CN114401310B - 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 advance in a visual cloud service feedback record is loaded to a convolutional neural network to perform visual cloud service feedback adjustment, so that derivative visual cloud service feedback is obtained; and updating historical visual cloud service feedback by combining the derived visual cloud service feedback to optimize the visual cloud service feedback record, so that in the process of optimizing the visual cloud service feedback record, the optimizing timeliness of the visual cloud service feedback record is improved on the history of compatibility accuracy, and the service feedback quality can be improved as much as possible through comprehensive historical visual cloud service feedback.
Description
Technical Field
The application relates to the technical field of visual service and data optimization, in particular to a visual cloud service data optimization method and a server.
Background
With the continuous progress of new generation information technology, the visual business has also been developed to a certain extent. However, in practical application, how to improve the optimization timeliness of the visual cloud service feedback record and to improve the service feedback quality as much as possible is a technical problem that needs to be further improved at present.
Disclosure of Invention
In order to improve 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 a visual cloud service feedback record contains historical visual cloud service feedback of a first service interaction item to be processed;
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 stored in advance in the visual cloud service feedback record into a 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;
Updating historical visual cloud service feedback of the first to-be-processed service interaction item by combining derivative visual cloud service feedback of the first to-be-processed service interaction item so as to optimize the visual cloud service feedback record;
The convolution 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 process of optimizing the convolution neural network, the historical visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as input, the associated visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as a benchmark of the convolution neural network, and network parameters of the convolution neural network are optimized until a set optimizing target is met.
In some optional embodiments, after the deriving the derivative visual cloud service feedback of the first pending service interaction item, the method further includes:
Inputting the first to-be-processed service interaction item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, and obtaining associated visual cloud service feedback of the first to-be-processed service interaction item;
Updating derivative visual cloud service feedback of the first to-be-processed service interaction item by combining the associated visual cloud service feedback of the first to-be-processed service interaction item so as to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by adopting the currently obtained visual cloud service feedback of the first to-be-processed service interaction item.
In some optional embodiments, inputting the first to-be-processed service interaction item into the tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, to obtain 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 item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, and obtaining associated visual cloud service feedback of the first to-be-processed service interaction item.
In some optional embodiments, the convolutional neural network includes a first model unit and a second model unit, where loading the historical visual cloud service feedback of the first to-be-processed service interaction item stored in advance in the visual cloud service feedback record to the convolutional neural network to perform 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 service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed;
And inputting the transitional visual cloud service feedback of the first to-be-processed service interaction item into the second model unit to perform visual cloud service feedback identification, so as to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item.
In some optional embodiments, the convolutional neural network includes a first model unit and a third model unit, where loading the historical visual cloud service feedback of the first to-be-processed service interaction item stored in advance in the visual cloud service feedback record to the convolutional neural network to perform 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 service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed;
Splicing the first to-be-processed service interaction item and the transitional visual cloud service feedback of the first to-be-processed service interaction item to obtain the spliced visual cloud service feedback of the first to-be-processed service interaction item;
Inputting the spliced visual cloud service feedback of the first service interaction item to be processed into the third model unit to perform visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first service interaction item to be processed.
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 implements the above method by reading a computer program from the memory and running it.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when run, implements the above method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the application, the history visual cloud service feedback stored in advance in the visual cloud service feedback record is loaded to the convolutional neural network to perform visual cloud service feedback adjustment, so that derivative visual cloud service feedback is obtained; and updating historical visual cloud service feedback by combining the derived visual cloud service feedback to optimize the visual cloud service feedback record, so that in the process of optimizing the visual cloud service feedback record, the optimizing timeliness of the visual cloud service feedback record is improved on the history of compatibility accuracy, and the service feedback quality can be improved as much as possible through comprehensive 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 application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a method for optimizing visual cloud service data according to an embodiment of the present application.
Fig. 2 is a schematic hardware structure of a data optimization server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
Referring to fig. 1 in combination, an embodiment of the present application provides a flow chart of a method for optimizing data of a visual cloud service, which is applied to a data optimization server, and further, the method specifically may include a technical scheme described in the following steps.
Step 100, determining whether the visualized cloud service feedback record contains historical visualized cloud service feedback of the first service interaction item to be processed.
Step 200, based on the visual cloud service feedback record including 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 stored in advance in the visual cloud service feedback record into a convolutional neural network to perform visual cloud service feedback adjustment, and obtaining derivative visual cloud service feedback of the first to-be-processed service interaction item.
And 300, updating historical visual cloud service feedback of the first to-be-processed service interaction item by combining the derived visual cloud service feedback of the first to-be-processed service interaction item so as to optimize the visual cloud service feedback record.
In the embodiment of the application, the convolutional neural network is optimized according to the historical visual cloud service feedback and the associated visual cloud service feedback of the plurality of second to-be-processed service interaction items, the historical visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as input in the optimizing process of the convolutional neural network, the associated visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as a benchmark of the convolutional neural network, and the network parameters of the convolutional neural network are optimized until the set optimizing target is met.
For some optional design ideas, after the deriving the derivative visual cloud service feedback of the first service interaction item to be processed, the method further includes: inputting the first to-be-processed service interaction item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, and obtaining associated visual cloud service feedback of the first to-be-processed service interaction item; updating derivative visual cloud service feedback of the first to-be-processed service interaction item by combining the associated visual cloud service feedback of the first to-be-processed service interaction item so as to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by adopting the currently obtained visual cloud service feedback of the first to-be-processed service interaction item.
For some optional design ideas, inputting the first to-be-processed service interaction item into the tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, to obtain associated visual cloud service feedback of the first to-be-processed service interaction item, including: and on the basis that the data optimization server is in an idle period, inputting the first to-be-processed service interaction item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, and obtaining associated visual cloud service feedback of the first to-be-processed service interaction item.
For some optional design ideas, the convolutional neural network includes a first model unit and a second model unit, where loading the historical visual cloud service feedback of the first to-be-processed service interaction item stored in advance in the visual cloud service feedback record to the convolutional neural network to perform 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 service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed; and inputting the transitional visual cloud service feedback of the first to-be-processed service interaction item into the second model unit to perform visual cloud service feedback identification, so as to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item.
For some optional design ideas, the convolutional neural network includes a first model unit and a third model unit, where loading the historical visual cloud service feedback of the first to-be-processed service interaction item stored in advance in the visual cloud service feedback record to the convolutional neural network to perform 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 service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed; splicing the first to-be-processed service interaction item and the transitional visual cloud service feedback of the first to-be-processed service interaction item to obtain the spliced visual cloud service feedback of the first to-be-processed service interaction item; inputting the spliced visual cloud service feedback of the first service interaction item to be processed into the third model unit to perform visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first service interaction item to be processed.
In summary, applied to the embodiment of the application, the history visual cloud service feedback stored in advance in the visual cloud service feedback record is loaded to the convolutional neural network to perform visual cloud service feedback adjustment, so as to obtain derivative visual cloud service feedback; the historical visual cloud service feedback is updated by combining the derived visual cloud service feedback to optimize the visual cloud service feedback record, so that in the process of optimizing the visual cloud service feedback record, the optimizing timeliness of the visual cloud service feedback record is improved on the history of compatibility accuracy, and the service feedback quality can be improved as much as possible through comprehensive historical visual cloud service feedback
In the above history, please refer to fig. 2 in combination, the present application further provides a hardware structure schematic diagram of the data optimization server 20, which specifically includes a memory 210, a processor 220, a network module 230 and a visualized cloud service data optimization device. The memory 210, the processor 220, and the network module 230 are electrically connected, either directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other by one or more communication buses or signal lines. The memory 210 stores therein a visual cloud service data optimizing apparatus including at least one software function module which may be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 operates the software programs and modules stored in the memory 210.
The Memory 210 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 210 is configured to store 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 (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc. The methods, steps and logic blocks disclosed in the 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 a network signal and data transceiving operation. The network signals may include wireless signals or wired signals.
Further, there is also provided a readable storage medium having stored thereon a program which when executed by a processor implements the above-described method.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected 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 trend of electronic information technology such as large scale integrated circuit technology and software hardware, it has become difficult to clearly divide the software and hardware boundaries of a computer system. Because any operations may be implemented in software or hardware. Execution of any instructions may be accomplished by hardware as well as 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, etc. Thus, it will be more straightforward and clear to one of ordinary skill in the electronic information arts that one of the solutions is described in terms of the individual operations in that solution. Those skilled in the art, knowing the operation to be performed, can directly design the product of interest based on consideration of the 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 aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage 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: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through 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 over 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 transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface 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.
Computer program instructions for carrying out operations of the present application may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source 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 be executed 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present application are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
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 having the instructions stored therein includes 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 flowcharts 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, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements 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 (3)
1. The visualized cloud service data optimization method is characterized by being applied to a data optimization server and comprising the following steps of:
Determining whether a visual cloud service feedback record contains historical visual cloud service feedback of a first service interaction item to be processed;
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 stored in advance in the visual cloud service feedback record into a 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;
Updating historical visual cloud service feedback of the first to-be-processed service interaction item by combining derivative visual cloud service feedback of the first to-be-processed service interaction item so as to optimize the visual cloud service feedback record;
The convolution 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 process of optimizing the convolution neural network, the historical visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as input, the associated visual cloud service feedback of the plurality of second to-be-processed service interaction items is taken as a benchmark of the convolution neural network, and network parameters of the convolution neural network are optimized until a set optimizing target is met;
after the derived visual cloud service feedback of the first service interaction item to be processed is obtained, the method further comprises:
Inputting the first to-be-processed service interaction item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, and obtaining associated visual cloud service feedback of the first to-be-processed service interaction item;
Updating derivative visual cloud service feedback of the first to-be-processed service interaction item by combining the associated visual cloud service feedback of the first to-be-processed service interaction item to optimize the visual cloud service feedback record, wherein the visual cloud service feedback record is optimized by adopting the currently obtained visual cloud service feedback of the first to-be-processed service interaction item;
The step of inputting the first to-be-processed service interaction item into the tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification, to obtain associated visual cloud service feedback of the first to-be-processed service interaction item, includes:
inputting the first to-be-processed service interaction item into a tuned first visual cloud service feedback mining network to perform visual cloud service feedback identification on the basis that the data optimization server is in an idle period, so as to obtain associated visual cloud service feedback of the first to-be-processed service interaction item;
The convolutional neural network comprises a first model unit and a second model unit, wherein the method comprises the steps of loading historical visual cloud service feedback of the first service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, to the convolutional neural network to perform visual cloud service feedback adjustment to obtain derivative visual cloud service feedback of the first service interaction item to be processed, and the method comprises the following steps:
inputting historical visual cloud service feedback of the first service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed;
Inputting the transition visual cloud service feedback of the first to-be-processed service interaction item into the second model unit to perform visual cloud service feedback identification, so as to obtain derivative visual cloud service feedback of the first to-be-processed service interaction item;
The convolutional neural network comprises a first model unit and a third model unit, wherein the method comprises the steps of loading historical visual cloud service feedback of the first service interaction item to be processed, 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 service interaction item to be processed, and the method comprises the following steps:
inputting historical visual cloud service feedback of the first service interaction item to be processed, which is stored in advance in the visual cloud service feedback record, into the first model unit to perform visual cloud service feedback recovery, so as to obtain transitional visual cloud service feedback of the first service interaction item to be processed;
Splicing the first to-be-processed service interaction item and the transitional visual cloud service feedback of the first to-be-processed service interaction item to obtain the spliced visual cloud service feedback of the first to-be-processed service interaction item;
Inputting the spliced visual cloud service feedback of the first service interaction item to be processed into the third model unit to perform visual cloud service feedback identification, and obtaining derivative visual cloud service feedback of the first service interaction item to be processed.
2. The data optimization server is characterized by 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 is configured to implement the method of claim 1 by reading a computer program from the memory and running the computer program.
3. A computer readable storage medium, characterized in that it has stored thereon a computer program, which, when run, implements the method of claim 1.
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