A method and system for intelligent management of enterprise business data

Abstract

The present application relates to an intelligent management method and system for enterprise business data, and relates to the field of data management technology. The method includes obtaining the previous operation time point of each data; determining the idle interval duration based on the current time point and the previous operation time point; judging whether the idle interval duration is greater than the preset long-term storage interval duration; if the idle interval duration is not greater than the long-term storage interval duration, defining the current data as normal use data; if the idle interval duration is greater than the long-term storage interval duration, defining the current data as useless placement data; controlling the normal use data to be stored normally in the database, and controlling the useless placement data to be compressed and stored in the database. The present application has the effect of improving the management effect of enterprise business data.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular to a method and system for intelligent management of enterprise business data. Background Art

[0002] Business operating data refers to various information generated and collected during the operation of an enterprise. These data reflect many aspects of the enterprise, such as its operating conditions, market performance, and internal management efficiency, and play a key role in the enterprise's decision-making and performance evaluation.

[0003] In related technologies, since business operating data plays a key role in an enterprise, historical data is generally effectively preserved. Currently, data is generally stored in a database. When staff need to view business operating data, they can issue a call request to call the data in the database.

[0004] In the above-mentioned related technologies, as time goes by, the amount of data in the database will continue to grow, which not only puts pressure on the database's data storage capacity, but also causes the large amount of data to affect the data viewing speed, resulting in poor data management effect. There is still room for improvement. Summary of the Invention

[0005] In order to improve the management effect of enterprise business data, this application provides an enterprise business data intelligent management method and system.

[0006] In the first aspect, the present application provides a method for intelligent management of enterprise business data, which adopts the following technical solutions:

[0007] A method for intelligent management of enterprise operation data, comprising:

[0008] Get the previous operation time point of each data;

[0009] Determine the length of the leisure interval based on the current time point and the previous operation time point;

[0010] Determine whether the leisure interval is longer than the preset long-term interval;

[0011] If the idle interval is not longer than the long-term interval, the current data is defined as normal usage data;

[0012] If the idle interval is longer than the long-term interval, the current data is defined as useless placement data;

[0013] Control the normal use data to be stored normally in the database, and control the useless placement data to be compressed and stored in the database.

[0014] Optionally, the method further includes a step of determining the duration of the long-term storage interval, which includes:

[0015] Get the storage data type of the data;

[0016] Determine similar data in the database according to the storage data type, and obtain the storage start time point and the single operation time point according to the similar data;

[0017] Construct a usage record axis based on the storage start time point and all single operation time points, and determine the operation interval duration based on each adjacent node on the usage record axis;

[0018] Constructing a similar duration interval based on the operation interval duration and the preset similar duration, and combining the remaining similar data of the same type with at least one operation interval duration within the similar duration interval with the current similar data of the same type to construct a similar set;

[0019] Count similar data based on type to determine the number of similar data, and count similar data within similar sets to determine the number within the set;

[0020] Calculate the proportion within the set based on the number of similar types and the number within the set, and define similar sets whose proportion within the set is greater than the preset benchmark demand proportion as valid sets;

[0021] In each valid set, the operation interval duration with the largest value is determined from each interval with similar duration according to a preset sorting rule, and the operation interval duration is defined as the long-term interval duration.

[0022] Optionally, after the operation interval is determined, the enterprise operation data intelligent management method further includes:

[0023] Determine whether the operation interval is less than a preset reference interval;

[0024] If the operation interval is not less than the reference interval, the currently determined operation interval is maintained;

[0025] If the operation interval is shorter than the reference interval, the two nodes corresponding to the operation interval are grouped into a preset node grouping set that is initially empty.

[0026] Counting the nodes in the node summary set to determine the number of nodes in the set, and determining whether the number of nodes in the set is two;

[0027] If the number of nodes in the set is two, one node is randomly deleted from the two nodes in the node summary set on the use record axis;

[0028] If the number of nodes in the set is not two, a node is randomly selected from all nodes in the node summary set as the original node, and the remaining nodes are defined as the same batch of nodes;

[0029] Determine the node interval duration based on the original node and the nodes in the same batch, and calculate the average of all node interval durations to determine the average interval duration;

[0030] The minimum mean interval duration is determined according to the sorting rules, and the nodes of the same batch corresponding to the minimum mean interval duration are deleted on the usage record axis.

[0031] Optionally, after the averaging interval is determined, the enterprise operation data intelligent management method further includes:

[0032] Determine whether there are at least two original nodes with the same and minimum mean interval duration;

[0033] If there are not at least two original nodes with the same and smallest mean interval duration, the nodes in the same batch are deleted according to the original node corresponding to the smallest mean interval duration;

[0034] If there are at least two original nodes with the same and smallest mean interval duration, the original node corresponding to the smallest mean interval duration is defined as the candidate node;

[0035] At the candidate node, data browsing time is obtained based on similar data of the same type;

[0036] The data browsing time with the largest value is determined according to the sorting rules, and the same batch of nodes corresponding to the candidate nodes corresponding to the data browsing time are deleted.

[0037] Optionally, the steps of controlling the compressed storage of useless data in the database include:

[0038] The storage data type corresponding to the useless placement data is defined as a useless placement type;

[0039] Determine type-equivalent data under each compression package in the database according to the useless placement type, and count the type-equivalent data under each compression package to determine the compression equivalent quantity;

[0040] Determine the storage interval length according to the storage start time of each data in the compressed package and the storage start time of the useless placement data, and define the data in the compressed package whose storage interval length is less than the preset same batch interval length as the same batch data;

[0041] Count the same batch of data to determine the same batch quantity, and calculate the compression equivalent quantity and the same batch quantity to determine the reasonable compression value;

[0042] The maximum reasonable compression value is determined according to the sorting rule, and the compression package corresponding to the reasonable compression value is defined as the target package, and the current useless placement data is compressed and stored in the target package.

[0043] Optionally, after the target package is determined, the enterprise operation data intelligent management method may further include:

[0044] Determine the amount of data storage space based on the target package;

[0045] Determine whether the data storage space is greater than a preset upper limit space;

[0046] If the data storage space is not greater than the upper limit, the currently useless data is compressed and stored in the target package;

[0047] If the data storage space is greater than the upper limit, two data are randomly selected from the target package to determine the data deviation value according to the storage data type and the storage start time point;

[0048] Determine the data deviation value with the largest value according to the sorting rule, define the data corresponding to the data deviation value as the central data, and define the remaining data in the target package as the supplementary data;

[0049] Construct a split package based on the central data, determine the incidental deviation value based on the central data and the incidental data, and store the incidental data in the split package corresponding to the central data with the smaller incidental deviation value;

[0050] A compression rationality value is re-determined in the two split packets according to the current useless placement data, so as to compress and store the current useless placement data in the corresponding split packets.

[0051] Secondly, this application provides an intelligent management system for enterprise business data, which adopts the following technical solutions:

[0052] An intelligent management system for enterprise operation data, comprising:

[0053] An acquisition module is used to obtain the previous operation time point of each data;

[0054] A processing module, connected to the acquisition module and the judgment module, for storing and processing information;

[0055] The judgment module is connected with the acquisition module and the processing module and is used for judging the information;

[0056] The processing module determines the length of the leisure interval according to the current time point and the previous operation time point;

[0057] The judgment module judges whether the leisure interval duration is greater than the preset long-term interval duration;

[0058] If the judgment module determines that the idle interval is not longer than the long-term interval, the processing module defines the current data as normal usage data;

[0059] If the judging module determines that the idle interval is longer than the long-term interval, the processing module defines the current data as useless placement data;

[0060] The processing module controls the normal use data to be stored normally in the database, and controls the useless placement data to be compressed and stored in the database.

[0061] In a third aspect, the present application provides a computer storage medium capable of storing corresponding programs, which has the characteristics of improving the management effect of enterprise business data, and adopts the following technical solutions:

[0062] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the above-mentioned methods for intelligent management of enterprise business data.

[0063] In summary, this application includes at least one of the following beneficial technical effects:

[0064] When storing business operation data, the viewing status of each data can be analyzed to compress data that is basically not likely to be viewed, thereby freeing up database storage space and improving the management effect of business operation data;

[0065] Different storage intervals can be set according to different data situations, so that different types of data can be processed differently to improve the accuracy of data analysis;

[0066] When compressing data, data with a high degree of correlation can be compressed to facilitate subsequent exploration of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of the intelligent management method of enterprise business data.

[0068] Figure 2 It is a module flow chart of the intelligent management method of enterprise business data. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 2 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0070] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0071] The present application discloses a method for intelligent management of business data. Figure 1 The method flow of the enterprise operation data intelligent management method includes the following steps:

[0072] Step S100: Obtain the previous operation time point of each data.

[0073] The previous operation time point is the time point at which each data is last operated in the database, where the operation referred to includes the input of instructions on the data such as storage and viewing.

[0074] Step S101: Determine the length of the leisure interval according to the current time point and the previous operation time point.

[0075] The idle interval duration is the interval duration between the previous operation time point and the current time point, that is, the duration during which the data is not operated.

[0076] Step S102: Determine whether the idle interval duration is greater than the preset long-term interval duration.

[0077] The long-term idle interval is the minimum idle interval set by the staff to determine whether the data has not been operated for a long time and the possibility of subsequent operation is also relatively small. This value can be a fixed value or determined according to steps S200-S206; the purpose of the judgment is to find out whether there is a situation where the data has not been called for viewing or other operations for a long time.

[0078] Step S1021: If the idle interval duration is not greater than the long-term storage interval duration, the current data is defined as normal usage data.

[0079] When the idle interval is not longer than the long-term interval, it means that the data has not been operated for a long time. In this case, it is defined as normal usage data to distinguish different data and facilitate subsequent analysis.

[0080] Step S1022: If the idle interval duration is longer than the long-term storage interval duration, the current data is defined as useless storage data.

[0081] When the idle interval is longer than the long-term interval, it means that the data has not been operated for a long time. In this case, it is defined as useless placement data to distinguish different data and facilitate subsequent analysis.

[0082] Step S103: Control the normal use data to be stored normally in the database, and control the useless placement data to be compressed and stored in the database.

[0083] By compressing useless data to reduce the storage space occupied by the data in the database, the database space is released. At this time, not only can the data with a high probability of being operated be quickly viewed, but the data with a low probability of being operated can also be effectively stored. When the useless data needs to be operated in the future, it only needs to be decompressed in the database to view the data. The data will not be unable to be viewed and can be effectively stored, thereby improving the management effect of the enterprise's operating data.

[0084] The method further includes a step of determining the duration of the long-term interval, which includes:

[0085] Step S200: Obtain the storage data type of the data.

[0086] The storage data type is the type of data, which can be determined by the data field, such as sales performance table, yield rate, etc. The storage data type of the specific song data is determined by the staff when entering the data.

[0087] Step S201: determining similar data in a database according to the storage data type, and obtaining a storage start time point and a single operation time point according to the similar data.

[0088] Similar type data refers to data of the same type as the current storage data type. The storage start time point refers to the time point when similar type data is stored in the database. The single operation time point refers to the time point when the similar type data is operated after being stored.

[0089] Step S202: constructing a usage record axis according to the storage start time point and all single operation time points, and determining the operation interval duration according to each adjacent node on the usage record axis.

[0090] The record axis is a coordinate axis formed by combining various time points, and the corresponding time nodes are set for each storage start time point and single operation time point on the coordinate axis. The operation interval duration is the duration between two adjacent operations on the data.

[0091] Step S203: constructing a similar duration interval according to the operation interval duration and the preset similar duration, and combining the remaining similar type data having at least one operation interval duration within the similar duration interval with the current similar type data to construct a similar set.

