Service Processed Method, Device, Medium and Electronic Device

Abstract

The present disclosure provides a service process handling method, apparatus, medium, and electronic device. The method includes: performing function segmentation processing on an image function model to obtain at least two image processing services; determining an image processing service among the at least two image processing services, and querying an image processing model including the image processing service; wherein the image processing model includes the image function model; obtaining service process information of the image processing service in the image processing model; and performing service process handling on the image processing service according to the service process information to obtain a service execution process of the image processing model. On the basis of ensuring the uniqueness of the image processing function, the present disclosure ensures the reusability of the image processing service. Further, performing service process handling on the image processing service saves labor costs and development resources, greatly reduces maintenance costs, and at the same time, can more flexibly access various image processing systems.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular, to a service process handling method, a service process handling device, a computer-readable medium, and an electronic device. Background Art

[0002] Image processing services are widely used in various image processing systems. When different services access the image processing system, it is necessary to use the image processing service to customize an exclusive image processing model to meet various service requirements and achieve rapid deployment and online operation.

[0003] However, this customization method requires a large amount of manpower, resulting in a long development cycle and high development costs. In addition, the interfaces of different image processing models cannot be reused, which also leads to a rapid increase in the maintenance costs of the image processing system.

[0004] In view of this, there is an urgent need in the art to develop a new service process handling method and device.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the technical background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a service process handling method, a service process handling device, a computer-readable medium, and an electronic device, so as to at least overcome to some extent the technical problems of too high labor costs and development costs, and too long development cycle.

[0007] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be partially learned through the practice of the present disclosure.

[0008] According to one aspect of the embodiments of the present disclosure, a service process handling method is provided, and the method includes:

[0009] Performing functional segmentation processing on an image function model to obtain at least two image processing services;

[0010] Determining an image processing service from the at least two image processing services, and querying an image processing model including the image processing service; wherein the image processing model includes the image function model;

[0011] Obtaining service process information of the image processing service in the image processing model;

[0012] Performing service process handling on the image processing service according to the service process information to obtain a service execution process of the image processing model.

[0013] According to one aspect of the embodiments of the present disclosure, a service process handling device is provided, and the device includes:

[0014] A function segmentation module, configured to perform function segmentation processing on an image function model to obtain at least two image processing services;

[0015] A service determination module, configured to determine an image processing service among the at least two image processing services, and query an image processing model including the image processing service; wherein, the image processing model includes the image function model or other image models other than the image function model;

[0016] An information acquisition module, configured to acquire service process information of the image processing service in the image processing model;

[0017] A process handling module, configured to perform service process handling on the image processing service according to the service process information to obtain a service execution process of the image processing model.

[0018] In some embodiments of the present disclosure, based on the above technical solution, the service process handling device further includes:

[0019] A path storage module, configured to store the image processing service in a service cluster to obtain service path information of the image processing service, and store the service path information in a path information table.

[0020] In some embodiments of the present disclosure, based on the above technical solution, the service determination module includes: an information monitoring sub-module, configured to determine an image processing model including the image processing service storing the service path information when it is monitored that the path information table stores the service path information.

[0021] In some embodiments of the present disclosure, based on the above technical solution, the service process handling device further includes: an identification generation module, configured to generate an execution process identification corresponding to the service execution process for calling the service execution process.

[0022] In some embodiments of the present disclosure, based on the above technical solution, the service process handling device further includes: a parameter receiving module, configured to receive service call parameters; wherein, the service call parameters include a service process identification;

[0023] A process determination module, configured to determine the service execution process corresponding to the service process identification according to the execution process identification, and perform image processing on the image through the service execution process corresponding to the service process identification to generate an image processing result.

[0024] In some embodiments of the present disclosure, based on the above technical solutions, the process determination module includes: an object download sub-module configured to download an image according to the download address information;

[0025] A service processing sub-module configured to perform image processing on the image based on the image processing information through the image processing service included in the service execution process corresponding to the service process identifier to obtain a service processing result;

[0026] A result summary sub-module configured to perform summary processing on the service processing result to obtain an image processing result.

