CN116719888A - Cloud computing-based enterprise group service method, system and storage medium - Google Patents
Cloud computing-based enterprise group service method, system and storage medium Download PDFInfo
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Abstract
The invention discloses an enterprise group service method, system and storage medium based on cloud computing, which are characterized in that enterprise group big data are uploaded to a cloud end and imported into a first decision classification model to carry out data classification, and government requirement analysis and retrieval are carried out on the classified data to obtain government requirement data; sending the government requirement data to a government terminal platform through a cloud; acquiring government affair public big data, importing the government affair public big data and enterprise group demand information into a second decision classification model to classify and retrieve demand data, and obtaining enterprise demand data; and sending the enterprise demand data to an enterprise terminal platform through the cloud. According to the invention, data mining can be effectively carried out on the aspects of enterprise demands and government affair demands from big data, and the data interaction capacity and the data utilization rate of the platforms of both government and enterprise sides are further effectively improved, so that the degree of close relation of the platforms of the government and enterprise is improved, and a good government and enterprise informatization environment is created.
Description
Technical Field
The invention relates to the field of cloud computing, in particular to an enterprise group service method, system and storage medium based on cloud computing.
Background
Cloud computing technology is an important trend and technology transformation in recent years, and has become one of core technologies for enterprise informatization construction. The cloud computing essence is that computing resources, storage resources, network resources and the like are provided for users in a service mode through the Internet, so that the users can use the resources anytime and anywhere as required without building and maintaining huge information technology infrastructures.
The method is limited by the conventional technology between the current government departments and enterprise groups, and data screening and classification are carried out on government and enterprise big data by means of manual experience, so that the data interaction and data mining efficiency of the government and enterprise parties are generally low, effective data utilization and data interaction between the government and enterprise are not facilitated, and therefore, how to combine the current efficient cloud computing technology and the data analysis technology to improve the data service efficiency between the current government and enterprise groups is an important problem to be solved urgently.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an enterprise group service method, an enterprise group service system and a storage medium based on cloud computing.
The first aspect of the invention provides an enterprise group service method based on cloud computing, which comprises the following steps:
Acquiring a plurality of enterprise group data;
performing data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data;
sending the government requirement data to a government terminal platform through a cloud;
analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
and sending the enterprise demand data to an enterprise terminal platform through the cloud.
In this scheme, will carry out the data fusion and form enterprise group big data with a plurality of enterprise group data, upload enterprise group big data to the high in the clouds, specifically do:
acquiring operation data, production data and management data of a plurality of enterprise groups through an enterprise terminal platform;
generating different enterprise group tags based on different enterprise groups;
performing data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
And carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
In this scheme, importing enterprise group big data into a first decision classification model to classify data, and performing government requirement analysis and retrieval on classified data to obtain government requirement data, specifically:
carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
And respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
In this scheme, import enterprise group big data into first decision classification model and carry out data classification, carry out government affair demand analysis and retrieval with the data after the classification, obtain government affair demand data, still include:
acquiring current user government requirement information in real time;
importing enterprise group big data into a first decision classification model to perform data classification to obtain classification data;
and carrying out condition screening on the classified data according to the user government requirement information, and obtaining government requirement data in real time.
In this scheme, based on enterprise terminal platform analysis out enterprise group demand information, obtain government affair public big data, with government affair public big data and the categorised model of enterprise group demand information import second decision to carry out demand data classification and retrieval, obtain enterprise demand data, include:
based on the enterprise terminal platform, obtaining platform use records of different enterprise groups;
Carrying out production operation association analysis on different enterprise groups to obtain association information among the different enterprise groups;
generating enterprise group demand information of different enterprise groups according to the platform use record and the associated information;
carrying out demand data feature analysis according to the enterprise group demand information, and generating N enterprise data demand decision conditions;
constructing a second decision classification model based on a decision tree based on N enterprise data demand decision conditions;
acquiring government affair disclosure big data;
carrying out data cleaning and data standardization on the government affair public big data, and importing a second decision classification model to carry out data classification to obtain second classification result data;
and based on different enterprise groups, carrying out secondary classification screening on the second classification result data to obtain corresponding enterprise demand data.
In this scheme, based on enterprise terminal platform analysis out enterprise group demand information, obtain government affair public big data, with government affair public big data and the categorised model of enterprise group demand information import second decision to carry out demand data classification and retrieval, obtain enterprise demand data, still include:
in a second analysis period, second association information between second platform usage records of different enterprise groups and the different enterprise groups is obtained;
Generating second enterprise group demand information of different enterprise groups based on the second platform usage record and second association information;
calculating and analyzing the information characteristic difference degree of the enterprise group demand information and the second enterprise group demand information, and if the difference degree is higher than a preset value, generating N second enterprise data demand decision conditions based on the second enterprise group demand information;
and optimizing the decision condition of the second decision classification model based on the N second enterprise data demand decision conditions, and obtaining an optimized second decision classification model.
The second aspect of the present invention also provides an enterprise group service system based on cloud computing, the system comprising: the cloud computing-based enterprise group service program is executed by the processor and comprises the following steps:
acquiring a plurality of enterprise group data;
performing data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data;
Sending the government requirement data to a government terminal platform through a cloud;
analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
and sending the enterprise demand data to an enterprise terminal platform through the cloud.
In this scheme, will carry out the data fusion and form enterprise group big data with a plurality of enterprise group data, upload enterprise group big data to the high in the clouds, specifically do:
acquiring operation data, production data and management data of a plurality of enterprise groups through an enterprise terminal platform;
generating different enterprise group tags based on different enterprise groups;
performing data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
and carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
In this scheme, importing enterprise group big data into a first decision classification model to classify data, and performing government requirement analysis and retrieval on classified data to obtain government requirement data, specifically:
Carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
and respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
The third aspect of the present invention also provides a computer readable storage medium, including a cloud computing-based enterprise group service program, where the cloud computing-based enterprise group service program, when executed by a processor, implements the steps of the cloud computing-based enterprise group service method according to any one of the above.
The invention discloses an enterprise group service method, system and storage medium based on cloud computing, which are characterized in that enterprise group big data are uploaded to a cloud end and imported into a first decision classification model to carry out data classification, and government requirement analysis and retrieval are carried out on the classified data to obtain government requirement data; sending the government requirement data to a government terminal platform through a cloud; acquiring government affair public big data, importing the government affair public big data and enterprise group demand information into a second decision classification model to classify and retrieve demand data, and obtaining enterprise demand data; and sending the enterprise demand data to an enterprise terminal platform through the cloud. According to the invention, data mining can be effectively carried out on the aspects of enterprise demands and government affair demands from big data, and the data interaction capacity and the data utilization rate of the platforms of both government and enterprise sides are further effectively improved, so that the degree of close relation of the platforms of the government and enterprise is improved, and a good government and enterprise informatization environment is created.
Drawings
FIG. 1 shows a flow chart of an enterprise group service method based on cloud computing of the present application;
FIG. 2 shows a flow chart of enterprise group big data acquisition of the present application;
FIG. 3 shows a flow chart of the government requirement data acquisition of the present application;
fig. 4 shows a block diagram of an enterprise group service system based on cloud computing according to the present application.
Description of the embodiments
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of an enterprise group service method based on cloud computing of the present application.
As shown in fig. 1, a first aspect of the present application provides a cloud computing-based enterprise group service method, including:
S102, acquiring a plurality of enterprise group data;
s104, carrying out data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
s106, importing enterprise group big data into a first decision classification model to classify data, and analyzing and searching government affair requirements of the classified data to obtain government affair requirement data;
s108, sending the government requirement data to a government terminal platform through a cloud;
s110, analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
s112, the enterprise demand data is sent to an enterprise terminal platform through the cloud.
The system comprises a cloud platform, an enterprise terminal platform and a government affair terminal platform, wherein the enterprise terminal platform is connected with a plurality of enterprise group terminals through the Internet, and the government affair terminal platform is connected with government affair terminals of a plurality of departments in a target area. In addition, the cloud platform is used for constructing a decision model and training and using the model through cloud computing capacity, so that hardware consumption of other terminal platforms is reduced, and use experience of a user terminal party is improved.
In the decision tree model, the specific core algorithm comprises one or more algorithms of ID3, C4.5, CART and the like.
FIG. 2 shows a flow chart of enterprise group big data acquisition of the present invention.
According to the embodiment of the invention, the data fusion is performed on the plurality of enterprise group data to form enterprise group big data, and the enterprise group big data is uploaded to a cloud, specifically:
s202, operation data, production data and management data of a plurality of enterprise groups are obtained through an enterprise terminal platform;
s204, generating different enterprise group labels based on different enterprise groups;
s206, carrying out data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
and S208, carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
It should be noted that, by generating the enterprise group label and forming the corresponding mapping relationship, the enterprise group to which the target data belongs can be rapidly located and retrieved when the data processing is performed subsequently. The enterprise group data includes operation data, production data, and management data.
