Intelligent auxiliary system based on instrument sharing platform

Abstract

The invention discloses an intelligent auxiliary system based on an instrument sharing platform in the technical field of information, and the system comprises an information collection module which is used for collecting basic information of an instrument sharing platform user and instrument use information; the database module is used for establishing a basic database based on the information acquired by the information acquisition module and managing and optimizing the information of the basic database; the statistical analysis module is used for performing statistical analysis on instrument use condition information and fund account information according to the collected instrument use information; the screening and error correction module is used for carrying out screening and error correction on the instrument use condition information according to the basic database; and the reporting module is used for forming a report form according to the information completing screening and error correction for reporting. The system is intelligent and efficient, the MATLAB is used for constructing a database to count and analyze the instrument use condition information and the required fund account information, intelligent data screening, error correction and reporting are carried out, the working efficiency of statistical analysis is improved, and orderly and efficient operation of an experiment teaching platform is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of information technology, and in particular is an intelligent auxiliary system based on an instrument sharing platform. Background Art

[0002] Computer information technology is changing with each passing day. Digital, networked, and multimedia information technology is increasingly being used in teaching and scientific research, and is gradually changing the teaching methods of colleges and universities. In recent years, the construction of digital campuses in colleges and universities has received widespread attention. As an important innovation in experimental teaching methods, the construction and application of digital experimental teaching platforms are constantly changing the status quo of experimental teaching in colleges and universities, and have greatly improved the teaching quality of experimental teaching and the smooth development of scientific research projects.

[0003] MATLAB has powerful data processing, analysis and visualization functions, and also provides the ability to interact with databases. Using MATLAB to design and develop experimental teaching systems has become a hot topic in colleges and universities in recent years. By sorting out the key processes of the experimental teaching platform, researching and formulating unified data specifications and unified classification codes, building a dynamic error correction database, and developing an intelligent auxiliary system, it can realize intelligent screening, intelligent error correction, intelligent classification summary, statistical analysis, and funding settlement of appointment information. The development and promotion of the system can not only promote the resource sharing, intelligentization, and digitalization of college experimental teaching platforms, but also promote the improvement of teaching and scientific research levels.

[0004] At present, many experimental teaching platforms are still using the traditional working mode to collect and manage basic information. Various records are completely printed manually, or even no records are available, let alone using the database to retrieve and trace the information on the platform. The traditional working mode is inefficient, and the calculation and statistics of data information are prone to errors, and it is even difficult to achieve accurate statistical analysis, and the data transmission has a certain lag. When the amount of data information is small, it can still be barely coped with, but when faced with a large number of scientific research and teaching tasks, the experimental teaching platform will generate a huge amount of data information, which may include instrument users, instrument managers, reserved instruments, uses, reservation times, test fees, and later payments. How to process and transmit relevant information in a timely manner and ensure the efficient operation and orderly management of relevant experimental teaching platforms is one of the problems currently faced by many platforms.

[0005] To this end, it is necessary to propose an intelligent auxiliary system based on an instrument sharing platform that can automatically perform information statistics and financial account analysis based on the information collected by the instrument sharing platform, and build a database to intelligently screen, correct and report the statistical analysis information. Summary of the invention

[0006] In order to solve the above problems, the purpose of the present invention is to provide an intelligent assistance system based on an instrument sharing platform, which collects users' instrument usage information, uses MATLAB to build a database to perform statistics and analysis on the instrument usage information and the required expense account information, and uses the functions of the database to intelligently screen, correct errors and report on the information data obtained from the statistical analysis, thereby improving the work efficiency of statistical analysis and ensuring the orderly and efficient operation of the experimental teaching platform.

[0007] In order to achieve the above-mentioned object, the technical solution of the present invention is as follows: an intelligent auxiliary system based on an instrument sharing platform, comprising an information acquisition module, a database module, a statistical analysis module, a screening and error correction module and a reporting module;

[0008] Information collection module, used to collect basic information of instrument sharing platform users and instrument usage information;

[0009] The database module is used to establish a basic database based on the basic user information collected by the information collection module, and to manage and optimize the basic database information;

[0010] The statistical analysis module is used to classify and analyze the instrument usage information and the fund account information required for the instrument use according to the basic database of the database module and the instrument use information collected by the information collection module;

[0011] A screening and error correction module is used to screen and correct missing and abnormal information of the instrument usage information according to the basic database of the database module;

[0012] The reporting module is used to generate reports based on the instrument usage information and instrument usage funding information for completed screening and error correction, and provide retrieval queries within the instrument sharing platform.