[0027] In some embodiments of the present disclosure, based on the above technical solutions, the image includes an image obtained by performing video frame extraction on a video, and the source of the image is determined by a category parameter in the service call parameter.

[0028] In some embodiments of the present disclosure, based on the above technical solutions, the download address information, the image processing information, and the category parameter are obtained through encoding processing.

[0029] In some embodiments of the present disclosure, based on the above technical solutions, the process determination module includes: an authentication processing sub-module configured to perform authentication processing on the identity parameter to obtain an authentication processing result;

[0030] An authentication success sub-module configured to determine the service execution process corresponding to the service process identifier according to the authentication processing result according to the execution process identifier.

[0031] In some embodiments of the present disclosure, based on the above technical solutions, the process determination module includes: a path determination sub-module configured to determine the image processing service included in the service execution process corresponding to the service process identifier as a service to be called, and determine the service path information of the service to be called in the path information table;

[0032] A path call sub-module configured to call the service to be called according to the service path information, and perform image processing on the image through the service to be called to generate an image processing result.

[0033] In some embodiments of the present disclosure, based on the above technical solutions, the path call sub-module includes: a cluster determination unit configured to determine the service cluster storing the service to be called according to the cluster identifier;

[0034] A service call unit configured to call the service to be called in the service cluster according to the network path information.

[0035] In some embodiments of the present disclosure, based on the above technical solution, the service process handling device further includes: a result storage sub-module configured to store the image processing result in a content delivery network.

[0036] According to one aspect of the embodiments of the present disclosure, there is provided a computer-readable medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the service process handling method in the above technical solution.

[0037] According to one aspect of the embodiments of the present disclosure, there is provided an electronic device, which includes: a processor; and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the service process handling method in the above technical solution by executing the executable instructions.

[0038] According to one aspect of the embodiments of the present disclosure, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the service process handling method provided in the above various alternative embodiments.

[0039] In the technical solution provided by the embodiments of the present disclosure, an image processing service is obtained through functional segmentation processing. On the basis of ensuring the uniqueness of the image processing function, the reusability of the image processing service is ensured. Further, service process handling is performed on the image processing service obtained through functional segmentation processing, which saves labor costs and development resources, greatly reduces maintenance costs, and at the same time, can access various image processing systems more flexibly, improving the efficiency of accessing the image processing system.

[0040] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings. In the drawings:

[0042] Figure 1 Schematically shows an architecture diagram of an exemplary system applying the technical solution of the present disclosure;

[0043] Figure 2 Schematically shows a flowchart of steps of a service process handling method in some embodiments of the present disclosure;

[0044] Figure 3 Schematically shows a flowchart of steps of a method for invoking a service execution process according to an execution process identifier in some embodiments of the present disclosure;

[0045] Figure 4 Schematically shows a flowchart of steps of a method for determining a service execution process in some embodiments of the present disclosure;

[0046] Figure 5 Schematically shows a flowchart of steps of a method for invoking a service execution process of a parallel structure in some embodiments of the present disclosure;

[0047] Figure 6 Schematically shows a flowchart of steps of a method for generating an image processing result in some embodiments of the present disclosure;

[0048] Figure 7 Schematically shows a flowchart of steps of a method for determining a service to be invoked in some embodiments of the present disclosure;

[0049] Figure 8 Schematically shows a module structure diagram of a vulgar person recognition model in an application scenario in some embodiments of the present disclosure;

[0050] Figure 9 Schematically shows a module structure diagram of a pornographic anime material detection model in an application scenario in some embodiments of the present disclosure;

[0051] Figure 10 Schematically shows a schematic structural diagram of a service process handling system in an application scenario in some embodiments of the present disclosure;

[0052] Figure 11 Schematically shows a schematic structural diagram of a service process handling process of a vulgar task recognition model of a parallel chain structure in some embodiments of the present disclosure;

[0053] Figure 12 Schematically shows a structural block diagram of a service process handling device in some embodiments of the present disclosure;

[0054] Figure 13 Schematically shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present disclosure. Detailed implementation manners

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

[0056] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will recognize that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0057] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0058] The flowcharts shown in the drawings are merely illustrative and do not necessarily include all the content and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0059] In the related art of the present field, image processing systems are widely adapted to various application scenarios. Among them, the main function of the image SaaS (Software as a Service) system of the News and Video Advertising Cooperation Department is to provide image processing capabilities for each module in the advertising placement, advertising retrieval, and advertising playback links, mainly using the image AI (Artificial Intelligence) model service for capacity output.

[0060] In the traditional solution, before each business accesses the image SaaS system, a dedicated image AI model service needs to be customized. This on-demand customization technical means can quickly and effectively meet the needs of various businesses and achieve rapid deployment and online operation.

[0061] However, this solution also has various drawbacks. First of all, each image model service needs to be customized during the access to the image SaaS system, including customizing communication protocols and interfaces, etc., which takes a large amount of human and time costs, has a long development cycle, and the development cost is very high. Then, due to the coupling of business logic and model processing logic, the interfaces of different image model services cannot be reused, resulting in an explosive growth of the number of interfaces and the number of image model services as the number of accessed services increases, and further leading to a rapid increase in system maintenance costs.

[0062] Based on the problems existing in the above solution, the present disclosure provides a service process flow processing method, a service process flow processing device, a computer-readable medium, and an electronic device based on artificial intelligence and cloud technology.

[0063] Artificial Intelligence (AI) is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines to enable the machines to have the functions of perception, reasoning, and decision-making.

[0064] Artificial intelligence technology is an interdisciplinary subject, involving a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0065] Among them, computer vision technology (CV) is a science that studies how to enable machines to "see". Further speaking, it refers to machine vision that uses cameras and computers to replace human eyes to identify, track, and measure targets, and further performs graphic processing to make the computer-processed images more suitable for human eyes to observe or be transmitted to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies and attempts to establish artificial intelligence systems that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc. technologies, and also includes common biometric recognition technologies such as face recognition and fingerprint recognition.

[0066] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0067] Cloud technology is the general term for network technology, information technology, integration technology, management platform technology, application technology, etc. based on the cloud computing business model. It can form a resource pool, be used on demand, and is flexible and convenient. Cloud computing technology will become an important support. The back-end services of technical network systems require a large amount of computing and storage resources, such as video websites, picture websites, and more portal websites. With the highly developed application of the Internet industry, in the future, each item may have its own identification mark and needs to be transmitted to the back-end system for logical processing. Data at different levels will be processed separately, and various industry data requires a powerful system back-end support, which can only be achieved through cloud computing.

[0068] Cloud computing is a computing model that distributes computing tasks on a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services according to needs. The network that provides resources is called the "cloud". The resources in the "cloud" seem to be infinitely expandable to users, and can be obtained at any time, used on demand, expanded at any time, and paid according to usage.

[0069] As a provider of basic cloud computing capabilities, a cloud computing resource pool (referred to as a cloud platform, generally referred to as an IaaS (Infrastructure as a Service) platform) will be established, and various types of virtual resources will be deployed in the resource pool for external customers to choose to use. The cloud computing resource pool mainly includes: computing devices (virtualized machines, including operating systems), storage devices, and network devices.

[0070] According to the logical function division, the PaaS (Platform as a Service) layer can be deployed on the IaaS (Infrastructure as a Service) layer, and the SaaS (Software as a Service) layer can be deployed on the PaaS layer. SaaS can also be deployed directly on IaaS. PaaS is a platform for software operation, such as databases, web containers, etc. SaaS is a variety of business software, such as web portals, SMS mass senders, etc. Generally speaking, SaaS and PaaS are upper layers relative to IaaS.

[0071] The service process processing method that utilizes computer vision technology and cloud technology in artificial intelligence saves labor costs and development and maintenance costs, and also improves the efficiency of accessing the image processing system.

[0072] Figure 1 A schematic diagram of an exemplary system architecture applying the technical solution of the present disclosure is shown.

[0073] like Figure 1 As shown, the system architecture 100 may include a terminal 110 , a network 120 , and a server 130 . The terminal 110 and the server 130 are connected via the network 120 .

[0074] The terminal 110 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The network 120 may be a communication medium of various connection types that can provide a communication link between the terminal 110 and the server 130, such as a wired communication link, a wireless communication link, or an optical fiber cable, etc., which is not limited in this application. The server 130 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0075] Specifically, the server side 130 performs function segmentation processing on the image function model to obtain at least two image processing services. Further, an image processing service is determined from the at least two image processing services, and an image processing model including the image processing service is queried; wherein, the image processing model includes the image function model. Then, the service process information of the image processing service in the image processing model is obtained, and the service process of the image processing service is processed according to the service process information to obtain the service execution process of the image processing model.

[0076] Moreover, an execution process identifier corresponding to the service execution process can also be generated to facilitate the terminal 110 to call the service execution process.

[0077] In addition, the service process processing method in the embodiments of the present disclosure can be applied to the terminal or the server side, and the present disclosure does not make special limitations on this. The embodiments of the present disclosure mainly take the service process processing method applied to the server side 130 as an example for illustration.

[0078] The service process processing method, service process processing device, computer-readable medium, and electronic device provided by the present disclosure will be described in detail below in conjunction with specific embodiments.

[0079] Figure 2 Schematically shows the step flowchart of the service process processing method in some embodiments of the present disclosure, as Figure 2 shown, the service process processing method mainly may include the following steps:

[0080] Step S210. Perform function segmentation processing on the image function model to obtain at least two image processing services.

[0081] Step S220. Determine an image processing service from the at least two image processing services, and query an image processing model including the image processing service; wherein, the image processing model includes the image function model.

[0082] Step S230. Obtain the service process information of the image processing service in the image processing model.

[0083] Step S240. Process the image processing service according to the service process information to obtain the service execution process of the image processing model.

[0084] In an exemplary embodiment of the present disclosure, an image processing service is obtained through functional segmentation processing. On the basis of ensuring the exclusivity of the image processing function, the reusability of the image processing service is ensured. Further, the service process of the image processing service obtained through functional segmentation processing is processed, which saves labor costs and development resources, and greatly reduces the maintenance cost. At the same time, various image processing systems can be accessed more flexibly, improving the efficiency of accessing the image processing system.

[0085] The following will describe each step of the service process processing method in detail.

[0086] In step S210, the image function model is subjected to functional segmentation processing to obtain at least two image processing services.

[0087] In an exemplary embodiment of the present disclosure, the image processing service is a service that can be provided by an image function module, and may include various service contents such as an image input service and an image detection service, or may be provided by other means. This exemplary embodiment does not make special limitations on this.

[0088] Specifically, the image function model may be a model with image processing capabilities, such as a vulgar person recognition model and a pornographic anime material detection model, or other image function models on the advertising placement, advertising retrieval, or advertising playback link. This exemplary embodiment does not make special limitations on this.

[0089] The image processing service may be a service provided by each functional module in the image function model. By abstracting the image function model according to the image processing function, the image processing services corresponding to each functional module can be determined.

[0090] For example, when the image function model is a vulgar person recognition model, the image processing service may be the service provided by the picture input module, the service provided by the person detection module, the service provided by the person vulgar classification module, and the service provided by the discrimination result output module, etc.; when the image function model is a pornographic anime material detection model, the image processing service may be the service provided by the picture input module, the service provided by the person detection module, the service provided by the anime character discrimination module, the service provided by the pornographic anime character discrimination module, and the service provided by the discrimination result output module, etc. When the image function model is other image function models on the advertising placement, advertising retrieval, or advertising playback link, the image processing service may also be other image processing services corresponding to other image function models. This exemplary embodiment does not make special limitations on this.

[0091] When the image processing service is provided by each functional module in the image function model, the image processing service can represent each functional module. Therefore, in order to perform functional segmentation processing on the image processing model according to functions, the image function model can be subjected to functional segmentation processing according to the image processing service to obtain the image processing service.

