Application calling method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an application program calling method and device, computer equipment and a storage medium, and the application program calling method comprises the following steps: obtaining an application program set; wherein the application program set is a set of target application programs which are identified by the preset model according to the target instruction and correspond to the target instruction; the preset model is used for receiving a target instruction and determining and starting a plurality of target application programs in the application program set according to the target instruction; obtaining a target instruction, and determining execution steps of a plurality of target application programs according to the target instruction; and calling the plurality of target application programs, and executing the target instruction according to the execution steps. The multiple target application programs are determined and started at the same time through the preset model, the running time is shortened, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, computer equipment and storage medium for calling an application program. Background Art

[0002] With the development of artificial intelligence (AI), its application scenarios are increasing, and the applications of AI are also increasing. The application process of AI includes obtaining tasks posted by users, semantic understanding of tasks, information integration, reasonable reasoning and planning, and finally calling external tools to complete tasks.

[0003] In the process of calling external tools, there are generally two ways, one is to directly call the relevant application programming interface (Application Programming Interface, API) of the interactive application, and the other is to simulate human actions and operate the interactive application interface. The applications mentioned in the present invention, unless otherwise specified, refer to such interactive applications. For example, when using the AI ​​big model to call social software to send messages, since the social software does not provide an application interface to the outside world, the AI ​​big model can only simulate people's click behavior and operate the social software to complete the sending of messages. However, the running speed of this method is greatly restricted. It is necessary to wait for the previous step to be completed before executing the next step, resulting in a long running time, a poor experience for users, and even completely unable to be applied to actual business scenarios. Summary of the invention

[0004] In view of this, the present invention provides a method, apparatus, computer device and storage medium for calling an application program to solve the problem of long running time.

[0005] In a first aspect, the present invention provides a method for calling an application, which is applied to an AI large model, and includes: obtaining an application set; wherein the application set is a set of target applications corresponding to a target instruction identified by a preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction; obtaining the target instruction, and determining the execution steps of the multiple target applications according to the target instruction; calling the multiple target applications, and executing the target instruction according to the execution steps.

[0006] The present invention obtains an application set, which is a set of target applications corresponding to a target instruction obtained by a preset model according to the target instruction, that is, the preset model is used to identify and start the target application corresponding to the target instruction according to the target instruction, determine the execution steps of multiple target applications according to the target instruction, and execute the target instruction according to the execution steps. The present invention starts the target application corresponding to the target instruction in advance, executes the target instruction according to the execution steps, and compared with the related art of opening an application to obtain the execution result and then opening the next application for execution, there is no need to wait for the completion of each step, which reduces the time for application startup and operation, improves the operation speed, and improves the user experience.

[0007] In an optional embodiment, determining the execution steps of multiple target applications based on the target instruction includes: identifying and decomposing the target instruction to obtain multiple sub-instructions, and determining the execution order of multiple target applications corresponding to the multiple sub-instructions; and determining the execution steps of the multiple target applications based on the execution order.

[0008] The present invention identifies and decomposes the target instruction to obtain different sub-instructions corresponding to different target applications, which can realize parallel execution between different target applications, thereby reducing the execution time of the target instruction. The present invention determines the execution order of multiple target applications corresponding to multiple sub-instructions, determines the execution steps of the multiple target applications according to the execution order, and can confirm the correlation between the target applications according to different sub-instructions. When there is no correlation between the sub-instructions, parallel sub-instructions can be set in the execution order, and the unrelated execution steps in the execution steps can be set to parallel execution, thereby shortening the running time and improving the efficiency of the target instruction execution.

[0009] In an optional implementation, the preset model is a small model specifically for fine-tuning and / or a rule engine.

[0010] In an optional embodiment, the application set is a set of target applications corresponding to the target instructions identified by the rule engine based on the target instructions; the rule engine is used to extract keywords of the target instructions, and to search the target user keyword database, the system dynamic keyword database, and the system static keyword database based on the keywords, and is also used to determine and start multiple target applications in the application set based on the search results.

