CN118365260A

CN118365260A - Enterprise management method, device, equipment and storage medium based on artificial intelligence

Info

Publication number: CN118365260A
Application number: CN202410223011.XA
Authority: CN
Inventors: 张浩然; 许萍
Original assignee: Nanyuanmen Street Office Beilin District Xi'an City
Current assignee: Nanyuanmen Street Office Beilin District Xi'an City
Priority date: 2024-02-28
Filing date: 2024-02-28
Publication date: 2024-07-19

Abstract

The application relates to the technical field of data processing, and provides an enterprise management method, device, equipment and storage medium based on artificial intelligence. Responding to an enterprise consultation request, and acquiring enterprise consultation text; the enterprise consultation text is an encrypted text; decrypting the enterprise consultation text by adopting a preset decryption algorithm to obtain a target text; inputting the target text into a preset text feature extraction model to obtain feature information of the target text; determining a target government department based on the characteristic information, and sending a target text to the target government department so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information; receiving the reply information sent by the target government department, encrypting the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise. The method can promote the communication between enterprises and government departments, so that the problems of the enterprises are solved in time.

Description

Enterprise management method, device, equipment and storage medium based on artificial intelligence

Technical Field

The present application relates to the field of data processing technologies, and in particular, to an enterprise management method, device, equipment and storage medium based on artificial intelligence.

Background

With the continuous development of the economy in China, various types of enterprises are continuously emerging, for assisting the development of the enterprises, first-line staff of government departments need to visit various enterprises from time to time, and consult or feed back the problems encountered by the enterprises in the operation process to related departments, the enterprises also consult or feed back the problems encountered in the work to the government, however, due to the fact that the related government departments are numerous and are influenced by the basic level treatment 'bar' relationship, the problems and the appeal of the enterprises are often not timely transmitted to the related government departments, or the related problems are caused to 'distortion' in layer-by-layer report, so that the problems of the enterprises cannot be effectively solved in time, and the development of the enterprises is influenced to a certain extent.

Disclosure of Invention

The application provides an enterprise management method, device, equipment and storage medium based on artificial intelligence, which are used for solving the problems set forth in the background technology.

In a first aspect, the present application provides an artificial intelligence based enterprise management method, including: responding to an enterprise consultation request, and acquiring enterprise consultation text; the enterprise consultation text is an encrypted text; decrypting the enterprise consultation text by adopting a preset decryption algorithm to obtain a target text; inputting the target text into a preset text feature extraction model to obtain feature information of the target text; determining a target government department based on the characteristic information, and sending the target text to the target government department so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information; receiving the reply information sent by the target government department, encrypting the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise.

In a second aspect, the present application provides an artificial intelligence based enterprise management apparatus, comprising:

The acquisition module is used for responding to the consultation request of the enterprise and acquiring the consultation text of the enterprise; the enterprise consultation text is an encrypted text;

The decryption module is used for decrypting the enterprise consultation text by adopting a preset decryption algorithm to obtain a target text;

the input module is used for inputting the target text into a preset text feature extraction model to obtain feature information of the target text;

the determining module is used for determining a target government department based on the characteristic information and sending the target text to the target government department so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information;

The receiving module is used for receiving the reply information sent by the target government department, carrying out encryption processing on the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise.

In a third aspect, the present application provides a terminal device comprising a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements an artificial intelligence based enterprise management method as described above.

In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements an artificial intelligence based enterprise management method as described above.

The application provides an enterprise management method, device, equipment and storage medium based on artificial intelligence, wherein the method comprises the steps of responding to an enterprise consultation request and acquiring enterprise consultation text; the enterprise consultation text is an encrypted text; decrypting the enterprise consultation text by adopting a preset decryption algorithm to obtain a target text; inputting the target text into a preset text feature extraction model to obtain feature information of the target text; determining a target government department based on the characteristic information, and sending the target text to the target government department so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information; receiving the reply information sent by the target government department, encrypting the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise. According to the method, on one hand, the enterprise consultation text is set to be an encryption text and the reply information is encrypted, so that the safety and reliability of the enterprise consultation text and the reply information in the transmission process can be ensured, the enterprise consultation text and the reply information are prevented from being stolen by unauthorized personnel, the normal operation of an enterprise is maintained, on the other hand, the target government department is determined based on the characteristic information, the enterprise consultation text can be accurately sent to the target government department, the effective communication between the enterprise and the target government department is promoted, the problem of the enterprise is effectively solved in time, and the normal development of the enterprise is ensured.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of an enterprise management method based on artificial intelligence according to an embodiment of the present application;

FIG. 2 is a schematic block diagram of an enterprise management device based on artificial intelligence according to an embodiment of the present application;

fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.

It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

With the continuous development of the economy in China, various types of enterprises are continuously emerging, for assisting the development of the enterprises, first-line staff of government departments need to visit various enterprises from time to time, and consult or feed back the problems encountered by the enterprises in the operation process to related departments, the enterprises also consult or feed back the problems encountered in the work to the government, however, due to the fact that the related government departments are numerous and are influenced by the basic level treatment 'bar' relationship, the problems and the appeal of the enterprises are often not timely transmitted to the related government departments, or the related problems are caused to 'distortion' in layer-by-layer report, so that the problems of the enterprises cannot be effectively solved in time, and the development of the enterprises is influenced to a certain extent. Therefore, the application provides an enterprise management method, device, equipment and storage medium based on artificial intelligence to solve the problems.

Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.

Referring to fig. 1, fig. 1 is a flowchart of an enterprise management method based on artificial intelligence according to an embodiment of the present application, and as shown in fig. 1, the enterprise management method based on artificial intelligence according to an embodiment of the present application includes steps S100 to S500.

Step S100, responding to an enterprise consultation request and acquiring enterprise consultation text; the enterprise consultation text is an encrypted text.

And step 200, adopting a preset decryption algorithm to decrypt the enterprise consultation text to obtain a target text.

And step S300, inputting the target text into a preset text feature extraction model to obtain feature information of the target text.

And step 400, determining a target government department based on the characteristic information, and sending the target text to the target government department, so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information.

Step S500, receiving the reply information sent by the target government department, encrypting the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise.

The execution subject of the embodiment of the present application may be an enterprise management device based on artificial intelligence, or may be a terminal or a server, and the embodiment is described by taking a server as an example.

In this embodiment, as described in step S100, the server responds to the consultation request of the enterprise to obtain the enterprise consultation text, specifically, when the enterprise user encounters a problem that needs to be consulted in the operation process, the enterprise user inputs the consultation request in the enterprise user interface, or when the government-based staff encounters a problem that needs to be consulted in the process of accessing the enterprise, the government-based staff inputs the consultation request in the enterprise user interface, and the server responds to the consultation request of the enterprise to obtain the enterprise consultation text. The enterprise consultation text is an encrypted text. It can be understood that the enterprise consultation text is set to be an encrypted text, so that the enterprise consultation text can be prevented from being tampered by unauthorized personnel, the authenticity and the reliability of the enterprise consultation text are ensured, and the normal operation of an enterprise is maintained.

As described in step S200, after the enterprise consultation text is obtained, the server adopts a preset decryption algorithm to decrypt the enterprise consultation text to obtain a target text. Specifically, the server firstly obtains department codes of all government departments and enterprise codes of enterprises, calculates the sum of all numbers of the enterprise codes for all enterprise codes to obtain a plurality of first numbers, calculates the sum of all numbers of the enterprise codes to obtain second numbers, then compares the first numbers with the second numbers for all the first numbers, and when the first numbers are smaller than the second numbers, takes the enterprise codes corresponding to the first numbers as first target enterprise codes, secondly sequentially arranges all the first target enterprise codes from top to bottom based on the sizes of the first numbers corresponding to all the first target enterprise codes to obtain a target matrix, and then sequentially arranges all the target matrix elements at specified positions of the target matrix as target matrix elements based on the positions of all the target matrix elements in the target matrix to obtain decryption passwords, and finally carries out decryption consultation on the enterprise based on the decryption passwords. Each department code and each enterprise code comprise letters and numbers, and first numbers corresponding to each row vector of the target matrix are sequentially increased from top to bottom.

After obtaining the target text, the server inputs the target text into a preset text feature extraction model to obtain feature information of the target text, as described in step S300. Specifically, after the target text is input into the text feature extraction model by the server, firstly, mapping the target text to a high-dimensional space through a word embedding layer of the text feature extraction model to obtain dense vectors corresponding to various vocabularies of the target text, then, performing deep learning on the dense vectors through a circulating neural network layer of the text feature extraction model to obtain global features of the target text, further, performing deep learning on the global features through a global maximum pooling layer of the text feature extraction model to obtain feature information of the target text, and finally, outputting the feature information through an output layer of the text feature extraction model.

After obtaining the feature information, the server determines a target government agency based on the feature information and transmits the target text to the target government agency, so that the target government agency replies to the consultation request of the enterprise based on the target text to obtain reply information, as described in step S400 above. The server firstly obtains a government department-function information mapping relation table in a preset database, respectively inputs each function information of the government department-function information mapping relation table into a preset semantic feature extraction model to obtain a plurality of first feature vectors, then inputs the feature information into the preset semantic feature extraction model to obtain a second feature vector, and secondly respectively calculates the similarity between each first feature vector and the second feature vector to obtain a plurality of similarities, determines the maximum similarity among all the similarities, determines the government department corresponding to the maximum similarity as a target government department, and finally sends the target text to the target government department so that the target government department replies to the enterprise's consultation request based on the target text to obtain reply information.

As described in the above step S500, after the target text is sent to the target government, the server receives the reply message sent by the target government, encrypts the reply message by using a preset encryption algorithm, and sends the encrypted reply message to the enterprise. Specifically, the server firstly receives the reply information sent by the target government department, then encrypts the reply information by adopting a preset encryption algorithm, and finally sends the encrypted reply information to the enterprise.

According to the method provided by the embodiment, on one hand, the enterprise consultation text is set to be the encryption text and the reply information is encrypted, so that the safety and reliability of the enterprise consultation text and the reply information in the transmission process can be ensured, the enterprise consultation text and the reply information are prevented from being stolen by unauthorized personnel, and the normal operation of an enterprise is maintained.

In some embodiments, the decrypting the enterprise consultation text by using a preset decrypting algorithm to obtain a target text includes the following steps:

Acquiring department codes of various government departments and enterprise codes of the enterprises; wherein each of said department codes and each of said business codes includes letters and numbers;

calculating the sum of the numbers of the enterprise codes aiming at each enterprise code to obtain a plurality of first numbers;

Calculating the sum of all the numbers of the enterprise codes to obtain a second number;

Comparing the first number with the second number for each first number, and taking the enterprise code corresponding to the first number as a first target enterprise code when the first number is smaller than the second number;

Based on the size of a first number corresponding to each first target enterprise code, sequentially arranging each first target enterprise code from top to bottom to obtain a target matrix; the first numbers corresponding to the row vectors of the target matrix are sequentially increased from top to bottom;

Taking matrix elements of the target matrix at the designated positions as target matrix elements, and sequentially arranging the target matrix elements based on the positions of the target matrix elements in the target matrix to obtain a decryption password;

And decrypting the enterprise consultation text based on the decryption password.

According to the method provided by the embodiment, the target matrix is generated based on the department codes of all government departments and the enterprise codes of the enterprises, matrix elements of the target matrix at the designated positions are used as target matrix elements, and all target matrix elements are sequentially arranged based on the positions of all target matrix elements in the target matrix, so that decryption passwords are obtained, the acquisition difficulty of the decryption passwords is improved, and therefore the safety of enterprise consultation texts is further improved.

In some embodiments, the inputting the target text into a preset text feature extraction model to obtain feature information of the target text includes the following steps:

mapping the target text to a high-dimensional space through a word embedding layer of the text feature extraction model to obtain dense vectors corresponding to various vocabularies of the target text; methods of mapping include, but are not limited to, word2Vec, gloVe;

deep learning is carried out on each dense vector through a cyclic neural network layer of the text feature extraction model, and global features of the target text are obtained;

and performing deep learning on the global features through a global maximum pooling layer of the text feature extraction model to obtain feature information of the target text.

According to the method provided by the embodiment, on one hand, the word embedding layer of the text feature extraction model is used for mapping the target text to a high-dimensional space to obtain dense vectors corresponding to various vocabularies of the target text, which is helpful for extracting effective information in the target text and capturing semantic and grammar information in the target text, on the other hand, the circulating neural network layer of the text feature extraction model is used for carrying out deep learning on various dense vectors to obtain global features of the target text, the global features in the target text including long-term dependence and context information can be learned, the target text can be more comprehensively understood, on the other hand, the global features are subjected to deep learning through the global maximum pooling layer of the text feature extraction model, the feature information of the target text can be obtained, the feature information with the most distinction in the target text can be extracted, the most important features in the target text are highlighted, and the key content of the target text can be accurately described.

In some embodiments, the determining the target government agency based on the characteristic information includes the steps of:

acquiring a government department-function information mapping relation table from a preset database; the government department-function information mapping relation table comprises a plurality of government department-function information mapping relations;

Inputting each piece of function information of the government department-function information mapping relation table into a preset semantic feature extraction model to obtain a plurality of first feature vectors;

inputting the feature information into the preset semantic feature extraction model to obtain a second feature vector;

And respectively calculating the similarity between each first feature vector and each second feature vector to obtain a plurality of similarities, determining the maximum similarity among all the similarities, and determining a government department corresponding to the maximum similarity as a target government department.

The method provided by the embodiment can automatically identify and determine the target government department, improves the management efficiency of the government to enterprises, ensures that the transmission of the target text is more targeted, can avoid the transmission redundancy of the target text in the transmission process, and ensures the authenticity and reliability of the target text.

In some embodiments, the encrypting the reply message includes the following steps:

Acquiring department codes of various government departments and enterprise codes of the enterprises; wherein each of said enterprise codes includes letters and numbers;

for each character of the enterprise codes, selecting enterprise codes which do not comprise the character from all enterprise codes as second target enterprise codes, and combining all second target enterprise codes corresponding to the character into a set corresponding to the character;

Determining a target set in all the sets; wherein the target set includes a maximum number of second target enterprise encodings;

Calculating the sum of the numbers of the second target enterprise codes aiming at the second target enterprise codes in the target set to obtain a plurality of third numbers;

based on the size of a third number corresponding to each second target enterprise code in the target set, arranging the second target enterprise codes in the target set in sequence from left to right to obtain an enterprise code combination; wherein, the third numbers corresponding to the second target enterprise codes in the enterprise code combination are sequentially increased from left to right;

extracting characters with the sequence number of prime numbers from the enterprise coding combination as target characters, and sequentially arranging the target characters based on the sequence of the target characters in the enterprise coding combination to obtain an encryption password;

And encrypting the reply information based on the encryption password.

The method provided by the embodiment can improve the cracking difficulty of the encrypted password, further improve the safety of the reply information in the transmission process, and further maintain the normal operation of the enterprise.

Referring to fig. 2, fig. 2 is a schematic block diagram of an enterprise management device 100 based on artificial intelligence according to an embodiment of the present application, and as shown in fig. 2, the enterprise management device 100 based on artificial intelligence includes:

An obtaining module 110, configured to respond to an enterprise consultation request and obtain an enterprise consultation text; the enterprise consultation text is an encrypted text.

And the decryption module 120 is configured to decrypt the enterprise consultation text by using a preset decryption algorithm to obtain a target text.

And the input module 130 is configured to input the target text into a preset text feature extraction model to obtain feature information of the target text.

And the determining module 140 is configured to determine a target government department based on the feature information, and send the target text to the target government department, so that the target government department replies to the consultation request of the enterprise based on the target text, and obtain reply information.

The receiving module 150 is configured to receive the reply message sent by the target government department, encrypt the reply message with a preset encryption algorithm, and send the encrypted reply message to the enterprise.

It should be noted that, for convenience and brevity of description, the specific working process of the above-described apparatus and each module may refer to the corresponding process in the foregoing embodiment of the enterprise management method based on artificial intelligence, which is not described herein again.

The artificial intelligence based enterprise management apparatus 100 provided in the above embodiments may be implemented in the form of a computer program that can be run on the terminal device 200 as shown in fig. 3.

Referring to fig. 3, fig. 3 is a schematic block diagram of a structure of a terminal device 200 according to an embodiment of the present application, where the terminal device 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected through a system bus 203, and the memory 202 may include a nonvolatile storage medium and an internal memory.

The non-volatile storage medium may store a computer program. The computer program comprises program instructions that, when executed by the processor 201, cause the processor 201 to perform any of the artificial intelligence based enterprise management methods described above.

The processor 201 is used to provide computing and control capabilities supporting the operation of the overall terminal device 200.

The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by the processor 201, causes the processor 201 to perform any of the artificial intelligence based enterprise management methods described above.

It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation of the terminal device 200 related to the present application, and that a specific terminal device 200 may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.

It should be appreciated that the Processor 201 may be a central processing unit (Central Processing Unit, CPU), and the Processor 201 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application SPECIFIC INTEGRATED Circuits (ASICs), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In some embodiments, the processor 201 is configured to execute a computer program stored in the memory to implement the following steps:

Responding to an enterprise consultation request, and acquiring enterprise consultation text; the enterprise consultation text is an encrypted text;

Decrypting the enterprise consultation text by adopting a preset decryption algorithm to obtain a target text;

inputting the target text into a preset text feature extraction model to obtain feature information of the target text;

Determining a target government department based on the characteristic information, and sending the target text to the target government department so that the target government department replies to the consultation request of the enterprise based on the target text to obtain reply information;

Receiving the reply information sent by the target government department, encrypting the reply information by adopting a preset encryption algorithm, and sending the encrypted reply information to the enterprise.

It should be noted that, for convenience and brevity of description, the specific working process of the terminal device 200 described above may refer to the corresponding process of the enterprise management method based on artificial intelligence, which is not described herein.

Embodiments of the present application also provide a computer-readable storage medium storing a computer program that, when executed by one or more processors, causes the one or more processors to implement an artificial intelligence based enterprise management method as provided by the embodiments of the present application.

The computer readable storage medium may be an internal storage unit of the terminal device 200 of the foregoing embodiment, for example, a hard disk or a memory of the terminal device 200. The computer readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like, which the terminal device 200 is equipped with.

While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.

Claims

1. An artificial intelligence based enterprise management method, comprising:

2. The enterprise management method based on artificial intelligence of claim 1, wherein the decrypting the enterprise consultation text using a preset decryption algorithm to obtain a target text comprises:

3. The method for enterprise management based on artificial intelligence according to claim 1, wherein the inputting the target text into a preset text feature extraction model to obtain feature information of the target text comprises:

mapping the target text to a high-dimensional space through a word embedding layer of the text feature extraction model to obtain dense vectors corresponding to various vocabularies of the target text;

4. The artificial intelligence based enterprise management method of claim 1, wherein the determining a target government agency based on the characteristic information comprises:

5. The artificial intelligence based enterprise management method of claim 1, wherein the encrypting the reply message comprises:

And encrypting the reply information based on the encryption password.

6. An artificial intelligence based enterprise management device, comprising:

7. A terminal device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the artificial intelligence based enterprise management method of any one of claims 1 to 5.

8. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, wherein the computer program, when executed by a processor, implements the artificial intelligence based enterprise management method of any one of claims 1 to 5.