CN114781378B - Enterprise data management method and system based on block chain - Google Patents

Abstract

The invention discloses a block chain-based enterprise data management method and system, relates to the technical field of data management, and is used for solving the problems that the existing system cannot manage and analyze enterprise data, so that the enterprise data is easy to generate conflict, abnormal and suspicious data, and the enterprise data is inaccurate and unique and the data quality is low; according to the enterprise data processing method, the enterprise data are identified and classified, and then the data are subjected to conflict, abnormity, suspicion and missing perception completion, so that the probability of errors of the enterprise data is reduced, and the quality of the enterprise data is improved; performing secondary processing on the integrated data, and linking up the certificate for storage, so that the certificate storage and source tracing of enterprise data are facilitated; the information of the content to be processed is processed through the exception handling module, so that corresponding enterprise personnel can be reminded to check and correct the content to be processed in time, the authenticity and reliability of enterprise data are ensured, and the data quality is improved.

Description

Enterprise data management method and system based on block chain

Technical Field

The invention relates to the technical field of data management, in particular to an enterprise data management method and system based on a block chain.

Background

Under the era background of comprehensive promotion of digital economy, the value of data serving as key production elements needs to be fully exerted in the market for cultivating and developing the data elements. Due to the ubiquitous conditions of difficult data management, difficult circulation, easy leakage and the like, data management becomes an important path for releasing the value of data elements, so that higher requirements are provided for the data management capability of various fields of various industries, and the final aim of the data management is to improve the value of data;

the existing enterprise data management system cannot manage and analyze enterprise data, so that the enterprise data is easy to generate data conflict, abnormity, suspicion and the like, and the enterprise data is inaccurate and unique and has low data quality.

Disclosure of Invention

The invention aims to solve the problems that the existing system can not treat and analyze enterprise data, so that the enterprise data is easy to generate conflict, abnormal and suspicious data, and the enterprise data is inaccurate and unique and has low data quality, and provides an enterprise data treatment method and system based on a block chain.

The purpose of the invention can be realized by the following technical scheme: the method for treating the enterprise data based on the block chain comprises the following steps:

the method comprises the following steps: classifying the enterprise data to obtain data elements and enterprise content corresponding to the data elements, and dividing the enterprise content into character content and digital content;

step two: integrating the enterprise content corresponding to the data elements, specifically:

s21: integrating the text content: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words;

s22: when a first word is included in a preset word group, judging whether the first word belongs to a conflict word group, when the first word is included in the conflict word group, matching all the other words in the conflict word group with all the words in the enterprise content, and if the words in the conflict word group are matched in the enterprise content, marking the matched words and the first word as conflict words;

s23: when the preset phrase does not have the first word, performing completion coefficient analysis on the first word and the word in the preset phrase to obtain a completion coefficient of the first word, and when the completion coefficient is larger than a set coefficient threshold, marking the word in the preset phrase with the maximum degree of repetition with the first word as the completion word of the first word;

s24: when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin with the pinyin of all words in a preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words;

step three: integrating the digital content: calling a preset numerical range and a numerical point group corresponding to the data element, comparing numerical values in the digital content with the corresponding preset numerical range or numerical point group, and marking the numerical values in the digital content as abnormal numbers when the numerical values in the digital content are not in the preset numerical range or the preset numerical values in the numerical point group do not include the numerical values in the digital content;

step four: marking the conflict words or the completion words or the suspicious words or the abnormal numbers as the contents to be processed and feeding back; receiving correction content corresponding to the content to be processed, replacing the correction content with corresponding enterprise content in the enterprise data, and marking the replaced enterprise data as integrated data;

step five: carrying out secondary processing on the integrated data, specifically comprising the following steps: acquiring all preset custom index models and parameters required by the custom index models, matching data elements required in the parameters with data elements in the integrated data, substituting the corresponding enterprise content in the integrated data into the custom index models when the data elements corresponding to the enterprise content in the integrated data comprise the data elements of all the parameters in the custom index models, outputting results through the custom index models, and marking the output results and the integrated data as processing data; and then, performing data chain storage on the processing data.

As a preferred embodiment of the present invention, the specific process for performing the completion coefficient analysis is as follows: marking the words containing the same characters of the first word in the preset word group as initial complementary words, wherein the number of the contained same characters can be one, two or more; counting the number of the first complementary words with the most number of the same words and marking the number as M; the value of M is a positive integer, and the minimum value is 1; substituting the formula BQ = 100/(M multiplied by kfM) to obtain a completion coefficient of the first word; kfM is a weight coefficient containing the number of the initial complementary words with a plurality of same words.