[0013] The principle of the basic plan is: to complete the collection of user basic information and instrument usage information through the information collection module, to enhance the efficiency and stability of the collected data with unified file data specifications, to build a basic database through the database module for the management, storage and retrieval of large-scale instrument usage data, to provide data support for subsequent screening and error correction, to statistically analyze the instrument usage data and instrument usage expense account information within a preset time through the statistical analysis module, to screen and correct the statistically obtained instrument usage information using database data and algorithms through the screening and error correction module, to form a report on the screening and error correction results and the instrument usage information and expense account information that have completed the screening and error correction through the reporting module, to provide users with their own information retrieval services on the instrument sharing platform, and to realize intelligent auxiliary functions in instrument reservation, information statistics, intelligent analysis, and expense settlement.

[0014] The beneficial effects of the basic scheme are: 1. Through the automated information collection module and efficient database management system, the errors of manual data entry can be greatly reduced, and the efficiency and accuracy of data processing can be improved. At the same time, the statistical analysis module can quickly classify and analyze a large amount of data, providing timely and accurate data support for decision-making.

[0015] 2. The screening and error correction module uses database data and algorithms to automatically screen and correct missing and abnormal information in instrument usage information, effectively avoiding statistical deviations caused by human negligence or errors, and further improving the quality of the data.

[0016] 3. Through statistical analysis of instrument usage and funding accounts, we can clearly understand the frequency of use, efficiency of use and funding allocation of each instrument, thereby providing decision support for laboratory managers, optimizing resource allocation and management, and improving resource utilization efficiency.

[0017] 4. The implementation of this system can ensure the orderly and efficient operation of the experimental teaching platform. Through intelligent auxiliary functions such as instrument reservation, information statistics, and fund settlement, it can simplify the management process, improve management efficiency, and provide students and researchers with a better user experience.

[0018] 5. Users can easily query their usage information and expense accounts through the instrument sharing platform, which improves the transparency and traceability of information. At the same time, the system's intelligent auxiliary function also reduces the burden on users and improves user satisfaction.

[0019] 6. This system provides a strong guarantee for the smooth progress of scientific research and teaching activities. Through timely and accurate instrument usage information and funding account information, scientific researchers can carry out experimental research more efficiently, and teaching staff can better arrange experimental teaching work.

[0020] Furthermore, the information collection module includes a basic information input unit and a report reading unit. The basic information input unit is used to input basic information such as personnel, team affiliation, funding account number and contact information of the instrument user;

[0021] The report reading unit is used to read the instrument reservation usage information report from the instrument sharing platform within a preset time.

[0022] The beneficial effects of the basic solution are: 1. By entering basic information such as the user's personnel, team affiliation, funding account and contact information, data standardization and normalization are achieved, providing a unified data format for subsequent data processing and analysis.

[0023] 2. By manually entering basic information, the accuracy and reliability of key information can be ensured, reducing analysis bias caused by data errors or omissions.

[0024] 3. The report reading unit can automatically read the instrument reservation usage information report from the instrument sharing platform, which greatly reduces the workload of manual input and improves data processing efficiency.

[0025] 4. By reading reports regularly or in real time, the system can obtain the latest information on instrument usage to ensure the timeliness and accuracy of statistical analysis and reporting results.

[0026] 5. Automatic reading of reports avoids errors and omissions that may occur during manual data entry, and improves the accuracy and reliability of data.

[0027] 6. The read report data can be directly used in the statistical analysis module to conduct in-depth analysis of instrument usage, financial accounts, etc., providing strong support for laboratory management.

[0028] Furthermore, the database module includes a basic database and a database optimization unit. The basic database is used to construct a non-relational database based on the basic information of the user entered by the basic information entry unit, and provide data support for the subsequent statistical analysis module and the screening and error correction module;

[0029] The database optimization unit is used to add the screened and error-corrected data into the basic database to increase the data scale, perform index optimization and query optimization on the existing basic database, and back up and restore the existing database data.

[0030] The beneficial effects of the basic solution are: 1. The basic database stores the basic information of users in a non-relational database, which is convenient for subsequent data retrieval, statistics and analysis. This centralized management method improves the consistency and integrity of data, and the non-relational database supports the needs of large-scale data and advanced data analysis, laying the foundation for the subsequent in-depth big data analysis of instrument usage data.

