Student accomplishment management platform based on comprehensive education

Abstract

The invention discloses a student literacy management platform based on comprehensive education, relates to the technical field of education management, and solves the problems that the collection of student data is often dispersed and lacks systematicness and comprehensiveness, and the storage and processing of the student data are lack of scientific planning, so that the utilization of storage resources is unreasonable, and the management efficiency is high. According to the method, the student data are classified according to three levels of high attainment, general attainment and low attainment, mass data can be quickly sorted, subsequent targeted analysis is facilitated, student data corresponding to each attainment class can be easily obtained, efficient data integration is realized, and the data processing efficiency is improved. The self-adaptive processing unit performs a series of processing on storage node management information, such as multi-system conversion, information extraction and recombination of a combined packet, further optimizes the data processing flow, generates detailed storage information, and provides powerful support for data management.

Description

Technical Field

[0001] The present invention relates to the technical field of education management, and specifically to a student quality management platform based on comprehensive education. Background Art

[0002] An education management system is a system that helps schools manage educational affairs with the network as the platform.

[0003] According to the patent application with the publication number CN109460913A, a student comprehensive quality evaluation system based on quality education is disclosed, including a security module, a basic data configuration module, a topology service module, an index and conduct management and warehousing module, a statistical report module, a database, and a professional evaluation module.

[0004] In the traditional student quality management mode, the collection of student data is often relatively scattered, lacking systematicness and comprehensiveness. The quality evaluation of students mostly depends on a single dimension, such as grades, and it is difficult to comprehensively consider the performance of students in multiple aspects such as learning behaviors, habits, and social practices.

[0005] At the same time, a large amount of student data is difficult to be efficiently integrated and analyzed, and it is impossible to provide accurate and targeted information on the formation mechanism of student quality for educators. In addition, there is a lack of scientific planning for the storage and processing of student data, resulting in unreasonable utilization of storage resources, low data processing efficiency, inability to track the changes in student status in real time, and difficulty in timely discovering problems existing in students' study and life. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a student quality management platform based on comprehensive education, which solves the problems that the collection of student data is often relatively scattered, lacking systematicness and comprehensiveness, and there is a lack of scientific planning for the storage and processing of student data, resulting in unreasonable utilization of storage resources and low data processing efficiency.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A student quality management platform based on comprehensive education, including: A data correlation analysis unit, which is used to classify the student data transmitted by the student data acquisition unit to obtain quality classification data, generate quality correlation information through big data analysis, and at the same time perform periodic monitoring in combination with the student data to generate a quality change prediction result, and transmit it to the management information display unit; A data storage management unit, which is used to bundle the student data transmitted by the data management analysis unit according to a fixed value to obtain a combined package, classify the combined package according to the data capacity to obtain high-capacity and low-capacity combined packages, and at the same time classify the storage nodes according to the available storage capacity to obtain the first and second storage nodes, and correspondingly store them with the high-capacity and low-capacity combined packages to generate node storage management information, and transmit it to the adaptive processing unit; An adaptive processing unit, which is used to analyze the node storage management information, convert the combined packet into a multi - base number to obtain conversion information, classify it to obtain base - number and non - base - number combined packets, simultaneously split the two, and insert them into each other to generate a recombined packet for storage, generate storage information, and transmit it to the management information display unit at the same time.

[0008] As a further solution of the present invention, it further includes a student data acquisition unit and a management information display unit; The student data acquisition unit is used to acquire the student data of students, and the student data includes literacy evaluation and education data; The management information display unit is used to display the literacy change prediction result to the corresponding management personnel and store the student data according to the storage information.

[0009] As a further solution of the present invention, the specific manner in which the data association analysis unit generates the literacy association information is as follows: Acquire the corresponding literacy evaluation in the student data, classify the student data according to the literacy evaluation to obtain the literacy classification data, simultaneously acquire the student data corresponding to the literacy classification data, and analyze the student data through big data to generate the literacy association information.

