Information processing method and device based on deep learning model, medium and product

Abstract

The embodiment of the invention discloses an information processing method and device based on a deep learning model, a medium and a product, and the method comprises the steps: obtaining configuration information corresponding to a system page when a configuration instruction for the system page of a target business system is received; determining to-be-processed information in the system page based on an enhancement option in the configuration information; and when a processing instruction for the to-be-processed information is received, processing the to-be-processed information by using the target deep learning model corresponding to the configuration information to obtain a processing result corresponding to the to-be-processed information. Therefore, under the condition that existing codes of the target service system are not transformed, the target service system can be enhanced through the deep learning model, the service capability of the target service system is improved, the technical difficulty of combination of the target deep learning model and the target service system is reduced, the development period is shortened, and the development efficiency is improved. And meanwhile, the target service system does not need to be shut down, and normal operation of the target service system is not affected.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence technology and system enhancement technology, and in particular to an information processing method, device, medium and product based on a deep learning model. Background Art

[0002] The enterprise business system is an important part of enterprise operations. The enterprise business system can be applied to enterprise businesses such as human resources management, financial / accounting management, and customer relationship management. In recent years, with the continuous development of artificial intelligence (AI) technology, the combination of AI technology and enterprise business systems can enhance the ability of enterprise business systems to handle business.

[0003] In the existing technology, the way to combine AI technology with enterprise business systems is usually to integrate deep learning models into enterprise business systems. However, since most enterprise business systems are already online, when integrating deep learning models into enterprise business systems, it is often necessary to deeply modify the existing codes in the enterprise business systems. This makes the development of deep learning models into enterprise business systems difficult and has a long development cycle. It also requires the enterprise business system to be shut down for maintenance, affecting the normal operation of the enterprise business. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present disclosure provide an information processing method, device, medium and product based on a deep learning model.

[0005] One aspect of an embodiment of the present disclosure provides an information processing method based on a deep learning model, including: in response to receiving a configuration instruction for a system page of a target business system, obtaining configuration information corresponding to the system page; based on enhancement options in the configuration information, determining information to be processed in the system page; in response to receiving a processing instruction for the information to be processed, processing the information to be processed using a target deep learning model corresponding to the configuration information to obtain a processing result corresponding to the information to be processed.

[0006] Another aspect of the embodiments of the present disclosure provides an information processing device based on a deep learning model, including: a first information acquisition module, used to obtain configuration information corresponding to a system page for a target business system in response to receiving a configuration instruction for the system page; a second information acquisition module, used to determine the information to be processed in the system page based on the enhancement options in the configuration information; an information processing module, used to process the information to be processed using a target deep learning model corresponding to the configuration information in response to receiving a processing instruction for the information to be processed, and obtain a processing result corresponding to the information to be processed.

[0007] Another aspect of the embodiments of the present disclosure provides an electronic device, comprising: a memory for storing a computer program; a processor for executing the computer program stored in the memory, and when the computer program is executed, the above-mentioned information processing method based on the deep learning model is implemented.

[0008] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned information processing method based on the deep learning model is implemented.

[0009] Another aspect of the embodiments of the present disclosure provides a computer program product, including computer program instructions, which implement the above-mentioned information processing method based on the deep learning model when executed by a processor.

[0010] The information processing method, device, medium and product based on the deep learning model in the disclosed embodiment, when receiving the configuration instruction for the system page of the target business system, obtain the configuration information corresponding to the system page of the target business system, and then determine the information to be processed in the system page based on the enhanced options in the configuration information; when receiving the processing instruction for the information to be processed, the target deep learning model corresponding to the configuration information is used to process the information to be processed, and the processing result corresponding to the information to be processed is obtained. Therefore, in the disclosed embodiment, the configuration information corresponding to the system page is used to determine the information to be processed in the system page, and the target deep learning model corresponding to the configuration information is used to process the information to be processed, so that without modifying the existing code of the target business system, that is, without integrating the deep learning model into the target business system, the target business system can also be enhanced by the deep learning model to improve the business capabilities of the target business system, reducing the technical difficulty of combining the target deep learning model with the target business system, shortening the development cycle, and at the same time, when the target deep learning model is combined with the target business system, the target business system does not need to be shut down, and does not affect the normal operation of the target business system.

[0011] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0013] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0014] Figure 1It is a flowchart of an information processing method based on a deep learning model provided by an exemplary embodiment of the present disclosure.

[0015] Figure 2 It is a flowchart of step S100 provided by an exemplary embodiment of the present disclosure.

[0016] Figure 3 It is a flowchart of an information processing method based on a deep learning model provided by another exemplary embodiment of the present disclosure.

[0017] Figure 4 It is a flowchart of a method for displaying processing results provided by an exemplary embodiment of the present disclosure.

[0018] Figure 5 It is a flowchart of an information processing method based on a deep learning model provided in an application example of the present disclosure.

[0019] Figure 6 It is a structural diagram of an information processing device based on a deep learning model provided by an exemplary embodiment of the present disclosure.

[0020] Figure 7 The figure is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. DETAILED DESCRIPTION

[0021] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0022] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0023] It should also be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0024] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0025] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0026] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0027] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0028] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0029] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0030] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0031] The embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0032] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0033] In the process of implementing the present disclosure, the inventors found that when integrating deep learning models into enterprise business systems, it is often necessary to deeply transform the enterprise business systems. Since different enterprise business systems have their own characteristics, the functional modules and data structures in the enterprise business systems are also different. This requires system analysis and customized development when deeply transforming the enterprise business systems. At the same time, it is also necessary to modify the front-end and back-end codes of the enterprise business systems, as well as develop large-scale data interfaces. This makes it difficult to develop deep learning models when they are integrated into enterprise business systems, with a long development cycle, and the enterprise business system needs to be shut down for maintenance, affecting the normal operation of the enterprise business.

[0034] Figure 1 FIG. 1 is a flowchart of an information processing method based on a deep learning model provided by an exemplary embodiment of the present disclosure. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the information processing method based on the deep learning model includes the following steps:

[0035] Step S100, in response to receiving a configuration instruction for a system page of a target business system, obtaining configuration information corresponding to the system page.

[0036] The target business system may be an enterprise business system. For example, the target business system may include a human resources management system, a financial management system, a customer relationship management system or an administrative management system.

[0037] The configuration information may include a method for processing corresponding information in the system page using a deep learning model.

[0038] In a specific implementation, the service system to be enhanced may include multiple system pages, and each system page corresponds to a piece of configuration information.

[0039] In one embodiment, a browser plug-in for obtaining configuration information and running the configuration information can be pre-set in the browser; when the user clicks on the browser plug-in to start the browser plug-in, it is determined that the configuration instruction for the current system page is received, and the browser plug-in can obtain the configuration information corresponding to the system page.

[0040] Step S110, based on the enhancement options in the configuration information, determine the information to be processed in the system page.

[0041] The configuration information may include an enhancement option. The enhancement option is used to indicate the content in the system page that can be processed by the deep learning model. The information to be processed may be the information corresponding to the enhancement option in the system page, and the information to be processed may be processed accordingly by the deep learning model. The information to be processed may include, for example, at least one of the following: text information, image information, and voice information.

[0042] Exemplarily, assuming that the system page includes resume information and the additional option is work experience, the information to be processed is the information corresponding to the work experience in the system page.

[0043] Step S120, in response to receiving a processing instruction for the information to be processed, using the target deep learning model corresponding to the configuration information to process the information to be processed, to obtain a processing result corresponding to the information to be processed.

[0044] Wherein, the configuration information corresponds to at least one target deep learning model. Exemplarily, the configuration information may include the Internet Protocol Address (IP) of the target deep learning model, and the information to be processed may be sent to the target deep learning model based on the IP address, and the target deep learning model processes the information to be processed to obtain a processing result. The processing result is used to represent the result obtained after the target deep learning model processes the information to be processed. For example, when the information to be processed is text information and the target deep learning model is a deep learning model that generates text summaries, the processing result includes summary information corresponding to the information to be processed.

[0045] The target deep learning model may be a pre-trained deep learning model. For example, the target deep learning model may include but is not limited to: a neural network language model (NNLM), a Transformer model, or a recurrent neural network (RNN).

[0046] In the disclosed embodiment, the configuration information corresponding to the system page is used to determine the information to be processed in the system page, and the target deep learning model corresponding to the configuration information is used to process the information to be processed. This achieves the situation where the existing code of the target business system does not need to be modified, that is, there is no need to integrate the deep learning model into the target business system. The target business system can also be enhanced through the deep learning model to improve the business capabilities of the target business system, reducing the technical difficulty of combining the target deep learning model with the target business system and shortening the development cycle. At the same time, when the target deep learning model is combined with the target business system, the target business system does not need to be shut down, which does not affect the normal operation of the target business system.

[0047] Figure 2 is a flow chart of step S100 provided by an exemplary embodiment of the present disclosure. In an optional implementation, Figure 2 As shown, step S100 in this embodiment may include the following steps:

[0048] Step S101: Obtain the page identifier of the system page.

[0049] The page identifier is used to identify the system page. Each system page of the target business system has a page identifier.

[0050] In a specific implementation, the target business system may include at least one module, and each module is used to implement a business function. The page identifier can be set according to the target business system to which the system page belongs and the module to which the system page belongs.

[0051] Exemplarily, the form of the page identifier can be <ai system="system" module="module">. Suppose the target business system to which the system page belongs is the Human Resources (HR) system, and the module to which the system page belongs is resume retrieval, then the page identifier of this system page can be expressed as 〈ai system="HR" module="resume retrieval"〉.

[0052] In one implementation, the system page can be, for example, a Hyper Text Markup Language (HTML) page. The page identifier can be set in the <head> corresponding to the system page in advance.

[0053] When the browser plugin is started, the browser plugin first determines whether the system page has a page identifier. When it is determined that the system page has a page identifier, it obtains the page identifier from the <head> corresponding to the system page; when it is determined that the system page does not have a page identifier, the operation ends.

[0054] Step S102: Obtain the configuration information based on the page identifier.

[0055] In a specific implementation, the configuration information corresponding to each system page can be set in advance, and the corresponding relationship between the configuration information and the page identifier can be established so that the configuration information can be obtained through the page identifier. The configuration information can be stored in the backend server or in the cloud, etc.

[0056] In the embodiments of the present disclosure, by setting a page identifier for the system page and corresponding the page identifier with the configuration information, the configuration information corresponding to the system page can be efficiently obtained through the page identifier.

[0057] In an optional implementation, in the embodiments of the present disclosure, the enhancement option includes the type information of the information to be processed.

[0058] The type information of the information to be processed may include the tags and element types (class) corresponding to the information to be processed in the source code of the system page.

[0059] Accordingly, step S110 in this embodiment may include: determining the information to be processed based on the type information in the enhancement option and the source code of the system page.

[0060] In one embodiment, the browser plug-in can determine whether the source code of the system page includes elements corresponding to the tags and element types in the type information based on the tags and element types in the type information. When the corresponding elements exist, that is, the system page includes the information to be processed, the information to be processed can be located in the system page through the source code of the system page based on the tags and element types in the type information to obtain the information to be processed; when the corresponding elements do not exist in the source code, that is, the system page does not include the information to be processed, the operation is terminated.

[0061] For example, assuming that the type information of the information to be processed is div.resume-content, where div is a tag and resume-content is an element type, a browser plug-in can be used to determine that the corresponding content of all div elements with an element type of resume-content in the source code of the system page is the information to be processed. The type information can be used to define a CSS selector in the system page, and the CSS selector can be used to determine all div elements with an element type of resume-content in the source code of the system page.

[0062] In the disclosed embodiment, by setting the type information of the information to be processed in the configuration information, the information to be processed can be quickly located in the system page through the source code of the system page based on the type information, thereby obtaining the information to be processed.

[0063] Figure 3 is a flowchart of an information processing method based on a deep learning model provided by another exemplary embodiment of the present disclosure. In some optional implementations, such as Figure 3 As shown, in the embodiment of the present disclosure, the information processing method based on the deep learning model may also include the following steps:

[0064] Step S200, determining the control display position and control display mode in the system page based on the control setting rule in the configuration information.

[0065] The configuration information may also include control setting rules. The control setting rules may include rules for creating controls corresponding to the information to be processed in the system page. Exemplarily, the control setting rules may include: the name, display mode, and display position of the control. The display mode may be, for example, a button, a key, etc.

[0066] The control display position is used to indicate the display position of the control in the system page, and the control display mode is used to indicate the display mode of the control in the system page. The display mode in the configuration information can be determined as the control display mode, and the display position in the configuration information can be determined as the control display position.

[0067] Step S210, based on the control display position and the control display mode, a control corresponding to the information to be processed is set in the system page, so that a processing instruction is sent when the control is triggered.

[0068] The browser plug-in sets the control on the browser page according to the display position and display mode of the control, and establishes an association between the control and the information to be processed, so as to complete setting the control corresponding to the information to be processed in the system page. When the user triggers the control by clicking the control or other means, it is determined that the processing instruction is received, the browser plug-in obtains the information to be processed, and sends the information to be processed to the target deep learning model, so that the target deep learning model processes the information to be processed, obtains the processing result, and displays the processing result on the system page.

