CN115018477B

CN115018477B - Big data analysis method and equipment based on enterprise OA system

Info

Publication number: CN115018477B
Application number: CN202210952129.7A
Authority: CN
Inventors: 张惠元
Original assignee: Big Bear Big Data Technology Changshu Co ltd
Current assignee: Big Bear Big Data Technology Changshu Co ltd
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-10-21
Anticipated expiration: 2042-08-09
Also published as: CN115018477A

Abstract

According to the big data analysis method and equipment based on the enterprise OA system, the feedback demand vector of the user to be processed corresponding to the instant demand item can be determined from the user feedback demand vector ReV1 based on the migration transformation result of the second function upgrading preference vector pointing to the first function upgrading preference vector, and the feedback demand vector of the user to be processed in the original enterprise OA operation information is cleaned, so that when the OA system upgrading decision analysis is carried out based on the user feedback demand vector ReV1 with the cleaned feedback demand vector of the user to be processed, misleading of the user feedback demand vector of the instant demand item to the precision and credibility of the OA system upgrading decision analysis is reduced, efficient, accurate and rapid OA system function module upgrading optimization is carried out based on the enterprise OA operation information for assisting the OA system upgrading decision, various service requirements of office operation of the enterprise are met as much as possible, and the operation intelligence degree of the OA system is improved.

Description

Big data analysis method and equipment based on enterprise OA system

Technical Field

The invention relates to the technical field of big data analysis, in particular to a big data analysis method and equipment based on an enterprise OA system.

Background

The enterprise OA system is an important office management system for enterprise informatization construction. The OA system can bring convenient information communication, perfect organization management and efficient process approval for enterprises, can realize remote intelligent office and scientific decision of the enterprises, and can be customized individually according to the development requirements of the enterprises to create exclusive cooperative office platforms of the enterprises. Currently, with the continuous development of the enterprise OA system, the number and types of user demands for the enterprise OA system are increased, and the optimization of the function upgrade of the enterprise OA system is urgent.

Disclosure of Invention

The invention at least provides a big data analysis method and equipment based on an enterprise OA system.

The invention provides a big data analysis method based on an enterprise OA system, which is applied to OA big data processing equipment and at least comprises the following steps: acquiring a user feedback demand vector ReV1 in original enterprise OA operation information and a user feedback demand vector ReV2 which has a matching relation with the user feedback demand vector ReV1 in target enterprise OA sample information corresponding to the original enterprise OA operation information; processing the user feedback demand vector ReV1 by utilizing an OA upgrade indication relation network, determining a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network, and processing the user feedback demand vector ReV2 by utilizing the OA upgrade indication relation network, determining a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relation network; determining a user feedback demand vector to be processed corresponding to an instant demand item from the user feedback demand vector ReV1 by combining preference correlation characteristics between the first function upgrading preference vector and the second function upgrading preference vector; and cleaning the user feedback demand vector to be processed in the original enterprise OA operation information, and determining the enterprise OA operation information for assisting the OA system upgrading decision.

Therefore, the feedback demand vector of the user to be processed corresponding to the instant demand item can be determined from the user feedback demand vector ReV1 based on the migration transformation result of the second function upgrading preference vector pointing to the first function upgrading preference vector, and the feedback demand vector of the user to be processed in the original enterprise OA operation information is cleaned, so that when OA system upgrading decision analysis is carried out based on the user feedback demand vector ReV1 with the cleaned feedback demand vector of the user to be processed, misleading of the user feedback demand vector of the instant demand item on the precision and reliability of the OA system upgrading decision analysis is reduced, efficient, accurate and rapid OA system function module upgrading optimization is carried out based on the enterprise OA operation information for assisting the OA system upgrading decision, various service requirements of office operation of the enterprise are met as much as possible, and the operation intelligence degree of the OA system is improved.

For some examples, before the obtaining the user feedback demand vector ReV1 in the original enterprise OA operation information and the user feedback demand vector ReV2 in the target enterprise OA sample information corresponding to the original enterprise OA operation information and having a matching relationship with the user feedback demand vector ReV1, the method further includes: and determining the target enterprise OA sample information for the original enterprise OA operation information based on a preset information sample determination rule.

For some examples, the determining the target enterprise OA sample information for the original enterprise OA operation information based on a preset information sample determination rule includes: analyzing whether the current selected OA operation information of the enterprise is matched with the preset information sample determining rule or not; in response to the selected enterprise OA operation information matching the preset information sample determination rule, determining the selected enterprise OA operation information as the target enterprise OA sample information; in response to the selected enterprise OA operation information not matching the preset information sample determination rule, determining first enterprise OA operation information as the target enterprise OA sample information; wherein the first enterprise OA operation information comprises: the collection time node is positioned in front of the original enterprise OA operation information and has the enterprise OA operation information with the minimum difference with the collection time period of the collection time node of the original enterprise OA operation information.

For some examples, further comprising: in response to the selected enterprise OA operation information not matching the pre-set information sample determination rule, determining the original enterprise OA operation information as additional selected enterprise OA operation information; the additional selected enterprise OA operational information is used to process a next set of original enterprise OA operational information.

Therefore, on the premise that the content between the target enterprise OA sample information and the original enterprise OA operation information is matched with the information sample determination rule, the information can be adjusted in a distributed mode, and the cleaning quality of the instant demand project is improved.

For some examples, the preset information sample determination rule includes one or more of: the difference between the sequence adjustment values of the original enterprise OA operation information and the selected enterprise OA operation information is smaller than a preset sequence adjustment limit value; the number of the user feedback demand vectors ReV2 which have a matching relation with the user feedback demand vector ReV1 in the selected enterprise OA operation information reaches a set number; the difference between the description values of the first acquisition state description value corresponding to the original enterprise OA operation information and the second acquisition state description value corresponding to the selected enterprise OA operation information is smaller than a description limit value configured in advance; the acquisition state description value is a variable for distinguishing different acquisition states obtained by performing characteristic value mapping processing on the multidimensional acquisition state information.

Therefore, the difference between the sequence adjustment values of the original enterprise OA operation information and the target enterprise OA sample information is smaller than the preset sequence adjustment limit value, so that sufficient information of the user feedback demand vector ReV1 and the user feedback demand vector ReV2 which can have a matching relation in the original enterprise OA operation information and the target enterprise OA sample information is guaranteed, and the user feedback demand vector to be processed corresponding to the instant demand item can be completely distinguished from the user feedback demand vector ReV 1; determining enterprise OA operation information with the number of user feedback demand vectors ReV2 of the user feedback demand vectors ReV1 having the matching relation reaching a set number as target enterprise OA sample information of original enterprise OA operation information, and more completely distinguishing the user feedback demand vectors to be processed corresponding to the instant demand items from the user feedback demand vectors ReV 1; on the premise that the difference between the description value of the first acquisition state corresponding to the OA operation information of the original enterprise and the description value of the second acquisition state corresponding to the OA sample information of the target enterprise is smaller than the pre-configured description limit value, the consistency of the requirement items in the OA operation information of the original enterprise and the OA sample information of the target enterprise can be ensured, and rich user feedback requirement vectors ReV1 can be determined from the OA operation information of the original enterprise.

For some examples, the processing the user feedback requirement vector ReV1 by using an OA upgrade indication relationship network to determine a first function upgrade preference vector of the user feedback requirement vector ReV1 in the OA upgrade indication relationship network includes: determining first module state data of an information statistic module when the original enterprise OA operation information is collected based on a module configuration link of the information statistic module in an enterprise OA operation environment when the target enterprise OA sample information is collected and first face of interest information of the information statistic module in the enterprise OA operation environment when the original enterprise OA operation information is collected; and in combination with the first module state data, migrating and transforming the user feedback demand vector ReV1 to the OA upgrade indication relation network, and determining a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network.

