Data management system and method based on data cockpit

Abstract

The invention discloses a data management system and method based on a data cockpit, and relates to the technical field of data management, and the method comprises the steps: obtaining a plurality of historical early warning records of a server, extracting and analyzing the resource occupation condition of each special cabin in the early warning records, and then extracting a feature record from the resource occupation condition; extracting a plurality of data records, calculating a target value corresponding to each data record in the special cabin corresponding to the feature record, and establishing a first matrix according to the target values; acquiring a period of time before the current moment, and establishing a second matrix; and calculating the early warning degree of the server at the current moment according to the first matrix and the second matrix, and judging whether to carry out early warning prompt on the network resource occupation of the server or not. According to the invention, the historical early warning condition of the server and the real-time data state of each special cabin are analyzed, the network resource occupation of the server is predicted, the resource occupation reliability of the server is improved, and the data management capability of the cockpit is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data management system and method based on a data cockpit. Background Art

[0002] The data cockpit plays a key role in integrating and analyzing the key data indicators of an enterprise. It can integrate data from multiple data sources for in-depth analysis and mining, and usually provides a centralized visual interface to help decision makers quickly understand business conditions. The data cockpit includes multiple special cabins, which are sections for analyzing and displaying data in the data cockpit. For example, for an enterprise, according to different businesses, they are divided into financial cabins, production cabins, human capacity cabins, and marketing cabins. Currently, multiple special cabins are deployed on the same server to share server resources. Although this is convenient for administrators to perform unified management and maintenance, channel blockage and resource preemption will occur, resulting in the problem of untimely updates of cockpit data. Therefore, based on the historical warning status of the server and the real-time data status of each special cabin, the server's warning status is predicted and prevented in advance, which helps to optimize the data management capabilities of the cockpit. Summary of the invention

[0003] The object of the present invention is to provide a data management system and method based on a data cockpit to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A data management method based on a data cockpit comprises the following steps: Step S100: obtaining several historical warning records of the server where the data cockpit application is deployed. The warning record is a record that the network resource usage of the server exceeds the normal usage range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted therefrom; Step S200: extracting data corresponding to the plate in the special cabin that needs to update data in real time, in the processing process before rendering to the plate, taking one processing process as one data record, calculating the target value corresponding to each data record in the special cabin corresponding to the feature record, and establishing a first matrix according to the target value; Step S300: Obtain a period of time before the current moment, obtain the time weight corresponding to each moment in the period, calculate the target value corresponding to each data record in the period of time, and establish a second matrix; Step S400: Calculate the warning level of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue a warning prompt for the network resource occupation of the server according to the warning level.

[0005] Furthermore, step S100 includes: Step S110: Obtain the normal occupancy range [0, V] corresponding to the server, where V is the maximum occupancy value preset by the system; obtain a number of warning records, extract the warning time range in the warning records, and the occupancy value of the network resources at each moment within the warning time range; deploy a network resource monitoring tool on the server, and set an independent monitoring point for each special cabin on the application corresponding to the data cockpit, and monitor the resource occupancy value corresponding to each monitoring point through the monitoring tool as the resource occupancy value of each special cabin; Step S120: according to the resource occupancy value of a certain special cabin T at each moment within the warning time range D1 of a certain warning record R, a first occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; taking the earliest moment in the warning time range D1 as the starting point and the resource occupancy value corresponding to the certain special cabin T at each moment within the previous time range D2, a second occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; Step S130: Calculate the average resource occupancy value V1 in the first occupancy function and the average resource occupancy value V2 in the second occupancy function to obtain the total difference V0=V1-V2, and calculate the corresponding value variance S according to all resource occupancy values ​​in the first occupancy function, and then obtain the characteristic occupancy value of a certain cabin T as X T =V0 / S, and obtain the characteristic occupancy value of each special compartment corresponding to a certain warning record R, and sum them up as the characteristic value of a certain warning record R. If the characteristic value is greater than the preset numerical threshold, the certain warning record R is taken as the characteristic record, and all the characteristic records are obtained.