[0031] 2. It provides efficient data support for basic information entry unit, statistical analysis module and screening and error correction module. Through the design of non-relational database, fast data reading and writing can be achieved to meet the system's requirements for real-time data.

[0032] 3. Non-relational databases are usually more flexible and can easily cope with changes in data structure. In the instrument sharing platform, as user information and instrument usage change, the database needs to constantly adapt to these changes, and non-relational databases can better meet this demand.

[0033] 4. Supplementing the screened and corrected data into the basic database not only improves the accuracy of the data, but also expands the data scale of the database. This helps to conduct more comprehensive statistical analysis and data mining, and provides richer information support for laboratory management.

[0034] 5. By optimizing the database index and query, the query speed and response time of the database can be significantly improved, which is of great significance to improving the overall performance of the system and user experience.

[0035] 6. The database optimization unit is also responsible for database backup and recovery. Regular database backup can ensure data security. In the event of data loss or damage, data can be quickly restored through recovery operations to ensure the normal operation of the system.

[0036] 7. Through the continuous work of the database optimization unit, potential problems in the database, such as performance bottlenecks, data redundancy, etc., can be discovered and solved in a timely manner, thereby enhancing the stability and reliability of the system.

[0037] Further, the statistical analysis module includes an information statistics unit and an account analysis unit, and the information statistics unit is used to count the usage of the instrument and user information within a preset time;

[0038] The account analysis unit is used to analyze the expense account information that the user needs to pay based on the usage information of the instrument that has completed the screening and error correction and the preset instrument usage fee information.

[0039] The beneficial effects of the basic plan are: 1. By counting the usage of instruments within a preset time, including frequency of use, duration of use, users, etc., laboratory managers can fully understand the usage status of the instruments and provide data support for subsequent resource allocation and scheduling.

[0040] 2. Statistics of user information help analyze user habits and needs, thereby optimizing the services of the instrument sharing platform and improving user experience. At the same time, it can also provide user behavior analysis reports for laboratory managers to assist in decision-making.

[0041] 3. The information statistics unit can visualize the statistical results in the form of charts, reports, etc., making the data more intuitive and easy to understand, so that laboratory managers can quickly grasp key information.

[0042] 4. Based on the usage information of the instruments that have completed the screening and error correction, the account analysis unit can accurately calculate the expenses that the user needs to pay. This avoids the problem of inaccurate accounting of expenses due to data errors or omissions, and ensures the fairness and transparency of fund management.

[0043] 5. Through the analysis of fund accounts, laboratory managers can understand the use and trend of funds, so as to optimize the allocation and use of funds. This helps to improve the efficiency of fund use and ensure the rational allocation of laboratory resources.

[0044] 6. The account analysis unit can also provide data support for the laboratory's budget management. By comparing actual expenditures with budget plans, laboratory managers can promptly identify budget deviations and take corresponding adjustment measures to ensure the achievement of budget goals.

[0045] Further, the screening and error correction module includes a missing value repair unit and an abnormal value repair unit, the missing value repair unit is used to use an algorithm to screen and mark missing values ​​in the instrument usage information, and to supplement and repair the determined information according to the data in the basic database;

[0046] An outlier repair unit is used to use algorithms and machine learning methods to learn and screen outliers in instrument usage information and mark them, and to supplement and repair the determined information based on the data in the basic database.

[0047] The beneficial effects of the basic scheme are: 1. The algorithm screens and marks the missing values ​​in the instrument usage information, and then uses the data in the basic database to supplement and repair the missing values, which can significantly improve the integrity of the data. The complete data set is the basis for subsequent statistical analysis and decision making.

[0048] 2. The existence of missing values ​​and outliers may lead to deviations in statistical results and affect the accuracy of analytical conclusions. By fixing missing values ​​and outliers, this deviation can be eliminated and the statistical results can be made more reliable.

[0049] 3. In the process of data analysis, processing missing values ​​often takes a lot of time and manpower. The automatic processing function of the missing value repair unit can significantly improve the analysis efficiency and reduce manpower consumption.

[0050] 4. Outliers are observations in a data set that appear abnormal or inconsistent compared to other observations. These values ​​may be caused by measurement errors, data entry errors, or extreme situations. Learning and screening through algorithms and machine learning methods can accurately identify and mark outliers, and repair them based on the data in the basic database, thereby improving the accuracy of the data.

