CN111679811B - Web service construction method and device - Google Patents
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- 238000010276 construction Methods 0.000 title claims abstract description 72
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Abstract
The application provides a Web service construction method and a device, wherein the method comprises the following steps: receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relation information between each target node; acquiring a current target node according to the target node information; and constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node. The application can improve the efficiency, flexibility and accuracy of constructing the Web service, and further can improve the accuracy of processing related business processes by applying the Web service.
Description
Technical Field
The present application relates to the field of Web services, and in particular, to a method and apparatus for building a Web service.
Background
The Web service is a platform independent, low-coupling, self-contained and programmable Web-based application; after training, the deep learning model cannot directly provide service to the outside, and the deep learning model needs to be packaged by a Web service technology, and meanwhile, relevant service logic is added for processing, so that relevant deep learning service can be provided for a calling party.
One Web service package is needed from model construction to external service provision, and one Web service can be connected with a plurality of trained deep learning models in series and can be added with business logic operation or other special preprocessing operation information. In the prior art, the Web service codes are required to be customized and developed according to different scenes, so that each scene corresponds to one independent Web service code, and the maintenance is not easy. And meanwhile, when the Web service is in butt joint with the same model, the butt joint codes are basically the same, and repeated development of the butt joint codes can be caused.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a Web service construction method and device, which can improve the efficiency, flexibility and accuracy of constructing Web services, and further can improve the accuracy of processing related business processes by using the Web services.
In a first aspect, the present application provides a Web service construction method, including:
receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relation information between each target node;
acquiring a current target node according to the target node information;
and constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node.
Further, each target node is respectively provided with different code group information; correspondingly, the constructing the target Web service for processing the current business based on the connection relation information between each target node and each target node includes: and combining the code group information in each target node according to the connection relation information between each target node so as to construct the target Web service for processing the current business.
Further, if the service includes: the image recognition service, after the target Web service for processing the current service is constructed, further comprises: executing a processing procedure aiming at the current image recognition service by applying the target nodes in the target Web service and the connection relation information among the target nodes; the processing procedure for the current image recognition service comprises the following steps: receiving an image to be identified, and determining the information of the area to be identified corresponding to the image to be identified based on a preset image positioning model, wherein the image positioning model is a deep learning model which is obtained through training in advance and is used for determining the information of the area to be identified corresponding to the image; cutting the image to be identified based on the area information to be identified to obtain an area image to be identified; and carrying out image text recognition on the region image to be recognized by using a preset image text recognition model, wherein the image text recognition model is a deep learning model which is obtained in advance and is used for recognizing the image text.
Further, the image positioning model is a deep learning model based on a deep algorithm.
Further, the image text recognition model is a convolutional recurrent neural network model.
In a second aspect, the present application provides a Web service construction apparatus, comprising:
the receiving module is configured to receive a target Web service construction request corresponding to a current service, where the target Web service construction request includes: target node information and connection relation information between each target node;
the target node acquisition module is used for acquiring a current target node according to the target node information;
and the construction module is used for constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node.
Further, each target node is respectively provided with different code group information; correspondingly, the construction module comprises: and the construction unit is used for combining the code group information in each target node according to the connection relation information between each target node so as to construct and obtain the target Web service for processing the current business.
Further, if the service includes: the image recognition service, the Web service construction apparatus further includes: the service processing module is used for executing a processing procedure aiming at the current image recognition service by applying the target nodes in the target Web service and the connection relation information among the target nodes; the processing procedure for the current image recognition service comprises the following steps: receiving an image to be identified, and determining the information of the area to be identified corresponding to the image to be identified based on a preset image positioning model, wherein the image positioning model is a deep learning model which is obtained through training in advance and is used for determining the information of the area to be identified corresponding to the image; cutting the image to be identified based on the area information to be identified to obtain an area image to be identified; and carrying out image text recognition on the region image to be recognized by using a preset image text recognition model, wherein the image text recognition model is a deep learning model which is obtained in advance and is used for recognizing the image text.
Further, the image positioning model is a deep learning model based on a deep algorithm.
Further, the image text recognition model is a convolutional recurrent neural network model.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the Web service construction method when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer instructions that, when executed, implement the Web service building method.