[0006] The data cockpit usually includes multiple special cabins. In this solution, the special cabin is used as a specific section. For example, for an enterprise, it is divided into a financial cabin, a production cabin, a human capacity cabin, and a marketing cabin according to different businesses. In actual applications, the screen displays of different special cabins are different. For example, the financial cabin includes many reports, and the production cabin includes many analysis charts. In this solution, the data in these reports and charts need to be updated in real time, and for network resources (the network resources here are memory resources), each special cabin that is updated in real time needs to occupy a large amount of network resources. Therefore, in this step S100, it is necessary to extract the warning information caused by these reasons, that is, the feature record in this solution. This helps to improve the reliability of the calculation results, specifically: For network resources, if the difference between range D2 and range D1 is large, that is, the total difference V0 is large, and for network resources within range D1, the occupancy is relatively stable, that is, the numerical variance S is small, then it can be said that a certain cabin T has a greater correlation with the server warning, that is, in this scheme, the characteristic occupancy value X is used. T To characterize the association relationship, if the sum of the association relationships of multiple special cabins exceeds the numerical threshold, it can be determined that the reason why the server's network resources issue an early warning is caused by these special cabins. Therefore, using such early warning records for the following analysis will help improve the reliability of the calculation results.

[0007] Further, step S200 includes: Step S210: Each special cabin includes several panels that need to update data in real time, wherein each panel updates data at a fixed time interval. Before rendering the real-time updated data to the panel corresponding to the special cabin, the process of extraction, conversion and loading is performed as a data record; extract a data record P of a special cabin T corresponding to a certain feature record R within the warning time range, and the data extracted from the data record P are stored in the database, and the number of data extracted from the record P Q1, the number of data whose format changes during the conversion process Q2, and the number of data rendered in the panel corresponding to the special cabin T by the extracted data Q3 are obtained; The extracted data is stored in the database. Generally, there are multiple data that need to be extracted. The specific number and type of data extracted are determined by actual conditions and will not be elaborated here. The converted data refers to the data whose format changes during the conversion process, including but not limited to: time format conversion, data type conversion, and character encoding conversion. Since the data will eventually be rendered to the corresponding report or chart, the number of data rendered to the corresponding report or chart after extraction and conversion is taken as Q3.

[0008] Step S220: Set the weights corresponding to the data quantities Q1, Q2 and Q3 to K1, K2 and K3 respectively, and K1, K2 and K3 are all greater than 0, so as to obtain the target value of record P. , e is a natural constant, and then according to the fixed time interval for updating the data, the target value of all data records of a certain compartment T within the warning time range is obtained, and the average value is calculated as the eigenvalue of a certain compartment T corresponding to a certain feature record R, and the first matrix A is established according to the eigenvalue of each compartment T corresponding to each feature record.

[0009] It should be noted that the function h=1-e -x When x is x≥0, h is [0,1), which is a function that h increases as x increases, and when x is small, the increase in h is larger than when x is large. In this scheme, the target value G PIt represents the degree of data change corresponding to the data record P. The greater the degree of data change, the greater the impact on the warning in the feature record. In this scheme, when the data quantity Q1, Q2 and Q3 are multiplied by the corresponding weights K1, K2 and K3 and the sum is calculated, the smaller the value obtained is, the greater the correlation between the degree of data change of record P and the degree of impact of the warning in the feature record. Therefore, the function h=1-e is used here. -x Design, and K1, K2 and K3 are weights, the specific values ​​should be determined according to the actual situation. Generally speaking, 0 <K1+K2+K3<1。

[0010] Furthermore, step S300 includes: Step S310: Obtain a time period F starting from the current time, obtain all data records in the time period F according to the fixed time interval for updating data, and sort the data records in the order of data record time from the beginning to the end; set the weight corresponding to each data record according to the condition that the larger the sequence number of the data record, the larger the weight, and the sum of the weights of all data records is 1; Step S320: Obtain the data quantity Q1, Q2 and Q3 corresponding to each data record of a certain cabin T in the time period F, and then calculate the target value corresponding to each data record; and according to the weight corresponding to each data record, obtain the current target cabin value of the certain cabin T: , where U is the number of data records in period F, W u is the weight of the u-th data record, H u is the target value of the u-th data record, and then the second matrix B is established according to the target value of each compartment.

