CN116862302A - Enterprise informatization management integrated platform based on big data - Google Patents
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Abstract
The application discloses an enterprise informatization management integrated platform based on big data, and particularly relates to the field of enterprise information management; the account number is divided into the same class set according to the interval judgment by counting the construction funds and the vermicelli quantity of the account number, so that finer management, distribution and comparison of company resources are facilitated; collecting emotion parameters and interaction parameters of the account, and obtaining a promotion coefficient by normalizing the emotion parameters and the interaction parameters, so that objective evaluation and ranking of the account are facilitated; the comparison result of the promotion coefficient and 0 is used for judging the popularity of the account number and further determining whether the account number is worth focusing on and investments. The account numbers are clearly evaluated and divided, so that more investment resources are concentrated on account numbers with higher welcome degree, the accuracy and the precision of investment are improved, investment risks and possible losses are reduced, the funds and resources of companies are protected, and the investment is prevented from being on account numbers with no potential or negative welcome degree.
Description
Technical Field
The application relates to the field of enterprise information management, in particular to an enterprise informatization management integrated platform based on big data.
Background
In media companies mainly using short video operation accounts, account operation is a core work business of the company and is also a root of survival and development of the company, so that the health degree of the account is very important to the company, and the health degree of the account directly influences the performance, brand image and market competitiveness of the company.
In the current commercial environment, many companies often face challenges of lost investment caused by data analysis errors when involved in account number support and investment decision, rely on erroneous or incomplete data for analysis, cause erroneous assessment of account number status, miss investment opportunities or select non-competitive accounts, and such data analysis errors negatively affect the development and return on investment of the company. The method has the advantages that the method causes the company to waste resources and funds, invests in invalid or low-efficiency accounts, cannot achieve expected market performance and business targets, cannot concentrate on the resources to prop up and cultivate the account with the highest potential, and is difficult for the company to achieve long-term sustainable results.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present application provides an enterprise informatization management integration platform based on big data to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the enterprise informatization management integrated platform based on big data comprises a statistics module, a data acquisition module, a comprehensive analysis module and an approval judgment module, wherein the modules are connected through signals;
the statistics module is used for counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of vermicelli accumulated until the account is developed, setting a funds interval used for account construction and a quantity interval accumulated until the account is developed, judging whether the funds used for account construction belong to the funds interval used for account construction or whether the quantity of vermicelli accumulated until the account is developed into the quantity interval accumulated until the account is developed, determining whether to divide the account into the same analogy set according to a judging result, generating a signal whether to divide the account into the same analogy set and sending the signal to the data acquisition module;
the data acquisition module acquires emotion parameters and interaction parameters of accounts belonging to the same analogy set, wherein the emotion parameters comprise comment recognition-detraction semantic overall indexes, and the interaction parameters comprise comment overall participation degree, generate parameter acquisition signals and send the parameter acquisition signals to the comprehensive analysis module;
the comprehensive analysis module normalizes the recognition-detraction semantic overall index and the evaluation overall participation degree to obtain a recognition coefficient, evaluates the account by using the recognition coefficient, generates a coefficient generation signal and sends the coefficient generation signal to the recognition judgment module;
and the acceptance judging module compares the promotion coefficient of the account with 0 and generates a statistical signal or a abandon signal according to the comparison result.
In a preferred embodiment, the statistics module operation includes the following:
counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of accumulated vermicelli developed so far, setting a funds interval used for account construction and a quantity interval accumulated by account development so far, and judging the accounts by using the following formula: x= { a e [ a ] min ,A max ]}||{B∈[B min ,B max ]In the formula, X represents a return result, A, B represents funds used for account construction and the quantity of accumulated vermicelli of account developed so far respectively, [ A ] min ,A max ]Represents the capital interval for construction, [ B ] min ,B max ]Representing the accumulated vermicelli quantity interval of account development so far, in this sectionIn the expression, E represents the logic belonging to the operation symbol, I represents the logic OR operation symbol, and the meaning of the whole expression is that if funds used for account construction are in a funds interval used for account construction or the quantity of accumulated vermicelli developed by the account is in a quantity interval accumulated by the account, the whole expression returns true, otherwise, fas le is returned.
In a preferred embodiment, the funds used for collecting account construction of each account and the quantity of accumulated vermicelli of account development so far are respectively substituted into a return result formula, if the return result is true, the account returned to true is classified into one analogy set, otherwise, if the return result is false, the account is not classified into one analogy set.
In a preferred embodiment, the logic for obtaining the global semantic indirection of the comment derogative is:
step A1: obtaining comment data of the account number under the recently rated number of works from a short video platform through a crawler, and taking a sentence with earliest comment time if the same ID comments for comments under the same work for a plurality of times;
step A2: preprocessing the collected comment text under each work, namely uniformly translating the comment text into the same language, and performing morphological reduction and stem extraction so as to facilitate subsequent emotion analysis;
step A3: carrying out emotion tendency analysis on each comment by using an emotion analysis algorithm, classifying the comments into positive, negative or neutral emotion based on an analysis result, and carrying out emotion classification by using a naive Bayesian classifier or using a vector machine classifier;
step A4: counting the number of positive, negative or neutral comments in all comments;
if the number of the neutral comment pieces is larger than or equal to the sum of the numbers of the positive comments and the negative comments, the work is not included in the analysis range, and a work with the closest release time is found again for analysis again;
if the number of the neutral comment pieces is smaller than the sum of the number of the positive comment pieces and the number of the negative comment pieces, comparing the number of the positive comment pieces with the number of the negative comment pieces;
if the number of positive comment pieces is larger than the number of negative comment pieces, judging that the work is a positive work;
if the number of the negative comments is greater than the number of the positive comments, judging that the work is a negative work;
step A5: calculating the positive degree value of the work according to the judgment result:
if the work is a front work, the front degree calculation formula is: frontal number of commentary/total number of commentary;
if the work is a negative work, the positive degree calculation formula is: negative comment score/comment total score 1;
step A6: and counting the front degree values of all rated works, and averaging to obtain the comment and recognition semantic integral index.
In a preferred embodiment, the overall participation score acquisition logic is:
step B1: acquiring time information of a published work in unit time and evaluation time information of comment users in a crawler mode, and recording;
step B2: counting the number of comments, the number of praise, the number of forwarding and the number of times of being coated, wherein if the same I D participates in praise, comment, forwarding and being coated at the same time, the same I D is recorded as one time, and the indexes are accumulated to obtain a total value;
step B3: selecting the works of the recent rated quantity in the account, counting the maximum total value and the minimum total value, and carrying the maximum total value and the minimum total value into the following formula to calculate and obtain the overall participation degree of the evaluation: overall score = (maximum total value-minimum total value)/(maximum total value + minimum total value).
In a preferred embodiment, the approval determination module operates to include the following:
after the promotion coefficient is obtained, comparing the promotion coefficient with 0, and if the promotion coefficient is more than or equal to 0, generating a statistical signal; otherwise, if the promotion coefficient is smaller than 0, generating a discarding signal.
The enterprise informatization management integrated platform based on big data has the technical effects and advantages that:
1. the account numbers can be screened and classified by collecting funds used for account construction of each account and the accumulated quantity of the accumulated vermicelli developed so far and adding the accumulated quantity of the accumulated vermicelli into a return result formula for judgment. If the funds used for account construction and the accumulated vermicelli quantity simultaneously meet the set interval requirement, namely the whole expression returns true, the accounts can be classified into an analogy set; the method is beneficial to statistics and analysis of the normally operated accounts, so that the characteristics and performances of each account in a company are better known, the accounts in the normal operation state are better distinguished from the accounts in other states, subjective bias is reduced based on analysis and judgment of actual data, and the accounts are initially screened and grouped;
2. through collecting emotion parameters and interaction parameters of accounts belonging to the same analog set, including comment identification semantic overall indexes and comment overall participation, normalizing and calculating a promotion coefficient, evaluating the account, and then comparing the promotion coefficient with 0 to generate a statistical signal or a abandon signal; thereby being beneficial to comprehensively evaluating and comparing account numbers; by comparing the push coefficient with 0, works of the accounts are evaluated, statistical signals or abandon signals are generated, and the accounts are clearly evaluated and divided, so that more investment resources can be concentrated on accounts with higher welcome degree, the investment accuracy and precision are improved, investment risks and possible losses are reduced, funds and resources of companies are protected, and investment is prevented from being on accounts with no potential or negative welcome degree.
Drawings
Fig. 1 is a schematic structural diagram of an enterprise informatization management integration platform based on big data.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
FIG. 1 shows an enterprise informatization management integrated platform based on big data, which comprises a statistics module, a data acquisition module, a comprehensive analysis module and an approval judgment module, wherein the modules are connected through signals;
the statistics module is used for counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of vermicelli accumulated until the account is developed, setting a funds interval used for account construction and a quantity interval accumulated until the account is developed, judging whether the funds used for account construction belong to the funds interval used for account construction or whether the quantity of vermicelli accumulated until the account is developed into the quantity interval accumulated until the account is developed, determining whether to divide the account into the same analogy set according to a judging result, generating a signal whether to divide the account into the same analogy set and sending the signal to the data acquisition module;
the data acquisition module acquires emotion parameters and interaction parameters of accounts belonging to the same analogy set, wherein the emotion parameters comprise comment recognition-detraction semantic overall indexes, and the interaction parameters comprise comment overall participation degree, generate parameter acquisition signals and send the parameter acquisition signals to the comprehensive analysis module;
the comprehensive analysis module normalizes the recognition-detraction semantic overall index and the evaluation overall participation degree to obtain a recognition coefficient, evaluates the account by using the recognition coefficient, generates a coefficient generation signal and sends the coefficient generation signal to the recognition judgment module;
and the acceptance judging module compares the promotion coefficient of the account with 0 and generates a statistical signal or a abandon signal according to the comparison result.
In some media companies, a short video account is considered one of the most important properties. These companies are aware of the strategic importance of multi-faceted flowering and therefore build multiple short video accounts and provide the same resources and support for each account. This strategy aims to expand coverage by developing multiple accounts, attract a wider audience population, and bring more exposure and business opportunities to the company.
As individual accounts develop, companies will conduct periodic account analysis and evaluation. Through comprehensive analysis of the data and indexes of each account, the performance of each account in aspects of development status, user interaction condition, content quality, market competitiveness and the like can be known. Such analysis may help companies identify accounts that are poorly developed or that fail to achieve the intended goal.
Based on the analysis results, companies can purposefully screen out account numbers with poor development, so as to save resources and cost. Thus, the development and the growth of the account with the highest potential can be further promoted by concentrating on the resources. Through centralized resources and optimized operation, the company can realize the maximization of income, and the influence and market position of the account number are improved.
The statistical module operation comprises the following contents:
counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of accumulated vermicelli developed so far, setting a funds interval used for account construction and a quantity interval accumulated by account development so far, and judging the accounts by using the following formula: x= { a e [ a ] min ,A max ]}||{B∈[B min ,B max ]In the formula, X represents a return result, A, B represents funds used for account construction and the quantity of accumulated vermicelli of account developed so far respectively, [ A ] min ,A max ]Represents the capital interval for construction, [ B ] min ,B max ]Representing the accumulated vermicelli quantity interval of account development so far, in the expression, epsilon represents that logic belongs to an operation symbol, and I represents that logic or operation symbol. The meaning of the whole expression is that if funds used for account construction are in a funds interval used for account construction or the quantity of accumulated vermicelli developed by the account is in a quantity interval accumulated by the account, the whole expression returns true, otherwise, fas le is returned.
And collecting funds used for account construction of each account and the quantity of accumulated vermicelli generated by account development until now, respectively bringing the funds and the quantity of accumulated vermicelli into a return result formula, classifying the account returned to true into an analogy set if the return result is true, otherwise, not classifying the account into the analogy set if the return result is false.
According to the application, funds used for account construction of each account and the accumulated quantity of the accumulated vermicelli developed so far are collected and are added into a return result formula for judgment, so that account numbers can be screened and classified. If the funds used for account construction and the accumulated vermicelli quantity simultaneously meet the set interval requirement, namely the whole expression returns true, the accounts can be classified into an analogy set; the method is beneficial to statistics and analysis of the normally operated accounts, so that the characteristics and performances of each account in a company are better known, the accounts in the normal operation state are better distinguished from the accounts in other states, subjective bias is reduced based on analysis and judgment of actual data, and the accounts are initially screened and grouped.
