An experimental data management system based on a network platform

Abstract

The present invention provides an experimental data management system based on a network platform. Belonging to the technical field of data management, the management method includes an experimental template manager and a data manager; the experimental template manager includes a project name and a research and development experimental template; the data manager includes experimental data and project personnel statistics; through data induction and integration by the network platform, the experimental data is visualized and classified. Through the network platform, the experimental data can be collected, stored, and processed in real time, avoiding the data lag and possible errors in the summary process in the traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to an experimental data management system based on a network platform. Background Art

[0002] Data-based management is a new management mode gradually formed during the process that domestic enterprises extensively learned and applied advanced management modes such as refined management, quality system certification, production methods, JIT, and performance management since the reform and opening up. At the same time, during the process of talent flow, it is also a management mode formed by the extensive exchange of information between industries and the fact that ordinary enterprises fully utilize the management methods based on data information in modern financial enterprises. Data-based management is the deepening of traditional ledger-style management, such as managers' notes, and it evolved with the development and popularization of computer technology and the development of industries such as finance and accounting that use data as the operation benchmark. Therefore, data-based management is not just about financial ledgers, and many enterprises in multiple industries are beginning to use data to monitor the business development status and guide the development of management work. Summary of the Invention

[0003] The present invention proposes an experimental data management system based on a network platform to solve the problems of data storage, transmission, and management. The technical solutions adopted are as follows:

[0004] An experimental data management system based on a network platform, the experimental data management system includes an input device 1, a database management device 3, and a system main station 4; wherein, the signal output end of the input device 1 is connected to the signal input end of the database sorting device 3, and the signal output end of the database sorting device 3 is connected to the signal input end of the system main station 4; data enters from the input device 1, passes through the database management device 3, and finally flows to the system main station 4.

[0005] Preferably, the database management system 3 further includes a communication device 7 for data communication; the signal output end of the communication device 7 is connected to the signal input end of the database management device 3.

[0006] Preferably, the database management system further includes a data storage device 2 for storing data; the signal input end of the data storage device 2 is connected to the signal output end of the input device 1, and the signal output end of the data storage device 2 is connected to the signal input end of the database management device 3.

[0007] Preferably, the database management system further includes a data retrieval device 5 for retrieval; the signal input end of the data retrieval device 5 is connected to the signal output end of the database management device 2.

[0008] Preferably, the database management system further includes a data statistics device 6 for counting data; the signal input end of the data statistics device 6 is connected to the signal output end of the database management device 2.

[0009] Preferably, the database management system further includes a cloud storage device 8 for storing data uploaded to the main station; the database management system further includes a cloud storage device 8 for storing data uploaded to the main station; the signal output end of the cloud storage device 8 is connected to the signal input end of the system main station 3.

[0010] Preferably, the database management system further includes a control device 9 for controlling data storage into and data reading from the cloud storage device; the signal output end of the control device 9 is connected to the signal input end of the cloud storage device 8.

[0011] Preferably, after receiving the experimental data, the data storage device 2 classifies the data according to the characteristics of the data, and then the data statistics device 6 performs experimental data analysis and obtains intuitive charts, including:

[0012] Step 1: The data storage device 2 receives the experimental data from the input device 1 and uses it as the first data.

[0013] Step 2: The data storage device 2 establishes databases according to the types of data based on the first information, including structured databases, semi-structured databases, and unstructured databases; alternatively, databases can be established according to the data sources, including enterprise internal databases, cloud platform databases, and network databases, and use them as the second data.

[0014] Step 3: The data statistics device 6 counts the data from the second data according to user requirements and obtains the discrete characteristics of the experimental data. The formula for the discrete characteristics of the experimental data is as follows:

[0015]

[0016] Wherein, R(t) represents the experimental data of the current month, U(t) represents the experimental data of other months, N1 represents the total number of months in the year when the experimental data is generated in the current month, N2 represents the total number of months in the year when the experimental data is generated in other months, and K represents the degree of change in the discrete degree of the experimental data generated in the current month phase compared to the average data generated in the year of the current month and the discrete degree of the experimental data generated in other months compared to the average data in the year of other months.

[0017] Step 4: The data statistics device 6 makes a chart based on the calculated data discreteness.

[0018] Preferably, the data transmission rate between the devices of the experimental data management system can be dynamically adjusted to keep the data transmission speed stable, including:

[0019] First step: The experimental data management system obtains the data transmission rate entering from the input device and uses it as the initial speed information; the experimental data management system obtains the initial speed through the formula as follows:

[0020]

[0021] Where Q represents the data transmission rate, B represents the maximum data transmission capacity of the transmission medium, usually measured in gigabits per second, CE represents the effective data volume that the coding algorithm can transmit under a given bandwidth, α represents a noise level constant, usually with a value range of 20 - 50 decibels, D represents the straight-line distance in space that the data passes from the sending point to the receiving point, M represents the degree of influence of the modulation signal on the amplitude of the modulation wave, when the value of M increases, the influence of the modulation signal on the carrier amplitude increases, T 1 represents the transmission delay, T 2 represents the total time theoretically required for data transmission, where the calculation method of the modulation coefficient M is as follows:

[0022]

[0023] Where Amax represents the maximum amplitude of the modulation wave, and Amin represents the minimum amplitude of the modulation wave;

[0024] Second step: The experimental data management device evaluates whether there is a problem with the noise.

[0025] When the noise exceeds 50 decibels, the experimental data management device issues an alarm to remind the user.

[0026] When the noise does not exceed 50 decibels, the experimental data management device will proceed to the next step;

[0027] Third step: The experimental data management device evaluates whether there is a problem with the transmission medium.

[0028] When there is a problem with the transmission medium, the experimental data management device issues an alarm to remind the user.

[0029] When there is no problem with the transmission medium, the experimental data management system will proceed to the next step;

[0030] Fourth step: When the experimental data management device evaluates that there is no problem with the transmission medium and the noise level does not exceed 50 decibels, the experimental data management device will increase the TCP window size or control the rapid opening of the TCP window, and reduce the data transmission delay;

[0031] Step 5: When the data transmission speed fluctuates, repeat Step 1 to Step 4 to ensure that the data transmission speed remains stable.

[0032] Advantages of the present invention: The present invention provides an experimental data management system for a network platform. Through the network platform, experimental data can be collected, stored, and processed in real time, avoiding the data lag and possible errors in the summarization process in traditional methods. The application of technologies such as cloud computing and distributed storage ensures the reliability and security of data storage, while improving the speed and efficiency of data processing. The network platform enables researchers in different laboratories, research teams, and even different regions to share experimental data, promoting scientific research collaboration and communication. By establishing a unified data access control mechanism and data sharing platform, the security and consistency of data are ensured. The experimental data on the network platform can be deeply mined through various data analysis tools and methods to discover the laws and trends in the data, providing strong support for scientific research decision-making. Using advanced technologies such as machine learning and artificial intelligence can further improve the accuracy and efficiency of data analysis. By recording environmental factors (such as temperature, humidity, etc.) during the experimental process through the network platform, it can help scientific researchers better analyze experimental results and optimize experimental conditions. Networked management can achieve dynamic management of experimental equipment, timely reflect the usage and maintenance requirements of the equipment, and improve the utilization rate and reliability of experimental equipment. The experimental data on the network platform has a clear recording method and a unified naming and numbering rule, avoiding the problems of data confusion and omission. Data cleaning, integration, and visualization and other processing means make the data results more intuitive and easy to understand, enhancing the interpretability and traceability of the data. Brief Description of the Drawings

[0033] Figure 1 It is a diagram of the experimental data management system described in the present invention;

[0034] Figure 2 It is a diagram of the database management device described in the present invention;

[0035] Figure 3 It is a diagram of the main station of the system described in the present invention. Detailed Embodiments

[0036] The following is a description of the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0037] The present invention provides an experimental data management system based on a network platform, characterized in that the experimental data management system includes an input device 1, a database management device 3, and a system main station 4; wherein, the signal output end of the input device 1 is connected to the signal input end of the database sorting device 3, and the signal output end of the database sorting device 3 is connected to the signal input end of the system main station 4; data enters from the input device 1, passes through the database management device 3, and finally flows to the system main station 4.

[0038] The working principle and effects of the above technical solution are as follows: The experimental data management system collects experimental data through user input or an automatic interface, supports the import of multiple data formats, such as Excel, CSV, etc., facilitating users to quickly import existing experimental data. Using cloud computing technology, the experimental data is saved to the cloud in real time to ensure the security and reliability of the data, and supports the storage of multiple versions of experimental data. Users can view and restore historical versions of experimental data at any time. It provides a data definition language (DDL) and a data manipulation language (DML) to facilitate users to define and modify the database structure, as well as query and update data. It supports database transaction processing to ensure data consistency and integrity. Adopting database indexing technology to improve data query efficiency, the system can automatically perform statistical analysis on users' experimental data, generate visual data analysis results, and provide various data analysis models, such as trend analysis, correlation analysis, variance analysis, etc., to meet different data analysis needs of users. Through automated and centralized data management, manual operations and errors are significantly reduced, and the management efficiency of experimental data is improved. Real-time data saving and multi-version management ensure data accuracy, reduce the risk of data loss and duplicate experiments. Data sharing and collaboration functions contribute to the communication and cooperation among researchers, accelerating the progress of scientific research. Cloud computing technology and data backup strategies ensure the security and integrity of experimental data.

[0039] In an embodiment of the present invention, the database management system 3 further includes a communication device 7 for data communication; the signal output end of the communication device 7 is connected to the signal input end of the database management device 3.

[0040] The working principle and effects of the above technical solution are as follows: At the sending end of the communication device, the data is processed through encoding and modulation and converted into a signal suitable for transmission on the transmission medium. The signal is transmitted through these media to the receiving end. At the receiving end, the signal is processed through demodulation and decoding and restored to the original data. The communication device greatly improves the data transfer efficiency and has strong anti-interference ability.

[0041] In an embodiment of the present invention, the database management system further includes a data storage device 2 for storing data; a signal input end of the data storage device 2 is connected to a signal output end of the input device 1, and a signal output end of the data storage device 2 is connected to a signal input end of the database management device 3.

[0042] The working principle and effect of the above technical solution are as follows: The data storage device relies on cloud storage to save data. When data needs to be stored, the computer converts the data into binary code and stores the data on the medium through the writing mechanism of the storage medium. When accessing the stored data, the computer retrieves the data through the reading mechanism of the storage medium. The data storage device can store a large amount of data, and based on the cloud network, it greatly improves the network access speed. The cloud storage medium also provides higher reliability and durability. At the same time, the stored data can be shared and accessed anytime and anywhere.

[0043] In an embodiment of the present invention, the database management system further includes a data retrieval device 5 for retrieval; a signal input end of the data retrieval device 5 is connected to a signal output end of the database management device 2.

[0044] The working principle and effects of the above technical solution are as follows: When a user needs to perform data retrieval, a retrieval request will be sent to the data retrieval device. This request usually contains information such as keywords and retrieval conditions. The data retrieval device will receive and analyze the keywords input by the user, and construct a retrieval formula based on the semantic and logical relationships of the keywords. In this process, operations such as keyword expansion, abbreviation, and synonym replacement may be involved to improve the accuracy and efficiency of retrieval. According to the constructed retrieval formula, the data retrieval device will search in the database. This usually involves operations such as traversing, matching, and filtering the data in the database. Modern data retrieval devices may adopt efficient indexing techniques and algorithms to speed up the search and improve the retrieval efficiency. After retrieving the matching data, the data retrieval device will sort the results according to certain rules (such as relevance, time, importance, etc.). Then, the sorted results will be presented to the user in a user-friendly manner (such as a list, chart, etc.). In addition to basic keyword retrieval, the data retrieval device may also provide a series of advanced retrieval functions, such as fuzzy retrieval, truncation retrieval, proximity operator retrieval, etc. These functions can help users more accurately locate and obtain the required information; the data retrieval device can achieve a quick response after the user submits a query. The data retrieval device can process large-scale data sets and return accurate results in a short time. The data retrieval device can accurately match the keywords and query conditions input by the user and return results highly relevant to the user's needs. This is measured by calculating the matching degree between the results returned by the system and the user's true needs. Common accuracy evaluation methods include precision. When transmitting and storing data, the data retrieval device will adopt encryption technology to protect the security of user data.

[0045] In an embodiment of the present invention, the database management system further includes a data statistics device 6 for statistics; the signal input end of the data statistics device 6 is connected to the signal output end of the database management device 2.