According to the embodiment of the invention, the enterprise group big data is imported into a first decision classification model for data classification, and the classified data is subjected to government requirement analysis and retrieval to obtain government requirement data, which comprises the following specific steps:
carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
And respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
In the step of randomly selecting the data of different enterprise groups from the initial enterprise big data, the size of the randomly selected data is a predetermined value, in addition, the training enterprise big data comprises the randomly selected data of all enterprise groups, and N is the number of enterprise group labels. The target government requirement data is historical existing government requirement data, is used for extracting characteristics of the government requirement data, and has certain representativeness. The magnitude of M is positively correlated with the magnitude of the data characteristic quantity. In the classification prediction and training of the first decision classification model, the training process can achieve the purpose of optimizing the decision tree according to the decision condition of actually replacing the root node and the sequential position of the decision condition corresponding to each node in the decision tree, so that the optimal model is trained.
Fig. 3 shows a flow chart of the government requirement data acquisition of the present invention.
According to an embodiment of the present invention, the importing the big data of the enterprise group into the first decision classification model to classify the data, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data, further includes:
s302, acquiring current user government requirement information in real time;
s304, importing enterprise group big data into a first decision classification model to perform data classification to obtain classification data;
and S306, performing condition screening on the classified data according to the user government requirement information, and obtaining government requirement data in real time.
The method includes the steps that large enterprise group data are imported into a first decision classification model to conduct data classification, classification data are obtained, the classification data are specifically classification result data based on government requirement characteristics of the large enterprise group data, the large enterprise group data have high availability and high utilization value for government platforms, data support and assistance can be provided for decisions made by governments, therefore, good business environments are built, and good government-enterprise relations are constructed. In the process of acquiring the current user government requirement information in real time, the government requirement information is obtained by government manager through inputting the requirement information, and the government requirement of different departments is different.
According to the embodiment of the invention, the enterprise group demand information is analyzed based on the enterprise terminal platform, government affair public big data is obtained, the government affair public big data and the enterprise group demand information are imported into a second decision classification model for demand data classification and retrieval, and enterprise demand data is obtained, and the method comprises the following steps:
based on the enterprise terminal platform, obtaining platform use records of different enterprise groups;
carrying out production operation association analysis on different enterprise groups to obtain association information among the different enterprise groups;
generating enterprise group demand information of different enterprise groups according to the platform use record and the associated information;
carrying out demand data feature analysis according to the enterprise group demand information, and generating N enterprise data demand decision conditions;
constructing a second decision classification model based on a decision tree based on N enterprise data demand decision conditions;
acquiring government affair disclosure big data;
carrying out data cleaning and data standardization on the government affair public big data, and importing a second decision classification model to carry out data classification to obtain second classification result data;
and based on different enterprise groups, carrying out secondary classification screening on the second classification result data to obtain corresponding enterprise demand data.
The association information between different enterprise groups includes production, operation interdependence, competing association relationship between a plurality of enterprise groups, for example, a commercial dependency relationship between a commodity production enterprise and a sales platform enterprise, a generation and supply relationship between a material provider group and a product manufacturing enterprise, and the like. In the enterprise group demand information, the N enterprise groups correspond to the N enterprise group demand information. Different enterprise groups correspond to different enterprise demand data.
According to the invention, the demand data characteristics of enterprises can be mastered from the data interaction level through the analysis platform, the relation among different enterprise groups can be analyzed, the efficiency in the process of carrying out data decision classification subsequently can be improved, and the mining of the enterprise demand data can be more accurately carried out from big data.
According to the embodiment of the invention, the enterprise group demand information is analyzed based on the enterprise terminal platform, government affair public big data is obtained, the government affair public big data and the enterprise group demand information are imported into a second decision classification model for demand data classification and retrieval, and enterprise demand data is obtained, and the method further comprises the following steps:
In a second analysis period, second association information between second platform usage records of different enterprise groups and the different enterprise groups is obtained;
generating second enterprise group demand information of different enterprise groups based on the second platform usage record and second association information;
calculating and analyzing the information characteristic difference degree of the enterprise group demand information and the second enterprise group demand information, and if the difference degree is higher than a preset value, generating N second enterprise data demand decision conditions based on the second enterprise group demand information;
and optimizing the decision condition of the second decision classification model based on the N second enterprise data demand decision conditions, and obtaining an optimized second decision classification model.
