Closed sampling system data management and analysis system

Abstract

The application discloses a closed sampling system data management and analysis system, and relates to the technical field of data transmission.The application collects target access data of an access user by input search data acquisition, acquires the target access data of the access user through a cloud server, monitors the transmission process of the target access data, periodically optimizes the target data packet in the transmission process, dynamically modifies the data capacity of the data carried in the target data packet in the next monitoring transmission cycle and the interval length of the next monitoring transmission cycle according to the transmission quality in the last monitoring transmission cycle, reduces the network resources occupied by the retransmission on one hand, and refines the data capacity of the target data packet on the other hand, dynamically adjusts the data capacity of the data carried in the lost target data packet, automatically calibrates the time sequence of the target data packet, improves the reliability of system data management, and greatly improves the data quality in the whole process of the closed sampling and the system operation stability.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to a data management and analysis system for a closed sampling system. Background Technology

[0002] In process industries such as chemical and pharmaceutical, the composition, purity, and physicochemical properties of materials directly determine product quality, production safety, and compliance. Closed sampling technology, as a professional sampling method that can avoid sample leakage, volatilization, or contact with the external environment, has been widely used in various high-risk and high-precision sampling scenarios. This technology completes the collection and transmission of samples through a closed-loop system, which can effectively ensure the safety of operators, prevent environmental pollution, and ensure the accuracy of sample analysis data.

[0003] With the large-scale application of closed sampling technology, the importance of various data generated during the sampling process has become increasingly prominent. These data are the core basis for product quality traceability, production process optimization, and safety risk prevention and control. Therefore, the efficient transmission, standardized management, and in-depth analysis of closed sampling data have become core requirements of the chemical and pharmaceutical industries, directly affecting the production efficiency and compliance level of enterprises.

[0004] However, during the transmission and management of closed-loop sampling data, data packets are prone to loss. Existing systems cannot dynamically adjust the segment size of closed-loop sampling data packets according to the real-time packet loss status. Fixed packet transmission significantly increases the retransmission load and wastes network resources when packet loss is high. Furthermore, the use of a fixed monitoring transmission cycle cannot dynamically adjust the cycle according to the packet loss distribution, resulting in an excessive amount of data lost in a single cycle.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] The purpose of this invention is to provide a data management and analysis system for a closed sampling system, in order to solve the problems mentioned in the background art.

[0007] This invention provides a data management and analysis system for a closed sampling system, comprising:

[0008] A client module is used to access closed sampling data from users. The client module includes an information acquisition unit. After the user enters search information, the information acquisition unit acquires the search information and simultaneously acquires the user's identity information. Based on the search information and identity information, the information acquisition unit generates the user's search index data.

[0009] The cloud server is used to retrieve relevant closed sampling data and generate target access data for each user after receiving the retrieval index data of the accessing user.

[0010] The cloud server is also used to transmit the target access data to the client module according to a preset transmission procedure;

[0011] The cloud server is also used to monitor and optimize the transmission process of all target data packets obtained by equally dividing the target access data, and optimize the transmission process of all remaining untransmitted target data packets within each monitoring transmission cycle based on the monitoring transmission cycle;

[0012] The cloud server is also used to calibrate the transmission timing of the target data packets every other monitoring transmission cycle, and select whether to sort all the remaining untransmitted target data packets based on the calibration result;

[0013] The data analysis module is used to store any target access data of any access user after its transmission is completed, and analyze it every time the number of stored target access data reaches a preset fixed amount, and update the corrected evaluation value stored in the cloud server based on the analysis result.

[0014] Further, the preset transmission procedure for transmitting the target access data is as follows:

[0015] S11: Obtain the data capacity size of the target access data of the access user, and mark it as K;

[0016] S12: Divide the target access data of the access user according to the data capacity size of the target access data to generate a number of target data packets. The specific division rules are as follows:

[0017] S121: If K < Kmin, equally divide the target access data of the access user into a number of target data packets, and the data capacity size of one target data packet is L1;

[0018] S122: If Kmin ≤ K ≤ Kmax, equally divide the target access data of the access user into a number of target data packets, and the data capacity size of one target data packet is L2;

[0019] S123: If K > Kmax, equally divide the target access data of the access user into a number of target data packets, and the data capacity size of one target data packet is L3;

[0020] The L1, L2, and L3 are preset division values, and the Kmin and Kmax are respectively the preset division standard critical minimum value and the division standard critical maximum value;

[0021] Based on the position of the data carried in each target data packet within the target user's target access data before it is divided equally, all target data packets obtained after the target access data is divided equally are transmitted sequentially from left to right, wherein the leftward position within the target access data, the earlier the transmission order.

