CN115795368A - Industrial training data processing method and system based on artificial intelligence - Google Patents
Industrial training data processing method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses an enterprise internal training data processing method and system based on artificial intelligence, and relates to the field of data processing, wherein the method comprises the following steps: classifying the intra-enterprise training resource library to obtain an intra-training resource distribution block; performing internal training value identification on the internal training resource distribution blocks based on a cloud processor to obtain resource value indexes; identifying the internal training resource distribution blocks by using the resource value indexes to generate an internal training resource sharing platform; when a terminal of the internal training resource sharing platform receives internal training resource request information of a first user, identity recognition is carried out on the first user, and an identity characteristic set is obtained; and acquiring and calling the internal training resources according to the identity characteristic set. The technical problems that in the prior art, management accuracy of the enterprise internal training resources is insufficient, the adaptability is low, and accordingly the enterprise internal training effect is poor are solved. The technical effects of improving the accuracy and the adaptability of the enterprise internal training resource management, improving the enterprise internal training quality and the like are achieved.
Description
Technical Field
The invention relates to the field of data processing, in particular to an enterprise internal training data processing method and system based on artificial intelligence.
Background
With the increasing competition of enterprises, how to improve the competitive advantage of enterprises is receiving wide attention. The internal training of the enterprise is an important way for improving the competitive advantage of the enterprise. Along with the wide application of the internal training of the enterprise, the internal training requirement of the enterprise develops towards diversification and complication. The traditional enterprise internal training mode has the defects of low internal training resource scheduling adaptation precision, poor internal training resource management effect, small internal training effect and the like. The method for carrying out optimization management on the training resources in the enterprise is researched and designed, and has very important practical significance.
In the prior art, the technical problems that the management accuracy of the training resources in the enterprise is insufficient, the adaptability is low, and the training effect in the enterprise is poor exist.
Disclosure of Invention
The application provides an enterprise internal training data processing method and system based on artificial intelligence. The problem of in the prior art to the enterprise within the training resources management precision not enough, the adaptation degree is low, and then cause the not good technique of the industry within the training effect is solved. The technical effects that the training resources in the enterprise are intelligently and reliably scheduled and managed, the accuracy and the adaptability of the training resource management in the enterprise are improved, the utilization rate of the training resources in the enterprise is improved, the training quality in the enterprise is improved, and the training cost in the enterprise is reduced are achieved.
In view of the above problems, the present application provides an enterprise internal training data processing method and system based on artificial intelligence.
In a first aspect, the present application provides an artificial intelligence-based method for processing internal training data of an enterprise, wherein the method is applied to an artificial intelligence-based internal training data processing system of an enterprise, and the method includes: acquiring an intra-enterprise training resource library of a target enterprise; classifying the enterprise internal training resource library to obtain internal training resource distribution blocks, wherein each internal training resource distribution block correspondingly stores a class of internal training resources, and the internal training resource distribution blocks are stored in the cloud processor; performing internal training value recognition on the internal training resource distribution blocks based on the cloud processor to obtain resource value indexes; identifying the internal training resource distribution blocks by the resource value indexes to generate an internal training resource sharing platform; acquiring internal training resource request information of a first user; when the terminal of the internal training resource sharing platform receives the internal training resource request information, the first user is subjected to identity recognition to obtain an identity characteristic set; and acquiring and calling internal training resources according to the identity characteristic set.
In a second aspect, the present application further provides an enterprise internal training data processing system based on artificial intelligence, wherein the system includes: the resource library acquisition module is used for acquiring an intra-enterprise training resource library of a target enterprise; a classification module, configured to classify the intra-enterprise trained resource pool to obtain intra-training resource distribution blocks, where each of the intra-training resource distribution blocks correspondingly stores a class of intra-training resources, and the intra-training resource distribution blocks are stored in the cloud processor; an internal training value identification module, configured to perform internal training value identification on the internal training resource distribution block based on the cloud processor, and obtain a resource value index; an identification module, configured to identify the trainee resource distribution block according to the resource value index, and generate a trainee resource sharing platform; the user internal training request acquisition module is used for acquiring internal training resource request information of a first user; the identity recognition module is used for carrying out identity recognition on the first user when the terminal of the internal training resource sharing platform receives the internal training resource request information to obtain an identity characteristic set; and the calling module is used for acquiring calling internal training resources according to the identity characteristic set.
In a third aspect, the present application further provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the enterprise internal training data processing method based on artificial intelligence when the executable instructions stored in the memory are executed.
In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor, implements the artificial intelligence based enterprise internal training data processing method provided in the present application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
classifying the intra-enterprise training resource library to obtain an intra-training resource distribution block; performing internal training value identification on the internal training resource distribution blocks through a cloud processor to obtain resource value indexes; identifying the internal training resource distribution blocks according to the resource value indexes to generate an internal training resource sharing platform; when a terminal of the internal training resource sharing platform receives internal training resource request information of a first user, identity recognition is carried out on the first user, and an identity characteristic set is obtained; and acquiring and calling the internal training resources according to the identity characteristic set. The technical effects that the training resources in the enterprise are intelligently and reliably scheduled and managed, the accuracy and the adaptability of the training resource management in the enterprise are improved, the utilization rate of the training resources in the enterprise is improved, the training quality in the enterprise is improved, and the training cost in the enterprise is reduced are achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is to be expressly understood that the drawings in the following description are directed to only some embodiments of the disclosure and are not intended as limitations of the disclosure.
FIG. 1 is a schematic flow chart of an enterprise internal training data processing method based on artificial intelligence according to the present application;
FIG. 2 is a schematic view illustrating a process of adjusting a resource value index in an enterprise internal training data processing method based on artificial intelligence according to the present application;
FIG. 3 is a schematic diagram of an enterprise internal training data processing system based on artificial intelligence according to the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Description of reference numerals: a repository acquisition module 11, a classification module 12, a training value identification module 13, an identification module 14, an in-user training request acquisition module 15, an identity identification module 16, a calling module 17, a processor 31, a memory 32, an input device 33, and an output device 34.
Detailed Description
The application provides an enterprise internal training data processing method and system based on artificial intelligence. The technical problems that in the prior art, management accuracy of the enterprise internal training resources is insufficient, the adaptability is low, and accordingly the enterprise internal training effect is poor are solved. The technical effects that intelligent and reliable scheduling management is carried out on the training resources in the enterprise, the accuracy and the adaptability of the management of the training resources in the enterprise are improved, the utilization rate of the training resources in the enterprise is improved, the training quality in the enterprise is improved, and the training cost in the enterprise is reduced are achieved.
Example one
Referring to fig. 1, the present application provides an intra-enterprise training data processing method based on artificial intelligence, wherein the method is applied to an intra-enterprise training data processing system based on artificial intelligence, the system is in communication connection with a cloud processor, and the method specifically includes the following steps:
step S100: acquiring an intra-enterprise training resource library of a target enterprise;
step S200: classifying the enterprise internal training resource library to obtain internal training resource distribution blocks, wherein each internal training resource distribution block correspondingly stores a class of internal training resources, and the internal training resource distribution blocks are stored in the cloud processor;
specifically, the target enterprise is subjected to internal training resource collection, an internal training resource library of the enterprise is obtained, the internal training resource library of the enterprise is classified, an internal training resource distribution block is obtained, and the internal training resource distribution block is stored to the cloud processor. The target enterprise can be any enterprise which uses the artificial intelligence based enterprise internal training data processing system for intelligent internal training management. The enterprise internal training resource library comprises various types of internal training resource data such as financial management internal training resources, product research and development internal training resources, enterprise business internal training resources, customer service internal training resources and the like of a target enterprise. Each block in the internal training resource distribution blocks correspondingly stores one type of internal training resources, namely, the internal training resource distribution blocks comprise a financial management internal training resource block, a product research and development internal training resource block, an enterprise management internal training resource block, a customer service internal training resource block and other various internal training resource blocks corresponding to the enterprise internal training resource library. The cloud processor is a large remote server taking cloud computing and cloud storage as cores in the prior art. The technical effect of classifying the internal training resource library of the enterprise to obtain the internal training resource distribution block and laying a foundation for subsequently identifying the internal training value of the internal training resource distribution block is achieved.
