Industrial chain-based evaluation method, electronic equipment and storage medium

Abstract

The invention provides an evaluation method based on an industrial chain, electronic equipment and a storage medium, and the method comprises the steps: determining a target total value corresponding to the industrial chain through feature data and keywords corresponding to a to-be-recognized enterprise, and evaluating whether the to-be-recognized enterprise can be associated with the industrial chain or not according to the target total value. And the accuracy of association between the to-be-identified enterprise and the industrial chain is improved.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to an evaluation method, electronic equipment and storage medium based on an industrial chain. Background Art

[0002] The industrial chain is a chain-like relationship formed between multiple industrial sectors based on factors such as technological and economic connections. It is used to describe the structure of a group of enterprises, that is, the relationship between different enterprises, for example, the upstream and downstream relationships between enterprises. Therefore, enterprises need to be associated with the corresponding nodes of the industrial chain.

[0003] In related technologies, the relationship between existing industrial chain nodes and enterprises is usually determined according to a single technology such as industry category, keyword matching, and prediction model. However, the accuracy of the relationship between enterprises and industrial chain nodes determined by a single technology is poor, which makes it impossible for upstream and downstream enterprises in the industrial chain to collaborate efficiently, reducing the operating efficiency of the entire industrial chain. Summary of the invention

[0004] The embodiments of the present application provide an evaluation method, electronic device and storage medium based on the industrial chain, which can solve the problem of poor accuracy of the association relationship between enterprises and industrial chain nodes.

[0005] In a first aspect, the embodiment of the present application provides an evaluation method based on the industrial chain, including:

[0006] Obtaining characteristic data corresponding to the enterprise to be identified;

[0007] Inputting the enterprise logo corresponding to the enterprise to be identified into a preset recognition model to obtain the keyword corresponding to the enterprise to be identified;

[0008] Calculate based on feature data and keywords to determine the target total value of the enterprise to be identified;

[0009] Based on the total target value, the enterprises to be identified are evaluated.

[0010] In a second aspect, an embodiment of the present application provides an evaluation device based on an industrial chain, including: a module for executing each step of the method described in the first aspect above.

[0011] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, wherein the processor is used to run a computer program stored in a memory to implement the method in the implementation manner of the first aspect.

[0012] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed, it is used to execute the method in the implementation manner of the first aspect.

[0013] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the method in the first aspect above.

[0014] The embodiment of the present application provides an evaluation method, electronic device and storage medium based on the industrial chain, which obtains the characteristic data corresponding to the enterprise to be identified; inputs the enterprise logo corresponding to the enterprise to be identified into a preset recognition model to obtain the keywords corresponding to the enterprise to be identified; calculates based on the characteristic data and keywords to determine the target total value of the enterprise to be identified; and evaluates the enterprise to be identified based on the target total value. The target total value corresponding to the industrial chain is determined through the characteristic data and keywords corresponding to the enterprise to be identified, and then based on the target total value, whether the enterprise to be identified can be associated with the industrial chain is evaluated, thereby improving the accuracy of the association between the enterprise to be identified and the industrial chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a schematic flow chart of an evaluation method based on an industrial chain provided in one embodiment of the present application;

[0017] Figure 2 It is a structural block diagram of an evaluation device based on the industrial chain provided in an embodiment of the present application;

[0018] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0020] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0021] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0023] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] The evaluation method based on the industrial chain provided by this application will be described below in conjunction with specific embodiments.

[0026] like Figure 1 As shown, Figure 1 The schematic flow chart of the evaluation method based on the industrial chain provided by the present application is shown. The evaluation method based on the industrial chain provided by the present application includes: Step 110 to Step 140, wherein:

[0027] Step 110, obtaining characteristic data corresponding to the enterprise to be identified.

[0028] In some embodiments, according to at least one pre-set characteristic dimension, dimensional data corresponding to each characteristic dimension can be obtained from the industrial chain data; for the dimensional data corresponding to each characteristic dimension, the characteristic data corresponding to the enterprise to be identified in the characteristic dimension is obtained.

[0029] Specifically, in the dimension of business characteristics, the business characteristics text information corresponding to the enterprise to be identified can be determined from the public data such as the enterprise profile, business scope (industrial and commercial data), and bidding information corresponding to the enterprise to be identified; in the dimension of technical characteristics, the technical field characteristics text information corresponding to the enterprise to be identified can be determined from the patent information, software information, press conferences, recruitment information, technical forums, etc. corresponding to the enterprise to be identified; in the dimension of product characteristics, the product characteristics text information corresponding to the enterprise to be identified can be determined through the enterprise profile (product introduction), bidding information, news and public opinion, etc. corresponding to the enterprise to be identified. As an example and not a limitation, the characteristic data corresponding to the enterprise to be identified include but are not limited to: the business characteristics text information corresponding to the enterprise to be identified, the technical field characteristics text information corresponding to the enterprise to be identified, and the product characteristics text information corresponding to the enterprise to be identified.

[0030] Step 120 , input the enterprise logo corresponding to the enterprise to be identified into a preset recognition model to obtain keywords corresponding to the enterprise to be identified.

[0031] In some embodiments, industry data may be acquired, and the acquired data may be trained to obtain a pre-set recognition model.

[0032] Exemplarily, according to the triggered operation, industry data is obtained. The industry data may include industry chain data and industry tags, the industry chain data includes: industry chain name, link name and node name, and the industry tag includes: tag code and at least one level industry tag.

[0033] In some embodiments, the industry chain data officially released by the government or authoritative industry research institutions can be crawled from the Internet through a web crawler program.

[0034] As an example but not limitation, the industrial chain data includes: industrial chain name, link name, node name, etc.

[0035] The embodiment of the present application takes the semiconductor and integrated circuit industry chain as an example to display the industry chain data, as shown in Table 1.

[0036] Table 1

[0037]

[0038] In some embodiments, industry labels may be constructed according to the National Economic Industry Classification (GB_T4754-2011).

[0039] As an example but not a limitation, the embodiment of the present application uses Table 2 as an example to display industry labels.

[0040] Table 2

[0041]

[0042]

[0043] In some embodiments, step 120 includes:

[0044] Step 1201, according to the enterprise identification corresponding to the enterprise to be identified, combined with the pre-acquired industrial chain data, determine the industry type corresponding to the enterprise to be identified.

[0045] In some embodiments, the industry type corresponding to the enterprise to be identified can also be obtained through the identification model. Exemplarily, the identification model includes: a first pre-training model, and the above step 1201 may include:

[0046] 1201A. Determine the name of the industrial chain corresponding to the enterprise to be identified based on the enterprise logo corresponding to the enterprise to be identified and in combination with the industrial chain data acquired in advance.

[0047] 1201B. Input the name of the industrial chain into the first pre-trained model, identify the name of the industrial chain through the first pre-trained model, and obtain the industry type corresponding to the enterprise to be identified.

[0048] In some embodiments, the industry chain name can be input into the first pre-trained model, and the first pre-trained model outputs the industry type to which the industry chain name belongs, thereby determining the industry type corresponding to the enterprise to be identified. The first pre-trained model is a model for determining the industry type corresponding to the industry chain. The first pre-trained model is a model in which the input is the industry chain name and the output is the industry type corresponding to the industry chain.

[0049] Exemplarily, the name of the industrial chain "semiconductors and integrated circuits" in step S101 is input into the first pre-trained model, and the first pre-trained model outputs "electronic information industry", thereby determining that the industry type corresponding to the semiconductor and integrated circuit industry chain is the electronic information industry.

[0050] In some embodiments, the industry type corresponding to the industrial chain is determined according to the industry characteristics in the industrial chain. For example, if the industry characteristics in the industrial chain are planting, breeding, etc., the industry type corresponding to the industrial chain may be agriculture; if the industry characteristics in the industrial chain are chip design, manufacturing, electronic equipment production, etc., the industry type corresponding to the industrial chain may be the electronic information industry, etc.

[0051] In some embodiments, the industry type of the enterprise to be identified is determined based on the industry information corresponding to the enterprise to be identified during its industrial and commercial registration.

[0052] It should be noted that after determining the industry type of the enterprise to be identified, the industrial chain nodes can be identified for the enterprises whose industry types are the same as the industry types corresponding to the industrial chain. By associating the industry types of the industrial chain and the enterprise to be identified, the time for the enterprise to be identified to associate with the industrial chain can be reduced, and the efficiency of the enterprise to be identified to associate with the industrial chain can be improved.

[0053] For example, there are a total of 100,000 enterprises to be identified, of which 10,000 are in the electronic information industry. Then, it is only necessary to identify the 10,000 enterprises to be identified in the electronic information industry with the nodes in the semiconductor and integrated circuit industry chain. By selecting enterprises to be identified whose industry types are the same as those corresponding to the industrial chain, the number of enterprises to be identified associated with the industrial chain is reduced, thereby reducing the time for the enterprises to be identified to be associated with the industrial chain and improving the efficiency of associating the enterprises to be identified with the industrial chain.

[0054] Step 1202: input the initial keywords corresponding to the industry type into the recognition model, expand the initial keywords through the recognition model, and obtain keywords corresponding to the enterprise to be identified.

[0055] In some embodiments, the recognition model includes: a second pre-trained model;

[0056] Step 1202 includes:

[0057] 1202A. Determine initial keywords based on industry type and industry chain data.

[0058] In 1202A, the initial keywords corresponding to the enterprise to be identified are determined based on the industry white paper of the industry type corresponding to the industrial chain, the industrial development plan, and the known listed company information at the industrial chain node.

[0059] In one possible implementation, the initial keywords corresponding to the enterprise to be identified can be determined based on at least one pre-set feature dimension. Among them, the at least one pre-set feature dimension can be determined based on the text information in professional data sheets such as authoritative research institutions, industry white papers, and research reports. It can be seen that the at least one pre-set feature dimension can change with the development of the industry. As an example and not a limitation, the at least one pre-set feature dimension may include at least one of business characteristics, technical characteristics, and product characteristics, wherein the business characteristics mainly involve the main business of the enterprise, the technical characteristics involve the technical information of the enterprise, and the product characteristics involve the products of the enterprise and the products corresponding to the industrial chain nodes.

[0060] Exemplarily, Table 3 shows the initial keywords corresponding to each feature dimension in at least one pre-set feature dimension of a logic chip design node in the semiconductor and integrated circuit industry chain.

[0061] Table 3

[0062]

[0063] 1202B. Input the initial keywords into the second pre-training model, expand the initial keywords through the second pre-training model, and obtain keywords corresponding to the enterprise to be identified.

[0064] In one possible implementation, the initial keywords corresponding to the enterprise to be identified are input into the second pre-trained model, and the second pre-trained model outputs the amplified keywords corresponding to the enterprise to be identified, thereby obtaining the amplified keywords corresponding to the enterprise to be identified; wherein the second pre-trained model can be an industry keyword large model, which is used to expand the keywords corresponding to each node of the industrial chain by recommending keywords related to the input words.

[0065] Exemplarily, at least one keyword of the keywords corresponding to the preset feature dimensions of the logic chip design: business features, technical features, and enterprise features can be input into the second pre-training model, and other keywords related to the at least one keyword can be output. For example, chip design is input, and keywords related to chip design such as chip design, integrated circuit design, logic design, physical design, timing design, power management, signal integrity, device modeling, circuit simulation, device layout, clock tree design, automatic place and route, power circuit design, low power design, and RF circuit design are output.

[0066] The industry big model can be determined according to the corpus or industry library corresponding to the industry type corresponding to the industrial chain. Specifically, according to the industry type corresponding to the industrial chain, the corpus or industry library corresponding to the industry type corresponding to the industrial chain is determined, and then the industry keyword big model is trained according to the corpus or industry library to obtain a usable industry keyword big model.

[0067] It should be noted that the corpus or industry library corresponding to different industry types can obtain corresponding words from industry associations, professional research institutions, professional forums, academic papers, development research reports and other channels, thereby forming a corpus or industry library corresponding to different industry types, including but not limited to business scope, business information, product category information, technical characteristics, and words corresponding to data information such as enterprise category information.

[0068] Step 130, performing calculations based on the characteristic data and keywords to determine the target total value of the enterprise to be identified.

[0069] In some embodiments, step 130 includes:

[0070] Step 1301: Obtain a matching score corresponding to each feature dimension according to feature data and keywords;

[0071] Specifically, step 1301 includes:

[0072] Step 1301A: For each feature dimension, obtain the number of texts corresponding to the feature data corresponding to the feature dimension in the industrial chain data.

[0073] Step 1301B: determine the number of matches corresponding to the keyword based on the feature data corresponding to the feature dimension, where the number of matches represents the number of words in the feature data that are identical to the keyword.

[0074] In some embodiments, a word in the characteristic data of the enterprise to be identified is matched one by one with each word in the keyword corresponding to the enterprise to be identified. If the two words are the same, the count is increased by 1 until the matching of each word in the characteristic data of the enterprise to be identified and each word in the keyword corresponding to the enterprise to be identified is completed, and the number of matches between the characteristic data corresponding to each characteristic dimension and the words that are the same as the keywords is determined.

[0075] Step 1301C: Calculate based on the number of texts and the number of matches to obtain the matching score corresponding to the feature dimension.

[0076] In some embodiments, for each dimension of the preset feature dimensions, the number of texts and the number of matches corresponding to each dimension are used to determine the matching score corresponding to each feature dimension.

[0077] Exemplarily, the matching score corresponding to the business characteristic dimension in the embodiment of the present application is calculated as follows: the number of text information in the business characteristic key information text set is represented by T1, the business characteristic key information is matched with the business information text of the enterprise, and the number of matched text information is represented by T2, and the final matching score corresponding to the business characteristic dimension is: T2*100 / T1. For example, there are 10 keywords of the business characteristic dimension at a certain industrial chain node, and the enterprise to be identified matches 5 keywords in the business characteristic dimension, then the matching score corresponding to the business characteristic dimension of the enterprise to be identified is: T2*100 / T1=5*100 / 10=50.

[0078] Step 1302: Calculate the target total value of the enterprise to be identified based on the matching score and weight corresponding to each feature dimension.

[0079] In one possible implementation, for each of the pre-set feature dimensions, the target score of each feature dimension is determined based on the matching score corresponding to each feature dimension and the weight corresponding to each feature dimension; for the target score of each feature dimension, the target total value of the enterprise to be identified in the current industrial chain is determined.

[0080] Exemplarily, in the preset feature dimensions, the score corresponding to each feature dimension is multiplied by the weight corresponding to each feature dimension to obtain the target score of each feature dimension; the target scores of each feature dimension are added in turn to obtain the target total value of the enterprise to be identified in the current industrial chain.

[0081] Specifically, taking the characteristic dimensions listed in the above embodiment as an example: the target total score = W1*the score corresponding to the operating characteristic dimension + W2*the score corresponding to the technical characteristic dimension + W3*the score corresponding to the product characteristic dimension, where W1 is the weight of the operating characteristic dimension, W2 is the weight of the technical characteristic dimension, and W3 is the weight corresponding to the product characteristic dimension.

[0082] Step 140, evaluating the enterprise to be identified according to the target total value.

[0083] In some embodiments, step 140 specifically includes:

[0084] Step 1401: Compare the target total value with a preset evaluation threshold to obtain a comparison result.

[0085] Step 1402: If the comparison result indicates that the target total value is greater than or equal to the evaluation threshold, the enterprise to be identified is prompted to pass the evaluation.

[0086] If the comparison result indicates that the total target value is less than the evaluation threshold, it is prompted that the enterprise to be identified has failed the evaluation.

[0087] Preferably, the evaluation standard is based on a percentage system, and the pre-set evaluation threshold can be 60 points. If the target total value is greater than or equal to 60 points, it indicates that the accuracy of the match between the enterprise to be identified and the industrial chain is high, then the enterprise to be identified has passed the evaluation and can be associated with the industrial chain; if the target total value is less than 60 points, it indicates that the accuracy of the match between the enterprise to be identified and the industrial chain is low, then the enterprise to be identified has not passed the evaluation, and there is no association between the enterprise to be identified and the industrial chain.

[0088] In some embodiments, step 140 may also include: adjusting the target total value according to the matching position between the keyword and the enterprise's information text to obtain an adjusted target total value, and evaluating the enterprise to be identified according to the adjusted target total value.

[0089] Furthermore, the embodiment of the present application can also optimize the matching results by adjusting the target total value, and can determine whether additional points are needed by the matching position of the keyword and the corporate logo (for example, the company name) of the company to be identified. For example, if the keyword of the node is "chip sales" and the name of the matching company is "XX Chip Sales Co., Ltd.", it can be considered that the position where the keyword "chip sales" appears is very critical, and 10 points are added on the basis of the target total value. If the total score exceeds 60 points at this time, the company to be identified has not passed the evaluation, and it can be determined that the company has an association relationship with the node.

[0090] The second is whether the keyword of the node appears in a complete form in the text information related to the enterprise name of the enterprise to be identified, such as "In recent years, the sales of a certain company's brand chips have gradually declined, and the domestic market share has continued to decline", among which "chip sales" is a continuous and complete form that can match the node keyword; if the enterprise name is "In recent years, the sales of a certain company's brand chips have increased year by year since their launch, and they are well received by the market," the "chips" and "sales" that appear in the text appear in an intermittent form, which cannot be called a complete form that matches the keyword. In this way, the complete and continuous form is counted as 1 keyword hit, and the intermittent form is counted as 0.8 keyword hits. It can be understood that the counting method of the keyword name hit is similar to the technical method of step 1301B, and the embodiments of the present application will not be repeated here.