[0010] As a further solution of the present invention, the specific manner in which the data association analysis unit generates the literacy change prediction result is as follows: Acquire the students corresponding to different literacy classification data, analyze the students with time t as the period, acquire the student data corresponding to the students within the time period t, and simultaneously predict the change situation of the students' literacy evaluation in combination with the literacy association information to generate the corresponding literacy change prediction result.

[0011] As a further solution of the present invention, the specific manner in which the data storage management unit obtains the high - capacity and low - capacity combined packets is as follows: After acquiring the student data, label the literacy classification data as a, where a takes values of 1, 2, 3. Select a group of literacy classification data, acquire all the student data of the same type inside it and label them as n, where n ranges from 1 to m, and m is the number of student data. Bundle the student data in units of h to generate a combined packet until all the student data is bundled. Measure the data capacity of the combined packet, and accordingly divide the combined packet into two categories: high - capacity and low - capacity.

[0012] As a further solution of the present invention, the specific manner in which the data storage management unit generates the node storage management information is as follows: Acquire all storage nodes and their available storage capacities, sort them from largest to smallest according to the capacity, divide the nodes into the first and second storage nodes according to the capacity threshold, store the high - capacity combined packets in the former and the low - capacity combined packets in the latter, generate the node storage management information and transmit it to the adaptive processing unit.

[0013] As a further solution of the present invention, the specific manner in which the adaptive processing unit analyzes the node storage management information is as follows: Obtain two types of storage nodes corresponding to the node storage management information, select any group as the target, obtain its corresponding combination package and perform decimal-to-hexadecimal conversion, respectively extract non-numeric and numeric information from the conversion information, and sequentially combine them to generate a non-radix combination package and a radix combination package.

[0014] As a further solution of the present invention, the specific manner in which the adaptive processing unit generates storage information is as follows: Obtain the non-radix and radix combination packages, divide the non-radix combination package into nine parts by a quantity of 2, and sequentially add the remaining non-radix numbers to the divided segments; Label the divided segments as k, where k ranges from 1 to 9, insert them in sequence at the equal division points of the nine-equal-part radix combination package to generate a recombined package, perform lossless compression on the recombined package and store it, generate storage information and transmit it to the management information display unit.

[0015] The present invention provides a student literacy management platform based on comprehensive education. Compared with the prior art, it has the following beneficial effects: By classifying student data according to three levels: high-level literacy, secondary literacy, and tertiary literacy, the present invention can quickly sort out a large amount of data, facilitate subsequent targeted analysis, easily obtain student data corresponding to each literacy classification, achieve efficient data integration, and improve data processing efficiency. The generated literacy correlation information clearly presents the internal relationship between student behavior and literacy evaluation, provides a key basis for educators to understand the mechanism of student literacy formation, and helps to formulate more targeted education strategies.

[0016] Through the data storage management unit, the present invention labels and groups the literacy classification data, classifies the combination packages according to the data capacity, and then reasonably classifies and allocates storage tasks according to the available storage capacity of the storage nodes, improving the utilization rate of storage resources. A series of processes of the adaptive processing unit on the storage node management information, such as multi-radix conversion, information extraction, and recombination of the combination package, further optimize the data processing flow, generate detailed storage information, and provide strong support for data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system principle block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1

[0020] Please refer to Figure 1 , this application provides a student literacy management platform based on comprehensive education, including a student data acquisition unit, a data correlation analysis unit, a data storage management unit, an adaptive processing unit, and a management information display unit, and in combination with Figure 1 it can be known that the above functional units are connected in a one-way electrical connection.

[0021] The student data acquisition unit is used to acquire all student data corresponding to students and transmit the student data to the data correlation analysis unit. Here, the student data includes literacy evaluation and educational information, and the specific educational information represents the situation of students receiving education.

[0022] The data correlation analysis unit is used to analyze the acquired student data. First, it acquires the corresponding literacy evaluation in the student data. Here, the literacy evaluation is given by the corresponding teacher, and classifies the student data according to the literacy evaluation to obtain literacy classification data. Here, the classification is based on the literacy evaluation level. The specific literacy evaluation levels include three levels: first-level literacy, second-level literacy, and third-level literacy. At the same time, it acquires the student data corresponding to the literacy classification data and analyzes the student data through big data to generate literacy correlation information. Here, the literacy correlation information represents the influence between the corresponding behaviors of students and the literacy evaluation; the platform uses big data analysis technology to deeply mine the student data under each category. After analysis, it is found that students in the first-level literacy category generally have good learning habits, such as regular previewing and reviewing, actively reading extracurricular books, etc. These behaviors help to improve their knowledge reserve and learning ability, and thus improve the literacy evaluation level. Among the students in the third-level literacy category, some students have problems such as being addicted to video games and lacking time management. These behaviors may affect their performance in learning and activities, resulting in a low literacy evaluation.

[0023] For example, Chinese teachers will give corresponding literacy evaluations to each student based on aspects such as the student's reading comprehension ability, writing level, and enthusiasm for classroom participation. Level 1 literacy: For example, Xiao Ming has performed well in the study of various subjects, actively speaks in class, completes homework with high quality, and actively participates in various subject competitions and social practice activities, and has achieved excellent results. The teachers of various subjects unanimously believe that Xiao Ming has level 1 literacy, and his relevant data is classified into the level 1 literacy category.

[0024] Level 2 literacy: Xiaohong's academic performance is at an average level. Her classroom performance is relatively stable and she can complete her homework on time, but she is slightly lacking in actively participating in extracurricular activities. The teachers comprehensively assessed Xiaohong as level 2 literacy, and her data was classified into the level 2 literacy category.

[0025] Level 3 literacy: Xiaogang has some difficulties in learning. He often delays his homework, has poor concentration in class, and is not very active in participating in activities. After evaluation, the teacher rated Xiaogang's literacy as level 3 literacy, and his data was classified as level 3 literacy.

[0026] Then, the students corresponding to different literacy classification data are obtained, and the students are analyzed with time t as a period, and the student data corresponding to the students in time period t are obtained. At the same time, the changes in students' literacy evaluation are predicted in combination with literacy-related information, and the corresponding literacy change prediction results are generated and transmitted to the management information display unit.

[0027] For example, taking one month (t=1 month) as the time period for analysis, the first-level literacy students are Xiao Li and Xiao Zhang, the second-level literacy students are Xiao Wang and Xiao Zhao, and the third-level literacy students are Xiao Chen and Xiao Sun. During this month, the platform continuously collects various data of students; For Xiao Li: Since he has maintained an active learning and activity status in this month, according to the literacy-related information, it is predicted that his literacy evaluation will remain at the first-level literacy level, and may even be further improved; For Xiao Wang: His performance fluctuates to a certain extent. His classroom speeches and homework completion are not ideal, but he also participates in class activities. Taking all factors into consideration, it is predicted that his literacy evaluation may still be at the level of Level 2 literacy, but if he can improve his learning behavior in the future, he has the potential to improve. For Xiao Chen: He has many bad behaviors, such as being distracted in class and seriously delaying his homework. Based on the literacy-related information, it is predicted that his literacy evaluation may continue to be at the third-level literacy level, and there is even a risk of further decline.

[0028] A management information display unit is used to display the acquired literacy change prediction results to corresponding managers.

[0029] Classify student data into three levels: primary literacy, secondary literacy, and tertiary literacy. Quickly sort out the vast amount of student data to make it clear, which is convenient for subsequent targeted analysis. At the same time, it can easily obtain the student data corresponding to each literacy classification data, realize the efficient integration of data, improve the data processing efficiency, generate literacy correlation information, and clearly present the impact between the corresponding behaviors of students and literacy evaluations. For example, discover the good learning habits of primary literacy students and the bad behavior problems of tertiary literacy students, providing a key basis for educators to understand the formation mechanism of students' literacy and helping to formulate more targeted educational strategies.

[0030] Analyze students with different literacy classifications in a cycle of time t, and combine the literacy correlation information to predict the changes in students' literacy evaluations. It can provide personalized development predictions for each student. At the same time, continuously collect various types of student data at a fixed time cycle to track the changes in students' status in real time and promptly discover the problems of students in learning and life.

[0031] Embodiment 2

[0032] As Embodiment 2 of the present invention, it is implemented on the basis of Embodiment 1, and the differences from Embodiment 1 are as follows: The data correlation analysis unit transmits the student data to the data storage and management unit, and the student data transmitted here includes the prediction results of literacy changes and the literacy classification data, and analyzes them through the data storage and management unit.

[0033] The data storage and management unit obtains the student data, labels the literacy classification data as a, and a = 1, 2, and 3. At the same time, takes one group of literacy classification data as the analysis object, and obtains all the student data of the same type within the analysis object, labels it as n, and n = 1, 2,..., m, where m represents the number of student data. Then obtains the student data with the value of h for bundling to generate a combined package, and so on for all student data bundling. At the same time, obtains the data capacity of the combined package, and classifies the combined package according to the data capacity into a high-capacity combined package and a low-capacity combined package. Here, the data capacity is compared with the capacity preset value, and the specific value of the preset value is set by the operator. The combined package with a data capacity greater than the capacity preset value is classified as a high-capacity combined package, and the one lower than the capacity preset value is classified as a low-capacity combined package; First, the system comprehensively obtains all the storage nodes in the platform. In order to more reasonably allocate storage tasks later, it will accurately collect the available storage capacity corresponding to each storage node. Then, sort these storage nodes in descending order according to the available storage capacity.

[0034] After sorting is completed, the storage nodes are classified according to a pre-set storage capacity threshold. The storage nodes with available storage capacity greater than the threshold are classified as first storage nodes, which will be specifically used to store high-capacity combination packages; while the storage nodes with available storage capacity less than or equal to the threshold are classified as second storage nodes, responsible for storing low-capacity combination packages.

[0035] After the classification of the storage nodes is completed, the system generates a detailed node storage management information. This information includes the identification, available storage capacity, category (first storage node or second storage node), and corresponding storage tasks (storing high-capacity or low-capacity combination packages) of each storage node. Finally, the generated node storage management information is transmitted to the adaptive processing unit in a timely and accurate manner, so that the adaptive processing unit can carry out subsequent data analysis and processing based on this information. The specific classification criteria are as follows: the storage nodes with available storage capacity greater than the threshold are classified as first storage nodes, and vice versa as second storage nodes; Obtain all the storage nodes in the platform and simultaneously obtain the available storage capacity corresponding to each storage node. For example, there are storage nodes A, B, C, etc. in the platform. The available storage capacity of storage node A is 500GB, the available storage capacity of storage node B is 300GB, and the available storage capacity of storage node C is 100GB. Sort the storage nodes in descending order of available storage capacity. In the above example, after sorting, it is storage nodes A, B, C.

[0036] Classify the storage nodes according to the available storage capacity into first storage nodes and second storage nodes. The specific classification criteria are: the storage nodes with available storage capacity greater than the threshold are classified as first storage nodes, and vice versa as second storage nodes. Assume that the set threshold is 200GB. Then storage nodes A and B are classified as first storage nodes because their available storage capacity is greater than 200GB; storage node C is classified as a second storage node because its available storage capacity is less than 200GB.

[0037] An adaptive processing unit, which is used to analyze the obtained node storage management information, and at the same time obtain the first storage node and the second storage node corresponding to the node storage management information, and analyze any group of storage nodes as the target object to obtain the combined package corresponding to the target object, and at the same time perform a multi - base conversion on the combined package. Specifically, here it is to convert decimal data into hexadecimal. The reason for choosing to convert decimal to hexadecimal is that hexadecimal has a certain compactness and convenience in representing data, which is convenient for subsequent extraction and processing of data according to specific rules. For example, for a certain data segment in the combined package, assuming its decimal value is 94, through the established base conversion algorithm, it is converted into the hexadecimal number 5E. In actual operation, the platform may perform such conversion operations on a series of decimal data in the combined package in sequence to obtain the conversion information, and extract the non - numerical information in the conversion information. For example, the "E" in the above - mentioned hexadecimal number, and combine the obtained non - numerical information in the extraction order to obtain a non - base combined package. Similarly, combine the remaining numerical information to obtain a base combined package; After the conversion is completed, carefully analyze the obtained hexadecimal conversion information and extract the non - numerical information from it. Taking the hexadecimal number 5E as an example, the non - numerical information here is "E". When actually processing a large amount of conversion information, combine all the extracted non - numerical information in the order of information extraction, so as to generate a non - base combined package. Suppose there is another hexadecimal number 1A later, extract "A", and combine it with the previous "E" in order to further enrich the content of the non - base combined package; Similarly, perform a combination operation on the numerical information in the conversion information. For example, for the hexadecimal number 5E, extract the digit "5" from it, and perform the same extraction operation on the numerical parts of other hexadecimal conversion information later, and combine them in order to obtain a base combined package. If there is another hexadecimal number 3F, combine "3" with the previous "5" to continuously improve the base combined package; Obtain the non - base combination package and the base combination package, and divide the non - base combination package into nine parts according to a quantity of 2 to obtain segments. For the remaining non - base numbers, add them to the segments in turn in a round - robin order. For example, if the number of non - base numbers obtained through analysis is 31, after dividing into nine parts, there are 13 remaining. Then, group them in pairs of two in order and combine them with the segments one by one. For example, for the non - base combination package ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f"], divide it into groups of 2 each. The first nine groups are ["A", "B"], ["C", "D"], ["E", "F"], ["G", "H"], ["I", "J"], ["K", "L"], ["M", "N"], ["O", "P"], ["Q", "R"]. Assume the number of non - base numbers is 31. After dividing into nine parts, there are 13 remaining. First, group these 13 non - base numbers in pairs in order to get ["S", "T"], ["U", "V"], ["W", "X"], ["Y", "Z"], ["a", "b"], ["c", "d"], ["e", "f"]. Then, add these groups to the previous segments in turn, starting from the first segment until all the remaining groups are added. For example, the first segment ["A", "B"] after adding ["S", "T"] becomes ["A", "B", "S", "T"], the second segment ["C", "D"] after adding ["U", "V"] becomes ["C", "D", "U", "V"], and so on. Then, combine the obtained segments with the base combination package for combination processing. And the specific combination processing method is: Obtain all the segment labels denoted as k, and k = 1, 2, …, 9. At the same time, insert the segments into the base combination package in ascending order of the label. The insertion point is selected by equally dividing the base combination package into nine parts, and inserting at the position corresponding to the base number at the equal - division point to generate a recombined package. At the same time, perform compression processing on the obtained recombined package. Here, the compression processing is carried out in a lossless compression manner, and store the compressed recombined package to generate storage information. Then, transmit it to the management information display unit.

[0038] For example, taking the above non - base combination package as an example, assume the base combination package is [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180]. After dividing it into nine equal parts, each part contains two numbers, namely [10, 20], [30, 40], [50, 60], [70, 80], [90, 100], [110, 120], [130, 140], [150, 160], [170, 180]. Taking the base numbers corresponding to the equal - division points as the insertion points, the segmented segments are inserted into the base combination package in ascending order of the labels. For example, the segmented segment with label 1 is inserted at the first equal - division point, that is, between [10, 20]; the segmented segment with label 2 is inserted at the second equal - division point, that is, between [30, 40], and finally a recombined package is generated. Assume the segmented segments are ["A", "B"], ["C", "D"], ["E", "F"], ["G", "H"], ["I", "J"], ["K", "L"], ["M", "N"], ["O", "P"], ["Q", "R"]. After insertion, the recombined package may become [10, "A", "B", 20, 30, "C", "D", 40, 50, "E", "F", 60, 70, "G", "H", 80, 90, "I", "J", 100, 110, "K", "L", 120, 130, "M", "N", 140, 150, "O", "P", 160, 170, "Q", "R", 180].

[0039] And so on, the same analysis is performed on all the first - level storage nodes and second - level storage nodes, and corresponding storage information is generated.

[0040] The management information display unit is used to perform storage according to the generated storage information.

[0041] Embodiment 3 As Embodiment 3 of the present invention, the key lies in combining the implementation processes of Embodiment 1 and Embodiment 2.

[0042] Some of the data in the above formula are taken for numerical calculation without substituting parameter units for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well - known to those skilled in the art.

[0043] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A student literacy management platform based on comprehensive education, characterized in that, Including: A data association analysis unit, which is used to classify the student data transmitted by the student data acquisition unit to obtain literacy classification data, generate literacy association information through big data analysis, and at the same time conduct periodic monitoring in combination with the student data to generate a prediction result of literacy change and transmit it to the management information display unit; A data storage management unit, which is used to bundle the student data transmitted by the data management analysis unit according to a fixed value to obtain a combined package, classify the combined package into high-capacity and low-capacity combined packages according to the data capacity of the combined package, and at the same time classify the storage nodes according to the available storage capacity to obtain the first and second storage nodes, and correspondingly store them with the high-capacity and low-capacity combined packages to generate node storage management information and transmit it to the adaptive processing unit; An adaptive processing unit, which is used to analyze the node storage management information, convert the combined package into a multi-base system to obtain conversion information, classify it into base and non-base combined packages, and at the same time split the two and insert them into each other to generate a recombined package for storage, generate storage information, and at the same time transmit it to the management information display unit.

2. The student accomplishment management platform based on comprehensive education according to claim 1, wherein It also includes a student data acquisition unit and a management information display unit; The student data acquisition unit is used to acquire the student data of students, and the student data includes literacy evaluation and educational data; The management information display unit is used to display the prediction result of literacy change to the corresponding management personnel and store the student data according to the storage information.

3. The student literacy management platform based on comprehensive education according to claim 1, characterized in that, The specific way for the data association analysis unit to generate literacy association information is: Obtain the corresponding literacy evaluation in the student data, classify the student data according to the literacy evaluation to obtain literacy classification data, at the same time obtain the student data corresponding to the literacy classification data, and analyze the student data through big data to generate literacy association information.

4. The student accomplishment management platform based on comprehensive education according to claim 1, characterized in that, The specific way for the data association analysis unit to generate a prediction result of literacy change is: Obtain the students corresponding to different literacy classification data, analyze the students with time t as the period, obtain the student data corresponding to the students within the time period t, and at the same time predict the change situation of the students' literacy evaluation in combination with the literacy association information to generate the corresponding prediction result of literacy change.

5. The student literacy management platform based on comprehensive education according to claim 1, characterized in that, The specific way for the data storage management unit to obtain high-capacity and low-capacity combined packages is: After obtaining the student data, label the literacy classification data as a, where a takes values of 1, 2, 3, select a group of literacy classification data, obtain all the same-type student data inside it and label it as n, where n ranges from 1 to m, and m is the number of student data. Bundle the student data in units of h to generate a combined package until all the student data is bundled. Measure the data capacity of the combined package, and accordingly divide the combined package into two categories: high-capacity and low-capacity.

6. The student accomplishment management platform based on comprehensive education according to claim 1, wherein The specific way for the data storage management unit to generate node storage management information is: Obtain all storage nodes and their available storage capacities, sort them from largest to smallest according to the capacity, and divide the nodes into the first and second storage nodes according to the capacity threshold. The former stores high-capacity combined packages, and the latter stores low-capacity combined packages, generate node storage management information and transmit it to the adaptive processing unit.

7. The student literacy management platform based on comprehensive education according to claim 1, characterized in that, The specific way for the adaptive processing unit to analyze the node storage management information is: Obtain two types of storage nodes corresponding to the node storage management information, select any one group as the target, obtain its corresponding combination package and perform decimal-to-hexadecimal conversion, respectively extract non-numeric and numeric information from the conversion information, and sequentially combine them to generate a non-base combination package and a base combination package.

8. The student literacy management platform based on comprehensive education according to claim 1, characterized in that, The specific manner in which the adaptive processing unit generates storage information is as follows: Obtain the non-base and base combination packages, divide the non-base combination package into nine parts according to the quantity 2, and sequentially add the remaining non-base numbers to the divided segments; Label the divided segments as k, where k ranges from 1 to 9, insert them in sequence at the equal division points of the nine-equal-part base combination package to generate a recombined package, perform lossless compression on the recombined package and then store it, generate storage information and transmit it to the management information display unit.

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