In the decision condition optimization of the second decision classification model, the specific optimization process is to introduce N decision conditions of the second enterprise data requirement into the model to update the decision conditions of a plurality of nodes, and each update can train and evaluate the second decision classification model and adjust the quality based on the evaluation result
In addition, in the enterprise group, the enterprise is influenced by market fluctuation, the change of the properties, the business scope, the operation mode and the type of manufactured products of each enterprise is likely to happen, furthermore, the corresponding enterprise platform usage record and the associated information among different enterprise groups are also changed, and the second decision classification model is updated and optimized in real time by analyzing the enterprise group platform usage record and the associated information in the current period in real time, so that the adaptability of the system is improved, more accurate classification data can be further obtained, and the data requirements of government enterprises are met.
According to an embodiment of the present invention, further comprising:
acquiring government requirement data and enterprise requirement data;
performing data coincidence degree and association degree analysis on government affair demand data and enterprise demand data to obtain government affair data association information;
extracting the primary information features and the secondary information features based on the government enterprise data association information to obtain government enterprise primary association features and government enterprise secondary association features;
generating a primary decision condition and a secondary decision condition according to the primary association characteristic of the government enterprise and the secondary association characteristic of the government enterprise;
constructing a government enterprise fusion decision tree model;
taking the main decision condition as a decision condition of a root node and a general child node of a government enterprise fusion decision tree model;
taking the secondary decision condition as a decision condition of leaf nodes in a government enterprise fusion decision tree model;
and acquiring government and enterprise fusion data, and carrying out the government and enterprise fusion data and obtaining fusion classification result data.
It should be noted that, the government enterprise data association information includes primary association information and secondary association information, where the primary association information and the secondary association information are obtained by analyzing based on the coincidence degree and the association degree and are used for analyzing the subsequent association features. The primary decision condition and the secondary decision condition each include one or more. The general child node is a non-leaf node.
It should be noted that in large government and enterprise data, due to the influence of data interaction and system operation, it is often necessary to classify and screen mixed data in the government and enterprise, that is, classify and screen government and enterprise fusion data, so as to improve the data mining capability of the system. According to the invention, through analyzing the association relation of government and enterprise data and constructing a corresponding decision tree model, the mining capability of the demand data in the government and enterprise data can be effectively improved, the rapid data classification and screening of the government and enterprise mixed data can be realized, and the data adaptability of the system can be improved.
Fig. 4 shows a block diagram of an enterprise group service system based on cloud computing according to the present invention.
The second aspect of the present invention also provides an enterprise group service system 4 based on cloud computing, the system comprising: a memory 41, and a processor 42, where the memory includes a cloud computing-based enterprise group service program, and when the cloud computing-based enterprise group service program is executed by the processor, the following steps are implemented:
acquiring a plurality of enterprise group data;
performing data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
Importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data;
sending the government requirement data to a government terminal platform through a cloud;
analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
and sending the enterprise demand data to an enterprise terminal platform through the cloud.
The system comprises a cloud platform, an enterprise terminal platform and a government affair terminal platform, wherein the enterprise terminal platform is connected with a plurality of enterprise group terminals through the Internet, and the government affair terminal platform is connected with government affair terminals of a plurality of departments in a target area. In addition, the cloud platform is used for constructing a decision model and training and using the model through cloud computing capacity, so that hardware consumption of other terminal platforms is reduced, and use experience of a user terminal party is improved.
According to the embodiment of the invention, the data fusion is performed on the plurality of enterprise group data to form enterprise group big data, and the enterprise group big data is uploaded to a cloud, specifically:
Acquiring operation data, production data and management data of a plurality of enterprise groups through an enterprise terminal platform;
generating different enterprise group tags based on different enterprise groups;
performing data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
and carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
It should be noted that, by generating the enterprise group label and forming the corresponding mapping relationship, the enterprise group to which the target data belongs can be rapidly located and retrieved when the data processing is performed subsequently. The enterprise group data includes operation data, production data, and management data.
