CN110597521B - Data processing method, electronic device, and medium - Google Patents
Data processing method, electronic device, and medium Download PDFInfo
- Publication number
- CN110597521B CN110597521B CN201910857324.XA CN201910857324A CN110597521B CN 110597521 B CN110597521 B CN 110597521B CN 201910857324 A CN201910857324 A CN 201910857324A CN 110597521 B CN110597521 B CN 110597521B
- Authority
- CN
- China
- Prior art keywords
- interface component
- functional unit
- interface
- selecting
- request information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/70—Software maintenance or management
- G06F8/71—Version control; Configuration management
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The present disclosure provides a data processing method, including obtaining request information, where the request information represents a request for a first functional unit, where the first functional unit is capable of being displayed in a display device in a first display manner, the first functional unit is capable of being connected to at least one second functional unit displayed in a second display manner in the display device in a first connection manner, where the first connection manner represents that data transmission is possible between the first functional unit and the second functional unit, selecting a first interface component from a plurality of interface components, constructing the first functional unit based on the first interface component, and calling the first functional unit to respond to the request information. The present disclosure also provides an electronic device and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to a data processing method, an electronic device, and a computer-readable storage medium.
Background
With the development of information technology, artificial intelligence has continuously gained outstanding achievements in various fields. Companies are developing artificial intelligence implementation algorithms for some common functions, such as voice recognition, image recognition, etc., and a large number of artificial intelligence implementation interfaces are available. The artificial intelligence interfaces of different channels have characteristics in function, and the interfaces of different channels need to be accessed in order to meet different requirements, however, the interfaces of different channels are different from each other, and are developed for accessing different interfaces respectively, so that the artificial intelligence interfaces are very inconvenient.
Disclosure of Invention
One aspect of the present disclosure provides a data processing method, including obtaining request information, where the request information represents a request for a first functional unit, where the first functional unit is capable of being displayed in a display device in a first display manner, where the first functional unit is capable of being connected to at least one second functional unit displayed in a second display manner in the display device in a first connection manner, where the first connection manner represents that data transmission is enabled between the first functional unit and the second functional unit, selecting a first interface component from a plurality of interface components, building the first functional unit based on the first interface component, and calling the first functional unit to respond to the request information.
Optionally, the plurality of interface components are built based on different training models.
Optionally, the selecting a first interface component from the plurality of interface components comprises determining at least one data characteristic based on the request information, and selecting the first interface component from the plurality of interface components according to the at least one data characteristic.
Optionally, the selecting the first interface component from the plurality of interface components includes determining a processing requirement in the request information, and selecting the first interface component from the plurality of interface components based on the processing requirement.
Optionally, the processing requirements include one or more of response time requirements, prediction accuracy requirements, and invocation cost requirements.
Optionally, the selecting, based on the processing requirement, a first interface component from a plurality of interface components includes selecting a second interface component from the plurality of interface components, executing, based on the request information, a first function unit constructed based on the second interface component, obtaining a processing result, and in a case that the processing result does not meet a predetermined condition, selecting the first interface component from the plurality of interface components to replace the second interface component for constructing the first function unit.
Optionally, the selecting the first interface component from the plurality of interface components comprises determining a predicted value of resource utilization of the at least one interface component, and selecting the first interface component from the at least one interface component based on the predicted value.
Optionally, the determining the predicted value of the resource utilization rate of the at least one interface component includes obtaining a resource status index corresponding to the interface component, and determining the predicted value of the resource utilization rate of the interface component based on the resource status index. The resource state index comprises one or more of processor temperature, processor occupancy rate, hard disk temperature, memory occupancy rate and network occupancy rate.
Another aspect of the disclosure provides a data processing apparatus including an obtaining module, a selecting module, and a calling module. The device comprises an obtaining module and a display module, wherein the obtaining module is used for obtaining request information, the request information represents a first function unit which is requested, the first function unit can be displayed in a display device in a first display mode, the first function unit can be connected with at least one second function unit which is displayed in a second display mode in the display device in a first connection mode, and the first connection mode represents that data transmission can be carried out between the first function unit and the second function unit. The selection module is used for selecting a first interface component from a plurality of interface components and constructing the first functional unit based on the first interface component. And the calling module is used for calling the first functional unit so as to respond to the request information.
Another aspect of the disclosure provides an electronic device comprising at least one processor and at least one memory storing one or more computer-readable instructions, wherein the one or more computer-readable instructions, when executed by the at least one processor, cause the processor to perform the method as described above.
Another aspect of the disclosure provides a non-volatile storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically shows an exemplary application scenario of a data processing method according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;
3A-3C schematically illustrate a flow diagram for selecting a first interface component from a plurality of interface components, according to an embodiment of the present disclosure;
FIG. 4 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and
FIG. 5 schematically shows a block diagram of a computer system according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a data processing method, which includes obtaining request information, wherein the request information represents a first function unit which is requested, the first function unit can be displayed in a display device in a first display mode, the first function unit can be connected with at least one second function unit which is displayed in a second display mode in the display device in a first connection mode, the first connection mode represents that data transmission can be carried out between the first function unit and the second function unit, a first interface component is selected from a plurality of interface components, the first function unit is constructed based on the first interface component, and the first function unit is called to respond to the request information.
Fig. 1 schematically shows an exemplary application scenario of a data processing method according to an embodiment of the present disclosure.
As shown in fig. 1, a visual artificial intelligence system can be represented by a flow chart comprising at least one input interface, an output interface and at least one functional unit. The data to be processed can be circulated in a mode represented by the flow chart, when the data to be processed passes through the functional unit, the data to be processed is processed by the functional unit and is output in a specified format to be circulated to the next functional unit or the output interface.
Wherein each functional unit may be an artificial intelligence model with a specific function. For example, an artificial intelligence system for recognizing the emotion of a user may include a functional unit of a face recognition model and a functional unit of a voice recognition model, which are fused to obtain an emotion judgment result as an output. Wherein, for a functional unit, there may be a plurality of interface components for constructing the functional unit, and one of the plurality of interface components may be selected as required for constructing the functional unit. For example, as shown in FIG. 1, interface component A, interface component B, and interface component C may be provided for building unit 1.
According to an embodiment of the present disclosure, the plurality of interface components are built based on different training models. For example, for a functional unit for speech recognition, companies such as associativity, ***, microsoft, amazon, science news, hundredths, Tencent, etc. currently provide interface components with speech recognition models for use, from which suitable interface components can be selected for building the functional unit as required.
According to the embodiment of the disclosure, the plurality of interface components are interface components of a unified input and output specification. Since the multiple interface components may come from different channels, the multiple interface components may be built based on different interface specifications, preprocessing manners, and the like. In this regard, the method of the embodiment of the present disclosure may further unify the input and output of each interface component through packaging, so that a selection may be arbitrarily made from a plurality of interface components when in use, and no compatibility problem occurs.
The scheme for constructing the artificial intelligence system provided by the embodiment of the disclosure has the advantages of visualization, low learning cost and low construction difficulty.
Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S230.
In operation S210, request information is obtained, where the request information represents a request for a first functional unit, the first functional unit is capable of being displayed in a display device in a first display manner, the first functional unit is capable of being connected to at least one second functional unit displayed in a second display manner in the display device in a first connection manner, and the first connection manner represents that data transmission is enabled between the first functional unit and the second functional unit.
According to the embodiment of the present disclosure, the first functional unit may be, for example, the unit 1 shown in fig. 1, and the second functional unit may be, for example, the unit 4 shown in fig. 1, the first functional unit and the second functional unit are connected in a first connection manner, and an output of the first functional unit serves as an input of the second functional unit. Alternatively, the first functional unit may be, for example, the unit 4 shown in fig. 1, and the second functional unit may be, for example, the unit 1 shown in fig. 1, the first functional unit and the second functional unit being connected in a first connection, and an output of the second functional unit being an input of the first functional unit.
In operation S220, a first interface component is selected from a plurality of interface components, and the first functional unit is built based on the first interface component.
According to an embodiment of the present disclosure, for example, interface component a as shown in fig. 1 may be selected for building unit 1, or interface component B or interface component C may also be selected for building unit 1.
In operation S230, the first functional unit is called in response to the request information. After the first functional unit is built, the first functional unit may process the data to be processed that is streamed to the functional unit. According to the embodiment of the disclosure, all the functional units in the artificial intelligence system can be constructed before the artificial intelligence system obtains input, and the functional units can also be constructed when the data stream is transferred to a certain functional unit.
According to the embodiment of the disclosure, the plurality of interface components have different characteristics, for example, some interface components have higher processing efficiency, some interface components have better prediction accuracy, some interface components have lower use cost, some interface components have better data processing effect on a specific scene, and the like. A method of selecting a first interface component from a plurality of interface components will be described with reference to fig. 3A to 3C.
FIG. 3A schematically illustrates a flow diagram for selecting a first interface component from a plurality of interface components according to an embodiment of the disclosure.
As shown in fig. 3A, the method includes operations S311 and S312.
In operation S311, at least one data characteristic is determined based on the request information.
In operation S312, the first interface component is selected from the plurality of interface components according to the at least one data characteristic.
For example, if the data size of the data to be processed is large, an interface component suitable for the large data size, such as an interface component with high processing efficiency, may be selected. For another example, if there are more images of black people in the data to be processed in a scene for processing facial images, an interface component generated by using a data model of black people face training may be selected. The method selects an interface component from a plurality of interface components based on differences in data characteristics.
FIG. 3B schematically illustrates a flow diagram for selecting a first interface component from a plurality of interface components according to another embodiment of the present disclosure.
As shown in fig. 3B, the method includes operations S321 and S322.
In operation S321, a processing requirement in the request information is determined. According to embodiments of the present disclosure, processing requirements may include one or more of response time requirements, prediction accuracy requirements, and call cost requirements.
In operation S322, a first interface component is selected from a plurality of interface components based on the processing requirement.
According to the embodiment of the disclosure, the user may specify the processing requirement in the request information, for example, the user may require the prediction accuracy to be more than 80%, and the system may select an interface component capable of meeting the processing requirement from a plurality of components for constructing the first functional unit.
According to the embodiment of the present disclosure, the selecting a first interface component from a plurality of interface components based on the processing requirement includes selecting a second interface component from the plurality of interface components, executing a first function unit constructed based on the second interface component based on the request information, obtaining a processing result, and selecting the first interface component from the plurality of interface components to replace the second interface component for constructing the first function unit if the processing result does not meet a predetermined condition. According to the method, when the second interface component cannot meet the processing requirement, the second interface component can be replaced by other interface components, and the first functional unit is reconstructed to meet the processing requirement.
FIG. 3C schematically shows a flow diagram for selecting a first interface component from a plurality of interface components according to another embodiment of the present disclosure.
As shown in fig. 3C, the method includes operations S331 and S332.
In operation S331, a predicted value of resource utilization of the at least one interface component is determined. According to embodiments of the present disclosure, different interface components may run on different platforms, e.g., there are at least two sets of servers, a first interface component running on a first set of servers and a second interface component running on a second set of servers. The first set of servers and the second set of servers have different resource utilization rates at different times. The resource utilization rate includes utilization rates of resources such as a processor, a memory, a hard disk, a network and the like.
According to the embodiment of the present disclosure, the resource utilization rate of the resources may be characterized by some resource status indicators, which may include, for example, one or more of processor temperature, processor occupancy, hard disk temperature, memory occupancy, and network occupancy. Determining the predicted value of the resource utilization of the at least one interface component may include obtaining a resource status indicator corresponding to the interface component, and determining the predicted value of the resource utilization of the interface component based on the resource status indicator.
In operation S332, a first interface component is selected from the at least one interface component based on the predicted value. For example, a first interface component running on a first group of servers may be selected for building a first functional unit when the resource occupancy of a second group of servers is too high.
Those skilled in the art will appreciate that the methods of fig. 3A-3C described above may be used in combination, and different selection policies may set different priorities to ultimately determine the first interface component from the plurality of interface components. For example, a plurality of interface components capable of meeting the data characteristics and the processing requirements may be determined from the plurality of interface components according to the data characteristics and the processing requirements, and then interface components corresponding to relatively idle platforms may be selected according to resource utilization rates of platforms on which the interface components operate to construct the first functional unit. Some of the conditions may be enforced, while others may be non-enforced. For example, in some embodiments, the invocation cost may be mandatory, while the response time requirement may be non-mandatory, and the interface component with the fastest response time may be selected among the interface components that meet the invocation cost.
The method of the embodiment of the disclosure combines a plurality of strategies to select one interface component from a plurality of interface components for constructing the functional unit, thereby being beneficial to fully and reasonably utilizing resources.
Based on the same inventive concept, the present disclosure also provides a data processing apparatus, and the data processing apparatus of the embodiment of the present disclosure is described below with reference to fig. 4.
Fig. 4 schematically shows a block diagram of a data processing apparatus 400 according to an embodiment of the present disclosure.
As shown in fig. 4, the data processing apparatus 400 includes an obtaining module 410, a selecting module 420, and a calling module 430. The apparatus 400 may perform the methods described above with reference to fig. 2, 3A-3C.
The obtaining module 410, for example, performs the operation S210 described above with reference to fig. 2, and is configured to obtain request information, where the request information represents a request for a first functional unit, the first functional unit is capable of being displayed in a first display manner in a display device, the first functional unit is capable of being connected to at least one second functional unit displayed in a second display manner in the display device in a first connection manner, and the first connection manner represents that data transmission is enabled between the first functional unit and the second functional unit.
The selection module 420, for example, performs operation S220 described above with reference to fig. 2, for selecting a first interface component from the plurality of interface components, building the first functional unit based on the first interface component.
The calling module 430, for example, performs the operation S230 described above with reference to fig. 2, for calling the first functional unit in response to the request information.
According to an embodiment of the present disclosure, the plurality of interface components are built based on different training models.
According to an embodiment of the present disclosure, the selecting a first interface component from a plurality of interface components includes determining at least one data characteristic based on the request information, and selecting the first interface component from the plurality of interface components according to the at least one data characteristic.
According to an embodiment of the present disclosure, the selecting the first interface component from the plurality of interface components includes determining a processing requirement in the request information, and selecting the first interface component from the plurality of interface components based on the processing requirement.
According to an embodiment of the present disclosure, the processing requirements include one or more of response time requirements, prediction accuracy requirements, and call cost requirements.
According to the embodiment of the present disclosure, the selecting a first interface component from a plurality of interface components based on the processing requirement includes selecting a second interface component from the plurality of interface components, executing a first function unit constructed based on the second interface component based on the request information, obtaining a processing result, and selecting the first interface component from the plurality of interface components to replace the second interface component for constructing the first function unit if the processing result does not meet a predetermined condition.
According to an embodiment of the present disclosure, the selecting a first interface component from a plurality of interface components includes determining a predicted value of a resource utilization of the at least one interface component, and selecting the first interface component from the at least one interface component based on the predicted value.
According to the embodiment of the disclosure, the determining the predicted value of the resource utilization rate of the at least one interface component includes obtaining a resource status index corresponding to the interface component, and determining the predicted value of the resource utilization rate of the interface component based on the resource status index. The resource state index comprises one or more of processor temperature, processor occupancy rate, hard disk temperature, memory occupancy rate and network occupancy rate.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the obtaining module 410, the selecting module 420, and the calling module 430 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 410, the selecting module 420, and the invoking module 430 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable manner of integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the obtaining module 410, the selecting module 420 and the calling module 430 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.
FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 5 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 5, computer system 500 includes a processor 510 and a computer-readable storage medium 520. The computer system 500 may perform a method according to an embodiment of the disclosure.
In particular, processor 510 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 510 may also include on-board memory for caching purposes. Processor 510 may be a single processing unit or a plurality of processing units for performing different actions of a method flow according to embodiments of the disclosure.
Computer-readable storage media 520, for example, may be non-volatile computer-readable storage media, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 520 may include a computer program 521, which computer program 521 may include code/computer-executable instructions that, when executed by the processor 510, cause the processor 510 to perform a method according to an embodiment of the disclosure, or any variation thereof.
The computer program 521 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 521 may include one or more program modules, including for example 521A, modules 521B, … …. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, and when these program modules are executed by the processor 510, the processor 510 may execute the method according to the embodiment of the present disclosure or any variation thereof.
According to an embodiment of the present invention, at least one of the reading module 410, the obtaining module 420, the executing module 430, the outputting module 440, the first executing sub-module 511, the updating sub-module 512, the first determining sub-module 521, the second executing sub-module 522, the second updating sub-module 523, the second determining sub-module 531, the third executing sub-module 532, the third updating sub-module 533, the determining module, and the recording module may be implemented as a computer program module described with reference to fig. 5, which, when executed by the processor 510, may implement the corresponding operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (8)
1. A data processing method for constructing an artificial intelligence system, the method comprising:
obtaining request information, wherein the request information represents a first function unit which requests to be displayed in a display device in a first display mode, the first function unit can be connected with at least one second function unit which is displayed in a second display mode in the display device in a first connection mode, and the first connection mode represents that data transmission can be carried out between the first function unit and the second function unit;
selecting a first interface component from a plurality of interface components, and constructing the first functional unit based on the first interface component;
calling the first functional unit to respond to the request information;
wherein said building said first functional unit comprises:
building all functional units in the artificial intelligence system before the system obtains input; or
Building the functional unit when the data stream is forwarded to the first functional unit;
wherein said selecting a first interface component from a plurality of interface components comprises:
determining a processing requirement in the request information;
selecting a first interface component from a plurality of interface components based on the processing requirement;
said selecting a first interface component from a plurality of interface components based on said processing requirement comprises:
selecting a second interface component from the plurality of interface components;
executing a first functional unit constructed based on the second interface component based on the request information to obtain a processing result;
and under the condition that the processing result does not meet the preset condition, selecting a first interface component from the plurality of interface components to replace the second interface component for constructing the first functional unit.
2. The method of claim 1, wherein the plurality of interface components are built based on different training models.
3. The method of claim 1, wherein the selecting a first interface component from a plurality of interface components comprises:
determining at least one data characteristic based on the request information;
selecting the first interface component from the plurality of interface components based on the at least one data characteristic.
4. The method of claim 1, wherein the processing requirements include one or more of response time requirements, prediction accuracy requirements, and invocation cost requirements.
5. The method of claim 1, wherein the selecting a first interface component from a plurality of interface components comprises:
determining a predicted value of resource utilization for the at least one interface component; and
based on the predicted value, a first interface component is selected from the at least one interface component.
6. The method of claim 5, wherein the determining a predicted value of resource utilization of the at least one interface component comprises:
obtaining a resource state index corresponding to the interface component;
determining a predicted value of resource utilization of the interface component based on the resource status indicator,
the resource state index comprises one or more of processor temperature, processor occupancy rate, hard disk temperature, memory occupancy rate and network occupancy rate.
7. An electronic device, comprising:
a processor; and
a memory having computer-readable instructions stored thereon that, when executed by the processor, cause the processor to construct an artificial intelligence system, the processor:
obtaining request information, wherein the request information represents a first function unit which requests to be displayed in a display device in a first display mode, the first function unit can be connected with at least one second function unit which is displayed in a second display mode in the display device in a first connection mode, and the first connection mode represents that data transmission can be carried out between the first function unit and the second function unit;
selecting a first interface component from a plurality of interface components, and constructing the first functional unit based on the first interface component;
calling the first functional unit to respond to the request information;
wherein said building said first functional unit comprises:
building all functional units in the artificial intelligence system before the system obtains input; or
Building the functional unit when the data stream is forwarded to the first functional unit;
wherein said selecting a first interface component from a plurality of interface components comprises:
determining a processing requirement in the request information;
selecting a first interface component from a plurality of interface components based on the processing requirement;
said selecting a first interface component from a plurality of interface components based on said processing requirement comprises:
selecting a second interface component from the plurality of interface components;
executing a first functional unit constructed based on the second interface component based on the request information to obtain a processing result;
and under the condition that the processing result does not meet the preset condition, selecting a first interface component from the plurality of interface components to replace the second interface component for constructing the first functional unit.
8. A computer-readable storage medium having computer-readable instructions stored thereon that, when executed by a processor, cause the processor to construct an artificial intelligence system, the processor:
obtaining request information, wherein the request information represents a first function unit which requests to be displayed in a display device in a first display mode, the first function unit can be connected with at least one second function unit which is displayed in a second display mode in the display device in a first connection mode, and the first connection mode represents that data transmission can be carried out between the first function unit and the second function unit;
selecting a first interface component from a plurality of interface components, and constructing the first functional unit based on the first interface component;
calling the first functional unit to respond to the request information;
wherein said building said first functional unit comprises:
building all functional units in the artificial intelligence system before the system obtains input; or
Building the functional unit when the data stream is forwarded to the first functional unit;
wherein said selecting a first interface component from a plurality of interface components comprises:
determining a processing requirement in the request information;
selecting a first interface component from a plurality of interface components based on the processing requirement;
said selecting a first interface component from a plurality of interface components based on said processing requirement comprises:
selecting a second interface component from the plurality of interface components;
executing a first functional unit constructed based on the second interface component based on the request information to obtain a processing result;
and under the condition that the processing result does not meet the preset condition, selecting a first interface component from the plurality of interface components to replace the second interface component for constructing the first functional unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910857324.XA CN110597521B (en) | 2019-09-10 | 2019-09-10 | Data processing method, electronic device, and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910857324.XA CN110597521B (en) | 2019-09-10 | 2019-09-10 | Data processing method, electronic device, and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110597521A CN110597521A (en) | 2019-12-20 |
| CN110597521B true CN110597521B (en) | 2021-11-16 |
Family
ID=68858781
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910857324.XA Active CN110597521B (en) | 2019-09-10 | 2019-09-10 | Data processing method, electronic device, and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110597521B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105897887A (en) * | 2016-04-08 | 2016-08-24 | 武汉邮电科学研究院 | Clouding computing-based remote sensing satellite big data processing system and method |
| AU2016277775A1 (en) * | 2016-01-04 | 2017-07-20 | Accenture Global Solutions Limited | Data processor for projects |
| CN108647316A (en) * | 2018-05-10 | 2018-10-12 | 北京中电普华信息技术有限公司 | Data processing method and device |
| CN109614088A (en) * | 2018-12-07 | 2019-04-12 | 北京知道创宇信息技术有限公司 | Form component generation method and device |
| CN109857569A (en) * | 2018-11-08 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Interface calling processing method and device, storage medium, computer equipment |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10056415A1 (en) * | 2000-11-14 | 2002-05-23 | Traian Internet Products Ag | Computer arrangement in packet-oriented network has software modules having uniform structure and same interface for setting functionality of data collectors |
| CN102571550B (en) * | 2010-12-30 | 2016-05-25 | 北京亿阳信通科技有限公司 | A kind of general information exchange platform and method |
| CN103501382B (en) * | 2013-09-17 | 2015-06-24 | 小米科技有限责任公司 | Voice service providing method, device and terminal |
| CN103559040B (en) * | 2013-11-12 | 2016-05-18 | 厦门卓讯信息技术有限公司 | A kind of system and method based on SDK rapid build mobile Internet application module |
| CN105049505A (en) * | 2015-07-09 | 2015-11-11 | 上海爱数软件有限公司 | Method and system for adapting various kinds of cloud storage interfaces |
| CN109684103A (en) * | 2018-12-17 | 2019-04-26 | 上海曼恒数字技术股份有限公司 | A kind of interface call method, device, server and storage medium |
-
2019
- 2019-09-10 CN CN201910857324.XA patent/CN110597521B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2016277775A1 (en) * | 2016-01-04 | 2017-07-20 | Accenture Global Solutions Limited | Data processor for projects |
| CN105897887A (en) * | 2016-04-08 | 2016-08-24 | 武汉邮电科学研究院 | Clouding computing-based remote sensing satellite big data processing system and method |
| CN108647316A (en) * | 2018-05-10 | 2018-10-12 | 北京中电普华信息技术有限公司 | Data processing method and device |
| CN109857569A (en) * | 2018-11-08 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Interface calling processing method and device, storage medium, computer equipment |
| CN109614088A (en) * | 2018-12-07 | 2019-04-12 | 北京知道创宇信息技术有限公司 | Form component generation method and device |
Non-Patent Citations (1)
| Title |
|---|
| 基于工作流技术的ERP***研究及开发;刘欣等;《成组技术与生产现代化 现代生产与管理技术》;20070730;21-25 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110597521A (en) | 2019-12-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20190340817A1 (en) | Learning opportunity based display generation and presentation | |
| US11574233B2 (en) | Suggestion and completion of deep learning models from a catalog | |
| CN109214674B (en) | Customer service system management method, customer service system and electronic equipment | |
| US20190394507A1 (en) | Methods and systems for managing viewer engagement of live streaming events | |
| CN110554865B (en) | Visual programming method, device, computing equipment and medium | |
| KR20210094639A (en) | Resource scheduling method and apparatus, electronic device and recording medium | |
| US11650983B2 (en) | Selecting an optimal combination of systems for query processing | |
| US20180012237A1 (en) | Inferring user demographics through categorization of social media data | |
| US20190018687A1 (en) | Cognitive thread management in a multi-threading application server environment | |
| US10013622B2 (en) | Removing unwanted objects from a photograph | |
| US20180157505A1 (en) | Automated image layer blacklisting in the cloud | |
| US10762089B2 (en) | Open ended question identification for investigations | |
| US10565202B2 (en) | Data write/import performance in a database through distributed memory | |
| US11182674B2 (en) | Model training by discarding relatively less relevant parameters | |
| CN110750295B (en) | Information processing method, device, electronic equipment and storage medium | |
| CN110597521B (en) | Data processing method, electronic device, and medium | |
| US11237890B2 (en) | Analytics initiated predictive failure and smart log | |
| US10924586B2 (en) | Aggregating virtual reality (VR) sessions | |
| US10762125B2 (en) | Sorting images based on learned actions | |
| US10397171B2 (en) | Managing content disclosure on social networking sites | |
| US20200051296A1 (en) | Determining image description specificity in presenting digital content | |
| US10417403B2 (en) | Automation authentication and access | |
| US20230105621A1 (en) | Extended reality content navigation | |
| CN114998068A (en) | Learning plan generation method and electronic equipment | |
| US11068552B2 (en) | Updating social media post based on subsequent related social media content |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |