Data closed-loop optimization processing method for enterprise data

Abstract

The present invention provides a data closed-loop optimization processing method for enterprise data, and relates to the technical field of big data analysis. The method includes collecting processing operation information data of different types of enterprise data, establishing a processing operation information database for enterprise data; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics to form data processing conversion guidance information; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing result characteristics to form data processing result guidance information; performing closed-loop optimization analysis based on the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data. The method optimizes, analyzes and judges by establishing a data processing information database of the closed-loop of target enterprise data, so as to achieve overall real-time and reasonable optimization of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a method and device for closed-loop optimization processing of enterprise data. Background Art

[0002] The data closed loop can make the data processing system more efficient in data analysis and processing, and can continuously optimize data processing based on the data processing flow to achieve the most efficient processing method. By monitoring each link of the data closed loop to track data changes in real time, it provides an important analytical basis for real-time improvement of closed-loop data processing methods to adapt to current data processing.

[0003] Data closed loops are increasingly being used in enterprise data processing. However, most enterprise data closed loops are still unable to efficiently achieve adaptive adjustment and optimization. Due to the diversity of data types, most of the time, only local or empirical adjustments are made manually, and the entire process of the data closed loop cannot be fully and efficiently optimized and adjusted.

[0004] Therefore, designing a data closed-loop optimization processing method for enterprise data, extracting reasonable processing information by establishing a data processing information database of the target enterprise data closed-loop, and then conducting targeted optimization analysis and judgment in the data processing process, so as to achieve overall real-time and reasonable optimization of data processing to ensure the efficiency and rationality of data processing, is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a data closed-loop optimization processing method for enterprise data, which establishes reasonable and complete important processing feature information in the closed-loop data processing process by collecting the processed operation information of the target enterprise data during the data closed-loop operation process, and determines the data closed-loop optimization method suitable for the current data type and data processing flow by reasonably analyzing and judging these data processing feature information. On the one hand, it can grasp the closed-loop data processing process as a whole from a macro perspective to avoid the negative impact of local monitoring and analysis on the entire processing process. On the other hand, it can also efficiently and reasonably realize the optimization and adjustment of the closed-loop data processing process, making the data processing process more efficient and reasonable, and highly timely.

[0006] In a first aspect, the present invention provides a data closed-loop optimization processing method for enterprise data, comprising collecting processing operation information data of different types of enterprise data and establishing a processing operation information database for the enterprise data; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics to form data processing conversion guidance information; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing result characteristics to form data processing result guidance information; performing closed-loop optimization analysis based on the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data.

[0007] In the present invention, the method establishes reasonable and complete important processing feature information in the closed-loop data processing process by collecting the processed operation information of the target enterprise data during the data closed-loop operation process, and determines the data closed-loop optimization method suitable for the current data type and data processing flow by reasonably analyzing and judging these data processing feature information. On the one hand, it can have a macro grasp of the closed-loop data processing process as a whole to avoid the negative impact of local monitoring and analysis on the entire processing process. On the other hand, it can also efficiently and reasonably realize the optimization and adjustment of the closed-loop data processing process, making the data processing process more efficient and reasonable, and highly timely.

[0008] As a possible implementation method, processing and operation information data of different types of enterprise data are collected to establish a processing and operation information database for enterprise data, including: collecting data on changes in the amount of data information of all types of enterprise data during the entire processing and operation process to form data on changes in the amount of information processing and operation of type data; collecting data on changes in information extraction time of all types of enterprise data during the entire processing and operation process to form data on changes in the extraction time of type data processing and operation; and collecting data on changes in the amount of information processing and operation of type data processing and operation extraction time corresponding to all types of enterprise data to establish a processing and operation information database.

[0009] In the present invention, the database for the processing and operation process of the enterprise's closed-loop data is a basic analysis and judgment information base for optimizing and analyzing the enterprise's closed-loop data processing process. By establishing a database, real-time data processing process information can be continuously obtained and reasonably classified and processed, thereby providing reasonable and effective reference data for subsequent extraction of database data to perform targeted optimization analysis of the data processing process.

[0010] As a possible implementation method, enterprise data processing information is extracted from a processing operation information database, and statistical analysis of the data processing information based on data processing conversion characteristics is performed to form data processing conversion guidance information, including: extracting information on changes in the amount of data information of each type of enterprise data during the processing operation process from the processing operation information database to form enterprise data information amount change information; extracting information on changes in data information extraction time of each type of enterprise data during the processing operation process from the processing operation information database to form enterprise data information extraction time change information; combining enterprise data information amount change information and enterprise data information extraction time change information to form data processing conversion guidance information.