[0092] The similar duration is the maximum difference allowed between the duration set by the staff for the recognition and operation interval duration that is close to each other and the operation interval duration. The similar duration interval is composed of the two time endpoints obtained by adding and subtracting the similar duration from the operation interval duration respectively; by constructing similar sets to identify data under the same processing conditions, it is convenient for subsequent analysis, and an example of the construction method of similar sets is as follows: for example, there are three data A, B, and C, wherein the operation interval durations of data A are 1, 2, and 3 respectively; the operation interval durations of data B are 1, 2, and 4 respectively; the operation interval durations of data C are 5, 6, and 4 respectively, and the similar duration is 0.5. When the similar interval is constructed with the operation interval duration of data A being 1, A and B can construct similar sets.

[0093] Step S204: Count the type-similar data to determine the type-similar quantity, and count the type-similar data in the similar set to determine the internal quantity of the set.

[0094] The number of similar types is the total number of all similar type data determined, and the number within the set is the total number of similar type data in the determined similar set.

[0095] Step S205: Calculate the internal proportion of the set based on the number of similar types and the number within the set, and define the similar set whose internal proportion is greater than the preset benchmark requirement proportion as a valid set.

[0096] The internal proportion of the set is the ratio of the type-similar data in a single similar set to all the type-similar data, which is determined by dividing the internal number of the set by the type-similar number; the benchmark requirement proportion is the minimum internal proportion of the set required by the staff to determine that there are enough type-similar data to represent the data of that type. By defining the valid set, different similar sets can be distinguished in advance, which is convenient for subsequent analysis.

[0097] Step S206 : determining the operation interval duration with the largest value from each interval with similar duration in each valid set according to a preset sorting rule, and defining the operation interval duration as the long-term interval duration.

[0098] The sorting rule is a method set by the staff to sort the size of the values, such as the bubble method. The sorting rule can be used to determine the operation interval with the largest value, that is, the operation interval is the maximum idle interval that will occur in the normal call of the current type of data. At this time, it is defined as the long-term interval to facilitate the analysis of the specific situation of the data.

[0099] After the operation interval is determined, the intelligent management method for enterprise business data also includes:

[0100] Step S300: Determine whether the operation interval is less than a preset reference interval.

[0101] The benchmark interval duration is the maximum operation interval duration allowed when the staff determines that two adjacent operations are the same operation. The purpose of the judgment is to know whether the operations corresponding to the current two nodes are the same operation. The same operation refers to the personnel who operate the data viewing the data for the same purpose. For example, after the staff views the sales performance table, they view the sales performance table again within a short period of time. It can be determined that the staff may have forgotten the data or re-analyzed the data, etc., which are all for the same purpose in the current situation. Therefore, it can be determined that it is the same operation.

[0102] Step S3001: If the operation interval duration is not less than the reference interval duration, the currently determined operation interval duration is maintained.

[0103] When the operation interval duration is not less than the reference interval duration, it indicates that the two operation nodes are not close to each other. In this case, the determined operation interval duration may be maintained for subsequent analysis.

[0104] Step S3002: If the operation interval duration is less than the reference interval duration, the two nodes corresponding to the operation interval duration are summarized into a preset node summary set that is initially empty.

[0105] When the operation interval is less than the benchmark interval, it means that the two operation nodes are close to each other, that is, the operations corresponding to the two operation nodes are for the same purpose. Therefore, the two nodes are summarized in the node summary set for subsequent analysis. The node summary method is as follows: when there are three nodes X, Y, and Z, the operation interval between the X node and the Y node is less than the benchmark interval, the operation interval between the Y node and the Z node is less than the benchmark interval, and the operation interval between the X node and the Z node is not less than the benchmark interval. Since Y can be summarized in the same node summary set with X and Z respectively, it can be determined that the operations corresponding to the three nodes X, Y, and Z are the same operation. Therefore, the three nodes X, Y, and Z can be summarized in the same node summary set.

[0106] Step S301: Count the nodes in the node summary set to determine the number of set nodes, and determine whether the number of set nodes is two.

[0107] The number of set nodes is the total number of nodes in the node summary set. The purpose of the judgment is to find out whether there are multiple nodes serving the same operation, which is to facilitate the determination of the actual operation situation.

[0108] Step S3011: If the number of nodes in the set is two, randomly delete one node from the two nodes in the node summary set on the usage record axis.

[0109] When the number of set nodes is two, it means that there are only two nodes serving the same operation. At this time, one of the nodes can be randomly deleted so as not to affect the subsequent determination of the operation interval duration.

[0110] Step S3012: If the number of nodes in the set is not two, randomly select one node from all nodes in the node summary set as the original node, and define the remaining nodes as nodes in the same batch.

[0111] When the number of set nodes is not two, it means that there are multiple nodes serving the same operation. At this time, it is more important to determine which node is the time node for the operation to facilitate the subsequent determination of the operation interval duration; define the original node and the same batch of nodes to distinguish different nodes for subsequent analysis.

[0112] Step S302: determining the node interval duration based on the original node and the nodes in the same batch, and performing an average calculation based on all the node interval durations to determine the average interval duration.

[0113] The node interval duration is the duration interval between the original node and the nodes in the same batch, and the mean interval duration is the average of all node interval durations determined under a single fixed original node.

[0114] Step S303: Determine the minimum average interval duration according to the sorting rule, and delete the same batch of nodes corresponding to the minimum average interval duration on the usage record axis.

[0115] The sorting rules can be used to determine the mean interval duration with the smallest value. That is, the distance between the original node corresponding to the mean interval duration and the remaining nodes is the shortest at this time, which means that the original node can be effectively represented by the remaining nodes. Therefore, the remaining nodes in the same batch except the original node can be deleted, so that the operation interval duration can be determined more accurately.

[0116] After the averaging interval is determined, the intelligent management method for enterprise business data also includes:

[0117] Step S400: Determine whether there are at least two original nodes with the same and minimum mean interval duration.

[0118] The purpose of the judgment is to find out whether there are multiple original nodes that meet the requirements, so as to facilitate subsequent analysis.

[0119] Step S4001: If there are not at least two original nodes with the same and smallest mean interval duration, the nodes in the same batch are deleted according to the original node corresponding to the smallest mean interval duration.

[0120] When there are not at least two original nodes with the same and smallest mean interval duration, it means that there is only one original node that meets the requirements. At this time, the nodes in the same batch can be deleted based on the original node.

[0121] Step S4002: If there are at least two original nodes with the same and smallest mean interval duration, the original node corresponding to the smallest mean interval duration is defined as a candidate node.

[0122] When there are at least two original nodes with the same and smallest mean interval duration, it means that there are multiple original nodes that meet the requirements. In this case, they are defined as candidate nodes to distinguish different original nodes and facilitate subsequent analysis.

[0123] Step S401: obtaining data browsing duration at a candidate node based on data of similar type.

[0124] The data browsing duration is the duration when an external person performs a call operation on similar data of the same type at an alternative node.

[0125] Step S402: determining the data browsing time with the largest value according to the sorting rule, and deleting the same batch of nodes corresponding to the candidate nodes corresponding to the data browsing time.

[0126] The sorting rules can be used to determine the data browsing time with the largest value. That is, the alternative node corresponding to the data browsing time is more representative of the operations performed by external personnel. At this time, the nodes in the same batch can be deleted based on the alternative node.

[0127] The steps to control the compression storage of useless data in the database include:

[0128] Step S500: defining the storage data type corresponding to the unused data as an unused type.

[0129] The useless placement type is defined to distinguish the storage data type of the useless placement data to facilitate subsequent analysis.

[0130] Step S501: determining type-identical data under each compression package in the database according to the useless placement type, and counting the type-identical data under each compression package to determine the compression equivalent quantity.

[0131] The type-identical data refers to data in the compressed package whose type is consistent with the useless placement type, and the compressed equivalent quantity refers to the amount of type-identical data determined in the compressed package.

[0132] Step S502: Determine the storage interval length according to the storage start time point of each data in the compressed package and the storage start time point of the useless placement data, and define the data in the compressed package with a storage interval length less than the preset same batch interval length as the same batch data.

[0133] The storage interval is the time interval between the data in the compressed package and the current useless data. The same batch interval is the maximum storage interval allowed when the staff identifies two data as the same batch and stores them together. By defining the same batch of data to identify different data, it is convenient for subsequent analysis.

[0134] Step S503: Count the same batch of data to determine the same batch quantity, and calculate the compression equivalent quantity and the same batch quantity to determine a reasonable compression value.

[0135] The number of the same batch is the total number of the determined same batch data. The reasonable compression value reflects the reasonable degree of compressing the current useless placement data into the compression package. The reasonable compression value also reflects the correlation degree between the current useless placement data and the rest of the data in the compression package. The larger the value, the higher the data correlation degree. The calculation formula of the reasonable compression value is σ=αM y *βM t , where σ is a reasonable value for compression, M y To compress the same quantity, M t is the same batch quantity, α and β are both positive fixed parameters used for calculation, and the values ​​are determined by the staff according to the actual situation.

[0136] Step S504: Determine the maximum reasonable compression value according to the sorting rule, define the compressed package corresponding to the reasonable compression value as the target package, and compress and store the current useless placement data into the target package.

[0137] The sorting rule can be used to determine the maximum reasonable compression value. That is, at this time, the data in the compressed package corresponding to the reasonable compression value has the highest correlation with the current useless placement data. At this time, it can be defined as the target package to compress the useless placement data, thereby compressing the data with a high degree of correlation together, which is convenient for subsequent data exploration, classification and other operations.

[0138] After the target package is determined, the intelligent management method for enterprise business data also includes:

[0139] Step S600: Determine the amount of data storage space according to the target package.

[0140] The data storage space amount is the storage space amount occupied by the data in the target package.

[0141] Step S601: Determine whether the data storage space is greater than a preset upper limit space.

[0142] The upper limit of space is the minimum amount of data storage space set by the staff to determine that a compressed package contains a lot of data. When there is a lot of data in the compressed package, the degree of correlation between the data may decrease, so further analysis is needed.

[0143] Step S6011: If the data storage space is not greater than the upper limit, the currently useless data is compressed and stored in the target package.

[0144] When the amount of data storage space is not greater than the upper limit, it indicates that there is not too much data in the target package, that is, the correlation between the data is high, and data compression can be performed normally.

[0145] Step S6012: If the data storage space is greater than the upper limit space, two data are randomly selected from the target packet to determine a data deviation value according to the storage data type and the storage start time point.

[0146] When the amount of data storage space is greater than the upper limit, it means that the correlation between the data in the target package is not high, so further analysis is needed. The data deviation value is the correlation between the two data. The larger the value, the lower the correlation between the two data. The calculation formula of the data deviation value is δ=γT s , where δ is the data deviation value, T s is the time interval between the storage start time points of the two data, γ is the parameter used for calculation, and the parameter is φ when the storage data types of the two data are the same, and ω when the storage data types of the two data are different, where φ<ω. The specific value is determined by the staff according to the actual situation.

[0147] Step S602: Determine the data deviation value with the largest value according to the sorting rule, define the data corresponding to the data deviation value as the central data, and define the remaining data in the target packet as the supplementary data.

[0148] The sorting rules can be used to determine the data deviation value with the largest value, that is, the correlation between the two corresponding data is the lowest at this time. At this time, the central data and the accompanying data are defined to distinguish different data, which is convenient for subsequent analysis.

[0149] Step S603: construct a split package based on the central data, determine the accompanying deviation value based on the central data and the accompanying data, and store the accompanying data in the split package corresponding to the central data with the smaller accompanying deviation value.

[0150] The split package is a compressed package, and the incidental deviation value is a parameter value representing the correlation between the central data and the incidental data. The calculation method of the incidental deviation value is consistent with the data deviation value and will not be described here. By storing the incidental data in the split package corresponding to the central data with a smaller incidental deviation value, the data with higher correlation can be stored together, which is convenient for subsequent data analysis. This method can be used to split the data again when the data volume in a single compressed package is large, thereby ensuring that each data in the database can be compressed according to the correlation.

[0151] Step S604 : re-determine the compression rationality value in the two split packets according to the current useless placement data, so as to compress and store the current useless placement data in the corresponding split packets.

[0152] By redetermining the association between data, currently useless data can be compressed and stored, thereby ensuring effective storage of data.

[0153] Reference Figure 2 Based on the same inventive concept, an embodiment of the present invention provides an intelligent management system for enterprise business data, including:

[0154] An acquisition module is used to obtain the previous operation time point of each data;

[0155] A processing module, connected to the acquisition module and the judgment module, for storing and processing information;

[0156] The judgment module is connected with the acquisition module and the processing module and is used for judging the information;

[0157] The processing module determines the length of the leisure interval according to the current time point and the previous operation time point;

[0158] The judgment module judges whether the leisure interval duration is greater than the preset long-term interval duration;

[0159] If the judgment module determines that the idle interval is not longer than the long-term interval, the processing module defines the current data as normal usage data;

[0160] If the judging module determines that the idle interval is longer than the long-term interval, the processing module defines the current data as useless placement data;

[0161] The processing module controls the normal use data to be stored in the database normally, and controls the useless placement data to be compressed and stored in the database;

[0162] A module for determining the duration of a storage interval for each type of data;

[0163] A usage record axis update module is used to analyze each node on the usage record axis to update the usage record axis;

[0164] The original node screening module is used to screen multiple original nodes that meet the requirements;

[0165] Data compression module, used to effectively compress each data;

[0166] The compressed packet splitting module is used to split compressed packets with large amounts of data compression.

[0167] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed by a method for intelligent management of enterprise business data.

[0169] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

Claims

1. A method for intelligent management of enterprise business data, characterized in that: include: Get the previous operation time point of each data; Determine the length of the leisure interval based on the current time point and the previous operation time point; Determine whether the leisure interval is longer than the preset long-term interval; If the idle interval is not longer than the long-term interval, the current data is defined as normal usage data; If the idle interval is longer than the long-term interval, the current data is defined as useless placement data; Control the normal storage of normal use data in the database, and control the compression storage of useless data in the database; The method further includes a step of determining the duration of the long-term interval, which includes: Get the storage data type of the data; Determine similar data in the database according to the storage data type, and obtain the storage start time point and the single operation time point according to the similar data; Construct a usage record axis based on the storage start time point and all single operation time points, and determine the operation interval duration based on each adjacent node on the usage record axis; Constructing a similar duration interval based on the operation interval duration and the preset similar duration, and combining the remaining similar data of the same type with at least one operation interval duration within the similar duration interval with the current similar data of the same type to construct a similar set; Count similar data based on type to determine the number of similar data, and count similar data within similar sets to determine the number within the set; Calculate the proportion within the set based on the number of similar types and the number within the set, and define similar sets whose proportion within the set is greater than the preset benchmark demand proportion as valid sets; In each valid set, the operation interval duration with the largest value is determined from each interval with similar duration according to a preset sorting rule, and the operation interval duration is defined as the long-term interval duration.

2. The method for intelligent management of business operation data according to claim 1, characterized in that: After the operation interval is determined, the intelligent management method for enterprise business data also includes: Determine whether the operation interval is less than a preset reference interval; If the operation interval is not less than the reference interval, the currently determined operation interval is maintained; If the operation interval is shorter than the reference interval, the two nodes corresponding to the operation interval are grouped into a preset node grouping set that is initially empty. Counting the nodes in the node summary set to determine the number of nodes in the set, and determining whether the number of nodes in the set is two; If the number of nodes in the set is two, one node is randomly deleted from the two nodes in the node summary set on the use record axis; If the number of nodes in the set is not two, a node is randomly selected from all nodes in the node summary set as the original node, and the remaining nodes are defined as the same batch of nodes; Determine the node interval duration based on the original node and the nodes in the same batch, and calculate the average of all node interval durations to determine the average interval duration; The minimum mean interval duration is determined according to the sorting rules, and the nodes of the same batch corresponding to the minimum mean interval duration are deleted on the usage record axis.

3. The method for intelligent management of business operation data according to claim 2, characterized in that: After the averaging interval is determined, the intelligent management method for enterprise business data also includes: Determine whether there are at least two original nodes with the same and minimum mean interval duration; If there are not at least two original nodes with the same and smallest mean interval duration, the nodes in the same batch are deleted according to the original node corresponding to the smallest mean interval duration; If there are at least two original nodes with the same and smallest mean interval duration, the original node corresponding to the smallest mean interval duration is defined as the candidate node; At the candidate node, data browsing time is obtained based on similar data of the same type; The data browsing time with the largest value is determined according to the sorting rules, and the same batch of nodes corresponding to the candidate nodes corresponding to the data browsing time are deleted.

4. The method for intelligent management of business operation data according to claim 1, characterized in that: The steps to control the compression storage of useless data in the database include: The storage data type corresponding to the useless placement data is defined as a useless placement type; Determine type-equivalent data under each compression package in the database according to the useless placement type, and count the type-equivalent data under each compression package to determine the compression equivalent quantity; Determine the storage interval length according to the storage start time of each data in the compressed package and the storage start time of the useless placement data, and define the data in the compressed package whose storage interval length is less than the preset same batch interval length as the same batch data; Count the same batch of data to determine the same batch quantity, and calculate the compression equivalent quantity and the same batch quantity to determine the reasonable compression value; The maximum reasonable compression value is determined according to the sorting rule, and the compression package corresponding to the reasonable compression value is defined as the target package, and the current useless placement data is compressed and stored in the target package.

5. The method for intelligent management of business operation data according to claim 4, characterized in that: After the target package is determined, the intelligent management method for enterprise business data also includes: Determine the amount of data storage space based on the target package; Determine whether the data storage space is greater than a preset upper limit space; If the data storage space is not greater than the upper limit, the currently useless data is compressed and stored in the target package; If the data storage space is greater than the upper limit, two data are randomly selected from the target package to determine the data deviation value according to the storage data type and the storage start time point; Determine the data deviation value with the largest value according to the sorting rule, define the data corresponding to the data deviation value as the central data, and define the remaining data in the target package as the supplementary data; Constructing a split package based on the central data, determining an incidental deviation value based on the central data and the incidental data, and storing the incidental data in the split package corresponding to the central data having the smaller incidental deviation value of the two central data; A compression rationality value is re-determined in the two split packets according to the current useless placement data, so as to compress and store the current useless placement data in the corresponding split packets.

6. An intelligent management system for enterprise business data, characterized in that: include: An acquisition module is used to obtain the previous operation time point of each data; A processing module, connected to the acquisition module and the judgment module, for storing and processing information; The judgment module is connected with the acquisition module and the processing module and is used for judging the information; The processing module determines the length of the leisure interval according to the current time point and the previous operation time point; The judgment module judges whether the leisure interval duration is greater than the preset long-term interval duration; If the judgment module determines that the idle interval is not longer than the long-term interval, the processing module defines the current data as normal usage data; If the judging module determines that the idle interval is longer than the long-term interval, the processing module defines the current data as useless placement data; The processing module controls the normal use data to be stored in the database normally, and controls the useless placement data to be compressed and stored in the database; The method further includes a step of determining the duration of the long-term interval, which includes: The storage data type of the data obtained by the acquisition module; The processing module determines similar data in the database according to the storage data type, and obtains the storage start time point and the single operation time point according to the similar data; The processing module constructs a usage record axis according to the storage start time point and all single operation time points, and determines the operation interval duration according to each adjacent node on the usage record axis; The processing module constructs a similar duration interval according to the operation interval duration and the preset similar duration, and combines the remaining similar data of the same type with the current similar data of the same type to construct a similar set; The processing module counts the type-similar data to determine the type-similar quantity, and counts the type-similar data within the similar set to determine the number within the set; The processing module calculates the internal ratio of the set based on the number of similar types and the number within the set, and defines the similar set whose internal ratio is greater than the preset benchmark requirement ratio as a valid set; The processing module determines the operation interval duration with the largest value from each interval with similar duration in each valid set according to a preset sorting rule, and defines the operation interval duration as the long-term interval duration.

7. A computer-readable storage medium, characterized in that The computer program is stored which can be loaded by a processor and execute the method for intelligent management of enterprise operation data as claimed in any one of claims 1 to 5.

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