Therefore, when the information statistics module collects original enterprise OA operation information, compared with the situation that when the information statistics module collects target enterprise OA sample information, only the update of the concerned surface exists, but the module state data in the enterprise OA operation environment does not exist, furthermore, the first module state data of the information statistics module when the information statistics module collects the original enterprise OA operation information can be determined based on the module configuration link of the information statistics module when the target enterprise OA sample information is collected in the enterprise OA operation environment and the first concerned surface information of the information statistics module when the original enterprise OA operation information is collected in the enterprise OA operation environment, therefore, the user feedback demand vector ReV1 in the original enterprise OA operation information is migrated to the OA upgrade indication relation network by using the content, and the process is based on the adjustment of the concerned surface of the information statistics module without aiming at the module state change of the information statistics module, so that the determination of the correlation characteristics of the first function upgrade preference vector and the second function upgrade preference vector can be realized, and the lightweight computing power of OA big data processing equipment is released.

For some examples, the processing the user feedback requirement vector ReV2 using the OA upgrade indication relational network to determine a second function upgrade preference vector of the user feedback requirement vector ReV2 in the OA upgrade indication relational network includes: and processing the user feedback demand vector ReV2 by utilizing the OA upgrading indication relation network in combination with second module state data of an information statistical module during the collection of the target enterprise OA sample information, and determining a second function upgrading preference vector of the user feedback demand vector ReV2 in the OA upgrading indication relation network.

For some examples, the determining a pending user feedback requirement vector corresponding to an immediate requirement item from the user feedback requirement vector ReV1 in combination with preference correlation features between the first functionality upgrade preference vector and the second functionality upgrade preference vector comprises: determining a migration transformation result pointing from the second function upgrade preference vector to the first function upgrade preference vector in combination with preference correlation characteristics between the first function upgrade preference vector and the second function upgrade preference vector; and combining the migration transformation result, and determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV1.

Therefore, whether the change of the migration transformation result corresponding to different user feedback demand vectors reflecting different demand items is the same as possible or not can be determined by utilizing the migration transformation result, and the user feedback demand vector to be processed is further distinguished from the user feedback demand vector ReV1.

For some examples, the determining, from the user feedback requirement vector ReV1 in combination with the migration transformation result, a pending user feedback requirement vector corresponding to the immediate requirement item includes: in the 1 st round of operation link, based on the set operation result of the migration transformation result, sequentially adjusting the migration transformation results corresponding to the user feedback demand vectors ReV 1; determining a target migration transformation result of the 1 st round operation link from the migration transformation results based on the sequence adjustment result and a set cleaning instruction; judging whether a preset termination rule is matched in a Y-th operation link; if so, determining a user feedback demand vector to be processed corresponding to the instant demand item from the user feedback demand vector ReV1 based on a target migration transformation result determined by the Y-1-th operation link; y is an integer greater than 1; the termination rule includes one or more of the following: the number of the operation links is not less than the preset number; the difference between the global operation result determined by the current operation link and the global operation result determined by the previous operation link is smaller than a set operation threshold value.

For some examples, further comprising: in the Y-th round operation link, in response to the fact that the termination rule is not matched, determining a global operation result based on a target migration transformation result determined by the Y-1-th round operation link; determining a target migration transformation result of the operation link of the Y-th round based on deviation information between a set operation result of each migration transformation result and the global operation result and the set cleaning instruction; judging whether the termination rule is matched or not in the Y +1 th operation link; and if so, determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV1 based on a target migration transformation result determined by the Y-th operation link.

Therefore, through the circulation of the plurality of operation links, the overall operation result tends to be stable as much as possible, and the feedback demand vector of the user to be processed corresponding to the instant demand item can be more accurately distinguished from the feedback demand vector ReV1 of the user.

For some examples, further comprising: determining third module state data of an information statistical module when the information statistical module collects the original enterprise OA operation information by combining an associated user feedback demand vector except the user feedback demand vector to be processed in the user feedback demand vector ReV1, a user feedback demand vector ReV3 which has a matching relation with the associated user feedback demand vector in the target enterprise OA sample information, and second module state data of the information statistical module collecting the target enterprise OA sample information; wherein the user feedback requirement vector ReV2 comprises the user feedback requirement vector ReV3.

Therefore, the module state data of the information statistics module during collection of the original enterprise OA operation information can be corrected, the accuracy of the determined third module state data is better, and the information collection precision of the information statistics module is improved.

For some examples, further comprising: migrating and converting the user feedback demand vector ReV3 into the original enterprise OA operation information again according to the third module state data, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information; determining secondary migration deviation information by combining the coverage area of the third function upgrade preference vector in the original enterprise OA operation information and the coverage area of the associated user feedback demand vector in the original enterprise OA operation information; determining an additional set cleaning indication in combination with the secondary migration deviation information; wherein the additional set cleansing instructions are for processing a next set of raw enterprise OA operation information.

For some examples, migrating and transforming the user feedback requirement vector ReV3 into the original enterprise OA operating information again according to the third module state data, and determining a third function upgrade preference vector for the user feedback requirement vector ReV3 in the original enterprise OA operating information includes: determining a relationship network transformation reference between a first OA operation relationship network corresponding to the original enterprise OA operation information and a second OA operation relationship network corresponding to the OA upgrade indication relationship network according to the third module state data; and in combination with the relational network transformation reference, migrating and transforming a second function upgrading preference vector of the user feedback demand vector ReV3 in the OA upgrading indication relational network into the original enterprise OA operation information, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information.

Therefore, the cleaning information of the OA operation information of the original enterprise is subjected to secondary migration deviation information determination based on the real-time operation link, and the cleaning proportion of the next group of OA operation information of the original enterprise is determined, so that the feedback demand vector of the user to be processed corresponding to the instant demand item can be determined from the subsequent OA operation information of the enterprise, and the continuous amplification of function upgrading misleading in multiple groups of OA operation information of the original enterprise caused by the existence of the instant demand item is improved.

Furthermore, by adjusting the set cleaning indication of the next set of original enterprise OA operation information, the feedback demand vector of the user to be processed corresponding to the instant demand item can be determined from the subsequent enterprise OA operation information, and the intelligent degree of OA information processing is improved.

The invention also provides an OA big data processing device, which comprises a processor and a memory; the processor is connected with the memory in communication, and the processor is used for reading the computer program from the memory and executing the computer program to realize the method.

For the description of the effects of the OA big data processing apparatus and the computer readable storage medium, refer to the description of the above method.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings required for the embodiments will be briefly described below, and the drawings herein incorporated in and forming a part of the specification illustrate embodiments consistent with the present invention and, together with the description, serve to explain the technical solution of the present invention. It is appreciated that the following drawings depict only some embodiments of the invention and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.

Fig. 1 is a block diagram of an OA big data processing apparatus according to an embodiment of the present invention.

Fig. 2 is a flowchart illustrating a big data analysis method based on an enterprise OA system according to an embodiment of the present invention.

Fig. 3 is a block diagram of a big data analysis apparatus based on an enterprise OA system according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.

Fig. 1 is a schematic structural diagram of an OA big data processing apparatus 10 according to an embodiment of the present invention, which includes a processor 102, a memory 104, and a bus 106. The memory 104 is used for storing execution instructions and includes a memory and an external memory, where the memory may also be understood as an internal memory and is used for temporarily storing operation data in the processor 102 and data exchanged with the external memory such as a hard disk, the processor 102 exchanges data with the external memory through the memory, and when the OA big data processing apparatus 10 runs, the processor 102 and the memory 104 communicate through the bus 106, so that the processor 102 executes the big data analysis method based on the enterprise OA system according to the embodiment of the present invention, and exemplarily, the processor 102 is used for reading a computer program from the memory 104 and executing the computer program, so as to implement the big data analysis method based on the enterprise OA system according to the embodiment of the present invention.

Referring to fig. 2, fig. 2 is a flowchart illustrating a big data analysis method based on an enterprise OA system according to an embodiment of the present invention, which is applied to an OA big data processing apparatus, and the method may include the following steps.

And step 11, obtaining a user feedback demand vector ReV1 in the OA operation information of the original enterprise and a user feedback demand vector ReV2 which has a matching relation with the user feedback demand vector ReV1 in the OA sample information of the target enterprise corresponding to the OA operation information of the original enterprise.

In the embodiment of the present invention, the OA operation information of the enterprise may be understood as Office user operation information, interaction information, or process operation data of the OA system, which are collected in an Office Automation (OA) operation process in the prior art.

