CN114298412A - Enterprise safety standardized operation method based on artificial intelligence and big data - Google Patents
Enterprise safety standardized operation method based on artificial intelligence and big data Download PDFInfo
- Publication number
- CN114298412A CN114298412A CN202111630177.6A CN202111630177A CN114298412A CN 114298412 A CN114298412 A CN 114298412A CN 202111630177 A CN202111630177 A CN 202111630177A CN 114298412 A CN114298412 A CN 114298412A
- Authority
- CN
- China
- Prior art keywords
- data
- enterprise
- indexes
- value
- artificial intelligence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an enterprise safety standardized operation method based on artificial intelligence and big data, belonging to the technical field of enterprise operation and comprising the following steps: building a big database, collecting historical operation data of an enterprise, carrying out primary processing on the data, carrying out feature collection on source data, classifying the source data according to features, and loading the processed data into the big database to serve as the basis of artificial intelligence processing and data mining; establishing an enterprise safety standardized operation index, and establishing an intelligent analysis basic data model of each category of data by combining a big database; the method comprises the steps of collecting enterprise operation data in real time, analyzing the real-time operation state of the enterprise through artificial intelligence and a data model, and predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model; and correcting the operation state of the enterprise according to the result to ensure that the enterprise can achieve complete safe and standardized operation.
Description
Technical Field
The invention relates to the technical field of enterprise operation, in particular to an enterprise safety standardized operation method based on artificial intelligence and big data.
Background
The enterprise safety standardized operation is an indispensable standard for long-term stable development of an enterprise, all departments in the enterprise operation cooperate with each other to operate, but simultaneously, the operation data of the departments are difficult to integrate, the operation data of all the departments cannot be quickly integrated and compared with the enterprise safety standardized operation indexes, the current operation data and the subsequent enterprise operation state can be evaluated to reach the judgment standard of the enterprise safety standardized operation, so that the enterprise management and the department work adjustment cannot timely follow the requirement of the enterprise safety standardized operation, and the enterprise develops and operates slowly.
Disclosure of Invention
The invention aims to provide an enterprise safety standardized operation method based on artificial intelligence and big data, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an enterprise safety standardized operation method based on artificial intelligence and big data comprises the following steps:
s1, building a big database, collecting historical operation data of an enterprise, carrying out primary processing on the data, carrying out feature collection on source data, classifying the source data according to features, and loading the processed data into the big database to serve as the basis of artificial intelligence processing and data mining;
s2, establishing an enterprise safety standardized operation index, and establishing an intelligent analysis basic data model of each category of data by combining a big database;
s3, collecting enterprise operation data in real time, analyzing the real-time operation state of the enterprise by combining artificial intelligence with a data model, predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model, and sharing the result;
and S4, extracting the data sources which are beneficial to the safety standardized operation of the enterprise and the data sources which are harmful to the safety standardized operation of the enterprise according to the result by the user, adjusting the working states and contents of relevant departments and personnel of the enterprise in time, and correcting the operation state of the enterprise to achieve the complete safety standardized operation.
In a preferred embodiment, in step S1, the method for collecting the historical operation data of the enterprise includes: based on cloud service and storage, according to characteristic data collection and a crawler tool, historical enterprise operating data in cloud data storage is extracted through comprehensive routing inspection, various types of key information is collected, historical enterprise operating data is analyzed and characteristic extraction is conducted through a search method based on a convolutional neural network, a large amount of redundancy, repetition and errors existing in the collected data are eliminated, the data are pure, original data integration is completed, and data accumulation is provided for a large database.
According to an optimized implementation case, after historical data of an enterprise are automatically collected through the characteristic data collection and crawler tool, the collected data are classified into corresponding categories according to characteristic information according to a pre-classification standard, the data of each category in the operation of the enterprise are conveniently and respectively mined, the collected data are checked for leakage in a manual rechecking mode, incomplete information in the historical operation data is manually searched for paper files and historical files for supplement, and data integrity and accuracy when a large database is built are ensured.