As can be seen from the technical scheme, the application provides a Web service construction method and device. Wherein the method comprises the following steps: receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relation information between each target node; acquiring a current target node according to the target node information; based on the connection relation information between each target node and each target node, constructing a target Web service for processing the current service, so that the efficiency, flexibility and accuracy of constructing the Web service can be improved, and the accuracy of processing related service processes by using the Web service can be further improved; the method can avoid the problems of accuracy and efficiency caused by repeated development, and reduce the cost of maintaining Web services and constructing Web services.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a Web service construction method in an embodiment of the application;
FIG. 2 is a flow chart of a Web service construction method according to another embodiment of the present application;
FIG. 3 is a schematic diagram of a Web service construction apparatus according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a Web service construction apparatus according to another embodiment of the present application;
FIG. 5 is a flow chart of a method for building Web services in a specific application example of the present application;
FIG. 6 is a schematic diagram of detailed information of different nodes and a logical sequence among nodes in a Web service in an embodiment of the present application;
FIG. 7 is a schematic diagram of a page of an upload custom algorithm in an embodiment of the present application;
FIG. 8 is a schematic diagram of a page for building a Web service in an embodiment of the application;
FIG. 9 is a schematic diagram of a page for modifying or updating published Web services in an embodiment of the application;
FIG. 10 is a schematic flow chart of a container control process in an embodiment of the application;
FIG. 11 is a flow chart of a method for building a Web service in another embodiment of the application;
fig. 12 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In consideration of the situations that in the prior art, each scene corresponds to a set of independent Web service codes, the docking codes are basically the same when the Web services are docked with the same model, and the like, the application provides a technical scheme for customizing scene business processes in a front-end page dragging mode and automatically constructing Web services by changing the existing Web service construction mode. Abstracting codes of different models or general preprocessing codes into different nodes respectively, adding related modifiable parameters to the nodes at the same time, and establishing a connection relation between the nodes by means of dragging front-end node icons to establish a flow of the whole Web service calling model; therefore, in a new business scenario, only a new business flow is required to be customized, and customized Web services can be automatically constructed without writing codes. Therefore, the problems of poor accuracy and low efficiency of Web service construction and high cost of Web service construction and maintenance caused by repeated code development are solved.
Based on this, in order to improve efficiency, flexibility and accuracy of building the Web service and further improve accuracy of processing related business processes of applying the Web service, the embodiment of the application provides a Web service building device, which may be a server or a client device, where the client device may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, an intelligent wearable device, and the like. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch and intelligent bracelet etc..
In practical applications, the part for constructing the Web service may be performed on the server side as described above, or all operations may be performed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.
The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.
Any suitable network protocol may be used for communication between the server and the client device, including those not yet developed on the filing date of the present application. The network protocols may include, for example, TCP/IP protocol, UDP/IP protocol, HTTP protocol, HTTPS protocol, etc. Of course, the network protocol may also include, for example, RPC protocol (Remote Procedure Call Protocol ), REST protocol (Representational State Transfer, representational state transfer protocol), etc. used above the above-described protocol.
The following examples are presented in detail.
As shown in fig. 1, in order to improve efficiency, flexibility and accuracy of building a Web service and further improve accuracy of processing related business processes of applying the Web service, the embodiment provides a Web service building method with an execution subject being a Web service building device, which specifically includes the following contents:
step 100: receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relationship information between respective target nodes.
In particular, a Web service construction request based on a click event of a front page may be received. The target node information may be identification information of a node for distinguishing between different nodes.
Step 200: and acquiring the current target node according to the target node information.
Specifically, the target node may be downloaded from a pre-stored node component library according to the target node information, where the node component library includes each optional node, and the node component library may be stored in a local or an independent server.
Step 300: and constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node.
Specifically, the target Web service may be a complete set of Web service codes, which may be provided in an independent Web server or may be provided in the Web service construction apparatus.
In order to obtain accurate code set information and further improve efficiency, flexibility and accuracy of constructing Web services, in one embodiment of the application, each target node is respectively provided with different code set information; correspondingly, step 300 comprises:
step 301: and combining the code group information in each target node according to the connection relation information between each target node so as to construct the target Web service for processing the current business.
Specifically, the code set information may be a set of code information corresponding to a deep learning model, or may be a set of code information corresponding to a business logic operation or other special preprocessing operations.
In order to improve the accuracy of the process of processing related services by using the Web service, in particular, to improve the accuracy of processing the image recognition service, referring to fig. 2, in one embodiment of the present application, if the service includes: the image recognition service further comprises, after step 300:
step 400: and executing a processing procedure aiming at the current image recognition service by applying the target nodes in the target Web service and the connection relation information among the target nodes.
The processing procedure for the current image recognition service in step 400 includes:
step 401: and receiving an image to be identified, and determining the information of the area to be identified corresponding to the image to be identified based on a preset image positioning model, wherein the image positioning model is a deep learning model which is obtained by training in advance and is used for determining the information of the area to be identified corresponding to the image.
Step 402: and cutting the image to be identified based on the area information to be identified to obtain the area image to be identified.
Specifically, the region information to be identified may be coordinate information of a region to be identified in the image to be identified. In order to further improve accuracy of obtaining the region image to be identified, filling and/or scaling processing can be performed on the region image to be identified according to preset cutting rules, wherein the preset cutting rules can be set according to actual conditions.
Step 403: and carrying out image text recognition on the region image to be recognized by using a preset image text recognition model, wherein the image text recognition model is a deep learning model which is obtained in advance and is used for recognizing the image text.
The image positioning model can be a deep learning model based on a deep algorithm; the image text recognition model may be a convolutional recurrent neural network crnn model.
Specifically, deep can process text information according to human thought, the processing speed is up to thousands of articles per second, the supported language is up to 20, and multiple deep neural network architectures including rolling and recurrent neural networks can be utilized to learn on the level of words and letters.
In one example, the target Web service includes a data receiving node, an image positioning node, a cutting node and an image text recognition node; the method comprises the steps that a data receiving node is used for receiving an image to be identified, an image positioning model preset in an image positioning node connected with the data receiving node is used for determining information of an area to be identified corresponding to the image to be identified, and the image positioning model is a deep learning model which is obtained in advance and used for determining the information of the area to be identified corresponding to the image; cutting the image to be identified based on the area information to be identified by using a cutting node connected with the image positioning node to obtain an area image to be identified; and carrying out image text recognition on the image of the area to be recognized by using an image text recognition model preset in an image text recognition node connected with the cutting node, wherein the image text recognition model is a deep learning model which is obtained in advance and used for recognizing the image text.
In order to improve efficiency, flexibility and accuracy of building a Web service and further improve accuracy of processing related business processes by applying the Web service, the application provides an embodiment of a Web service building device for implementing all or part of contents in the Web service building method, referring to fig. 3, where the Web service building device specifically includes:
the receiving module 10 is configured to receive a target Web service construction request corresponding to a current service, where the target Web service construction request includes: target node information and connection relationship information between respective target nodes.
The acquiring target node module 20 is configured to acquire a current target node according to the target node information.
A construction module 30, configured to construct a target Web service for processing the current business based on the connection relationship information between each target node and each target node.
In one embodiment of the present application, each of the target nodes is provided with different code set information, respectively; correspondingly, the construction module comprises:
and the construction unit is used for combining the code group information in each target node according to the connection relation information between each target node so as to construct and obtain the target Web service for processing the current business.
Referring to fig. 4, in one embodiment of the present application, if the service includes: the image recognition service, the Web service construction apparatus further includes:
a service processing module 40, configured to execute a processing procedure for a current image recognition service by applying the target nodes in the target Web service and connection relationship information between the target nodes; the processing procedure for the current image recognition service comprises the following steps:
and receiving an image to be identified, and determining the information of the area to be identified corresponding to the image to be identified based on a preset image positioning model, wherein the image positioning model is a deep learning model which is obtained by training in advance and is used for determining the information of the area to be identified corresponding to the image.
And cutting the image to be identified based on the area information to be identified to obtain the area image to be identified.
And carrying out image text recognition on the region image to be recognized by using a preset image text recognition model, wherein the image text recognition model is a deep learning model which is obtained in advance and is used for recognizing the image text.
Specifically, the image positioning model is a deep learning model based on a deep algorithm; the image text recognition model is a convolutional recurrent neural network model.
In order to further explain the present solution, referring to fig. 5, the present application further provides a specific application example of a Web service construction method, including S11: customizing a service flow, S12: defining parameters of each node in the process; s13: automatically constructing Web service; s14: invoking Web service; the specific flow is as follows:
1) It is first necessary to customize each node in the Web service and add relevant parameter information to the node.