Further, a series of user feedback demand points (such as office service demand items) are performed on the original enterprise OA operation information (to-be-processed enterprise OA operation information) to perform mining analysis based on a relevant artificial intelligence technology, so as to obtain a user feedback demand vector ReV1 (which may also be understood as a first user feedback demand vector, and ReV may be understood as an abbreviation of a requirement vector). Similarly, target enterprise OA sample information (which may be understood as target enterprise OA reference/template information) corresponding to the original enterprise OA operation information is matched with the user feedback demand vector ReV1, so as to obtain a user feedback demand vector ReV2 (which may be understood as a second user feedback demand vector). The user feedback demand vector is represented by the existing characteristic vector or characteristic array.

And step 12, processing the user feedback demand vector ReV1 by using an OA upgrade indication relation network, determining a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network, and processing the user feedback demand vector ReV2 by using the OA upgrade indication relation network, and determining a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relation network.

In this embodiment of the present invention, the OA upgrade indication relationship network may be a preset OA upgrade indication relationship network, or may be an upgrade indication relationship network (which may also be understood as reference information or multidimensional vector space for performing vector projection processing) searched from a set relationship network (such as, but not limited to, a conventional feature vector relationship list/feature vector mapping list/feature vector library) according to a user requirement. Further, vector migration transformation (such as feature mapping or vector matching processing) is performed on the user feedback demand vector ReV1 according to the OA upgrade indication relation network to obtain a first function upgrade preference vector (for example, a first function upgrade preference feature point may be determined based on a mapping position of the user feedback demand vector ReV1 in the OA upgrade indication relation network, which is essentially traditional vector pairing processing or classification matching processing). Correspondingly, vector migration transformation is carried out on the user feedback demand vector ReV2 according to the OA upgrading indication relation network, and a second function upgrading preference vector (such as a second function upgrading preference characteristic point) is obtained.

And step 13, determining a feedback demand vector of the user to be processed corresponding to the instant demand item from the feedback demand vector ReV1 of the user by combining preference correlation characteristics between the first function upgrading preference vector and the second function upgrading preference vector.

In the embodiment of the present invention, the preference associating feature may be understood as a relative preference association relationship between the first function upgrade preference vector and the second function upgrade preference vector (such as a relative distribution situation in an OA upgrade indication relationship network). In addition, the instant demand items can be understood as temporary user demands, which are not very suitable for the requirements of the actual OA system operation conditions, and the demand items do not contribute much to the function upgrade of the entire OA system, or the audience population of the demand items is not wide, or the demand items are non-mainstream demand items. Further, target user feedback demand analysis is performed on the user feedback demand vector ReV1 corresponding to the instant demand items, so as to obtain a user feedback demand vector to be processed (which can be understood as a target user feedback demand feature point, and the target user feedback demand feature point can be understood as a feature point with a service upgrade interference effect or a misleading effect).

And 14, cleaning the feedback demand vector of the user to be processed in the original enterprise OA operation information, and determining the enterprise OA operation information for assisting the OA system upgrading decision.

In the embodiment of the present invention, cleaning the feedback demand vector of the user to be processed in the OA operation information of the original enterprise may be understood as removing/deleting the feedback demand vector of the user to be processed from the OA operation information of the original enterprise, or labeling the operation information set pointed by the feedback demand vector of the user to be processed, so as to ignore this part of the operation information set when performing the OA system upgrade decision in the subsequent stage, or assigning a weight to the operation information set pointed by the feedback demand vector of the user to be processed, where the occupation ratio of the weight may be small, so as to reduce the influence of this part of the operation information set on the decision result when performing the OA system upgrade decision in the subsequent stage.

Under some embodiments, functional items needed for OA system upgrade may be predicted based on enterprise OA operation information used to assist OA system upgrade decisions, and then targeted upgrade optimization may be performed on these functional items to meet user needs. For example, upgrade optimization is performed for an information protection function, and upgrade optimization is performed for an OA visualization interface.

It can be understood that, in the embodiment of the present invention, the user feedback demand vector ReV1 in the original enterprise OA operation information and the user feedback demand vector ReV2 corresponding to the user feedback demand vector ReV1 in the target enterprise OA sample information are respectively migrated and transformed to the OA upgrade indication relationship network, and a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relationship network and a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relationship network are determined; then based on the preference correlation characteristics of the second function upgrading preference vector and the first function upgrading preference vector, a user feedback requirement vector to be processed corresponding to the instant requirement item is determined from the user feedback requirement vector ReV1, the user feedback requirement vector to be processed of the instant requirement item is cleaned from the user feedback requirement vector ReV1, so that the user feedback requirement vector to be processed corresponding to the instant requirement item can be cleaned from the user feedback requirement vector ReV1 based on the variation of the user feedback requirement vector corresponding to the instant requirement item and the continuous requirement item (the mainstream user requirement item) in an OA upgrading indication relation network, misleading of the user feedback requirement vector of the instant requirement item to the precision and reliability of OA system upgrading decision analysis is reduced, efficient, accurate and rapid decision-making module upgrading optimization is carried out based on enterprise OA operation information for assisting OA system upgrading, various service requirements of enterprise office operation are met as much as possible, and the operation intelligence degree of the OA system is improved.

For some embodiments, in step 11, the original enterprise OA operation information may be, for example, one set of enterprise OA operation information determined when performing OA system upgrade decision analysis in combination with the decision analysis network or one set of enterprise OA operation information determined in the enterprise OA operation record acquired by accessing the enterprise OA operation environment.

In some examples, when collecting the original enterprise OA operation information, for example, the enterprise OA operation record of the enterprise OA operation environment may be obtained by collecting the enterprise OA operation environment based on the information statistics module, and the original enterprise OA operation information may be determined from the enterprise OA operation information included in the enterprise OA operation record. Further, the enterprise OA operation environment may be determined according to an actual operation environment in which the enterprise OA operates the information processing application; in some examples, the OA operating environment of the enterprise may be selected according to actual circumstances, and is not limited herein.

In the embodiment of the present invention, when the enterprise OA operation record collection is performed on the enterprise OA operation environment, for example, the enterprise OA operation record of the enterprise OA operation environment may be obtained based on an information statistics module disposed in the enterprise OA operation environment. Further, after enterprise OA operation records of the enterprise OA operation environment are collected, each set of enterprise OA operation information may be determined as original enterprise OA operation information one by one, and target enterprise OA sample information of the original enterprise OA operation information is determined in the enterprise OA operation records; the target enterprise OA sample information determined for the original enterprise OA operation information is generally the enterprise OA operation information whose time node is before the original enterprise OA operation information.

In addition, the process of acquiring the enterprise OA operation record of the enterprise OA operation environment and the process of determining the original enterprise OA operation information and processing the original enterprise OA operation information can be performed simultaneously or not. If the two processes are performed simultaneously, the original enterprise OA operation information may be determined from the acquired enterprise OA operation information while the enterprise OA operation records of the enterprise OA operation environment are collected. If the two processes are not performed simultaneously, the OA operation records of the enterprise may be obtained first, and after the OA operation records of the enterprise are collected, the OA operation information of the original enterprise may be determined from the obtained OA operation records of the enterprise.

For some possible examples, the target enterprise OA sample information corresponding to the original enterprise OA operation information may be implemented as follows: and determining the OA sample information of the target enterprise for the OA operation information of the original enterprise based on a preset information sample determination rule. In the embodiment of the invention, the target enterprise OA sample information can be understood as target enterprise OA reference information.

An exemplary implementation manner for determining the target enterprise OA sample information for the original enterprise OA operation information based on a preset information sample determination rule may include the following contents recorded in steps 21 to 23.

And step 21, analyzing whether the current OA operation information of the selected enterprise is matched with the preset information sample determination rule.

In the embodiment of the present invention, the preset information sample determination rule may be understood as a preset information screening requirement/a preset information screening condition.

And step 22, in response to the selected enterprise OA operation information matching the preset information sample determination rule, determining the selected enterprise OA operation information as the target enterprise OA sample information.

And step 23, in response to the fact that the selected enterprise OA operation information does not match the preset information sample determination rule, determining the first enterprise OA operation information as the target enterprise OA sample information.

In an embodiment of the present invention, the OA operation information of the first enterprise may include: the collection time node is positioned in front of the original enterprise OA operation information and has the enterprise OA operation information with the minimum difference with the collection time period of the collection time node of the original enterprise OA operation information. Furthermore, the acquisition period difference may be understood as an acquisition time gap.

For some possible examples, in response to the selected enterprise OA operational information not matching the pre-set information sample determination rule, the following may be included: determining the original enterprise OA operational information as additional selected enterprise OA operational information.

In an embodiment of the present invention, the additional selected enterprise OA operational information is used to process a next set of raw enterprise OA operational information. In addition, the additional selected enterprise OA operational information may also be understood as new selected enterprise OA operational information/current selected enterprise OA operational information.

In the embodiment of the present invention, when each set of enterprise OA operation information in one enterprise OA operation record is processed, for example, the 1 st set of enterprise OA operation information in the enterprise OA operation record may be determined as current selected enterprise OA operation information (which may be understood as key enterprise OA operation information/target enterprise OA operation information), and then, for the 2 nd set of enterprise OA operation information in the enterprise OA operation record, whether the current selected enterprise OA operation information (the 1 st set of enterprise OA operation information) matches a preset information sample determination rule is analyzed; and determining and matching a preset information sample determination rule, determining the current selected enterprise OA operation information as target enterprise OA sample information of the 2 nd group of enterprise OA operation information, and processing the 2 nd group of enterprise OA operation information by means of the target enterprise OA sample information.

Analyzing whether the current selected enterprise OA operation information (the 1 st group of enterprise OA operation information) matches with a preset information sample determination rule or not for the 3 rd group of enterprise OA operation information in the enterprise OA operation record; and determining and matching a preset information sample determination rule, determining the current selected enterprise OA operation information as target enterprise OA sample information of the 3 rd group of enterprise OA operation information, and processing the 3 rd group of enterprise OA operation information by using the target enterprise OA sample information.

Calculating one by one, and processing the OA operation information of the 4 th group of enterprises to the OA operation information of the 8 th group of enterprises in sequence, wherein the current OA operation information (the OA operation information of the 1 st group of enterprises) of the selected enterprises can be matched with a preset information sample determination rule, so that the OA operation information of the 4 th group of enterprises to the OA operation information of the 8 th group of enterprises can be processed through the current OA operation information (the OA operation information of the 1 st group of enterprises) of the selected enterprises.

Analyzing whether the current selected enterprise OA operation information (the 1 st group of enterprise OA operation information) matches with a preset information sample determination rule or not for the 9 th group of enterprise OA operation information in the enterprise OA operation record; if the currently selected enterprise OA operation information (the 1 st group of enterprise OA operation information) does not match the preset information sample determination rule, the 8 th group of enterprise OA operation information is determined as the target enterprise OA sample information of the 9 th group of enterprise OA operation information, and the 9 th group of enterprise OA operation information is processed by using the target enterprise OA sample information (the 8 th group of enterprise OA operation information). The 9 th set of enterprise OA operational information is determined to be additional selected enterprise OA operational information.

Analyzing whether the current selected enterprise OA operation information (9 th group of enterprise OA operation information) matches with a preset information sample determination rule or not for the 10 th group of enterprise OA operation information in the enterprise OA operation record; and determining and matching preset information sample determination rules, determining the current selected enterprise OA operation information as target enterprise OA sample information of the 10 th group of enterprise OA operation information, and processing the 10 th group of enterprise OA operation information by using the target enterprise OA sample information.

And circularly implementing the processes until all the OA operation information of the enterprise needing to be processed in the OA operation record of the enterprise finishes the OA operation information processing process of the enterprise. Further, when analyzing whether the current selected enterprise OA operation information matches the preset information sample determination rule, whether the current selected enterprise OA operation information matches the preset information sample determination rule may be determined according to conditions of different dimensions.

In some embodiments, the pre-configured information sample determination rule may include at least one of the following rules a, B, and C, but is not limited thereto.

Rule A: the difference between the sequence adjustment values of the original enterprise OA operational information and the selected enterprise OA operational information is less than a pre-configured sequence adjustment limit.

In the embodiment of the invention, the order of acquiring the OA operation information of each group of enterprises in the OA operation records acquired by acquiring the OA operation environment of the enterprises based on the information statistical module is used for sequencing the OA operation information of the enterprises acquired in each link, and the order adjustment value of the OA operation information of each group of enterprises is determined. In some examples, the information statistics module obtains the OA operation environment once every 0.03S, and sets a sequence adjustment value for the start of the first operation link as the OA operation information of each link, for example, the sequence adjustment value for the OA operation information of the first operation link is "Va", the sequence adjustment value for the OA operation information of the second operation link is "Vb", the sequence adjustment value for the OA operation information of the third operation link is "Vc", and so on, the sequence adjustment value for the OA operation information of the twentieth operation link is "Vd", and a difference between the sequence adjustment value for the OA operation information of the twentieth operation link and the sequence adjustment value for the OA operation information of the first operation link is "Ve" - "Va" = "Ve").

If the difference between the original enterprise OA operation information and the sequence adjustment value of the current selected enterprise OA operation information is smaller than the preset sequence adjustment limit value, the current selected enterprise OA operation information is determined as target enterprise OA sample information, so that sufficient information of a user feedback demand vector ReV1 and a user feedback demand vector ReV2 which are in a matching relation can be ensured in the original enterprise OA operation information and the target enterprise OA sample information, the user feedback demand vector to be processed corresponding to the instant demand item can be more completely distinguished from the user feedback demand vector ReV1, and after the user feedback demand vector to be processed is distinguished from the user feedback demand vector ReV1, the original enterprise OA operation information can be more completely processed by using the other user feedback demand vector ReV1 in the user feedback demand vector ReV1, for example, the OA system function upgrading processing can be performed by combining the operation information.

Rule B: and the number of the user feedback demand vectors ReV2 which have a matching relation with the user feedback demand vector ReV1 in the selected enterprise OA operation information reaches a set number.

In some examples, after the user feedback demand vector in the original enterprise OA operation information is extracted, the user feedback demand vector in the original enterprise OA operation information is determined, the user feedback demand vector in the target enterprise OA sample information is extracted, and the user feedback demand vector in the target enterprise OA sample information is determined, the user feedback demand vector in the original enterprise OA operation information and the user feedback demand vector in the target enterprise OA sample information are also paired. And determining a user feedback demand vector ReV1 in the OA operation information of the original enterprise and a user feedback demand vector ReV2 which can be successfully paired with the user feedback demand vector ReV1 in the OA sample information of the target enterprise. The user feedback demand vector ReV1 and the user feedback demand vector ReV2 are successfully paired, and it can be understood that the user feedback demand vector ReV1 and the user feedback demand vector ReV2 represent the same user feedback demand vector in the same demand project. If the number of the user feedback demand vectors ReV2 which have the matching relation with the user feedback demand vector ReV1 in the current selected enterprise OA operation information meets the set number value, the current selected enterprise OA operation information is determined as target enterprise OA sample information, and therefore the user feedback demand vector to be processed corresponding to the instant demand item can be completely distinguished from the user feedback demand vector ReV1.

For example, a user feedback requirement vector obtained by mining user feedback requirements in the original enterprise OA operation information may include: demand vector _ a1-Demand vector _ a100; and mining a user feedback Demand vector obtained by the user feedback Demand of the current selected enterprise OA operation information, wherein the user feedback Demand vector can comprise a Demand vector _ b 1-a Demand vector _ b200, and the set numerical value is 66.

On one hand, if it can be determined from the Demand vector _ a1-Demand vector _ a100 that there are user feedback Demand vectors in matching relationship for at least 66 user feedback Demand vectors in the Demand vector _ b1-Demand vector _ b200, the current selected enterprise OA operation information is determined as the target enterprise OA sample information of the original enterprise OA operation information.

On the other hand, if it is not possible to determine the user feedback Demand vectors having the matching relationship for at least 66 user feedback Demand vectors in Demand vector _ b1-Demand vector _ b200 from among Demand vector _ a1-Demand vector _ a100, it is not possible to determine the current selected enterprise OA operation information as the target enterprise OA sample information of the original enterprise OA operation information.

Rule C: and the difference of description values between the first collection state description value corresponding to the original enterprise OA operation information and the second collection state description value corresponding to the selected enterprise OA operation information is smaller than a preset description limit value.

In the embodiment of the invention, on the premise that the difference between the description value of the first collection state corresponding to the OA operation information of the original enterprise and the description value of the second collection state corresponding to the OA sample information of the target enterprise is smaller than the pre-configured description limit value, the consistency of the requirement items in the OA operation information of the original enterprise and the OA sample information of the target enterprise can be ensured, and rich user feedback requirement vector ReV1 can be determined from the OA operation information of the original enterprise.

For example, when different information is collected, states such as states of a collection thread and states of a running environment can be used as distinguishing features of the different information, and the different states can be converted into a state feature value/state description value form to be recorded through a related state feature mapping processing mode, so that the different collection states can be distinguished. For example, the first collection state description value corresponding to the original enterprise OA operation information is "1001" (indicating that the collection thread is in a real-time collection state, and the OA operation environment is an authority authentication operation environment), and the second collection state description value corresponding to the target enterprise OA sample information is "1010" (indicating that the collection thread is in a delayed collection state, and the OA operation environment is a non-authentication operation environment), so that different collection states can be recorded by using a quantitative processing thought. In other words, the acquisition state description value is a variable obtained by performing eigenvalue mapping processing on the multidimensional acquisition state information to distinguish different acquisition states. Further, the eigenvalue mapping can be adapted to the existing mapping techniques.

In some examples, the first set of enterprise OA operational information may be immediately set as the first set of sample operational information when determining the target enterprise OA sample information. If the difference between the acquisition time periods of the original enterprise OA operation information and the first group of sample operation information is greater than the set time period, it can be understood that the number of the enterprise OA operation information time period differences between the original enterprise OA operation information and the first group of sample operation information is greater than the set number, the original enterprise OA operation information is set as additional sample operation information; setting the original enterprise OA operation information as additional sample operation information, wherein the number of successfully existing matching relations between the user feedback demand vector ReV1 of the original enterprise OA operation information and the user feedback demand vector ReV2 of the first group of sample operation information is less than a number threshold value; and setting the OA operation information of the original enterprise as additional sample operation information, wherein the difference between the description values of the first collection state description value corresponding to the OA operation information of the original enterprise and the second collection state description value corresponding to the OA operation information of the selected enterprise in the first group is not less than the preset description limit value.

In the embodiment of the invention, the user feedback demand vector ReV1 is the user feedback demand vector in the OA operation information of the original enterprise, which is obtained by extracting the user feedback demand vector from the OA operation information of the original enterprise; wherein the user feedback demand vector ReV1 comprises: for example, the user feedback demand vector ReV1 in the first enterprise OA operation information may be mined through a preset residual neural network.

In the embodiment of the invention, the target enterprise OA sample information can be enterprise OA operation information obtained by an information statistics module collecting enterprise OA operation environment in a previous processing link, and the user feedback demand vector ReV2 is a user feedback demand vector which is obtained in the target enterprise OA sample information by carrying out user feedback demand vector extraction on the target enterprise OA sample information and has a matching relationship with the user feedback demand vector ReV1. The user feedback requirement vector ReV2 is identified in a similar manner to the user feedback requirement vector ReV1.

In some examples, there are one continuous demand item "file sharing function update item" and one instant demand item "face recognition item" in the enterprise OA running environment, there are three user feedback Preference vectors, reference vector-1, reference vector-2, reference vector-3 on the "file sharing function update item", and there are two user feedback Preference vectors, reference vector-4, reference vector-5 on the "face recognition item"; a user feedback demand vector ReV1-10 corresponding to a user feedback Preference vector Preferencevector-1 of a file sharing function updating item, a user feedback demand vector ReV1-20 corresponding to a user feedback Preference vector Preferencevector-2, a user feedback demand vector ReV1-30 corresponding to a user feedback Preference vector Preferencevector-3, a user feedback demand vector ReV1-40 corresponding to a user feedback Preference vector Preferencevector-4 of a 'face recognition item' and a user feedback demand vector ReV1-50 corresponding to a user feedback Preference vector Preferencevector-5 exist in the original enterprise OA operation information; a user feedback demand vector ReV2-100 corresponding to a user feedback Preference vector Preference vector-1 of a 'file sharing function updating item' exists in the OA sample information of the target enterprise, a user feedback demand vector ReV2-200 corresponding to the user feedback Preference vector Preference-2, a user feedback demand vector ReV2-300 corresponding to the user feedback Preference vector Preference-3, a user feedback demand vector ReV2-400 corresponding to a user feedback Preference vector Preference vector-4 of a 'face recognition item' exists, and a user feedback demand vector ReV2-500 corresponding to the user feedback Preference vector Preference vector-5; 10 and 100 may be used to represent a user feedback Preference vector Preference-1 for "file sharing function update item" in an enterprise OA execution environment, 20 and 200 may be used to represent a user feedback Preference vector Preference-2 for "file sharing function update item" in an enterprise OA execution environment, 30 and 300 may be used to represent a user feedback Preference vector Preference-4 for "file sharing function update item" in an enterprise OA execution environment, 40 and 400 may be used to represent a user feedback Preference vector Preference-4 for "face recognition item" in an enterprise OA execution environment, 50 and 500 may be used to represent a user feedback Preference vector Preference-5 for "face recognition item" in an enterprise OA execution environment.

In addition, if the process of performing enterprise OA operation information processing on target enterprise OA sample information is completed before current original enterprise OA operation information is processed, it can be understood that in the previous processing link, the target enterprise OA sample information is determined to be original enterprise OA operation information, and the enterprise OA operation information processing is performed, and since the user feedback requirement mining processing is performed on the target enterprise OA sample information, a user feedback requirement vector obtained by the user feedback requirement mining processing on the target enterprise OA sample information can be stored in the previous link; in the current processing link, the requirement mining processing can be carried out only on the feedback of the current OA operation information user of the original enterprise; and obtaining a user feedback demand vector of the OA sample information of the target enterprise from the pre-stored data, and then pairing the user feedback demand vector of the OA operation information of the original enterprise and the user feedback demand vector of the OA sample information of the target enterprise to determine a user feedback demand vector ReV1 and a user feedback demand vector ReV2.

In step 12, the OA upgrade indication relationship network may be a relationship network determined in advance in a relationship network distribution vector corresponding to the OA operating environment of the enterprise.

In the embodiment of the invention, when the same group of original enterprise OA operation information is processed, only one OA upgrade indication relation network is generally determined; different OA operation information of the original enterprise can correspond to different OA upgrading indication relationship networks.

In the embodiment of the invention, after the OA upgrading indication relational network is determined in the enterprise OA operation environment, the actual module state data of the OA upgrading indication relational network in the relational network distribution vector corresponding to the enterprise OA operation environment is determined, and it can be understood that the relational network transformation reference between the relational network distribution vector and the OA upgrading indication relational network can be determined. On the premise that the state data of a first module in an enterprise OA operation environment when the information statistical module obtains original enterprise OA operation information and the state data of a second module in the enterprise OA operation environment when the information of a target enterprise OA sample is obtained are determined, the OA upgrading indication relation network can be used for processing the user feedback demand vector ReV1, a first function upgrading preference vector of the user feedback demand vector ReV1 in the OA upgrading indication relation network is determined, the OA upgrading indication relation network is used for processing the user feedback demand vector ReV2, and a second function upgrading preference vector of the user feedback demand vector ReV2 in the OA upgrading indication relation network is determined.

When the OA updating indication relation network is used for processing the user feedback demand vector ReV1, the first module state data determined for the OA operation information of the original enterprise is prediction module state data. When the first module state data is determined, based on that when the information statistics module collects original enterprise OA operation information, compared with that when the information statistics module collects target enterprise OA sample information, only update of a focus plane (data feature or information characteristic focused in the information collection process) exists, and no update exists in the module state data in the enterprise OA operation environment, further, based on a module configuration link of the information statistics module in the enterprise OA operation environment when collecting the target enterprise OA sample information and first focus plane information of the information statistics module in the enterprise OA operation environment when collecting the original enterprise OA operation information, the first module state data of the information statistics module when collecting the original enterprise OA operation information can be determined. And then processing the user feedback demand vector ReV1 by utilizing an OA upgrade indication relation network based on the first module state data, and determining a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network.