[0011] In the present invention, in addition to the preset model that can identify the target instructions, the rule engine can also realize the recognition of the target instructions. Based on the extraction of keywords and multiple searches, multiple target applications can be determined, so that the determined multiple target applications are more accurate and more in line with the target instructions.

[0012] In an optional embodiment, the execution step includes a parallel execution step and a serial execution step, wherein the parallel execution step refers to an execution step in which multiple execution steps are processed in parallel; the serial execution step refers to an order relationship between the execution steps, and when the first execution step is completed, the second execution step is executed.

[0013] In the present invention, the execution steps include parallel execution steps and serial execution steps, that is, the execution steps include execution steps that can be processed in parallel and execution steps that are executed separately. Parallel processing improves the execution efficiency of target instructions, shortens the running time, and improves the user experience.

[0014] In an optional implementation, after executing the target instruction according to the execution step, the method further includes: obtaining multiple execution results sent by multiple target applications, combining the multiple execution results to obtain a target execution result, and displaying the target execution result.

[0015] In the present invention, the target execution result is displayed so that the user can clearly know the execution status of the target instruction and ensure that it is executed according to the user's true intention.

[0016] In a second aspect, the present invention provides an application calling system, comprising: a preset model, used to receive a target instruction, and used to identify an application set corresponding to the target instruction according to the target instruction, and used to start multiple target applications in the application set according to the target instruction; wherein the application set represents a set of target applications corresponding to the target instruction; a preset model, used to send the application set to an AI big model; the AI ​​big model, used to obtain the target instruction, and also used to determine the execution steps of multiple target applications according to the target instruction; the AI ​​big model, used to call multiple target applications and execute the target instruction according to the execution steps.

[0017] The preset model of the present invention is used to receive the target instruction, and to identify the application set corresponding to the target instruction according to the target instruction, and to start multiple target applications in the application set according to the target instruction; wherein the application set represents the set of target applications corresponding to the target instruction; the preset model is used to send the application set to the AI ​​big model, the AI ​​big model is used to obtain the target instruction, and is also used to determine the execution steps of multiple target applications according to the target instruction; the AI ​​big model is used to call multiple target applications and execute the target instruction according to the execution steps. The present invention starts the target application corresponding to the target instruction in advance, and executes the target instruction according to the execution steps. Compared with the related art, which opens an application to obtain the execution result and then opens the next application to execute, there is no need to wait for the completion of each step, which reduces the time for application startup and operation, improves the operation speed, and improves the user experience.

[0018] In an optional implementation, the preset model is a small model specifically for fine-tuning and / or a rule engine.

[0019] In an optional implementation, the rule engine receives a target instruction input by a target user, and determines a target application based on the interaction among a keyword management module, a target user keyword database, a system dynamic keyword database, and a system static keyword database.

[0020] In an optional embodiment, the system also includes: an AI big model, which is also used to obtain multiple execution results sent by multiple target applications; the AI ​​big model is also used to combine the multiple execution results to obtain the target execution result; and is used to display the target execution result.

[0021] In a third aspect, the present invention provides an application calling device, which is applied to an AI large model, and the device includes: an application set acquisition module, which is used to acquire an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by a preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction; the target instruction acquisition module is used to acquire the target instruction and determine the execution steps of the multiple target applications according to the target instruction; the target instruction execution module is used to call multiple target applications and execute the target instruction according to the execution steps.

[0022] In a fourth aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for calling an application of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0023] In a fifth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute a method for calling an application program of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1is a flowchart of an application calling method in a related technology according to an embodiment of the present invention;

[0026] Figure 2 is a flowchart of a method for calling an application according to an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of a design example of a rule engine according to an embodiment of the present invention;

[0028] Figure 4 is a flowchart of a rule engine execution method according to an embodiment of the present invention;

[0029] Figure 5 is a flowchart of a calling method of another application according to an embodiment of the present invention;

[0030] Figure 6 is a flowchart of a method for calling another application according to an embodiment of the present invention;

[0031] Figure 7 is a flowchart of a method for operating a dedicated small model or a rule engine according to an embodiment of the present invention;

[0032] Figure 8 is a schematic diagram of the process of calling the system of an application according to an embodiment of the present invention;

[0033] Fig. 9 is a structural block diagram of a device for calling an application according to an embodiment of the present invention;

[0034] Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, it is a flowchart of an application calling method in the related art. In the related art, multiple target applications are called and target instructions are executed according to the execution steps, such as Figure 1As shown, after receiving the instruction, the AI ​​large model control application operation control module first starts application A according to the instruction, and controls application A to execute the instruction to obtain execution result a, starts application B according to execution result a, and controls application B to execute the instruction to obtain execution result b, starts application C according to execution result b, and controls application C to execute the instruction to obtain execution result c.

[0037] Therefore, in the related art, the next application can be started only after each application is executed, resulting in a long running time and low efficiency.

[0038] The embodiment of the present invention provides a method for calling an application program, which determines and simultaneously starts multiple target application programs through a preset model, so as to shorten the running time and improve the user experience.

[0039] According to an embodiment of the present invention, an embodiment of a method for calling an application is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] In this embodiment, a method for calling an application is provided, which can be used for an AI large model. Figure 2 is a flow chart of a method for calling an application according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0041] Step S201, obtaining an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by the preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction.

[0042] In some optional implementations, the preset model is a small model specifically for fine-tuning and / or a rule engine.

[0043] The preset model may be a small model dedicated to fine tuning, may be a rule engine, or may be a small model dedicated to fine tuning and a rule engine.

[0044] The preset model is used to identify the target command and determine multiple target applications corresponding to the target command according to the target command.

[0045] Among them, the target instruction is the instruction to be executed input by the target user, and the input form can be voice input, text input, gesture input, etc. The target application is the application corresponding to the target instruction, that is, the target application is the application that needs to be run to execute the target instruction.

[0046] In the embodiment of the present invention, unless otherwise specified, the target applications are all interactive applications.

[0047] For example, if the target instruction is "summarize the 10 hottest news today and send the summary to L via email", the preset model identifies the target instruction and obtains the target application as a news application and an email client; generates an application set, which includes a news application and an email client; and starts the target application, that is, starts the news application and the email client.

[0048] In some optional embodiments, the application set is a set of target applications corresponding to the target instructions identified by the rule engine based on the target instructions; the rule engine is used to extract keywords of the target instructions, and to search the target user keyword database, the system dynamic keyword database, and the system static keyword database based on the keywords, and is also used to determine and start multiple target applications in the application set based on the search results.

[0049] Among them, the rule engine is a component embedded in the application. The rules of the rule engine can be customized according to actual needs. In the embodiment of the present invention, the rule engine has the same function as the preset model, but the operation process is different. After receiving the target instruction, the rule engine extracts the keywords of the target instruction, searches the target user keyword database, the system dynamic keyword database and the system static keyword database according to the keywords, and determines the target application according to the search results.

[0050] In the rule engine, the target user keyword database refers to a database that stores commonly used keywords of target users who frequently use the AI ​​big model; the system dynamic keyword database refers to a database that stores keywords that change in real time; and the system static keyword database refers to a database that stores keywords that do not change.

[0051] like Figure 3 As shown in the figure, it is a schematic diagram of the design example of the rule engine. Figure 3 In the process, a target instruction input by a target user is received, keywords of the target instruction are extracted in a keyword management module, and a target user keyword database, a system dynamic keyword database, and a system static keyword database are searched according to the keywords to obtain search results, and a target application is determined according to the search results.

[0052] In the embodiment of the present invention, Figure 4As shown in FIG. 1 , it is a schematic diagram of the rule engine execution method, including: according to the rule engine, receiving the target instruction input by the target user, and determining the target application according to the interaction between the target user keyword database of the keyword management module, the system dynamic keyword database and the system static keyword database. Figure 4 As shown, exemplarily, for keywords such as "send message", "contact", "notify", and "say something", the corresponding target application is a social application; for keywords such as "buy for me", "place an order", "price", "promotion", and "live shopping", the corresponding target application is a shopping application; for keywords such as "hot keywords", "economic keywords", "political keywords", and "celebrity keywords", the corresponding target application is a news application; for keywords such as "search", "calculate", "time keywords", and "weather keywords", the corresponding target application is a tool application.