[0046] The working principle and effects of the above technical solution are as follows: The data statistics device first collects data from the data source. Next, the device will clean and preprocess the collected data. This includes handling missing values, outliers, and adjusting data formats, etc., aiming to eliminate noise and errors in the data and ensure the quality of the data. After the data is ready, the data statistics device will perform descriptive statistical analysis, analyzing relevant characteristics such as the dispersion degree and distribution of the data through mathematical operations. Based on the results of descriptive statistics, the device can perform inferential statistical analysis. These analyses include hypothesis testing, confidence interval estimation, regression analysis, analysis of variance, etc. Inferential statistical analysis helps draw conclusions about the population from sample data and verify research hypotheses. Finally, the data statistics device will interpret and report the results of statistical analysis. This involves interpreting statistical indicators, answering research questions, providing conclusions and suggestions. The result interpretation and report should be transparent, accurate, and understandable so that users can correctly understand and apply the results of statistical analysis.

[0047] In an embodiment of the present invention, the database management system further includes a cloud storage device 8 for storing data uploaded to the main station; the database management system further includes a cloud storage device 8 for storing data uploaded to the main station; the signal output end of the cloud storage device 8 is connected to the signal input end of the system main station 3.

[0048] The working principle and effects of the above technical solution are as follows: The cloud storage device automatically splits a huge computing and processing program into countless smaller sub-programs through the network, and then after calculation and analysis by a huge system composed of multiple servers, the processing results are sent back to the user for operation on the cloud page.

[0049] R & D project management R & D project Management project R & D test record More functions Set test template

[0050] , experimental records can be added and uploaded to the cloud in this way. The cloud storage device solves the problem of wasted storage space through virtualization technology, can automatically reallocate data, improves the utilization rate of storage space, and the cloud storage has load balancing and fault redundancy functions, ensuring the reliability and high availability of data. Moreover, it can achieve economies of scale and elastic expansion, reduce operating costs, and avoid resource waste. The cloud storage can accommodate more metadata and provide excellent custom control for data of specific business and system functions. The cloud storage uses a distributed storage system and a redundancy mechanism to ensure the high availability of data. Even if a server fails, the data is still available. It provides an authentication scheme based on public and private keys and powerful ACL permission control, can adapt to flexible business requirements and ensure data security. Cloud storage providers usually set up multiple data centers globally, and users can choose the data center closest to their location for storage to obtain a low-latency and fast data access experience. Users can conveniently access and manage the stored data at any time and place through the public Internet or a private network.

[0051] In an embodiment of the present invention, the database management system further includes a control device 9 for controlling data storage to the cloud storage device 8 and reading data from the cloud storage device 8; the signal output end of the control device 9 is connected to the signal input end of the cloud storage device 8.

[0052] The working principle and effects of the above technical solution are as follows: The user uploads data to the server of cloud storage through methods such as API, web page, or client. During the upload process, the data may be encoded and compressed to reduce the file size and optimize the transmission efficiency. The control device is responsible for receiving the data uploaded by the user and ensuring the integrity and security of the data. The cloud storage server stores the received data in its storage devices, which usually adopt distributed storage technology to store the data on multiple physical devices to improve the reliability and availability of the data. The control device is responsible for managing these storage devices to ensure the correct storage and backup of the data. The user can manage the data stored on the cloud storage server through methods such as API, web page, or client, such as operations like uploading, downloading, deleting, and renaming. These operations are all processed and coordinated by the control device. The user can access the data stored on the cloud storage server through methods such as API, web page, or client to perform operations such as online preview, downloading, and sharing. The control device is responsible for verifying the identity and permissions of the user to ensure that only authorized users can access the data. The control device is also responsible for processing data sharing requests to ensure the security and privacy protection during the sharing process. The cloud storage server guarantees the security of the data through access control, encryption, backup, etc. The control device is responsible for implementing these security measures to ensure the security of the data during storage, transmission, and use. The access control function ensures that only authorized users can access the data. Encryption technology is used to protect the confidentiality of the data during transmission and storage. The backup mechanism is used to prevent data loss and damage. The control device is responsible for monitoring the operating status and performance of the cloud storage system, including the status of storage devices, the usage of network bandwidth, etc. If the system has abnormalities or performance bottlenecks, the control device can perform automatic or manual fault recovery or resource expansion to ensure the stability and reliability of the system. The control device can also be extended according to the needs of the user to provide a larger storage capacity and higher performance to meet different business requirements. Cloud storage uses virtualization technology to improve the utilization rate of storage resources. The control device is responsible for integrating the storage resources on multiple servers to form a unified storage resource pool for users to use on demand. Through virtualization technology, the control device can achieve dynamic allocation and management of storage resources, improving the flexibility and scalability of the system.