[0051] 5. Based on accurate and reliable data sets, laboratory managers can make more informed decisions. For example, based on the analysis results of instrument usage, they can optimize resource allocation, adjust usage strategies, etc. to improve laboratory operating efficiency and benefits.

[0052] Further, the reporting module includes a statistical reporting unit and a platform query unit, the statistical reporting unit is used to form a report based on the usage information of the instruments that have completed the screening and error correction and the screening results, and to form a report based on the expense account information obtained by analyzing the usage information of the instruments that have completed the screening and error correction;

[0053] The platform query unit is used to transmit the instrument usage information and financial account information to the instrument sharing platform for users to retrieve and view their own relevant information items.

[0054] The beneficial effects of the basic scheme are: 1. The statistical reporting unit achieves information transparency by forming a report containing the instrument usage information and financial account information for completed screening and error correction. This not only helps users understand their own usage and financial expenditure, but also enhances users' trust in the platform.

[0055] 2. The data and analysis results in the report can provide powerful decision-making support for laboratory managers. Managers can evaluate the efficiency of instrument use, optimize resource allocation, adjust budgets, etc. based on the information in the report, thereby improving the overall operating efficiency of the laboratory.

[0056] 3. The report of fund account information records the user's fund expenditure in detail, which helps to ensure the compliance of fund use. This is of great significance for maintaining the financial discipline and reputation of the laboratory.

[0057] 4. The platform query unit allows users to transfer instrument usage information and expense account information to the instrument sharing platform for users to retrieve and review at any time. This self-service query method greatly improves the convenience of users and enables users to obtain the required information anytime and anywhere.

[0058] 5. Users can search and view relevant information items on the instrument sharing platform according to their needs to achieve personalized services. This service method not only improves user satisfaction, but also enhances the user stickiness of the platform.

[0059] 6. By transmitting information to the instrument sharing platform, different users can share and collaborate on data. For example, team members can jointly view the usage of instruments and coordinate usage plans, thereby improving team work efficiency.

[0060] Furthermore, index optimization is used to improve the indexing performance of the database using Boolean indexing and caching techniques.

[0061] The benefits of the basic solution are: 1. Through indexing, the database system can quickly locate the physical location of the data, thereby reducing the number of disk reads, especially when processing large amounts of data. Indexing enables the database to respond quickly to query requests, especially for columns or fields that are frequently queried. By creating indexes on these columns, the query speed can be significantly improved.

[0062] 2. Since indexes can speed up queries, the database system can process query requests faster, thereby reducing CPU usage time and making more efficient use of CPU resources. Caching technologies (such as database caching and application caching) can store frequently accessed data in memory, reducing access to disk and thus improving memory usage efficiency.

[0063] 3. Through reasonable index design, the lock contention in concurrent query and update operations of the database can be reduced, thereby improving the concurrent performance of the system. Caching technology can effectively reduce the pressure on the database in high-concurrency scenarios, because the data in the cache can be accessed directly without accessing the database.

[0064] 4. Cache technology can maintain the consistency between cache data and database data through reasonable cache update strategies (such as cache invalidation strategies). Although index optimization itself does not directly enhance data security, it can reduce potential security risks caused by system performance bottlenecks by improving the overall performance and response speed of the system.

[0065] 5. Through the combination of Boolean index logic, query statements that need to consider multiple conditions at the same time can be optimized to improve query efficiency.

[0066] 6. By regularly checking and optimizing indexes (such as deleting useless indexes, rebuilding indexes, etc.), you can maintain good database performance and simplify index maintenance. Index optimization can reduce the load on the database system and reduce system crashes or instability caused by performance problems.

[0067] Furthermore, query optimization is used to improve database performance by using efficient query statements and configuring database connection pools.

[0068] The beneficial effects of the basic solution are: 1. By optimizing query statements, such as reducing unnecessary column selection, using appropriate aggregate functions, avoiding complex subqueries, etc., the query execution time can be significantly reduced and the database response speed can be improved. Query optimization will analyze query statements and try to generate the best query execution plan to reduce data access and computational complexity, thereby speeding up query speed.

[0069] 2. Optimized queries can return results faster, improve the responsiveness of the user interface, and enhance the user experience. Fast queries and efficient data processing can increase user satisfaction and trust in the system.

[0070] 3. By reducing query complexity and resource consumption, the database load can be reduced, and the maintenance workload caused by performance issues can be reduced. The optimized query statements are more concise and clear, and are easier to understand and maintain.

[0071] 4. Database connection pool can reuse existing database connections, avoiding the overhead of frequently creating and destroying connections. Connection pool can manage multiple database connections, so that the database can respond to requests faster in high concurrency scenarios. By properly configuring the size and parameters of the connection pool, the utilization of database resources can be optimized to avoid resource waste and excessive competition.

[0072] Furthermore, the missing value repair unit uses the ismissing function to perform missing value screening.

[0073] The benefits of the basic solution are: 1. The ismissing function can accurately identify missing values ​​in the data, whether it is NaN (Not a Number) in numeric data or specific missing value identifiers of other data types (such as strings, dates, etc.). This accurate identification is the prerequisite for subsequent missing value processing and helps avoid mistakenly treating non-missing values ​​as missing values. By timely screening and repairing missing values, data errors caused by missing values ​​can be reduced, and the accuracy and reliability of data analysis can be improved.

[0074] 2. The ismissing function can be used to automatically complete the missing value screening, reduce the burden of manual screening, and improve the efficiency of data processing. Missing value screening is the first step in missing value processing. Through this step, it can be clear which data needs to be repaired or replaced, providing a clear goal and direction for subsequent missing value processing (such as filling, deletion, etc.).

[0075] 3. By screening and uniformly processing missing values ​​through the ismissing function, data from different sources can have a unified missing value identification and processing method when integrated and shared, reducing the difficulty of data integration and sharing. The data after repairing the missing values ​​is more complete and reliable, which helps to improve the availability of data and enable the data to better serve subsequent needs.

[0076] Furthermore, the outlier repair unit uses the interquartile range method and isolation forest machine learning method to perform outlier learning and screening.

[0077] The beneficial effects of the basic scheme are: 1. The IQR method calculates the quartiles and interquartile range of the data and sets reasonable upper and lower limits to identify outliers. This method has no strict assumptions about the data distribution and is applicable to many types of data. The isolation forest method detects outliers by constructing multiple isolation trees, which can effectively identify abnormal points in the data. The combination of these two methods can more accurately identify outliers in the data, thereby improving the quality of the data. The existence of outliers often has an adverse effect on the results of data analysis. Repairing or deleting outliers through the repair module can reduce data errors and improve the accuracy and reliability of data analysis.

[0078] 2. The outlier repair unit can automatically complete the screening and repair of outliers, reduce the burden of manual processing, and improve the efficiency of data processing. The IQR method and the isolation forest method have their own characteristics. The IQR method is simple and fast and suitable for most situations; the isolation forest method is more suitable for processing large data sets and complex data structures. The combination of the two methods can be flexibly selected according to actual conditions to optimize the data processing process.

[0079] 3. In complex data analysis scenarios, the presence of outliers may seriously affect the accuracy of the analysis results. The outlier repair unit can timely repair or delete outliers to ensure the accuracy and reliability of data analysis. In machine learning models, outliers may cause model overfitting or performance degradation. Processing outliers through the repair module can enhance the robustness of the model and improve the generalization ability of the model.

[0080] 4. Repairing outliers can improve the overall distribution of data, making the data more consistent with the actual situation, thereby improving the availability of data. The repaired data is more complete and reliable, and can support more application scenarios, such as data mining, machine learning model training, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 Schematic diagram of an intelligent assistance system based on an instrument sharing platform in an embodiment of the present invention.

[0082] Figure 2 Schematic diagram of the information collection module of the intelligent assistance system based on the instrument sharing platform in an embodiment of the present invention.

[0083] Figure 3 Schematic diagram of the database module of the intelligent assistance system based on the instrument sharing platform in an embodiment of the present invention.

[0084] Figure 4 Schematic diagram of a statistical analysis module of an intelligent assistance system based on an instrument sharing platform in an embodiment of the present invention.

[0085] Figure 5Schematic diagram of a screening and error correction module of an intelligent assistance system based on an instrument sharing platform in an embodiment of the present invention.

[0086] Figure 6 Schematic diagram of a reporting module of an intelligent assistance system based on an instrument sharing platform in an embodiment of the present invention.

[0087] Figure 7 It is a functional schematic diagram of a database optimization unit of an intelligent assistance system based on an instrument sharing platform in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The following is further described in detail through specific implementation methods:

[0089] Basically as attached Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown: an intelligent auxiliary system based on an instrument sharing platform, including an information collection module, a database module, a statistical analysis module, a screening and error correction module and a report module;

[0090] An information collection module is used to collect basic information of instrument sharing platform users and instrument usage information. The information collection module includes a basic information input unit and a report reading unit. The basic information input unit is used to input basic information such as personnel, team affiliation, funding account number and contact information of instrument users. The report reading unit is used to read the instrument reservation usage information report from the instrument sharing platform within a preset time.

[0091] The database module is used to establish a basic database based on the basic user information collected by the information collection module, and to manage and optimize the basic database information. The database module includes a basic database and a database optimization unit. The basic database is used to build a non-relational database based on the basic user information entered by the basic information entry unit, and provide data support for the subsequent statistical analysis module and the screening and error correction module. The database optimization unit is used to add the screened and error-corrected data to the basic database to increase the data scale, use Boolean index and cache technology to optimize the index of the existing basic database, use efficient query statements and configure the database connection pool to optimize the query, and back up and restore the existing database data;

[0092] The statistical analysis module is used to classify and analyze the instrument usage information and the expense account information required for the instrument use according to the basic database of the database module and the instrument usage information collected by the information collection module. The statistical analysis module includes an information statistics unit and an account analysis unit. The information statistics unit is used to count the instrument usage and user information within a preset time. The account analysis unit is used to analyze the expense account information that the user needs to pay according to the usage information of the instrument that has completed the screening and error correction and the preset instrument usage fee information;

[0093] A screening and error correction module is used to screen and correct missing information and abnormal information of the instrument usage information according to the basic database of the database module. The screening and error correction module includes a missing value repair unit and an abnormal value repair unit. The missing value repair unit is used to use the ismissing function to screen and mark missing values ​​in the instrument usage information, and to supplement and repair the confirmed information according to the data in the basic database. The abnormal value repair unit is used to use the interquartile range method and the isolation forest machine learning method to learn and screen abnormal values ​​in the instrument usage information and mark them, and to supplement and repair the confirmed information according to the data in the basic database;

[0094] The reporting module is used to generate reports based on the instrument usage information and instrument usage funding information that have completed screening and error correction, and provide retrieval and query within the instrument sharing platform. The reporting module includes a statistical reporting unit and a platform query unit. The statistical reporting unit is used to generate reports based on the instrument usage information and screening results that have completed screening and error correction, and to generate reports based on the funding account information obtained by analyzing the instrument usage information that has completed screening and error correction. The platform query unit is used to transmit the instrument usage information and funding account information to the instrument sharing platform for users to retrieve and review their own related information items.

[0095] The specific implementation process is as follows: when creating an account on the instrument sharing platform, laboratory members need to enter their own name, grade, contact information, laboratory director, college and laboratory number and other basic information as the basic information for the account and subsequent construction of the database. The manual entry of basic information can ensure the accuracy and reliability of key information. During the use of the instrument sharing platform, instrument reservations need to fill in the user's name, date of use, duration of use, number of samples, presence or absence of consumables, contact information, laboratory number and college and other information. These reservation information is generated by the instrument sharing platform and passed to the next statistical analysis module, which greatly reduces the workload of manual entry and improves data processing efficiency.

[0096] At the same time, the database module has built a basic database based on the basic information data using the document-based database model MongoDB. The MongoDB database model is suitable for big data and high-speed read and write operations. The information statistics unit of the statistical analysis module categorizes the instrument reservation information report and generates statistical results for the same instrument, the same laboratory, and the same person, namely, the number of times the instrument is used by researchers within the preset time, the proportion of the number of times a single researcher uses the instrument in the total number of times the instrument is used within the preset time, the number and proportion of the number of times the team or unit to which the researcher belongs to reserve the use of the instrument, and the proportion of the number of times the instrument is reserved and used on the instrument sharing platform in the total number of times the instrument is used within the preset time. These statistical results can help instrument users plan the instrument reservation plan within the next preset time, help the unit to which the instrument belongs to optimize the allocation of instrument resources, and improve the efficiency of the use of instrument assets.

[0097] After that, the system will pass the statistical results to the screening and error correction module. The missing value repair unit of the screening and error correction module uses the ismissing function to screen and mark the missing values ​​in the instrument usage information. The code example is as follows:

[0098] Use the ismissing function to detect missing values

[0099] data = [12NaN 45];

[0100] missing_values=missing(data);

[0101] The confirmed information is supplemented and repaired according to the data in the basic database. For example, if the user misses to fill in the contact information or unit code in the reservation information, the missing value repair unit can repair and correct the missing information according to the relatively clear confirmed information such as the unit and contact information corresponding to the user's name in the basic database. However, if the reservation information is missing relatively vague and accidental information such as the usage duration and usage time, the missing value repair unit will mark it to remind the system administrator to manually repair it.

[0102] The same is true for the outlier repair unit. The interquartile range method and the isolation forest machine learning method are used to learn and screen the instrument usage information. The interquartile range method code example is as follows:

[0103] Use the iqr function to calculate the interquartile range

[0104] data = [1234510];

[0105] iqr_value = iqr(data);

[0106] Mark values ​​that exceed 1.5 times the interquartile range as outliers

[0107] threshold=1.5*iqr_value;

[0108] outliers=data>(max(data)-threshold);

[0109] The code example of the isolation forest machine learning method is as follows:

[0110] Use the IsolationForest function to create an isolation forest model

[0111] data = [1234510];

[0112] model=IsolationForest.fit(data);

[0113] Predicting outliers in data

[0114] outliers=predict(model,data);

[0115] After an abnormal value is detected, the confirmed information is supplemented and repaired and error correction prompts are given according to the data in the basic database. For example, if the user's contact information is only 10 digits long, the abnormal value repair unit can identify the abnormal information and use the corresponding confirmed information in the basic database to repair and mark the instrument usage information, eliminate statistical deviations, and make the statistical results more reliable.

[0116] When the screening and error correction module encounters a situation where vague and accidental information such as usage duration and usage time is missing in the reservation information, and the missing value repair unit and the outlier repair unit are difficult to repair using technical means, the screening and error correction module will use a variety of special table background colors to mark the statistical report table columns with different errors and provide error type prompts. For example, when someone uses the instrument sharing platform to reserve an instrument but forgets to fill in the usage time, the missing data is difficult to supplement and repair based on the basic database data. In the final statistical report table, this column will be marked with a pink background color, and the "missing usage time" will be described in the error correction prompt; when a member of a new team uses the instrument sharing platform for the first time When there is a new user in the basic database, the screening and error correction module will mark this column with a gold background color because there is no relevant data in the basic database, and describe "new user" in the error correction prompt to remind the staff to manually verify and check the basic information data of the new user; when more than 3 error entries are exactly the same in the statistical table of the appointment information of the instrument sharing platform, it may be that there is an error in the basic information data. These error entries will not be repaired in the statistical report and will be marked in red. The error correction prompt will describe "the error entries are consistent" to remind the staff to manually verify and modify the data in the basic database, so as to improve the self-evolution and correction function of the basic database.

[0117] The information on the instrument usage after the screening and error correction is transmitted back to the database optimization unit of the database module to increase the total data size of the database. At the same time, the database optimization unit also performs index optimization and query optimization on the existing data. The index optimization uses Boolean index and cache technology to optimize the database index performance. Boolean index refers to the use of logical operators (==, >, <, etc.) to create Boolean indexes, which can quickly select array elements that meet specific conditions. For example, A(A>5) returns the Boolean index of elements in array A that are greater than 5. The code example is as follows:

[0118] Boolean Indexing

[0119] D=A>0.5;

[0120] E = A(D);

[0121] Caching technology refers to storing frequently accessed array elements in cache, which can reduce memory access time, such as using persistent variables or memory functions to cache data.

[0122] Query optimization uses efficient query statements and configures database connection pools to improve database performance. Efficient query statements refer to avoiding multiple uses of the SELECT command and using more WHERE and JOIN query commands. This is because using the SELECT command will retrieve all columns in the table, which is usually unnecessary and will increase the amount of data transmission. Using the WHERE and JOIN clauses can filter out unnecessary data and reduce the size of the result set. The WHERE clause code example is as follows:

[0123] sqlquery="SELECT employee_id,name FROM employees WHERE department="Sales"';

[0124] The following is a sample JOIN clause code:

[0125] sqlquery=['SELECT e.name,d.department_name'...

[0126] 'FROM employees e'...