Step S300: performing internal training value recognition on the internal training resource distribution blocks based on the cloud processor to obtain resource value indexes;
further, step S300 of the present application further includes:
step S310: acquiring a first inner training resource block, a second inner training resource block …, an nth inner training resource block, wherein N is the total number of blocks of the inner training resource distribution block, and N is a positive integer greater than 0;
step S320: reading the resource data in the first inner training resource block, the second inner training resource block … and the nth inner training resource block to obtain first read data and second read data … nth read data;
specifically, the method is connected to the cloud processor, and performs internal training resource block reading on the cloud processor to obtain a first internal training resource block and a second internal training resource block …, and performs resource data reading on the first internal training resource block and the second internal training resource block …, and the nth training resource block respectively to obtain first read data and second read data … nth read data. The first internal training resource block, the second internal training resource block … the Nth internal training resource block comprise financial management internal training resource blocks, product research and development internal training resource blocks, enterprise management internal training resource blocks, customer service internal training resource blocks and other multi-type internal training resource blocks in the internal training resource distribution block. N is the total number of blocks of the midamble resource distribution block, and N is a positive integer greater than 0. The first read data … and the second read data … include multiple types of internal training resource data such as financial management internal training resources, product research and development internal training resources, enterprise management internal training resources, customer service internal training resources and the like corresponding to the first internal training resource block, the second internal training resource block … and the Nth internal training resource block.
Step S330: inputting the first reading data, the second reading data … and the Nth reading data into an internal training resource value analysis model to obtain the resource value index, wherein the resource value index comprises a first resource value index and a second resource value index … Nth resource value index.
Further, step S330 of the present application further includes:
step S331: configuring a plurality of analysis network layers, wherein the plurality of analysis network layers comprise a block type identification layer, a characteristic dimension determination layer, a data characteristic analysis layer and a resource value calculation layer;
step S332: and connecting the input end and the output end among the layers according to the block type identification layer, the characteristic dimension determination layer, the data characteristic analysis layer and the resource value calculation layer, and building the internal training resource value analysis model.
Further, step S332 of the present application further includes:
step S3321: receiving, by the resource value calculation layer, block type information based on the block type identification layer;
step S3322: receiving, by the resource value calculation layer, a plurality of feature dimensions based on the feature dimension determination layer;
step S3323: receiving, by the resource value computing layer, a resource feature data set based on the data feature analysis layer;
root step S3324: and analyzing the block type information, the characteristic dimensions and the resource characteristic data set according to the resource value calculation layer, and outputting the resource value index, wherein the resource value calculation layer comprises a plurality of preset demand indexes corresponding to the characteristic dimensions.
Specifically, the first read data … and the second read data … are used as input information, and the input information is input into an internal training resource value analysis model to obtain a resource value index. The internal training resource value analysis model comprises an input layer, a block type identification layer, a characteristic dimension determination layer, a data characteristic analysis layer, a resource value calculation layer and an output layer.
The first read data … and the nth read data are used as input information and input to the block type identification layer to obtain the block type information. Then, the first read data, the second read data … nth read data, and the block type information are used as input information, and the input information is input to the characteristic dimension determination layer to obtain a plurality of characteristic dimensions. And then, the first read data, the second read data … Nth read data and a plurality of characteristic dimensions are used as input information and input into a data characteristic analysis layer to obtain a resource characteristic data set. And further, inputting the block type information, the multiple characteristic dimensions and the resource characteristic data set as input information into a resource value calculation layer to obtain a resource value index.
The block type identification layer is used for identifying the types of a plurality of input read data to obtain block type information. The block type information includes first block type information, second block type information … nth block type information. The first and second block type information … and the nth block type information 5363 include the corresponding training resource type information of the first and second read data … and the nth read data. The input information of the characteristic dimension determination layer comprises a plurality of read data and block type information. The characteristic dimension determining layer is used for performing characteristic analysis on the input multiple read data according to the block type information to obtain multiple characteristic dimensions. The block type information is different, and the feature analysis dimensionality of the corresponding read data is also different. The plurality of feature dimensions includes a first feature dimension, a second feature dimension … nth feature dimension. The first characteristic dimension … and the second characteristic dimension … include data information such as internal training effect, internal training importance and internal training meaning corresponding to the first read data and the second read data … and the nth read data. For example, the first read data includes financial management internal training resource data. Then, the feature dimension determination layer performs feature analysis on the first read data mainly from the financial management dimension. The first characteristic dimension comprises the improvement of financial management capabilities of budget management, fund checking and the like brought by financial management internal training resource data, and internal training directions, internal training functions and the like of the financial management internal training resource data. The input information of the data characteristic analysis layer comprises a plurality of characteristic dimensions and a plurality of read data. The data characteristic analysis layer is used for matching the plurality of read data according to the plurality of characteristic dimensions and outputting a resource characteristic data set. The resource characteristic data sets include a first resource characteristic data set, a second resource characteristic data set … nth resource characteristic data set. The first resource characteristic data set and the second resource characteristic data set … the Nth resource characteristic data set comprise reading data corresponding to a plurality of characteristic dimensions. The resource value calculation layer comprises a plurality of preset demand indexes corresponding to a plurality of characteristic dimensions. The preset requirement indexes comprise a plurality of training requirement information determined by target enterprise self-adaptive setting. The resource value index comprises a first resource value index, a second resource value index … Nth resource value index. The higher the resource value index, the higher the midamble value of the midamble resource blocks in the corresponding midamble resource distribution block.
Illustratively, when a resource value calculation layer is constructed, historical data query is carried out on the basis of block type information, a plurality of characteristic dimensions and a resource characteristic data set, and a plurality of groups of constructed data sets are obtained. Each set of construction data set comprises historical block type information, a plurality of historical characteristic dimensions, a historical resource characteristic data set and a historical resource value index. And continuously self-training and learning the multiple groups of constructed data sets to a convergence state, so as to obtain a resource value calculation layer. The construction resource value calculation layer has the functions of intelligently analyzing the input block type information, a plurality of characteristic dimensions and a resource characteristic data set and evaluating the resource value. In addition, the block type identification layer, the feature dimension determination layer, and the data feature analysis layer are constructed in the same manner as the resource value calculation layer, and for the sake of brevity of the description, no further description is given here. And sequentially connecting the input end and the output end among the block type identification layer, the characteristic dimension determination layer, the data characteristic analysis layer and the resource value calculation layer to obtain the internal training resource value analysis model.
The technical effects that the internal training resource distribution blocks are subjected to multi-dimensional internal training value identification through the internal training resource value analysis model, accurate resource value indexes are obtained, comprehensiveness of internal training resource management is improved, and accordingly accuracy of internal training in enterprises is improved are achieved.
Step S400: identifying the internal training resource distribution blocks by the resource value indexes to generate an internal training resource sharing platform;
further, step S400 of the present application further includes:
step S410: determining the department to which the resource belongs according to the internal training resource distribution block;
step S420: performing relevance analysis on the services among the departments to which the resources belong to obtain a service relevance index;
step S430: connecting the internal training resource distribution blocks according to the business association index to generate a resource sharing circuit;
step S440: and embedding the resource sharing line in the internal training resource sharing platform, and realizing internal training resource sharing among business association departments.
Specifically, the internal training resource distribution blocks are identified according to the resource value index, and an internal training resource sharing platform is obtained. And further, carrying out enterprise department matching on the internal training resource distribution blocks to obtain the departments to which the resources belong. And performing service relevance analysis on the department to which the resource belongs to obtain a service relevance index, and connecting the internal training resource distribution blocks according to the service relevance index to obtain a resource sharing line. And embedding the resource sharing line in the internal training resource sharing platform, thereby realizing the internal training resource sharing among business association departments. The inter-training resource sharing platform comprises an inter-training resource distribution block and a resource value index corresponding to the inter-training resource distribution block. The departments to which the resources belong comprise a first resource department and a second resource department …, wherein the Nth resource department belongs to. That is, the resource affiliated department includes information of multiple resource affiliated departments, such as a financial management department, a product research and development department, an enterprise operation department, a customer service department, and the like corresponding to multiple types of internal training resource blocks in the internal training resource distribution block. The business association index comprises a first business association index and a second business association index … Nth business association index. And the business association index and the department to which the resource belongs have a corresponding relation. For example, the first business relevance index includes business relevance parameters between the department to which the first resource belongs and the other multiple resource-belonging departments. The resource sharing lines comprise a first resource sharing line and a second resource sharing line … Nth resource sharing line. And connecting the internal training resource distribution blocks according to the service association index to obtain the resource sharing line. The internal training resource sharing is achieved through the internal training resource sharing platform and the resource sharing line, so that the utilization rate of the internal training resources of the enterprise is improved, the internal training cost of the enterprise is reduced, and the comprehensiveness of the internal training resource management is improved.
Step S500: acquiring the internal training resource request information of a first user;
step S600: when the terminal of the internal training resource sharing platform receives the internal training resource request information, the first user is subjected to identity recognition to obtain an identity characteristic set;
specifically, the internal training resource request information of the first user is sent to a terminal of the internal training resource sharing platform, and the first user is identified through the internal training resource sharing platform to obtain an identity characteristic set. Wherein the first user may be any employee of the target enterprise. The internal training resource request information comprises the request identity of the first user, the internal training resource demand type and the internal training resource demand quantity. The identity characteristic set comprises employee names, employee types, employee department attributes, employee titles and the like corresponding to the internal training resource request information. The technical effects that the first user is subjected to identity recognition to obtain the identity characteristic set, and accordingly the adaptability and the reliability of internal training resource calling are improved are achieved.
Step S700: and acquiring and calling internal training resources according to the identity characteristic set.
Further, step S700 of the present application further includes:
step S710: determining internal training resource authority according to the identity characteristic set;
step S720: acquiring inner training resource blocks capable of being called according to the inner training resource authority, wherein the inner training resource blocks capable of being called correspond to resource value indexes;
step S730: and performing forward serialization processing on the internal training resource block which can be called according to the resource value index, and sending a processed resource block chain to the first user.
Specifically, resource authority matching is carried out based on the identity feature set, and internal training resource authority is obtained. Calling an internal training resource distribution block in the internal training resource sharing platform according to the internal training resource authority to obtain a callable internal training resource block, wherein the callable internal training resource block corresponds to a resource value index, performing positive serialization processing on the callable internal training resource block according to the resource value index, and sending a processed resource block chain to a first user to obtain the callable internal training resource of the first user. The internal training resource authority comprises internal training resource distribution block calling authority information corresponding to the identity characteristic set. The callable inner training resource block includes an inner training resource block corresponding to an inner training resource privilege. For example, the set of identity characteristics indicates that the first user belongs to a financial management department. Then, the internal training resource permissions include that the first user can invoke the financial management internal training resource block in the internal training resource distribution block. The callable intra-training resource block comprises a financial management intra-training resource block. When the first user belongs to the advanced management layer, the internal training resource authority of the first user is more, and the internal training resource block capable of being called comprises a plurality of internal training resource blocks. The positive serialization processing refers to the arrangement of the callable internal training resource blocks according to the size of the resource value index. The resource block chain includes the callable inner training resource blocks after the serialization processing. The call internal training resource comprises a resource block chain. The technical effect that the internal training resources are matched with the first user through the identity characteristic set to obtain the calling internal training resources, and therefore the quality of the internal training resource management of the enterprise is improved is achieved.
Further, as shown in fig. 2, after step S700, the method further includes:
step S810: acquiring resource feedback information of the calling internal training resource according to the first user;
step S820: storing the resource feedback information, and acquiring a value adjustment coefficient based on the resource feedback information when the resource feedback information is accumulated to a preset information amount;
step S830: and adjusting the resource value index according to the value adjustment coefficient.
Specifically, after the first user obtains the internal training resources, feedback information acquisition is performed on the first user, resource feedback information is obtained, and the resource feedback information is stored. And when the resource feedback information is accumulated to the preset information amount, evaluating the resource feedback information to obtain a value adjustment coefficient, and adjusting the resource value index according to the value adjustment coefficient. The resource feedback information comprises an evaluation score of the first user for calling the internal training resource. The preset information amount comprises a data amount threshold of the preset and determined resource feedback information. Illustratively, the resource feedback information includes a plurality of evaluation scores when the resource feedback information is accumulated to a preset information amount. And calculating the average value of the evaluation scores to obtain a value adjustment coefficient. And multiplying the value adjustment coefficient by a resource value index corresponding to the called internal training resource to obtain an adjusted resource value index, and updating the original resource value index according to the adjusted resource value index. The resource value index is adaptively adjusted according to the resource feedback information, so that the real-time performance and the accuracy of the resource value index are improved, and the reliability of the resource management in the enterprise is improved.
In summary, the enterprise internal training data processing method based on artificial intelligence provided by the application has the following technical effects:
1. classifying the in-enterprise training resource library to obtain an in-enterprise training resource distribution block; performing internal training value identification on the internal training resource distribution blocks through a cloud processor to obtain resource value indexes; identifying the internal training resource distribution blocks according to the resource value indexes to generate an internal training resource sharing platform; when a terminal of the internal training resource sharing platform receives internal training resource request information of a first user, identity recognition is carried out on the first user, and an identity characteristic set is obtained; and acquiring and calling the internal training resources according to the identity characteristic set. The technical effects that the training resources in the enterprise are intelligently and reliably scheduled and managed, the accuracy and the adaptability of the training resource management in the enterprise are improved, the utilization rate of the training resources in the enterprise is improved, the training quality in the enterprise is improved, and the training cost in the enterprise is reduced are achieved.
2. The internal training resource distribution blocks are subjected to multi-dimensional internal training value identification through the internal training resource value analysis model, accurate resource value indexes are obtained, comprehensiveness of internal training resource management is improved, and therefore accuracy of internal training of enterprises is improved.
3. The internal training resource sharing is realized through the internal training resource sharing platform and the resource sharing line, so that the utilization rate of the internal training resources of the enterprise is improved, the internal training cost of the enterprise is reduced, and the comprehensiveness of the internal training resource management of the enterprise is improved.
4. And the resource value index is adaptively adjusted according to the resource feedback information, so that the real-time performance and the accuracy of the resource value index are improved, and the reliability of the resource management in the enterprise is improved.
Example two
Based on the same inventive concept as the method for processing the internal standard data of the enterprise based on the artificial intelligence in the foregoing embodiment, the present invention further provides an internal standard data processing system of the enterprise based on the artificial intelligence, please refer to fig. 3, where the system includes:
the resource library acquisition module 11, where the resource library acquisition module 11 is configured to acquire an intra-enterprise training resource library of a target enterprise;
a classification module 12, where the classification module 12 is configured to classify the intra-enterprise trained resource pool to obtain intra-training resource distribution blocks, where each block in the intra-training resource distribution blocks correspondingly stores a class of intra-training resources, and the intra-training resource distribution blocks are stored in the cloud processor;
an internal training value recognition module 13, wherein the internal training value recognition module 13 is configured to perform internal training value recognition on the internal training resource distribution block based on the cloud processor, and obtain a resource value index;
an identification module 14, wherein the identification module 14 is configured to identify the trainee resource distribution block by the resource value index, and generate a trainee resource sharing platform;
an in-user training request obtaining module 15, where the in-user training request obtaining module 15 is configured to obtain in-user training resource request information of a first user;
an identity recognition module 16, wherein the identity recognition module 16 is configured to perform identity recognition on the first user to obtain an identity feature set when the terminal of the internal training resource sharing platform receives the internal training resource request information;
and the calling module 17 is used for obtaining and calling the internal training resources according to the identity feature set.
Further, the system further comprises:
an inner training resource block acquisition module, configured to acquire a first inner training resource block, a second inner training resource block …, an nth inner training resource block, where N is a total number of blocks of the inner training resource distribution block, and N is a positive integer greater than 0;
a data reading module, configured to read resource data in the first inner training resource block, the second inner training resource block …, and the nth training resource block to obtain first read data and second read data … nth read data;
the data input module is used for inputting the first reading data … and the Nth reading data 5363 into an internal training resource value analysis model to obtain the resource value index, wherein the resource value index comprises a first resource value index and a second resource value index … Nth resource value index.
Further, the system further comprises:
the network layer configuration module is used for configuring a plurality of analysis network layers, and the analysis network layers comprise a block type identification layer, a characteristic dimension determination layer, a data characteristic analysis layer and a resource value calculation layer;
and the building module is used for connecting the input end and the output end among all layers according to the block type identification layer, the characteristic dimension determination layer, the data characteristic analysis layer and the resource value calculation layer, and building the internal training resource value analysis model.
Further, the system further comprises:
a first receiving module for receiving, by the resource value calculation layer, block type information based on the block type identification layer;
a second receiving module for receiving, by the resource value calculation layer, a plurality of feature dimensions based on the feature dimension determination layer;
a third receiving module for receiving, by the resource value computing layer, a resource feature data set based on the data feature analysis layer;
and the resource value index output module is used for analyzing the block type information, the characteristic dimensions and the resource characteristic data set according to the resource value calculation layer and outputting the resource value index, wherein the resource value calculation layer comprises a plurality of preset requirement indexes corresponding to the characteristic dimensions.
Further, the system further comprises:
the resource authority determining module is used for determining the internal training resource authority according to the identity characteristic set;
a resource block determination module, configured to obtain a callable inner training resource block according to the inner training resource permission, where the callable inner training resource block has a resource value index;
and the data sending module is used for carrying out forward serialization processing on the invokable internal training resource block according to the resource value index and sending the processed resource block chain to the first user.
Further, the system further comprises:
a department determining module, configured to determine a department to which a resource belongs according to the internal training resource distribution block;
the relevance analysis module is used for carrying out relevance analysis on the business among the departments to which the resources belong to obtain a business relevance index;
a shared line generation module, configured to connect the internal training resource distribution blocks according to the service association index to generate a resource shared line;
and the resource sharing module is used for embedding the resource sharing line in the internal training resource sharing platform and realizing internal training resource sharing among business related departments.
Further, the system further comprises:
a resource feedback information acquisition module, configured to acquire resource feedback information of the calling internal training resource according to the first user;
the value adjustment coefficient acquisition module is used for storing the resource feedback information, and acquiring a value adjustment coefficient based on the resource feedback information when the resource feedback information is accumulated to a preset information amount;
and the adjusting module is used for adjusting the resource value index according to the value adjusting coefficient.
The internal enterprise training data processing system based on artificial intelligence provided by the embodiment of the invention can execute the internal enterprise training data processing method based on artificial intelligence provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Each included module is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be realized; in addition, the specific names of the functional modules are only for convenience of distinguishing from each other and are not used for limiting the protection scope of the present invention.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an electronic device provided in the third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing the embodiment of the present invention. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of the processors 31 in the electronic device may be one or more, one processor 31 is taken as an example in fig. 4, the processor 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or in other ways, and the connection by the bus is taken as an example in fig. 4.
The memory 32 is a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to an artificial intelligence based enterprise internal training data processing method in the embodiment of the present invention. The processor 31 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the memory 32, so as to implement the above-mentioned method for processing data based on artificial intelligence in the enterprise.
The application provides an industrial training data processing method based on artificial intelligence, wherein the method is applied to an industrial training data processing system based on artificial intelligence, and the method comprises the following steps: classifying the in-enterprise training resource library to obtain an in-enterprise training resource distribution block; performing internal training value identification on the internal training resource distribution blocks through a cloud processor to obtain resource value indexes; identifying the internal training resource distribution blocks according to the resource value indexes to generate an internal training resource sharing platform; when a terminal of the internal training resource sharing platform receives internal training resource request information of a first user, identity recognition is carried out on the first user, and an identity characteristic set is obtained; and acquiring and calling the internal training resources according to the identity characteristic set. The technical problems that in the prior art, management accuracy of the enterprise internal training resources is insufficient, the adaptability is low, and accordingly the enterprise internal training effect is poor are solved. The technical effects that the training resources in the enterprise are intelligently and reliably scheduled and managed, the accuracy and the adaptability of the training resource management in the enterprise are improved, the utilization rate of the training resources in the enterprise is improved, the training quality in the enterprise is improved, and the training cost in the enterprise is reduced are achieved.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An industrial training data processing method based on artificial intelligence is applied to an industrial training data processing system based on artificial intelligence, the system is in communication connection with a cloud processor, and the method comprises the following steps:
acquiring an intra-enterprise training resource library of a target enterprise;
classifying the enterprise internal training resource library to obtain internal training resource distribution blocks, wherein each internal training resource distribution block correspondingly stores a class of internal training resources, and the internal training resource distribution blocks are stored in the cloud processor;
performing internal training value recognition on the internal training resource distribution blocks based on the cloud processor to obtain resource value indexes;
identifying the internal training resource distribution blocks by the resource value indexes to generate an internal training resource sharing platform;
acquiring the internal training resource request information of a first user;
when the terminal of the internal training resource sharing platform receives the internal training resource request information, the first user is subjected to identity recognition, and an identity characteristic set is obtained;
and acquiring and calling internal training resources according to the identity characteristic set.
2. The method of claim 1, wherein the intra-training value identification is performed on the intra-training resource distribution block to obtain a resource value index, the method further comprising:
acquiring a first inner training resource block, a second inner training resource block …, an nth inner training resource block, wherein N is the total number of blocks of the inner training resource distribution block, and N is a positive integer greater than 0;
reading the resource data in the first inner training resource block, the second inner training resource block … and the nth inner training resource block to obtain first read data and second read data … nth read data;
inputting the first read data … and the Nth read data 5363 into an internal training resource value analysis model to obtain the resource value index, wherein the resource value index comprises a first resource value index and a second resource value index … Nth resource value index.
3. The method of claim 2, wherein the method further comprises:
configuring a plurality of analysis network layers, wherein the plurality of analysis network layers comprise a block type identification layer, a characteristic dimension determination layer, a data characteristic analysis layer and a resource value calculation layer;
and connecting the input end and the output end among the layers according to the block type identification layer, the characteristic dimension determination layer, the data characteristic analysis layer and the resource value calculation layer, and building the internal training resource value analysis model.
4. The method of claim 3, wherein the method further comprises:
receiving, by the resource value calculation layer, block type information based on the block type identification layer;
receiving, by the resource value calculation layer, a plurality of feature dimensions based on the feature dimension determination layer;
receiving, by the resource value computing layer, a resource feature data set based on the data feature analysis layer;
and analyzing the block type information, the characteristic dimensions and the resource characteristic data set according to the resource value calculation layer, and outputting the resource value index, wherein the resource value calculation layer comprises a plurality of preset demand indexes corresponding to the characteristic dimensions.
5. The method of claim 1, wherein the call in-training resource is obtained according to the set of identity features, the method further comprising:
determining internal training resource authority according to the identity characteristic set;
acquiring internal training resource blocks capable of being called according to the internal training resource authority, wherein the internal training resource blocks capable of being called all correspond to resource value indexes;
and performing forward serialization processing on the internal training resource block which can be called according to the resource value index, and sending a processed resource block chain to the first user.
6. The method of claim 5, wherein the method further comprises:
determining the department to which the resource belongs according to the internal training resource distribution block;
performing relevance analysis on the services among the departments to which the resources belong to obtain a service relevance index;
connecting the internal training resource distribution blocks according to the business association index to generate a resource sharing circuit;
and embedding the resource sharing line in the internal training resource sharing platform, and realizing internal training resource sharing among business association departments.
7. The method of claim 1, wherein the method further comprises:
acquiring resource feedback information of the calling internal training resource according to the first user;
storing the resource feedback information, and acquiring a value adjustment coefficient based on the resource feedback information when the resource feedback information is accumulated to a preset information amount;
and adjusting the resource value index according to the value adjustment coefficient.
8. An artificial intelligence based enterprise internal training data processing system, wherein the system is in communication connection with a cloud processor, the system comprising:
the resource library acquisition module is used for acquiring an intra-enterprise training resource library of a target enterprise;
a classification module, configured to classify the intra-enterprise resource pool to obtain intra-training resource distribution blocks, where each block in the intra-training resource distribution blocks correspondingly stores a class of intra-training resources, and the intra-training resource distribution blocks are stored in the cloud processor;
an internal training value identification module, configured to perform internal training value identification on the internal training resource distribution block based on the cloud processor, and obtain a resource value index;
an identification module, configured to identify the trainee resource distribution block according to the resource value index, and generate a trainee resource sharing platform;
the user internal training request acquisition module is used for acquiring internal training resource request information of a first user;
the identity recognition module is used for carrying out identity recognition on the first user when the terminal of the internal training resource sharing platform receives the internal training resource request information to obtain an identity characteristic set;
and the calling module is used for acquiring calling internal training resources according to the identity characteristic set.
9. An electronic device, characterized in that the electronic device comprises:
a memory for storing executable instructions;
a processor for implementing an artificial intelligence based enterprise internal training data processing method as claimed in any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method of processing data in an enterprise based on artificial intelligence, as claimed in any one of the claims 1 to 7.
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