[0011] Further, step S400 includes: summing up all the target special compartment values ​​in the second matrix B to obtain a total target value; if the total target value is greater than a preset target numerical threshold, dividing each target special compartment value by the total target value to obtain a target ratio corresponding to each special compartment at the current moment; and then establishing a target ratio matrix C based on all the target ratios, and multiplying the first matrix A by the target ratio matrix C to obtain a matrix D; and taking the maximum value of the elements in the matrix D as the warning degree; if the warning degree is greater than the preset degree threshold, a warning prompt is issued for the network resource occupancy of the current server, and relevant personnel are prompted to make adjustments.

[0012] If the total target value obtained according to the second matrix B is small, it means that the current network resource usage is not caused by the special cabin, so it is not within the analysis scope of this plan, and the total target value is large, within the analysis scope of this plan, so it is necessary to establish a target ratio matrix C based on the second matrix whose total target value is greater than the preset target value threshold; when the maximum value in the matrix D after multiplying the first matrix A by the currently obtained target ratio matrix C is greater than the preset degree threshold, it means that the warning situation of the historical feature record corresponding to the maximum value is relatively similar, so it is necessary to issue an early warning prompt for the network resource usage of the server and prompt relevant personnel to make adjustments.

[0013] A data management system based on a data cockpit, comprising a feature record extraction module, a first matrix establishment module, a second matrix establishment module and an early warning prompt module; Feature record extraction module: used to obtain several warning records of the server history on which the data cockpit application is deployed. Warning records are records of network resource usage of the server exceeding the normal range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted from it; The first matrix establishment module is used to extract the data corresponding to the plate in the special cabin that needs to update the data in real time. In the processing process before rendering to the plate, one processing process is regarded as one data record, and the target value corresponding to each data record in the special cabin corresponding to the feature record is calculated, and the first matrix is ​​established according to the target value; The second matrix establishment module is used to obtain a period of time before the current moment, obtain the time weight corresponding to each moment in the period, calculate the target value corresponding to each data record in the period of time, and establish the second matrix; Early warning prompt module: used to calculate the early warning degree of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue an early warning prompt for the network resource usage of the server according to the early warning degree.

[0014] Further, the feature record extraction module includes a resource occupancy value calculation unit, an occupancy function establishment unit and a feature record extraction unit; Resource occupancy value calculation unit: used to extract the warning time range in the warning record and the network resource occupancy value at each moment within the warning time range; deploy network resource monitoring tools and monitor the resource occupancy value of each dedicated cabin; Occupancy function establishment unit: used for establishing a first occupancy function and a second occupancy function in which the resource occupancy value corresponding to the special cabin changes with time according to the warning time range of the warning record; The characteristic record extraction unit is used to obtain the characteristic occupancy value of the special cabin according to each resource occupancy value in the first occupancy function and the second occupancy function; and obtain the characteristic value of the early warning record, and then extract the characteristic record from the early warning record.