In the embodiment of the present invention, the information statistics module collects the first information of interest in the OA operation environment of the enterprise when collecting the original OA operation information of the enterprise (the information statistics module collects certain conditions in some columns of the OA operation information of the enterprise, such as collecting information of which scenes, collecting information of which clients, and the like).

Further, whereas module status data of other enterprise OA operation information whose collection time node precedes the original enterprise OA operation information has been determined in a previous processing link, in view of this, when some other set of enterprise OA operation information whose collection time node precedes the original enterprise OA operation information is determined as target enterprise OA sample information of the original enterprise OA operation information, second module status data at which the information statistics module acquires the target enterprise OA sample information is determined. The user feedback demand vector ReV2 can be processed by using the OA upgrade indication relationship network in combination with second module state data of the information statistical module during collection of the OA sample information of the target enterprise, and a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relationship network is determined.

Further, the second module state data may be module state data corresponding to the target enterprise OA sample information (for example, which key nodes the information statistics module is located in the information collection process, etc.) obtained after module state prediction is performed on the target enterprise OA sample information; in some examples, the second module state data of the target enterprise OA sample information, such as the third module state data of the current primary enterprise OA operation information, is provided in the post-processing flow to determine the current primary enterprise OA operation information as the target enterprise OA sample information.

In the embodiment of the invention, after the second module state data when the information statistics module acquires the OA sample information of the target enterprise is determined, the OA upgrade indication relation network is utilized to process the user feedback demand vector ReV2 based on the second module state data, and a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relation network is determined.

In some examples, the embodiment of the present invention takes as an example that the OA upgrade indication relationship network is used to process the user feedback requirement vector ReV1 based on the first module status data: determining relative module state data between the information statistical module and the OA upgrading indication relation network based on the module state data of the OA upgrading indication relation network under the relation network distribution vector and the first module state data; and then determining a relationship network transformation reference between the OA upgrading indication relationship network and the OA operation relationship network when the information statistical module collects the original enterprise OA operation information based on the relative module state data and the collection principle of the information statistical module, and then processing the user feedback demand vector ReV1 by utilizing the OA upgrading indication relationship network according to the relationship network transformation reference. An exemplary scheme for processing the user feedback demand vector ReV2 using the OA upgrade indication relational network is similar to the above-described exemplary scheme for migrating and transforming the user feedback demand vector ReV1 to the OA upgrade indication relational network.

In step 13, when determining the to-be-processed user feedback demand vector corresponding to the immediate demand item from the user feedback demand vector ReV1, this can be achieved by: determining a migration transformation result pointing from the second function upgrade preference vector to the first function upgrade preference vector based on preference correlation characteristics between the first function upgrade preference vector and the second function upgrade preference vector; and determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV1 based on the migration transformation result.

In the embodiment of the invention, the preference correlation characteristics are generated based on the updating of the distribution characteristics of the information statistical module when the information statistical module acquires the OA sample information of the target enterprise and the information statistical module acquires the OA operation information of the original enterprise, and for the continuous user feedback demand vectors, when the distribution characteristics of the information statistical module are updated, the module state changes of different continuous user feedback demand vectors in the function upgrading preference vectors of the OA upgrading indication relationship network are approximately the same; for the instant user feedback demand vector, when the distribution characteristics of the information statistics module are updated, generally speaking, the module state change of the function upgrade preference vector of the instant user feedback demand vector in the OA upgrade indication relationship network is not consistent with the module state change of the function upgrade preference vector of the continuous user feedback demand vector in the OA upgrade indication relationship network, and it can be understood that the user feedback demand vector to be processed is determined from the user feedback demand vector ReV1 based on the different changes of different user feedback demand vectors in the OA upgrade indication relationship network.

The following is an idea of a migration transformation result of a first function upgrade preference vector and a second function upgrade preference vector of an OA upgrade indication relationship network, wherein the first function upgrade preference vector interest _ A corresponds to the second function upgrade preference vector interest _ A _0 with a matching relationship, the first function upgrade preference vector interest _ B has the second function upgrade preference vector interest _ B _0 with a matching relationship, the first function upgrade preference vector interest _ C has the second function upgrade preference vector interest _ C _0 with a matching relationship, the first function upgrade preference vector interest _ D has the second function upgrade preference vector interest _ D _0 with a matching relationship, and the first function upgrade preference vector interest _ E has the second function upgrade preference vector interest _ E _0 with a matching relationship; the migration transformation result is a feature vector pointing from the second function upgrade preference vector to the first function upgrade preference vector.

Further, the first function upgrade preference vector interest _ a, the first function upgrade preference vector interest _ B, the first function upgrade preference vector interest _ C, and the first function upgrade preference vector interest _ D are all function upgrade preference vectors corresponding to the continuous demand items; the first function upgrading preference vector interest _ E is a function upgrading preference vector of the instant demand item; by combining the above contents, the vector mode and the vector direction state of the migration transformation result from the second function upgrade preference vector interest _ a _0 to the first function upgrade preference vector interest _ a, the migration transformation result from the second function upgrade preference vector interest _ B _0 to the first function upgrade preference vector interest _ B, the migration transformation result from the second function upgrade preference vector interest _ C _0 to the first function upgrade preference vector interest _ C, and the migration transformation result R from the second function upgrade preference vector interest _ D _0 to the first function upgrade preference vector interest _ D are all close to the same; the migration transformation result from the second function upgrade preference vector interest _ E _0 to the first function upgrade preference vector interest _ E is inconsistent with the vector mode and the vector direction state of the migration transformation result.

For some possible embodiments, when determining the pending user feedback requirement vector corresponding to the immediate requirement item from the user feedback requirement vector ReV1 based on the migration transformation result, this may be achieved by: in the 1 st round of operation link, based on the set operation result of the migration transformation result, sequentially adjusting the migration transformation results corresponding to the user feedback demand vectors ReV 1; determining a target migration transformation result of the 1 st round operation link from the migration transformation results based on the sequence adjustment result and a set cleaning instruction; judging whether a termination rule configured in advance is matched in a Y-th operation link; if so, determining a user feedback demand vector to be processed corresponding to the instant demand item from the user feedback demand vector ReV1 based on a target migration transformation result determined by the Y-1-th operation link; wherein Y is an integer greater than 1.

In an embodiment of the present invention, the termination rule includes one or more of the following: the number of the operation links is not less than the preset number; the difference between the global operation result determined by the current operation link and the global operation result determined by the previous operation link is smaller than a set operation threshold.

In addition, in the Y-th round operation link, in response to the fact that the termination rule is not matched, determining a global operation result based on a target migration transformation result determined by the Y-1-th round operation link; determining a target migration transformation result of the operation link of the Y-th round based on deviation information between a set operation result of each migration transformation result and the global operation result and the set cleaning instruction; judging whether the termination rule is matched or not in the Y +1 th operation link; and if so, determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV1 based on a target migration transformation result determined by the Y-th operation link.

In the embodiment of the present invention, the migration transformation result corresponding to the continuous requirement item is formed only by the change of the information statistics module, the attention plane and the setting operation result on the OA upgrade indication relationship network are the same, and the target migration transformation result corresponding to the immediate requirement item is integrated into the update of the information statistics module and the update of the related requirement item, and the attention plane and the setting operation result on the OA upgrade indication relationship network are distinguished from the migration transformation result of the continuous requirement item, so that the to-be-processed user feedback requirement vector corresponding to the immediate requirement item can be determined from the user feedback requirement vector ReV1.

In the embodiment of the present invention, in the 1 st round of operation, when determining the target migration transformation result of the 1 st round of operation from the migration transformation results based on the sequence adjustment result and the set purge instruction, for example, the migration transformation results may be sequentially adjusted based on the order of the set operation results from high to low. Then, according to the setting cleaning instruction, based on the order of the setting calculation results from high to low, a plurality of migration conversion results with longer setting calculation results are cleaned from the plurality of migration conversion results, and the other migration conversion results are the target migration conversion results determined by the 1 st round of operation.

In the operation link of the Y-th round, if it is determined that the termination rule is matched, when the feedback demand vector of the user to be processed corresponding to the immediate demand item is determined from the feedback demand vector ReV1 of the user based on the target migration transformation result determined in the operation link of the Y-1 th round, for example, the feedback demand vector ReV1 of the user corresponding to the target migration transformation result may be determined from the feedback demand vectors ReV1 of the users, the feedback demand vector ReV1 of the user corresponding to the target migration transformation result is cleaned from the feedback demand vectors ReV1 of the users in the original enterprise OA operation information, and the feedback demand vector ReV1 of the other user becomes the feedback demand vector of the user to be processed.

In some possible embodiments, in the Y-th operation segment, in response to a mismatch between the termination rule and the global operation result, when determining the target migration transformation result of the Y-th operation segment based on the deviation information between the setting operation result and the global operation result of each migration transformation result and the setting cleansing indication, the following may be implemented: and performing order adjustment on each migration transformation result based on the order of the deviation information between the setting operation result and the global operation result of each migration transformation result from high to low. Then, according to the set cleaning instruction, based on the order of the deviation information from high to low, a plurality of migration transformation results with larger deviation information are cleaned from the plurality of migration transformation results, and the other migration transformation results are the target migration transformation results determined by the Y-th operation link.

In addition, the cleaning proportion configured in advance may be set in advance, or may be determined in the process of processing other original enterprise OA operation information in the previous processing link. Further, the cleaning ratio configured in advance is determined in the process of processing other original enterprise OA operation information based on the previous link, and is not limited herein.

For example, there are 200 user feedback demand vectors ReV1, and 200 migration transformation results are formed, and the elimination rate of the cleaning instruction is set to 0.2, and then the following operation steps are performed.

In the 1 st round of operation: based on the set operation result of 200 migration transformation results, sequentially adjusting 200 migration transformation results; in the embodiment of the present invention, according to the setting cleaning instruction, based on the order of the setting operation results from high to low, 40 longer setting operation results (modulo of vector) are cleaned from 200 migration transformation results, and the other 160 migration transformation results become the target migration transformation results determined by the 1 st operation segment.

In the 2 nd operational link: the judgment is not matched with the termination rule configured in advance. In the embodiment of the invention, a global operation result is determined based on 160 target migration transformation results determined by the 1 st round of operation link, and deviation information between the set operation result of 200 migration transformation results and the global operation result is determined. And sequentially adjusting the 200 migration transformation results based on the sequence of the deviation information between the set operation results of the 200 migration transformation results and the global operation results from high to low. And then, based on the sequence of the deviation information from high to low, cleaning 40 migration transformation results with larger deviation information from 200 migration transformation results, wherein the other 160 migration transformation results are the target migration transformation results determined by the 2 nd operation link.

In the 3 rd operation link: judging that the rule does not match the preset termination rule. In the embodiment of the invention, a global operation result is determined based on 160 target migration transformation results determined in the 2 nd operation link, and deviation information between the set operation result of 200 migration transformation results and the global operation result is determined. And sequentially adjusting the 200 migration transformation results based on the sequence of the deviation information between the set operation results of the 200 migration transformation results and the global operation results from high to low. Then, based on the order of the deviation information from high to low, the 40 migration transformation results with larger deviation information are cleaned from the 200 migration transformation results, and the other 160 migration transformation results are the target migration transformation results determined by the 3 rd operation link.

In the nth operation link: and judging and matching the preset termination rule. In the embodiment of the invention, according to 160 target migration transformation results determined in the (n-1) th operation link, 66 user feedback requirement vectors ReV1 corresponding to the target migration transformation results are determined from a plurality of user feedback requirement vectors ReV1, the 66 user feedback requirement vectors ReV1 corresponding to the target migration transformation results are cleaned from 200 user feedback requirement vectors ReV1 in the OA operation information of the original enterprise, and the other 40 user feedback requirement vectors ReV1 are changed into user feedback requirement vectors to be processed.

Based on the circulation of the plurality of operation links, the overall operation result tends to be stable as much as possible, and the user feedback demand vector to be processed corresponding to the instant demand item can be completely and accurately distinguished from the user feedback demand vector ReV1.

The big data analysis method based on the enterprise OA system according to another embodiment of the present invention may further include the following steps: determining third module state data of an information statistical module when the information statistical module collects the original enterprise OA operation information by combining an associated user feedback demand vector except the user feedback demand vector to be processed in the user feedback demand vector ReV1, a user feedback demand vector ReV3 which has a matching relation with the associated user feedback demand vector in the target enterprise OA sample information, and second module state data of the information statistical module collecting the target enterprise OA sample information; wherein the user feedback requirement vector ReV2 comprises the user feedback requirement vector ReV3.

Therefore, the module state data of the information statistics module during collection of the original enterprise OA operation information can be improved, the accuracy of the determined third module state data is better, and the information collection precision of the information statistics module can be improved.

In other cases, in order to reduce interference caused by analysis accuracy of the original enterprise OA operation information in a post-processing link of the instant demand item and reduce continuous radiation of deviation information in a plurality of processing links, the embodiment of the invention can also determine secondary migration deviation information of cleaning information of the original enterprise OA operation information based on a real-time operation link and determine a set cleaning instruction of the next set of original enterprise OA operation information, so that a user feedback demand vector to be processed corresponding to the instant demand item can be determined from the subsequent enterprise OA operation information.

In this embodiment of the present invention, after obtaining the third module state data of the original enterprise OA operation information, the following contents may also be included: migrating and converting the user feedback demand vector ReV3 into the original enterprise OA operation information again according to the third module state data, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information; determining secondary migration deviation information by combining a coverage area (such as vector position information) of the third function upgrade preference vector in the original enterprise OA operation information and a coverage area of the associated user feedback demand vector in the original enterprise OA operation information; determining an additional set cleaning indication in combination with the secondary migration deviation information; wherein the additional set cleaning instructions are used for processing the next group of original enterprise OA operation information.

In some examples, if the secondary migration deviation information is smaller than the deviation information configured in advance, which indicates that the probability of the instant demand item existing in the current OA operation information of the original enterprise is low, the set cleansing instruction of the next set of OA operation information of the original enterprise may be adjusted correspondingly (for example, the value is reduced), or the set cleansing instruction of the next set of OA operation information of the original enterprise may be kept unchanged. If the secondary migration deviation information is not less than the deviation information configured in advance, which indicates that the probability of the instant demand item existing in the current OA operation information of the original enterprise is high, the set cleaning indication (such as the numerical value is increased) of the OA operation information of the next group of the original enterprise can be updated correspondingly, so that the user feedback demand vector corresponding to the instant demand item can be cleaned more comprehensively when the OA operation information of the next group of the original enterprise is processed.

In the embodiment of the present invention, when migrating and transforming the user feedback demand vector ReV3 into the original enterprise OA operation information again according to the third module state data and determining the third function upgrade preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information, for example, a relationship network transformation reference (relationship network mapping indication) between a first OA operation relationship network corresponding to the original enterprise OA operation information and a second OA operation relationship network corresponding to the OA upgrade indication relationship network may be determined according to the third module state data; and in combination with the relational network transformation reference, migrating and transforming a second function upgrading preference vector of the user feedback demand vector ReV3 in the OA upgrading indication relational network into the original enterprise OA operation information, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information.

On the basis of the above, please refer to fig. 3, the present invention further provides a block diagram of a big data analysis apparatus 30 based on an enterprise OA system, which includes the following functional modules.

The requirement vector acquiring module 31 is configured to acquire a user feedback requirement vector ReV1 in the OA operation information of the original enterprise, and a user feedback requirement vector ReV2 having a matching relationship with the user feedback requirement vector ReV1 in the OA sample information of the target enterprise corresponding to the OA operation information of the original enterprise.

A preference vector determining module 32, configured to process the user feedback requirement vector ReV1 by using an OA upgrade indication relationship network, determine a first function upgrade preference vector of the user feedback requirement vector ReV1 in the OA upgrade indication relationship network, process the user feedback requirement vector ReV2 by using the OA upgrade indication relationship network, and determine a second function upgrade preference vector of the user feedback requirement vector ReV2 in the OA upgrade indication relationship network.

And the demand vector analysis module 33 is configured to determine, by combining with the preference correlation feature between the first function upgrade preference vector and the second function upgrade preference vector, a to-be-processed user feedback demand vector corresponding to the immediate demand item from the user feedback demand vector ReV1.

And the requirement vector cleaning module 34 is configured to clean the requirement vector fed back by the user to be processed in the original enterprise OA operation information, and determine enterprise OA operation information used for assisting an OA system upgrade decision.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus exemplarily described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

Claims

1. A big data analysis method based on an enterprise OA system is applied to OA big data processing equipment and at least comprises the following steps:

acquiring a user feedback demand vector ReV1 in original enterprise OA operation information and a user feedback demand vector ReV2 which has a matching relation with the user feedback demand vector ReV1 in target enterprise OA sample information corresponding to the original enterprise OA operation information;

processing the user feedback demand vector ReV1 by utilizing an OA upgrade indication relation network to determine a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network, and processing the user feedback demand vector ReV2 by utilizing the OA upgrade indication relation network to determine a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relation network;

determining a user feedback demand vector to be processed corresponding to an instant demand item from the user feedback demand vector ReV1 by combining preference correlation characteristics between the first function upgrading preference vector and the second function upgrading preference vector; cleaning the feedback demand vector of the user to be processed in the original enterprise OA operation information, and determining enterprise OA operation information for assisting an OA system upgrading decision;

before obtaining the user feedback demand vector ReV1 in the original enterprise OA operation information and the user feedback demand vector ReV2 which has a matching relationship with the user feedback demand vector ReV1 in the target enterprise OA sample information corresponding to the original enterprise OA operation information, the method further includes: determining the OA sample information of the target enterprise for the OA operation information of the original enterprise based on a preset information sample determination rule; the determining of the target enterprise OA sample information for the original enterprise OA operation information based on a preset information sample determining rule comprises the following steps: analyzing whether the current selected OA operation information of the enterprise is matched with the preset information sample determining rule or not; in response to the selected enterprise OA operation information matching the preset information sample determination rule, determining the selected enterprise OA operation information as the target enterprise OA sample information; in response to the selected enterprise OA operation information not matching the preset information sample determination rule, determining first enterprise OA operation information as the target enterprise OA sample information; wherein the first enterprise OA operation information comprises: the acquisition time node is positioned in front of the original enterprise OA operation information and has the smallest difference with the acquisition time period of the acquisition time node of the original enterprise OA operation information;

wherein the method further comprises: in response to the selected enterprise OA operation information not matching the pre-set information sample determination rule, determining the original enterprise OA operation information as additional selected enterprise OA operation information; wherein the additional selected enterprise OA operational information is used to process a next set of original enterprise OA operational information; wherein the preset information sample determination rule comprises one or more of the following: a first rule that a difference between the sequence adjustment values of the original enterprise OA operational information and the selected enterprise OA operational information is less than a pre-configured sequence adjustment limit; according to a second rule, the number of the user feedback demand vectors ReV2 which are in the matching relation with the user feedback demand vector ReV1 in the OA operation information of the selected enterprise reaches a set numerical value; a third rule, a difference between description values of a first collection state description value corresponding to the original enterprise OA operation information and a second collection state description value corresponding to the selected enterprise OA operation information is smaller than a description limit value configured in advance; the acquisition state description value is a variable used for distinguishing different acquisition states obtained by carrying out characteristic value mapping processing on the multidimensional acquisition state information;

wherein, the determining, by combining preference correlation characteristics between the first function upgrade preference vector and the second function upgrade preference vector, a to-be-processed user feedback demand vector corresponding to an instant demand item from the user feedback demand vector ReV1 includes: determining a migration transformation result pointing from the second function upgrade preference vector to the first function upgrade preference vector in combination with preference correlation characteristics between the first function upgrade preference vector and the second function upgrade preference vector; determining a user feedback demand vector to be processed corresponding to the instant demand item from the user feedback demand vector ReV1 by combining the migration transformation result; wherein, the determining, in combination with the migration transformation result, a to-be-processed user feedback requirement vector corresponding to the immediate requirement item from the user feedback requirement vector ReV1 includes: in the 1 st round of operation link, based on the set operation result of the migration transformation result, sequentially adjusting the migration transformation results corresponding to the user feedback demand vectors ReV 1; determining a target migration transformation result of the 1 st round operation link from the migration transformation results based on the sequence adjustment result and a set cleaning instruction; judging whether a preset termination rule is matched in a Y-th operation link; if the current demand item is matched with the target migration transformation result determined by the Y-1 th round of operation link, determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV 1; wherein Y is an integer greater than 1, and the termination rule includes one or more of: the number of the operation links is not less than the preset number; the difference between the global operation result determined by the current operation link and the global operation result determined by the previous operation link is smaller than a set operation threshold;

wherein the method further comprises: in the Y-th operation link, in response to the fact that the termination rule is not matched, determining a global operation result based on a target migration transformation result determined by the Y-1-th operation link; determining a target migration transformation result of the operation link of the Y-th round based on deviation information between a set operation result of each migration transformation result and the global operation result and the set cleaning instruction; judging whether the termination rule is matched or not in the Y +1 th operation link; if the current demand item is matched with the target migration transformation result, determining a to-be-processed user feedback demand vector corresponding to the instant demand item from the user feedback demand vector ReV1 based on the target migration transformation result determined by the Y-th operation link;

wherein the method further comprises: determining third module state data of an information statistical module when the information statistical module collects the original enterprise OA operation information by combining an associated user feedback demand vector except the user feedback demand vector to be processed in the user feedback demand vector ReV1, a user feedback demand vector ReV3 which has a matching relation with the associated user feedback demand vector in the target enterprise OA sample information, and second module state data of the information statistical module collecting the target enterprise OA sample information; wherein the user feedback requirement vector ReV2 comprises the user feedback requirement vector ReV3;

wherein the method further comprises: migrating and converting the user feedback demand vector ReV3 into the original enterprise OA operation information again according to the third module state data, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information; determining secondary migration deviation information by combining the coverage area of the third function upgrade preference vector in the original enterprise OA operation information and the coverage area of the associated user feedback demand vector in the original enterprise OA operation information; determining an additional set cleaning indication in combination with the secondary migration deviation information; wherein the additional set cleaning instruction is used for processing the next group of original enterprise OA operation information; wherein, according to the third module state data, migrating and converting the user feedback demand vector ReV3 into the original enterprise OA operation information again, and determining a third function upgrade preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information, including: according to the third module state data, determining a relation network transformation reference between a first OA operation relation network corresponding to the original enterprise OA operation information and a second OA operation relation network corresponding to the OA upgrade indication relation network; and in combination with the relational network transformation reference, migrating and transforming a second function upgrading preference vector of the user feedback demand vector ReV3 in the OA upgrading indication relational network into the original enterprise OA operation information, and determining a third function upgrading preference vector of the user feedback demand vector ReV3 in the original enterprise OA operation information.

2. The enterprise OA system-based big data analysis method of claim 1, wherein the processing the user feedback requirement vector ReV1 by using an OA upgrade indication relationship network to determine a first function upgrade preference vector of the user feedback requirement vector ReV1 in the OA upgrade indication relationship network comprises:

determining first module state data of an information statistic module when the original enterprise OA operation information is collected based on a module configuration link of the information statistic module in an enterprise OA operation environment when the target enterprise OA sample information is collected and first face of interest information of the information statistic module in the enterprise OA operation environment when the original enterprise OA operation information is collected;

and in combination with the first module state data, migrating and transforming the user feedback demand vector ReV1 to the OA upgrade indication relation network, and determining a first function upgrade preference vector of the user feedback demand vector ReV1 in the OA upgrade indication relation network.

3. The enterprise OA system-based big data analysis method according to claim 1, wherein the processing the user feedback requirement vector ReV2 by using the OA upgrade indication relationship network to determine a second function upgrade preference vector of the user feedback requirement vector ReV2 in the OA upgrade indication relationship network comprises: and processing the user feedback demand vector ReV2 by utilizing the OA upgrade indication relation network in combination with second module state data of the information statistical module during the collection of the OA sample information of the target enterprise, and determining a second function upgrade preference vector of the user feedback demand vector ReV2 in the OA upgrade indication relation network.

4. An OA big data processing device is characterized by comprising a processor and a memory; the processor is connected in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 3.