CN114401310A - Visual cloud service data optimization method and server - Google Patents
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Abstract
The application relates to a visual cloud service data optimization method and a server, wherein historical visual cloud service feedback stored in a visual cloud service feedback record in advance is loaded to a convolutional neural network for visual cloud service feedback adjustment to obtain derivative visual cloud service feedback; and updating historical visual cloud service feedback by combining the derivative visual cloud service feedback to optimize the visual cloud service feedback record, so that the optimization timeliness of the visual cloud service feedback record is improved in the history of compatible accuracy in the process of optimizing the visual cloud service feedback record, and the service quality of the service feedback can be improved as much as possible by integrating the historical visual cloud service feedback.
Description
Technical Field
The application relates to the technical field of visual business and data optimization, in particular to a visual cloud business data optimization method and a server.
Background
With the continuous progress of new generation information technology, visualization service is also developed to a certain extent. However, in practical application, how to improve the optimization timeliness of the visual cloud service feedback record and improve the service quality of the service feedback as much as possible is a technical problem that needs to be further improved at present.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a visual cloud service data optimization method and a server.
The application provides a visual cloud service data optimization method, which is applied to a data optimization server and comprises the following steps:
determining whether the visual cloud service feedback record contains historical visual cloud service feedback of a first to-be-processed service interaction project;
on the basis that the visual cloud service feedback record contains the historical visual cloud service feedback of the first to-be-processed service interaction item, loading the historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in advance in the visual cloud service feedback record, to a convolutional neural network for visual cloud service feedback adjustment, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction item;
updating historical visual cloud service feedback of the first to-be-processed service interaction project by combining with the derivative visual cloud service feedback of the first to-be-processed service interaction project so as to optimize the visual cloud service feedback record;
the convolutional neural network is optimized according to historical visual cloud service feedback and associated visual cloud service feedback of a plurality of second to-be-processed service interaction projects, in the optimization process of the convolutional neural network, the historical visual cloud service feedback of the second to-be-processed service interaction projects is regarded as input, the associated visual cloud service feedback of the second to-be-processed service interaction projects is regarded as the reference of the convolutional neural network, and network parameters of the convolutional neural network are optimized until a set optimization target is met.
In some optional embodiments, after the obtaining of the derived visual cloud service feedback of the first to-be-processed service interaction item, the method further includes:
inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain an associated visual cloud service feedback of the first to-be-processed service interaction project;
and updating the derivative visual cloud service feedback of the first to-be-processed service interaction project by combining the associated visual cloud service feedback of the first to-be-processed service interaction project to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by using the currently obtained visual cloud service feedback of the first to-be-processed service interaction project.
In some optional embodiments, the inputting the first to-be-processed service interaction item into the optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain the associated visual cloud service feedback of the first to-be-processed service interaction item includes:
and on the basis that the data optimization server is in an idle period, inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification, and obtaining the associated visual cloud service feedback of the first to-be-processed service interaction project.
In some optional embodiments, the convolutional neural network includes a first model unit and a second model unit, where loading historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in the visual cloud service feedback record in advance, to the convolutional neural network for visual cloud service feedback adjustment, to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item, includes:
inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project;
and inputting the transition visual cloud service feedback of the first to-be-processed service interaction project into the second model unit for visual cloud service feedback identification to obtain derivative visual cloud service feedback of the first to-be-processed service interaction project.
In some optional embodiments, the convolutional neural network includes a first model unit and a third model unit, where loading historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in the visual cloud service feedback record in advance, to the convolutional neural network for visual cloud service feedback adjustment, to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item, includes:
inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project;
splicing the first to-be-processed service interaction project and the transition visual cloud service feedback of the first to-be-processed service interaction project to obtain a spliced visual cloud service feedback of the first to-be-processed service interaction project;
inputting the spliced visual cloud service feedback of the first to-be-processed service interaction project into the third model unit for visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction project.
The application also provides a data optimization server, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when run, implements the above-described method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the application, historical visual cloud service feedback stored in the visual cloud service feedback record in advance is loaded to a convolutional neural network for visual cloud service feedback adjustment, so that derivative visual cloud service feedback is obtained; and updating historical visual cloud service feedback by combining the derivative visual cloud service feedback to optimize the visual cloud service feedback record, so that the optimization timeliness of the visual cloud service feedback record is improved in the history of compatible accuracy in the process of optimizing the visual cloud service feedback record, and the service quality of the service feedback can be improved as much as possible by integrating the historical visual cloud service feedback.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a visual cloud service data optimization method provided in an embodiment of the present application.
Fig. 2 is a schematic hardware structure diagram of a data optimization server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a flow diagram of a visualized cloud service data optimization method, which is applied to a data optimization server, and further, the method may specifically include the following steps.
Step 100, determining whether the visual cloud service feedback record contains historical visual cloud service feedback of the first to-be-processed service interaction project.
200, on the basis that the visual cloud service feedback record contains the historical visual cloud service feedback of the first to-be-processed service interaction item, loading the historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in the visual cloud service feedback record in advance, to a convolutional neural network for visual cloud service feedback adjustment, so as to obtain a derivative visual cloud service feedback of the first to-be-processed service interaction item.
Step 300, updating historical visual cloud service feedback of the first to-be-processed service interaction project by combining the derived visual cloud service feedback of the first to-be-processed service interaction project so as to optimize the visual cloud service feedback record.
In the embodiment of the application, the convolutional neural network is optimized according to historical visual cloud service feedback and associated visual cloud service feedback of a plurality of second to-be-processed service interaction items, in the optimization process of the convolutional neural network, the historical visual cloud service feedback of the plurality of second to-be-processed service interaction items is regarded as input, the associated visual cloud service feedback of the plurality of second to-be-processed service interaction items is regarded as the reference of the convolutional neural network, and network parameters of the convolutional neural network are optimized until a set optimization target is met.
For some optional design considerations, after the obtaining of the derived visual cloud service feedback of the first to-be-processed service interaction item, the method further includes: inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain an associated visual cloud service feedback of the first to-be-processed service interaction project; and updating the derivative visual cloud service feedback of the first to-be-processed service interaction project by combining the associated visual cloud service feedback of the first to-be-processed service interaction project to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by using the currently obtained visual cloud service feedback of the first to-be-processed service interaction project.
For some optional design ideas, the inputting the first to-be-processed service interaction item into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain an associated visual cloud service feedback of the first to-be-processed service interaction item includes: and on the basis that the data optimization server is in an idle period, inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification, and obtaining the associated visual cloud service feedback of the first to-be-processed service interaction project.
For some optional design ideas, the convolutional neural network includes a first model unit and a second model unit, where the historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in the visual cloud service feedback record in advance, is loaded to the convolutional neural network for visual cloud service feedback adjustment, so as to obtain a derivative visual cloud service feedback of the first to-be-processed service interaction item, and the method includes: inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project; and inputting the transition visual cloud service feedback of the first to-be-processed service interaction project into the second model unit for visual cloud service feedback identification to obtain derivative visual cloud service feedback of the first to-be-processed service interaction project.
For some optional design ideas, the convolutional neural network includes a first model unit and a third model unit, where the historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in the visual cloud service feedback record in advance, is loaded to the convolutional neural network for visual cloud service feedback adjustment, so as to obtain a derivative visual cloud service feedback of the first to-be-processed service interaction item, and the method includes: inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project; splicing the first to-be-processed service interaction project and the transition visual cloud service feedback of the first to-be-processed service interaction project to obtain a spliced visual cloud service feedback of the first to-be-processed service interaction project; inputting the spliced visual cloud service feedback of the first to-be-processed service interaction project into the third model unit for visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction project.
In summary, when the method is applied to the embodiment of the application, historical visual cloud service feedback stored in the visual cloud service feedback record in advance is loaded to the convolutional neural network for visual cloud service feedback adjustment, so that derivative visual cloud service feedback is obtained; historical visual cloud service feedback is updated by combining the derivative visual cloud service feedback to optimize the visual cloud service feedback record, so that the optimization timeliness of the visual cloud service feedback record is improved in the history of compatible accuracy in the process of optimizing the visual cloud service feedback record, and the service quality of the service feedback can be improved as much as possible by integrating the historical visual cloud service feedback
In the above history, please refer to fig. 2 in combination, the present application further provides a hardware structure diagram of the data optimization server 20, which specifically includes a memory 210, a processor 220, a network module 230, and a visual cloud service data optimization apparatus. The memory 210, the processor 220, and the network module 230 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 210 stores a visual cloud service data optimization device, which includes at least one software functional module that can be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 executes software programs and modules stored in the memory 210.
The Memory 210 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 210 is used for storing a program, and the processor 220 executes the program after receiving an execution instruction.
The processor 220 may be an integrated circuit chip having data processing capabilities. The Processor 220 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 230 is configured to establish a communication connection between the data optimization server 20 and other communication terminal devices through a network, so as to implement transceiving operations of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. Accordingly, it will be apparent to those skilled in the art of electronic information technology that a more direct and clear description of one embodiment is provided by describing the various operations within the embodiment. Knowing the operations to be performed, the skilled person can directly design the target product based on the consideration of said non-technical factors.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
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 application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (7)
1. A visual cloud service data optimization method is applied to a data optimization server and comprises the following steps:
determining whether the visual cloud service feedback record contains historical visual cloud service feedback of a first to-be-processed service interaction project;
on the basis that the visual cloud service feedback record contains the historical visual cloud service feedback of the first to-be-processed service interaction item, loading the historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in advance in the visual cloud service feedback record, to a convolutional neural network for visual cloud service feedback adjustment, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction item;
updating historical visual cloud service feedback of the first to-be-processed service interaction project by combining with the derivative visual cloud service feedback of the first to-be-processed service interaction project so as to optimize the visual cloud service feedback record;
the convolutional neural network is optimized according to historical visual cloud service feedback and associated visual cloud service feedback of a plurality of second to-be-processed service interaction projects, in the optimization process of the convolutional neural network, the historical visual cloud service feedback of the second to-be-processed service interaction projects is regarded as input, the associated visual cloud service feedback of the second to-be-processed service interaction projects is regarded as the reference of the convolutional neural network, and network parameters of the convolutional neural network are optimized until a set optimization target is met.
2. The method according to claim 1, wherein after the obtaining of the derived visual cloud service feedback of the first pending service interaction item, the method further comprises:
inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain an associated visual cloud service feedback of the first to-be-processed service interaction project;
and updating the derivative visual cloud service feedback of the first to-be-processed service interaction project by combining the associated visual cloud service feedback of the first to-be-processed service interaction project to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by using the currently obtained visual cloud service feedback of the first to-be-processed service interaction project.
3. The method of claim 2, wherein the inputting the first to-be-processed service interaction item into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification to obtain an associated visual cloud service feedback of the first to-be-processed service interaction item comprises:
and on the basis that the data optimization server is in an idle period, inputting the first to-be-processed service interaction project into an optimized first visual cloud service feedback mining network for visual cloud service feedback identification, and obtaining the associated visual cloud service feedback of the first to-be-processed service interaction project.
4. The method according to any one of claims 1 to 3, wherein the convolutional neural network comprises a first model unit and a second model unit, wherein the step of loading historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in advance in the visual cloud service feedback record, to the convolutional neural network for visual cloud service feedback adjustment to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item comprises the steps of:
inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project;
and inputting the transition visual cloud service feedback of the first to-be-processed service interaction project into the second model unit for visual cloud service feedback identification to obtain derivative visual cloud service feedback of the first to-be-processed service interaction project.
5. The method according to any one of claims 1 to 3, wherein the convolutional neural network comprises a first model unit and a third model unit, wherein the step of loading historical visual cloud service feedback of the first to-be-processed service interaction item, which is stored in advance in the visual cloud service feedback record, to the convolutional neural network for visual cloud service feedback adjustment to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item comprises the steps of:
inputting historical visual cloud service feedback of the first to-be-processed service interaction project stored in the visual cloud service feedback record in advance into the first model unit for visual cloud service feedback recovery to obtain transition visual cloud service feedback of the first to-be-processed service interaction project;
splicing the first to-be-processed service interaction project and the transition visual cloud service feedback of the first to-be-processed service interaction project to obtain a spliced visual cloud service feedback of the first to-be-processed service interaction project;
inputting the spliced visual cloud service feedback of the first to-be-processed service interaction project into the third model unit for visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction project.
6. A data optimization server, comprising a memory, a processor, and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-5 by reading the computer program from the memory and running it.
7. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-5.
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