[0015] Further, the second matrix establishing module includes a weight setting unit and a second matrix establishing unit; Weight setting unit: used to obtain all data records in the period from the current time as the starting point, and set the weight corresponding to each data record; The second matrix establishing unit is used to calculate the target value corresponding to each data record, and obtain the target value of the special cabin according to the weight corresponding to each data record, and then establish the second matrix.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a data management system and method based on a data cockpit, including: obtaining several warning records of the server history, extracting and analyzing the resource occupancy of each special cabin in the warning records, and then extracting feature records therefrom; extracting several data records, calculating the target value corresponding to each data record in the special cabin corresponding to the feature record, and establishing a first matrix according to the target value; obtaining a period of time before the current moment, and establishing a second matrix; calculating the warning degree of the server at the current moment according to the first matrix and the second matrix, and judging whether to issue a warning prompt for the network resource occupancy of the server. The present invention predicts the network resource occupancy of the server by analyzing the historical warning situation of the server and the real-time data status of each special cabin, which helps to improve the reliability of server resource occupancy and optimize the data management capability of the cockpit. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a data management method based on a data cockpit of the present invention; Figure 2 This is a structural diagram of a data management system based on a data cockpit of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example: Figure 1 As shown, the present invention provides a data management method and technical solution based on a data cockpit, comprising the following steps: Step S100: obtaining several historical warning records of the server where the data cockpit application is deployed. The warning record is a record that the network resource usage of the server exceeds the normal usage range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted therefrom; Step S110: Obtain the normal occupancy range [0, V] corresponding to the server, where V is the maximum occupancy value preset by the system; obtain a number of warning records, extract the warning time range in the warning records, and the occupancy value of the network resources at each moment within the warning time range; deploy a network resource monitoring tool on the server, and set an independent monitoring point for each special cabin on the application corresponding to the data cockpit, and monitor the resource occupancy value corresponding to each monitoring point through the monitoring tool as the resource occupancy value of each special cabin; Step S120: according to the resource occupancy value of a certain special cabin T at each moment within the warning time range D1 of a certain warning record R, a first occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; taking the earliest moment in the warning time range D1 as the starting point and the resource occupancy value corresponding to the certain special cabin T at each moment within the previous time range D2, a second occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; Step S130: Calculate the average resource occupancy value V1 in the first occupancy function and the average resource occupancy value V2 in the second occupancy function to obtain the total difference V0=V1-V2, and calculate the corresponding value variance S according to all resource occupancy values ​​in the first occupancy function, and then obtain the characteristic occupancy value of a certain cabin T as X T =V0 / S, and obtain the characteristic occupancy value of each special compartment corresponding to a certain warning record R, and sum them up as the characteristic value of a certain warning record R. If the characteristic value is greater than the preset numerical threshold, the certain warning record R is taken as the characteristic record, and all the characteristic records are obtained.

[0020] Step S200: extracting data corresponding to the plate in the special cabin that needs to update data in real time, in the processing process before rendering to the plate, taking one processing process as one data record, calculating the target value corresponding to each data record in the special cabin corresponding to the feature record, and establishing a first matrix according to the target value; Step S210: Each special cabin includes several panels that need to update data in real time, wherein each panel updates data at a fixed time interval. Before rendering the real-time updated data to the panel corresponding to the special cabin, the process of extraction, conversion and loading is performed as a data record; extract a data record P of a special cabin T corresponding to a certain feature record R within the warning time range, and the data extracted from the data record P are stored in the database, and the number of data extracted from the record P Q1, the number of data whose format changes during the conversion process Q2, and the number of data rendered in the panel corresponding to the special cabin T by the extracted data Q3 are obtained; For the finance cabin, production cabin, human capacity cabin and marketing cabin, there are sections that need to update data in real time. The sections here are the reports or charts mentioned above. Before the data is rendered on the reports or charts, it needs to be extracted, transformed and loaded. In this solution, the processing process is ETL. ETL is the abbreviation of Extract-Transform-Load in English, which is used to describe the process of extracting (extract), transforming (transform), and loading (load) data from the source end to the destination end. It is an existing technology.

[0021] Step S220: Set the weights corresponding to the data quantities Q1, Q2 and Q3 to K1, K2 and K3 respectively, and K1, K2 and K3 are all greater than 0, so as to obtain the target value of record P. , e is a natural constant, and then according to the fixed time interval for updating the data, the target value of all data records of a certain compartment T within the warning time range is obtained, and the average value is calculated as the eigenvalue of a certain compartment T corresponding to a certain feature record R, and the first matrix A is established according to the eigenvalue of each compartment T corresponding to each feature record.

[0022] In this embodiment, the number of feature records is M, the number of special cabins is N, and the first matrix established is ,in is the characteristic value of the first cabin in the first characteristic record, and so on. It is the characteristic value of the Nth special cabin in the Mth characteristic record.

[0023] Step S300: Obtain a period of time before the current moment, obtain the time weight corresponding to each moment in the period, calculate the target value corresponding to each data record in the period of time, and establish a second matrix; Step S310: Obtain a time period F starting from the current time, obtain all data records in the time period F according to the fixed time interval for updating data, and sort the data records in the order of data record time from the beginning to the end; set the weight corresponding to each data record according to the condition that the larger the sequence number of the data record, the larger the weight, and the sum of the weights of all data records is 1; Step S320: Obtain the data quantity Q1, Q2 and Q3 corresponding to each data record of a certain cabin T in the time period F, and then calculate the target value corresponding to each data record; and according to the weight corresponding to each data record, obtain the current target cabin value of the certain cabin T: , where U is the number of data records in period F, W u is the weight of the u-th data record, H u is the target value of the u-th data record, and then the second matrix B is established according to the target value of each compartment.

[0024] Since the number of special cabins is N, according to the target special cabin value of each special cabin, the second matrix is ​​established , where Y1 is the target value of the first dedicated cabin, and so on, Y N is the target cabin value of the Nth cabin.

[0025] Step S400: Calculate the warning level of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue a warning prompt for the network resource occupation of the server according to the warning level.

[0026] Step S400 includes: summing up all the target special compartment values ​​in the second matrix B to obtain a total target value. If the total target value is greater than a preset target numerical threshold, each target special compartment value is divided by the total target value to obtain the target ratio corresponding to each special compartment at the current moment. Then, based on all the target ratios, a target ratio matrix C is established, and the first matrix A is multiplied by the target ratio matrix C to obtain a matrix D. The maximum value of the elements in the matrix D is used as the warning degree. If the warning degree is greater than the preset degree threshold, a warning prompt is issued for the network resource occupancy of the current server, and relevant personnel are prompted to make adjustments.

[0027] If the total target value obtained according to the second matrix B is small, it means that the current network resource occupation is not caused by the dedicated cabin, so it is not within the analysis scope of this solution, and the total target value is large, within the analysis scope of this solution, so it is necessary to establish a target ratio matrix C according to the second matrix whose total target value is greater than the preset target value threshold. In this embodiment , where R1 is the target ratio of the first dedicated cabin, R Nis the target ratio of the Nth special cabin; since matrix A is an M×N matrix (M rows and N columns), and matrix C is an N×1 matrix (N rows and 1 column), the matrix D obtained by multiplying the two is an M×1 matrix (M rows and 1 column). , where Z 1 is the value of the first element, Z M is the value of the Mth element. Since M represents the number of feature records, when the largest value in the matrix D obtained by multiplying the current target ratio matrix C with the first matrix A is greater than the preset degree threshold, it means that the warning situation of the historical feature record corresponding to the largest value is relatively similar. Therefore, it is necessary to issue an early warning prompt for the network resource usage of the server and prompt relevant personnel to make adjustments.

[0028] In addition, this solution also provides a data management system based on the data cockpit, such as Figure 2 As shown, it includes a feature record extraction module, a first matrix establishment module, a second matrix establishment module and an early warning prompt module; Feature record extraction module: used to obtain several warning records of the server history on which the data cockpit application is deployed. Warning records are records of network resource usage of the server exceeding the normal range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted from it; The first matrix establishment module is used to extract the data corresponding to the plate in the special cabin that needs to update the data in real time. In the processing process before rendering to the plate, one processing process is regarded as one data record, and the target value corresponding to each data record in the special cabin corresponding to the feature record is calculated, and the first matrix is ​​established according to the target value; The second matrix establishment module is used to obtain a period of time before the current moment, obtain the time weight corresponding to each moment in the period, calculate the target value corresponding to each data record in the period of time, and establish the second matrix; Early warning prompt module: used to calculate the early warning degree of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue an early warning prompt for the network resource usage of the server according to the early warning degree.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A data management method based on a data cockpit, characterized in that: The following steps are involved: Step S100: obtaining several historical warning records of the server where the data cockpit application is deployed, wherein the warning record is a record of the network resource usage of the server exceeding the normal usage range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted therefrom; Step S200: extracting data corresponding to a plate in a special cabin that needs to update data in real time, in a processing process before rendering to the plate, taking one processing process as one data record, calculating a target value corresponding to each data record in the special cabin corresponding to the feature record, and establishing a first matrix according to the target value; Step S300: obtaining a period of time before the current moment, and obtaining the time weight corresponding to each moment therein, calculating the target value corresponding to each data record in the period of time, and establishing a second matrix; Step S400: Calculate the warning level of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue a warning prompt for the network resource occupation of the server according to the warning level.

2. The data management method based on the data cockpit according to claim 1, characterized in that: Step S100 includes: Step S110: Obtain the normal occupancy range [0, V] corresponding to the server, where V is the maximum occupancy value preset by the system; obtain a number of warning records, extract the warning time range in the warning records, and the occupancy value of the network resources at each moment within the warning time range; deploy a network resource monitoring tool on the server, and set an independent monitoring point for each special cabin on the application corresponding to the data cockpit, and monitor the resource occupancy value corresponding to each monitoring point through the monitoring tool as the resource occupancy value of each special cabin; Step S120: according to the resource occupancy value of a certain special cabin T at each moment within the warning time range D1 of a certain warning record R, a first occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; taking the earliest moment in the warning time range D1 as the starting point and the resource occupancy value corresponding to the certain special cabin T at each moment within the previous time range D2, a second occupancy function of the resource occupancy value corresponding to the certain special cabin T changing with time is established; Step S130: Calculate the average resource occupancy value V1 in the first occupancy function and the average resource occupancy value V2 in the second occupancy function to obtain the total difference V0=V1-V2, and calculate the corresponding value variance S according to all resource occupancy values ​​in the first occupancy function, and then obtain the characteristic occupancy value of a certain cabin T as X T =V0 / S, and obtain the characteristic occupancy value of each special compartment corresponding to the warning record R, and sum them up as the characteristic value of the warning record R. If the characteristic value is greater than the preset numerical threshold, the warning record R is taken as the characteristic record, and all the characteristic records are obtained.

3. The data management method based on the data cockpit according to claim 1, characterized in that: Step S200 includes: Step S210: Each special cabin includes several panels that need to update data in real time, wherein each panel updates data at a fixed time interval. Before rendering the real-time updated data to the panel corresponding to the special cabin, the data is extracted, converted and loaded as a data record; extract a data record P of a special cabin T corresponding to a certain feature record R within the warning time range, and the data extracted from the data record P are stored in the database, and the number of data extracted from the record P Q1, the number of data whose format changes during the conversion process Q2, and the number of data rendered in the panel corresponding to the said special cabin T by the extracted data Q3 are obtained; Step S220: Set the weights corresponding to the data quantities Q1, Q2 and Q3 to K1, K2 and K3 respectively, and K1, K2 and K3 are all greater than 0, so as to obtain the target value of record P. , e is a natural constant, and then according to the fixed time interval for updating the data, the target value of all data records of a certain compartment T within the warning time range is obtained, and the average value is calculated as the eigenvalue of a certain compartment T corresponding to the certain feature record R, and the first matrix A is established according to the eigenvalue of each special compartment T corresponding to each feature record.

4. The data management method based on the data cockpit according to claim 3 is characterized in that: Step S300 includes: Step S310: Obtain a time period F starting from the current time, obtain all data records in the time period F according to the fixed time interval for updating data, and sort the data records in the order of data record time from the front to the back; set the weight corresponding to each data record according to the condition that the larger the sequence number of the data record, the larger the weight, and the sum of the weights of all data records is 1; Step S320: Obtain the data quantity Q1, Q2 and Q3 corresponding to each data record of a certain cabin T in the time period F, and then calculate the target value corresponding to each data record; and according to the weight corresponding to each data record, obtain the current target cabin value of the certain cabin T: , where U is the number of data records in period F, W u is the weight of the u-th data record, H u is the target value of the u-th data record, and then the second matrix B is established according to the target value of each compartment.

5. The data management method based on the data cockpit according to claim 1, characterized in that: Step S400 includes: summing up all the target special compartment values ​​in the second matrix B to obtain a total target value; if the total target value is greater than a preset target numerical threshold, dividing each target special compartment value by the total target value to obtain a target ratio corresponding to each special compartment at the current moment; and then establishing a target ratio matrix C based on all the target ratios; and multiplying the first matrix A by the target ratio matrix C to obtain a matrix D; and taking the maximum value of the elements in the matrix D as the warning degree; if the warning degree is greater than the preset degree threshold, a warning prompt is issued for the network resource occupancy of the current server, and relevant personnel are prompted to make adjustments.

6. A data management system, used to execute the data management method based on a data cockpit according to any one of claims 1 to 5, characterized in that: The system includes a feature record extraction module, a first matrix establishment module, a second matrix establishment module and an early warning prompt module; Feature record extraction module: used to obtain several warning records of the server history on which the data cockpit application is deployed. The warning record is a record that the server's network resource usage exceeds the normal usage range. The data cockpit includes several special cabins, which are sections for analyzing and displaying data in the data cockpit. The resource usage of each special cabin in the warning record is extracted and analyzed, and then feature records are extracted from it; The first matrix establishment module is used to extract the data corresponding to the plate in the special cabin that needs to update the data in real time. In the processing process before rendering to the plate, one processing process is regarded as one data record, and the target value corresponding to each data record in the special cabin corresponding to the feature record is calculated, and the first matrix is ​​established according to the target value; The second matrix establishment module is used to obtain a period of time before the current moment, obtain the time weight corresponding to each moment in the period, calculate the target value corresponding to each data record in the period of time, and establish the second matrix; Early warning prompt module: used to calculate the early warning degree of the server at the current moment according to the first matrix and the second matrix, and determine whether to issue an early warning prompt for the network resource usage of the server according to the early warning degree.

7. A data management system according to claim 6, characterized in that: The feature record extraction module includes a resource occupancy value calculation unit, an occupancy function establishment unit and a feature record extraction unit; Resource occupancy value calculation unit: used to extract the warning time range in the warning record and the network resource occupancy value at each moment within the warning time range; deploy network resource monitoring tools and monitor the resource occupancy value of each dedicated cabin; Occupancy function establishment unit: used for establishing a first occupancy function and a second occupancy function in which the resource occupancy value corresponding to the special cabin changes with time according to the warning time range of the warning record; A characteristic record extraction unit is used to obtain a characteristic occupancy value of the special cabin according to each resource occupancy value in the first occupancy function and the second occupancy function; And obtain the characteristic values ​​of the warning records, and then extract the characteristic records from the warning records.

8. A data management system according to claim 6, characterized in that: The second matrix establishment module includes a weight setting unit and a second matrix establishment unit; Weight setting unit: used to obtain all data records in the period from the current time as the starting point, and set the weight corresponding to each data record; The second matrix establishing unit is used to calculate the target value corresponding to each data record, and obtain the target value of the special cabin according to the weight corresponding to each data record, and then establish the second matrix.

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