The enterprise data management system based on the block chain comprises a data collection module, a data integration module, an exception handling module and a data processing module;

the data collection module is used for collecting enterprise data uploaded by enterprise personnel through the intelligent terminal and sending the enterprise data to the data integration module;

the data integration module is used for integrating and processing enterprise data, and the specific integration processing process comprises the following steps:

classifying the enterprise data to obtain data elements and enterprise content corresponding to the data elements, and then dividing the enterprise content into character content and digital content;

integrating the text content: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words; the words can be words, short sentences and the like consisting of two, three or more words; when a first word is included in a preset word group, judging whether the first word belongs to a conflict word group, when the first word is included in the conflict word group, matching all the other words in the conflict word group with all the words in the enterprise content, and if the words in the conflict word group are matched in the enterprise content, marking the matched words and the first word as conflict words; when the first word does not exist in the preset word group, performing completion coefficient analysis on the first word and the word in the preset word group to obtain a completion coefficient of the first word, and when the completion coefficient is larger than a set coefficient threshold value, marking the word in the preset word group with the maximum degree of repetition with the first word as the completion word of the first word; when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin of the first word with the pinyin of all the words in the preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words;

integrating the digital content: calling a preset numerical range and a numerical point group corresponding to the data element, comparing a numerical value in the digital content with the corresponding preset numerical range or the corresponding numerical point group, and marking the numerical value in the digital content as an abnormal number when the numerical value in the digital content is not in the preset numerical range or the preset numerical value in the numerical point group does not comprise the numerical value in the digital content; the numerical value point group consists of a plurality of preset numerical values;

marking the conflict words or the completion words or the suspicious words or the abnormal numbers as the contents to be processed, and sending the contents to be processed to an abnormal processing module; receiving the correction content fed back by the exception handling module, replacing the correction content with corresponding enterprise content in the enterprise data, marking the replaced enterprise data as integrated data and sending the integrated data to the data processing module;

and the data processing module carries out secondary processing on the integrated data to obtain processed data, and then carries out data chaining and evidence saving on the processed data.

As a preferred embodiment of the present invention, the data processing module performs a secondary processing on the integrated data in a specific process: acquiring all preset custom index models and parameters required by the custom index models, matching data elements required in the parameters with data elements in the integrated data, substituting corresponding enterprise content in the integrated data into the custom index models when the data elements corresponding to the enterprise content in the integrated data comprise the data elements of all the parameters in the custom index models, outputting results through the custom index models, and marking the output results and the integrated data as processing data; the user-defined index model comprises a character encryption model and a formula calculation model.

As a preferred embodiment of the present invention, the exception handling module is configured to receive and process a content to be processed, and a specific processing process is as follows:

acquiring enterprise personnel uploaded correspondingly to the content to be processed, sending a content correction request to an intelligent terminal of the enterprise personnel, and receiving a request processing result fed back by the enterprise personnel through the intelligent terminal within a preset time range;

when receiving the fed back request processing result in the preset time range, analyzing the request processing result, specifically: when the request processing result is a receiving confirmation instruction, the content to be processed is sent to the intelligent terminal of the enterprise personnel and is subjected to reminding operation, wherein the reminding operation is as follows: extracting a data element corresponding to the content to be processed, acquiring a preset interval duration corresponding to the data element, calculating the duration of the time for sending the content to be processed and the current time of the system, and generating and processing instructions and feeding the instructions back to the intelligent terminal of the corresponding enterprise personnel for monitoring and analyzing when the calculated duration is equal to the preset interval duration and the corrected content corresponding to the content to be processed is not received; when the request processing result is a checking instruction and a communication number, sending the content to be processed to an intelligent terminal corresponding to the communication number and carrying out reminding operation;

when the feedback request processing result is not received within the preset time range, sending a content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel and receiving a transmitted instruction fed back by the management personnel through the intelligent terminal, stopping sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel after the transmitted instruction is received, and sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel at regular time after the transmitted instruction is not received.

As a preferred embodiment of the present invention, the specific process of monitoring and analyzing is as follows: counting the first time and the total number of times of sending of a first sending and processing instruction, calculating the time difference between the first time and the current time of a system to obtain the total sending time, extracting the values of the total sending time and the preset interval time, establishing a right-angle triangle by taking the values of the total sending time and the preset interval time as right-angle sides, selecting the middle point of the hypotenuse of the right-angle triangle, taking the middle point as a starting point to be a straight-line segment perpendicular to the hypotenuse, wherein the direction of the straight-line segment is a direction far away from the intersection point of the two right-angle sides, and the length value of the straight-line segment is equal to the value of the total sending times; the method comprises the steps that end points of a straight line section are respectively connected with the middle points of the right-angle sides of two right-angle sides to obtain section bevel edges of two corresponding sides of the straight line section, a closed quadrangle constructed by the section bevel edges of the two corresponding sides of the straight line section and the two right-angle sides extracts the numerical value of the area of the closed quadrangle and marks the numerical value as a reminding numerical value, and when the reminding numerical value is larger than a set reminding threshold value, a content correction request and the work number of enterprise personnel are sent to an intelligent terminal of an enterprise personnel corresponding to a manager.

Compared with the prior art, the invention has the beneficial effects that:

1. according to the enterprise data processing method, the enterprise data are classified, and then conflict, abnormity, suspicion and missing perception completion are carried out on the data, so that the probability of errors of the enterprise data is reduced, and the quality of the enterprise data is improved; performing secondary processing on the integrated data, and linking up the certificate for storage, so that the certificate storage and source tracing of enterprise data are facilitated;

2. according to the invention, the information of the content to be processed is processed through the exception handling module, so that corresponding enterprise personnel can be reminded to check and correct the content to be processed in time, the authenticity and reliability of enterprise data are ensured, and the data quality is further improved.

Drawings

In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.

Fig. 1 is a schematic block diagram of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, the enterprise data governance system based on the block chain is used in a block chain platform, and the block chain platform is internally provided with a data storage module, a data collection module, a data integration module, an exception handling module and a data processing module;

the data storage module stores personnel information of enterprise personnel, a preset user-defined index model, an integrated word bank corresponding to the data elements, a preset numerical range and a numerical point set; the personnel information of the enterprise personnel comprises names, work numbers, intelligent terminal communication numbers, work numbers of the management personnel, corresponding intelligent terminal communication numbers and the like of the personnel;

enterprise personnel upload enterprise data to the data collection module through the intelligent terminal, and the data collection module collects the received enterprise data and transmits the collected enterprise data to the data integration module;

the data integration module integrates enterprise data, and specifically comprises the following steps:

classifying the enterprise data to obtain data elements and enterprise content corresponding to the data elements, and then dividing the enterprise content into character content and digital content;

integrating the text content: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words; the words can be words, short sentences and the like consisting of two, three or more words; when the preset phrase comprises a first word, judging whether the first word belongs to a conflict phrase, when the conflict phrase comprises the first word, matching all the rest words in the conflict phrase with all the words in the enterprise content, and if the words in the conflict phrase are matched in the enterprise content, marking the matched words and the first word as conflict words; when no first word exists in the preset word group, performing completion coefficient analysis on the first word and the word in the preset word group to obtain a completion coefficient of the first word; the analysis process is as follows:

marking the words containing the same characters of the first word in the preset word group as initial complementary words, wherein the number of the contained same characters can be one, two or more; counting the number of the initial supplement words with the maximum number of the same words and marking the number as M; the value of M is a positive integer, and the minimum value is 1; substituting the formula BQ = 100/(M multiplied by kfM) to obtain a completion coefficient of the first word; kfM is a weight coefficient containing the number of initial complementary words with a plurality of same words; when M is the minimum value, the completion coefficient is the maximum, namely only one word with the same characters as the first word is in the preset word group;

when the completion coefficient is larger than a set coefficient threshold value, marking the word with the maximum repetition degree with the first word in the preset word group as the completion word of the first word, wherein the repetition degree is composed of the factors of the same repeated word number and the consistent number of the corresponding positions of the repeated characters, the more the same repeated word number is, the more the consistent number of the corresponding positions of the repeated characters is, the greater the repetition degree is, and if the first word is ABCDE; the complement words are ABCDGF and ABCDG; the same number of words between ABCDGF and ABCDE is 4, and the consistent number of positions corresponding to repeated characters is 4; the same number of words between the ABCDG and the ABCDE is 4, and the consistent number of positions corresponding to repeated characters is 2; the repeatability of the ABCDGF is greater than that of the ABCDG;

