts with a certain experimental material, the action recognition algorithm is used to identify the student's hand movements, read the standard hand movement image data stored in the system, mark the operation steps involving material consumption, find the matching operation steps for comparison, and only when an operation step involves excessive material consumption is it necessary to judge whether there is an error.

[0046] S1-2, Decompose the standard hand movement image data of each experiment into several operational steps. ,in The quantity is determined by dividing each step into different operation step types. ,in The number of operation step types is defined as follows: one operation step necessarily corresponds to one type, and one type can correspond to multiple operation steps. Mark all operations involving material consumption. When it is determined that a student's action in a certain operation step differs from the standard operation step by more than a set value, record the type of operation step, and then record the number of times the student made an error in each operation step type. ;

[0047] S2 specifically refers to:

[0048] S2-1. When experimental materials are placed into the experimental material package at the production end, the system records the types, quantities, and weights of the materials in the package. Before each experiment, when students remove the materials, the system records the types and quantities of materials removed based on the weight difference before and after each removal. After each experiment, when the materials are returned, the RFID tag is aligned again with the RFID reader inside the experimental material package to calculate the weight difference for all repeatedly consumed materials. ,in For the types and quantities of reusable materials, then based on the total weight difference. The formula for recording single-use material loss is as follows: ,in For the first The weight of each single-use consumable material has been measured in S0. This is the first time during this experimental learning session. The quantity of consumable materials extracted per transaction. This refers to the number of different types of consumable materials in the current experimental material package, and , The amount of consumable material lost per transaction can be calculated using the formula.

[0049] S2-2. Calculate the average weight difference of each consumable material and the average quantity of each consumable material lost per transaction for all students who operated correctly in this experiment. This is the normal loss caused by correct operation. Subtract the average weight difference and average quantity of loss from the weight difference and quantity in S2-1 to obtain the material loss increment caused by the current student's incorrect operation steps. Match the material loss increment with the type of incorrect operation steps involved to obtain the average increment of material loss per transaction caused by the current student in each type of incorrect operation step. The increment is defined as weight for consumable materials and quantity for consumable materials per transaction.

[0050] By statistically analyzing the weight, the specific types, weights, and quantities of material losses can be recorded. The weight only needs to be tested when multiple consumable materials are taken out, avoiding confusion between the weight loss and single-use consumable materials. Students do not need to manually perform the statistics or report to the teacher, achieving a fully intelligent tracking effect.

[0051] The custom material replenishment packs in S3 are as follows:

[0052] S3-1. Each time a material replenishment pack is customized, the average weight and quantity of materials lost by students who operate correctly in subsequent experiments are calculated based on the types, weights, and quantities of materials remaining in the current student experimental material packs. The types, weights, and quantities of materials that need to be replenished are statistically analyzed, and then material replenishment packs corresponding to the students are customized as needed.

[0053] The S3 experimental material package can be customized in the following ways:

[0054] S3-2. After the previous experiment, statistically analyze the incorrect operations related to material consumption made by each student in this experiment, record the types of operational steps, and compare the number of incorrect operations with the number of correct operations to obtain the probability of error. ,in This refers to the number of operation steps for a certain type of operation step in this experimental learning. This represents the number of incorrect operation steps performed by a student in this operation step type.

[0055] S3-3. Before the next experimental learning session, first analyze the standard hand movement image data stored in the system, break it down into several operation steps, then merge them into various operation step types, record the number of operation steps in each operation step type, and determine the current error probability of students in each operation step type. By combining the average increase in material loss per instance caused by the student's various incorrect operation steps, the three factors are multiplied together to obtain the increase in material loss for the student in the next experiment. Based on this data, the weight and quantity of materials in the student's experimental material package are customized.

[0056] By summarizing each step and categorizing them into multiple types, students are more likely to make mistakes in the same type of operation when encountering similar steps in subsequent experiments, leading to increased material waste. By analyzing the degree of material waste increase for each student in the next experiment, their experimental material kits can be customized to ensure that each student's materials can withstand the material waste caused by their mistakes, reducing the number of times additional material refill kits are requested and minimizing unnecessary material waste.

[0057] S4 specifically refers to:

[0058] S4-1. For students who are doing an experiment for the first time and have not collected experimental learning data, it is necessary to customize the experimental material package according to the rated material ratio. First, count all types of experimental materials consumed by all students in a certain experimental learning, then count the average weight of consumable materials lost, and the average number of consumable materials lost per time. Based on these two indicators, customize the experimental material package for this experimental learning.

[0059] By capturing and analyzing images of students' operational behaviors during experimental learning, the types and frequency of behaviors that lead to material loss are marked, thereby determining which type of materials they are more likely to lose during experimental learning. Files are created for experimental projects to optimize the rated material ratio of experimental material packages, and files are created for individual students to personalize experimental material packages for their subsequent experiments, making the initial material ratio more reasonable.

[0060] By establishing RFID tags and weighing reusable materials in the experimental material kits, and weighing single-use materials, the material loss after students' experimental learning is statistically analyzed. Based on the subsequent material needs of the experimental project, material replenishment kits are customized for each student. There is no need for students to manually provide feedback to the teacher. With accurate data support, all kinds of materials can be used to the fullest extent and no waste is caused.

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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.

[0062] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A product resource allocation system based on industry-education integration, characterized in that: The system includes a behavior capture module, a loss analysis module, and a resource allocation module. The behavior capture module is used to perform image recognition and behavior analysis on students' operational behaviors during the experiment, identify the types and frequencies of misoperations, and establish a competency profile. The loss analysis module performs statistical analysis on the actual usage of various materials in the experimental material package, and accurately records consumption data through RFID and weighing methods. The resource allocation module intelligently adjusts the material ratio based on the student's competency profile and the type of experimental task, realizing personalized customization and on-demand replenishment, and reducing resource waste. The behavior capture module includes an image acquisition unit, a behavior recognition module, a misoperation discrimination module, a material loss marking module, a material type differentiation module, and a capability profile building module. The image acquisition unit is electrically connected to the behavior recognition module and the misoperation discrimination module in sequence, and the material loss marking module is electrically connected to the material type differentiation module. The image acquisition unit is used to acquire image information of students during experimental learning. The behavior recognition module is used to identify the behavior actions of each student during experimental learning. The misoperation discrimination module is used to compare the acquired behavior actions with the stored standard experimental actions and to identify erroneous actions that cause additional material loss. The material loss marking module is used to mark the material loss caused by various erroneous actions. The material type differentiation module is used to classify the types of material loss. The capability profile building module is used to build a unique capability profile for each student and to statistically analyze their error-prone actions. The loss analysis module includes a pressure sensing unit, an RFID tag, an RFID reading unit, a weight change calculation module, and a loss recording module. The pressure sensing unit is electrically connected to the weight change calculation module, and the RFID tag is wirelessly connected to the RFID reading unit. The pressure sensing unit is used to detect the bottom pressure of the experimental material package. The RFID tag is used to establish RFID tags for repeatedly consumable materials. The RFID reading unit is used to read RFID tag information and identify materials. The weight change calculation module is used to identify the weight change caused by the bottom pressure of the experimental material package. The loss recording module is used to record the degree of loss of various materials during students' experimental learning. The resource allocation module includes a material replenishment calculation module, a personalized customization module, and a rated material ratio module. The material replenishment calculation module is electrically connected to the loss recording module, and the personalized customization module is electrically connected to the ability profile establishment module and the loss recording module. The material replenishment calculation module is used to calculate the replenishment amount of various experimental materials. The personalized customization module is used to personalize experimental material packages and replenishment material packages for different students. The rated material ratio module is used to statistically analyze the material consumption of all students and optimize the ratio of various experimental materials.

2. The product resource allocation system based on industry-education integration according to claim 1, characterized in that: The system works as follows: S0. When producing experimental materials, the experimental materials are divided into multiple-consumption type and other types. Multiple-consumption type refers to materials that are consumed in each experiment. Other types refer to materials that can be reused and materials that are consumed only once. Weigh each type of material and record its weight. Establish RFID tags for multiple-consumption type materials. S1. In the first batch of experimental learning, image recognition and behavior comparison were used to identify whether there were any errors in the students' operation actions or misuse of tools. Operations that caused material consumption were classified and marked to establish a student ability profile. S2. Before students take out consumable materials, they should align the RFID tag with the RFID reading unit in the experimental material package to record the removal. Combined with the detection results of the weight change calculation module, the types and amounts of materials consumed in each experiment should be statistically analyzed. The loss situation should be compared with the average loss for verification, and the material loss caused by misoperation should be analyzed. S3. Based on the student's ability profile and the types of misoperations and material losses caused by the misoperations in previous experimental learning, customize material replenishment packages for the student's current experimental project and personalize the experimental material packages for the student's other subsequent experiments. S4. Compile statistics on the material consumption of all students in the current experimental project, and establish files for the experimental project to customize the rated material ratio of the experimental material package.

3. A product resource allocation system based on industry-education integration according to claim 2, characterized in that: Specifically, S1 is: S1-1. When students begin their experimental learning, their hand movements are continuously collected, and the types of experimental materials on the experimental table are identified. When a student interacts with a certain experimental material, the action recognition algorithm is used to identify the student's hand movements, read the standard hand movement image data stored in the system, mark the operation steps involving material consumption, find the matching operation steps for comparison, and only when an operation step involves excessive material consumption is it necessary to judge whether there is an error. S1-2, Break down the standard hand movement image data of each experimental learning session into several operational steps. ,in The quantity is determined by dividing each step into different operation step types. ,in The number of operation step types is defined as follows: one operation step necessarily corresponds to one type, and one type can correspond to multiple operation steps. Mark all operations involving material consumption. When it is determined that a student's action in a certain operation step differs from the standard operation step by a set value, record the type of this operation step, and then record the number of times the student made an error in each operation step type. .

4. A product resource allocation system based on industry-education integration according to claim 3, characterized in that: Specifically, S2 is: S2-1. When experimental materials are placed into the experimental material package at the production end, the system records the types, quantities, and weights of the materials in the package. Before each experiment, when students remove the materials, the system records the types and quantities of materials removed based on the weight difference before and after each removal. After each experiment, when the materials are returned, the RFID tag is aligned again with the RFID reader inside the experimental material package to calculate the weight difference for all repeatedly consumed materials. ,in For the types and quantities of reusable materials, then based on the total weight difference. The formula for recording single-use material loss is as follows: ,in For the first The weight of each single-use consumable material has been measured in S0. This is the first time during this experimental learning session. The quantity of consumable materials extracted per transaction. This refers to the number of different types of consumable materials in the current experimental material package, and , The amount of consumable material lost per transaction can be calculated using the formula. S2-2. Calculate the average weight difference of each consumable material and the average quantity of each consumable material lost per transaction for all students who performed the correct operation in this experiment. This represents the normal loss caused by correct operation. Subtract the average weight difference and average quantity of loss from the weight difference and quantity in S2-1 to obtain the material loss increment caused by the current student's incorrect operation steps. Correspond the material loss increment to the type of incorrect operation steps involved to obtain the average increment of material loss per transaction caused by the current student in each type of incorrect operation step. The increment is defined as weight for consumable materials and quantity for consumable materials per transaction.

5. A product resource allocation system based on industry-education integration according to claim 4, characterized in that: The customized material replenishment package in S3 specifically refers to: S3-1. Each time a material replenishment kit is customized, the average weight and quantity of materials lost by students who perform the correct operations in subsequent experiments are calculated based on the types, weights, and quantities of materials remaining in the current student experimental material kit. The types, weights, and quantities of materials that need to be replenished are statistically analyzed, and then material replenishment kits corresponding to the students are customized as needed.

6. A product resource allocation system based on industry-education integration according to claim 5, characterized in that: The personalized customization of the experimental material package in S3 is specifically as follows: S3-2. After the previous experiment, statistically analyze the incorrect operations related to material consumption made by each student in this experiment, record the types of operational steps, and compare the number of incorrect operations with the number of correct operations to obtain the probability of error. ,in This refers to the number of operation steps for a certain type of operation step in this experimental learning. This represents the number of incorrect operation steps performed by a student in this operation step type. S3-3. Before the next experimental learning session, first analyze the standard hand movement image data stored in the system, break it down into several operation steps, then merge them into various operation step types, record the number of operation steps in each operation step type, and determine the current error probability of students in each operation step type. By combining the average increase in material loss per instance caused by the student's various incorrect operation steps, the three factors are multiplied together to obtain the increase in material loss for the student in the next experiment. Based on this data, the weight and quantity of materials in the student's experimental material package are customized.

7. A product resource allocation system based on industry-education integration according to claim 6, characterized in that: Specifically, S4 is: S4-1. For students who are doing an experiment for the first time and have not collected experimental learning data, it is necessary to customize the experimental material package according to the rated material ratio. First, count all types of experimental materials consumed by all students in a certain experimental learning, then count the average weight of consumable materials lost, and the average number of consumable materials lost per time. Based on these two indicators, customize the experimental material package for this experimental learning.

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