A data management system and method based on a data cockpit

Abstract

The present invention discloses a data management system and method based on a data cockpit, which relates to the technical field of data management, and includes: obtaining a number of historical warning records of a server, extracting and analyzing the resource occupancy of each special cabin in the warning records, and then extracting feature records therefrom; extracting a number of data records, calculating target values corresponding to each data record in the special cabin corresponding to the feature records, and establishing a first matrix according to the target values; obtaining a period of time before the current moment, and establishing a second matrix; according to the first matrix and the second matrix, calculating the warning degree of the server at the current moment, and determining whether to give a warning prompt for 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, the present invention predicts the network resource occupancy of the server, which helps to improve the reliability of server resource occupancy and optimize the data management ability of the cockpit.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and specifically 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, conduct in-depth analysis and mining, and usually provides a centralized visual interface to help decision-makers quickly understand the business situation. The data cockpit includes multiple specialized cockpits, which are the sections for analyzing and displaying the data in the data cockpit. For example, for an enterprise, according to different businesses, it is divided into a financial specialized cockpit, a production specialized cockpit, a human resources specialized cockpit, a marketing specialized cockpit, etc. There is a situation where multiple specialized cockpits are deployed on the same server to share the resources of the server. Although this is convenient for administrators to conduct unified management and maintenance, it will cause problems such as channel blockage and resource preemption, resulting in the problem that the cockpit data is not updated in a timely manner. Therefore, predicting the warning situation of the server based on the historical warning situation of the server and the real-time data status of each specialized cockpit, and taking preventive measures in advance, helps to optimize the data management ability of the cockpit. Summary of the Invention

[0003] The purpose 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 purpose, the present invention provides the following technical solutions:

[0005] A data management method based on a data cockpit includes the following steps:

[0006] Step S100: Obtain a number of historical warning records of the server on which the data cockpit application program is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes a number of specialized cockpits, which are the sections for analyzing and displaying the data in the data cockpit. Extract and analyze the resource occupancy situation of each specialized cockpit in the warning records, and then extract the characteristic records from them;

[0007] Step S200: Extract the data corresponding to the sections in the specialized cockpit that need to be updated in real time. In the processing process before rendering to the section, take one processing process as one data record, calculate the target value corresponding to each data record in the specialized cockpit corresponding to the characteristic record, and establish a first matrix according to the target value;

[0008] Step S300: Obtain a period of time before the current moment, and obtain the time weight corresponding to each moment therein. Calculate the target value corresponding to each data record within the period of time, and establish a second matrix;

[0009] Step S400: Calculate the warning level of the server at the current moment based on the first matrix and the second matrix, and determine whether to give a warning prompt for the network resource occupancy of the server according to the warning level.

[0010] Further, step S100 includes:

[0011] 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 values of network resources at each moment within the warning time range; deploy a network resource monitoring tool on the server, and set up independent monitoring points for each special cabin on the application program corresponding to the data cockpit, and monitor the resource occupancy values corresponding to each monitoring point through the monitoring tool as the resource occupancy values of each special cabin;

[0012] Step S120: Based on the resource occupancy values at each moment corresponding to a special cabin T within the warning time range D1 of a warning record WR, establish a first occupancy function of the resource occupancy value corresponding to the special cabin T changing with time; obtain the resource occupancy values at each moment corresponding to a special cabin T within the time range D2 going back from the earliest moment in the warning time range D1, and establish a second occupancy function of the resource occupancy value corresponding to the special cabin T changing with time;

[0013] 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, obtain the total difference V0 = V1 - V2, and calculate the corresponding numerical variance S based on all the resource occupancy values in the first occupancy function, and further obtain the characteristic occupancy value X of a special cabin T T = V0 / S, and obtain the characteristic occupancy values of each special cabin corresponding to a warning record WR, and sum them up as the characteristic value of the warning record WR. If the characteristic value is greater than the preset numerical threshold, regard the warning record WR as a characteristic record, and obtain all the characteristic records.

[0014] There are usually multiple dedicated cabins under the data cockpit. In this solution, a dedicated cabin is regarded as a specific module. For example, for an enterprise, according to different businesses, it can be divided into a finance dedicated cabin, a production dedicated cabin, a human resources dedicated cabin, a marketing dedicated cabin, etc. And in actual applications, the screen displays of different dedicated cabins are different. For example, the finance dedicated cabin includes many reports, while the production dedicated cabin includes many analysis charts. In this solution, the data in these reports and charts needs to be updated in real time. And for network resources (the network resources here are memory resources), each dedicated cabin that is updated in real time requires 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 records in this solution. Doing so helps to improve the reliability of the calculation results. Specifically:

[0015] For network resources, if the difference between range D2 and range D1 is relatively large, that is, the total difference V0 is large, and for the network resources within range D1, the occupancy is relatively stable, that is, the numerical variance S is small, then it can be shown that a certain dedicated cabin T has a relatively large correlation with the server warning. That is, in this solution, the feature occupancy value X T is used to characterize this correlation. And if the sum of the correlations of multiple dedicated cabins exceeds the numerical threshold, then it can be determined that the reason for the server's network resource warning is caused by these dedicated cabins. Therefore, using such warning records for the following analysis helps to improve the reliability of the calculation results.

[0016] Further, step S200 includes:

[0017] Step S210: Each dedicated cabin includes several data blocks that need to be updated in real time. Among them, each block updates data at a fixed time interval. The process of extracting, transforming, and loading the data to be updated in real time before rendering it to the corresponding block of the dedicated cabin is regarded as a data record; extract a data record P of a certain dedicated cabin T corresponding to a certain feature record FR within the warning time range. The data extracted by the data record P is stored in the database. Obtain the number Q1 of data extracted by the data record P, the number Q2 of data whose format changes during the transformation process, and the number Q3 of data rendered in the corresponding block of a certain dedicated cabin T for the extracted data;

[0018] The extracted data is stored in the database. Generally, there are multiple data to be extracted, and the specific number and type of extracted data are determined by the actual situation and will not be elaborated here; the transformed data is the data whose format changes during the transformation process, including but not limited to: time format conversion, data type conversion, character encoding conversion, etc.; since the data will ultimately be rendered onto the corresponding report or chart, the number of data rendered onto the corresponding report or chart after extraction and transformation is used as Q3.

[0019] 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 the data record P. e is a natural constant. 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 FR. The first matrix A is established based on the eigenvalue of each compartment T corresponding to each feature record.

[0020] 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 P It 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 quantities 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 the data record P and the 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。

[0021] Furthermore, step S300 includes:

[0022] Step S310: Obtain a time period F starting from the current time. Based on the fixed time interval for data update, obtain all data records within time period F and sort the data records in chronological order from the beginning to the end. Set a weight for each data record based on the condition that the larger the data record number, the greater the weight, and the sum of the weights of all data records equals 1.

[0023] Step S320: Obtain the data quantities Q1, Q2, and Q3 corresponding to each data record of a certain dedicated cabin T in the time period F, and then calculate the target value corresponding to each data record; and obtain the current target dedicated cabin value of the certain dedicated cabin T based on the weight corresponding to each data record: 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 based on the target value of each compartment.

[0024] Further, step S400 includes: summing all the target special cabin values in the second matrix B to obtain the total target value; if the total target value is greater than the preset target value threshold, dividing each target special cabin value by the total target value to obtain the target ratio corresponding to each special cabin at the current moment, and then establishing a target ratio matrix C based on all the target ratios; 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 level. If the warning level is greater than the preset level threshold, a warning prompt is given for the network resource occupancy of the current server, and relevant personnel are prompted to make adjustments.

[0025] If the total target value obtained from the second matrix B is small, it indicates that the current network resource occupancy is not caused by the special cabin, so it is not within the scope of analysis of this solution. While if the total target value is large and within the scope of analysis of this solution, a target ratio matrix C needs to be established based on the second matrix whose total target value is greater than the preset target value threshold; when it meets the condition that a certain maximum value in the matrix D obtained by multiplying the current target ratio matrix C by the first matrix A is greater than the preset level threshold, it also means that the warning situation of the historical feature record corresponding to the maximum value is relatively similar currently, so a warning prompt for the network resource occupancy of the server needs to be given, and relevant personnel are prompted to make adjustments.