[0127] 'JOIN departments d ON e.department_id=d.department_id'];

[0128] Configuring a database connection pool means connecting a group of databases to each other. Applications can reuse these connections without having to re-establish a connection for each database operation. The database optimization unit can also back up and restore existing database data.

[0129] The instrument usage information that has completed screening and error correction will also be transmitted back to the account analysis unit of the statistical analysis module, and the financial account information that users need to pay for using shared instruments will be analyzed. The instrument usage information and financial account information that have completed screening and error correction will be transmitted to the statistical reporting unit of the reporting module to form corresponding statistical reports and financial account reports for reporting. At the same time, these reports will also be uploaded to the instrument sharing platform so that users can search and view the instrument usage information items and financial accounts related to themselves in the platform query unit, which greatly improves user convenience and enhances the user stickiness of the platform.

[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0131] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An intelligent assistance system based on an instrument sharing platform, characterized in that: It includes information collection module, database module, statistical analysis module, screening and error correction module and report module; Information collection module, used to collect basic information of instrument sharing platform users and instrument usage information; The database module is used to establish a basic database based on the basic user information collected by the information collection module, and to manage and optimize the basic database information; The statistical analysis module is used to classify and analyze the instrument usage information and the fund account information required for the instrument use according to the basic database of the database module and the instrument use information collected by the information collection module; A screening and error correction module is used to screen and correct missing and abnormal information of the instrument usage information according to the basic database of the database module; The reporting module is used to generate reports based on the instrument usage information and instrument usage funding information for completed screening and error correction, and provide retrieval queries within the instrument sharing platform.

2. The intelligent assistance system based on the instrument sharing platform according to claim 1 is characterized in that: The information collection module includes a basic information input unit and a report reading unit. The basic information input unit is used to input basic information such as personnel, team affiliation, funding account number and contact information of the instrument user; The report reading unit is used to read the instrument reservation usage information report from the instrument sharing platform within a preset time.

3. The intelligent assistance system based on the instrument sharing platform according to claim 2 is characterized in that: The database module includes a basic database and a database optimization unit. The basic database is used to construct a non-relational database based on the basic information of users entered into the basic information entry unit, and to provide data support for subsequent statistical analysis modules and screening and error correction modules. The database optimization unit is used to add the screened and error-corrected data into the basic database to increase the data scale, perform index optimization and query optimization on the existing basic database, and back up and restore the existing database data.

4. The intelligent assistance system based on the instrument sharing platform according to claim 3 is characterized in that: The statistical analysis module includes an information statistics unit and an account analysis unit. The information statistics unit is used to count the usage of the instrument and user information within a preset time; The account analysis unit is used to analyze the expense account information that the user needs to pay based on the usage information of the instrument that has completed the screening and error correction and the preset instrument usage fee information.

5. The intelligent assistance system based on the instrument sharing platform according to claim 4 is characterized in that: The screening and error correction module includes a missing value repair unit and an abnormal value repair unit. The missing value repair unit is used to use an algorithm to screen and mark missing values ​​in the instrument usage information, and to supplement and repair the confirmed information according to the data in the basic database; An outlier repair unit is used to use algorithms and machine learning methods to learn and screen outliers in instrument usage information and mark them, and to supplement and repair the determined information based on the data in the basic database.

6. The intelligent assistance system based on the instrument sharing platform according to claim 5 is characterized in that: The reporting module includes a statistical reporting unit and a platform query unit. The statistical reporting unit is used to generate a report based on the usage information of the instruments that have completed the screening and error correction and the screening results, and to generate a report based on the expense account information obtained by analyzing the usage information of the instruments that have completed the screening and error correction; The platform query unit is used to transmit the instrument usage information and financial account information to the instrument sharing platform for users to retrieve and view their own relevant information items.

7. The intelligent assistance system based on the instrument sharing platform according to claim 3 is characterized in that: Index optimization is used to improve the index performance of the database using Boolean indexing and caching techniques.

8. The intelligent assistance system based on the instrument sharing platform according to claim 3 is characterized in that: Query optimization is used to improve database performance by using efficient query statements and configuring database connection pools.

9. The intelligent assistance system based on the instrument sharing platform according to claim 5, characterized in that: The missing value repair unit uses the ismissing function to screen for missing values.

10. The intelligent assistance system based on the instrument sharing platform according to claim 5, characterized in that: The outlier repair unit uses the interquartile range method and isolation forest machine learning method to learn and screen outliers.

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