In a preferred embodiment, in step S2, the enterprise safety standardized operation indexes include human resource indexes, financial data indexes, business data indexes, administrative data indexes, legal status data indexes, intellectual property data indexes, equity data indexes, supply chain data indexes, production and marketing data indexes, and customer survey indexes, and the enterprise operation data in steps S1 and S3 are collected and normalized according to the data classification of the enterprise safety standardized operation indexes.
In a preferred embodiment, the human resource indexes mainly comprise KPI analysis reports and working capacity figures of enterprise employees, the financial data indexes comprise net assets, net profits and tax data, the administrative data indexes comprise enterprise administrative data and national government preferential policy data, and the customer survey indexes are enterprise existing customer opinion information collected through questionnaire survey and potential customer intention information and suggestions of randomly extracting related industries.
In a preferred embodiment, in step S3, the method for analyzing the real-time operation status of the enterprise includes: the method comprises the steps of forming a state diagram of each category of operation data by using time as an abscissa and using data quantity as an ordinate according to the category of the enterprise operation data collected in real time, comparing and analyzing each category of data with a formulated enterprise safety standardized operation index threshold value, judging the department standard reaching condition corresponding to each category of data, comprehensively judging the overall operation state of the enterprise according to a weight ratio, obtaining excellent and poor evaluation, and facilitating visual detection of the enterprise safety standardized operation state.
In a preferred embodiment, in step S3, the method for predicting the operation status of the enterprise at the next stage according to the real-time operation data and the model includes: the method comprises the steps of calculating an exponential smooth value according to a state diagram of each category of operation data formed by enterprise operation state data collected in real time, big data model data and an enterprise safety standardized operation index threshold value, predicting the enterprise operation data of the future category by matching with a time sequence prediction model to obtain a predicted value, predicting subsequent state information of enterprise operation in advance, judging whether the department working state corresponding to each operation data meets the enterprise safety standardized operation index, and conveniently adjusting the work of each department in time.
In a preferred embodiment, the method for calculating the exponential smoothing value is a linear quadratic exponential smoothing method:
s301, primary smooth prediction
Y(t+1)'＝ayt+a(yt-yt') in which Y(t+1)' is a predicted value of t +1 phase, i.e., a smoothed value S of this phase (t phase)t(ii) a yt is the actual value of t period; y ist' is a predicted value of t period, i.e. a smoothed value S of the previous period(t-1)The next predicted value is the sum of the current predicted value and the error of the current actual value and predicted value discounted by a;
s302, quadratic exponential smoothing prediction
The second exponential smoothing is a re-smoothing of the first exponential smoothing, and the formula is as follows:
St＝αSt+(1-α)S(t-1)
Y(t+T)＝at+btT
at＝2St-St
bt＝(α/1-α)(St-St)
wherein StOnce exponential smoothing value of the time t period; s(t-1)Is the second exponential smoothing value of period t; α is a smoothing coefficient; y is(t+T)Is the T + T stage prediction value; and T is the period number which is shifted backwards from the period T, so that the operation state of the enterprise is predicted by a linear quadratic exponential smoothing method.
In a preferred embodiment, a is 0.2-0.6, alpha is 0.6-1, and the initial value of the model is taken as the smooth value S of the previous stage when the prediction is performed for the first time(t-1)The real-time status parameter is used as the actual value yt.
In step S3, the method for analyzing the real-time operation status of the enterprise is to normalize the standard deviation of each data of the enterprise, where x 'is (x- μ)/σ, where x is the original data, x' is the data after the normalization of the standard deviation, μ is the mean of all the feature value columns of the original data set, and σ is the standard deviation of all the feature value columns of the original data set, then perform gaussian distribution calculation based on the normalized data and the model data,
The invention has the beneficial effects that:
1. calculating an exponential smoothing value according to a state diagram of each category of operation data formed by the enterprise operation state data collected in real time, big data model data and an enterprise safety standardized operation index threshold value, predicting the enterprise operation data of the future category by matching with a time sequence prediction model to obtain a predicted value, thereby predicting the subsequent state information of the enterprise operation in advance, judging whether the department working state corresponding to the operation data meets the enterprise safety standardized operation index, and conveniently and timely adjusting the work of each department, thereby realizing real-time and accurate adjustment of the enterprise operation state and meeting the enterprise safety standardized operation requirement;
2. the method is simple and convenient, the system is easy to build, and the method is favorable for the rapid and safe development requirements of enterprises.
Drawings
Fig. 1 is a schematic structural diagram of an enterprise security standardized operation method based on artificial intelligence and big data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Example (b): as shown in fig. 1, the present invention provides an enterprise security standardized operation method based on artificial intelligence and big data, comprising the following steps:
s1, building a big database, collecting historical operation data of an enterprise, carrying out primary processing on the data, carrying out feature collection on source data, classifying the source data according to features, and loading the processed data into the big database to serve as the basis of artificial intelligence processing and data mining;
s2, establishing an enterprise safety standardized operation index, and establishing an intelligent analysis basic data model of each category of data by combining a big database;
s3, collecting enterprise operation data in real time, analyzing the real-time operation state of the enterprise by combining artificial intelligence with a data model, predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model, and sharing the result;
and S4, extracting the data sources which are beneficial to the safety standardized operation of the enterprise and the data sources which are harmful to the safety standardized operation of the enterprise according to the result by the user, adjusting the working states and contents of relevant departments and personnel of the enterprise in time, and correcting the operation state of the enterprise to achieve the complete safety standardized operation.
Further, in step S1, the method for collecting the enterprise historical operation data includes: based on cloud service and storage, according to characteristic data collection and a crawler tool, historical enterprise operating data in cloud data storage is extracted through comprehensive routing inspection, various types of key information is collected, historical enterprise operating data is analyzed and characteristic extraction is conducted through a search method based on a convolutional neural network, a large amount of redundancy, repetition and errors existing in the collected data are eliminated, the data are pure, original data integration is completed, and data accumulation is provided for a large database.
Furthermore, after historical data of an enterprise are automatically collected through the characteristic data collection and crawler tool, the collected data are classified into corresponding categories according to the characteristic information according to the pre-classification standard, the data of each category in the operation of the enterprise can be conveniently mined respectively, the collected data are checked for leakage in a manual rechecking mode, incomplete information in the historical operation data is manually searched for paper files and historical files for supplement, and the data integrity and accuracy when a large database is built are ensured.
Further, in step S2, the enterprise safety standardized operation indexes include human resource indexes, financial data indexes, business data indexes, administrative data indexes, legal status data indexes, intellectual property data indexes, equity data indexes, supply chain data indexes, production and marketing data indexes, and customer survey indexes, and the enterprise operation data in steps S1 and S3 are collected and normalized according to the data classification of the enterprise safety standardized operation indexes.
Furthermore, the human resource indexes mainly comprise KPI analysis reports and working capacity portrayals of enterprise employees, the financial data indexes comprise net assets, net profits and tax data, the administrative data indexes comprise enterprise administrative management data and national government preferential policy data, and the customer survey indexes are enterprise existing customer opinion information collected through questionnaire survey and potential customer intention information and suggestions of randomly extracting related industries.
Further, in step S3, the method for analyzing the real-time operation status of the enterprise includes: the method comprises the steps of forming a state diagram of each category of operation data by using time as an abscissa and using data quantity as an ordinate according to the category of the enterprise operation data collected in real time, comparing and analyzing each category of data with a formulated enterprise safety standardized operation index threshold value, judging the department standard reaching condition corresponding to each category of data, comprehensively judging the overall operation state of the enterprise according to a weight ratio, obtaining excellent and poor evaluation, and facilitating visual detection of the enterprise safety standardized operation state.
Further, in step S3, the method for predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model includes: the method comprises the steps of calculating an exponential smooth value according to a state diagram of each category of operation data formed by enterprise operation state data collected in real time, big data model data and an enterprise safety standardized operation index threshold value, predicting the enterprise operation data of the future category by matching with a time sequence prediction model to obtain a predicted value, predicting subsequent state information of enterprise operation in advance, judging whether the department working state corresponding to each operation data meets the enterprise safety standardized operation index, and conveniently adjusting the work of each department in time.
Further, the method for calculating the exponential smoothing value is a linear quadratic exponential smoothing method:
s301, primary smooth prediction
Y(t+1)'＝ayt+a(yt-yt') in which Y(t+1)' is a predicted value of t +1 phase, i.e., a smoothed value S of this phase (t phase)t(ii) a yt is the actual value of t period; y ist' is a predicted value of t period, i.e. a smoothed value S of the previous period(t-1)The next predicted value is the sum of the current predicted value and the error of the current actual value and predicted value discounted by a;
s302, quadratic exponential smoothing prediction
The second exponential smoothing is a re-smoothing of the first exponential smoothing, and the formula is as follows:
St＝αSt+(1-α)S(t-1)
Y(t+T)＝at+btT
at＝2St-St
bt＝(α/1-α)(St-St)
wherein StOnce exponential smoothing value of the time t period; s(t-1)Is the second exponential smoothing value of period t; α is a smoothing coefficient; y is(t+T)Is the T + T stage prediction value; and T is the period number which is shifted backwards from the period T, so that the operation state of the enterprise is predicted by a linear quadratic exponential smoothing method.
Furthermore, the value of a is 0.2-0.6, the value of alpha is 0.6-1, and when the prediction is carried out for the first time, the initial value of the model is taken as the smooth value S of the previous period(t-1)The real-time status parameter is used as the actual value yt.
In step S3, the method for analyzing the real-time operation status of the enterprise is to normalize the standard deviation of each data of the enterprise, where x 'is (x- μ)/σ, where x is the original data, x' is the data after the normalization of the standard deviation, μ is the mean of all the feature value columns of the original data set, and σ is the standard deviation of all the feature value columns of the original data set, then perform gaussian distribution calculation based on the normalized data and the model data,
When the intelligent analysis system is used, based on cloud service and storage, according to characteristic data collection and a crawler tool, enterprise historical operating data in the cloud data storage is extracted through comprehensive routing inspection, various types of key information is collected, the enterprise historical operating data is analyzed and extracted through a search method based on a convolutional neural network, a large amount of redundancy, repetition and errors existing in the collected data are eliminated, the data are pure, original data integration is completed, data accumulation is provided for a large database, the large data are complete and accurate in processing through manual review, enterprise safety standardized operation indexes are formulated, an intelligent analysis basic data model of each type of data is established in combination with the large database, a state diagram of each type of operating data is formed according to the enterprise operating state data collected in real time, and the large data model data and an enterprise safety standardized operation index threshold value are formed, the index smooth value is calculated, the enterprise operation data of the future category is predicted by matching with the time sequence prediction model, and the predicted value is obtained, so that the subsequent state information of the enterprise operation is predicted in advance, whether the department working state corresponding to the operation data meets the enterprise safety standardized operation index or not is judged, the work of each department is convenient to adjust in time, the enterprise operation state is accurately adjusted in real time, and the enterprise safety standardized operation requirement is met.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An enterprise safety standardization operation method based on artificial intelligence and big data is characterized by comprising the following steps:
s1, building a big database, collecting historical operation data of an enterprise, carrying out primary processing on the data, carrying out feature collection on source data, classifying the source data according to features, and loading the processed data into the big database to serve as the basis of artificial intelligence processing and data mining;
s2, establishing an enterprise safety standardized operation index, and establishing an intelligent analysis basic data model of each category of data by combining a big database;
s3, collecting enterprise operation data in real time, analyzing the real-time operation state of the enterprise by combining artificial intelligence with a data model, predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model, and sharing the result;
and S4, extracting the data sources which are beneficial to the safety standardized operation of the enterprise and the data sources which are harmful to the safety standardized operation of the enterprise according to the result by the user, adjusting the working states and contents of relevant departments and personnel of the enterprise in time, and correcting the operation state of the enterprise to achieve the complete safety standardized operation.
2. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 1, characterized in that: in step S1, the method for collecting the enterprise historical operation data includes: based on cloud service and storage, according to characteristic data collection and a crawler tool, historical enterprise operating data in cloud data storage is extracted through comprehensive routing inspection, various types of key information is collected, historical enterprise operating data is analyzed and characteristic extraction is conducted through a search method based on a convolutional neural network, a large amount of redundancy, repetition and errors existing in the collected data are eliminated, the data are pure, original data integration is completed, and data accumulation is provided for a large database.
3. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 2, characterized in that: after historical data of an enterprise are automatically collected through the characteristic data collection and crawler tool, the collected data are classified into corresponding categories according to characteristic information according to the pre-classification standard, the data of each category in the operation of the enterprise are conveniently mined respectively, the collected data are checked for leakage through a manual rechecking mode, incomplete information in the historical operation data is stored through manual searching paper and is supplemented through historical files, and data integrity and accuracy in the process of establishing a large database are guaranteed.
4. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 1, characterized in that: in step S2, the enterprise safety standardized operation indexes include human resource indexes, financial data indexes, business data indexes, administrative data indexes, legal status data indexes, intellectual property data indexes, equity data indexes, supply chain data indexes, production and marketing data indexes, and customer survey indexes, and meanwhile, the enterprise operation data in steps S1 and S3 are collected and normalized according to the data classification of the enterprise safety standardized operation indexes.
5. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 4, characterized in that: the human resource indexes mainly comprise KPI analysis reports and working capacity portrayals of enterprise employees, the financial data indexes comprise net assets, net profits and tax data, the administrative data indexes comprise enterprise administrative management data and national government preferential policy data, and the customer survey indexes are enterprise existing customer opinion information collected through questionnaire survey and potential customer intention information and suggestions of randomly extracting related industries.
6. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 1, characterized in that: in step S3, the method for analyzing the real-time operation state of the enterprise includes: the method comprises the steps of forming a state diagram of each category of operation data by using time as an abscissa and using data quantity as an ordinate according to the category of the enterprise operation data collected in real time, comparing and analyzing each category of data with a formulated enterprise safety standardized operation index threshold value, judging the department standard reaching condition corresponding to each category of data, comprehensively judging the overall operation state of the enterprise according to a weight ratio, obtaining excellent and poor evaluation, and facilitating visual detection of the enterprise safety standardized operation state.
7. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 6, characterized in that: in step S3, the method for predicting the operation state of the enterprise at the next stage according to the real-time operation data and the model includes: the method comprises the steps of calculating an exponential smooth value according to a state diagram of each category of operation data formed by enterprise operation state data collected in real time, big data model data and an enterprise safety standardized operation index threshold value, predicting the enterprise operation data of the future category by matching with a time sequence prediction model to obtain a predicted value, predicting subsequent state information of enterprise operation in advance, judging whether the department working state corresponding to each operation data meets the enterprise safety standardized operation index, and conveniently adjusting the work of each department in time.
8. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 7, characterized in that: the method for calculating the exponential smoothing value is a linear quadratic exponential smoothing method:
s301, primary smooth prediction
Y(t+1)'＝ayt+a(yt-yt') in which Y(t+1)' is a predicted value of t +1 phase, i.e., a smoothed value S of this phase (t phase)t(ii) a yt is the actual value of t period; y ist' is a predicted value of t period, i.e. a smoothed value S of the previous period(t-1)The next predicted value is the sum of the current predicted value and the error of the current actual value and predicted value discounted by a;
s302, quadratic exponential smoothing prediction
The second exponential smoothing is a re-smoothing of the first exponential smoothing, and the formula is as follows:
St＝αSt+(1-α)S(t-1)
Y(t+T)＝at+btT
at＝2St-St
bt＝(α/1-α)(St-St)
wherein StOnce exponential smoothing value of the time t period; s(t-1)Is the second exponential smoothing value of period t; α is a smoothing coefficient; y is(t+T)Is the T + T stage prediction value; and T is the period number which is shifted backwards from the period T, so that the operation state of the enterprise is predicted by a linear quadratic exponential smoothing method.
9. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 8, characterized in that: a is 0.2-0.6, alpha is 0.6-1, and the initial value of the model is taken as the smooth value S of the upper stage when the prediction is carried out for the first time(t-1)The real-time status parameter is used as the actual value yt.
10. The enterprise security standardized operation method based on artificial intelligence and big data as claimed in claim 1, characterized in that: in step S3, the method for analyzing the real-time operation status of the enterprise is to normalize the standard deviation of each data of the enterprise, where x 'is (x- μ)/σ, where x is the original data, x' is the data after the normalization of the standard deviation, μ is the mean of all the feature value columns of the original data set, and σ is the standard deviation of all the feature value columns of the original data set, then perform gaussian distribution calculation based on the normalized data and the model data,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111630177.6A CN114298412A (en) | 2021-12-28 | 2021-12-28 | Enterprise safety standardized operation method based on artificial intelligence and big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111630177.6A CN114298412A (en) | 2021-12-28 | 2021-12-28 | Enterprise safety standardized operation method based on artificial intelligence and big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114298412A true CN114298412A (en) | 2022-04-08 |
Family
ID=80971088
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111630177.6A Pending CN114298412A (en) | 2021-12-28 | 2021-12-28 | Enterprise safety standardized operation method based on artificial intelligence and big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114298412A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115422146A (en) * | 2022-06-09 | 2022-12-02 | 中国标准化研究院 | Sinkiang region enterprise standardized database construction and application method |
| CN115687470A (en) * | 2022-09-28 | 2023-02-03 | 江苏科技大学 | Enterprise management method and system based on cloud platform |
| CN116303688A (en) * | 2023-05-17 | 2023-06-23 | 北京德钧科技服务有限公司 | Digital analysis method, system, equipment and medium based on Internet |
-
2021
- 2021-12-28 CN CN202111630177.6A patent/CN114298412A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115422146A (en) * | 2022-06-09 | 2022-12-02 | 中国标准化研究院 | Sinkiang region enterprise standardized database construction and application method |
| CN115422146B (en) * | 2022-06-09 | 2023-05-16 | 中国标准化研究院 | Construction and application method of enterprise standardized database in Xinjiang region |
| CN115687470A (en) * | 2022-09-28 | 2023-02-03 | 江苏科技大学 | Enterprise management method and system based on cloud platform |
| CN116303688A (en) * | 2023-05-17 | 2023-06-23 | 北京德钧科技服务有限公司 | Digital analysis method, system, equipment and medium based on Internet |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114298412A (en) | Enterprise safety standardized operation method based on artificial intelligence and big data | |
| CN107633265B (en) | Data processing method and device for optimizing credit evaluation model | |
| CN110852856B (en) | Invoice false invoice identification method based on dynamic network representation | |
| You et al. | Applications of artificial intelligence for coal mine gas risk assessment | |
| CN109242171A (en) | A kind of shield-tunneling construction Ground surface settlement method based on BIM and RS-SVR | |
| CN114529038A (en) | Intelligent matching business recruitment strategy system and method based on enterprise demands | |
| CN112364182A (en) | Graph feature-based enterprise risk conduction prediction method and device and storage medium | |
| CN114493535B (en) | Asset model-based data center system for data driving | |
| CN116894648A (en) | Government engineering digital project management system | |
| Liu | Research on risk management of big data and machine learning insurance based on internet finance | |
| CN117807374A (en) | Spare part abnormal leading data identification method, device and computer equipment | |
| CN109344171A (en) | A kind of nonlinear system characteristic variable conspicuousness mining method based on Data Stream Processing | |
| Quan et al. | Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction | |
| CN109949101B (en) | Big data concept-based cigarette material cost analysis method | |
| CN115292274B (en) | Data warehouse topic model construction method and system | |
| Shi | A Machine Learning Study on the Model Performance of Human Resources Predictive Algorithms | |
| Liu et al. | Early warning control model and simulation study of engineering safety risk based on a convolutional neural network | |
| CN115545437A (en) | Financial enterprise operation risk early warning method based on multi-source heterogeneous data fusion | |
| CN113902532A (en) | Invoice false-open risk identification method | |
| Sun | Management Research of Big Data Technology in Financial Decision-Making of Enterprise Cloud Accounting | |
| CN114240318A (en) | Target object oriented information processing method and device and computer equipment | |
| Hessami | Business risk evaluation and management of Iranian commercial insurance companies | |
| CN113254512A (en) | Military and civil fusion policy information data analysis and optimization system | |
| CN113379211A (en) | Block chain-based logistics information platform default risk management and control system and method | |
| CN112070107A (en) | Electronic port ship harboring control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |