Data processing method, device and equipment, computer readable storage medium and product

Abstract

The invention provides a data processing method, device and equipment, a computer readable storage medium and a product, and relates to the field of artificial intelligence, in particular to the field of big data. The method is applied to a data processing device, and the data processing device is in communication connection with a preset data processing model and at least one service system. According to the specific implementation scheme, a data processing request is obtained, and the data processing request comprises a data processing result obtained by carrying out data processing on the basis of a natural language generated by a user through a data processing model and identification information of a to-be-called target service system; determining service association information corresponding to the target service system; performing data optimization operation on the data processing result according to the service association information to obtain target data; and calling the target business system to execute a task matched with the target data based on the target data. Therefore, the problem that the service processing accuracy of a current data processing model for a specific service field is not high is solved.

Description

Technical Field

[0001] The present disclosure relates to big data in artificial intelligence, and more particularly to a data processing method, apparatus, device, computer-readable storage medium, and product. Background Art

[0002] With the development of artificial intelligence technology, big models are gradually entering the lives of users. Users can input natural language into the big model, so that the big model can process data based on natural language and output data processing results that match the natural language.

[0003] Currently, big models are increasingly being used in different business fields. How to improve the accuracy of big models in business processing in specific business fields has become an urgent problem to be solved. Summary of the invention

[0004] The present disclosure provides a data processing method, apparatus, device, computer-readable storage medium and product for improving the processing accuracy of a data processing model.

[0005] According to a first aspect of the present disclosure, a data processing method is provided, which is applied to a data processing device, wherein the data processing device is respectively connected to a preset data processing model and at least one business system in communication; the method comprises:

[0006] Obtaining a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on the natural language generated by the user, and identification information of a target business system to be called;

[0007] Determine the business association information corresponding to the target business system;

[0008] Performing data optimization operations on the data processing results according to the business association information to obtain target data;

[0009] The target business system is called based on the target data to execute a task matching the target data.

[0010] According to a second aspect of the present disclosure, a data processing device is provided, wherein the data processing device is respectively connected to a preset data processing model and at least one business system for communication; the data processing device comprises:

[0011] An acquisition module, used to acquire a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called;

[0012] A determination module, used to determine the business association information corresponding to the target business system;

[0013] An optimization module, used to perform data optimization operations on the data processing results according to the business association information to obtain target data;

[0014] A calling module is used to call the target business system based on the target data to execute a task matching the target data.

[0015] According to a third aspect of the present disclosure, there is provided a data processing system, comprising a data processing device, at least one business system, and a preset data processing model;

[0016] Wherein, the data processing device is respectively connected to the at least one business system and the preset data processing model in communication;

[0017] The data processing device is used to obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called;

[0018] The data processing device is used to determine the business association information corresponding to the target business system, perform data optimization operations on the data processing result according to the business association information to obtain target data, and call the target business system to execute a task matching the target data based on the target data;

[0019] The business system is used to execute tasks matching the target data.

[0020] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0021] at least one processor; and

[0022] a memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data processing method described in the first aspect.

[0024] According to a fifth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the data processing method as described in the first aspect.

[0025] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising: a computer program, wherein the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program so that the electronic device executes the method described in the first aspect.

[0026] The technology disclosed in the present invention solves the problem that the current data processing model has low accuracy in business processing for specific business fields.

[0027] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0029] Figure 1 A system architecture diagram of a data processing system on which the present disclosure is based;

[0030] Figure 2 A flowchart of a data processing method provided by an embodiment of the present disclosure;

[0031] Figure 3 A schematic diagram of an application scenario provided for an embodiment of the present disclosure;

[0032] Figure 4 A flowchart of a data processing method provided by another embodiment of the present disclosure;

[0033] Figure 5 A flowchart of a data processing method provided by another embodiment of the present disclosure;

[0034] Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present disclosure;

[0035] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0037] The present disclosure provides a data processing method, device, equipment, computer-readable storage medium and product, which are applied to big data in artificial intelligence to achieve the technical effect of improving the data processing accuracy of the data processing model for specific business scenarios.

[0038] It should be noted that the data processing method, apparatus, device, computer-readable storage medium and product provided by the present disclosure can be applied to any application scenario of data processing based on a large model.

[0039] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0040] With the development of artificial intelligence technology, artificial intelligence technology has entered the era of big models. Users can input natural language into the big model according to actual needs. The natural language can be in the form of voice, text, etc. The big model can perform semantic analysis on the natural language and perform corresponding data processing operations based on the analysis results. For example, the user can output the natural language of "New To-Do Operation: Meet with Xiao Ming at 2 o'clock tomorrow" to the big model. The big model can create a new to-do task based on the natural language and send reminders to the user based on the to-do task.

[0041] At present, big models are increasingly being used in business systems. Big models can often connect to multiple business systems and provide data processing capabilities for multiple business systems. However, when big models are combined with business systems, the accuracy of data processing by big models for specific businesses is not high, so the output results of big models cannot accurately call business systems, and thus cannot meet the actual needs of users.

[0042] In the process of solving the above technical problems, the inventors have found through research that in order to improve the accuracy of the data processing results output by the large model and make the data processing results more compatible with the business needs of the business system, an intermediate layer can be built between the business system and the large model, and the intermediate layer can be specifically the data processing device described in the present disclosure. After obtaining the data processing results output by the large model, the data processing device can perform data optimization operations on the data processing results based on the business association information corresponding to the preset business system to obtain the target data, and call the business system based on the target data.

[0043] The inventors further discovered that when the big model provides services for multiple different business systems, the interface information of the call interfaces of different business systems is different, wherein the interface information includes but is not limited to host address, request parameters, response format, etc. For the heterogeneous call interface information of different business systems, in order to enable the big model to provide services for different business systems, the data processing device can also convert the heterogeneous call interface information into interface information of the target format that the big model can recognize.

[0044] In order to enable readers to more deeply understand the implementation principle of the present disclosure, the following Figure 1-Figure 7 The embodiments of the present disclosure are further refined.

[0045] Figure 1 is a system architecture diagram of the data processing system on which the present disclosure is based, such as Figure 1 As shown, the data processing system 11 at least includes a server 12, at least one business system 13 and a data processing model 14. The server 12 is respectively connected to the at least one business system 13 and the data processing model 14. The server 12 may be provided with a data processing device, which may be written in a language such as C / C++, Java, Shell or Python.

[0046] Based on the above system architecture, the data processing device is used to obtain a data processing request, and input the natural language into the data processing model 14 based on the data processing request, wherein the data processing request includes the natural language generated by the user and the identification information of the target business system to be called;

[0047] The data processing device is used to obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called;

[0048] The data processing device is used to determine the business association information corresponding to the target business system, perform data optimization operations on the data processing results according to the business association information to obtain target data, and call the target business system 13 based on the target data to execute tasks matching the target data;

[0049] The business system 13 is used to execute tasks matching the target data.

[0050] This can improve the accuracy of the target data, and the target data can match the data requirements of the business system.

[0051] Figure 2A flow chart of a data processing method provided in an embodiment of the present disclosure, wherein the data processing method is applied to a data processing device, wherein the data processing device is respectively connected to a preset data processing model and at least one business system in communication; Figure 2 As shown, the method includes:

[0052] Step 201: Obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called.

[0053] The execution subject of the present disclosure is a data processing device, which can be coupled to a server. The server can communicate with the data processing model and at least one business system respectively, so that the data processing device can exchange information with the data processing model and at least one business system respectively.

[0054] The data processing model can be a large model of artificial intelligence, which can parse the natural language input by the user and perform corresponding data processing operations based on the parsing results. For example, the data processing model can interact with the user based on the natural language input by the user. Alternatively, the data processing model can perform text analysis based on natural language, determine the instructions corresponding to the natural language, and execute the corresponding instructions. Alternatively, the data processing model can create literary content related to natural language based on natural language, etc. The present disclosure does not limit the data processing capabilities of the data processing model based on natural language.

[0055] In this embodiment, the data processing model can provide data processing capabilities to at least one business system respectively, and call the business system to perform corresponding tasks based on the processed target data. The data processing model can be connected to the data processing device in communication, and the data processing device is connected to at least one business system in communication.

[0056] Based on the above system architecture, the data processing device can obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model through data processing based on a natural language generated by a user, and identification information of a target business system to be called.

[0057] Optionally, the data processing result can be obtained by the user directly inputting the natural language into the data processing model. It can also be obtained by the user inputting the natural language into the data processing device, and the data processing device inputting the natural language into the data processing model. The present disclosure does not limit this.

[0058] Step 202: Determine the business association information corresponding to the target business system.

[0059] In this embodiment, in order to enable the data processing results output by the data processing model to match the actual business needs of the business system and improve the accuracy of the data processing results, after obtaining the data processing request, the business association information corresponding to the target business system can be obtained based on the identification information of the target business system in the data processing request.

