CN116628206A - Enterprise credit analysis management system based on data analysis - Google Patents
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Abstract
The invention discloses an enterprise credit analysis management system based on data analysis, which relates to the technical field of enterprise credit analysis and comprises a data grabbing module, an enterprise information database, a report output module, a self credit analysis module, a social credit analysis module, a comprehensive analysis module and a keyword characteristic database. The enterprise credit degree and the social credit degree are analyzed, so that the management level and the social responsibility of the enterprise are reflected, the enterprise credit awareness is further consolidated, the enterprise credit is comprehensively analyzed on the basis, the enterprise credit is more comprehensively estimated, the singleness of the traditional enterprise credit estimation is changed, the audience group of the enterprise credit estimation is wider, more comprehensive and accurate enterprise credit information can be provided for multiple roles of investors, suppliers, clients, job seekers and the like, and the public trust of the enterprise credit estimation is greatly improved.
Description
Technical Field
The invention relates to the technical field of enterprise credit analysis, in particular to an enterprise credit analysis management system based on data analysis.
Background
Credit is an inevitable product of social and economic development, and is an indispensable ring in the economic operation of modern markets. Maintaining and developing credit relationships is an important premise for protecting social economic order, and enterprise credit is not only focused by investors or borrowers in the financial market, but also by multiple parties in the general trading market, and with the development of economic contracts, enterprise credit becomes a prerequisite for cooperation and trading. Thus, enterprise credit system construction is a major concern for the construction of the entire social credit system.
At present, the phenomenon of enterprise credit deficiency is aggravated, the credit awareness is light, and the traditional enterprise credit assessment method mainly depends on assessing enterprise credit from two aspects of finance and law, so that the assessment result is one-sided due to excessive attention to asset guarantee, so that the enterprise credit assessment public trust is poor, service objects have limitations, further the audience group for enterprise credit assessment is small, and more comprehensive and accurate information is difficult to provide for multiple roles of investors, clients, job seekers and the like.
Disclosure of Invention
The invention aims to provide an enterprise credit analysis management system based on data analysis, so as to solve the problems set forth in the background technology.
The aim of the invention can be achieved by the following technical scheme: the enterprise credit analysis management system based on data analysis comprises a data capture module, an enterprise information database, a report output module, a self credit analysis module, a social credit analysis module, a comprehensive analysis module and a keyword characteristic database;
the data grabbing module is used for acquiring basic data of an enterprise and sending the basic data to the enterprise information database for storage;
the self credit analysis module is used for acquiring the business registration information, employee comments and labor dispute records of the enterprise, analyzing the self credit of the enterprise, generating a self credit good signal or a self credit warning signal according to the comparison between the self credit of the enterprise and a preset self credit threshold of the enterprise, and transmitting the self credit good signal or the self credit warning signal to the comprehensive analysis module;
the social credit analysis module is used for collecting social responsibility information and complaint records of enterprises, analyzing the social reliability of the enterprises, comparing the social reliability of the enterprises with a preset social reliability threshold corresponding to the enterprises to generate a social credit good signal or a social credit warning signal, and sending the social credit good signal or the social credit warning signal to the comprehensive analysis module;
the comprehensive analysis module is used for analyzing comprehensive credit of the enterprise, collecting financial and legal risk information of the enterprise, analyzing to obtain conventional credit of the enterprise, comparing the conventional credit of the enterprise with a preset conventional credit threshold of the enterprise, generating a conventional credit good signal or a conventional credit warning signal, receiving signals sent by the credit analysis module and the social credit analysis module to judge comprehensive credit of the enterprise, and sending a judging result to the report output module;
the report output module is used for receiving the judging result of the comprehensive analysis module, generating a prompting signal and prompting correspondingly when the comprehensive credit of the enterprise is general, and outputting an enterprise comprehensive credit analysis report based on the prompting signal when the comprehensive credit of the enterprise is excellent or good;
the enterprise information database is used for storing basic information of enterprises;
the keyword characteristic database is used for storing comment characteristic keywords and comprises a positive keyword list, a negative keyword list and a neutral keyword list, wherein the positive keyword list is used for storing keywords with vocabulary properties of positive, the negative keyword list is used for storing keywords with vocabulary properties of negative, and the neutral keyword list is used for storing keywords with vocabulary properties of neutral.
Preferably, the analysis of the enterprise's own credibility is performed as follows:
the method comprises the steps of researching real information of an enterprise in a staff questionnaire mode, wherein the real information of the enterprise comprises an actual office address, an actual staff number, an actual operation scale and an actual paramedic number, and acquiring the business registration information of the enterprise, wherein the business registration information comprises the office address, the staff number, the operation scale and the paramedic number;
comparing office addresses corresponding to business registration information of enterprises with actual office addresses, employee numbers corresponding to business registration information of the enterprises with actual employee numbers, enterprise operation scale with actual operation scale, and participants corresponding to business registration information of the enterprises with actual participants, generating real signals when the comparison is successful, generating false signals, counting the occurrence times of the real signals and the false signals, respectively marking the real signals and the false signals as fh and xj, and analyzing the occurrence times of the real signals and the false signals to obtain the authenticity ZS of the business registration information of the enterprises.
Collecting reviews of enterprise on-staff and off-staff, namely enterprise reviews, qualitatively judging the nature of the enterprise reviews through a keyword feature database, and counting the number of positive evaluations, negative evaluations and neutral evaluations, thereby analyzing the review coefficient PL of the enterprise;
acquiring labor dispute records in an enterprise, counting the occurrence times of the labor dispute records, and analyzing to obtain a labor specification coefficient LD corresponding to the enterprise;
the enterprise self-credit ME is obtained through the enterprise business registration information authenticity, the comment coefficient and the labor specification coefficient, the self-credit corresponding to the enterprise is compared with a preset self-credit threshold, when the self-credit corresponding to the enterprise is smaller than the preset self-credit threshold, a self-credit warning signal is generated, when the self-credit corresponding to the enterprise is larger than the preset self-credit threshold, a self-credit good signal is generated, and the self-credit warning signal or the self-credit good signal is sent to the comprehensive analysis module and is sent to the comprehensive analysis module.
Preferably, the enterprise comments are qualitatively determined through the keyword feature database, and the specific process is as follows:
obtaining enterprise comments as sample data, removing stop words in the sample data, simplifying the sample data, processing each simplified sample data by using a word segmentation tool, inputting each simplified sample data into the word segmentation tool to obtain vocabulary corresponding to each sample data, and forming a vocabulary list CH corresponding to each sample data by the vocabulary corresponding to each sample data r ' r represents the number of each sample data, r=1, 2, t, t is a positive integer, and the parts of speech of the preset vocabulary are "positive", "negative" and "neutral";
selecting the vocabulary in each vocabulary list, matching the vocabulary with the keywords stored in the keyword feature database, outputting the part of speech corresponding to the vocabulary if the vocabulary in each vocabulary list is successfully matched with the keywords stored in the keyword feature database, and counting the occurrence times of the parts of speech of positive, negative and neutral; if the matching of the vocabulary in each vocabulary list and the keywords stored in the keyword feature database is unsuccessful, invoking an emotion analysis interface to perform vocabulary emotion analysis on the vocabulary, if the vocabulary part is positive, increasing the occurrence frequency of the positive part of speech once, and sending the vocabulary to a positive keyword table in the keyword feature database for storage, if the vocabulary part is negative, increasing the occurrence frequency of the negative part of speech once, and sending the vocabulary to the positive keyword table in the keyword feature database for storage, if the vocabulary part is neutral, increasing the occurrence frequency of the neutral part of speech once, and sending the vocabulary to a neutral keyword table in the keyword feature database for storage, finally obtaining the occurrence frequency of each part of speech corresponding to each sample data, and recording the occurrence frequency as the occurrence frequency of each part of speech respectively
Obtaining an evaluation coefficient DX of each sample data by analysis r When each sample data evaluation coefficient is equal to the preset sample dataMarking the comment as a neutral comment when the evaluation coefficient threshold value is evaluated, marking the comment as a positive comment when the evaluation coefficient of each sample data is larger than the preset sample data evaluation coefficient threshold value, marking the comment as a negative comment when the evaluation coefficient of each sample data is smaller than the preset sample data evaluation coefficient threshold value, and counting the times of positive, neutral and negative in enterprise comments, wherein the times are respectively marked as Q Frontal comments 、Q Negative comments 、Q Neutral comments 。
Preferably, the social credibility of the enterprise is analyzed, and the analysis process is as follows:
the method comprises the steps of obtaining social responsibility information of enterprises, wherein the social responsibility information comprises environment protection information and public welfare information, the environment protection information comprises total investment TZ, environment protection investment TZ', environment protection behavior records, the public welfare behavior records are extracted, records violating environment protection regulations in the environment protection behavior records are counted and recorded as WG, and the public welfare information comprises public welfare times G, total beneficiaries G and total duration T;
obtaining a first evaluation coefficient HB corresponding to the social credibility of the enterprise through analysis;
acquiring complaint records of an enterprise, extracting complaint time points and processing completion time points corresponding to the complaint records of the enterprise, and calculating the difference to obtain the corresponding acceptance time t of the complaint records B And also extracts the satisfaction score D corresponding to each complaint record of the enterprise B And complaint number ts;
obtaining a second evaluation coefficient TS corresponding to the enterprise through analysis;
and comparing the social reliability corresponding to the enterprise with a preset social reliability threshold value through analysis, generating a social credit warning signal when the social reliability corresponding to the enterprise is smaller than the preset social reliability threshold value, generating a social credit good signal when the social reliability corresponding to the enterprise is larger than the preset social reliability threshold value, and transmitting the social credit warning signal or the social credit good signal to the comprehensive analysis module.
Preferably, the comprehensive credibility of the enterprise is analyzed, and the specific analysis process is as follows:
collecting financial and legal risk information corresponding to an enterprise, counting the number CW of financial risk records and the number FW of legal risk records, obtaining conventional credit CT corresponding to the enterprise through analysis, comparing the conventional credit corresponding to the enterprise with a preset conventional credit threshold, generating a conventional credit warning signal when the conventional credit corresponding to the enterprise is smaller than the preset conventional credit threshold, and generating a conventional credit good signal when the conventional credit corresponding to the enterprise is larger than the preset conventional credit threshold;
acquiring self credit, social credit and conventional credit corresponding to an enterprise, analyzing to obtain comprehensive credit corresponding to the enterprise, comparing the comprehensive credit with a set comprehensive credit threshold, generating a primary credit signal if the comprehensive credit corresponding to the enterprise is greater than a preset comprehensive credit threshold, generating a secondary credit signal if the comprehensive credit corresponding to the enterprise is equal to the preset comprehensive credit threshold, and generating a tertiary credit signal if the comprehensive credit corresponding to the enterprise is less than the preset comprehensive credit threshold;
and when any one of the self credit warning signal, the social credit warning signal and the conventional credit warning signal and the primary credit signal or the secondary credit signal are identified, the comprehensive credit of the enterprise is judged to be good, and when any two or more of the self credit warning signal, the social credit warning signal and the conventional credit warning signal and any one of the secondary credit signal and the tertiary credit signal are identified, the comprehensive credit of the enterprise is judged to be general, and the judgment result is sent to the report output module.
The invention has the beneficial effects that:
1. by analyzing the credit rating and the social credit rating of the enterprise, the management level and the social responsibility of the enterprise are well reflected, and the credit awareness of the enterprise is further consolidated;
2. according to the enterprise credit evaluation method, the enterprise credit is comprehensively analyzed, the enterprise credit is more comprehensively evaluated, the singleness of the traditional enterprise credit evaluation is changed, the audience group of the enterprise credit evaluation is wider, more comprehensive and accurate enterprise credit information can be provided for multiple roles of investors, suppliers, clients, job seekers and the like, and the public trust of the enterprise credit evaluation is greatly improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses an enterprise credit analysis management system based on data analysis, which comprises a data capture module, an enterprise information database, a report output module, a self credit analysis module, a social credit analysis module, a comprehensive analysis module and a keyword characteristic database;
the data grabbing module is used for acquiring basic data of an enterprise and sending the basic data to the enterprise information database for storage.
The self credit analysis module is used for acquiring the business registration information, the employee comments and the labor dispute records of the enterprise, so that the self credit of the enterprise is analyzed, and the analysis steps are as follows:
the real information of the enterprise is researched in a staff questionnaire mode, the real information of the enterprise comprises an actual office address, an actual staff number, an actual operation scale and an actual participant number, and the staff registration information of the enterprise comprises the office address, the staff number, the operation scale and the participant number.
Comparing office addresses corresponding to business registration information of enterprises with actual office addresses, employee numbers corresponding to business registration information of enterprises with actual employee numbers, enterprise operation scale with actual operation scale, and participants corresponding to business registration information of enterprises with actual participants, generating real signals when the comparison is successful, otherwise generating false signals, counting the occurrence times of the real signals and the false signals, respectively recording as fh and xj, and according to the formulaAnd calculating to obtain the authenticity ZS of the business registration information of the enterprise.
The method comprises the steps of collecting comments of enterprise incumbent personnel and offstaff on the enterprise, namely enterprise comments, taking the enterprise comments as sample data, and removing stop words in each sample data, wherein the stop words comprise English characters, numbers, digital characters, punctuation marks and single Chinese characters with high use frequency, so that each sample data is simplified.
Processing each simplified sample data by using word segmentation tool, inputting each simplified sample data into word segmentation tool to obtain vocabulary corresponding to each sample data, and forming vocabulary list CH corresponding to each sample data by vocabulary corresponding to each sample data r ' r denotes the number of each sample data, r=1, 2,.., the s-th vocabulary corresponding to the r-th sample data is represented, s represents the number of each vocabulary, s=1, 2.
Selecting the vocabulary in each vocabulary list, matching the vocabulary with the keywords stored in the keyword feature database, and outputting the vocabulary if the vocabulary in each vocabulary list is successfully matched with the keywords stored in the keyword feature databaseThe corresponding parts of speech, and count the occurrence times of the parts of speech of positive, negative and neutral; if the matching of the vocabulary in each vocabulary list and the keywords stored in the keyword feature database is unsuccessful, invoking an emotion analysis interface to perform vocabulary emotion analysis on the vocabulary, if the vocabulary part is positive, increasing the occurrence frequency of the positive part of speech once, and sending the vocabulary to a positive keyword table in the keyword feature database for storage, if the vocabulary part is negative, increasing the occurrence frequency of the negative part of speech once, and sending the vocabulary to the positive keyword table in the keyword feature database for storage, if the vocabulary part is neutral, increasing the occurrence frequency of the neutral part of speech once, and sending the vocabulary to a neutral keyword table in the keyword feature database for storage, finally obtaining the occurrence frequency of each part of speech corresponding to each sample data, and recording the occurrence frequency as the occurrence frequency of each part of speech respectively
According to the embodiment of the invention, the unsuccessfully matched vocabulary is subjected to vocabulary emotion analysis, and the unsuccessfully matched vocabulary is stored into different part-of-speech storage tables according to different parts-of-speech, so that a keyword feature library is continuously perfected, and more accurate judgment on the comment properties of enterprises is conveniently made.
Calculation by formulaObtaining the data evaluation coefficient DX of each model r ，/>For the preset positive evaluation word weight factor, neutral evaluation word weight factor, negative evaluation word weight factor, ++>Is neutralThe reference appearance times of words, delta C is the allowable difference of the preset neutral word appearance times, e is a natural constant, when each model data evaluation coefficient is smaller than a preset model data evaluation coefficient threshold value, the comment is marked as a negative comment, when each model data evaluation coefficient is equal to the preset model data evaluation coefficient threshold value, the comment is marked as a neutral comment, and when each model data evaluation coefficient is larger than the preset model data evaluation coefficient threshold value, the comment is marked as a positive comment.
The comment properties of the target enterprise corresponding to each comment are obtained, the occurrence times of the comments are counted and respectively recorded as Q Frontal comments 、Q Negative comments 、Q Neutral comments 。
By the formulaAnd calculating to obtain the comment coefficient PL of the enterprise.
Acquiring labor dispute records in enterprises, counting the occurrence times W of the labor dispute records, and passing through a formulaCalculating to obtain labor specification coefficients LD and R corresponding to enterprises Actual practice is that of Is the number of actual workers corresponding to the enterprise.
By the formula me=zs×β 1 +PL×β 2 +LD×β 3 Obtaining the self confidence of enterprises, beta 1 、β 2 、β 3 For a preset true degree weight factor, a comment coefficient weight factor and a labor specification coefficient weight factor, comparing the self-credit degree corresponding to the enterprise with a preset self-credit degree threshold, generating a self-credit warning signal when the self-credit degree corresponding to the enterprise is smaller than the preset self-credit degree threshold, generating a self-credit good signal when the self-credit degree corresponding to the enterprise is larger than the preset self-credit degree threshold, and transmitting the self-credit warning signal or the self-credit good signal to the comprehensive analysis module.
Further, the enterprise credit evaluation method and the enterprise credit evaluation system analyze the business registration information, the employee comments and the labor dispute records of the enterprise to obtain the self-credit of the enterprise, and the self-credit is used as one of the standards for evaluating the credit of the enterprise, so that the uniqueness of the credit evaluation of the traditional enterprise is changed.
The social credit analysis module is used for acquiring social responsibility information and complaint records of the enterprise, so that the social reliability of the enterprise is analyzed, and the analysis steps are as follows:
the method comprises the steps of obtaining social responsibility information of enterprises, wherein the social responsibility information comprises environment protection information and public welfare information, the environment protection information comprises total investment TZ, environment protection investment TZ', environment protection behavior records, the public welfare behavior records are extracted, records violating environment protection regulations in the environment protection behavior records are counted, the records are recorded as WG, and the public welfare information comprises public welfare times G, total beneficiaries G and total duration T.
According to the formulaObtaining a first evaluation coefficient HB, < ->Respectively a preset environmental protection investment weight factor, an environmental protection record weight factor and a public welfare weight factor.
According to the method and the device for the enterprise social credit assessment, the social responsibility information of the enterprise is obtained and analyzed and is used as a part of the enterprise social credit, so that the enterprise social credit can be more accurately reflected, the enterprise credit assessment range is wider, and the comprehensive performance is stronger.
Acquiring complaint records of an enterprise, extracting complaint time points and processing completion time points corresponding to the complaint records of the enterprise, and calculating the difference to obtain the corresponding acceptance time t of the complaint records B And also extracts the satisfaction score D corresponding to each complaint record of the enterprise B And the number of complaints ts.
According to the formulaCalculating a second evaluation coefficient TS corresponding to the enterprise, wherein t 'is a preset processing time threshold value, and D' is a preset valueTs' is a preset complaint number threshold, +.>The method comprises the steps of setting a preset acceptance duration influence factor, a satisfaction score influence factor and a complaint frequency influence factor.
Through the formula sh=hb×β 4 +TS×β 5 Obtaining social confidence level, beta, corresponding to the enterprise 4 And beta 5 And for the preset first evaluation coefficient weight factor and the second evaluation coefficient weight factor, when the social reliability corresponding to the enterprise is smaller than a preset social reliability threshold, generating a social credit warning signal, and when the social reliability corresponding to the enterprise is larger than the preset social reliability threshold, generating a social credit good signal, and transmitting the social credit warning signal or the social credit good signal to the comprehensive analysis module.
Further, the social reliability of the enterprise is analyzed, so that the social responsibility of the enterprise and the social image of the enterprise are comprehensively displayed, and the credit assessment public trust of the enterprise is increased.
The comprehensive analysis module is used for analyzing the comprehensive credibility of the enterprise, and the specific analysis steps are as follows:
collecting financial and legal risk information corresponding to enterprises, counting the number of financial risk records and legal risk records, respectively recording as CW and FW, and calculating through a formulaObtaining the conventional confidence level beta corresponding to the enterprise 6 And beta 7 And comparing the conventional credit corresponding to the enterprise with a preset conventional credit threshold for the preset financial risk influence factor and the legal risk influence factor, generating a conventional credit warning signal when the conventional credit corresponding to the enterprise is smaller than the preset conventional credit threshold, and generating a conventional credit good signal when the conventional credit corresponding to the enterprise is larger than the preset conventional credit threshold.
Acquiring self credit, social credit and conventional credit corresponding to an enterprise through a formulaObtaining the comprehensive credibility F, gamma corresponding to the enterprise 1 、γ 2 、γ 3 The method comprises the steps of comparing the comprehensive credit corresponding to an enterprise with a set comprehensive credit threshold, generating a primary credit signal if the comprehensive credit corresponding to the enterprise is greater than the preset comprehensive credit threshold, generating a secondary credit signal if the comprehensive credit corresponding to the enterprise is equal to the preset comprehensive credit threshold, and generating a tertiary credit signal if the comprehensive credit corresponding to the enterprise is less than the preset comprehensive credit threshold;
and when any one of the self credit warning signal, the social credit warning signal and the conventional credit warning signal and the primary credit signal or the secondary credit signal are identified, the comprehensive credit of the enterprise is judged to be good, and when any two or more of the self credit warning signal, the social credit warning signal and the conventional credit warning signal and any one of the secondary credit signal and the tertiary credit signal are identified, the comprehensive credit of the enterprise is judged to be general, and the judgment result is sent to the report output module.
The report output module is used for receiving the judging result of the comprehensive analysis module, generating a prompting signal and prompting correspondingly when the comprehensive credit of the enterprise is good or common, and outputting an enterprise comprehensive credit analysis report based on the prompting signal when the comprehensive credit of the enterprise is excellent.
The enterprise information database is used for storing basic information of enterprises.
The keyword feature database is used for storing comment feature keywords.
The foregoing is merely illustrative and explanatory of the invention, as it is apparent to those skilled in the art that various modifications and additions can be made to the specific embodiments described or in a similar manner without departing from the structure of the invention or beyond the scope of the invention as defined in the appended claims.
Claims (5)
1. The enterprise credit analysis management system based on data analysis comprises a data grabbing module, an enterprise information database and a report output module, and is characterized by also comprising a self credit analysis module, a social credit analysis module, a comprehensive analysis module and a keyword characteristic database;
the data grabbing module is used for acquiring basic data of an enterprise and sending the basic data to the enterprise information database for storage;
the self credit analysis module is used for analyzing the self credit degree of the enterprise to obtain the self credit degree of the enterprise, comparing the self credit degree of the enterprise with a preset self credit degree threshold value, generating a self credit signal according to a comparison result, wherein the self credit signal comprises a self credit good signal and a self credit warning signal, and transmitting the self credit signal of the enterprise to the comprehensive analysis module;
the social credit analysis module is used for analyzing the social credibility of the enterprise to obtain the social credibility of the enterprise, comparing the social credibility of the enterprise with a preset social credibility threshold value, generating a social credit signal according to a comparison result, wherein the social credit signal comprises a social credit good signal and a social credit warning signal, and transmitting the social credit signal of the enterprise to the comprehensive analysis module;
the comprehensive analysis module is used for analyzing the comprehensive credit of the enterprise, collecting financial and legal risk information of the enterprise, analyzing to obtain the conventional credit of the enterprise, receiving signals sent by the credit analysis module and the social credit analysis module, judging the comprehensive credit of the enterprise, and sending the judging result to the report output module;
the report output module is used for receiving the judging result of the comprehensive analysis module and outputting an enterprise comprehensive credit analysis report;
the enterprise information database is used for storing basic information of enterprises;
the keyword feature database is used for storing comment feature keywords.
2. The enterprise credit analysis management system based on data analysis of claim 1, wherein the analysis of the enterprise's own credit is performed as follows:
the method comprises the steps of researching the real information of enterprises in a staff questionnaire mode;
acquiring business registration information of an enterprise, comparing the business registration information with real information of the enterprise, generating real signals or false signals according to comparison results, counting the occurrence times of the real signals or the false signals, and analyzing the occurrence times of the real signals and the occurrence times of the false signals to obtain the authenticity of the business registration information of the enterprise;
collecting reviews of enterprise on staff and off staff, namely enterprise reviews, qualitatively judging the enterprise reviews through a keyword feature database, judging the properties of the enterprise reviews, wherein the properties of the reviews comprise positive evaluation, negative evaluation and neutral evaluation, and counting the number of the positive evaluation, the negative evaluation and the neutral evaluation, so that the review coefficients of the enterprise are analyzed;
acquiring labor dispute records in an enterprise, counting the occurrence times of the labor dispute records, and obtaining a labor specification coefficient corresponding to the enterprise through analysis;
the method comprises the steps of analyzing the authenticity of the business registration information, the comment coefficient and the labor specification coefficient of an enterprise to obtain the self-credit corresponding to the enterprise, comparing the self-credit corresponding to the enterprise with a preset self-credit threshold, generating a self-credit warning signal when the self-credit corresponding to the enterprise is smaller than the preset self-credit threshold, generating a self-credit good signal when the self-credit corresponding to the enterprise is larger than the preset self-credit threshold, and transmitting the self-credit warning signal or the self-credit good signal to an integrated analysis module.
3. The enterprise credit analysis management system based on data analysis of claim 2, wherein the enterprise comments are characterized by the keyword feature database, and the specific process is as follows:
the method comprises the steps of obtaining enterprise comments as sample data, simplifying and word segmentation processing is carried out on the sample data to obtain vocabularies corresponding to each sample data, forming a vocabulary list corresponding to each sample data by the vocabularies corresponding to each sample data, and presetting the parts of speech of the vocabularies as positive, negative and neutral;
selecting the vocabulary in each vocabulary list, matching the vocabulary with the keywords stored in the keyword feature database, and outputting the part of speech corresponding to the vocabulary if the vocabulary in each vocabulary list is successfully matched with the keywords stored in the keyword feature database; if the matching of the vocabulary in each vocabulary list and the keywords stored in the keyword feature database is unsuccessful, invoking an emotion analysis interface to perform vocabulary emotion analysis on the vocabulary, if the vocabulary part of speech is positive, sending the vocabulary to a positive keyword table in the keyword feature database to be stored, if the vocabulary part of speech is negative, sending the vocabulary to a negative keyword table in the keyword feature database to be stored, if the vocabulary part of speech is neutral, sending the vocabulary to a neutral keyword table in the keyword feature database to be stored, counting the occurrence times of each part of speech in each vocabulary list, and finally obtaining the occurrence times of each part of speech corresponding to each sample data;
and (3) obtaining an evaluation coefficient of each sample data through analysis, marking the comment as a negative comment when each sample data evaluation coefficient is smaller than a preset sample data evaluation coefficient threshold value, marking the comment as neutral when each sample data evaluation coefficient is equal to the preset sample data evaluation coefficient threshold value, marking the comment as positive when each sample data evaluation coefficient is larger than the preset sample data evaluation coefficient threshold value, and counting the occurrence times of positive evaluation, negative evaluation and neutral evaluation.
4. The enterprise credit analysis management system based on data analysis of claim 1, wherein the analysis of the social credibility of the enterprise is performed as follows:
acquiring social responsibility information of enterprises, including environmental protection information and public welfare information;
obtaining a first evaluation coefficient corresponding to an enterprise through analysis;
acquiring complaint records of an enterprise, extracting complaint time points and processing completion time points corresponding to the complaint records of the enterprise, calculating the difference value to obtain the acceptance duration corresponding to the complaint records, and extracting satisfaction scores and complaint times corresponding to the complaint records of the enterprise;
obtaining a second evaluation coefficient corresponding to the enterprise through analysis;
and analyzing the first evaluation coefficient and the second evaluation coefficient corresponding to the enterprise to obtain social reliability corresponding to the enterprise, comparing the social reliability corresponding to the enterprise with a preset social reliability threshold, generating a social credit warning signal when the social reliability corresponding to the enterprise is smaller than the preset social reliability threshold, generating a social credit good signal when the social reliability corresponding to the enterprise is larger than the preset social reliability threshold, and transmitting the social credit warning signal or the social credit good signal to the comprehensive analysis module.
5. The enterprise credit analysis management system based on data analysis of claim 1, wherein the analysis of the integrated credit of the enterprise is performed by the following specific analysis process:
collecting financial and legal risk information corresponding to an enterprise, counting the number of financial risk records and legal risk records, obtaining conventional credit corresponding to the enterprise through analysis, comparing the conventional credit corresponding to the enterprise with a preset conventional credit threshold, generating a conventional credit warning signal when the conventional credit corresponding to the enterprise is smaller than the preset conventional credit threshold, and generating a conventional credit good signal when the conventional credit corresponding to the enterprise is larger than the preset conventional credit threshold;
acquiring self credit, social credit and conventional credit corresponding to an enterprise, analyzing to obtain comprehensive credit corresponding to the enterprise, comparing the comprehensive credit with a set comprehensive credit threshold to generate a grade signal, generating a primary credit signal if the comprehensive credit corresponding to the enterprise is greater than the preset comprehensive credit threshold, generating a secondary credit signal if the comprehensive credit corresponding to the enterprise is equal to the preset comprehensive credit threshold, and generating a tertiary credit signal if the comprehensive credit corresponding to the enterprise is less than the preset comprehensive credit threshold;
and receiving signals of the self credit analysis module and the social credit analysis module, extracting a first-level credit signal, a second-level credit signal and a third-level credit signal, identifying the signals, judging that the comprehensive credit of the enterprise is excellent when the first-level credit signal, the self credit good signal, the social credit good signal and the conventional credit good signal are simultaneously identified, judging that the comprehensive credit of the enterprise is good when any one of the self credit warning signal, the social credit warning signal and the conventional credit warning signal and the first-level credit signal or the second-level credit signal are identified, judging that the comprehensive credit of the enterprise is common when other conditions are identified, and sending a judging result to the report output module.
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