[0011] In the present invention, the optimization analysis of the data closed loop is aimed at optimizing data processing. Therefore, when performing the optimization analysis, it is necessary to consider that the data extracted from the data closed loop is information closely related to the realization of data value. Here, the size of the data volume generated by the enterprise data in the data closed loop process and the time consumed for processing are extracted as the basic parameters for optimization analysis. As for the size of the data volume, it directly determines the size of the volume that is effectively extracted and analyzed and the effective data volume that can be formed by the data closed loop operation. Of course, this volume also comes from the data feature extraction method determined in the data closed loop, that is, the algorithm for data extraction, etc. Therefore, the size of the extracted data also reflects the effect and ability of data feature extraction. As for the processing consumption time, on the one hand, it determines the cost information consumed by the corresponding type of realization data when it is effectively extracted, which is not limited to resource consumption, man-hour consumption, etc. On the other hand, it also reflects the processing capacity and processing effect of the data feature extraction method. It should be noted that since the data processing in the data closed-loop process is time-sensitive, these parameters should be considered in the time dimension when extracting the amount of data and the processing time. Only in this way can the analysis result data have a strong timeliness, and then guide the data closed-loop to perform real-time and reasonable optimization and adjustment processing, so as to ensure the real-time and rationality of data closed-loop management.

[0012] As a possible implementation method, the data information amount change information of each type of enterprise data in the processing operation process is extracted from the processing operation information database to form the enterprise data information amount change information, including: determining the closed-loop optimization analysis cycle, extracting the data information amount size formed by each operation processing of each type of enterprise data in the processing operation information database, and forming the enterprise data processing information amount change curve within the closed-loop optimization analysis cycle = , where n represents the number of different types of enterprise data; according to the change curve of enterprise data processing information volume = , determine the corresponding enterprise data processing information volume change rate ,in, for The derivative in the time dimension.

[0013] In the present invention, the analysis of data size takes into account that all data in the data closed loop are analyzed and processed in real time, or even continuously and uninterruptedly, so when analyzing the data size in the time dimension, it is necessary to consider confirming the unit value of the data size. In this application, although the realization data is mostly processed and generated continuously and uninterruptedly, the extraction and analysis of the original data has sub-items and collection restrictions, so it can be performed in units of each extraction of sub-items, or in units of the maximum collection limit allowed for a single time. Here, the data size analysis is performed in units of the amount of data generated each time. Of course, considering that the optimization analysis needs to analyze the changing characteristics of the data size, the rate of change of the data size should be determined during the analysis to provide an important data basis for the subsequent optimization processing analysis.

[0014] As a possible implementation method, extracting the data information extraction time variation information of each type of enterprise data in the processing operation process from the processing operation information database to form the enterprise data information extraction time variation information includes: extracting the data information extraction time formed by each operation processing of each type of enterprise data in the processing operation information database, and forming the enterprise data processing information extraction time variation curve within the closed-loop optimization analysis cycle = ; Extract the duration change curve based on enterprise data processing information = , determine the corresponding enterprise data processing information extraction time change rate ,in, for The derivative in the time dimension.

[0015] In the present invention, similarly, for the data processing time, the maximum time consumed by the secondary extraction can be used as a unit of measurement, or the maximum time consumed by the data collected by the unit can be used as a unit of measurement to analyze the parameters in the time dimension. Here, in order to ensure the comparability of subsequent data analysis, the maximum time consumed by the single extracted data is used as a unit of measurement for analysis. After obtaining the change data of the processing time in the time dimension, the change rate data of the processing time is extracted to provide an important data basis for subsequent analysis.

[0016] As a possible implementation method, enterprise data processing information is extracted from the processing operation information database, and statistical analysis is performed on the data processing information based on the characteristics of the data processing results to form data processing result guidance information, including: extracting information on the amount of information extracted from the amount of data information generated during the processing operation of each type of enterprise data from the processing operation information database, and forming a curve of the change in the amount of enterprise data extraction = ; According to the change curve of enterprise data extraction volume , determine the change rate of enterprise data extraction volume of the corresponding enterprise data during the optimization analysis cycle ,in, for The derivative in the time dimension.

[0017] In the present invention, the value realization of enterprise data formed by the data closed loop is directly reflected in its ability to be extracted and utilized. Therefore, the information obtained for the realization application is a parameter represented by the amount of data extracted for realization in this application. Similarly, the amount of enterprise data extracted is time-sensitive due to the specific circumstances of demand and value realization, so the amount of extracted data is expressed in the time dimension so that the formed data is also time-sensitive, and then the analysis result data is made time-sensitive during the optimization analysis, thereby ensuring the timeliness and rationality of the data closed loop optimization analysis. Of course, the rate of change of the extracted amount is also a parameter data that needs to be focused on in the optimization analysis.

[0018] As a possible implementation method, a closed-loop optimization analysis is performed based on the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data, including: based on the change rate of the enterprise data processing information volume and the rate of change in enterprise data extraction , perform the following optimization analysis: If and Both show a gradual upward trend, so combined with the change rate of enterprise data processing information volume , Change rate of enterprise data extraction And the change rate of enterprise data processing information extraction time , to optimize the processing and analysis of the continuous increase in the amount of information, and form the optimization processing and analysis result information of the continuous increase in the amount of information with the same trend; if There is a non-gradual upward trend. It shows a gradual upward trend, so the change rate of enterprise data processing information extraction time is combined , to optimize the processing and analysis of the increase in information volume, and form the optimization processing and analysis result information of the asynchronous trend of continuous increase in information volume; if If the trend is not gradual, then the enterprise data extraction volume change curve , Change rate of enterprise data processing information extraction time and the rate of change in the amount of data extracted by the enterprise , and perform non-continuous increase in optimization processing and analysis result information.

[0019] In the present invention, after obtaining the real-time effective parameter data for optimization analysis, in order to realize the reasonable and accurate optimization adjustment of the data closed loop operation, it is necessary to reasonably analyze these parameter data to form important and accurate guiding data for reference of optimization adjustment analysis. Here, for the three parameter data of the amount of data formed, the processing time, and the amount of data extracted, the amount of data extracted and the amount of data formed directly determine the specific situation in which the data provided by the data closed loop is fully utilized, that is, how much of the formed data is the effective transformation of value realization. Therefore, when performing optimization analysis, the changes of these two parameters are analyzed and judged first, and other parameter data are obtained for different situations based on the judgment results for analysis and processing, so as to obtain the guiding data required for optimization analysis. Of course, according to the analysis of the parameter data, the analysis results for different types of enterprise data will be formed, and these situations are different. Therefore, different optimization processing guiding information is formed for the formation, which provides an important data basis for the optimization analysis of the data closed loop, so as to effectively ensure the real-time and rationality of the data closed loop. It should be noted here that there are various ways to judge the gradually rising trend. It can be to judge whether the span of the entire growth amount meets expectations within the optimization analysis period, or to judge whether the growth rate is greater than zero within the optimization analysis period occupies the expected proportion of time, or to judge whether the growth rate is less than zero within the optimization analysis period and whether it is less than the expected proportion of time, etc. When conducting analysis, reasonable judgment criteria are selected in combination with different types of monetization data to ensure the correctness and rationality of trend analysis.

[0020] As a possible implementation method, if and Both show a gradual upward trend, so combined with the change rate of enterprise data processing information volume , Change rate of enterprise data extraction And the change rate of enterprise data processing information extraction time , to optimize the processing and analysis of the continuous increase in the amount of information, and to form the optimization processing and analysis result information of the continuous increase in the amount of information with the same trend, including: setting the stable threshold range of the processing time change rate of the corresponding enterprise data =[ , ], Indicates the stable threshold range of the processing time change rate The minimum value of Indicates the stable threshold range of the processing time change rate The maximum value of , and the following optimization analysis is performed: < , > , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the information volume occupied and reserved curve; when < , ∈ , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when < , < , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when ≥ , then determine the change curve of the remaining information volume in the time dimension after the information volume of enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the information volume saving curve.

[0021] In the present invention, when both the amount of generated data and the amount of extracted data are showing an upward trend, it is first considered that the demand for the corresponding type of enterprise data is large and the value realization ability is strong. Combined with the change rate analysis, if the change rate of the amount of data extracted does not exceed the change rate of the amount of data formed, then in the case of a strong growth in the processing time, it can be considered that this type of enterprise data requires a long time to form. Although the amount of data formed is gradually increasing, due to the low frequency of formation, the amount of data remaining unextracted will gradually decrease with the increase in the amount of extraction. Therefore, considering that the storage space of the remaining amount of data after the formation and extraction of this type of enterprise data will decrease, the change curve of the remaining amount of data is output to provide guidance data for the optimization of the storage space of this type of enterprise data. For the case where the rate of change of the amount of data does not exceed the rate of change of the amount of data formed, and the processing time tends to be stable or has a decreasing trend, although the amount of data formed is gradually increased, considering that the formation frequency remains unchanged or the frequency increases, the amount of data remaining will increase, and then more storage space will be occupied, so the change curve of the remaining amount is formed to provide a guidance reference for the expansion optimization of the storage of the remaining amount of data of this type. When the rate of change of the amount of extracted data is greater than or equal to the rate of change of the amount of generated data, it is considered that more data will be extracted later, so the amount of remaining data will gradually decrease, thus forming a curve of the change in the amount of remaining data, providing guiding reference data for the reasonable reduction and optimization of the storage space of this type of enterprise data.

[0022] As a possible implementation method, if There is a non-gradual upward trend. It shows a gradual upward trend, so the change rate of enterprise data processing information extraction time is combined , optimize the processing and analysis of the increase in information volume, and form the information volume continues to increase asynchronous trend optimization processing analysis result information: set the information volume processing time threshold corresponding to different enterprise data , and perform the following optimization analysis: ≤ , then the corresponding enterprise data is calibrated as stable object data; when > , then the corresponding enterprise data is calibrated as the object data to be optimized, and the information processing time threshold is determined Limit the time for information processing.

[0023] In the present invention, when the amount of data formed by the extracted unchanged amount shows an upward trend and the amount of data formed shows a non-increasing trend, it means that the proportion of this type of enterprise data being effectively extracted is increasing, and the processing time for forming this type of enterprise data becomes an important factor that directly restricts its completion of the data closed loop. Of course, the processing time of any type of enterprise data will have a theoretically reasonable limit value, and the gap between the actual processing time and the theoretical limit value is often determined by factors such as the system data processing performance and the limited nature of data processing, and can be optimized and adjusted. Therefore, if the processing time is less than this limit value, it is considered reasonable and can meet the needs of data extraction, and if it exceeds this limit value, it is necessary to use the limit value as the optimization target to perform feature extraction optimization based on the optimization target, so this type of enterprise data is calibrated, and this time limit value is provided as a reference guide for optimization adjustment.

[0024] As a possible implementation method, if If the trend is not gradual, then the enterprise data extraction volume change curve , Change rate of enterprise data processing information extraction time and the rate of change in the amount of data extracted by the enterprise , to perform non-continuous optimization processing and analysis of the result information, including: setting data information volume limits for different types of enterprise data , and perform the following optimization analysis: There is a gradual upward trend, and < , the corresponding enterprise data is marked as stable object data; when There is a gradual upward trend, and ≥ , the corresponding enterprise data is marked as the object data to be optimized, and the data information volume limit value is determined is the information extraction limit; when When the trend is not gradually increasing, set the information extraction unit value of different types of enterprise data , and perform the following optimization analysis: > , > , the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value of information processing time; > , ∈ , then the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value of information processing time; > , < , the corresponding enterprise data is marked as stable object data; when ≤ , the corresponding realization data is marked as deleted object data.

[0025] In the present invention, the amount of data extracted shows a non-increasing trend. It can be considered that with the change of time and the change of demand, the demand for this type of data volume will gradually decrease. Therefore, in order to optimize the data composition of the data closed loop and provide more resources for other types of enterprise data with large demand, a certain degree of optimization and adjustment is required. If the amount of data formed is still gradually increasing, then consider setting a reasonable data volume formation limit for this type of monetization data, and analyze and judge whether to optimize or not based on this limit. For those that need to be optimized, the limit is used as a target for feature extraction optimization guidance reference. For the situation where the amount of data formed is also gradually decreasing, consider whether it is necessary to reasonably dismantle or adjust this type of monetization data. First, consider the amount of data extracted. If the amount of data extracted is still greater than the set minimum threshold, it is considered that this type of enterprise data still has a certain value realization significance. When the processing time is not less than the set time threshold, the set time threshold is used as the target for optimization and adjustment guidance. When the processing time is less than the set time threshold, no optimization and adjustment processing is performed. If the amount of extracted data is less than the set minimum threshold, it is considered that this type of enterprise data has no reasonable value realization significance and can be deleted to provide resources for other enterprise data to improve the role and effect of the data closed loop.

[0026] The data closed-loop optimization processing method and device for enterprise data provided by the present invention have the following beneficial effects:

[0027] This method establishes reasonable and complete important processing feature information in the closed-loop data processing process by collecting the processed operation information of the target enterprise data during the data closed-loop operation process, and determines the data closed-loop optimization method suitable for the current data type and data processing flow through reasonable analysis and judgment of these data processing feature information. On the one hand, it can have a macro grasp of the closed-loop data processing process as a whole to avoid the negative impact of local monitoring and analysis on the entire processing process. On the other hand, it can also efficiently and reasonably realize the optimization and adjustment of the closed-loop data processing process, making the data processing process more efficient and reasonable, and highly timely. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A step diagram of a data closed-loop optimization processing method for enterprise data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] The data closed loop can make the data processing system more efficient in data analysis and processing, and can continuously optimize data processing based on the data processing flow to achieve the most efficient processing method. By monitoring each link of the data closed loop to track data changes in real time, it provides an important analytical basis for real-time improvement of closed-loop data processing methods to adapt to current data processing.

[0032] Data closed loops are increasingly being used in enterprise data processing. However, most enterprise data closed loops are still unable to efficiently achieve adaptive adjustment and optimization. Due to the diversity of data types, most of the time, only local or empirical adjustments are made manually, and the entire process of the data closed loop cannot be fully and efficiently optimized and adjusted.

[0033] refer to Figure 1The embodiment of the present invention provides a data closed-loop optimization processing method for enterprise data. The method establishes reasonable and complete important processing feature information in the closed-loop data processing process by collecting the processed operation information of the target enterprise data in the data closed-loop operation process, and determines the data closed-loop optimization method suitable for the current data type and data processing flow by reasonably analyzing and judging these data processing feature information. On the one hand, the closed-loop data processing process can be grasped at a macro level as a whole to avoid the negative impact of local monitoring and analysis on the entire processing process. On the other hand, the closed-loop data processing process can be optimized and adjusted efficiently and reasonably, making the data processing process more efficient and reasonable, and highly timely.

[0034] The data closed-loop optimization processing method for enterprise data specifically includes the following steps:

[0035] S1: Collect processing and operation information data of different types of enterprise data and establish a processing and operation information database of enterprise data.

[0036] Collect processing and operation information data of different types of enterprise data, and establish a processing and operation information database for enterprise data, including: collecting data on changes in the amount of data information of all types of enterprise data during the entire processing and operation process, to form data on changes in the amount of information processing and operation of type data; collecting data on changes in information extraction time of all types of enterprise data during the entire processing and operation process, to form data on changes in the extraction time of type data processing and operation; and collecting data on changes in the amount of information processing and operation of type data processing and operation extraction time corresponding to all types of enterprise data, to establish a processing and operation information database.

[0037] The database for the processing and operation process of the enterprise's closed-loop data is a basic analysis and judgment information base for optimizing the enterprise's closed-loop data processing process. By establishing a database, real-time data processing process information can be continuously obtained and reasonably classified and processed, thereby providing reasonable and effective reference data for the subsequent extraction of database data to perform targeted optimization analysis of the data processing process.

[0038] S2: extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics to form data processing conversion guidance information.

[0039] Enterprise data processing information is extracted from the processing operation information database, and statistical analysis of the data processing information based on data processing conversion characteristics is performed to form data processing conversion guidance information, including: extracting information on changes in the amount of data information of each type of enterprise data during the processing operation process from the processing operation information database to form enterprise data information amount change information; extracting information on changes in data information extraction time of each type of enterprise data during the processing operation process from the processing operation information database to form enterprise data information extraction time change information; combining enterprise data information amount change information and enterprise data information extraction time change information to form data processing conversion guidance information.

[0040] The optimization analysis of the data closed loop aims at optimizing data processing. Therefore, when conducting the optimization analysis, it is necessary to consider that the data extracted from the data closed loop is information closely related to the realization of data value. Here, the amount of data generated by the enterprise data in the data closed loop process and the time consumed for processing are extracted as the basic parameters for optimization analysis. The size of the data volume directly determines the size of the volume that is effectively extracted and analyzed and the effective data volume that can be formed by the data closed loop operation. Of course, this volume also comes from the data feature extraction method determined in the data closed loop, that is, the data extraction algorithm, etc. Therefore, the size of the extracted data also reflects the effect and ability of data feature extraction. For the processing consumption time, on the one hand, it determines the cost information consumed by the corresponding type of realization data when it is effectively extracted, which is not limited to resource consumption, man-hour consumption, etc. On the other hand, it also reflects the processing capacity and processing effect of the data feature extraction method. It should be noted that since the data processing in the data closed-loop process is time-sensitive, these parameters should be considered in the time dimension when extracting the amount of data and the processing time. Only in this way can the analysis result data have a strong timeliness, and then guide the data closed-loop to perform real-time and reasonable optimization and adjustment processing, so as to ensure the real-time and rationality of data closed-loop management.

[0041] Extracting the data information volume change information of each type of enterprise data in the processing and operation process from the processing and operation information database to form the enterprise data information volume change information, including: determining the closed-loop optimization analysis cycle, extracting the data information volume size formed by each type of enterprise data in the processing and operation information database each time the operation is processed, and forming the enterprise data processing information volume change curve within the closed-loop optimization analysis cycle. = , where n represents the number of different types of enterprise data; according to the change curve of enterprise data processing information volume = , determine the corresponding enterprise data processing information volume change rate ,in, for The derivative in the time dimension.

[0042] The analysis of the data size takes into account that all data in the data closed loop are analyzed and processed in real time, or even continuously and uninterruptedly. Therefore, when analyzing the data size in the time dimension, it is necessary to consider confirming the unit value of the data size. In this application, although the realization data is mostly processed and generated continuously and uninterruptedly, the extraction and analysis of the original data are subject to sub-items and collection restrictions. Therefore, it can be performed in units of each extraction of sub-items, or in units of the maximum collection limit allowed for a single time. Here, the data size analysis is performed in units of the amount of data generated each time. Of course, considering that the optimization analysis requires analysis of the changing characteristics of the data size, the rate of change of the data volume should be determined during the analysis to provide an important data basis for the subsequent optimization processing analysis.

[0043] Extracting the data information extraction time variation information of each type of enterprise data in the processing and operation process from the processing and operation information database to form the enterprise data information extraction time variation information, including: extracting the data information extraction time formed by each operation and processing of each type of enterprise data in the processing and operation information database, and forming the enterprise data processing information extraction time variation curve within the closed-loop optimization analysis cycle. = ; Extract the duration change curve based on enterprise data processing information = , determine the corresponding enterprise data processing information extraction time change rate ,in, for The derivative in the time dimension.

[0044] Similarly, for data processing time, the maximum time consumed by the secondary extraction can be used as a unit of measurement, or the maximum time consumed by the data collected by the unit can be used as a unit of measurement to analyze the parameters in the time dimension. Here, in order to ensure the comparability of subsequent data analysis, the maximum time consumed by the single extraction of data is also used as a unit of measurement for analysis. After obtaining the change data of the processing time in the time dimension, the change rate data of the processing time is extracted to provide an important data basis for subsequent analysis.

[0045] S3: extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on the characteristics of the data processing results to form data processing result guidance information.

[0046] Extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on the characteristics of the data processing results to form data processing result guidance information, including: extracting information on the amount of information extracted from the amount of data information generated during the processing operation of each type of enterprise data from the processing operation information database, and forming a curve of the change in the amount of enterprise data extraction = ; According to the change curve of enterprise data extraction volume , determine the change rate of enterprise data extraction volume of the corresponding enterprise data during the optimization analysis cycle ,in, for The derivative in the time dimension.

[0047] For the enterprise data formed by the data closed loop, its value realization is directly reflected in its ability to be extracted and utilized. Therefore, the information obtained for the realization application is expressed in this application as a parameter of the amount of data extracted for realization. Similarly, the amount of enterprise data extracted is time-sensitive due to the specific circumstances of demand and value realization, so the amount of extracted data is expressed in the time dimension so that the formed data is also time-sensitive, and then the analysis result data is made time-sensitive during the optimization analysis, thus ensuring the timeliness and rationality of the data closed loop optimization analysis. Of course, the rate of change of the extracted amount is also a parameter data that needs to be focused on in the optimization analysis.

[0048] S4: Perform closed-loop optimization analysis based on the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data.

[0049] According to the data processing conversion guidance information and data processing result guidance information, closed-loop optimization analysis is carried out to form closed-loop optimization guidance data, including: according to the change rate of enterprise data processing information and the rate of change in enterprise data extraction , perform the following optimization analysis: If and Both show a gradual upward trend, so combined with the change rate of enterprise data processing information volume , Change rate of enterprise data extraction And the change rate of enterprise data processing information extraction time , to optimize the processing and analysis of the continuous increase in the amount of information, and form the optimization processing and analysis result information of the continuous increase in the amount of information with the same trend; if There is a non-gradual upward trend. It shows a gradual upward trend, so the change rate of enterprise data processing information extraction time is combined , to optimize the processing and analysis of the increase in information volume, and form the optimization processing and analysis result information of the asynchronous trend of continuous increase in information volume; if If the trend is not gradual, then the enterprise data extraction volume change curve , Change rate of enterprise data processing information extraction time and the rate of change in the amount of data extracted by the enterprise , and perform non-continuous increase in optimization processing and analysis result information.

[0050] After obtaining the real-time effective parameter data for optimization analysis, in order to achieve reasonable and accurate optimization and adjustment of the data closed-loop operation, it is necessary to reasonably analyze these parameter data and form important and accurate guiding data for reference for optimization and adjustment analysis. Here, for the three parameter data of the amount of data formed, the processing time, and the amount of data extracted, the amount of data extracted and the amount of data formed directly determine the specific situation of the data provided by the data closed loop being fully utilized, that is, how much of the formed data has completed the effective transformation of value realization. Therefore, when performing optimization analysis, it is preferred to analyze and judge the changes in these two parameters, and on the basis of the judgment results, other parameter data are obtained for different situations for analysis and processing, and then the guiding data required for optimization analysis is obtained. Of course, according to the analysis of parameter data, the analysis results for different types of enterprise data will be formed, and these situations are different. Therefore, different optimization processing guiding information is formed for the formation, which provides an important data basis for the optimization analysis of the data closed loop, so as to effectively ensure the real-time and rationality of the data closed loop. It should be noted here that there are various ways to judge the gradually rising trend. It can be to judge whether the span of the entire growth amount meets expectations within the optimization analysis period, or to judge whether the growth rate is greater than zero within the optimization analysis period occupies the expected proportion of time, or to judge whether the growth rate is less than zero within the optimization analysis period and whether it is less than the expected proportion of time, etc. When conducting analysis, reasonable judgment criteria are selected in combination with different types of monetization data to ensure the correctness and rationality of trend analysis.

[0051] like and Both show a gradual upward trend, so combined with the change rate of enterprise data processing information volume , Change rate of enterprise data extraction And the change rate of enterprise data processing information extraction time , to optimize the processing and analysis of the continuous increase in the amount of information, and to form the optimization processing and analysis result information of the continuous increase in the amount of information with the same trend, including: setting the stable threshold range of the processing time change rate of the corresponding enterprise data =[ , ], Indicates the stable threshold range of the processing time change rate The minimum value of Indicates the stable threshold range of the processing time change rate The maximum value of , and the following optimization analysis is performed: < , > , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the information volume occupied and reserved curve; when < , ∈ , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when < , < , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when ≥ , then determine the change curve of the remaining information volume in the time dimension after the information volume of enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the information volume saving curve.

[0052] When both the amount of generated data and the amount of extracted data are showing an upward trend, it is first considered that the demand for the corresponding type of enterprise data is large and the value realization ability is strong. Combined with the change rate analysis, if the change rate of the amount of extracted data does not exceed the change rate of the amount of data formed, then in the case of a strong growth in the processing time, it can be considered that this type of enterprise data requires a long time to form. Although the amount of data formed is gradually increasing, due to the low frequency of formation, the amount of remaining unextracted data will gradually decrease with the increase in the amount of extraction. Therefore, considering that the storage space of the remaining amount of data after this type of enterprise data is formed and extracted will decrease, the change curve of the remaining data volume is output to provide guidance data for the optimization of the storage space of this type of enterprise data. When the data volume change rate does not exceed the change rate of the amount of data formed, and the processing time tends to be stable or has a decreasing trend, although the amount of data formed is gradually increased, considering that the frequency of formation remains unchanged or the frequency increases, the amount of remaining data will increase, which will occupy more storage space. Therefore, the change curve of the remaining amount is formed to provide a guidance reference for the expansion and optimization of the storage of the remaining data volume of this type. When the rate of change of the amount of extracted data is greater than or equal to the rate of change of the amount of generated data, it is considered that more data will be extracted later, so the amount of remaining data will gradually decrease, thus forming a curve of the change in the amount of remaining data, providing guiding reference data for the reasonable reduction and optimization of the storage space of this type of enterprise data.

[0053] like There is a non-gradual upward trend. It shows a gradual upward trend, so the change rate of enterprise data processing information extraction time is combined , optimize the processing and analysis of the increase in information volume, and form the information volume continues to increase asynchronous trend optimization processing analysis result information: set the information volume processing time threshold corresponding to different enterprise data , and perform the following optimization analysis: ≤ , then the corresponding enterprise data is calibrated as stable object data; when > , then the corresponding enterprise data is calibrated as the object data to be optimized, and the information processing time threshold is determined Limit the time for information processing.

[0054] When the amount of data formed by the extracted constant is not increasing while the amount of data being extracted is increasing, it means that the proportion of this type of enterprise data being effectively extracted is increasing, and the processing time of this type of enterprise data becomes an important factor that directly restricts the completion of the data closed loop. Of course, there will be a theoretically reasonable limit value for the processing time of any type of enterprise data. The gap between the actual processing time and the theoretical limit value is often determined by factors such as the system data processing performance and the limited nature of data processing, and can be optimized and adjusted. Therefore, if the processing time is less than this limit, it is considered reasonable and can meet the needs of data extraction. If it exceeds this limit, it is necessary to optimize the feature extraction based on the optimization target with the limit as the optimization target. Therefore, this type of enterprise data is calibrated, and this time limit is provided as a reference guide for optimization adjustment.

[0055] like If the trend is not gradual, then the enterprise data extraction volume change curve , Change rate of enterprise data processing information extraction time and the rate of change in the amount of data extracted by the enterprise , to perform non-continuous optimization processing and analysis of the result information, including: setting data information volume limits for different types of enterprise data , and perform the following optimization analysis: There is a gradual upward trend, and < , the corresponding enterprise data is marked as stable object data; when There is a gradual upward trend, and ≥ , the corresponding enterprise data is marked as the object data to be optimized, and the data information volume limit value is determined is the information extraction limit; when When the trend is not gradually increasing, set the information extraction unit value of different types of enterprise data , and perform the following optimization analysis: > , > , then the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value of information processing time; > , ∈ , the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value of information processing time; > , < , the corresponding enterprise data is marked as stable object data; when ≤ , the corresponding realization data is marked as deleted object data.

[0056] The amount of data extracted shows a non-increasing trend. It can be considered that with the change of time and demand, the demand for this type of data will gradually decrease. Therefore, in order to optimize the data structure of the data closed loop and provide more resources for other types of enterprise data with high demand, a certain degree of optimization and adjustment is required. If the amount of data generated is still gradually increasing, then consider setting a reasonable data amount formation limit for this type of monetization data, and analyze and judge whether to optimize it based on this limit. For those that need to be optimized, use the limit as a target for optimization guidance reference for feature extraction. For the case where the amount of data generated is also gradually decreasing, consider whether it is necessary to reasonably dismantle or adjust this type of monetization data. First consider the amount of data extracted. If the amount of data extracted is still greater than the set minimum threshold, it is considered that this type of enterprise data still has a certain value realization significance. When the processing time is not less than the set time threshold, the set time threshold is used as the target for optimization and adjustment guidance. When the processing time is less than the set time threshold, no optimization and adjustment processing is performed. If the amount of extracted data is less than the set minimum threshold, it is considered that this type of enterprise data has no reasonable value realization significance and can be deleted to provide resources for other enterprise data to improve the role and effect of the data closed loop.

[0057] In summary, the data closed-loop optimization processing method and device for enterprise data provided by the embodiments of the present invention have the following beneficial effects:

[0058] This method establishes reasonable and complete important processing feature information in the closed-loop data processing process by collecting the processed operation information of the target enterprise data during the data closed-loop operation process, and determines the data closed-loop optimization method suitable for the current data type and data processing flow through reasonable analysis and judgment of these data processing feature information. On the one hand, it can have a macro grasp of the closed-loop data processing process as a whole to avoid the negative impact of local monitoring and analysis on the entire processing process. On the other hand, it can also efficiently and reasonably realize the optimization and adjustment of the closed-loop data processing process, making the data processing process more efficient and reasonable, and highly timely.

[0059] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0060] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0061] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0062] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0063] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0064] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0065] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. These three situations, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0066] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0069] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0070] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0071] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0078] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data closed-loop optimization processing method for enterprise data, characterized in that: include: Collect processing and operation information data of different types of enterprise data and establish a processing and operation information database of enterprise data; Extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics to form data processing conversion guidance information; Extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing result characteristics to form data processing result guidance information; Performing closed-loop optimization analysis according to the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data; Among them, the processing and operation information data of different types of enterprise data are collected to establish a processing and operation information database of enterprise data, including: Collect the data information volume change data of all types of enterprise data during the entire processing and operation process to form the data volume change data of type data processing and operation; Collect the information extraction time variation data of all types of enterprise data during the entire processing and operation process to form the type data processing and operation extraction time variation data; Gathering the data on the amount of information on the type of data processing and operation and the data on the duration of extraction of the type of data processing and operation corresponding to all types of enterprise data, and establishing the processing and operation information database; extracting enterprise data processing information from the processing and operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics, to form data processing conversion guidance information, including: Extracting the data information volume change information of each type of the enterprise data during the processing operation process from the processing operation information database to form the enterprise data information volume change information; Extracting the data information extraction time length variation information of each type of the enterprise data during the processing operation process from the processing operation information database to form the enterprise data information extraction time length variation information; Combining the enterprise data information volume change information and the enterprise data information extraction time change information to form the data processing conversion guidance information; extracting the data information volume change information of each type of the enterprise data during the processing operation process from the processing operation information database to form the enterprise data information volume change information, including: Determine the closed-loop optimization analysis cycle, extract the amount of data information generated by each type of enterprise data in the processing operation information database each time the data is processed, and form a curve of the amount of enterprise data processing information within the closed-loop optimization analysis cycle = , where n represents the number of different types of enterprise data; According to the change curve of the enterprise data processing information volume = , determine the corresponding enterprise data processing information volume change rate ,in, for The derivative in the time dimension; extracting the data information extraction time change information of each type of the enterprise data in the processing operation process from the processing operation information database to form the enterprise data information extraction time change information, including: Extract the data information extraction time formed by each operation processing of each type of enterprise data in the processing operation information database, and form a change curve of the enterprise data processing information extraction time within the closed-loop optimization analysis cycle = ; Extract the time variation curve according to the enterprise data processing information = , determine the corresponding enterprise data processing information extraction time change rate ,in, for The derivative in the time dimension; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on the data processing result characteristics to form data processing result guidance information, including: Extracting information about the amount of information extracted from the processing and operation information database for each type of enterprise data in the data information amount generated during the processing and operation process, and forming a curve of the amount of enterprise data extraction = ; According to the change curve of the enterprise data extraction volume , determine the corresponding enterprise data extraction volume change rate of the enterprise data within the optimization analysis cycle ,in, for The derivative in the time dimension; performing closed-loop optimization analysis according to the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data, including: According to the change rate of the enterprise data processing information volume and the rate of change in the amount of data extracted by the enterprise , perform the following optimization analysis and processing: like and Both show a gradual upward trend, so combined with the change rate of the enterprise data processing information volume , the change rate of the amount of data extracted by the enterprise And the change rate of the enterprise data processing information extraction time , to optimize the processing and analysis of the continuously increasing amount of information, and to form the optimization processing and analysis result information of the continuously increasing amount of information with the same trend; like There is a non-gradual upward trend. The enterprise data processing information extraction time change rate is combined with the enterprise data processing information extraction time change rate. , to optimize the processing and analysis of the increase in information volume, and form the optimization processing and analysis result information of the asynchronous trend of continuous increase in information volume; like If the trend is not gradual, then the enterprise data extraction volume change curve is combined with the above , the change rate of the enterprise data processing information extraction time and the rate of change in the amount of data extracted from the enterprise , and perform non-continuous increase in optimization processing and analysis result information.

2. The data closed-loop optimization processing method for enterprise data according to claim 1 is characterized in that: If and Both show a gradual upward trend, so combined with the change rate of the enterprise data processing information volume , the change rate of the amount of data extracted by the enterprise And the change rate of the enterprise data processing information extraction time , to optimize the processing and analysis of the continuous increase in the amount of information, and to form the optimization processing and analysis result information of the continuous increase in the amount of information with the same trend, including: Set the stable threshold range of the processing time change rate corresponding to the enterprise data =[ , ], Indicates the stable threshold range of the processing time change rate The minimum value of Indicates the stable threshold range of the processing time change rate The maximum value of , and perform the following optimization analysis: when < , > , then determine the variation curve of the remaining information volume of the enterprise data after the information volume of the enterprise data is extracted in the closed-loop optimization analysis cycle in the time dimension, and mark it as the information volume occupied and reserved curve; when < , ∈ , then determine the change curve of the remaining information volume of the enterprise data in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when < , < , then determine the change curve of the remaining information volume of the enterprise data in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the occupied information volume to be expanded curve; when ≥ , then determine the change curve of the remaining information volume in the time dimension after the information volume of the enterprise data is extracted during the closed-loop optimization analysis cycle, and mark it as the information volume saving curve.

3. The data closed-loop optimization processing method for enterprise data according to claim 2 is characterized in that: If There is a non-gradual upward trend. The enterprise data processing information extraction time change rate is combined with the enterprise data processing information extraction time change rate. , to optimize the processing and analysis of the increase in information volume, and form the optimization processing and analysis result information of the asynchronous trend of continuous increase in information volume: Set the information processing time threshold corresponding to different enterprise data , and perform the following optimization analysis: when ≤ , then the corresponding enterprise data is calibrated as stable object data; when > , then the corresponding enterprise data is marked as the object data to be optimized, and the information processing time threshold is determined Limit the time for information processing.

4. The data closed-loop optimization processing method for enterprise data according to claim 3 is characterized in that: If If the trend is not gradual, then the enterprise data extraction volume change curve is combined with the above , the change rate of the enterprise data processing information extraction time and the rate of change in the amount of data extracted from the enterprise , perform non-continuous increase optimization processing analysis result information, including: Set data information volume limit values ​​for different types of enterprise data , and perform the following optimization analysis: when There is a gradual upward trend, and < , then the corresponding enterprise data is marked as stable object data; when There is a gradual upward trend, and ≥ , then the corresponding enterprise data is marked as the object data to be optimized, and the data information volume limit value is determined is the limit value for the amount of information to be extracted; when When the trend is not gradually increasing, the information extraction unit value of different types of enterprise data is set , and perform the following optimization analysis: when > , > , then the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value for information processing time; when > , ∈ , then the corresponding enterprise data is marked as the object data to be optimized, and the maximum value of the change rate of enterprise data information processing time is determined is the target value for information processing time; when > , < , then the corresponding enterprise data is marked as stable object data; when ≤ , the corresponding enterprise data is marked as deletion object data.

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