[0022] Furthermore, the data analysis module performs the following analysis for any given time:

[0023] S41: Re-label all target access data whose storage capacity for this analysis is greater than P1 as data to be parsed, where P1 is a preset threshold.

[0024] S42: For any data to be parsed, determine the parsing period of the data to be parsed based on the time from the start of transmission to the end of transmission. Divide the parsing period into parsing segments of equal duration, and label the a parsing segments of the parsing period as A1, A2, ..., Aa, where a>1;

[0025] S43: Taking a parsing segment as an example, obtain the packet loss rate B1 and the average transmission rate C1 when transmitting the data to be parsed within a parsing segment of the parsing period; obtain the sum of the data capacity D1 of all data packets transmitted within a parsing segment of the parsing period;

[0026] S44: The process quality response number E1 of transmitting the data to be parsed within one parsing segment of the parsing cycle is calculated using the formula E1=α1×(1-B1)+α2×(C1 / Cmax)+η1×(D1 / Dtotal). Here, α1, α2, and η1 are preset coefficients, Cmax is the preset maximum allowable transmission rate of the transmission segment, and Dtotal is the data capacity of the data to be parsed.

[0027] S45: Calculate and obtain the process mass reaction numbers E1, E2, ..., Ea that transmit the data to be parsed within the a parsing segments of the parsing cycle according to S42 to S43;

[0028] S46: Use the discrete point filtering algorithm to process all the calculated process quality response numbers, and calculate the average value of all remaining process quality response numbers after data processing. Then, define the average value as the transmission quality number G1 of the parsed media data.

[0029] Get the total number of process quality response numbers that are greater than the transmission quality number G1 among all process quality response numbers deleted after data processing, and re-label them as abnormal quality numbers, marking them as G2;

[0030] S47: Calculate and obtain the process quality evaluation value H1 of transmitting the to-be-analyzed data within a parsing periods of the parsing period a by using the formula H1 = G1×(1 - G2 / a).

[0031] S48: Calculate and obtain the process quality evaluation values of all the to-be-analyzed data according to S42 to S47, perform data processing on all the calculated process quality evaluation values by using a discrete point filtering algorithm, calculate the average value of all the remaining process quality evaluation values after data processing, calibrate the average value as a corrected evaluation value, and mark it as J.

[0032] The data analysis module transmits the corrected evaluation value to a cloud server for updated storage.

[0033] Further, the content of optimizing the transmission process of all the remaining untransmitted target data packets within a monitoring transmission period based on the monitoring transmission period is as follows:

[0034] S21: Obtain the total number of lost target data packets within the monitoring transmission period, mark it as m. During the transmission process, the start time of the initial monitoring transmission period is the loss time of the first lost target data packet monitored during the transmission process, and the end time of the monitoring transmission period is the loss time + the monitoring delay duration Q1. The value of the monitoring delay duration Q1 within the initial monitoring transmission period is a preset delay duration threshold.

[0035] S22: Compare the magnitudes of m and a preset packet loss scalar threshold n1. If 0 ≤ m < n1, the monitoring optimization unit does not perform any processing; otherwise, obtain the time differences between the loss times of two adjacent lost target data packets within the monitoring transmission period, and mark them as M1, M2,..., Mm-1.

[0036] S23: Use the formula to calculate and obtain the discrete value Z1 of the time differences between the loss times of two adjacent lost target data packets within the monitoring transmission period, compare the magnitudes of Z1 and Z. If Z1 ≥ Z, delete the corresponding Mq values in descending order of |Mq - M| and calculate the discrete value Z1 of the remaining Mq values, and compare the magnitudes of Z1 and Z again until Z < Z1. Z is a preset time difference discrete threshold, and M is the average value of the target data packet loss time differences within the current monitoring transmission period participating in the discrete value calculation. When Z < Z1, calibrate the average value of all the remaining time differences within the monitoring transmission period participating in the calculation of Z1 as a time difference value, and mark it as N1.