[0053] An embodiment of the present invention is characterized in that after receiving the experimental data, the data storage device 2 classifies the data according to the characteristics of the data, and then the data statistics device 6 performs experimental data analysis and obtains an intuitive chart, including:

[0054] Step 1: The data storage device 2 receives the experimental data from the input device 1 and uses it as the first data;

[0055] Step 2: The data storage device 2 establishes databases according to the first information, including a structured database, a semi-structured database, and an unstructured database, based on the types of data; or it can also establish databases according to the data sources, including an enterprise internal database, a cloud platform database, and a network database, and use them as the second data;

[0056] Step 3: The data statistics device 6 statistically analyzes the data from the second data according to user requirements and obtains the discrete characteristics of the experimental data. The formula for the discrete characteristics of the experimental data is as follows:

[0057]

[0058] Among them, R(t) represents the experimental data of the current month, U(t) represents the experimental data of other months, N1 represents the total number of months in the year when the experimental data is generated in the current month, N2 represents the total number of months in the year when the experimental data is generated in other months, and K represents the degree of change in the discrete degree between the experimental data generated in the current month phase and the average data generated in the year when the current month is located compared to the discrete degree between the experimental data generated in other months and the average data in the year when other months are located;

[0059] Step 4: The data statistics device 6 makes a chart based on the calculated data discreteness.

[0060] The working principle and effects of the above technical solution are as follows: Select appropriate data sources to ensure the representativeness and reliability of the data. This may include experimental records, financial statements, historical data, etc. According to the research objectives, select representative samples and use methods such as random sampling and systematic sampling to reduce errors. Process and organize the collected original data, including data cleaning, data conversion, etc. The organized data can be visually displayed through statistical tables, statistical charts, etc. for further analysis. Use mathematical operations and statistical methods to deeply analyze the organized data. This may include calculating descriptive statistics such as the mean, median, and mode, and using methods such as time series analysis, regression analysis, and correlation analysis to explore the changing patterns of experimental costs. In particular, time series analysis can reveal the long-term trend changes, seasonal changes, cyclic changes, and irregular changes of experimental costs over a period of time. According to the results of data analysis, interpret and infer the changing patterns of experimental costs. This may include identifying key factors affecting experimental costs, predicting future trends of experimental costs, and assessing potential risks of experimental cost changes. The results of interpretation and inference can provide a scientific basis for decision-makers to help them formulate more effective experimental budgets and cost control strategies. Present the analysis results in the form of reports, charts, etc. to relevant personnel for their understanding and application. Decision-makers can adjust experimental strategies, optimize resource allocation, reduce experimental costs, etc. according to the analysis results.

[0061] An embodiment of the present invention is characterized in that the data transmission rate between devices of the experimental data management system can be dynamically adjusted to keep the data transmission speed stable, including:

[0062] First step: The experimental data management system obtains the data transmission rate entering from the input device and uses it as the initial speed information; the experimental data management system obtains the initial speed through the formula as follows:

[0063]

[0064] Where Q represents the data transmission rate, B represents the maximum data transmission capacity of the transmission medium, usually measured in gigabits per second, CE represents the effective data volume that the coding algorithm can transmit under a given bandwidth, α represents a noise level constant, usually with a value range of 20 - 50 decibels, D represents the straight-line distance in space that the data passes from the sending point to the receiving point, M represents the influence degree of the modulation signal on the amplitude of the modulation wave, when the value of M increases, the influence of the modulation signal on the carrier amplitude enhances, T 1 represents the transmission delay, T 2 represents the total time theoretically required for data transmission, where the calculation method of the modulation coefficient M is as follows:

[0065]

[0066] Where Amax represents the maximum amplitude of the modulation wave, and Amin represents the minimum amplitude of the modulation wave;

[0067] Second step: The experimental data management device evaluates whether there is a problem with the noise.

[0068] When the noise exceeds 50 decibels, the experimental data management device issues an alarm to remind the user.

[0069] When the noise does not exceed 50 decibels, the experimental data management device will proceed to the next step;

[0070] Third step: The experimental data management device evaluates whether there is a problem with the transmission medium.

[0071] When there is a problem with the transmission medium, the experimental data management device issues an alarm to remind the user.

[0072] When there is no problem with the transmission medium, the experimental data management system will proceed to the next step;

[0073] Fourth step: When the experimental data management device evaluates that there is no problem with the transmission medium and the noise level does not exceed 50 decibels, the experimental data management device will increase the TCP window size or control the TCP window to open quickly, and reduce the data transmission delay;

[0074] Step 5: After the data transfer speed fluctuates, repeat Steps 1 to 4 to ensure that the data transfer speed remains stable.

[0075] The working principle and effect of the above technical solution are as follows: When the experimental data management device detects fluctuations in the data, it will evaluate the transmission medium, and based on the values of various parameter indicators of the transmission medium, determine whether there is a problem with the transmission medium. The judgment factors include the average broadband utilization rate, transmission delay, and signal-to-noise ratio.

[0076] Among them, the average broadband utilization rate represents the average ratio of the actually used bandwidth to the total bandwidth within a certain period of time. Its calculation formula is:

[0077]

[0078] Among them, H represents the average broadband utilization rate, Xi represents the amount of broadband used in a single unit of time, with the unit of gigabits per second, D represents the sum of multiple units of time, that is, the total time of using broadband, and Y represents the total broadband amount;

[0079] The signal-to-noise ratio represents the ratio of the signal to the noise. It is an important indicator to measure the signal quality. Its formula is as follows:

[0080]

[0081] Among them, SNR represents the signal-to-noise ratio, P1 represents the transmission power of the signal, and P2 represents the frequency of the noise;

[0082]

[0083] Among them, G represents the index value when the experimental data management device evaluates the transmission medium.

[0084] When the index value of G > 238.77, the experimental data management device issues an alarm to prompt the user.

[0085] When the index value of G < 238.77, the experimental data management device proceeds to Step 3.

[0086] The data transmission rate mainly depends on the optimization and improvement in multiple aspects. The increase in network bandwidth is the key to enhancing the data transmission rate. A higher bandwidth means that more data can be transmitted within the same time. By reducing network interruptions and failures to ensure the continuity and efficiency of data transmission, the application of new coding technologies such as compression algorithms and error correction codes can reduce the volume of data and the transmission error rate, thereby improving the transmission speed and reliability. Optimizing and organizing the data to remove redundant and invalid information can also improve the efficiency and speed of data transmission. By means of reasonable network planning and traffic control mechanisms to reduce network congestion, the data transmission speed can be increased; a high data transmission rate means that data can be transmitted within a shorter time, thus improving the overall communication efficiency. In applications that require quick response or real-time interaction, such as online games, video conferencing, and real-time stock trading, a high transmission rate can ensure that users obtain an instant and smooth experience. For the transmission of large files or data sets, a high data transmission rate can significantly reduce the time required for transmission, thereby improving work efficiency. A high data transmission rate can utilize network bandwidth resources more effectively and reduce bandwidth waste. In the case of limited network bandwidth, a high transmission rate can ensure that multiple users or applications can obtain sufficient bandwidth resources simultaneously, thus avoiding network congestion and performance degradation. A high data transmission rate can support the rapid processing and analysis of real-time data. In applications such as big data analysis, the Internet of Things, and cloud computing, a high transmission rate can ensure the rapid transmission and processing of real-time data, thereby supporting more efficient decision-making and response. For remote work and team collaboration, a high data transmission rate can ensure the rapid sharing and transmission of files, data, and information. This can significantly improve the efficiency and convenience of remote work, and promote team collaboration and communication. A high data transmission rate can support more concurrent connections and concurrent data transmissions. In applications that need to handle a large number of concurrent requests, such as online game servers, cloud computing platforms, and social media platforms, a high transmission rate can ensure that each user can obtain sufficient bandwidth resources, thus maintaining the stability and availability of the service.

[0087] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. An experimental data management system based on a network platform, characterized in that: The experimental data management system comprises an input device (1), a database management device (3) and a system main station (4); wherein the signal output end of the input device (1) is connected to the signal input end of the database arrangement device (3), and the signal output end of the database arrangement device (3) is connected to the signal input end of the system main station (4); data enters from the input device (1) through the database management device (3) and finally flows to the system main station (4), and the data transmission rate between the various devices of the experimental data management system can be dynamically adjusted to keep the data transmission speed stable, including: Step 1: The experimental data management system obtains the data transmission rate from the input device and uses it as initial speed information; the experimental data management system obtains the initial speed through a formula, which is as follows: Among them, Q represents the data transmission rate, B represents the maximum data transmission capacity of the transmission medium, usually measured in gigabits per second, CE represents the effective amount of data that the coding algorithm can transmit under a given bandwidth, α represents a noise level constant, usually in the range of 20-50 decibels, D represents the straight-line distance in space that the data passes from the sending point to the receiving point, M represents the influence of the modulation signal on the amplitude of the modulation wave, when the value of M increases, the influence of the modulation signal on the carrier amplitude increases, T1 represents the transmission delay, T2 represents the total time required for data transmission in theory, and the modulation coefficient M is calculated as follows: Among them, Amax represents the maximum amplitude of the modulation wave, and Amin represents the minimum amplitude of the modulation wave; Step 2: The experimental data management device evaluates whether there is a problem with noise, When the noise exceeds 50 decibels, the experimental data management device will sound an alarm to remind the user. When the noise does not exceed 50 and a half minutes, the experimental data management device will proceed to the next step; Step 3: The experimental data management device evaluates whether there is a problem with the transmission medium. When there is a problem with the transmission medium, the experimental data management device sends out an alarm to remind the user. When there is no problem with the transmission medium, the experimental data management system will proceed to the next step; Step 4: When the experimental data management device evaluates that there is no problem with the transmission medium and the noise level does not exceed 50 decibels, the experimental data management device will increase the TCP window size or control the TCP window to open quickly, and reduce the data transmission delay; Step 5: When the data transmission speed fluctuates, repeat steps 1 to 4 to ensure that the data transmission speed remains stable.

2. The experimental data management system according to claim 1, characterized in that: The database management system (3) also includes a communication device (7) for data communication; the signal output end of the communication device (7) is connected to the signal input end of the database management device (3).

3. The experimental data management system according to claim 1, characterized in that: The database management system further comprises a data storage device (2) for storing data; the signal input end of the data storage device (2) is connected to the signal output end of the input device (1), and the signal output end of the data storage device (2) is connected to the signal input end of the database management device (3).

4. The experimental data management system according to claim 3, characterized in that: After receiving the experimental data, the data storage device (2) classifies the data according to the characteristics of the data, and then the data statistics device (6) analyzes the experimental data and obtains intuitive charts, including: Step 1: The data storage device (2) receives the experimental data from the input device (1) and uses it as the first data; Step 2: The data storage device (2) establishes a database based on the first data and the type of data, including a structured database, a semi-structured database, and an unstructured database; it may also establish a database based on the source of the data, including an internal enterprise database, a cloud platform database, and a network database, and use them as the second data; Step 3: The data statistics device (6) collects the data from the second data according to the user's needs and obtains the discrete characteristics of the experimental data. The discrete characteristics formula of the experimental data is as follows: Among them, R(t) represents the experimental data of the current month, U(t) represents the experimental data of other months, N1 represents the total number of months in the year in which the current month generates experimental data, N2 represents the total number of months in the year in which other months generate experimental data, and K represents the degree of change in the degree of dispersion of the experimental data generated in the current month and the average data generated in the year in which the current month is located compared with the degree of dispersion of the experimental data generated in other months and the average data of the year in which other years are located; Step 4: The data statistics device (6) makes a chart based on the calculated data discreteness.

5. The experimental data management system according to claim 1, characterized in that: The database management system also includes a data retrieval device (5) for retrieval; the signal input end of the data retrieval device (5) is connected to the signal output end of the database management device (2).

6. The experimental data management system according to claim 1, characterized in that: The database management system further comprises a data statistics device (6) for statistical data; the signal input end of the data statistics device (6) is connected to the signal output end of the database management device (2).

7. The experimental data management system according to claim 1, characterized in that: The database management system also includes a cloud storage device (8) for storing data uploaded to the main station; the signal output end of the cloud storage device (8) is connected to the signal input end of the system main station (3).

8. The experimental data management system according to claim 7, characterized in that: The database management system also includes a control device (9) for controlling data storage in the cloud storage device and reading data from the cloud storage device; the signal output end of the control device (9) is connected to the signal input end of the cloud storage device (8).

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