[0026] A data management system based on a data cockpit includes a feature record extraction module, a first matrix establishment module, a second matrix establishment module, and a warning prompt module;

[0027] The feature record extraction module: is used to obtain a number of historical warning records of the server where the data cockpit application program is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes several special cabins, and the special cabin is a section for analyzing and displaying data in the data cockpit. Extract and analyze the resource occupancy situation of each special cabin in the warning records, and then extract feature records from them;

[0028] The first matrix establishment module: is used to extract the data corresponding to the section in the special cabin that needs to be updated in real time. During the processing before rendering to the section, regard one processing process as one data record, calculate the target value corresponding to each data record in the special cabin corresponding to the feature record, and establish a first matrix based on the target values;

[0029] The second matrix establishment module: is used to obtain a period of time before the current moment, and obtain the time weight value corresponding to each moment therein, calculate the target value corresponding to each data record within the period of time, and establish a second matrix;

[0030] Early warning prompt module: used to calculate the early warning level of the server at the current moment according to the first matrix and the second matrix, and determine whether to give an early warning prompt for the network resource occupancy of the server according to the early warning level.

[0031] Furthermore, the feature record extraction module includes a resource occupancy value calculation unit, an occupancy function establishment unit, and a feature record extraction unit;

[0032] Resource occupancy value calculation unit: used to extract the early warning time range in the early warning record, and the occupancy value of network resources at each moment within the early warning time range; deploy a monitoring tool for network resources, and monitor the resource occupancy value of each special cabin;

[0033] Occupancy function establishment unit: used to establish a first occupancy function and a second occupancy function for the resource occupancy value corresponding to the special cabin changing with time according to the early warning time range of the early warning record;

[0034] Feature record extraction unit: 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 feature record from the early warning record.

[0035] Furthermore, the second matrix establishment module includes a weight setting unit and a second matrix establishment unit;

[0036] Weight setting unit: used to obtain all data records within the time period starting from the current moment and set the weight corresponding to each data record;

[0037] Second matrix establishment unit: used to calculate the target value corresponding to each data record, and obtain the target special cabin value of the special cabin according to the weight corresponding to each data record, and then establish the second matrix.

[0038] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a data management system and method based on a data cockpit, including: obtaining a number of historical early warning records of the server, extracting and analyzing the resource occupancy of each special cabin in the early warning record, and then extracting feature records therefrom; extracting a number of 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 early warning level of the server at the current moment according to the first matrix and the second matrix, and determining whether to give an early warning prompt for the network resource occupancy of the server. By analyzing the historical early warning situation of the server and the real-time data status of each special cabin, the present invention predicts the network resource occupancy of the server, which helps to improve the reliability of server resource occupancy and optimize the data management ability of the cockpit. Description of the Drawings

[0039] Figure 1 Schematic flow chart of a data management method based on a data cockpit according to the present invention;

[0040] Figure 2 Structure diagram of a data management system based on a data cockpit according to the present invention. Detailed implementation manners

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment: As Figure 1 shown, the present invention provides a technical solution for a data management method based on a data cockpit, including the following steps:

[0043] Step S100: Obtain a number of historical warning records of a server on which a data cockpit application program is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes a number of specialized cabins, and the specialized cabins are sections for analyzing and displaying data in the data cockpit. Extract and analyze the resource occupancy of each specialized cabin in the warning records, and then extract characteristic records therefrom;

[0044] 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 resource at each moment within the warning time range; deploy a monitoring tool for the network resource on the server, and set independent monitoring points for each specialized cabin on the application program corresponding to the data cockpit. Monitor the resource occupancy value corresponding to each monitoring point through the monitoring tool as the resource occupancy value of each specialized cabin;

[0045] Step S120: According to the resource occupancy values of each moment of a specialized cabin T corresponding to a warning record WR within the warning time range D1, establish a first occupancy function of the resource occupancy value of the specialized cabin T changing with time; obtain the resource occupancy values of each moment of a specialized cabin T corresponding to the time range D2 going back from the earliest moment in the warning time range D1, and establish a second occupancy function of the resource occupancy value of the specialized cabin T changing with time;

[0046] 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, obtain the total difference V0 = V1 - V2, and calculate the corresponding value variance S based on all the resource occupancy values in the first occupancy function, and then obtain the characteristic occupancy value X of a specific special cabin T T = V0 / S, and obtain the characteristic occupancy value of each special cabin corresponding to a certain warning record WR, and sum them up as the characteristic value of a certain warning record WR. If the characteristic value is greater than the preset numerical threshold, regard a certain warning record WR as a characteristic record, and obtain all the characteristic records.

[0047] Step S200: Extract the data corresponding to the section that needs to be updated in real time in the special cabin. In the processing process before rendering to the section, regard one processing process as one data record, calculate the target value corresponding to each data record in the special cabin corresponding to the characteristic record, and establish the first matrix according to the target value;

[0048] Each special cabin includes several sections that need to be updated in real time. Among them, each section updates data at a fixed time interval. The process of extracting, transforming, and loading the data updated in real time before rendering to the corresponding section of the special cabin is regarded as one data record; Extract a data record P of a specific special cabin T of a certain characteristic record FR within the warning time range. All the data extracted by the data record P are stored in the database. Obtain the number Q1 of the data extracted by the data record P, the number Q2 of the data whose format changes during the transformation process, and the number Q3 of the data rendered in the section corresponding to a specific special cabin T of the extracted data;

[0049] For the financial special cabin, the production special cabin, the human resource special cabin, and the marketing special cabin, there are sections that need to be updated in real time. The sections here are the reports or charts mentioned above; Before the data is rendered on the report or chart, it needs to go through the process of extraction, transformation, and loading. In this solution, this process is ETL, which is the abbreviation of the English Extract - Transform - Load, used to describe the process of extracting data from the source end, transforming it, and loading it to the destination end, which is an existing technology.

[0050] Step S220: Set the weights corresponding to the data quantities Q1, Q2, and Q3 to K1, K2, and K3 respectively, where K1, K2, and K3 are all greater than 0, and then obtain the target value of the data record P as Let \(e\) be the natural constant. Then, according to the fixed time interval for updating data, obtain the target values of all data records of a specific cabin \(T\) within the warning time range, and calculate the average value as the characteristic value of the specific cabin \(T\) corresponding to a certain characteristic record \(FR\). And based on the characteristic values of each specific cabin \(T\) corresponding to each characteristic record, establish the first matrix \(A\).

[0051] In this embodiment, the number of characteristic records is \(M\), and the number of specific cabins is \(N\). The established first matrix where is the characteristic value of the first specific cabin in the first characteristic record, and so on is the characteristic value of the \(N\)th specific cabin in the \(M\)th characteristic record.

[0052] Step S300: Obtain a period of time before the current moment, and get the time weight corresponding to each moment therein. Calculate the target value corresponding to each data record within a period of time, and establish the second matrix;

[0053] Step S310: Obtain the period \(F\) starting from the current moment going forward. According to the fixed time interval for updating data, obtain all data records within the period \(F\), and sort the data records in the order from the earliest to the latest according to the data record time; Set the weight corresponding to each data record according to the condition that the larger the serial number of the data record, the larger the weight, and the sum of the weights of all data records is 1;

[0054] Step S320: Obtain the data quantities \(Q1\), \(Q2\), and \(Q3\) corresponding to each data record of a specific cabin \(T\) within the period \(F\), and then calculate the target value corresponding to each data record; And according to the weight corresponding to each data record, the target specific cabin value of the current specific cabin \(T\) is obtained as: where \(U\) is the number of data records within the 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. Then, according to the target specific cabin values of each specific cabin, establish the second matrix \(B\).

[0055] Since the number of specific cabins is \(N\), according to the target specific cabin values of each specific cabin, the established second matrix where \(Y1\) is the target specific cabin value of the first specific cabin, and so on, \(Y\) N is the target specific cabin value of the \(N\)th specific cabin.

[0056] 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 give a warning prompt for the server's network resource occupancy according to the warning level.

[0057] Step S400 includes: summing up all the target special cabin values in the second matrix B to obtain the total target value. If the total target value is greater than the preset target value threshold, divide each target special cabin value by the total target value to obtain the target ratio corresponding to each special cabin at the current moment. Then, based on all the target ratios, establish the target ratio matrix C, multiply the first matrix A by the target ratio matrix C to obtain the matrix D, and take the maximum value of the elements in the matrix D as the warning level. If the warning level is greater than the preset level threshold, give a warning prompt for the network resource occupancy of the current server and prompt the relevant personnel to make adjustments.

[0058] If the total target value obtained from the second matrix B is small, it indicates that the current network resource occupancy is not caused by the special cabin, so it is not within the scope of analysis of this solution. While if the total target value is large and within the scope of analysis of this solution, it is necessary to establish the target ratio matrix C based on 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 special cabin, R N is 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 multiplied matrix D is an M×1 matrix (M rows and 1 column). In this solution, the obtained matrix 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 maximum value in the matrix D obtained by multiplying the current target ratio matrix C by the first matrix A is greater than the preset level threshold, it also means that the warning situation of the historical feature record corresponding to the maximum value is relatively similar. Therefore, it is necessary to give a warning prompt for the network resource occupancy of the server and prompt the relevant personnel to make adjustments.

[0059] In addition, this solution also provides a data management system based on the data cockpit, as Figure 2 shown, including a feature record extraction module, a first matrix establishment module, a second matrix establishment module, and a warning prompt module;

[0060] Feature record extraction module: used to obtain a number of historical warning records of the server where the data cockpit application program is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes several special cabins, and the special cabin is a section for analyzing and displaying the data in the data cockpit. Extract and analyze the resource occupancy situation of each special cabin in the warning records, and then extract the feature records from them;

[0061] The first matrix establishment module: It is used to extract the data corresponding to the sections in the dedicated cabin that need to be updated in real time. During the processing before rendering to the sections, one processing process is regarded as one data record, calculate the target value corresponding to each data record in the dedicated cabin corresponding to the feature record, and establish the first matrix according to the target value;

[0062] The second matrix establishment module: It is used to obtain a period of time before the current moment, and obtain the time weight corresponding to each moment therein, calculate the target value corresponding to each data record within a period of time, and establish the second matrix;

[0063] The early warning prompt module: It is used to calculate the early warning level of the server at the current moment according to the first matrix and the second matrix, and judge whether to give an early warning prompt for the network resource occupancy of the server according to the early warning level.

[0064] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A data management method based on a data cockpit, characterized in that Including the following steps: Step S100: Obtain a number of historical warning records of the server on which the data cockpit application is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes several dedicated cabins, and the dedicated cabin is a section for analyzing and displaying data in the data cockpit. Extract and analyze the resource occupancy of each dedicated cabin in the warning records, and then extract feature records therefrom; Step S200: Extract the data corresponding to the sections in the dedicated cabin that need to be updated in real time. Before rendering the data to the section, take one processing process as one data record. Calculate the target value corresponding to each data record in the dedicated cabin corresponding to the feature record, and establish a first matrix according to the target value; Step S300: Obtain a period of time before the current moment, and obtain the time weight corresponding to each moment therein. Calculate the target value corresponding to each data record within the period of time, and establish a second matrix; Step S400: According to the first matrix and the second matrix, calculate the warning level of the server at the current moment, and determine whether to give a warning prompt for the network resource occupancy of the server according to the warning level.

2. The data management method based on a data cockpit according to claim 1, wherein 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 resource at each moment within the warning time range; deploy a monitoring tool for the network resource on the server, and set an independent monitoring point for each dedicated 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 dedicated cabin; Step S120: According to a certain warning record WR within the warning time range D1, establish a first occupancy function of the resource occupancy value corresponding to a certain dedicated cabin T changing with time for each moment corresponding to the dedicated cabin T; obtain the resource occupancy value corresponding to each moment of a certain dedicated cabin T within the time range D2 before starting from the earliest moment in the warning time range D1, and establish a second occupancy function of the resource occupancy value corresponding to the dedicated cabin T changing with time; 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, obtain the total difference V0 = V1 - V2, and calculate the corresponding numerical variance S based on all the resource occupancy values in the first occupancy function, thereby obtaining the characteristic occupancy value X of a specific cabin T T = V0 / S, obtain the characteristic occupancy value of each cabin corresponding to the specific warning record WR, and sum them up as the characteristic value of the specific warning record WR. If the characteristic value is greater than the preset numerical threshold, regard the specific warning record WR as a characteristic record, and obtain all the characteristic records.

3. The data management method based on a data cockpit according to claim 1, characterized in that, Step S200 includes: Step S210: Each dedicated cabin includes several sections that need to be updated in real time. Among them, each section updates data at a fixed time interval. Take the process of extraction, transformation, and loading of the real-time updated data before rendering it to the section corresponding to the dedicated cabin as one data record; extract a data record P in a certain dedicated cabin T corresponding to a certain feature record FR within the warning time range. The data extracted by the data record P is stored in the database. Obtain the number Q1 of data extracted by the data record P, the number Q2 of data whose format changes during the transformation process, and the number Q3 of data rendered in the section corresponding to the dedicated cabin T of the extracted data; Step S220: Set the weights corresponding to the data quantities Q1, Q2, and Q3 to K1, K2, and K3 respectively, where K1, K2, and K3 are all greater than 0, and then obtain the target value of the data record P as e is the natural constant. Then, according to the fixed time interval for updating data, obtain the target values of all data records of a certain special cabin T within the early warning time range, and calculate the average value as the eigenvalue of the certain special cabin T corresponding to the certain feature record FR. And establish the first matrix A according to the eigenvalues of each special cabin T corresponding to each feature record.

4. A data management method based on a data cockpit according to claim 3, characterized in that, Step S300 includes: Step S310: Obtain the time period F starting from the current moment. According to the fixed time interval for updating data, obtain all data records within the time period F, and sort each data record in the order of data record time from front to back; Set the weight corresponding to each data record according to the condition that the larger the serial 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 quantities Q1, Q2, and Q3 corresponding to each data record of a specific cabin T within a time period F, and then calculate the target value corresponding to each data record; and based on the weight corresponding to each data record, obtain the target cabin value of the current specific cabin T as follows: where U is the number of data records within the time period F, and W u is the weight of the u-th data record, and H u is the target value of the u-th data record. Then, based on the target cabin values of each cabin, establish the second matrix B.

5. A data management method based on a data cockpit according to claim 1, characterized in that Step S400 includes: Summing all the target special cabin values in the second matrix B to obtain the total target value. If the total target value is greater than the preset target numerical threshold, divide each target special cabin value by the total target value to obtain the target ratio corresponding to each special cabin at the current moment. Then, according to all the target ratios, establish the target ratio matrix C, multiply the first matrix A by the target ratio matrix C to obtain the matrix D, and use the maximum value of the elements in the matrix D as the warning level. If the warning level is greater than the preset level threshold, give a warning prompt for the network resource occupancy of the current server and prompt relevant personnel to make adjustments.

6. A data management system for implementing a data management method based on a data cockpit according to any one of claims 1-5, characterized in that, The system includes a feature record extraction module, a first matrix establishment module, a second matrix establishment module, and a warning prompt module. Feature record extraction module: Used to obtain a number of historical warning records of the server where the data cockpit application program is deployed. The warning records are records where the network resource occupancy of the server exceeds the normal occupancy range. The data cockpit includes several special cabins, and the special cabin is a section for analyzing and displaying the data in the data cockpit. Extract and analyze the resource occupancy situation of each special cabin in the warning records, and then extract the feature records from them. First matrix establishment module: Used to extract the data corresponding to the sections in the special cabin that need to be updated in real time. In the processing process before rendering to the section, regard one processing process as one data record, calculate the target value corresponding to each data record in the special cabin corresponding to the feature record, and establish the first matrix according to the target value. Second matrix establishment module: Used to obtain a period of time before the current moment, and obtain the time weight corresponding to each moment therein, calculate the target value corresponding to each data record within the period of time, and establish the second matrix. Warning prompt module: Used to calculate the warning level of the server at the current moment according to the first matrix and the second matrix, and judge whether to give a warning prompt for the network resource occupancy of the server according to the warning level.

7. A data management system according to claim 6, wherein 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 occupancy value of the network resource at each moment within the warning time range; Deploy a monitoring tool for network resources and monitor the resource occupancy value of each special cabin. Occupancy function establishment unit: Used to establish the first occupancy function and the second occupancy function of the resource occupancy value corresponding to the special cabin changing with time according to the warning time range of the warning record. Feature record extraction unit: 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 values of the warning records, and then extract the characteristic records from the warning records.

8. A data management system according to claim 6, wherein The second matrix establishment module includes a weight setting unit and a second matrix establishment unit; The weight setting unit: used to obtain all data records within the time period starting from the current moment and going forward, and set the weight corresponding to each data record; The second matrix establishment unit: used to calculate the target value corresponding to each data record, and obtain the target special cabin value of the special cabin according to the weight corresponding to each data record, and then establish a second matrix.

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