CN118277916A

CN118277916A - Enterprise industry identification method, device, equipment and storage medium

Info

Publication number: CN118277916A
Application number: CN202410399464.8A
Authority: CN
Inventors: 丁汉其; 潘晓英; 贡丹燕
Original assignee: Shencai Technology Co ltd
Current assignee: Shencai Technology Co ltd
Priority date: 2024-04-03
Filing date: 2024-04-03
Publication date: 2024-07-02

Abstract

The invention discloses an enterprise belonging industry identification method, device, equipment and storage medium. The method comprises the following steps: acquiring at least one enterprise production element of an enterprise to be identified; determining a first reference industry category and a reference category probability corresponding to each enterprise production element of an enterprise to be identified according to a pre-established element industry relation mapping model between the generated element information and the industry category; determining at least one candidate industry category of an enterprise to be identified and candidate category probabilities of all candidate industry categories according to the reference category probabilities of enterprise production elements belonging to the same first reference industry category; and determining the target industry class of the enterprise to be identified according to the probability of each candidate class. The technical scheme of the embodiment of the invention realizes the automatic identification of the industry to which the enterprise belongs, and improves the accuracy of the industry identification result.

Description

Enterprise industry identification method, device, equipment and storage medium

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for identifying industries of enterprises.

Background

In the construction and use process of the information system, analysis data aiming at the industry is often provided for users to carry out business deployment or policy decision, and if the industry data is inaccurate, the deployment and decision result of the users can be influenced.

However, depending on manually-filled industry information, a situation of misinformation or false alarm may be caused by factors such as understanding of an operator's definition of an industry, unclear industrial positioning of an enterprise, and the like. For example: the advertisement company mainly customizes hanging tags, packages and related propaganda articles for clothing manufacturers, and some of the advertisement companies consider the advertisement industry, and some of the advertisement companies consider the clothing industry. Therefore, how to automatically and accurately identify industries to which enterprises belong becomes a problem to be solved at present.

Disclosure of Invention

The invention provides an enterprise belonging industry identification method, device, equipment and storage medium, which are used for realizing automatic identification of the enterprise belonging industry and improving accuracy of an industry identification result.

According to an aspect of the present invention, there is provided an industry identification method to which an enterprise belongs, the method including:

Acquiring at least one enterprise production element of an enterprise to be identified;

Determining a first reference industry category and a reference category probability corresponding to each enterprise production element of the enterprise to be identified according to a pre-established element industry relation mapping model between the generated element information and the industry category;

determining at least one candidate industry category of an enterprise to be identified and candidate category probabilities of the candidate industry categories according to the reference category probabilities of enterprise production elements belonging to the same first reference industry category;

and determining the target industry category of the enterprise to be identified according to the probability of each candidate category.

According to another aspect of the present invention, there is provided an industry identification device to which an enterprise belongs, the device comprising:

the element information acquisition module is used for acquiring at least one enterprise production element of the enterprise to be identified;

the reference category determining module is used for determining a first reference industry category and a reference category probability corresponding to each enterprise production element of the enterprise to be identified respectively according to a pre-constructed element-industry relation mapping model between the generated element information and the industry category;

the candidate category determining module is used for determining at least one candidate industry category of the enterprise to be identified and the candidate category probability of each candidate industry category according to the reference category probability of the enterprise production factors belonging to the same first reference industry category;

and the target category determining module is used for determining the target industry category of the enterprise to be identified according to the probability of each candidate category.

According to another aspect of the present invention, there is provided an electronic apparatus including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the business identification method of any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for identifying industries to which an enterprise according to any embodiment of the present invention belongs.

The technical scheme of the embodiment of the invention comprises the steps of obtaining at least one enterprise production element of an enterprise to be identified; determining a first reference industry category and a reference category probability corresponding to each enterprise production element of an enterprise to be identified according to a pre-established element industry relation mapping model between the generated element information and the industry category; determining at least one candidate industry category of an enterprise to be identified and candidate category probabilities of all candidate industry categories according to the reference category probabilities of enterprise production elements belonging to the same first reference industry category; and determining the target industry class of the enterprise to be identified according to the probability of each candidate class. According to the technical scheme, the automatic identification of industries to which the enterprises belong is realized, the industry prediction is performed through the element industry relation mapping model, factors of enterprise production elements are considered in the prediction process, the accurate prediction of industry categories is realized, category probability factors are combined in the prediction process, and the accuracy of the industry category prediction to which the enterprises belong is further improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

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 apparent that the drawings in the following description are only 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 flow chart of an enterprise industry identification method according to a first embodiment of the present invention;

FIG. 2 is a flowchart of an enterprise industry identification method according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of an enterprise-related industry recognition device according to a third embodiment of the present invention;

Fig. 4 is a schematic structural diagram of an electronic device implementing an enterprise-related industry identification method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of an enterprise industry identification method according to an embodiment of the present invention, where the method may be performed by an enterprise industry identification device, and the enterprise industry identification device may be implemented in hardware and/or software, and the enterprise industry identification device may be configured in an electronic device. As shown in fig. 1, the method includes:

S110, at least one enterprise production element of the enterprise to be identified is obtained.

The enterprise to be identified may be an enterprise to be identified by the industry to which the enterprise belongs. The enterprise production element may be product/service information of the enterprise, and may include information such as product/service names, primary raw materials, auxiliary materials, and measurement units. The enterprise production elements corresponding to different enterprises are different, and for any enterprise, the corresponding enterprise production elements can be one type or multiple types.

S120, determining a first reference industry category and a reference category probability corresponding to each enterprise production element of the enterprise to be identified according to a pre-constructed element industry relation mapping model between the generated element information and the industry categories.

The element industry relation mapping model can be constructed in advance by relevant technicians. By way of example, the enterprise production elements of each enterprise in the history period can be obtained, the data is cleaned, the nonstandard data is removed, and the removed data is subjected to unified formatting. Reading enterprise production elements of each product, binding the relationship between the enterprise production elements of the enterprise and each industry, and increasing the ticket obtaining number of the enterprise production elements of the enterprise belonging to each industry; and determining probability values between the enterprise production elements and each industry according to the ticket number between the enterprise production elements and each industry, storing the results into a relational library, and taking the relational library as an element industry relation mapping model.

It should be noted that, in the industry relationship mapping model, for an enterprise production element of any enterprise, there may be at least one industry corresponding to each industry, and there is a corresponding probability value for each corresponding industry. For example, for a certain enterprise production element, the associated industries are industry a, industry B and industry C, the corresponding element industry association similarity probability value between industry a is 90%, the element industry association similarity probability value between industry a is 80%, and the element industry association similarity probability value between industry C is 70%. The probability values of similarity between the model and industry are stored in an industry relation mapping model.

For example, a first reference industry category corresponding to each enterprise production element of the enterprise to be identified and a reference category probability between each reference industry category may be obtained from a pre-constructed element industry relationship model.

In order to further improve the accuracy of determining the industry category and category probability, the information of the enterprise production elements can be predicted in a network model mode, so that the corresponding industry category and category probability can be obtained.

In an alternative embodiment, the element industry relationship mapping model is generated as follows: acquiring historical production element information of enterprises of at least one different industry under a historical period; inputting the historical production element information into a pre-constructed classification network model to obtain a prediction industry category output by the model; and carrying out model training on the classification network model according to the prediction industry category and the standard industry category corresponding to the corresponding historical production element information until a preset model iteration ending condition is met, so as to obtain an element industry relation mapping model.

The classification network model may be a deep learning model, for example, a convolutional neural network model, or the like. The model iteration result condition can be preset by a related technician, for example, a preset iteration number threshold is reached, or the loss value tends to be stable.

Illustratively, enterprise historical production element information of different industries in a historical period is obtained, and a large amount of enterprise historical production element information is input into a pre-constructed classification network model to obtain a predicted industry category and category probability value output by the model. And model training is carried out on the classification network model according to the prediction industry class and the standard industry class corresponding to the corresponding historical production element information. The standard industry category may be sample tag data, among others. And obtaining the element industry relation mapping model when the preset model iteration ending condition is met.

The method includes the steps that each enterprise production element of an enterprise to be identified is input into an element industry relation mapping model which is obtained through training in advance, and a first reference industry category and a reference category probability which are output by the model and respectively correspond to each enterprise production element are obtained.

S130, determining at least one candidate industry category of the enterprise to be identified and candidate category probabilities of the candidate industry categories according to the reference category probabilities of the enterprise production elements belonging to the same first reference industry category.

For example, a probability average of reference category probabilities of enterprise production elements of the same first reference industry category may be determined, and the probability average is greater as the candidate industry category.

In an alternative embodiment, determining at least one candidate business category of the business to be identified and a candidate category probability for each candidate business category based on the reference category probabilities of the business production elements belonging to the same first reference business category includes: determining a target category probability of at least one identical first reference industry category according to the reference category probabilities of enterprise production elements belonging to the identical first reference industry category; selecting candidate industry categories from the same first reference industry categories according to the target category probabilities of the same first reference industry categories; and determining the target class probability corresponding to the candidate industry class as the candidate class probability.

For example, if for a certain enterprise to be identified, the corresponding enterprise production elements include an enterprise production element a, an enterprise production element b, an enterprise production element c, an enterprise production element d and an enterprise production element e; the first reference industry category corresponding to the enterprise production element a and the enterprise production element c is industry M; the first reference industry category corresponding to the enterprise production element b and the enterprise production element e is industry N; the first reference industry category corresponding to the enterprise production element d is industry P. The reference class probability of the first reference industry M corresponding to the enterprise production element a is 90%; the probability of the reference category N of the first reference industry corresponding to the enterprise production element b is 80%; the reference class probability of the first reference industry M corresponding to the enterprise production element c is 70%; the reference class probability of the first reference industry P corresponding to the enterprise production element d is 60%; the reference class probability of the first reference industry N corresponding to the enterprise production element e is 40%.

And determining the target category probability of at least one identical first reference industry category according to the reference category probability of the enterprise production factors belonging to the identical first reference industry category. The target class probability can be determined by a reference class probability mean value of the same first reference industry class. For example, the target category probability for the same first reference industry category M for enterprise production element a and enterprise production element c is 80%. The target class probability for the same first reference industry class M corresponding to the enterprise production element b and the enterprise production element e is 60%. The reference class probability of the first reference industry N corresponding to the enterprise production element e is 40%; the reference class probability of the first reference industry P corresponding to the enterprise production element d is 60%.

Candidate industry categories are selected from each of the same first reference industry categories. For example, the target category probability is not smaller than a preset probability average value and can be used as the candidate industry category. Continuing the above example, if the preset probability average value is 50%, the candidate industry category is the first reference industry category M and the first reference industry category P. The candidate category probability of the corresponding candidate industry category M is 60%, and the candidate category probability of the candidate industry category N is 60%.

And S140, determining the target industry category of the enterprise to be identified according to the probability of each candidate category.

For example, a corresponding candidate industry category with the highest probability value can be selected from the candidate category probabilities, and the candidate industry category is used as the target industry category of the enterprise to be identified. For example, if there are two candidate industry categories, namely industry a and industry B, and the probability of the candidate category corresponding to industry a is 90% and the probability of the candidate category corresponding to industry B is 85%, industry a is taken as the target industry category of the enterprise to be identified.

Example two

Fig. 2 is a flowchart of an enterprise industry identification method according to a second embodiment of the present invention, where the present embodiment is optimized and improved based on the above technical solutions.

Further, the step of determining the probability difference value between the probabilities of the candidate categories by 'refining the target industry category of the enterprise to be identified into' according to the probabilities of the candidate categories; if the probability difference values are not larger than a preset difference value threshold value, acquiring enterprise basic information of the enterprise to be identified; and determining the target industry category of the enterprise to be identified according to the enterprise basic information. To refine the determination of the target industry category. In the embodiments of the present invention, the descriptions of other embodiments may be referred to in the portions not described in detail.

As shown in fig. 2, the method comprises the following specific steps:

S210, at least one enterprise production element of the enterprise to be identified is obtained.

S220, determining a first reference industry category and a reference category probability corresponding to each enterprise production element of the enterprise to be identified according to a pre-established element industry relation mapping model between the generated element information and the industry categories.

S230, determining at least one candidate industry category of the enterprise to be identified and candidate category probabilities of the candidate industry categories according to the reference category probabilities of the enterprise production elements belonging to the same first reference industry category.

S240, determining a probability difference value between the probabilities of the candidate categories.

Illustratively, the probability difference between the probabilities of the candidate categories is determined separately from the probabilities of the candidate categories. For example, there is a 90% probability of candidate category corresponding to candidate industry category a, 88% probability of candidate category corresponding to candidate industry category B, and 40% probability of candidate category corresponding to candidate industry category C. The probability difference between candidate industry class a and candidate industry class B is 2%; the probability difference between the candidate industry class A and the candidate industry class C is 50%; the probability difference between candidate industry category B and candidate industry category C is 44%.

S250, if the probability difference values are not larger than a preset difference value threshold value, acquiring enterprise basic information of the enterprise to be identified.

The difference threshold may be preset by a person skilled in the art, for example, the difference threshold may be set to 5%. The closer the probability difference value is, the closer the relationship between the enterprise and two industries with the close difference value is, and the further judgment based on the enterprise production factors cannot be performed in the two industries; the larger the probability difference value is, the closer the enterprise is to the industry with the larger probability value, and the higher the probability of being in the large probability value is.

Therefore, for example, if the probability differences are not greater than the preset difference threshold, it indicates that the enterprise is very close to or matched with each candidate industry category, the candidate industry category may be manually determined or manually selected by a related technician, or enterprise basic information of the enterprise to be identified may be obtained, and further prediction may be performed according to the enterprise basic information.

Optionally, if any probability difference value is greater than a preset difference value threshold, the matching degree between the enterprise and each candidate industry category is greatly different, and the highest probability of the candidate category can be selected from each candidate industry category as the target industry category, and the higher the category probability is, the higher the matching degree with the industry to which the enterprise belongs is.

S260, determining the target industry category of the enterprise to be identified according to the enterprise basic information.

The business basic information may include information such as a business name and a business organization structure code.

In an alternative embodiment, determining the target industry category of the enterprise to be identified according to the enterprise basic information includes: determining a second reference industry category corresponding to the enterprise basic information of the enterprise to be identified based on an information industry relation mapping model between the basic information and the industry category, which is constructed in advance, according to the enterprise basic information; and if the matching industry category matched with the second reference industry category exists in the candidate industry categories, determining the matching industry category as the target industry category.

The information industry relation mapping model can be trained by related technicians in advance. For example, the basic information of the historical enterprise can be used as a sample training set, and model training can be carried out on a pre-constructed semantic analysis network model to obtain an information industry relation mapping model. The information industry relation mapping model is used for predicting the industry category to which the enterprise belongs according to the basic information of the enterprise.

The enterprise basic information is input into an information industry relation mapping model to obtain a second reference industry category corresponding to the enterprise basic information of the enterprise to be identified. Or the information industry relation mapping model can also be that a related technician manually constructs the association relation between corresponding industry categories according to the history enterprise basic information in the history period in advance, and forms an association relation library, and then the association relation library is continuously updated to continuously optimize and perfect the association relation library. When the enterprise basic information of the enterprise to be identified is obtained, searching a second reference industry category corresponding to the enterprise basic information according to the association relation library.

And if the matching industry category matched with the second reference industry category exists in the candidate industry categories, determining the matching industry category as the target industry category.

Optionally, if the candidate industry category does not have a matching industry category matching with the second reference industry category, determining category similarity between the second reference industry category and each candidate industry category respectively; and determining the target industry class of the enterprise to be identified according to the similarity of each class. The category similarity may be determined based on a text matching manner, which is not described in detail in this embodiment.

According to the technical scheme, the probability difference value between the probabilities of the candidate classes is determined, if the probability difference value is not larger than the preset difference value threshold, the enterprise basic information of the enterprise to be identified is obtained, the target industry class of the enterprise to be identified is determined according to the enterprise basic information, and the correlation degree between the enterprise and the industry to which the enterprise belongs is determined by considering the probability difference value in the process of determining the target industry class, so that the accurate determination of the industry to which the enterprise belongs is further realized.

Example III

Fig. 3 is a schematic structural diagram of an enterprise-related industry recognition device according to a third embodiment of the present invention. The device for identifying the industry of the enterprise provided by the embodiment of the invention can be suitable for the condition of automatically identifying the industry of the enterprise, and the device for identifying the industry of the enterprise can be realized in a form of hardware and/or software, as shown in fig. 3, and specifically comprises: element information acquisition module 301, reference category determination module 302, candidate category determination module 303, and target category determination module 304. Wherein,

An element information obtaining module 301, configured to obtain at least one enterprise production element of an enterprise to be identified;

The reference category determining module 302 is configured to determine a first reference industry category and a reference category probability corresponding to each enterprise production element of the enterprise to be identified according to a pre-constructed element-industry relationship mapping model between generated element information and industry categories;

A reference category determination module 303, configured to determine at least one candidate industry category of an enterprise to be identified and a candidate category probability of each candidate industry category according to a reference category probability of enterprise production elements belonging to the same first reference industry category;

The target category determining module 304 is configured to determine a target industry category of the enterprise to be identified according to each candidate category probability.

Optionally, the model generation mode of the element industry relation mapping model is as follows:

Acquiring historical production element information of enterprises of at least one different industry under a historical period;

inputting the historical production element information into a pre-constructed classification network model to obtain a prediction industry category output by the model;

And carrying out model training on the classification network model according to the prediction industry category and the standard industry category corresponding to the corresponding historical production element information until a preset model iteration ending condition is met, so as to obtain an element industry relation mapping model.

Optionally, the candidate category determining module 303 includes:

the target category probability determining unit is used for determining the target category probability of at least one identical first reference industry category according to the reference category probability of the enterprise production factors belonging to the identical first reference industry category;

the candidate category selection unit is used for selecting candidate industry categories from the same first reference industry categories according to the target category probabilities of the same first reference industry categories;

And the candidate category determining unit is used for determining the target category probability corresponding to the candidate industry category as the candidate category probability.

Optionally, the target category determining module 304 includes:

A probability difference value determining unit, configured to determine a probability difference value between probabilities of the candidate categories;

The enterprise basic information acquisition unit is used for acquiring enterprise basic information of an enterprise to be identified if the probability difference value is not greater than a preset difference value threshold value;

and the target category determining unit is used for determining the target industry category of the enterprise to be identified according to the enterprise basic information.

Optionally, the target category determining unit includes:

A second industry category determining subunit, configured to determine, according to the basic information of the enterprise, a second reference industry category corresponding to the basic information of the enterprise to be identified based on an information industry relationship mapping model between pre-constructed basic information and industry categories;

and the target industry category determining subunit is used for determining the matching industry category as the target industry category if the matching industry category matched with the second reference industry category exists in the candidate industry categories.

Optionally, the target category determining unit further includes:

A category similarity determining subunit, configured to determine, if no matching industry category matching the second reference industry category exists in the candidate industry categories, a category similarity between the second reference industry category and each candidate industry category;

And the target industry category determining subunit is used for determining the target industry category of the enterprise to be identified according to the category similarity.

Optionally, the target category determining module 304 includes:

and the probability difference comparison unit is used for selecting the candidate category with the highest probability from the candidate industry categories as the target industry category if any probability difference value is larger than a preset difference threshold value.

The enterprise industry identification device provided by the embodiment of the invention can execute the enterprise industry identification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 4 shows a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 may also be stored. The processor 41, the ROM 42 and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.

Various components in electronic device 40 are connected to I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 41 may be various general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the business identification method to which the business belongs.

In some embodiments, the business-to-business identification method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the business-to-business identification method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the business-to-business identification method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. An enterprise industry identification method is characterized by comprising the following steps:

2. The method of claim 1, wherein the element industry relationship mapping model is generated as follows:

3. The method of claim 1, wherein determining at least one candidate business category for the business to be identified and a candidate category probability for each of the candidate business categories based on the reference category probabilities for business production elements belonging to the same first reference business category comprises:

determining a target category probability of at least one identical first reference industry category according to the reference category probabilities of enterprise production elements belonging to the identical first reference industry category;

Selecting candidate industry categories from the same first reference industry categories according to the target category probabilities of the same first reference industry categories;

And determining the target category probability corresponding to the candidate industry category as the candidate category probability.

4. The method of claim 1, wherein determining the target industry category of the business to be identified based on each of the candidate category probabilities comprises:

Determining a probability difference between probabilities of the candidate categories;

If the probability difference value is not greater than a preset difference value threshold value, acquiring enterprise basic information of the enterprise to be identified;

And determining the target industry category of the enterprise to be identified according to the enterprise basic information.

5. The method of claim 4, wherein determining the target industry category of the business to be identified based on the business base information comprises:

Determining a second reference industry category corresponding to the enterprise basic information of the enterprise to be identified based on an information industry relation mapping model between the basic information and the industry category, which is constructed in advance, according to the enterprise basic information;

And if the matching industry category matched with the second reference industry category exists in the candidate industry categories, determining the matching industry category as a target industry category.

6. The method of claim 5, wherein the method further comprises:

if the candidate industry category does not have the matching industry category matched with the second reference industry category, determining the category similarity between the second reference industry category and each candidate industry category respectively;

And determining the target industry category of the enterprise to be identified according to the category similarity.

7. The method according to claim 4, wherein the method further comprises:

If any probability difference value is larger than a preset difference value threshold value, selecting the candidate category with the highest probability from the candidate industry categories as a target industry category.

8. An enterprise-affiliated industry recognition device, comprising:

9. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the business identification method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the business identification method of any one of claims 1-7.