[0060] Among them, the business-related information includes but is not limited to the domain knowledge content of the field in which the business system is located, the data requirements corresponding to the business system, etc., among which the domain knowledge content includes time processing content, location processing content, personnel processing content, domain dictionary content, screening condition content, etc., and the data requirements include but are not limited to data formats and other content.

[0061] Step 203: Perform data optimization operations on the data processing results according to the business association information to obtain target data.

[0062] In this embodiment, after the business association information of the business system is acquired, a data optimization operation may be performed on the data processing result based on the business association information to obtain target data.

[0063] The data optimization includes but is not limited to optimization operations such as vocabulary replacement, supplementation, and splitting, so that the obtained target data can match the actual business needs of the business system and improve the accuracy of the data processing results.

[0064] Step 204: Based on the target data, the target business system is called to execute a task matching the target data.

[0065] In this embodiment, after data optimization is performed on the data processing results to obtain the target data, the target business system can be called based on the target data to execute a task matching the target data.

[0066] Figure 3 A schematic diagram of an application scenario provided by an embodiment of the present disclosure, such as Figure 3 As shown, the user can initiate a data processing request according to actual needs, and the data processing request can carry natural language. For example, the natural language can be "meeting with Xiao Ming at 2 pm tomorrow". After obtaining the data processing request, the data processing device 31 can input the natural language into the data processing model 32. The data processing model 32 can parse the natural language and perform corresponding data processing to obtain the data processing result. The data processing device 31 can optimize the data processing result to obtain the target data, and feed the target data back to the business system 33.

[0067] Continuing with the above example, the data processing result may be “To-do list: start a meeting; Subject: me, Xiao Ming; Time: tomorrow at 2 pm”. However, in the data processing result, the data referred to by “me”, “Xiao Ming” and “tomorrow” are not clear enough, resulting in low accuracy of the data processing result. The data processing device can determine the identity information of “me” based on the identification information of the currently logged-in user, and determine the identity information of “Xiao Ming” based on the pre-stored address book of users using the business system, and obtain the current time information in real time, and determine the specific time of “tomorrow” based on the current time information. Therefore, based on the above business-related information, the data processing result can be optimized to “To-do list: start a meeting; Subject: Zhang San, Li Ming; Time: 2 o’clock on July 12, 2023”. Through the above data optimization processing, the results of data processing can be improved, so that users can perform business processing more accurately.

[0068] The data processing method provided in this embodiment constructs a data processing device between a data processing model and at least one business system, so that the data processing device can perform data optimization operations on the output results of the data processing model based on business-related content, so that the obtained target data matches the actual business needs of the business system, thereby improving the accuracy of the data processing results.

[0069] Figure 4 A flowchart of a data processing method provided by another embodiment of the present disclosure is shown below. Based on any of the above embodiments, before step 201, Figure 4 As shown, it also includes:

[0070] Step 401: respectively obtain the interface entry request sent by each of the business systems, wherein the interface entry request includes the interface information corresponding to the business system, and the format of the interface information corresponding to different business systems is different.

[0071] Step 402: parse the interface information to determine the format corresponding to the interface information.

[0072] Step 403: Convert the format of the interface information into a target format that matches the data processing model.

[0073] In this embodiment, the data processing model can often provide pre-processing functions for multiple business systems. The interface information corresponding to the call interface of different business systems is often different. The interface information includes but is not limited to host address, request parameters, response format and other information.

[0074] Since the interface information of the data processing model is often different from the interface information of the business system, it is impossible to directly call the business system based on the data processing result. In order to solve the above technical problems, the data processing device can perform conversion operations on heterogeneous interface information.

[0075] Optionally, before obtaining the data processing request, the data processing device may respectively obtain the interface entry request sent by each of the business systems, wherein the interface entry request includes interface information corresponding to the business system, and the formats of the interface information corresponding to different business systems are different.

[0076] For each piece of interface information, the interface information is parsed to determine the format content corresponding to the interface information, and the format of the interface information is converted into a target format that matches the data processing model.

[0077] The data processing method provided in this embodiment converts the interface information of the heterogeneous calling interfaces of various business parties, so that the data processing device can implement calling operations on different business systems.

[0078] Further, based on any of the above embodiments, step 204 includes:

[0079] The target business system is called based on the target data according to the interface information of the target format to execute a task matching the target data.

[0080] In this embodiment, after the interface information corresponding to each business system is converted into a target format that matches the data processing model, the data processing model can now exchange information with the business system. After obtaining the target data output by the data processing model, the target business system can be called based on the target data according to the interface information in the target format to perform tasks that match the target data.

[0081] The data processing method provided in this embodiment converts the interface information of the heterogeneous calling interfaces of various business parties, so that the data processing device can implement calling operations on different business systems.

[0082] Figure 5 A flowchart of a data processing method provided by another embodiment of the present disclosure is provided. Based on any of the above embodiments, Figure 5 As shown, step 201 includes:

[0083] Step 501: Obtain natural language generated by a user.

[0084] Step 502: input the natural language into the data processing model to obtain the data processing result.

[0085] Step 503: Generate the data processing request based on the data processing result and the target business system determined by the user.

[0086] In this embodiment, the user can input natural language into the data processing model according to actual needs, and generate a data processing request based on the natural language. Alternatively, the user can also input natural language into the data processing device, and the data processing device inputs the natural language into the data processing model.

[0087] Optionally, the data processing device can obtain natural language generated by the user. The natural language is input into the data processing model to obtain the data processing result. In addition, the user can also determine the target business system to be called according to actual needs. Thus, a data processing request can be generated based on the data processing result and the target business system determined by the user.

[0088] The data processing method provided in this embodiment obtains the natural language generated by the user by the data processing device and inputs the natural language into the data processing model, so that the natural language can be pre-processed according to actual needs before being input into the data processing model, thereby further improving the accuracy of data processing.

[0089] Further, based on any of the above embodiments, step 502 includes:

[0090] Perform a keyword recognition operation on the natural language to obtain at least one keyword corresponding to the natural language.

[0091] If the number of keywords corresponding to the natural language is less than a preset quantity threshold, the natural language is input into the data processing model.

[0092] If the number of keywords corresponding to the natural language is greater than a preset threshold value, a preset number of target keywords that meet preset conditions are selected from the multiple keywords corresponding to the natural language according to preset data editing information, and the preset number of target keywords are input into the data processing model.

[0093] In this embodiment, when the data processing model is used to process natural language, keywords corresponding to the natural language can be extracted, and subsequent data processing can be performed based on the keywords. However, when the natural language is more complex, that is, when there are more keywords, the calculation amount of the data processing model may be larger, affecting the data processing results.

[0094] Therefore, after obtaining the natural language, a keyword recognition operation can be performed on the natural language to obtain at least one keyword corresponding to the natural language. If the number of keywords corresponding to the natural language is less than a preset quantity threshold, that is, the data processing model can accurately perform data processing based on the keyword, the natural language can be input into the data processing model. On the contrary, if the number of keywords corresponding to the natural language is greater than the preset quantity threshold, it may cause the data processing model to have a large amount of calculation, affecting the data processing result. Therefore, a preset number of target keywords that meet preset conditions can be selected from multiple keywords corresponding to the natural language according to the preset data editing information, and the preset number of target keywords can be input into the data processing model.

[0095] Among them, the selection conditions of the target keywords are defined in the data editing information. For example, the keywords can be sorted according to importance, and the top N keywords can be selected as the target keywords. Alternatively, N keywords with fewer words than a preset threshold can be selected as the target keywords. The data editing information can be set by the user according to actual needs, or the data editing information can be preset in the data processing device. The present disclosure does not limit this.

[0096] The data processing method provided in this embodiment can reduce the computational pressure of the data processing model and improve data processing efficiency by determining a preset number of target keywords corresponding to the natural language before inputting the natural language into the data processing model when the number of keywords corresponding to the natural language is greater than a preset threshold number, and inputting the target keywords into the data processing model.

[0097] Optionally, based on any of the above embodiments, the method further includes:

[0098] Determine the task information corresponding to the target data.

[0099] Check whether the task information meets the preset splitting condition.

[0100] If so, the task information is split according to the preset data editing information to obtain at least one subtask.

[0101] Step 204 includes:

[0102] The target business system is called respectively based on the target data to execute the at least one subtask.

[0103] In this embodiment, after optimizing the data processing results and obtaining the target data, the business system can be called based on the target data to process the task corresponding to the target data. However, the task corresponding to the target data may be relatively complex and may be composed of multiple simple tasks. When the business system processes complex tasks, it may be difficult and cannot achieve the desired effect.

[0104] Therefore, in order to improve the accuracy of business processing, after obtaining the target data, the task information corresponding to the target data can be determined. Check whether the task information meets the preset splitting conditions. Among them, the execution difficulty, execution time, execution steps and other factors corresponding to the task information can be determined, and based on the above factors, it is determined whether the task information meets the preset splitting conditions. Alternatively, other methods can be used to determine whether the task information meets the preset splitting conditions, and the present disclosure does not limit this.

[0105] Furthermore, if it is detected that the task information meets the preset splitting condition, the task information can be split according to the preset data editing information to obtain at least one subtask. The data editing information can be set by the user according to actual needs, or the data editing information can be preset in the data processing device. The present disclosure does not limit this.

[0106] The data processing method provided by the present disclosure detects whether the task information meets the preset splitting conditions before calling the business system based on the target data, and performs a splitting operation on the task information when the splitting conditions are met, thereby reducing the amount of calculation for the business system to process the business and improving the data processing efficiency.

[0107] Optionally, based on any of the above embodiments, the method further includes:

[0108] In response to the data editing operation triggered by the user, parameter arrangement information and / or task splitting information generated by the user is obtained.

[0109] The parameter arrangement information and / or the task splitting information is determined as the data editing information.

[0110] In this embodiment, the data editing information can be set by the user according to actual needs. The data editing information can specifically include parameter arrangement information and / or task splitting information. The parameter arrangement information is used to select the target keyword from multiple keywords. The task splitting information is used to split complex tasks.

[0111] Optionally, in order to realize the generation of data editing information, in response to the data editing operation triggered by the user, the parameter arrangement information and / or task splitting information generated by the user may be obtained and the parameter arrangement information and / or task splitting information may be determined as the data editing information.

[0112] The data processing method provided by the present disclosure determines data editing information in advance based on parameter arrangement information and / or task splitting information generated by the user, so that during the data processing process, target keywords can be selected and / or complex tasks can be split based on the data editing information, thereby reducing the amount of data in the data processing process and improving the efficiency of data processing.

[0113] Optionally, based on any of the above embodiments, the business association information includes domain knowledge content corresponding to the target business system.

[0114] Step 203 includes:

[0115] Determine the business-specific vocabulary in the data processing result.

[0116] The business-specific vocabulary is replaced based on the domain knowledge content to obtain the target data.

[0117] In this embodiment, in order to enable the data processing results output by the data processing model to match the actual business needs of the business system and improve the accuracy of the data processing results, after obtaining the data processing request, the data processing results can be optimized based on the business-related information corresponding to the target business system.

[0118] Optionally, the business association information corresponding to the target business system may be domain knowledge content corresponding to the target business system, wherein the domain knowledge content includes time processing content, location processing content, personnel processing content, domain dictionary content, screening condition content, and the like.

[0119] After obtaining the data processing results, the data processing results may include some non-standard business-specific vocabulary. In order to improve the accuracy of subsequent data processing, the business-specific vocabulary in the data processing results can be determined. The standard vocabulary matching the business-specific vocabulary is determined in the domain indication content, and the business-specific vocabulary is replaced based on the standard vocabulary in the domain knowledge content to obtain the target data.

[0120] The data processing method provided by the present disclosure can improve the accuracy of target data and provide a basis for subsequent business calls by replacing business-specific vocabulary based on standard vocabulary in domain knowledge content.

[0121] Optionally, based on any of the above embodiments, the business association information includes data requirement information corresponding to the target business system, and the data requirement information is obtained by parsing interface information in an interface entry request sent by the target business system.

[0122] Step 203 includes:

[0123] The data format of the data processing result is adjusted according to the data requirements corresponding to the business system to obtain the target data.

[0124] In this embodiment, the business association information includes data requirement information corresponding to the target business system, which is obtained by parsing the interface information in the interface input request sent by the target business system. The data requirement information may include data formats required by the business system.

[0125] After obtaining the data processing result, the data format of the data processing result can be adjusted according to the data requirements of the business system, and the format of the data processing result is adjusted to the data format corresponding to the data requirements to obtain the target data.

[0126] The data processing method provided by the present disclosure adjusts the data format of the data processing result based on the data requirements of the business system, so that the adjusted target data can better meet the actual requirements of the business system.

[0127] Optionally, based on any of the above embodiments, the business association information includes domain knowledge content and data demand information corresponding to the target business system.

[0128] Step 203 includes:

[0129] Determine the referential vocabulary in the data processing result.

[0130] The reference vocabulary is replaced according to the domain knowledge content and the data demand information to obtain the target data.

[0131] In this embodiment, the business association information includes domain knowledge content and data requirement information corresponding to the target business system. The domain knowledge content includes time processing content, location processing content, personnel processing content, domain dictionary content, screening condition content, etc. The data requirement information can be the data format required by the business system.

[0132] After obtaining the data processing result, the referential vocabulary in the data processing result can be determined. For example, the natural language can be "Hold a meeting with Xiao Ming at 2 pm tomorrow". The data processing result can be "To-do items: hold a meeting; Objects: me, Xiao Ming; Time: 2 pm tomorrow". Among them, "I", "Xiao Ming", and "tomorrow" are all referential data.

[0133] However, the above reference information is often not clear enough, and subsequent business calls based on the data processing results may result in low accuracy. Therefore, the reference words can be replaced based on the domain knowledge content and data demand information to obtain the target data.

[0134] Continuing with the above example, the identity information of "I" can be determined based on the identification information of the currently logged-in user, and the identity information of "Xiao Ming" can be determined based on the pre-stored address book of users using the business system, and the current time information can be obtained in real time, and the specific time of "tomorrow" can be determined based on the current time information. This achieves the optimization operation of data processing results.

[0135] The data processing method provided in this embodiment replaces the reference words based on the domain knowledge content and data demand information, so that the obtained target data can match the actual business needs of the business system, thereby improving the accuracy of the data processing results.

[0136] Figure 6 The data processing device provided in the embodiment of the present disclosure is a schematic diagram of the structure of the data processing device, wherein the data processing device is respectively connected to a preset data processing model and at least one business system for communication. Figure 6 As shown, the device includes: an acquisition module 61, a determination module 62, an optimization module 63 and a calling module 64. The acquisition module 61 is used to obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model based on the natural language generated by the user, and identification information of the target business system to be called. The determination module 62 is used to determine the business association information corresponding to the target business system. The optimization module 63 is used to perform data optimization operations on the data processing result according to the business association information to obtain target data. The calling module 64 is used to call the target business system based on the target data to perform a task matching the target data.

[0137] Furthermore, based on any of the above embodiments, the device also includes: an acquisition module, used to respectively acquire the interface entry requests sent by each of the business systems, wherein the interface entry requests include interface information corresponding to the business system, and the formats of the interface information corresponding to different business systems are different; a parsing module, used to parse the interface information and determine the format corresponding to the interface information; and a conversion module, used to convert the format of the interface information into a target format that matches the data processing model.

[0138] Further, based on any of the above embodiments, the calling module includes: a calling submodule, which is used to call the target business system based on the target data according to the interface information of the target format to execute a task matching the target data.

[0139] Further, based on any of the above embodiments, the acquisition module includes: an acquisition sub-module, used to acquire natural language generated by a user; a processing sub-module, used to input the natural language into the data processing model to obtain the data processing result; and a generation sub-module, used to generate the data processing request based on the data processing result and the target business system determined by the user.

[0140] Further, based on any of the above embodiments, the processing submodule includes: an identification unit, used to perform a keyword recognition operation on the natural language to obtain at least one keyword corresponding to the natural language; a first input unit, used to input the natural language into the data processing model if the number of keywords corresponding to the natural language is less than a preset number threshold; a second unit, used to select a preset number of target keywords that meet preset conditions from multiple keywords corresponding to the natural language according to preset data editing information if the number of keywords corresponding to the natural language is greater than a preset number threshold, and input the preset number of target keywords into the data processing model.

[0141] Furthermore, based on any of the above embodiments, the device also includes: a determination module, used to determine the task information corresponding to the target data; a detection module, used to detect whether the task information meets the preset splitting conditions; a splitting module, used to, if so, split the task information according to preset data editing information to obtain at least one subtask; the calling module includes: a calling sub-module, used to call the target business system based on the target data to execute the at least one subtask.

[0142] Further, based on any of the above embodiments, the device further comprises: an acquisition module, configured to acquire the parameter arrangement information and / or task splitting information generated by the user in response to the data editing operation triggered by the user; and a splitting module, configured to determine the parameter arrangement information and / or task splitting information as the data editing information.

[0143] Further, based on any of the above embodiments, the business association information includes the domain knowledge content corresponding to the target business system. The optimization module includes: a determination submodule, used to determine the business-specific vocabulary in the data processing result. A replacement submodule, used to replace the business-specific vocabulary based on the domain knowledge content to obtain the target data.

[0144] Further, based on any of the above embodiments, the business association information includes data requirement information corresponding to the target business system, and the data requirement information is obtained by parsing the interface information in the interface entry request sent by the target business system. The optimization module includes: an adjustment submodule, which is used to adjust the data format of the data processing result according to the data requirement corresponding to the business system to obtain the target data.

[0145] Further, based on any of the above embodiments, the business association information includes the domain knowledge content and data requirement information corresponding to the target business system. The optimization module includes: a determination submodule, used to determine the reference vocabulary in the data processing result. A replacement submodule, used to replace the reference vocabulary according to the domain knowledge content and the data requirement information to obtain the target data.

[0146] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0147] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, including:

[0148] At least one processor. And

[0149] A memory in communication with the at least one processor.

[0150] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data processing method described in any of the above embodiments.

[0151] According to an embodiment of the present disclosure, the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the data processing method as described in any of the above embodiments.

[0152] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0153] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure, such as Figure 7As shown, electronic devices are 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. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, 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 disclosure described and / or claimed herein.

[0154] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0155] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0156] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as data processing methods. For example, in some embodiments, the data processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the above-described data processing method in any other appropriate manner (e.g., by means of firmware).

[0157] 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), complex 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.

[0158] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code 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.

[0159] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. 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.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein may be implemented in a computing system that includes back-end 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 front-end 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 back-end components, middleware components, or front-end 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), and the Internet.

[0162] A computer 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 relationship between the client and the server 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 ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0163] 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 recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0164] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. 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 disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, applied to a data processing device, wherein the data processing device is respectively connected to a preset data processing model and at least one business system in communication; comprising: Obtaining a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model through data processing based on the natural language generated by the user, and identification information of a target business system to be called; Determine the business association information corresponding to the target business system; Performing data optimization operations on the data processing results according to the business association information to obtain target data; The target business system is called based on the target data to execute a task matching the target data.

2. The method according to claim 1, before obtaining the data processing request, further comprising: Respectively obtaining interface entry requests sent by each of the business systems, wherein the interface entry requests include interface information corresponding to the business system, and the formats of the interface information corresponding to different business systems are different; Performing a parsing operation on the interface information to determine a format corresponding to the interface information; The format of the interface information is converted into a target format matching the data processing model.

3. The method according to claim 2, wherein the step of calling the business system based on the target data to execute a task matching the target data comprises: The target business system is called based on the target data according to the interface information of the target format to execute a task matching the target data.

4. The method according to claim 1, wherein obtaining a data processing request comprises: Obtain user-generated natural language; Inputting the natural language into the data processing model to obtain the data processing result; The data processing request is generated based on the data processing result and a target business system determined by a user.

5. The method according to claim 4, wherein inputting the natural language into the data processing model comprises: Performing a keyword recognition operation on the natural language to obtain at least one keyword corresponding to the natural language; If the number of keywords corresponding to the natural language is less than a preset number threshold, inputting the natural language into the data processing model; If the number of keywords corresponding to the natural language is greater than a preset threshold value, a preset number of target keywords that meet preset conditions are selected from the multiple keywords corresponding to the natural language according to preset data editing information, and the preset number of target keywords are input into the data processing model.

6. The method according to claim 1, further comprising: Determining task information corresponding to the target data; Detecting whether the task information meets the preset splitting condition; If yes, splitting the task information according to the preset data editing information, or splitting the task information based on the data processing model to obtain at least one subtask; The calling the target business system based on the target data to execute the task matching the target data includes: The target business system is called respectively based on the target data to execute the at least one subtask.

7. The method according to claim 5 or 6, further comprising: In response to the data editing operation triggered by the user, obtaining parameter arrangement information and / or task splitting information generated by the user; The parameter arrangement information and / or the task splitting information is determined as the data editing information.

8. The method according to any one of claims 1 to 6, wherein the business association information includes domain knowledge content corresponding to the target business system; The performing a data optimization operation on the data processing result according to the business association information to obtain target data includes: Determining business-specific vocabulary in the data processing results; The business-specific vocabulary is replaced based on the domain knowledge content to obtain the target data.

9. The method according to any one of claims 1 to 6, wherein the business association information includes data requirement information corresponding to the target business system, and the data requirement information is obtained by parsing the interface information in the interface entry request sent by the target business system; The performing a data optimization operation on the data processing result according to the business association information to obtain target data includes: The data format of the data processing result is adjusted according to the data requirements corresponding to the business system to obtain the target data.

10. The method according to any one of claims 1 to 6, wherein the business association information includes domain knowledge content and data demand information corresponding to the target business system; The performing a data optimization operation on the data processing result according to the business association information to obtain target data includes: Determining a reference word in the data processing result; The reference vocabulary is replaced according to the domain knowledge content and the data demand information to obtain the target data.

11. A data processing device, the data processing device being respectively connected to a preset data processing model and at least one business system in communication; comprising: An acquisition module, used to acquire a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called; A determination module, used to determine the business association information corresponding to the target business system; An optimization module, used to perform data optimization operations on the data processing results according to the business association information to obtain target data; A calling module is used to call the target business system based on the target data to execute a task matching the target data.

12. The device according to claim 11, further comprising: An acquisition module, used to respectively acquire the interface entry request sent by each of the business systems, wherein the interface entry request includes the interface information corresponding to the business system, and the format of the interface information corresponding to different business systems is different; A parsing module, used to parse the interface information and determine the format corresponding to the interface information; The conversion module is used to convert the format of the interface information into a target format that matches the data processing model.

13. The device according to claim 12, wherein the calling module comprises: The calling submodule is used to call the target business system based on the target data according to the interface information in the target format to execute a task matching the target data.

14. The device according to claim 11, wherein the acquisition module comprises: The acquisition submodule is used to acquire the natural language generated by the user; A processing submodule, used for inputting the natural language into the data processing model to obtain the data processing result; The generating submodule is used to generate the data processing request based on the data processing result and the target business system determined by the user.

15. The method according to claim 14, wherein the processing submodule comprises: a recognition unit, configured to perform a keyword recognition operation on the natural language to obtain at least one keyword corresponding to the natural language; A first input unit, configured to input the natural language into the data processing model if the number of keywords corresponding to the natural language is less than a preset number threshold; The second unit is used to select a preset number of target keywords that meet preset conditions from multiple keywords corresponding to the natural language according to preset data editing information if the number of keywords corresponding to the natural language is greater than a preset number threshold, and input the preset number of target keywords into the data processing model.

16. The apparatus according to claim 11, further comprising: A determination module, used to determine the task information corresponding to the target data; A detection module, used to detect whether the task information meets the preset splitting conditions; a splitting module, configured to split the task information according to preset data editing information to obtain at least one subtask; The calling module includes: The calling submodule is used to call the target business system to execute the at least one subtask based on the target data.

17. The device according to claim 15 or 16, further comprising: An acquisition module, configured to acquire parameter arrangement information and / or task splitting information generated by the user in response to a data editing operation triggered by the user; A splitting module is used to determine the parameter arrangement information and / or task splitting information as the data editing information.

18. The device according to any one of claims 11 to 16, wherein the business association information includes domain knowledge content corresponding to the target business system; The optimization module includes: A determination submodule, used to determine the business-specific vocabulary in the data processing result; The replacement submodule is used to perform a replacement operation on the business-specific vocabulary based on the domain knowledge content to obtain the target data.

19. The device according to any one of claims 11 to 16, wherein the business association information includes data demand information corresponding to the target business system, and the data demand information is obtained by parsing the interface information in the interface entry request sent by the target business system; The optimization module includes: The adjustment submodule is used to adjust the data format of the data processing result according to the data requirements corresponding to the business system to obtain the target data.

20. The device according to any one of claims 11 to 16, wherein the business association information includes domain knowledge content and data demand information corresponding to the target business system; The optimization module includes: A determination submodule, used to determine the referential vocabulary in the data processing result; The replacement submodule is used to perform a replacement operation on the reference vocabulary according to the domain knowledge content and the data demand information to obtain the target data.

21. A data processing system, comprising a data processing device, at least one business system and a preset data processing model; in, The data processing device is respectively connected to the at least one business system and the preset data processing model for communication; The data processing device is used to obtain a data processing request, wherein the data processing request includes a data processing result obtained by the data processing model performing data processing based on a natural language generated by a user, and identification information of a target business system to be called; The data processing device is used to determine the business association information corresponding to the target business system, perform data optimization operations on the data processing result according to the business association information to obtain target data, and call the target business system to execute a task matching the target data based on the target data; The business system is used to execute tasks matching the target data.

22. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1 to 10.

23. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the data processing method according to any one of claims 1 to 10.

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

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