when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin of the first word with the pinyin of all the words in the preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words;

integrating the digital content: calling a preset numerical range and a numerical point group corresponding to the data element, comparing numerical values in the digital content with the corresponding preset numerical range or numerical point group, and marking the numerical values in the digital content as abnormal numbers when the numerical values in the digital content are not in the preset numerical range or the preset numerical values in the numerical point group do not include the numerical values in the digital content; the numerical value point group consists of a plurality of preset numerical values;

marking the conflict words or the completion words or the suspicious words or the abnormal numbers as the contents to be processed, and sending the contents to be processed to an abnormal processing module; and receiving the correction content fed back by the exception handling module, replacing the correction content with the corresponding enterprise content in the enterprise data, marking the replaced enterprise data as integrated data and transmitting the integrated data to the data processing module.

The data processing module carries out secondary processing on the integrated data to obtain all preset custom index models and parameters required by the custom index models, data elements required in the parameters are matched with data elements in the integrated data, when the data elements corresponding to enterprise contents in the integrated data comprise the data elements of all parameters in the custom index models, the corresponding enterprise contents in the integrated data are substituted into the custom index models, results are output through the custom index models, and the output results and the integrated data are marked as processing data; the user-defined index model comprises a character encryption model and a formula calculation model; performing data chaining certificate storage on the processing data; if the data element is the total income of the enterprise, when the character encryption model comprises the total income of the enterprise, the amount corresponding to the total income of the enterprise is encrypted through the character encryption model to obtain encrypted data, and the processed data is marked as processed data.

The exception handling module receives and handles the content to be handled, and the specific handling process is as follows:

acquiring enterprise personnel uploaded correspondingly to the content to be processed, sending a content correction request to an intelligent terminal of the enterprise personnel, and receiving a request processing result fed back by the enterprise personnel through the intelligent terminal within a preset time range;

when a request processing result fed back is received within a preset time range, analyzing the request processing result, specifically: when the request processing result is a receiving confirmation instruction, the content to be processed is sent to the intelligent terminal of the enterprise personnel and is subjected to reminding operation, wherein the reminding operation is as follows: extracting a data element corresponding to the content to be processed, acquiring preset interval duration corresponding to the data element, calculating duration of the time for sending the content to be processed and the current time of the system, and generating and processing instructions and feeding the instructions back to the intelligent terminal of the corresponding enterprise personnel for monitoring and analyzing when the calculated duration is equal to the preset interval duration and the corrected content corresponding to the content to be processed is not received, wherein the method specifically comprises the following steps: counting the first time and the total number of times of sending of a first sending and processing instruction, calculating the time difference between the first time and the current time of a system to obtain the total sending time, extracting the values of the total sending time and the preset interval time, establishing a right-angle triangle by taking the values of the total sending time and the preset interval time as right-angle sides, selecting the middle point of the hypotenuse of the right-angle triangle, taking the middle point as a starting point to be a straight-line segment perpendicular to the hypotenuse, wherein the direction of the straight-line segment is a direction far away from the intersection point of the two right-angle sides, and the length value of the straight-line segment is equal to the value of the total sending times; respectively connecting the end points of the straight-line segment with the middle points of the right-angle sides of the two right-angle sides to obtain segment bevel edges of the two corresponding sides of the straight-line segment, extracting the numerical value of the area of the closed quadrangle and marking the numerical value as a reminding numerical value through the segment bevel edges of the two corresponding sides of the straight-line segment and the closed quadrangle constructed by the two right-angle sides, and sending a content correction request and the work number of an enterprise worker to an intelligent terminal of a manager corresponding to the enterprise worker when the reminding numerical value is greater than a set reminding threshold value;

when the request processing result is a checking instruction and a communication number, sending the content to be processed to an intelligent terminal corresponding to the communication number and carrying out reminding operation;

when a feedback request processing result is not received within a preset time range, sending a content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel and receiving a transmitted instruction fed back by the management personnel through the intelligent terminal, stopping sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel after the transmitted instruction is received, and sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel at regular time after the transmitted instruction is not received;

the information of the content to be processed is processed through the exception handling module, so that corresponding enterprise personnel can be reminded to check and correct the content to be processed in time, the authenticity and reliability of enterprise data are ensured, and the data quality is improved.

