Data processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring to-be-identified information of a to-be-discriminated enterprise, and determining an enterprise type corresponding to the to-be-discriminated enterprise according to the to-be-identified information; if the to-be-discriminated enterprise is an industrial enterprise type, determining a target enterprise scale determination model corresponding to the to-be-discriminated enterprise based on a preset enterprise list; and determining an enterprise scale category to which the to-be-discriminated enterprise belongs based on the target enterprise scale determination model. The method solves the problems of time consumption, labor consumption and poor accuracy in a manual judgment mode when the enterprise scale category of the enterprise is judged in the prior art, and realizes the reduction of the influence of subjective factors by automatically determining the enterprise scale category of the to-be-judged enterprise according to the enterprise information of the to-be-judged enterprise, thereby improving the judgment accuracy of the enterprise scale category of the to-be-judged enterprise. And the judgment efficiency and accuracy of the enterprise scale category are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of data processing technology, and in particular, to a data processing method, device, electronic device and storage medium. Background Art

[0002] Accurately determining the scale of an enterprise organization is conducive to making adaptive development plans based on the scale of the enterprise organization, thereby providing a guarantee for improving the competitiveness and sustainable development capabilities of the enterprise organization.

[0003] At present, for the corporate organizations of temporary regulatory enterprises, when judging the corporate scale of the corporate organizations, it is usually necessary to merge the corporate related information of corporate organizations from multiple channels and make judgments through manual labeling. However, such a judgment method is not only labor-intensive and inefficient, but also has a certain degree of subjectivity. Therefore, a more objective, accurate and rapid judgment method is needed for judging temporary regulatory enterprises.

[0004] In order to solve the above problems, it is necessary to improve the way of determining the enterprise scale of enterprises. Summary of the invention

[0005] The embodiments of the present invention provide a data processing method, device, electronic device and storage medium to solve the problems in the prior art of manually determining the enterprise scale of an enterprise, which are subject to certain subjective factors, low processing efficiency and low accuracy.

[0006] In a first aspect, an embodiment of the present invention provides a data processing method, including:

[0007] Acquire information to be identified of the enterprise to be identified, and determine the enterprise type corresponding to the enterprise to be identified according to the information to be identified; wherein the enterprise type includes an industrial enterprise type or other enterprise types except the industrial enterprise type;

[0008] If the enterprise to be identified is the type of industrial enterprise, a target enterprise scale determination model corresponding to the enterprise to be identified is determined based on a preset enterprise list; wherein the preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a potential-to-regulate enterprise determination model;

[0009] Based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined.

[0010] In a second aspect, an embodiment of the present invention further provides a data processing device, including:

[0011] An enterprise type determination module, used to obtain information to be identified of an enterprise to be identified, and determine the enterprise type corresponding to the enterprise to be identified according to the information to be identified; wherein the enterprise type includes an industrial enterprise type or other enterprise types except the industrial enterprise type;

[0012] A model determination module, for determining a target enterprise scale determination model corresponding to the enterprise to be determined based on a preset enterprise list if the enterprise to be determined is the industrial enterprise type; wherein the preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-deregulation enterprise determination model or a potential-to-regulate enterprise determination model;

[0013] The enterprise size category determination module is used to determine the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0015] one or more processors;

[0016] A storage device for storing one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method described in any embodiment of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method described in any embodiment of the present invention.

[0019] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the data processing method as described in any embodiment of the present invention.

[0020] The embodiment of the present invention provides a data processing method, device, electronic device and storage medium, which obtains the information to be identified of the enterprise to be identified, and determines the enterprise type corresponding to the enterprise to be identified according to the information to be identified; if the enterprise to be identified is an industrial enterprise type, then the target enterprise scale determination model corresponding to the enterprise to be identified is determined based on the preset enterprise list; wherein the preset enterprise list is a preset list of above-standard enterprises or a preset list of below-standard enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a model for enterprises expected to enter the regulations; based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined. In this technical solution, by constructing a neural network model for determining the scale of an enterprise, and when the enterprise to be identified is an industrial enterprise type, the target enterprise scale determination model corresponding to the enterprise to be identified is determined according to the enterprise-related data of the enterprise to be identified, thereby automatically determining the scale category of the enterprise to be identified based on the target enterprise scale determination model. The invention solves the problem that the enterprise size category of an enterprise in the prior art is time-consuming, labor-intensive and inaccurate based on the manual determination method. By automatically determining the enterprise size category of the enterprise to be determined based on the enterprise information of the enterprise to be determined, the influence of subjective factors is reduced and the efficiency and accuracy of determining the enterprise size category are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts. In the drawings:

[0022] Figure 1 is a flow chart of a data processing method provided in an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a data processing method provided in an embodiment of the present invention;

[0024] Figure 3 is a structural schematic diagram of a data processing device provided in an embodiment of the present invention;

[0025] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0028] Among them, the acquisition, storage, use and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. It should be noted that in the embodiments of this application, some existing solutions in the industry such as certain software, components or models may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a data processing method provided in an embodiment of the present invention. This embodiment can be applied to determine the enterprise type according to the relevant information of the enterprise, and then perform targeted automatic analysis on the enterprise-related information based on the pre-built enterprise scale determination model corresponding to the enterprise type, so as to accurately determine the enterprise scale corresponding to the enterprise. The method of this embodiment can be executed by a data processing device, which can be implemented in hardware and / or software. The method specifically includes the following steps:

[0032] S110, obtaining information to be identified of the enterprise to be identified, and determining the enterprise type corresponding to the enterprise to be identified according to the information to be identified.

[0033] In this technical solution, the enterprise to be identified refers to an industrial enterprise organization that needs to be identified in terms of enterprise scale category. The information to be identified refers to information containing the enterprise attributes of the enterprise to be identified, such as the industry code and enterprise type code to which the enterprise to be identified belongs. Enterprise types include industrial enterprise types or other enterprise types other than industrial enterprise types.

[0034] In practical applications, the development of corporate organizations is closely related to their scale. For the enterprises to be judged, accurately positioning the scale of the enterprises to be judged is of great significance to the development strategy, structural adjustment, promotion of transformation and increase of stamina of the corporate organizations.

[0035] Based on this, the present technical solution provides a method for determining the enterprise scale of an enterprise to be determined. Optionally, before obtaining the information to be identified corresponding to the enterprise to be determined, it also includes: obtaining historical enterprise association information of at least one identified enterprise corresponding to a preset enterprise scale, and dividing each group of historical enterprise association information to obtain at least one group of data sets to be used; training a pre-constructed initial enterprise scale determination model based on at least one group of data sets to be used, and determining target model parameters corresponding to the initial enterprise scale determination model according to a loss function in the training process; adjusting the initial model parameters of the initial enterprise scale determination model based on the target model parameters to obtain a target enterprise scale determination model, so as to determine the enterprise scale category corresponding to the enterprise to be determined based on the target enterprise scale determination model.

[0036] Among them, the preset enterprise scale is the preset above-scale enterprise or the preset below-scale enterprise, and the data set to be used includes training set, validation set and test set. The so-called preset above-scale enterprise refers to the enterprise that meets the current enterprise scale classification standard. For example, the enterprise whose main business profit data of the enterprise organization is greater than 20 million yuan is an above-scale enterprise. The so-called preset below-scale enterprise refers to the enterprise that does not meet the current enterprise scale classification standard. For example, the enterprise whose main business profit data of the enterprise organization is less than 20 million yuan is a below-scale enterprise.

[0037] Among them, the identified enterprise can be understood as an enterprise organization that has been determined by the enterprise scale. Historical enterprise association information refers to enterprise information related to the identified enterprise, such as enterprise association information including basic enterprise information data of the identified enterprise, enterprise electricity consumption data over the years, enterprise financial and tax data over the years, enterprise registration information, monthly operating data, monthly gain data and enterprise type information, etc. The data set to be used includes a training set, a validation set and a test set. The initial enterprise scale determination model refers to a model used to determine the enterprise scale of the enterprise based on the enterprise association information, and the initial enterprise scale determination model refers to a neural network model that has not been trained or parameter-adjusted, such as the initial enterprise scale determination model can be a BP neural network. The target enterprise scale determination model refers to a model training of the initial enterprise scale determination model based on at least one set of data sets to be used, and the model parameters are adjusted to the optimal model. The enterprise scale category is a business scale category with a risk of deregulation or a business scale category that is expected to be regulated. In this technical solution, the target enterprise scale determination model is a pre-built deregulation risk enterprise determination model or a regulated enterprise determination model.

[0038] In order to more clearly introduce the present technical solution, the construction process of the target enterprise scale determination model is introduced in detail here.

[0039] In a specific example, historical enterprise association information of at least one identified enterprise is obtained, and the enterprise scale corresponding to each identified enterprise is marked. For example, the enterprise scale of the identified enterprise is a risk-of-deregulation enterprise and a promising-to-regulate enterprise. Among them, based on the data of the list of newly regulated enterprises in previous years in the historical enterprise association information, it can be determined whether the identified enterprise is a promising-to-regulate enterprise, and based on the data of the list of deregulated enterprises in previous years in the historical enterprise association information, it can be determined whether the identified enterprise is a risk-of-deregulation enterprise.

[0040] On this basis, after obtaining the historical enterprise association information of at least one identified enterprise, the historical enterprise association information of each identified enterprise is randomly divided for data normalization to obtain the corresponding data set to be used. Specifically, 60% of the historical enterprise association information is used as the training set, 20% of the historical enterprise association information is used as the validation set, and the remaining 20% ​​of the historical enterprise association information is used as the test set. On this basis, taking the initial enterprise scale determination model as the BP neural network model as an example, the model structure parameters and hyperparameters of the BP neural network model are set, the number of neurons in the input layer is 30, the activation function between the input layer and the hidden layer is the relu function, the number of hidden neurons is 20, the activation function between the hidden layer and the output layer is set to the softmax function, and the number of neurons in the output layer is 1; the loss function is set to the CrossEntropyLoss function; the optimization algorithm is set to the Adam algorithm, and the learning rate is set to 0.02; the number of training iterations is set to 2000 times.

[0041] Inputting at least one set of data sets to be used into the initial enterprise scale determination model to adjust the initial model parameters of the initial enterprise scale determination model to obtain a target enterprise scale determination model. The target enterprise scale determination model may be a model for determining enterprises at risk of deregulation or a model for determining enterprises that are expected to enter regulation.

[0042] S120. If the enterprise to be identified is an industrial enterprise type, a target enterprise scale determination model corresponding to the enterprise to be identified is determined based on a preset enterprise list.

[0043] The preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises. The preset list of above-scale enterprises includes at least one enterprise information that has been determined as an above-scale enterprise, and the preset list of below-scale enterprises includes at least one enterprise information that has been determined as a below-scale enterprise. It should be noted that the enterprise information in the preset enterprise list needs to be updated accordingly according to the changes in the current enterprise scale type.

[0044] In practical applications, when the enterprise to be identified is an industrial enterprise type, it is usually necessary to further determine the enterprise size category of the enterprise to be identified in order to determine whether the enterprise to be identified has the risk of being deregulated or is likely to become an enterprise that is eligible for regulation.

[0045] Based on this, in the present technical solution, the information to be identified is set as the industry code to be identified and / or the enterprise type code to be identified of the enterprise to be identified, and the enterprise type corresponding to the enterprise to be identified is determined according to the information to be identified, including: if the information to be identified is the industry code to be identified, then when the industry code to be identified is greater than the first preset industry code and less than the second preset industry code, the enterprise type of the enterprise to be identified is determined to be an industrial enterprise type; and / or if the information to be identified is the enterprise type code to be identified, then when the enterprise type code to be identified is greater than the first preset enterprise type code and less than the second preset enterprise type code, the enterprise type of the enterprise to be identified is determined to be an industrial enterprise type.

[0046] For example, the first preset industry code is defined as "0600", the second preset industry code is defined as "4700", the first preset enterprise code is defined as "100", and the second preset enterprise code is defined as "400". After obtaining the information to be identified of the enterprise to be identified, the enterprise type corresponding to the enterprise to be identified is determined according to the industry code and enterprise type code corresponding to the enterprise to be identified.

[0047] Specifically, if the industry code of the enterprise to be identified is greater than "0600" and less than "4700", the enterprise to be identified is determined to be an industrial enterprise type. If the enterprise type code of the enterprise to be identified is greater than "100" and less than "400", the enterprise type of the enterprise to be identified is determined to be an industrial enterprise type.

[0048] S130. Determine the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model.

[0049] Specifically, the enterprise-related information of the enterprise to be identified is input into the target enterprise scale determination model, so as to determine the enterprise scale of the enterprise to be identified based on the target enterprise scale determination model, and determine whether the enterprise to be identified is an enterprise at risk of deregulation or an enterprise that is expected to be regulated.

[0050] The embodiment of the present invention provides a data processing method, device, electronic device and storage medium, which obtains the information to be identified of the enterprise to be identified, and determines the enterprise type corresponding to the enterprise to be identified according to the information to be identified; if the enterprise to be identified is an industrial enterprise type, then the target enterprise scale determination model corresponding to the enterprise to be identified is determined based on the preset enterprise list; wherein the preset enterprise list is a preset list of above-standard enterprises or a preset list of below-standard enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a model for enterprises expected to enter the regulations; based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined. In this technical solution, by constructing a neural network model for determining the scale of an enterprise, and when the enterprise to be identified is an industrial enterprise type, the target enterprise scale determination model corresponding to the enterprise to be identified is determined according to the enterprise-related data of the enterprise to be identified, thereby automatically determining the scale category of the enterprise to be identified based on the target enterprise scale determination model. The invention solves the problem that the enterprise size category of an enterprise in the prior art is time-consuming, labor-intensive and inaccurate based on the manual determination method. By automatically determining the enterprise size category of the enterprise to be determined based on the enterprise information of the enterprise to be determined, the influence of subjective factors is reduced and the efficiency and accuracy of determining the enterprise size category are improved.

[0051] Embodiment 2

[0052] Figure 2 Flow chart of a data processing method provided in an embodiment of the present invention. The embodiment of the present invention further optimizes the above embodiment on the basis of the above embodiment, and the embodiment of the present invention can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the data processing method provided in the embodiment of the present invention may include the following steps:

[0053] S210: When receiving an enterprise size category identification request, receiving information to be identified uploaded by the enterprise to be identified.

[0054] Among them, the enterprise size category identification request can be understood as request information for determining the enterprise size category of the enterprise to be identified.

[0055] In actual applications, if it is necessary to determine the enterprise size category of the enterprise to be determined, an enterprise size category identification request can be sent through the enterprise size determination platform to obtain the information to be identified of the enterprise to be determined after parsing the enterprise size category identification request, and then determine the enterprise size category of the enterprise to be determined based on the information to be identified.

[0056] Alternatively, when receiving an enterprise size category identification request, the information to be identified corresponding to the enterprise to be identified can be determined from a pre-set information repository according to the enterprise identification information carried in the enterprise size category identification request. The information repository refers to a database for storing enterprise-related information and information to be identified uploaded by at least one enterprise.

[0057] S220. If the enterprise to be identified is an industrial enterprise type, a target enterprise scale determination model corresponding to the enterprise to be identified is determined based on a preset enterprise list.

[0058] In actual applications, the target enterprise size determination model corresponding to the enterprise to be identified is determined based on the preset enterprise list, including: obtaining the current latest preset enterprise list based on the authorized data acquisition channel; if the preset above-scale enterprise list includes the enterprise to be identified, the enterprise to be identified is determined to be an above-scale enterprise, and the target enterprise size determination model corresponding to the enterprise to be identified is determined to be a deregulation risk enterprise determination model; if the preset below-scale enterprise list includes the enterprise to be identified, the enterprise to be identified is determined to be a below-scale enterprise, and the target enterprise size determination model corresponding to the enterprise to be identified is determined to be a potential entry-level enterprise determination model.

[0059] In this technical solution, the target enterprise scale determination model includes a model for determining enterprises at risk of deregulation or a model for determining enterprises that are expected to be regulated.

[0060] If the enterprise to be identified is an above-scale enterprise, the enterprise determination model for enterprises at risk of deregulation is used as the target enterprise scale determination model corresponding to the enterprise to be identified; if the enterprise to be identified is an below-scale enterprise, the enterprise determination model for enterprises that are expected to enter the scale is used as the target enterprise scale determination model corresponding to the enterprise to be identified. After determining the target enterprise scale determination model corresponding to the enterprise to be identified, the enterprise-related information of the enterprise to be identified is input into the target enterprise scale determination model to determine the enterprise scale category corresponding to the enterprise to be identified.

[0061] S230: Determine the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model.

[0062] Specifically, based on the target enterprise size determination model, the enterprise size category to which the enterprise to be identified belongs is determined, including: performing data analysis on the enterprise-related data of the enterprise to be identified based on the target enterprise size determination model to determine the enterprise size type corresponding to the enterprise to be identified.

[0063] Among them, enterprise-related data includes enterprise operating data and / or enterprise scale change data.

[0064] Exemplarily, if the enterprise to be identified is an enterprise above designated size, a target enterprise scale determination model corresponding to the enterprise to be identified is further determined based on at least one enterprise-related data. For example, if the operating data of the enterprise to be identified in the previous year is less than the preset maximum operating data, or the enterprise to be identified is a newly added enterprise above designated size, the enterprise determination model for deregulation risk is used as the target enterprise scale determination model for the enterprise to be identified.

[0065] Exemplarily, if the enterprise to be identified is an enterprise below the standard, the target enterprise scale determination model corresponding to the enterprise to be identified is further determined based on at least one enterprise-related data. For example, if the operating data of the enterprise to be identified in the previous year is greater than the preset minimum operating data, or the enterprise to be identified is recently changed to an enterprise below the standard, the enterprise determination model that is expected to enter the standard will be used as the target enterprise scale determination model for the enterprise to be identified.

[0066] Furthermore, after the enterprise-related data of the enterprise to be identified is input into the corresponding target enterprise determination model, the enterprise scale type corresponding to the enterprise to be identified is automatically determined based on the target enterprise determination model.

[0067] In addition, when the enterprise to be identified is an enterprise with a risk of deregulation or an enterprise under regulation, the enterprise development strategy of the enterprise to be identified is determined based on the enterprise-related information of the enterprise to be identified, and a strategy recommendation report is generated.

[0068] In practical applications, enterprise scale has very important guiding significance for the development of enterprises. If the enterprise to be identified is a risky enterprise to be deregulated or an enterprise below the regulations, it indicates that there are certain deficiencies in determining the enterprise to be identified based on the enterprise-related information of the enterprise to be identified. The enterprise-related information of the enterprise to be identified can be compared one by one, and corresponding development suggestions can be made for the enterprise-related information that does not meet the requirements of above-scale enterprises. Based on at least one development suggestion obtained, an enterprise development strategy recommendation report corresponding to the enterprise to be identified is generated, so as to provide better and effective suggestions for the future development of the enterprise to be identified.

[0069] The embodiment of the present invention provides a data processing method, device, electronic device and storage medium, which obtains the information to be identified of the enterprise to be identified, and determines the enterprise type corresponding to the enterprise to be identified according to the information to be identified; if the enterprise to be identified is an industrial enterprise type, then the target enterprise scale determination model corresponding to the enterprise to be identified is determined based on the preset enterprise list; wherein the preset enterprise list is a preset list of above-standard enterprises or a preset list of below-standard enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a model for enterprises expected to enter the regulations; based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined. In this technical solution, by constructing a neural network model for determining the scale of an enterprise, and when the enterprise to be identified is an industrial enterprise type, the target enterprise scale determination model corresponding to the enterprise to be identified is determined according to the enterprise-related data of the enterprise to be identified, thereby automatically determining the scale category of the enterprise to be identified based on the target enterprise scale determination model. The invention solves the problem that the enterprise size category of an enterprise in the prior art is time-consuming, labor-intensive and inaccurate based on the manual determination method. By automatically determining the enterprise size category of the enterprise to be determined based on the enterprise information of the enterprise to be determined, the influence of subjective factors is reduced and the efficiency and accuracy of determining the enterprise size category are improved.

[0070] Embodiment 3

[0071] Figure 3 1 is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. The device can be implemented by software and / or hardware and is generally integrated on any electronic device with network communication function, including but not limited to: servers, computers, personal digital assistants and other devices. Figure 3 As shown, the data processing device provided in this embodiment may include: an enterprise type determination module 310, a model determination module 320 and an enterprise scale category determination module 330.

[0072] The enterprise type determination module 310 is used to obtain the information to be identified of the enterprise to be identified, and determine the enterprise type corresponding to the enterprise to be identified according to the information to be identified; wherein the enterprise type includes an industrial enterprise type or other enterprise types except the industrial enterprise type;

[0073] The model determination module 320 is used to determine the target enterprise scale determination model corresponding to the enterprise to be determined based on the preset enterprise list if the enterprise to be determined is an industrial enterprise type; wherein the preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a potential-to-regulate enterprise determination model;

[0074] The enterprise size category determination module 330 is used to determine the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model.

[0075] The embodiment of the present invention provides a data processing method, device, electronic device and storage medium, which obtains the information to be identified of the enterprise to be identified, and determines the enterprise type corresponding to the enterprise to be identified according to the information to be identified; if the enterprise to be identified is an industrial enterprise type, then the target enterprise scale determination model corresponding to the enterprise to be identified is determined based on the preset enterprise list; wherein the preset enterprise list is a preset list of above-standard enterprises or a preset list of below-standard enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a model for enterprises expected to enter the regulations; based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined. In this technical solution, by constructing a neural network model for determining the scale of an enterprise, and when the enterprise to be identified is an industrial enterprise type, the target enterprise scale determination model corresponding to the enterprise to be identified is determined according to the enterprise-related data of the enterprise to be identified, thereby automatically determining the scale category of the enterprise to be identified based on the target enterprise scale determination model. The invention solves the problem that the enterprise size category of an enterprise in the prior art is time-consuming, labor-intensive and inaccurate based on the manual determination method. By automatically determining the enterprise size category of the enterprise to be determined based on the enterprise information of the enterprise to be determined, the influence of subjective factors is reduced and the efficiency and accuracy of determining the enterprise size category are improved.

[0076] On the basis of the above embodiment, optionally, the data processing device further includes: a data set determination module, which is used to obtain historical enterprise association information of at least one identified enterprise corresponding to a preset enterprise scale, and divide each group of historical enterprise association information into data to obtain at least one group of data sets to be used; wherein the preset enterprise scale is an enterprise above the preset scale or an enterprise below the preset scale, and the data sets to be used include a training set, a verification set and a test set;

[0077] A parameter determination module, used to train a pre-built initial enterprise scale determination model based on at least one set of data sets to be used, and determine target model parameters corresponding to the initial enterprise scale determination model according to a loss function in the training process;

[0078] The enterprise scale category determination module is used to adjust the initial model parameters of the initial enterprise scale determination model based on the target model parameters to obtain the target enterprise scale determination model, so as to determine the enterprise scale category corresponding to the enterprise to be identified based on the target enterprise scale determination model.

[0079] On the basis of the above embodiment, optionally, the enterprise type determination module includes: a first information receiving unit, configured to receive information to be identified uploaded by the enterprise to be identified upon receiving an enterprise scale category identification request; or

[0080] The second information receiving unit is used to determine the information to be identified corresponding to the enterprise to be identified from a preset information storage repository according to the enterprise identification information carried in the enterprise size category identification request when receiving the enterprise size category identification request.

[0081] On the basis of the above embodiment, optionally, the model determination module includes: a first enterprise type determination unit, which is used to determine that the enterprise type of the enterprise to be identified is an industrial enterprise type if the information to be identified is an industry code to be identified and the industry code to be identified is greater than the first preset industry code and less than the second preset industry code; and / or

[0082] The second enterprise type determination unit is used to determine that the enterprise type of the enterprise to be identified is an industrial enterprise type if the information to be identified is the enterprise type code to be identified and the enterprise type code to be identified is greater than the first preset enterprise type code and less than the second preset enterprise type code.

[0083] Based on the above embodiment, optionally, the model determination module includes: a list acquisition unit, used to acquire the latest preset enterprise list based on the authorized data acquisition channel;

[0084] A unit for determining a model for determining a risky enterprise to be deregulated, for determining that the enterprise to be determined is an enterprise above the designated size if the preset list of enterprises above the designated size includes the enterprise to be determined, and determining that the target enterprise scale determination model corresponding to the enterprise to be determined is a model for determining a risky enterprise to be deregulated;

[0085] The unit for determining the model for enterprises that are likely to comply with regulations is used to determine that the enterprise to be identified is an enterprise that is not subject to regulations if the preset list of enterprises that are not subject to regulations includes the enterprise to be identified, and to determine that the target enterprise size determination model corresponding to the enterprise to be identified is the model for determining enterprises that are likely to comply with regulations.

[0086] On the basis of the above embodiment, optionally, an enterprise scale category determination unit is used to perform data analysis on the enterprise-related data of the enterprise to be identified based on the target enterprise scale determination model to determine the enterprise type corresponding to the enterprise to be identified; wherein the enterprise-related data includes enterprise operating data and / or enterprise scale change data.

[0087] The data processing device provided in the embodiment of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functions and beneficial effects of executing the data processing method. For detailed process, please refer to the relevant operations of the data processing method in the above embodiment.

[0088] Embodiment 4

[0089] Figure 41 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0090] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0091] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a data processing method.

[0093] In some embodiments, the data processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method in any other appropriate manner (e.g., by means of firmware).

[0094] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0096] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein may 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 the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

[0098] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0099] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0100] Embodiment 5

[0101] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the data processing method provided in any embodiment of the present application.

[0102] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0103] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0104] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire information to be identified of the enterprise to be identified, and determine the enterprise type corresponding to the enterprise to be identified according to the information to be identified; wherein the enterprise type includes an industrial enterprise type or other enterprise types except the industrial enterprise type; If the enterprise to be identified is the type of industrial enterprise, a target enterprise scale determination model corresponding to the enterprise to be identified is determined based on a preset enterprise list; wherein the preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-delisting enterprise determination model or a potential-to-regulate enterprise determination model; Based on the target enterprise scale determination model, the enterprise scale category to which the enterprise to be identified belongs is determined.

2. The method according to claim 1, characterized in that Before obtaining the information to be identified corresponding to the enterprise to be identified, the method further includes: Acquire historical enterprise association information of at least one identified enterprise corresponding to a preset enterprise scale, and divide each group of historical enterprise association information into data to obtain at least one group of data sets to be used; wherein the preset enterprise scale is an enterprise above the preset scale or an enterprise below the preset scale, and the data sets to be used include a training set, a validation set, and a test set; Training a pre-built initial enterprise scale determination model based on at least one set of the data sets to be used, and determining target model parameters corresponding to the initial enterprise scale determination model according to a loss function in the training process; The initial model parameters of the initial enterprise scale determination model are adjusted based on the target model parameters to obtain the target enterprise scale determination model, so as to determine the enterprise scale category corresponding to the enterprise to be identified based on the target enterprise scale determination model.

3. The method according to claim 1, characterized in that The obtaining of the information to be identified corresponding to the enterprise to be identified includes: Upon receiving a request for enterprise size category identification, receiving information to be identified uploaded by the enterprise to be identified; or When the enterprise size category identification request is received, the information to be identified corresponding to the enterprise to be identified is determined from a preset information storage repository according to the enterprise identification information carried in the enterprise size category identification request.

4. The method according to claim 1, characterized in that: The information to be identified is the industry code to be identified and / or the enterprise type code to be identified of the enterprise to be identified, and determining the enterprise type corresponding to the enterprise to be identified according to the information to be identified includes: If the information to be identified is the industry code to be identified, then when the industry code to be identified is greater than the first preset industry code and less than the second preset industry code, the enterprise type of the enterprise to be identified is determined to be the industrial enterprise type; and / or If the information to be identified is the enterprise type code to be identified, then when the enterprise type code to be identified is greater than the first preset enterprise type code and less than the second preset enterprise type code, it is determined that the enterprise type of the enterprise to be identified is the industrial enterprise type.

5. The method according to claim 1, characterized in that The determining model of the target enterprise scale corresponding to the enterprise to be identified based on the preset enterprise list includes: Based on the authorized data acquisition channels, obtain the latest list of preset companies; If the enterprise to be identified is included in the preset list of enterprises above designated size, the enterprise to be identified is determined to be an enterprise above designated size, and the target enterprise scale determination model corresponding to the enterprise to be identified is determined to be the risk-of-delisting enterprise determination model; If the enterprise to be identified is included in the preset list of regulated enterprises, the enterprise to be identified is determined to be an regulated enterprise, and the target enterprise scale determination model corresponding to the enterprise to be identified is determined to be the model for determining the enterprise that is expected to be regulated.

6. The method according to claim 1, characterized in that The step of determining the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model includes: Based on the target enterprise scale determination model, data analysis is performed on the enterprise-related data of the enterprise to be identified to determine the enterprise scale type corresponding to the enterprise to be identified; wherein the enterprise-related data includes enterprise operating data and / or enterprise scale change data.

7. A data processing device, characterized in that: The device comprises: An enterprise type determination module, used to obtain information to be identified of an enterprise to be identified, and determine the enterprise type corresponding to the enterprise to be identified according to the information to be identified; wherein the enterprise type includes an industrial enterprise type or other enterprise types except the industrial enterprise type; A model determination module, for determining a target enterprise scale determination model corresponding to the enterprise to be determined based on a preset enterprise list if the enterprise to be determined is the industrial enterprise type; wherein the preset enterprise list is a preset list of above-scale enterprises or a preset list of below-scale enterprises, and the target enterprise scale determination model is a pre-constructed risk-of-deregulation enterprise determination model or a potential-to-regulate enterprise determination model; The enterprise size category determination module is used to determine the enterprise size category to which the enterprise to be identified belongs based on the target enterprise size determination model.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method described in any one of claims 1 to 6.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the data processing method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the data processing method according to any one of claims 1 to 6.

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