[0069] In an embodiment of the present disclosure, a control setting rule is set in a configuration file, so that controls corresponding to the information to be processed can be set in a system page according to the control setting rule, so that whether to use the target deep learning model to process the information to be processed can be selected according to the control, thereby improving the flexibility of processing the information to be processed by the target deep learning model.

[0070] In some optional implementations, in the embodiments of the present disclosure, the configuration information also includes address information of the target deep learning model.

[0071] Correspondingly, step S120 in the embodiment of the present disclosure may include: sending the information to be processed to the target deep learning model based on the address information of the target deep learning model, so that the target deep learning model processes the information to be processed and obtains a processing result.

[0072] In one embodiment, the information to be processed can be input into a target deep learning model, and the target deep learning model outputs a processing result.

[0073] For example, assuming that the target business system to which the system page belongs is the Human Resources (HR) system, and the module to which the system page belongs is resume retrieval, the page identifier of the system page can be expressed as 〈ai system="HR"module="resume retrieval"〉, which is set in the 〈head〉 corresponding to the system page, and a browser plug-in for obtaining configuration information is pre-set in the browser.

[0074] When the browser plug-in is started, the browser plug-in obtains the page identifier and obtains the configuration information corresponding to the page identifier from the backend. The configuration information can be expressed as: {"parsers": [{"system":"HR","modules":[{"module":"Resume screening","rules":[{"targetElement":"div.resume-content":"enhancement":{"type":"button","text":"AI screening","apiEndpoint":"https: / / api.example.com / scanResume","position":"div.resume"}]}]}]}.

[0075] parsers represents the parser (configuration information). Each enhanced system can correspond to a parser. The parser can be an array including all systems and their functional parsing rules. system represents the target business system to which the parser is applicable. In this example, the value of system is HR, indicating that the parser can be applied to the human resources system. modules can be an array containing parsing rules for different modules. module represents the module in the target business system to which the current parsing rule is applicable. In this embodiment, the value of module is resume screening, indicating that the parser can be applied to the resume screening module in the human resources system. rules is an array containing specific rules. Each rule defines a selector for a system page (for example, a CSS selector) and a way to process the information to be processed. tElement, enhancement, and position can each represent a rule; targetElement is an enhancement option, and the value div.resume-content corresponding to targetElement is the type information; enhancement represents the information of the element (control) inserted into the system page; type represents the enhancement type (the display mode of the control). In this example, the value corresponding to type is button, indicating that the display mode of the control is button; text represents the text content of the control (the name of the control). In this example, the value corresponding to text is AI screening, which means that the name of the control displayed on the control is AI screening; apiEndpoint represents the Uniform Resource Locator (URL) address of the target deep learning model. In this example, the value corresponding to apiEndpoint is the URL address of the target deep learning model https: / / api.example.com / scanResume; position represents the display position of the control in the system page. In this example, the value corresponding to position is div.resume, indicating that the control is inserted at the corresponding content in the system page in the div element with the element type resume in the source code of the system page.

[0076] In the embodiments of the present disclosure, by setting the address information of the target deep learning model in the configuration information, the information to be processed can be sent to the target deep learning model through the address information, so that the information to be processed can be processed by the target deep learning model, thereby realizing the enhancement of the target business system through the deep learning model. At the same time, when the target business system needs to be replaced with a new deep learning model, it is only necessary to replace the address information of the new target deep learning model in the configuration information, without resetting the target business system, thereby improving the flexibility of the combination of the target business system and the deep learning model and enhancing the scalability of the target business model.

[0077] In some optional implementations, after step S120 in the embodiment of the present disclosure, the method further includes: displaying the processing result on the system page.

[0078] In one embodiment, a browser plug-in may be used to display the processing result in a system page.

[0079] In the disclosed embodiment, the processing result of the information to be processed is displayed on the system page, which makes it convenient for the user to view the processing result, thereby improving the user experience.

[0080] Figure 4 1 is a flowchart of a method for displaying processing results provided by an exemplary embodiment of the present disclosure. In some optional implementations, in this embodiment, the processing results can be displayed in the system page in the following manner:

[0081] Step S300, based on the information display rules in the configuration information, determine the display position of the processing result in the system page.

[0082] The configuration information also includes information display rules, which may be rules for determining whether the processing results are displayed on the system page.

[0083] In one embodiment, the information display rule may include: the display position of the information. The browser plug-in may determine the display position of the information as the display position of the processing result. Exemplarily, the location information of the information may be div.resume, which indicates that the div element with the element type of resume in the source code of the system page displays the processing result at the corresponding content in the system page.

[0084] Step S310, displaying the processing result on the system page based on the display position of the processing result.

[0085] In one embodiment, when the processing result fed back by the target deep learning model is received, the browser plug-in can display the processing result in the system page according to the display position of the processing result.

[0086] In the disclosed embodiment, the display position of the processing result in the system page can be determined by the information display rules in the configuration information, and the processing result can be displayed at the display position in the system page, thereby allowing the processing result to be flexibly displayed on the system page, thereby improving the user experience.

[0087] Figure 5 1 is a flowchart of an information processing method based on a deep learning model provided by an application example of the present disclosure. In this application example, a browser plug-in is pre-set in the browser. Figure 5As shown, the information processing method based on the deep learning model includes the following steps:

[0088] S1, when the user starts the browser plug-in by clicking the browser plug-in, that is, receives the configuration instruction, the browser plug-in detects whether the system page includes the page identifier. If the system page includes the page identifier, step S2 is executed. If the system page does not include the page identifier, step S3 is executed.

[0089] S2, end the operation, and no further steps of this example will be executed;

[0090] S3, the browser plug-in obtains configuration information based on the page identifier;

[0091] S4, the browser plug-in determines whether the system page includes the to-be-processed information corresponding to the enhanced option in the configuration information. When the to-be-processed information does not exist in the page system, step S5 is executed. When the to-be-processed information exists in the page system, step S6 is executed.

[0092] S5, end the operation, and no subsequent steps of this example will be executed;

[0093] S6, the browser plug-in obtains the information to be processed in the system page according to the type information of the information to be processed in the enhanced option;

[0094] S7, the browser plug-in determines the control display position and control display mode in the system page based on the control setting rule in the configuration information;

[0095] S8, the browser plug-in sets a control corresponding to the information to be processed in the system page based on the control display position and the control display mode;

[0096] S9, triggering the control to send a processing instruction by clicking the control, and upon receiving the processing instruction, the browser plug-in sends the information to be processed to the target deep learning model based on the address information of the target deep learning model in the configuration information;

[0097] S10, the target deep learning model processes the information to be processed, obtains the processing result, and feeds the processing result back to the system page;

[0098] S11, the browser plug-in determines the display position of the processing result in the system page based on the information display rule in the configuration information;

[0099] S12, displaying the processing result on the system page based on the display position of the processing result.

[0100] Please refer to the corresponding beneficial technical effects of the above-mentioned corresponding exemplary method part, which will not be repeated here.

[0101] Figure 6is a schematic diagram of the structure of an information processing device based on a deep learning model provided by an exemplary embodiment of the present disclosure. Figure 6 As shown, the device of this embodiment may include: a first information acquisition module 400 , a second information acquisition module 410 and an information processing module 420 .

[0102] The first information acquisition module 400 is used to obtain configuration information corresponding to the system page in response to receiving a configuration instruction for the system page of the target business system;

[0103] A second information acquisition module 410 determines the information to be processed in the system page based on the enhancement options in the configuration information;

[0104] The information processing module 420, in response to receiving a processing instruction for the information to be processed, processes the information to be processed using a target deep learning model corresponding to the configuration information to obtain a processing result corresponding to the information to be processed.

[0105] In some possible implementations of the present disclosure, the first information acquisition module 400 in the embodiment of the present disclosure includes:

[0106] A first information acquisition submodule, used to acquire a page identifier of the system page;

[0107] The first information determination submodule is used to obtain the configuration information based on the page identifier.

[0108] In some possible implementations of the present disclosure, the enhancement options in the embodiments of the present disclosure include type information of the information to be processed;

[0109] The second information acquisition module 410 is specifically configured to determine the information to be processed based on the type information and the source code of the system page.

[0110] In some possible implementations of the present disclosure, the information processing device based on the deep learning model in the embodiment of the present disclosure further includes:

[0111] A control information determination module, which determines a control display position and a control display mode in the system page based on the control setting rules in the configuration information;

[0112] A control setting module is used to set a control corresponding to the information to be processed in the system page based on the control display position and the control display mode, so that the processing instruction is sent when the control is triggered.

[0113] In some possible implementations of the present disclosure, the configuration information in the embodiments of the present disclosure includes address information of the target deep learning model;

[0114] The information processing module 420 is specifically used to send the information to be processed to the target deep learning model based on the address information, so that the target deep learning model processes the information to be processed to obtain the processing result.

[0115] In some possible implementations of the present disclosure, the information processing device based on the deep learning model in the embodiment of the present disclosure further includes:

[0116] An information display module is used to display the processing result in the system page.

[0117] In some possible implementations of the present disclosure, the information display module in the embodiment of the present disclosure is specifically used to determine the display position of the processing result in the system page based on the information display rule in the configuration information; and display the processing result in the system page based on the display position of the processing result.

[0118] The information processing device based on the deep learning model in the embodiment of the present disclosure corresponds to the embodiment of the information processing method based on the deep learning model in the present disclosure, and the relevant contents can be referenced to each other and will not be repeated here.

[0119] The beneficial technical effects corresponding to the exemplary embodiment of the information processing device based on the deep learning model of the embodiment of the present disclosure can be found in the corresponding beneficial technical effects of the above-mentioned corresponding exemplary method part, which will not be repeated here.

[0120] In addition, an embodiment of the present disclosure further provides an electronic device, including:

[0121] Memory for storing computer programs;

[0122] A processor is used to execute the computer program stored in the memory, and when the computer program is executed, the information processing method based on the deep learning model described in any of the above embodiments of the present disclosure is implemented.

[0123] Figure 7 This is a schematic diagram of the structure of an application embodiment of the electronic device disclosed in the present invention. Figure 7 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signals from them.

[0124] like Figure 7 As shown, the electronic device includes one or more processors and memory.

[0125] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0126] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the information processing method based on the deep learning model of each embodiment of the present disclosure described above and / or other desired functions.

[0127] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0128] In addition, the input device may also include, for example, a keyboard, a mouse, and the like.

[0129] The output device can output various information to the outside, including the determined distance information, direction information, etc. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0130] Of course, to simplify, Figure 7 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0131] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the information processing method based on the deep learning model according to various embodiments of the present disclosure described in the above part of this specification.

[0132] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0133] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the information processing method based on the deep learning model according to various embodiments of the present disclosure described in the above part of this specification.

[0134] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 above.

[0135] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0136] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limiting. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0137] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0138] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0139] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0140] It should also be noted that in the apparatus, device and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0141] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0142] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. An information processing method based on a deep learning model, characterized in that: include: In response to receiving a configuration instruction for a system page of a target business system, obtaining configuration information corresponding to the system page; Determining information to be processed in the system page based on the enhancement options in the configuration information; In response to receiving a processing instruction for the information to be processed, the information to be processed is processed using a target deep learning model corresponding to the configuration information to obtain a processing result corresponding to the information to be processed.

2. The method according to claim 1, characterized in that The obtaining of configuration information corresponding to the system page includes: Obtaining a page identifier of the system page; The configuration information is obtained based on the page identifier.

3. The method according to claim 1 or 2, characterized in that: The enhancement options include type information of the information to be processed; The determining, based on the enhancement option in the configuration information, the information to be processed in the system page includes: The information to be processed is determined based on the type information and the source code of the system page.

4. The method according to any one of claims 1 to 3, characterized in that: After determining the information to be processed in the system page based on the enhancement options in the configuration information, the method further includes: Determining a control display position and a control display mode in the system page based on the control setting rules in the configuration information; Based on the control display position and the control display mode, a control corresponding to the information to be processed is set in the system page, so that the processing instruction is sent when the control is triggered.

5. The method according to any one of claims 1 to 4, characterized in that: The configuration information includes address information of the target deep learning model; The processing of the information to be processed by using the target deep learning model corresponding to the configuration information includes: The information to be processed is sent to the target deep learning model based on the address information, so that the target deep learning model processes the information to be processed to obtain the processing result.

6. The method according to any one of claims 1 to 5, characterized in that: After obtaining the processing result, the method further includes: The processing result is displayed on the system page.

7. The method according to claim 6, characterized in that The displaying of the processing result on the system page includes: Based on the information display rules in the configuration information, determine the display position of the processing result in the system page; Based on the display position of the processing result, the processing result is displayed in the system page.

8. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to execute the computer program stored in the memory, and when the computer program is executed, the information processing method based on the deep learning model described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the information processing method based on the deep learning model described in any one of claims 1 to 7.

10. A computer program product comprising computer program instructions, characterized in that When the computer program instructions are executed by a processor, the information processing method based on the deep learning model described in any one of claims 1 to 7 is implemented.

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