When the invention is used, firstly enterprise data is classified to obtain data elements and enterprise content corresponding to the data elements, then the enterprise content is divided into character content and digital content, then the enterprise content corresponding to the data elements is integrated, and the character content is integrated: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words; when the preset phrase comprises a first word, judging whether the first word belongs to a conflict phrase, when the conflict phrase comprises the first word, matching all the rest words in the conflict phrase with all the words in the enterprise content, and if the words in the conflict phrase are matched in the enterprise content, marking the matched words and the first word as conflict words; when the preset phrase does not have the first word, performing completion coefficient analysis on the first word and the word in the preset phrase to obtain a completion coefficient of the first word, and when the completion coefficient is larger than a set coefficient threshold, marking the word in the preset phrase with the maximum degree of repetition with the first word as the completion word of the first word; when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin of the first word with the pinyin of all the words in the preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words; and secondly, integrating the digital content: calling a preset numerical range and a numerical point group corresponding to the data element, comparing a numerical value in the digital content with the corresponding preset numerical range or the corresponding numerical point group, and marking the numerical value in the digital content as an abnormal number when the numerical value in the digital content is not in the preset numerical range or the preset numerical value in the numerical point group does not comprise the numerical value in the digital content; marking the conflict words or completion words or suspicious words or abnormal numbers as the content to be processed and feeding back; receiving correction content corresponding to the content to be processed, replacing the correction content with enterprise content corresponding to the enterprise data, and marking the replaced enterprise data as integration data; and finally, carrying out secondary processing on the integrated data, specifically comprising the following steps: acquiring all preset custom index models and parameters required by the custom index models, matching data elements required in the parameters with data elements in the integrated data, substituting the corresponding enterprise content in the integrated data into the custom index models when the data elements corresponding to the enterprise content in the integrated data comprise the data elements of all the parameters in the custom index models, outputting results through the custom index models, and marking the output results and the integrated data as processing data; and then, performing data chain storage on the processing data.

The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (3)

1. The block chain-based enterprise data governance method is characterized by comprising the following steps:

the method comprises the following steps: classifying the enterprise data to obtain data elements and enterprise content corresponding to the data elements, and then dividing the enterprise content into character content and digital content;

step two: integrating the enterprise content corresponding to the data element, which specifically comprises the following steps:

s21: integrating the text content: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words;

s22: when the preset phrase comprises a first word, judging whether the first word belongs to a conflict phrase, when the conflict phrase comprises the first word, matching all the rest words in the conflict phrase with all the words in the enterprise content, and if the words in the conflict phrase are matched in the enterprise content, marking the matched words and the first word as conflict words;

s23: when the preset phrase does not have the first word, performing completion coefficient analysis on the first word and the word in the preset phrase to obtain a completion coefficient of the first word, and when the completion coefficient is larger than a set coefficient threshold, marking the word in the preset phrase with the maximum degree of repetition with the first word as the completion word of the first word;

s24: when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin of the first word with the pinyin of all the words in the preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words;

step three: integrating the digital content: calling a preset numerical range and a numerical point group corresponding to the data element, comparing numerical values in the digital content with the corresponding preset numerical range or numerical point group, and marking the numerical values in the digital content as abnormal numbers when the numerical values in the digital content are not in the preset numerical range or the preset numerical values in the numerical point group do not include the numerical values in the digital content;

step four: marking the conflict words or completion words or suspicious words or abnormal numbers as contents to be processed and feeding back; receiving correction content corresponding to the content to be processed, replacing the correction content with enterprise content corresponding to the enterprise data, and marking the replaced enterprise data as integration data;

step five: carrying out secondary processing on the integrated data, specifically comprising the following steps: acquiring all preset custom index models and parameters required by the custom index models, matching data elements required in the parameters with data elements in the integrated data, substituting corresponding enterprise content in the integrated data into the custom index models when the data elements corresponding to the enterprise content in the integrated data comprise the data elements of all the parameters in the custom index models, outputting results through the custom index models, and marking the output results and the integrated data as processing data; then, performing data chaining certificate storage on the processing data;

the concrete process for analyzing the completion coefficient is as follows: marking the words containing the same characters of the first word in the preset word group as initial complementary words, counting the number of the initial complementary words containing the maximum number of the same characters and marking as M; substituting the formula BQ = 100/(M multiplied by kfM) to obtain a completion coefficient of the first word; kfM is a weight coefficient containing the number of the initial complementary words with a plurality of same words.

2. The enterprise data management system based on the block chain is characterized by comprising a data collection module, a data integration module, an exception handling module and a data processing module;

the data collection module is used for collecting enterprise data uploaded by enterprise personnel through the intelligent terminal and sending the enterprise data to the data integration module;

the data integration module is used for integrating and processing enterprise data, and the specific integration processing process comprises the following steps:

classifying the enterprise data to obtain data elements and enterprise content corresponding to the data elements, and then dividing the enterprise content into character content and digital content;

integrating the text content: calling an integrated word stock corresponding to the data elements, extracting words of enterprise content corresponding to the data elements, matching the words with preset phrases in the integrated word stock, and marking the words of the enterprise content as first words; when a first word is included in a preset word group, judging whether the first word comprises a conflict word group, when the conflict word group comprises the first word, matching all the other words in the conflict word group with all the words in the enterprise content, and if the words in the conflict word group are matched in the enterprise content, marking the matched words and the first word as conflict words; when the preset phrase does not have the first word, performing completion coefficient analysis on the first word and the word in the preset phrase to obtain a completion coefficient of the first word, and when the completion coefficient is larger than a set coefficient threshold, marking the word in the preset phrase with the maximum degree of repetition with the first word as the completion word of the first word; when the completion coefficient is less than or equal to the set coefficient threshold, extracting the pinyin of the first word, matching the pinyin of the first word with the pinyin of all the words in the preset phrase, and when the corresponding pinyin is matched, marking the first word as a suspicious word; when the corresponding pinyin is not matched, marking the words of the enterprise content as abnormal words;

integrating the digital content: calling the data elements to correspond to a preset numerical range and a numerical point group, comparing numerical values in the digital content with the corresponding preset numerical range or the corresponding numerical point group, and marking the numerical values in the digital content as abnormal numbers when the numerical values in the digital content are not in the preset numerical range or the preset numerical values in the numerical point group do not include the numerical values in the digital content;

marking the conflict words or the completion words or the suspicious words or the abnormal numbers as the contents to be processed, and sending the contents to be processed to an abnormal processing module; receiving the correction content fed back by the exception handling module, replacing the correction content with corresponding enterprise content in the enterprise data, marking the replaced enterprise data as integrated data and sending the integrated data to the data processing module;

the data processing module carries out secondary processing on the integrated data to obtain processed data, and then carries out data chaining and evidence saving on the processed data;

the data processing module performs secondary processing on the integrated data in the specific process that: acquiring all preset custom index models and parameters required by the custom index models, matching data elements required in the parameters with data elements in the integrated data, substituting corresponding enterprise content in the integrated data into the custom index models when the data elements corresponding to the enterprise content in the integrated data comprise the data elements of all the parameters in the custom index models, outputting results through the custom index models, and marking the output results and the integrated data as processing data; the user-defined index model comprises a character encryption model and a formula calculation model.

3. The system according to claim 2, wherein the exception handling module is configured to receive and process the content to be processed, and the specific processing procedure is as follows:

acquiring enterprise personnel uploaded correspondingly to the content to be processed, sending a content correction request to an intelligent terminal of the enterprise personnel, and receiving a request processing result fed back by the enterprise personnel through the intelligent terminal within a preset time range;

when a request processing result fed back is received within a preset time range, analyzing the request processing result, specifically: when the request processing result is the first result, the content to be processed is sent to the intelligent terminal of the enterprise personnel and reminding operation is carried out, wherein the reminding operation is as follows: extracting a data element corresponding to the content to be processed, acquiring a preset interval duration corresponding to the data element, calculating the duration of the time for sending the content to be processed and the current time of the system, and generating and processing instructions and feeding the instructions back to the intelligent terminal of the corresponding enterprise personnel for monitoring and analyzing when the calculated duration is equal to the preset interval duration and the corrected content corresponding to the content to be processed is not received; when the request processing result is a second result, the second result is a checking instruction and a communication number, and the content to be processed is sent to the intelligent terminal corresponding to the communication number and is subjected to reminding operation;

when the feedback request processing result is not received within the preset time range, sending a content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel and receiving a transmitted instruction fed back by the management personnel through the intelligent terminal, stopping sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel after the transmitted instruction is received, and sending the content correction request and the work number of the enterprise personnel to the intelligent terminal of the management personnel corresponding to the enterprise personnel at regular time after the transmitted instruction is not received.

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