[0053] For example, in the target user keyword database, "send a message" means that for the target user, the application used is the social software V; in the system dynamic keyword database, "news hot spots" refers to the hot spots on the news software S; in the system static keyword database, "set an alarm" means setting an alarm on the alarm software.

[0054] In the embodiment of the present invention, in addition to the preset model that can identify the target instructions, the rule engine can also realize the recognition of the target instructions, and determine multiple target applications based on the extraction of keywords and multiple searches, so that the determined multiple target applications are more accurate and more in line with the target instructions.

[0055] Step S202, obtaining a target instruction, and determining execution steps of a plurality of target application programs according to the target instruction.

[0056] Among them, the method of obtaining the target instruction can be that the user sends a target instruction in the form of voice to the AI ​​big model, or the target user types in the form of text, etc.

[0057] In some optional implementations, the target instruction is identified and decomposed to obtain multiple sub-instructions, and the execution order of multiple target applications corresponding to the multiple sub-instructions is determined; based on the execution order, the execution steps of the multiple target applications are determined.

[0058] Exemplarily, for the target instruction "summarize the 10 hottest news today and send the summary to L via email", the target instruction is decomposed to obtain the first sub-instruction, running the news application; the second sub-instruction, summarizing the content of the first 10 news; the third sub-instruction, opening the email client; the fourth sub-instruction, entering L's email address in the address bar of the email client; the fifth sub-instruction, pasting the content summarized in the second sub-instruction into the content bar of the email client and sending it; then the execution order of the target application is to start the news application and the email client at the same time; then the execution steps of the target application are: the first sub-instruction and the third sub-instruction are executed in parallel; the second sub-instruction and the fourth sub-instruction are executed in parallel; and finally the fifth sub-instruction is executed.

[0059] In some optional embodiments, the execution steps include parallel execution steps and serial execution steps, wherein the parallel execution steps represent execution steps in which multiple execution steps are processed in parallel; the serial execution steps represent that there is a sequential relationship between the execution steps, and when the first execution step is completed, the second execution step is executed.

[0060] The order relationship between the first execution step and the second execution step is that the first execution step precedes the second execution step.

[0061] In some optional implementations, there is no dependency between parallel execution steps, but there is a dependency between serial execution steps. The dependency indicates the order of execution steps. In an embodiment of the present invention, when there is a dependency between execution steps, the content of the dependent execution steps is filled into the preset position to achieve the setting of the dependency.

[0062] Exemplarily, running a news application and opening an email client can be executed in parallel, and therefore, running a news application and opening an email client are parallel execution steps, while pasting the content summarized in the second sub-instruction into the content bar of the email client and sending it can only be executed separately, and therefore, pasting the content summarized in the second sub-instruction into the content bar of the email client and sending it is a serial execution step.

[0063] In an embodiment of the present invention, the execution steps include parallel execution steps and serial execution steps, that is, the execution steps include execution steps that can be processed in parallel and execution steps that are executed separately. Parallel processing improves the execution efficiency of target instructions, shortens the running time, and improves the user experience.

[0064] Step S203, calling multiple target applications, and executing target instructions according to the execution steps.

[0065] In the embodiment of the present invention, the target execution result is displayed so that the user can clearly know the execution status of the target instruction and ensure that it is executed according to the user's intention.

[0066] The application calling method provided in this embodiment obtains an application set, and the application set is a set of target applications corresponding to the target instruction obtained by the preset model according to the target instruction, that is, the preset model is used to identify and start the target application corresponding to the target instruction according to the target instruction, determine the execution steps of multiple target applications according to the target instruction, and execute the target instruction according to the execution steps. The embodiment of the present invention starts the target application corresponding to the target instruction in advance, executes the target instruction according to the execution steps, and compared with the related art of opening an application to obtain the execution result and then opening the next application for execution, there is no need to wait for the completion of each step, which reduces the time for application startup and operation, improves the operation speed, and improves the user experience.

[0067] In this embodiment, a method for calling an application is provided, which can be used for the above-mentioned AI big model. Figure 5 is a flowchart of a calling method of another application according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0068] Step S501, obtaining an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by the preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction. For details, please refer to Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0069] Step S502, obtain the target instruction, and determine the execution steps of multiple target applications according to the target instruction. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0070] In some optional implementations, the above step S502 includes:

[0071] Step S5021, identifying and decomposing the target instruction to obtain multiple sub-instructions, and determining the execution order of multiple target application programs corresponding to the multiple sub-instructions.

[0072] Step S5022: Determine the execution steps of multiple target applications according to the execution order.

[0073] Step S503, calling multiple target applications, and executing the target instructions according to the execution steps. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0074] Step S504, obtaining multiple execution results sent by multiple target applications, combining the multiple execution results to obtain a target execution result, and displaying the target execution result.

[0075] The calling method of another application provided in this embodiment identifies and decomposes the target instruction to obtain different sub-instructions corresponding to different target applications, which can realize parallel execution between different target applications, thereby reducing the execution time of the target instruction. The present invention determines the execution order of multiple target applications corresponding to multiple sub-instructions, determines the execution steps of the multiple target applications according to the execution order, and confirms the correlation between the target applications according to different sub-instructions. When there is no correlation between the sub-instructions, parallel sub-instructions can be set in the execution order, and the unrelated execution steps in the execution steps can be set to parallel execution, thereby shortening the running time and improving the efficiency of the target instruction execution.

[0076] In this embodiment, a method for calling an application is provided, which can be used for the above-mentioned AI big model. Figure 6 is a flowchart of a method for calling another application according to an embodiment of the present invention. Figure 6 As shown, including:

[0077] Obtain the target instruction, determine and start application A and application B corresponding to the target instruction according to the dedicated small model or rule engine; control the application operation control module according to the AI ​​large model to decompose the target instruction into the first sub-instruction, the second sub-instruction, the third sub-instruction and the fourth sub-instruction; control application A to execute the third sub-instruction and the fourth sub-instruction, and control application B to execute the first sub-instruction and the second sub-instruction; thereby obtaining the third execution result and the fourth execution result of application A and the second execution result and the first execution result of application B.

[0078] exist Figure 6 In the figure, the second sub-instruction and the fourth sub-instruction represented by the bold lines are executed in parallel, and the first sub-instruction and the third sub-instruction represented by the thin lines are executed in parallel. The execution order of the embodiment of the present invention is to first execute the first sub-instruction and the third sub-instruction of the thin lines, and then execute the second sub-instruction and the fourth sub-instruction of the bold lines; the bold dotted lines represent the second execution result and the fourth execution result corresponding to the second sub-instruction and the fourth sub-instruction, and the thin dotted lines represent the first execution result and the third execution result corresponding to the first sub-instruction and the third sub-instruction.

[0079] In the embodiment of the present invention, the first sub-instruction and the third sub-instruction are executed first, wherein the first sub-instruction and the third sub-instruction are executed in parallel, and the application A corresponding to the first sub-instruction and the application B of the third sub-instruction are run in parallel; then the second sub-instruction and the fourth sub-instruction are executed, wherein the second sub-instruction and the fourth sub-instruction are executed in parallel, and the application A corresponding to the fourth sub-instruction and the application B of the second sub-instruction are run in parallel. In the embodiment of the present invention, the application A and the application B are executed simultaneously according to the sub-instructions, without waiting for the execution of one application to end before executing another application, thereby improving the degree of parallelism, reducing the running time, and improving the execution efficiency of the target instruction.

[0080] In an embodiment of the present invention, a dedicated small model or rule engine is used to determine and start an application, for example, Figure 7 As shown, it is a schematic diagram of the operation method of a dedicated small model or rule engine. The dedicated small model or rule engine determines multiple applications corresponding to the target instruction according to the target instruction, can prepare the operating environment of multiple applications for application A to application N, and start multiple applications, and use the specially trained small model of the operation control module to control multiple applications in parallel and execute the target instruction.

[0081] Among them, the specially trained small model is a machine learning model for a specific task. Usually, when training a small model, sufficient samples in a specific field are given. These samples are generally question-answering samples, that is, given questions and answers, and by learning these samples, the reasoning skills in the field are mastered.

[0082] In the implementation manner of the present invention, the specially trained small model is only used in specific scenarios, so the inference computing power will be greatly reduced, and the inference cost will be greatly reduced; the response time of the specially trained small model is very fast, because the number of parameters is small and there is a special training process, so the response speed is even faster.

[0083] In this embodiment, a system for calling an application program is provided. Figure 8 FIG. 1 is a flowchart of the calling system operation of an application according to an embodiment of the present invention. Figure 8 As shown, the process includes the following steps:

[0084] Step S801, a preset model is used to receive a target instruction, and to identify an application set corresponding to the target instruction according to the target instruction, and to start multiple target applications in the application set according to the target instruction; wherein the application set represents a set of target applications corresponding to the target instruction.

[0085] Step S802, preset model, used to send the application set to the AI ​​big model.

[0086] Step S803, the AI ​​large model is used to obtain the target instruction and also to determine the execution steps of multiple target applications according to the target instruction.

[0087] Step S804, the AI ​​big model is used to call multiple target applications and execute target instructions according to the execution steps.

[0088] In some optional implementations, the preset model is a small model specifically for fine-tuning and / or a rule engine.

[0089] In some optional embodiments, the rule engine receives a target instruction input by a target user, and determines a target application based on the interaction among a keyword management module, a target user keyword database, a system dynamic keyword database, and a system static keyword database.

[0090] In some optional embodiments, the above system also includes: an AI big model, which is also used to obtain multiple execution results sent by multiple target applications; the AI ​​big model is also used to combine multiple execution results to obtain target execution results; and is used to display the target execution results.

[0091] In an embodiment of the present invention, a preset model is used to receive a target instruction, and to identify an application set corresponding to the target instruction according to the target instruction, and to start multiple target applications in the application set according to the target instruction; wherein the application set represents a set of target applications corresponding to the target instruction; the preset model is used to send the application set to the AI ​​big model, the AI ​​big model is used to obtain the target instruction, and is also used to determine the execution steps of multiple target applications according to the target instruction; the AI ​​big model is used to call multiple target applications and execute the target instruction according to the execution steps. The present invention starts the target application corresponding to the target instruction in advance, and executes the target instruction according to the execution steps. Compared with the related art, which opens an application to obtain the execution result and then opens the next application to execute, there is no need to wait for the completion of each step, which reduces the time for application startup and operation, improves the operation speed, and improves the user experience.

[0092] In the present embodiment, a device for calling an application is also provided, and the device is used to implement the above-mentioned embodiment and preferred implementation mode, and the descriptions have been made no further. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0093] This embodiment provides a device for calling an application program, such as Fig. 9 As shown, including:

[0094] The application set acquisition module 901 is used to acquire an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by the preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction.

[0095] The target instruction acquisition module 902 is used to acquire the target instruction and determine the execution steps of multiple target application programs according to the target instruction.

[0096] The target instruction execution module 903 is used to call multiple target application programs and execute the target instructions according to the execution steps.

[0097] In some optional implementations, the target instruction acquisition module 902 includes:

[0098] The execution order determination unit is used to identify and decompose the target instruction to obtain multiple sub-instructions and determine the execution order of multiple target application programs corresponding to the multiple sub-instructions.

[0099] The execution step determination unit is used to determine the execution steps of multiple target application programs according to the execution order.

[0100] In some optional embodiments, the above device further includes:

[0101] The execution result display module is used to obtain multiple execution results sent by multiple target applications, combine the multiple execution results, obtain the target execution result, and display the target execution result.

[0102] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0103] The calling device of the application in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0104] The embodiment of the present invention also provides a computer device having the above Fig. 9 The calling device of the application shown.