According to the embodiment of the invention, the enterprise group big data is imported into a first decision classification model for data classification, and the classified data is subjected to government requirement analysis and retrieval to obtain government requirement data, which comprises the following specific steps:
carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
Carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
and respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
In the step of randomly selecting the data of different enterprise groups from the initial enterprise big data, the size of the randomly selected data is a predetermined value, in addition, the training enterprise big data comprises the randomly selected data of all enterprise groups, and N is the number of enterprise group labels. The target government requirement data is historical existing government requirement data, is used for extracting characteristics of the government requirement data, and has certain representativeness. The magnitude of M is positively correlated with the magnitude of the data characteristic quantity. In the classification prediction and training of the first decision classification model, the training process can achieve the purpose of optimizing the decision tree according to the decision condition of actually replacing the root node and the sequential position of the decision condition corresponding to each node in the decision tree, so that the optimal model is trained.
According to an embodiment of the present invention, the importing the big data of the enterprise group into the first decision classification model to classify the data, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data, further includes:
acquiring current user government requirement information in real time;
importing enterprise group big data into a first decision classification model to perform data classification to obtain classification data;
and carrying out condition screening on the classified data according to the user government requirement information, and obtaining government requirement data in real time.
The method includes the steps that large enterprise group data are imported into a first decision classification model to conduct data classification, classification data are obtained, the classification data are specifically classification result data based on government requirement characteristics of the large enterprise group data, the large enterprise group data have high availability and high utilization value for government platforms, data support and assistance can be provided for decisions made by governments, therefore, good business environments are built, and good government-enterprise relations are constructed. In the process of acquiring the current user government requirement information in real time, the government requirement information is obtained by government manager through inputting the requirement information, and the government requirement of different departments is different.
According to the embodiment of the invention, the enterprise group demand information is analyzed based on the enterprise terminal platform, government affair public big data is obtained, the government affair public big data and the enterprise group demand information are imported into a second decision classification model for demand data classification and retrieval, and enterprise demand data is obtained, and the method comprises the following steps:
based on the enterprise terminal platform, obtaining platform use records of different enterprise groups;
carrying out production operation association analysis on different enterprise groups to obtain association information among the different enterprise groups;
generating enterprise group demand information of different enterprise groups according to the platform use record and the associated information;
carrying out demand data feature analysis according to the enterprise group demand information, and generating N enterprise data demand decision conditions;
constructing a second decision classification model based on a decision tree based on N enterprise data demand decision conditions;
acquiring government affair disclosure big data;
carrying out data cleaning and data standardization on the government affair public big data, and importing a second decision classification model to carry out data classification to obtain second classification result data;
and based on different enterprise groups, carrying out secondary classification screening on the second classification result data to obtain corresponding enterprise demand data.
The association information between different enterprise groups includes production, operation interdependence, competing association relationship between a plurality of enterprise groups, for example, a commercial dependency relationship between a commodity production enterprise and a sales platform enterprise, a generation and supply relationship between a material provider group and a product manufacturing enterprise, and the like. In the enterprise group demand information, the N enterprise groups correspond to the N enterprise group demand information. Different enterprise groups correspond to different enterprise demand data.
According to the invention, the demand data characteristics of enterprises can be mastered from the data interaction level through the analysis platform, the relation among different enterprise groups can be analyzed, the efficiency in the process of carrying out data decision classification subsequently can be improved, and the mining of the enterprise demand data can be more accurately carried out from big data.
According to the embodiment of the invention, the enterprise group demand information is analyzed based on the enterprise terminal platform, government affair public big data is obtained, the government affair public big data and the enterprise group demand information are imported into a second decision classification model for demand data classification and retrieval, and enterprise demand data is obtained, and the method further comprises the following steps:
In a second analysis period, second association information between second platform usage records of different enterprise groups and the different enterprise groups is obtained;
generating second enterprise group demand information of different enterprise groups based on the second platform usage record and second association information;
calculating and analyzing the information characteristic difference degree of the enterprise group demand information and the second enterprise group demand information, and if the difference degree is higher than a preset value, generating N second enterprise data demand decision conditions based on the second enterprise group demand information;
and optimizing the decision condition of the second decision classification model based on the N second enterprise data demand decision conditions, and obtaining an optimized second decision classification model.
In the decision condition optimization of the second decision classification model, the specific optimization process is to introduce N decision conditions of the second enterprise data requirement into the model to update the decision conditions of a plurality of nodes, and each update can train and evaluate the second decision classification model and adjust the quality based on the evaluation result
In addition, in the enterprise group, the enterprise is influenced by market fluctuation, the change of the properties, the business scope, the operation mode and the type of manufactured products of each enterprise is likely to happen, furthermore, the corresponding enterprise platform usage record and the associated information among different enterprise groups are also changed, and the second decision classification model is updated and optimized in real time by analyzing the enterprise group platform usage record and the associated information in the current period in real time, so that the adaptability of the system is improved, more accurate classification data can be further obtained, and the data requirements of government enterprises are met.
According to an embodiment of the present invention, further comprising:
acquiring government requirement data and enterprise requirement data;
performing data coincidence degree and association degree analysis on government affair demand data and enterprise demand data to obtain government affair data association information;
extracting the primary information features and the secondary information features based on the government enterprise data association information to obtain government enterprise primary association features and government enterprise secondary association features;
generating a primary decision condition and a secondary decision condition according to the primary association characteristic of the government enterprise and the secondary association characteristic of the government enterprise;
constructing a government enterprise fusion decision tree model;
taking the main decision condition as a decision condition of a root node and a general child node of a government enterprise fusion decision tree model;
taking the secondary decision condition as a decision condition of leaf nodes in a government enterprise fusion decision tree model;
and acquiring government and enterprise fusion data, and carrying out the government and enterprise fusion data and obtaining fusion classification result data.
It should be noted that, the government enterprise data association information includes primary association information and secondary association information, where the primary association information and the secondary association information are obtained by analyzing based on the coincidence degree and the association degree and are used for analyzing the subsequent association features. The primary decision condition and the secondary decision condition each include one or more. The general child node is a non-leaf node.
It should be noted that in large government and enterprise data, due to the influence of data interaction and system operation, it is often necessary to classify and screen mixed data in the government and enterprise, that is, classify and screen government and enterprise fusion data, so as to improve the data mining capability of the system. According to the invention, through analyzing the association relation of government and enterprise data and constructing a corresponding decision tree model, the mining capability of the demand data in the government and enterprise data can be effectively improved, the rapid data classification and screening of the government and enterprise mixed data can be realized, and the data adaptability of the system can be improved.
The third aspect of the present invention also provides a computer readable storage medium, including a cloud computing-based enterprise group service program, where the cloud computing-based enterprise group service program, when executed by a processor, implements the steps of the cloud computing-based enterprise group service method according to any one of the above.
The invention discloses an enterprise group service method, system and storage medium based on cloud computing, which are characterized in that enterprise group big data are uploaded to a cloud end and imported into a first decision classification model to carry out data classification, and government requirement analysis and retrieval are carried out on the classified data to obtain government requirement data; sending the government requirement data to a government terminal platform through a cloud; acquiring government affair public big data, importing the government affair public big data and enterprise group demand information into a second decision classification model to classify and retrieve demand data, and obtaining enterprise demand data; and sending the enterprise demand data to an enterprise terminal platform through the cloud. According to the invention, data mining can be effectively carried out on the aspects of enterprise demands and government affair demands from big data, and the data interaction capacity and the data utilization rate of the platforms of both government and enterprise sides are further effectively improved, so that the degree of close relation of the platforms of the government and enterprise is improved, and a good government and enterprise informatization environment is created.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to 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 each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention 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 invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An enterprise group service method based on cloud computing, which is characterized by comprising the following steps:
acquiring a plurality of enterprise group data;
performing data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data;
sending the government requirement data to a government terminal platform through a cloud;
analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
And sending the enterprise demand data to an enterprise terminal platform through the cloud.
2. The cloud computing-based enterprise group service method according to claim 1, wherein the data fusion of the plurality of enterprise group data is performed to form enterprise group big data, and the enterprise group big data is uploaded to a cloud, specifically:
acquiring operation data, production data and management data of a plurality of enterprise groups through an enterprise terminal platform;
generating different enterprise group tags based on different enterprise groups;
performing data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
and carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
3. The cloud computing-based enterprise group service method according to claim 1, wherein the step of importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data is specifically as follows:
carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
Based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
and respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
4. The cloud computing-based enterprise group service method of claim 1, wherein the importing enterprise group big data into the first decision classification model for data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data, further comprises:
Acquiring current user government requirement information in real time;
importing enterprise group big data into a first decision classification model to perform data classification to obtain classification data;
and carrying out condition screening on the classified data according to the user government requirement information, and obtaining government requirement data in real time.
5. The cloud computing-based enterprise group service method of claim 4, wherein the enterprise terminal platform-based analysis of enterprise group demand information, obtaining government affair disclosure big data, importing the government affair disclosure big data and enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data, comprises:
based on the enterprise terminal platform, obtaining platform use records of different enterprise groups;
carrying out production operation association analysis on different enterprise groups to obtain association information among the different enterprise groups;
generating enterprise group demand information of different enterprise groups according to the platform use record and the associated information;
carrying out demand data feature analysis according to the enterprise group demand information, and generating N enterprise data demand decision conditions;
constructing a second decision classification model based on a decision tree based on N enterprise data demand decision conditions;
Acquiring government affair disclosure big data;
carrying out data cleaning and data standardization on the government affair public big data, and importing a second decision classification model to carry out data classification to obtain second classification result data;
and based on different enterprise groups, carrying out secondary classification screening on the second classification result data to obtain corresponding enterprise demand data.
6. The cloud computing-based enterprise group service method of claim 5, wherein the enterprise terminal platform-based enterprise group demand information is analyzed to obtain government affair disclosure big data, the government affair disclosure big data and the enterprise group demand information are imported into a second decision classification model to classify and retrieve demand data, and enterprise demand data is obtained, and further comprising:
in a second analysis period, second association information between second platform usage records of different enterprise groups and the different enterprise groups is obtained;
generating second enterprise group demand information of different enterprise groups based on the second platform usage record and second association information;
calculating and analyzing the information characteristic difference degree of the enterprise group demand information and the second enterprise group demand information, and if the difference degree is higher than a preset value, generating N second enterprise data demand decision conditions based on the second enterprise group demand information;
And optimizing the decision condition of the second decision classification model based on the N second enterprise data demand decision conditions, and obtaining an optimized second decision classification model.
7. A cloud computing-based enterprise group service system, the system comprising: the cloud computing-based enterprise group service program is executed by the processor and comprises the following steps:
acquiring a plurality of enterprise group data;
performing data fusion on the plurality of enterprise group data to form enterprise group big data, and uploading the enterprise group big data to a cloud;
importing enterprise group big data into a first decision classification model to perform data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data;
sending the government requirement data to a government terminal platform through a cloud;
analyzing enterprise group demand information based on an enterprise terminal platform, acquiring government affair public big data, importing the government affair public big data and the enterprise group demand information into a second decision classification model for demand data classification and retrieval, and obtaining enterprise demand data;
And sending the enterprise demand data to an enterprise terminal platform through the cloud.
8. The cloud computing-based enterprise group service system of claim 7, wherein the data fusion of the plurality of enterprise group data and the formation of the enterprise group data, the uploading of the enterprise group data to the cloud, specifically, is:
acquiring operation data, production data and management data of a plurality of enterprise groups through an enterprise terminal platform;
generating different enterprise group tags based on different enterprise groups;
performing data mapping on the operation data, the production data and the management data and the corresponding enterprise group labels to form a label mapping relation;
and carrying out data fusion on all enterprise group labels and corresponding operation data, production data and management data to obtain enterprise group big data.
9. The cloud computing-based enterprise group service system of claim 7, wherein the importing enterprise group big data into the first decision classification model performs data classification, and performing government requirement analysis and retrieval on the classified data to obtain government requirement data, specifically:
carrying out data cleaning, normalization and missing value preprocessing on the enterprise group big data to obtain initial enterprise big data;
Based on enterprise group labels, randomly selecting data of different enterprise groups from the initial enterprise big data to obtain N groups of training enterprise big data;
carrying out feature analysis on N groups of training enterprise big data, and carrying out decision condition conversion according to feature analysis results to obtain N preliminary decision feature conditions;
acquiring target government requirement data;
performing data feature analysis based on the target government requirement data, and generating M requirement decision feature conditions based on the size of the data feature quantity;
constructing a first decision classification model based on a decision tree based on the N preliminary decision feature conditions and the M demand decision feature conditions;
selecting a demand decision feature condition as a decision condition of a root node, and taking the rest demand decision feature conditions and the preliminary decision feature conditions as decision conditions of child nodes and leaf nodes;
and respectively importing N groups of training enterprise big data and target government affair demand data serving as training data and verification data into a first decision classification model to conduct classification prediction and training until the classification result data characteristics obtained in the first decision classification model accord with the characteristics in the target government affair demand data.
10. A computer readable storage medium, wherein a cloud computing based enterprise group service program is included in the computer readable storage medium, which when executed by a processor, implements the steps of the cloud computing based enterprise group service method of any of claims 1 to 6.
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