The data acquisition module operates to include the following:
and collecting emotion parameters and interaction parameters of the account numbers belonging to the same analogy set, wherein the emotion parameters comprise comment and recognition semantic overall indexes, and the interaction parameters comprise comment overall participation.
The comment and recognition semantic overall index obtaining logic is as follows:
step A1: obtaining comment data of the account number under the recently rated number of works from a short video platform through a crawler, and taking a sentence with earliest comment time if the same ID comments for comments under the same work for a plurality of times;
step A2: preprocessing the collected comment text under each work, namely uniformly translating the comment text into the same language, and performing morphological reduction and stem extraction so as to facilitate subsequent emotion analysis;
step A3: carrying out emotion tendency analysis on each comment by using an emotion analysis algorithm, classifying the comments into positive, negative or neutral emotion based on analysis results, and carrying out emotion classification by using a naive Bayesian classifier or using a vector machine classifier;
step A4: counting the number of positive, negative or neutral comments in all comments;
if the number of the neutral comment pieces is larger than or equal to the sum of the numbers of the positive comment pieces and the negative comment pieces, the work is not included in the analysis range, and the work with the closest release time is found again for analysis again.
If the number of the neutral comment pieces is smaller than the sum of the number of the positive comment pieces and the number of the negative comment pieces, comparing the number of the positive comment pieces with the number of the negative comment pieces;
if the number of positive comment pieces is larger than the number of negative comment pieces, judging that the work is a positive work;
if the number of the negative comments is greater than the number of the positive comments, judging that the work is a negative work;
step A5: calculating the positive degree value of the work according to the judgment result:
if the work is a front work, the front degree calculation formula is: frontal number of commentary/total number of commentary;
if the work is a negative work, the positive degree calculation formula is: negative comment score/comment total score 1;
step A6: and counting the front degree values of all rated works, and averaging to obtain the comment and recognition semantic integral index.
For example, the number of the cells to be processed,
comment data are obtained:
comment data of the account number under the recently rated number of works is obtained from the short video platform through a crawler. Assuming that 3 pieces of comment data of works are collected, 300 pieces of comments, 200 pieces of comments and 400 pieces of comments are respectively provided, and each piece of comment is the same I D comment statement with earliest time;
preprocessing comment text:
preprocessing comments of each work, namely uniformly translating comment texts into English, and performing morphological reduction and stem extraction;
emotion analysis:
using an emotion analysis algorithm (such as a naive Bayes classifier or a vector machine classifier) to analyze emotion tendencies of each comment and classifying comments into positive, negative or neutral emotion;
statistics of comment emotion categories:
counting the number of positive, negative and neutral comments in all comments to obtain the following results:
number of positive evaluation strips of work 1: 200 strips
Number of negative comments for work 1: 50 strips
Number of neutral comments in work 1: 50 strips
Number of positive comments for work 2: 100 strips
Number of negative reviews for work 2: 80 strips
Number of neutral reviews for work 2: 20 strips
Number of positive evaluation strips of work 3: 300 strips
Number of negative reviews for work 3: 50 strips
Number of neutral reviews for work 3: 50 strips
Judging work classification:
the number of neutral comment pieces for work 1 is less than the sum of the positive and negative comment pieces, i.e., 50< (200+50).
The number of neutral comment pieces for work 2 is less than the sum of the positive and negative comment pieces, i.e., 20< (100+80).
The number of neutral comment pieces of work 3 is smaller than the sum of the numbers of positive and negative comment pieces, i.e., 50< (300+50)
Calculating the front degree:
work 1 is judged to be a front work, and the front degree is calculated according to the front comment score number and the total comment score number:
positive degree = positive number of comments/total number of comments = 200/300 = 0.67
Work 2 is determined to be a negative work, and the positive degree is calculated according to the number of negative comments and the total number of comments:
positive degree = -1 x negative number of comment pieces/total number of comment pieces = -1 x 80/200 = -0.4
Work 3 is judged to be a front work, and the front degree is calculated according to the front comment score number and the total comment score number:
positive degree = positive number of comments/total number of comments = 300/400 = 0.75
Results:
work 1 was judged to be a front work, and the front degree was 0.67.
Work 2 was judged as a negative work, with a positive level of-0.4.
Work 3 was judged to be a front work, with a front degree of 0.75.
Calculating the front degree values of all rated works and averaging:
the positive degree values of all works are counted and averaged:
(0.67+(-0.4)+0.75)/3≈0.34
results:
the comment and recognition semantic overall index is 0.34.
The comment desquamation semantic overall index is used for reflecting the overall evaluation emotion tendency of the recently rated number of works under the account, the front degree of each work can be obtained by carrying out emotion analysis and calculation of the front degree value on comments under each work, statistics and average value calculation are carried out on the front degree values of all rated works, and the obtained comment desquamation semantic overall index can be used for comprehensively evaluating the overall evaluation of the works of the account in audience;
when the comment and the commend-detraction semantic overall index is larger than 0 and larger, the overall evaluation of the work representing the account in the audience is more positive. This means that in the comments collected by the works of the account, the proportion of positive emotion is higher, and the audience evaluates the works more positively, in favor of or satisfied with the comments, so that the audience considers the works to be higher in quality, attractive in content and in line with the expectations of the audience, and interests and favorites are generated for the works of the account;
when the comment and the commend-detraction semantic overall index is smaller than 0 and smaller, the overall evaluation of the work representing the account in the audience is more negative. This means that in the comments collected by the works of the account, the proportion of negative emotions is higher, and the audience evaluates the works more negatively, criticizes or is dissatisfied, so that the audience considers the works to be low in quality, unattractive in content or not in line with their expectations, and thus loses interest in the works of the account.
The overall participation degree of the evaluation is obtained by the following logic:
step B1: acquiring time information of a published work in unit time and evaluation time information of comment users in a crawler mode, and recording;
step B2: counting the number of comments, the number of praise, the number of forwarding and the number of times of being coated, wherein if the same I D participates in praise, comment, forwarding and being coated at the same time, the same I D is recorded as one time, and the indexes are accumulated to obtain a total value;
step B3: selecting the works of the recent rated quantity in the account, counting the maximum total value and the minimum total value, and carrying the maximum total value and the minimum total value into the following formula to calculate and obtain the overall participation degree of the evaluation: overall score = (maximum total value-minimum total value)/(maximum total value + minimum total value).
For example, suppose we choose the recent rated number of works for a certain account, of which there are 3, their comment number, praise number, forwarding number and number of times it is @ as follows:
work 1: comment quantity: 50, praise number: 100, forwarding number: 80, number of times @: 20
Work 2: comment quantity: 80, praise number: 120, number of forwarding: 60, by @ times: 30
Work 3: comment quantity: 70, praise number: 90, number of forwarding: 100, by @ times: 25
According to step B2, we count the total value of these indices:
total number of comments = 50+80+70 = 200
Praise number total value=100+120+90=310
Total value of forwarding number=80+60+100=240
Total number of times of quilt @ times = 20+30+25 = 75
Then we calculate the maximum total value and the minimum total value:
maximum total value=max (200,310,240,75) =310
Minimum total value=mi n (200,310,240,75) =75
Next, according to step B3, we bring the maximum and minimum total values into the formula to calculate the overall participation of the evaluation:
overall score = (310-75)/(310+75) =0.656
The overall participation degree of the evaluation is used for measuring the fluctuation degree of the audience of the recent account works, when the overall participation degree of the evaluation is larger, the participation degree of the audience is obviously different among the works, which means that some works of the account are concerned and interacted by more users, and the participation degree of other works is lower, which means that a larger promotion space exists in the aspects of popularization, marketing or user interaction of the account; when the overall participation degree of the evaluation is smaller, the participation degree of the audience is indicated to be smaller in the works, which means that the participation degree of the users in the works of the account is balanced, which means that the diversity and attraction of account contents are indicated, and the interactive quality between the account and the audience is higher, which is helpful to enhance the overall attraction and the user loyalty of the account.
The comprehensive analysis module operation comprises the following contents:
normalizing the commend-detracting semantic integral index and the comment integral participation degree to obtain a advocating coefficient;
for example, the favorability coefficient may be derived by the following formula: ara=α×ssi+β×eai, where ARA, SSI, EAI represents a promoting coefficient, a desquamation semantic overall index, and an overall participation degree of the evaluation, α and β are preset scaling coefficients of the desquamation semantic overall index and the overall participation degree of the evaluation, respectively, and α is greater than 0, and β is less than 0;
the promotion coefficient represents the overall evaluation and acceptance degree of the audience to the account work, the larger promotion coefficient represents the higher promotion degree of the audience to the work and the higher acceptance degree to the account, otherwise, the smaller promotion coefficient represents the lower promotion degree of the audience to the work and the lower acceptance degree to the account number.
The approval judging module operates including the following:
after the promoting coefficient is obtained, comparing the promoting coefficient with 0, if the promoting coefficient is more than or equal to 0, indicating that the evaluation of works in the account is positive and the overall enthusiasm is high, the works are more concerned and favored, generating statistical signals, sorting accounts according to the promoting coefficient from large to small, distributing funds to the investment account according to the current stage of the company, and distributing the funds to the account for generating the statistical signals for investment; on the contrary, if the promoting coefficient is smaller than 0, the popularity of the works in the account is lower, which means that the evaluation of the works by the audience is negative and the participation degree is negative, the works are less concerned and favored, and the abandon signal is generated.
According to the application, through collecting emotion parameters and interaction parameters of accounts belonging to the same analogy set, including comment identification semantic overall indexes and comment overall participation, normalizing and calculating a promotion coefficient, evaluating the account, and then comparing the promotion coefficient with 0 to generate a statistical signal or a abandon signal; thereby being beneficial to comprehensively evaluating and comparing account numbers; by comparing the push coefficient with 0, works of the accounts are evaluated, statistical signals or abandon signals are generated, and the accounts are clearly evaluated and divided, so that more investment resources can be concentrated on accounts with higher welcome degree, the investment accuracy and precision are improved, investment risks and possible losses are reduced, funds and resources of companies are protected, and investment is prevented from being on accounts with no potential or negative welcome degree.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the systems, apparatuses and units described above may refer to the corresponding procedures in the foregoing embodiments, and are not repeated here.
In the several embodiments provided in the present application, it should be understood that the disclosed system and apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (6)
1. The enterprise informatization management integrated platform based on big data is characterized by comprising a statistics module, a data acquisition module, a comprehensive analysis module and an approval judgment module, wherein the modules are connected through signals;
the statistics module is used for counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of vermicelli accumulated until the account is developed, setting a funds interval used for account construction and a quantity interval accumulated until the account is developed, judging whether the funds used for account construction belong to the funds interval used for account construction or whether the quantity of vermicelli accumulated until the account is developed into the quantity interval accumulated until the account is developed, determining whether to divide the account into the same analogy set according to a judging result, generating a signal whether to divide the account into the same analogy set and sending the signal to the data acquisition module;
the data acquisition module acquires emotion parameters and interaction parameters of accounts belonging to the same analogy set, wherein the emotion parameters comprise comment recognition-detraction semantic overall indexes, and the interaction parameters comprise comment overall participation degree, generate parameter acquisition signals and send the parameter acquisition signals to the comprehensive analysis module;
the comprehensive analysis module normalizes the recognition-detraction semantic overall index and the evaluation overall participation degree to obtain a recognition coefficient, evaluates the account by using the recognition coefficient, generates a coefficient generation signal and sends the coefficient generation signal to the recognition judgment module;
and the acceptance judging module compares the promotion coefficient of the account with 0 and generates a statistical signal or a abandon signal according to the comparison result.
2. The enterprise informatization management integration platform based on big data according to claim 1, wherein: the statistical module operation comprises the following contents:
counting all accounts in normal operation in a company, collecting funds used for account construction and the quantity of accumulated vermicelli developed so far, setting a funds interval used for account construction and a quantity interval accumulated by account development so far, and judging the accounts by using the following formula: x= { a e [ a ] min ,A max ]}||{B∈[B min ,B max ]In the formula, X represents a return result, A, B represents funds used for account construction and the quantity of accumulated vermicelli of account developed so far respectively, [ A ] min ,A max ]Represents the capital interval for construction, [ B ] min ,B max ]In the expression, epsilon represents logic belonging to an operation symbol, and I represents logic or operation symbol, wherein the meaning of the whole expression is that if funds used for account construction are in a fund interval used for account construction or the quantity of the accumulated vermicelli developed so far is in the accumulated vermicelli developed so far, the whole expression returns true, otherwise, fasle is returned.
3. The enterprise informatization management integration platform based on big data according to claim 2, wherein:
and collecting funds used for account construction of each account and the quantity of accumulated vermicelli generated by account development until now, respectively bringing the funds into a return result formula, classifying the account returned to true into an analogy set if the return result is true, otherwise, not classifying the account into the analogy set if the return result is false.
4. The enterprise informatization management integration platform based on big data according to claim 3, wherein: the comment and recognition semantic overall index obtaining logic is as follows:
step A1: obtaining comment data of the account number under the recently rated number of works from a short video platform through a crawler, and taking a sentence with earliest comment time if the same ID comments for comments under the same work for a plurality of times;
step A2: preprocessing the collected comment text under each work, namely uniformly translating the comment text into the same language, and performing morphological reduction and stem extraction so as to facilitate subsequent emotion analysis;
step A3: carrying out emotion tendency analysis on each comment by using an emotion analysis algorithm, classifying the comments into positive, negative or neutral emotion based on an analysis result, and carrying out emotion classification by using a naive Bayesian classifier or using a vector machine classifier;
step A4: counting the number of positive, negative or neutral comments in all comments;
if the number of the neutral comment pieces is larger than or equal to the sum of the numbers of the positive comments and the negative comments, the work is not included in the analysis range, and a work with the closest release time is found again for analysis again;
if the number of the neutral comment pieces is smaller than the sum of the number of the positive comment pieces and the number of the negative comment pieces, comparing the number of the positive comment pieces with the number of the negative comment pieces;
if the number of positive comment pieces is larger than the number of negative comment pieces, judging that the work is a positive work;
if the number of the negative comments is greater than the number of the positive comments, judging that the work is a negative work;
step A5: calculating the positive degree value of the work according to the judgment result:
if the work is a front work, the front degree calculation formula is: frontal number of commentary/total number of commentary;
if the work is a negative work, the positive degree calculation formula is: negative comment score/comment total score 1;
step A6: and counting the front degree values of all rated works, and averaging to obtain the comment and recognition semantic integral index.
5. The enterprise informatization management integration platform based on big data according to claim 4, wherein: the overall participation degree of the evaluation is obtained by the following logic:
step B1: acquiring time information of a published work in unit time and evaluation time information of comment users in a crawler mode, and recording;
step B2: counting the number of comments, the number of praise, the number of forwarding and the number of times of being coated, wherein if the same ID participates in praise, comment, forwarding and being coated at the same time, the same ID is recorded as one time, and the indexes are accumulated to obtain a total value;
step B3: selecting the works of the recent rated quantity in the account, counting the maximum total value and the minimum total value, and carrying the maximum total value and the minimum total value into the following formula to calculate and obtain the overall participation degree of the evaluation: overall score = (maximum total value-minimum total value)/(maximum total value + minimum total value).
6. The enterprise informatization management integration platform based on big data according to claim 5, wherein: the approval judging module operates including the following:
after the promotion coefficient is obtained, comparing the promotion coefficient with 0, and if the promotion coefficient is more than or equal to 0, generating a statistical signal; otherwise, if the promotion coefficient is smaller than 0, generating a discarding signal.
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| CN117370424A (en) * | 2023-12-07 | 2024-01-09 | 深圳市易图资讯股份有限公司 | Mobile application comment data analysis mining method and system for economic information analysis |
| CN117370424B (en) * | 2023-12-07 | 2024-02-13 | 深圳市易图资讯股份有限公司 | Mobile application comment data analysis mining method and system for economic information analysis |
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Application publication date: 20231010 |