[0091] The embodiment of the present application provides an evaluation method based on the industrial chain, which obtains the characteristic data corresponding to the enterprise to be identified; inputs the enterprise logo corresponding to the enterprise to be identified into a pre-set recognition model to obtain the keywords corresponding to the enterprise to be identified; calculates based on the characteristic data and keywords to determine the target total value of the enterprise to be identified; and evaluates the enterprise to be identified based on the target total value. The target total value corresponding to the industrial chain is determined through the characteristic data and keywords corresponding to the enterprise to be identified, and then based on the target total value, whether the enterprise to be identified can be associated with the industrial chain is evaluated, thereby improving the accuracy of the association between the enterprise to be identified and the industrial chain. Further, it makes it impossible for upstream and downstream enterprises in the industrial chain to collaborate efficiently, reducing the operating efficiency of the entire industrial chain.

[0092] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0093] Corresponding to the evaluation method based on the industrial chain in the above embodiment, Figure 2 A structural block diagram of an evaluation device based on an industrial chain provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0094] Reference Figure 2 , the evaluation device based on the industrial chain includes:

[0095] An acquisition module 210 is used to acquire characteristic data corresponding to the enterprise to be identified;

[0096] The identification module 220 is used to input the enterprise logo corresponding to the enterprise to be identified into a preset identification model to obtain the keyword corresponding to the enterprise to be identified;

[0097] A determination module 230 is used to perform calculations based on the characteristic data and keywords to determine the target total value of the enterprise to be identified;

[0098] The evaluation module 240 is used to evaluate the enterprise to be identified according to the target total value.

[0099] In some embodiments, the identification module 220 is further configured to:

[0100] According to the corporate logo corresponding to the enterprise to be identified and combined with the pre-acquired industrial chain data, the industry type corresponding to the enterprise to be identified is determined; the initial keywords corresponding to the industry type are input into the recognition model, and the initial keywords are expanded through the recognition model to obtain the keywords corresponding to the enterprise to be identified.

[0101] In some embodiments, the recognition model includes: a first pre-trained model; the recognition module 220 is also used to: determine the name of the industrial chain corresponding to the enterprise to be identified based on the enterprise logo corresponding to the enterprise to be identified, combined with the pre-acquired industrial chain data; input the industrial chain name into the first pre-trained model, and identify the industrial chain name through the first pre-trained model to obtain the industry type corresponding to the enterprise to be identified.

[0102] In some embodiments, the recognition model includes: a second pre-trained model; the recognition module 220 is further used to:

[0103] According to the industry type and combined with the industrial chain data, the initial keywords are determined; the initial keywords are input into the second pre-training model, and the initial keywords are expanded through the second pre-training model to obtain the keywords corresponding to the enterprises to be identified.

[0104] In some embodiments, the acquisition module 210 is further used to: acquire dimension data corresponding to each characteristic dimension from the industrial chain data according to at least one preset characteristic dimension;

[0105] For the dimensional data corresponding to each characteristic dimension, obtain the characteristic data corresponding to the enterprise to be identified in the characteristic dimension.

[0106] In some embodiments, the determination module 230 is also used to: obtain the matching score corresponding to each feature dimension based on the feature data and keywords; calculate according to the matching score and weight corresponding to each feature dimension to obtain the target total value of the enterprise to be identified.

[0107] In some embodiments, the determination module 230 is also used to: for each feature dimension, obtain the number of texts corresponding to the feature data corresponding to the feature dimension in the industrial chain data; determine the number of matches corresponding to the keyword based on the feature data corresponding to the feature dimension, and the number of matches represents the number of words in the feature data that are the same as the keyword; calculate based on the number of texts and the number of matches to obtain the matching score corresponding to the feature dimension.

[0108] In some embodiments, the evaluation module 240 is also used to: compare the target total value with a pre-set evaluation threshold to obtain a comparison result; if the comparison result indicates that the target total value is greater than or equal to the evaluation threshold, then the enterprise to be identified is prompted to have passed the evaluation; if the comparison result indicates that the target total value is less than the evaluation threshold, then the enterprise to be identified is prompted to have failed the evaluation.

[0109] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0110] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0111] The embodiment of the present application further provides an electronic device 300, which includes: at least one processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the at least one processor 301, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program 303. Alternatively, the processor 301 implements the functions of each module / unit in the above-mentioned device embodiments when executing the computer program 303, for example Figure 2 The functions of the acquisition module 210, the identification module 220, the determination module 230 and the evaluation module 240 are shown.

[0112] Exemplarily, the computer program 303 may be divided into one or more modules / units, one or more modules / units are stored in the memory 302, and are executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 303 in the electronic device 300.

[0113] Those skilled in the art will understand that Figure 3 It is only an example of the electronic device 300 and does not constitute a limitation of the electronic device 300. The electronic device 300 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 300 may also include input and output devices, network access devices, buses, etc.

[0114] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0115] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to an evaluation device / electronic device based on the industrial chain, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0117] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An evaluation method based on the industrial chain, characterized in that: The method comprises: Obtaining characteristic data corresponding to the enterprise to be identified; Inputting the enterprise logo corresponding to the enterprise to be identified into a preset recognition model to obtain a keyword corresponding to the enterprise to be identified; Calculate based on the characteristic data and the keywords to determine the target total value of the enterprise to be identified; The enterprise to be identified is evaluated according to the target total value.

2. The method according to claim 1, characterized in that The step of inputting the enterprise logo corresponding to the enterprise to be identified into a preset recognition model to obtain a keyword corresponding to the enterprise to be identified includes: Determine the industry type corresponding to the enterprise to be identified based on the enterprise identifier corresponding to the enterprise to be identified and in combination with the pre-acquired industrial chain data; The initial keywords corresponding to the industry type are input into the recognition model, and the initial keywords are expanded through the recognition model to obtain keywords corresponding to the enterprise to be recognized.

3. The method according to claim 2, characterized in that The recognition model includes: a first pre-training model; The step of determining the industry type corresponding to the enterprise to be identified based on the enterprise identifier corresponding to the enterprise to be identified and combining the pre-acquired industrial chain data includes: Determine the name of the industrial chain corresponding to the enterprise to be identified according to the enterprise identifier corresponding to the enterprise to be identified and in combination with the pre-acquired industrial chain data; The industrial chain name is input into the first pre-trained model, and the industrial chain name is identified by the first pre-trained model to obtain the industry type corresponding to the enterprise to be identified.

4. The method according to claim 2, characterized in that The recognition model includes: a second pre-training model; The step of inputting the initial keywords corresponding to the industry type into the recognition model and expanding the initial keywords through the recognition model to obtain keywords corresponding to the enterprise to be recognized includes: According to the industry type and in combination with the industry chain data, initial keywords are determined; The initial keywords are input into the second pre-training model, and the initial keywords are expanded by the second pre-training model to obtain keywords corresponding to the enterprise to be identified.

5. The method according to claim 1, characterized in that The step of obtaining characteristic data corresponding to the enterprise to be identified includes: According to at least one preset characteristic dimension, dimensional data corresponding to each characteristic dimension is acquired from the industrial chain data; For each dimension data corresponding to the characteristic dimension, the characteristic data corresponding to the enterprise to be identified in the characteristic dimension is obtained.

6. The method according to claim 1, characterized in that The step of calculating according to the characteristic data and the keywords to determine the target total value of the enterprise to be identified includes: According to the feature data and the keyword, obtain a matching score corresponding to each feature dimension; The target total value of the enterprise to be identified is obtained by calculating according to the matching score and weight corresponding to each of the feature dimensions.

7. The method according to claim 6, characterized in that The obtaining, according to the feature data and the keyword, a matching score corresponding to each feature dimension includes: For each of the feature dimensions, obtaining the number of texts corresponding to the feature data corresponding to the feature dimension in the industrial chain data; Determine, according to the feature data corresponding to the feature dimension, a matching number corresponding to the keyword, wherein the matching number represents the number of words in the feature data that are identical to the keyword; A calculation is performed based on the number of texts and the number of matches to obtain a matching score corresponding to the feature dimension.

8. The method according to any one of claims 1 to 7, characterized in that: The step of evaluating the enterprise to be identified according to the target total value includes: Comparing the target total value with a preset evaluation threshold to obtain a comparison result; If the comparison result indicates that the target total value is greater than or equal to the evaluation threshold, prompting the enterprise to be identified to pass the evaluation; If the comparison result indicates that the target total value is less than the evaluation threshold, it is prompted that the enterprise to be identified has failed the evaluation.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the method according to any one of claims 1 to 8 when calling the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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