2) And determining the processing flow of the whole Web service through the connection relation between the nodes.
3) And automatically constructing the deep learning Web service according to the processing flow of the whole Web service, and accessing the Web service in an http mode after the construction is completed.
In the process of customizing deep learning Web services, the core is partly the determination of nodes in the Web services, the customization of node parameters and the connection among the nodes.
In order to further explain the scheme, the application provides a specific application example of customizing the detailed information of different nodes of Web service and the connection relation between the nodes in an image recognition application scene, wherein the specific application example comprises a deep learning model for positioning and a deep learning model for recognizing image text, and the specific application example is as follows:
s21: and adding a data preprocessing node into the target Web service, wherein the data preprocessing node is provided with parameters of related preprocessing processes, such as parameters corresponding to basic preprocessing processes of image enhancement, image rotation, image binarization and the like.
S22: calling a deep learning positioning model after preprocessing an image to obtain a region to be identified in the image; a deep learning positioning model processing node is added into the target Web service, and the node is provided with a trained deep learning positioning model, and an algorithm and a version adopted by the deep learning positioning model.
S23: after the image is positioned, a process of cutting the image is required to be customized, if a code corresponding to cutting the image does not exist in a preset deep learning code library, related codes can be edited and uploaded according to a certain specification, user group definition cutting picture nodes corresponding to the codes are added in a target Web service, and parameters of the user group definition cutting picture nodes can be customized.
S24: calling a recognition service of deep learning after cutting the image; it is necessary to add a deep learning recognition model processing node to the target Web service, where the deep learning recognition model processing node is provided with a deep learning recognition algorithm and a version number of the model. The Optical Character Recognition (OCR) results of the image can be obtained by applying the deep learning recognition model processing node.
S25: and finally, defining a result processing node, and customizing a processing mode of the deep learning recognition model output result by self and outputting the processed result.
Specifically, fig. 6 shows detailed information of different nodes of the Web service and a logic sequence among the nodes in this specific application example, where the logic sequence is a data preprocessing node, a deep learning positioning model processing node, a user group definition cutting picture node, a deep learning identification model processing node, and a result processing node which are sequentially connected.
The specific operation steps of the user on the page are as follows:
s31: as shown in FIG. 7, there are some common node component icons predefined on the right side of the page, if the user needs to upload the custom algorithm, the user can select the button "upload custom algorithm" on the upper left side of the page, then the user can upload the local algorithm to the node component library, and the node component icons can be dragged for use.
S32: as shown in FIG. 8, the custom algorithm uploaded by the user has been displayed in the node component icon on the right side of the page and is available for use. When a user needs to start building a service, a 'build service' button on the left upper side of the page can be selected, and then the whole processing flow of the service can be built by dragging the node component icon on the right side of the page, and the user needs to formulate the node attribute of the component while dragging the node component icon. Different node properties, such as nodes related to the deep learning model, must select the algorithm used by the model and the already trained deep learning model version number.
S33: after the connection relation of the node components in the Web service is determined, the 'release' button on the upper right side of the page can be clicked to release the customized service.
S34: in the published service, if there is an update of the deep learning model version, or modification of the parameters in the node, the "modify service" button on the upper left of the page may be clicked, and the parameters of the node to be modified are modified, as shown in fig. 9, the model version of the positioning model in fig. 8 is modified, and the model version is modified from "20190101124000" to "202000211153400". After the modification is finished, clicking the release button at the upper right of the page again to update the service.
As shown in fig. 10, when the container control interface receives a customization request sent by a user from a page, a container start request is sent to Kubernets, and when resources meet requirements, the Kubernets starts a container of a Web service according to the requirements and distributes the required resources to the container of the Web service, and customized Web service codes are automatically built in the container.
The Kubernets is a container orchestration engine of Google open source, and supports automatic deployment, large-scale scalability and application containerization management.
FIG. 11 is a flow chart of an automatic construction of customized deep learning Web service code. The method is characterized in that each node is regarded as an independent module code through a modularized concept, and then the module codes are combined together according to a specific mode to form Web service according to a customized flow, wherein the specific flow is as follows:
s41: and obtaining a deep learning model and a docking code from the deep learning model library and the deep learning code library.
Specifically, when receiving the customized Web service request, the customized related node detailed information is received, the deep learning model selected by the user is downloaded from the deep learning model library through the related node detailed information, and the related code of the corresponding algorithm is downloaded from the deep learning code library.
S42: the downloaded deep learning model is loaded into a starting service TF (TensorFlow Serving) of the deep learning model for hosting.
Wherein TensorFlow Serving is a service system of Google open source, which is suitable for deploying machine learning model.
S43: and constructing Web service according to the flash+guricorn mode and the user customization flow, and calling a corresponding model in TF service if model processing exists.
Wherein, flash is a lightweight Web application framework written using Python; the guricorn is a high performance Web Server Gateway Interface (WSGI) server.
Specifically, according to the detailed information of the related nodes, web service codes are built based on a flash+guricorn mode, and when a part needing to call a model is encountered, the corresponding model in TF service is automatically called. In a service framework based on a flash+gun+TF service mode, TF service is used as a starting service of a deep learning model, is responsible for loading the deep learning model, and allocates corresponding GPU or CPU resources for the deep learning model; the flash+guricorn is used for providing a service interface to the outside, and the relevant deep learning model in TF service is called internally according to service logic.
S44: a customized Web service is provided to a user. Specifically, a service interface is exposed to the outside for user invocation.
As can be seen from the foregoing description, the present application provides a method and an apparatus for constructing a Web service, by determining a required trained deep learning model in a front end page configuration manner, determining a processing flow in a service in a form of a drag frame, and finally combining a plurality of deep learning models and providing a customized Web service to the outside, which can improve efficiency, flexibility and accuracy of constructing the Web service, and further can improve accuracy of processing related business processes of applying the Web service; the method can avoid the problems of accuracy and efficiency caused by repeated development, and reduce the cost of maintaining Web services and constructing Web services.
In order to improve efficiency, flexibility and accuracy of building Web services and further improve accuracy of processing related business processes by applying Web services from a hardware level, the application provides an embodiment of an electronic device for implementing all or part of contents in the Web service building method, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the Web service construction device and related equipment such as a user terminal; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to an embodiment for implementing the Web service construction method and an embodiment for implementing the Web service construction apparatus according to the embodiments, and the contents thereof are incorporated herein, and are not repeated here.
Fig. 12 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 12, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 12 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one or more embodiments of the application, the Web service building functions may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:
step 100: receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relationship information between respective target nodes.
Step 200: and acquiring the current target node according to the target node information.
Step 300: and constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node.
From the above description, it can be seen that the electronic device provided by the embodiment of the present application can improve efficiency, flexibility and accuracy of building the Web service, and further can improve accuracy of processing related business processes by applying the Web service.
In another embodiment, the Web service construction apparatus may be configured separately from the central processor 9100, for example, the Web service construction apparatus may be configured as a chip connected to the central processor 9100, and the Web service construction function is implemented by control of the central processor.
As shown in fig. 12, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 12; in addition, the electronic device 9600 may further include components not shown in fig. 12, and reference may be made to the related art.
As shown in fig. 12, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.
The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.
The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, images, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.
As can be seen from the above description, the electronic device provided by the embodiment of the present application can improve efficiency, flexibility and accuracy of building the Web service, and further can improve accuracy of processing related business processes by applying the Web service.
An embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the Web service construction method in the above embodiment, the computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements all the steps in the Web service construction method in the above embodiment, for example, the processor implementing the steps when executing the computer program:
step 100: receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relationship information between respective target nodes.
Step 200: and acquiring the current target node according to the target node information.
Step 300: and constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node.
As can be seen from the above description, the computer readable storage medium provided by the embodiments of the present application can improve efficiency, flexibility and accuracy of building the Web service, and further can improve accuracy of processing related business processes by applying the Web service.
The embodiments of the method of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment mainly describes differences from other embodiments. For relevance, see the description of the method embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described 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 flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, 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, embedded processor, 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 specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present application have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A Web service construction method, comprising:
receiving a target Web service construction request corresponding to a current service, wherein the target Web service construction request comprises: target node information and connection relation information between each target node;
acquiring a current target node according to the target node information;
constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node, wherein the target Web service comprises a data receiving node, an image positioning node, a cutting node and an image text recognition node;
after the target Web service for processing the current business is constructed, the method further comprises:
the method comprises the steps that a data receiving node is used for receiving an image to be identified, an image positioning model preset in an image positioning node connected with the data receiving node is used for determining information of an area to be identified corresponding to the image to be identified, and the image positioning model is a deep learning model which is obtained in advance and used for determining the information of the area to be identified corresponding to the image; cutting the image to be identified based on the area information to be identified by using a cutting node connected with the image positioning node to obtain an area image to be identified; performing image text recognition on the region image to be recognized by using an image text recognition model preset in an image text recognition node connected with the cutting node;
the receiving the target Web service construction request corresponding to the current service comprises the following steps:
if a button of a self-defined algorithm at the left upper part of the page is triggered, uploading a local self-defined algorithm file to a node assembly library, wherein the self-defined algorithm is displayed in a node assembly icon at the right side of the page;
while dragging the node component icon, if the node component icon corresponds to a node of the deep learning model, using an algorithm and a version number in the page selection model;
after the connection relation of the node components in the Web service is determined, if a release option at the upper right side of the page is triggered, a target Web service construction request corresponding to the current service is received;
the construction of the target Web service for processing the current business based on the connection relation information between each target node and each target node comprises the following steps:
TF Serving is used as a starting service of the deep learning model, is responsible for loading the deep learning model and distributes corresponding GPU or CPU resources for the deep learning model; and providing a service interface for the outside by using the flash+guricorn, and calling a relevant deep learning model in the TF service according to the service logic to construct a target Web service for processing the current service.
2. The Web service construction method according to claim 1, wherein each of the target nodes is provided with different code group information, respectively;
correspondingly, the constructing the target Web service for processing the current business based on the connection relation information between each target node and each target node includes:
and combining the code group information in each target node according to the connection relation information between each target node so as to construct the target Web service for processing the current business.
3. The Web service construction method according to claim 1, wherein the image localization model is a deep learning model based on a deep algorithm.
4. The Web service building method according to claim 1, wherein the image text recognition model is a convolutional recurrent neural network model.
5. A Web service construction apparatus, comprising:
the receiving module is configured to receive a target Web service construction request corresponding to a current service, where the target Web service construction request includes: target node information and connection relation information between each target node;
the target node acquisition module is used for acquiring a current target node according to the target node information;
the construction module is used for constructing a target Web service for processing the current business based on the connection relation information between each target node and each target node, wherein the target Web service comprises a data receiving node, an image positioning node, a cutting node and an image text recognition node;
the Web service construction apparatus is further configured to:
the method comprises the steps that a data receiving node is used for receiving an image to be identified, an image positioning model preset in an image positioning node connected with the data receiving node is used for determining information of an area to be identified corresponding to the image to be identified, and the image positioning model is a deep learning model which is obtained in advance and used for determining the information of the area to be identified corresponding to the image; cutting the image to be identified based on the area information to be identified by using a cutting node connected with the image positioning node to obtain an area image to be identified; performing image text recognition on the region image to be recognized by using an image text recognition model preset in an image text recognition node connected with the cutting node;
the receiving module is specifically configured to:
if a button of a self-defined algorithm at the left upper part of the page is triggered, uploading a local self-defined algorithm file to a node assembly library, wherein the self-defined algorithm is displayed in a node assembly icon at the right side of the page;
while dragging the node component icon, if the node component icon corresponds to a node of the deep learning model, using an algorithm and a version number in the page selection model;
after the connection relation of the node components in the Web service is determined, if a release option at the upper right side of the page is triggered, a target Web service construction request corresponding to the current service is received;
the construction module is specifically used for:
TF Serving is used as a starting service of the deep learning model, is responsible for loading the deep learning model and distributes corresponding GPU or CPU resources for the deep learning model; and providing a service interface for the outside by using the flash+guricorn, and calling a relevant deep learning model in the TF service according to the service logic to construct a target Web service for processing the current service.
6. The Web service construction apparatus according to claim 5, wherein each of the target nodes is provided with different code group information, respectively;
correspondingly, the construction module comprises:
and the construction unit is used for combining the code group information in each target node according to the connection relation information between each target node so as to construct and obtain the target Web service for processing the current business.
7. The Web service building apparatus according to claim 5, wherein the image localization model is a deep learning model based on a deep algorithm.
8. The Web service building apparatus of claim 5 wherein the image text recognition model is a convolutional recurrent neural network model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the Web service construction method of any one of claims 1 to 4 when the program is executed by the processor.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed implement the Web service building method of any of claims 1 to 4.
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