[0105] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10As shown, the computer device includes: one or more processors 1010, memory 1020, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 1010 is taken as an example.

[0106] The processor 1010 may be a central processing unit, a network processor or a combination thereof. The processor 1010 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0107] The memory 1020 stores instructions executable by at least one processor 1010, so that the at least one processor 1010 executes the method shown in the above embodiment.

[0108] The memory 1020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 1020 may optionally include a memory remotely arranged relative to the processor 1010, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The memory 1020 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 1020 may also include a combination of the above types of memory.

[0110] The computer device also includes a communication interface 1030, which is used for the computer device to communicate with other devices or a communication network.

[0111] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0112] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for calling an application, characterized in that: Applied to the AI ​​big model, the method includes: Acquire an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by a preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction; Acquire the target instruction, and determine the execution steps of the plurality of target applications according to the target instruction; The plurality of target application programs are called, and the target instructions are executed according to the execution steps.

2. The method according to claim 1, characterized in that: The step of determining the execution of the plurality of target applications according to the target instruction comprises: Identifying and decomposing the target instruction to obtain a plurality of sub-instructions, and determining an execution order of the plurality of target application programs corresponding to the plurality of sub-instructions; According to the execution order, the execution steps of the multiple target application programs are determined.

3. The method according to claim 1 or 2, characterized in that: The preset model is a small model dedicated to fine-tuning and / or a rule engine.

4. The method according to claim 3, characterized in that The application set is a set of target applications corresponding to the target instruction identified by the rule engine based on the target instruction; the rule engine is used to extract keywords of the target instruction, and to search the target user keyword database, the system dynamic keyword database and the system static keyword database based on the keywords, and is also used to determine and start multiple target applications in the application set based on the search results.

5. The method according to claim 1 or 2, characterized in that: The execution steps include parallel execution steps and serial execution steps, wherein the parallel execution steps represent execution steps in which multiple execution steps are processed in parallel; the serial execution steps represent that there is a sequential relationship between the execution steps, and when the first execution step is completed, the second execution step is executed.

6. The method according to claim 1 or 2, characterized in that: After executing the target instruction according to the execution step, the method further includes: Acquire multiple execution results sent by the multiple target applications, combine the multiple execution results to obtain a target execution result, and display the target execution result.

7. A system for calling an application program, characterized in that: The system comprises: A preset model, used for receiving a target instruction, and for identifying an application set corresponding to the target instruction according to the target instruction, and for starting a plurality of target applications in the application set according to the target instruction; wherein the application set represents a set of target applications corresponding to the target instruction; The preset model is used to send the application set to the AI ​​big model; The AI ​​big model is used to obtain the target instruction and determine the execution steps of the multiple target applications according to the target instruction; The AI ​​big model is used to call the multiple target applications and execute the target instructions according to the execution steps.

8. The system according to claim 7, characterized in that The preset model is a small model dedicated to fine-tuning and / or a rule engine.

9. The system according to claim 8, characterized in that The rule engine receives a target instruction input by a target user, and determines a target application according to the interaction among a keyword management module, a target user keyword database, a system dynamic keyword database, and a system static keyword database.

10. The system according to claim 7, characterized in that The system further comprises: The AI ​​big model is also used to obtain multiple execution results sent by the multiple target applications; The AI ​​big model is also used to combine the multiple execution results to obtain the target execution result; and to display the target execution result.

11. A device for calling an application, characterized in that: Applied to AI large model, the device includes: An application set acquisition module, used to acquire an application set; wherein the application set is a set of target applications corresponding to the target instruction identified by a preset model according to the target instruction; the preset model is used to receive the target instruction, and to determine and start multiple target applications in the application set according to the target instruction; A target instruction acquisition module, used to acquire the target instruction and determine the execution steps of the plurality of target application programs according to the target instruction; The target instruction execution module is used to call the multiple target application programs and execute the target instructions according to the execution steps.

12. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for calling the application program according to any one of claims 1 to 6 by executing the computer instructions.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for calling the application program according to any one of claims 1 to 6.

Images