[0037] S24: Calculate the segmentation correction rate R1 of the target data packet in the current monitoring transmission period using the formula R1=β1×(m / mMax)+λ1×(1-K1 / K), where β1 and λ1 are preset factors, mMax is the maximum allowable number of packet losses in the monitoring transmission period, and K1 is the sum of the data capacity of all target data packets transmitted in the monitoring transmission period.

[0038] The delay correction rate R2 of the target data packet within the monitoring transmission period is calculated using the formula R2=β2×(N1 / Q1)+δ1×(Vmax-V / Vmax)+δ2×(1-J / Jmax), where V is the average transmission rate within the monitoring transmission period, Vmax is the maximum allowed transmission rate within any preset monitoring transmission period, β2 and δ1 are preset factors, Jmax is the maximum correction evaluation value within any preset monitoring transmission period, and J is the correction evaluation value stored in the current cloud server.

[0039] S25: Calculate the optimized segmentation amount T1 of the target data packet based on the monitoring transmission period during the transmission process using the formula T1=LN×(1-R1)×(1-R2), where LN is the data capacity of a single target data packet within the monitoring transmission period;

[0040] S26: Calculate the new monitoring delay duration value U1 during the transmission process using the formula U1=Q1×(1-R2)×(N1 / Q1); update the monitoring delay duration Q1 to U1 during the transmission process based on the new monitoring delay duration value U1.

[0041] Furthermore, in S25, based on the optimized segmentation amount T1 of the target data packet during the monitoring transmission cycle during the transmission process, all remaining untransmitted target data packets are re-integrated and then equally divided into several new target data packets. At this time, the data capacity of any target data packet after equal division is T1, and the transmission and monitoring of all remaining untransmitted target data packets continue.

[0042] Furthermore, the calibration of the transmission timing of the target data packets during a monitoring transmission cycle is as follows:

[0043] S31: Obtain the total number of target data packets lost m, time difference N1, optimized segmentation amount T1, and data packet size LN within the monitoring transmission period;

[0044] S32: The timing calibration coefficient Y is calculated using the formula Y=(m×N1)×LN / (Q1×T1). The timing calibration coefficient Y is used to quantitatively characterize the degree of disorder in the timing of the target data packet transmission.

[0045] S33: If Y ≤ Y1, it is determined that the data packet transmission timing is normal, and the transmission is performed according to the original timing; if Y1 < Y < Y2, it is determined that the timing is slightly offset, and the remaining all untransmitted target data packets are subjected to timing fine-tuning, and after the fine-tuning, they are transmitted; if Y ≥ Y2, it is determined that the timing is disordered, and the remaining all untransmitted target data packets are re-sorted, and after the re-sorting, they are transmitted.

[0046] Further, the cloud server is further configured to re-transmit all the lost target data packets within the monitoring transmission cycle to the client module every other monitoring transmission cycle.

[0047] Further, the client module further includes a display unit. After the client module receives all the target data packets of the accessing user for transmission, it transmits them to the display unit. After the display unit receives the target data packets of the accessing user transmitted by the client module, it integrates and displays them for the accessing user to view.

[0048] Compared with the prior art, the following beneficial effects are achieved:

[0049] In this invention, by setting an information collection unit to collect the typed search data of the accessing user, based on the typed search data of the accessing user, the cloud server obtains the target access data of the accessing user and transmits it to the display unit for the accessing user to view, monitors the process of transmitting the target access data of the accessing user, and performs periodic equal division optimization on the target data packets of the target access data during the transmission process. During the equal division optimization process, the data capacity size of the data carried in the target data packets in the next monitoring transmission cycle is dynamically modified in combination with the transmission quality of the previous monitoring transmission cycle, and the interval duration of the next monitoring transmission cycle is synchronously modified. By this means, on the one hand, the network resources occupied by retransmission are reduced, and on the other hand, the data capacity size of the transmitted target data packets is refined, and the data capacity size of the data carried in the lost target data packets is dynamically adjusted, which is convenient for the later integration and viewing of the target data packets; at the same time, the automatic calibration of the target data packet timing during the transmission process is realized, the reliability of the system data management is improved, and the data quality of the whole process of closed sampling and the system operation stability are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figure 1 This application provides a closed sampling system data management and analysis system, including a client module, a cloud server and a data analysis module;

[0053] The client module is used to access user access to closed sampling data, and the client module includes an information acquisition unit and a display unit.

[0054] The information collection unit is used to obtain the search information and simultaneously obtain the identity information of the user after the user enters the search information. The search information includes, but is not limited to, sampling number, sampling time interval, sampling point number, closed sampling equipment number, sample type, batch number, and test items, which are used as query conditions to match and obtain target closed sampling data in the cloud server.

[0055] Identity information includes, but is not limited to, the user's account, name, department, access level, employee ID, unique identifier of the login device, and login IP address, which are used to verify user identity, control access permissions, and trace operational behavior.

[0056] Closed-loop sampling data includes, but is not limited to, all dimensions of sampling-related data, including sampling time, sampling location, sampling equipment, sample parameters, pressure, temperature, flow rate, environmental parameters, operators, sampling duration, anomaly markers, sampling curves, original sampling documents, test results, and compliance records.

[0057] The information collection unit generates the search index data of the accessing user based on the acquired search information and identity information, and transmits the search index data to the cloud server;

[0058] The cloud server is used for cloud-based management of closed sampling data, and the cloud server stores closed sampling data that can be publicly accessed by authorized users.

[0059] After receiving the access user's search index data transmitted by the client module, the cloud server uses the access user's search index data as a query condition to query and obtain relevant closed sampling data in the cloud server, and generates the access user's target access data based on the closed sampling data and the access user's search index data.

[0060] The cloud server is also used to transmit the target access data to the client module according to a preset transmission step, and the preset transmission step is as follows:

[0061] S11: Obtain the data capacity size of the target access data of the access user, and mark it as K;

[0062] S12: Divide the target access data of the access user according to the data capacity size of the target access data to generate a number of target data packets. The specific division rules are as follows:

[0063] S121: If K < Kmin, divide the target access data of the access user equally into a number of target data packets, and the data capacity size of one target data packet is L1;

[0064] S122: If Kmin ≤ K ≤ Kmax, divide the target access data of the access user equally into a number of target data packets, and the data capacity size of one target data packet is L2;

[0065] S123: If K > Kmax, divide the target access data of the access user equally into a number of target data packets, and the data capacity size of one target data packet is L3;

[0066] The L1, L2, and L3 are preset division values, and the Kmin and Kmax are respectively the preset division standard critical minimum value and the division standard critical maximum value;

[0067] Transmit all the target data packets obtained by dividing the target access data in order from left to right according to the position of the data carried in each target data packet in the target access data of the target user before equal division. Among them, the closer the position is to the left in the target access data, the earlier the transmission order;

[0068] The cloud server is also used to monitor and optimize the transmission process of all the target data packets obtained by dividing the target access data, and optimize the transmission process of all the remaining untransmitted target data packets within each monitoring transmission cycle based on the monitoring transmission cycle. The optimization content is as follows:

[0069] S21: Obtain the total number of lost target data packets within the monitoring transmission cycle, and mark it as m. During the transmission process, the start time of the initial monitoring transmission cycle is the loss time of the first lost target data packet monitored during the transmission process, and the end time of the monitoring transmission cycle is the loss time + the monitoring delay duration Q1. The value of the monitoring delay duration Q1 within the initial monitoring transmission cycle is the preset delay duration threshold;

[0070] S22: Compare the value of m with the preset packet loss scalar threshold n1. If 0 ≤ m < n1, the monitoring optimization unit does not perform any processing; otherwise, obtain the time differences between the loss times of two adjacent lost target data packets within the monitoring transmission period, and label them as M1, M2, ..., Mm-1;

[0071] S23: Use the formula to calculate and obtain the discrete value Z1 of the time differences between the loss times of two adjacent lost target data packets within the monitoring transmission period. Compare the value of Z1 with Z. If Z1 ≥ Z, delete the corresponding Mq values in descending order of | Mq - M | and calculate the discrete value Z1 of the remaining Mq. Compare the value of Z1 with Z again until Z < Z1. Here, Z is the preset time difference discrete threshold, and M is the average value of the time differences of the target data packet losses within the current monitoring transmission period participating in the discrete value calculation. When Z < Z1, the average value of all the remaining time differences within the monitoring transmission period participating in the calculation of Z1 is calibrated as the time difference and labeled as N1;

[0072] S24: Use the formula R1 = β1×(m / mMax) + λ1×(1 - K1 / K) to calculate and obtain the segmentation correction rate R1 of the target data packets within the current monitoring transmission period. Here, β1 and λ1 are preset factors, mMax is the maximum allowable packet loss number within the monitoring transmission period, and K1 is the sum of the data capacity sizes of all the target data packets transmitted within the monitoring transmission period;

[0073] Use the formula R2 = β2×(N1 / Q1) + δ1×(Vmax - V / Vmax) + δ2×(1 - J / Jmax) to calculate and obtain the delay correction rate R2 of the target data packets within the monitoring transmission period. Here, V is the average transmission rate within the monitoring transmission period, Vmax is the maximum allowable transmission rate within any preset monitoring transmission period, β2 and δ1 are preset factors, Jmax is the maximum correction evaluation value within any preset monitoring transmission period, and J is the correction evaluation value stored in the current cloud server at this time;

[0074] S25: Use the formula T1 = LN×(1 - R1)×(1 - R2) to calculate and obtain the optimized segmentation amount T1 of the target data packets based on the monitoring transmission period during the transmission process. Here, LN is the data capacity size of a single target data packet within the monitoring transmission period;

[0075] Re - integrate all the remaining untransmitted target data packets according to the optimized segmentation amount T1 of the target data packets based on the monitoring transmission period during the transmission process. After integration, equally divide them into several new target data packets. At this time, the data capacity size of any one of the equally divided target data packets is T1;

[0076] And continue to transmit and monitor all the remaining target data packets that have not been transmitted;

[0077] S26: Calculate and obtain the value U1 of the new monitoring delay duration in the transmission process by using the formula U1 = Q1×(1 - R2)×(N1 / Q1);

[0078] Update the monitoring delay duration Q1 to U1 in the transmission process according to the value U1 of the new monitoring delay duration;

[0079] The cloud server is further configured to calibrate the transmission timing of the target data packets every other monitoring transmission cycle;

[0080] S31: Obtain the total number m of target data packets lost, the time difference value N1, the optimized segmentation amount T1, and the data packet size LN within the monitoring transmission cycle;

[0081] S32: Calculate and obtain the timing calibration coefficient Y by using the formula Y = (m×N1)×LN / (Q1×T1). The timing calibration coefficient Y is used to quantitatively characterize the disorder degree of the transmission timing of the target data packets: the larger the Y value, the more chaotic the timing of the target data packets; the smaller the Y value, the more stable the target transmission timing;

[0082] It should be noted here that the larger the value of the total number m, the more data packets are lost and the easier it is to be out-of-order. N1 indicates the fluctuation of the packet loss time interval of the target data packets. The more unstable the interval fluctuation, the easier it is to be out-of-order. T1 indicates the data capacity size of a single data packet after integration. The smaller it is, the faster the transmission and the easier it is to be out-of-order. Q1 is the interval duration of the monitoring transmission cycle. The longer the cycle, the lower the probability of being out-of-order. LN refers to the data capacity size of a single data packet before integration. The larger it is, the smaller the out-of-order;

[0083] S33: If Y ≤ Y1, it is determined that the data packet transmission timing is normal and transmitted according to the original timing; if Y1 < Y < Y2, it is determined that the timing is slightly offset, and the remaining all untransmitted target data packets are subjected to timing fine-tuning and then transmitted after fine-tuning; if Y ≥ Y2, it is determined that the timing is disordered, and the remaining all untransmitted target data packets are re-ordered and then transmitted after re-ordering;

[0084] The cloud server is further configured to re-transmit all the target data packets lost within the monitoring transmission cycle to the client module every other monitoring transmission cycle;

[0085] After receiving all the target data packets of the access user transmitted, the client module transmits them to the display unit. After receiving the target data packets of the access user transmitted by the client module, the display unit integrates and displays them for the access user to view;

[0086] Once the target access data for each user has been transmitted, the data analysis module stores the target access data.

[0087] The data analysis module is used to analyze the target access data stored therein after the number of data reaches a preset fixed amount, and update the storage correction evaluation value based on the analysis results.

[0088] The content of any one analysis is as follows:

[0089] S51: Re-label all target access data whose storage capacity for this analysis is greater than P1 as data to be parsed, where P1 is a preset threshold.

[0090] S52: For any data to be parsed, determine the parsing period of the data to be parsed based on the start time of the data transmission to the end time of the data transmission, divide the parsing period into parsing segments, divide the parsing period into a parsing segments of equal length, and label the a parsing segments of the parsing period as A1, A2, ..., Aa, where a>1;

[0091] S53: Taking a parsing segment as an example, obtain the packet loss rate B1 and the average transmission rate C1 when transmitting the data to be parsed within a parsing segment of the parsing period; obtain the sum of the data capacity D1 of all data packets transmitted within a parsing segment of the parsing period;

[0092] S54: The process quality response number E1 of transmitting the data to be parsed within one parsing segment of the parsing cycle is calculated using the formula E1=α1×(1-B1)+α2×(C1 / Cmax)+η1×(D1 / Dtotal). Here, α1, α2, and η1 are preset coefficients, Cmax is the preset maximum allowable transmission rate of the transmission segment, and Dtotal is the data capacity of the data to be parsed.

[0093] S55: Calculate and obtain the process mass reaction numbers E1, E2, ..., Ea that transmit the data to be parsed within the a parsing segments of the parsing cycle according to S52 to S53;

[0094] S56: Use the discrete point filtering algorithm to process all the calculated process quality response numbers, and calculate the average value of all remaining process quality response numbers after data processing. Then, define the average value as the transmission quality number G1 of the parsed media data.

[0095] In this application, the discrete point filtering algorithm is the H-score filtering algorithm;

[0096] Get the total number of process quality response numbers that are greater than the transmission quality number G1 among all process quality response numbers deleted after data processing, and re-label them as abnormal quality numbers, marking them as G2;

[0097] S57: Calculate the process quality evaluation value H1 for transmitting the data to be parsed within a parsing segment of the parsing cycle using the formula H1=G1×(1-G2 / a);

[0098] S58: Following S52 to S57, calculate and obtain the process quality evaluation values ​​of all data to be parsed. Use a discrete point filtering algorithm to process all the calculated process quality evaluation values, and calculate the average value of all remaining process quality evaluation values ​​after data processing. Label this average value as the corrected evaluation value, and denot it as J.

[0099] The data analysis module transmits the corrected evaluation value to the cloud server for updating and storage.

[0100] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0101] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A closed sampling system data management and analysis system, characterized in that, Including: A client module for accessing the airtight sampling data accessed by a user. The client module includes an information collection unit. After the accessed user types in the retrieval information, the information collection unit obtains the retrieval information and synchronously obtains the identity information of the accessed user, and generates the retrieval index data of the accessed user based on the retrieval information and the identity information; A cloud server for querying and obtaining relevant airtight sampling data as a query condition after receiving the retrieval index data of each accessed user and generating the target access data of the accessed user; The cloud server is further configured to transmit the target access data to the client module according to a preset transmission step; The cloud server is further configured to monitor and optimize the transmission process of all target data packets obtained by dividing the target access data, and optimize the transmission process of all remaining untransmitted target data packets within each monitoring transmission cycle based on the monitoring transmission cycle; The cloud server is also configured to calibrate the transmission timing of the target data packets every other monitoring transmission cycle, and select whether to sort all the remaining untransmitted target data packets based on the calibration result; A data analysis module for storing any target access data after it is transmitted for any accessed user, and analyzing it every time the number of stored target access data reaches a preset fixed amount, and updating the corrected evaluation value stored in the cloud server based on the analysis result.

2. The closed sampling system data management and analysis system according to claim 1, characterized in that, The preset transmission step for transmitting the target access data is as follows: S11: Obtain the data capacity size of the target access data of the accessed user, and mark it as K; S12: Divide the target access data of the accessed user according to the data capacity size of the target access data to generate a number of target data packets. The specific division rules are as follows: S121: If K < Kmin, divide the target access data of the accessed user equally into a number of target data packets, and the data capacity size of one target data packet is L1; S122: If Kmin ≤ K ≤ Kmax, divide the target access data of the accessed user equally into a number of target data packets, and the data capacity size of one target data packet is L2; S123: If K > Kmax, divide the target access data of the accessed user equally into a number of target data packets, and the data capacity size of one target data packet is L3; The L1, L2, and L3 are preset division values, and the Kmin and Kmax are respectively the preset division standard critical minimum value and the division standard critical maximum value; Transmit all the target data packets obtained by dividing the target access data in order from left to right according to the position of the data carried in each target data packet in the target access data of the target user before equal division, where the closer the position is to the left in the target access data, the earlier the transmission order.

3. The closed sampling system data management and analysis system according to claim 1, characterized in that, The content of any analysis performed by the data analysis module is as follows: S41: Re-label all target access data whose storage capacity for this analysis is greater than P1 as data to be parsed, where P1 is a preset threshold. S42: For any data to be parsed, determine the parsing period of the data to be parsed based on the time from the start of transmission to the end of transmission. Divide the parsing period into parsing segments of equal duration, and label the a parsing segments of the parsing period as A1, A2, ..., Aa, where a>1; S43: Taking a parsing segment as an example, obtain the packet loss rate B1 and the average transmission rate C1 when transmitting the data to be parsed within a parsing segment of the parsing period; obtain the sum of the data capacity D1 of all data packets transmitted within a parsing segment of the parsing period; S44: The process quality response number E1 of transmitting the data to be parsed within one parsing segment of the parsing cycle is calculated using the formula E1=α1×(1-B1)+α2×(C1 / Cmax)+η1×(D1 / Dtotal). Here, α1, α2, and η1 are preset coefficients, Cmax is the preset maximum allowable transmission rate of the transmission segment, and Dtotal is the data capacity of the data to be parsed. S45: Calculate and obtain the process mass reaction numbers E1, E2, ..., Ea that transmit the data to be parsed within the a parsing segments of the parsing cycle according to S42 to S43; S46: Use the discrete point filtering algorithm to process all the calculated process quality response numbers, and calculate the average value of all remaining process quality response numbers after data processing. Then, define the average value as the transmission quality number G1 of the parsed media data. Get the total number of process quality response numbers that are greater than the transmission quality number G1 among all process quality response numbers deleted after data processing, and re-label them as abnormal quality numbers, marking them as G2; S47: Calculate the process quality evaluation value H1 for transmitting the data to be parsed within a parsing segment of the parsing cycle using the formula H1=G1×(1-G2 / a); S48: Following S42 to S47, calculate and obtain the process quality evaluation values ​​of all data to be parsed. Use a discrete point filtering algorithm to process all the calculated process quality evaluation values, and calculate the average value of all remaining process quality evaluation values ​​after data processing. Label this average value as the corrected evaluation value, and denot it as J. The data analysis module transmits the corrected evaluation value to the cloud server for updating and storage.

4. The closed sampling system data management and analysis system according to claim 3, characterized in that, The optimization of the transmission process of all remaining untransmitted target data packets within a monitoring transmission cycle is as follows: S21: Obtain the total number of target data packets lost during the monitoring transmission period, denoted as m. During the transmission process, the start time of the initial monitoring transmission period is the loss time of the first lost target data packet monitored during the transmission process, and the end time of the monitoring transmission period is the loss time + monitoring delay duration Q1. During the initial monitoring transmission period, the value of the monitoring delay duration Q1 is the preset delay duration threshold; S22: Compare the magnitudes of m and the preset packet loss scalar threshold n1. If 0 ≤ m < n1, the monitoring optimization unit does not perform any processing; otherwise, obtain the time differences between the loss times of two adjacent lost target data packets during the monitoring transmission period, denoted as M1, M2,..., Mm-1; S23: Use the formula to calculate and obtain the discrete value Z1 of the time difference between the loss times of two adjacent lost target data packets within the monitored transmission period, compare the magnitudes of Z1 and Z. If Z1 ≥ Z, then delete the corresponding Mq values in descending order of |Mq - M| and calculate the discrete value Z1 of the remaining Mq values, and compare the magnitudes of Z1 and Z again until Z < Z1. The Z is a preset discrete threshold of the time difference, and M is the mean of the time differences of the loss of the target data packets within the current monitored transmission period participating in the discrete value calculation. When Z < Z1, the average value of all the remaining time differences within the monitored transmission period participating in the calculation of Z1 is calibrated as the time difference and marked as N1; S24: Calculate and obtain the segmentation correction rate R1 of the target data packets during the current monitoring transmission period using the formula R1 = β1×(m / mMax)+λ1×(1-K1 / K). β1 and λ1 are preset factors, mMax is the maximum allowable packet loss quantity during the monitoring transmission period, and K1 is the sum of the data capacity sizes of all target data packets transmitted during the monitoring transmission period; Calculate and obtain the delay correction rate R2 of the target data packets during the monitoring transmission period using the formula R2 = β2×(N1 / Q1)+δ1×(Vmax-V / Vmax)+δ2×(1-J / Jmax). V is the average transmission rate during the monitoring transmission period, Vmax is the maximum allowable transmission rate in any preset monitoring transmission period, β2 and δ1 are preset factors, Jmax is the maximum correction evaluation value in any preset monitoring transmission period, and J is the current correction evaluation value stored in the cloud server at this time; S25: Calculate and obtain the optimized segmentation quantity T1 of the target data packets based on the monitoring transmission period during the transmission process using the formula T1 = LN×(1-R1)×(1-R2). LN is the data capacity size of a single target data packet during the monitoring transmission period; S26: Calculate and obtain the value U1 of the new monitoring delay duration during the transmission process using the formula U1 = Q1×(1-R2)×(N1 / Q1); update the monitoring delay duration Q1 to U1 during the transmission process according to the value U1 of the new monitoring delay duration.

5. The closed sampling system data management and analysis system according to claim 4, characterized in that, In S25, re-integrate all the remaining untransmitted target data packets according to the optimized segmentation quantity T1 of the target data packets based on the monitoring transmission period during the transmission process, equally divide them into several new target data packets. At this time, the data capacity size of any equally divided target data packet is T1, and continue to transmit and monitor all the remaining untransmitted target data packets.

6. The data management and analysis system for a closed sampling system according to claim 4, characterized in that, The content for calibrating the transmission timing of target data packets in one monitoring transmission period is as follows: S31: Obtain the total number m of target data packets lost during the monitoring transmission period, the time difference N1, the optimized segmentation quantity T1, and the data packet size LN; S32: Calculate and obtain the timing calibration coefficient Y using the formula Y = (m × N1) × LN / (Q1 × T1). The timing calibration coefficient Y is used to quantitatively characterize the disorder degree of the target data packet transmission timing. S33: If Y ≤ Y1, it is determined that the data packet transmission timing is normal, and the transmission is performed according to the original timing. If Y1 < Y < Y2, it is determined that the timing has a slight deviation, and the remaining all untransmitted target data packets are subjected to timing fine-tuning, and then transmitted after the fine-tuning. If Y ≥ Y2, it is determined that the timing is disordered, and the remaining all untransmitted target data packets are re-sorted, and then transmitted after the re-sorting.

7. The closed sampling system data management and analysis system according to claim 1, characterized in that, The cloud server is further configured to re-transmit all the lost target data packets within the monitoring transmission cycle to the client module every other monitoring transmission cycle.

8. The closed sampling system data management and analysis system according to claim 1, characterized in that, The client module further includes a display unit. After receiving all the target data packets of the accessing user transmitted, the client module transmits them to the display unit. After receiving the target data packets of the accessing user transmitted by the client module, the display unit integrates them and displays them for the accessing user to view.

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