[0092] When the image processing service is the service provided by the picture input module, the person detection module, the person vulgarity classification module, and the discrimination result output module, functional segmentation processing can be performed according to the image processing service to obtain the picture input service, the person detection service, the person vulgarity classification service, and the discrimination result output service; when the image processing service is the service provided by the picture input module, the person detection module, the anime character discrimination module, the pornographic anime character discrimination module, and the discrimination result output module, functional segmentation processing can be performed according to the image processing service to obtain the picture input service, the person detection service, the anime character discrimination service, the pornographic anime character discrimination service, and the discrimination result output service.

[0093] In this exemplary embodiment, performing functional segmentation processing on the image function model according to the image processing service to obtain at least two image processing services provides a data basis for subsequent service process flow processing and is also beneficial to improving the reusability of the image processing service.

[0094] After determining the image processing service, each image processing service can be deployed to generate service path information.

[0095] In an alternative embodiment, the image processing service is stored in a service cluster to obtain the service path information of the image processing service, and the service path information is stored in a path information table.

[0096] The service cluster is used to store the image processing services obtained by functional segmentation processing. Multiple image processing services can be stored in one service cluster. Moreover, there can be one or multiple service clusters, and this exemplary embodiment does not make special limitations on this.

[0097] To solve the load problem caused by excessive deployed image processing services, the service path information for deploying the image processing service can be specified through the cluster identifier of the service cluster and the network path information of the image processing service. That is, the service path information includes the cluster identifier of the service cluster and the network path information of the image processing service.

[0098] Among them, the cluster identifier may be the identifier information of the load-balanced cluster. One cluster identifier can specify a set of network path information, and one network path information can specify an image processing service. The network path information may be the routing information of the image processing service or other information, and this exemplary embodiment does not make special limitations thereon. The routing information may be the storage path of the image processing service, and the corresponding image processing service can be obtained through the routing information.

[0099] And the path information table can store the service path information of the image processing service each time a new image processing service is monitored.

[0100] The path information table may be a table for storing service path information. When the service path information includes the cluster identifier of the service cluster and the network path information of the image processing service, the cluster identifier and the network path information corresponding to the image processing service are stored in the path information table. That is, the service path information corresponding to the image processing service can be queried in the path information table.

[0101] In this exemplary embodiment, after determining the image processing service obtained by function segmentation processing, the image processing service can be further deployed, and the service path information related to the deployment location can be stored to facilitate subsequent querying and process flow processing of the corresponding image processing service through the service path information.

[0102] In step S220, determine an image processing service among at least two image processing services, and query the image processing model including the image processing service; wherein, the image processing model includes an image function model.

[0103] In the exemplary embodiment of the present disclosure, in order to perform process flow processing on multiple image processing services obtained by function segmentation processing, an image processing service to be subjected to process flow processing can be first determined among at least two image processing services. This image processing service can be randomly determined or specified according to actual needs, and this exemplary embodiment does not make special limitations thereon.

[0104] Furthermore, determine the image processing model including the image processing service to perform process flow processing on the image processing service.

[0105] Specifically, in order to perform process flow processing on the image processing service, the path information table can be monitored to determine the timing of process flow processing.

[0106] In an alternative embodiment, when it is monitored that the service path information is stored in the path information table, determine the image processing model including the image processing service storing the service path information.

[0107] Since the service path information of the image processing service is stored in the path information table when the image processing service is deployed, after the deployment, the image processing model can be determined for service process flow processing. In addition, the timing of other service process flow processing can also be set according to actual needs, and this exemplary embodiment does not make special limitations on this.

[0108] Since the image processing service is obtained by performing function segmentation processing on the image function model, and there are the same image processing services and unique image processing services in the image processing model to be processed by the process flow, an image processing model including the image processing service can be determined to perform service process flow processing on the image processing services included in the image processing model.

[0109] When the image processing service is a general image processing service, at least two image processing models including the image processing service can be determined; when the image processing service is a unique image processing service, only one image processing model including the image processing service can be determined.

[0110] For example, the services jointly included in the vulgar person recognition model and the pornographic anime material detection model are the picture input service, the person detection service, and the discrimination result output service. Therefore, the image processing models including the image input service, the person detection service, and the discrimination result output service can be the vulgar person recognition model and the pornographic anime material detection model.

[0111] In addition, the vulgar person recognition model further includes a unique person vulgar classification service, and the pornographic anime material detection model further includes a unique anime character discrimination service and a pornographic anime character discrimination service. Therefore, the image processing model including the person vulgar classification service can be the vulgar person recognition model, and the image processing model including the anime character discrimination service and the pornographic anime character discrimination service can be the pornographic anime material detection model.

[0112] In step S230, obtain the service process information of the image processing service in the image processing model.

[0113] In the exemplary embodiment of the present disclosure, since each image processing service in the image processing model is executed in sequence, each image processing service has its own execution order in the image processing model, and this execution order is represented by service process information.

[0114] The service process information can be represented by means of numbers, words, letters, etc., or can be characterized by referring to the execution order of one service, or other types of information, and this exemplary embodiment does not make special limitations on this.

[0115] For example, the service process information of the image input service in the vulgar person recognition model is 1, the service process information of the person detection service is 2, the service process information of the person vulgarity classification service is 3, and the service process information of the discrimination result output service is 4; in the pornographic anime material detection model, the service process information of the image input service is 1, the service process information of the person detection service is 2, the service process information of the anime character discrimination service is 3, the service process information of the pornographic anime character discrimination service is 4, and the service process information of the discrimination result output service is 5.

[0116] Therefore, the service process information of the same image processing service in different image processing models may be the same or different.

[0117] In step S240, the image processing service is processed according to the service process information to obtain the service execution process of the image processing model.

[0118] In an exemplary embodiment of the present disclosure, after determining the service process information of the image processing service in the image processing model, the image processing service may be processed according to the service process information to obtain a service execution process corresponding to the image processing model.

[0119] Among them, the service process processing is a processing method of connecting other image processing services of the image processing service in the image processing model according to the service process information of an image processing service. And, since there is a service connection relationship between each image processing service after the connection processing, the processing result of the previous image processing service is the input parameter of the next image processing service. Since all the image processing services included in the image processing model have been processed in terms of service process, the service execution process of the image processing model can be obtained after all the image processing services are processed in terms of service process. This service image process characterizes the service connection relationship between each image processing service in the image processing model.

[0120] It should be noted that since the image function model is included in the image processing model, the image processing model for service process processing can be the image function model that has been recently subjected to function segmentation processing for service process processing, that is, directly performing service process processing after performing function segmentation processing on the image function model.

[0121] In addition, the image processing model for service process processing also includes other image processing models other than the image function model, and the other image processing models are image processing models including the determined image processing services.

[0122] Moreover, the image processing model for service process handling may also include an image processing model obtained after updating an image processing model. That is to say, the image processing model will be updated according to actual needs, and after the update, service process handling can be performed according to the image processing services included in the updated image processing model. And, according to the actual situation, the service execution process of the unupdated image processing model can also be deleted to save storage space, or the service execution process of the image processing model before the update can be saved as an expansion method. This exemplary embodiment does not make special limitations on this.

[0123] For example, the service process information of the image input service in the vulgar person recognition model is 1, the service process information of the person detection service is 2, the service process information of the person vulgar classification service is 3, and the service process information of the discrimination result output service is 4. Therefore, by performing service process handling on the image input service, person detection service, person vulgar classification service, and discrimination result output service included in the vulgar person recognition model respectively, the service execution process of the image processing service in the vulgar person recognition model can be obtained as image input service → person detection service → person vulgar classification service → discrimination result output service; the service process information of the image input service in the pornographic anime material detection model is 1, the service process information of the person detection service is 2, the service process information of the anime character discrimination service is 3, the service process information of the pornographic anime character discrimination service is 4, and the service process information of the discrimination result output service is 5. Therefore, by performing service process handling on the image input service, person detection service, anime character discrimination service, pornographic anime character discrimination service, and discrimination result output service included in the pornographic anime material detection model respectively, the service execution process of the pornographic anime material detection model can be obtained as image input service → person detection service → anime character discrimination service → pornographic anime character discrimination service → discrimination result output service.

[0124] Moreover, the service execution process can be in a chain structure, and this chain structure can be a serial chain structure, a parallel chain structure, or other structures. This exemplary embodiment does not make special limitations on this.

[0125] After generating the service execution process of the image processing model, corresponding execution identification information can be generated for invocation.

[0126] In an alternative embodiment, an execution process identifier corresponding to the service execution process is generated for invoking the service execution process.

[0127] The execution process identifier can be the identification information of the service execution process, and a service execution process uniquely corresponds to one execution process identifier. Through this execution process identification information, a service execution process can be uniquely determined.

[0128] In an alternative embodiment, Figure 3 FIG. Figure 3 shows a step flowchart of a method for invoking a service execution process according to an execution process identifier, as Figure 3 shown. The method at least includes the following steps: In step S310, service call parameters are received; wherein, the service call parameters include a service process identifier.

[0129] When the service end requests to invoke a service execution process, a service request is sent. The service call parameters are carried in the service request, and in order to clarify the service execution process to be invoked, the execution process identifier is included in the service call parameters.

[0130] In step S320, the service execution process corresponding to the service process identifier is determined according to the execution process identifier, and image processing is performed on the image through the service execution process corresponding to the service process identifier to generate an image processing result.

[0131] Since there is a one-to-one correspondence between the execution process identifier and the service execution process, after obtaining the service process identifier in the service call parameters, the service execution process to be invoked can be determined according to the execution process identifier identical to the service process identifier, so as to perform image processing on the image through the service execution process to generate an image processing result.

[0132] When invoking, not all application programs that initiate a service request have the qualification to invoke a service execution process. Therefore, the service request can be authenticated.

[0133] In an alternative embodiment, the service call parameters include identity parameters. Figure 4 FIG. Figure 4 shows a schematic diagram of a step flow of a method for determining a service execution process, as Figure 4 shown. The method at least includes the following steps: In step S410, the identity parameters are authenticated to obtain an authentication result.

[0134] The identity parameters may be the identification information and signature information of the application program that initiates the service request, that is, appid and app_sign. Among them, the signature information may be the signature information corresponding to the service request generated when the service end initiates the service request, so as to send the identification information and signature information for verification. The signature information may be generated by encrypting the access request according to a key, or may be generated in other ways. This exemplary embodiment does not make special limitations on this.

[0135] The method for authenticating the identity parameters may be to determine whether the identification information and signature information of the application program that can invoke the service execution process include the identification information and signature information carried in the service request, or there may be other authentication methods. This exemplary embodiment does not make special limitations on this.

[0136] In step S420, based on the authentication processing result, the service execution process corresponding to the service process identifier is determined according to the execution process identifier.

[0137] When the identification information and signature information of the application that can call the service execution process include the identification information and signature information carried in the business request, the authentication processing result is determined to be authentication passed, so as to further use the execution process identifier to determine the service execution process.

[0138] In this exemplary embodiment, identity parameters are used to authenticate the application program that initiates the service request, thereby ensuring the information security and privacy of the service execution process.

[0139] It is worth noting that if the service execution process is a serial structure, the image processing result can be obtained by directly calling the service execution process to perform image processing on the image; and when the service execution process is a parallel structure, after calling the service execution process to perform image processing on the image, the processing results can also be summarized to obtain the image processing result.

[0140] In an optional embodiment, the service call parameters also include download address information and image processing information. Figure 5 A schematic flow chart showing the steps of a method for calling a service execution process of a parallel structure is shown. Figure 5 As shown, the method at least includes the following steps: In step S510, the image is downloaded according to the download address information.

[0141] The download address information is the location information where the image to be processed is stored. The image can be downloaded according to the location information to perform subsequent image processing on the image.

[0142] In an optional embodiment, the image includes an image obtained by performing frame extraction processing on a video, and the source of the image is determined by a category parameter in the service call parameter.

[0143] Since the service end also carries a category parameter in the service request when sending the service request, it can be determined whether the downloaded image is obtained by performing video frame extraction processing on the video according to the category parameter. Among them, the video frame extraction processing is to extract several frames at a certain interval from the video to be processed, simulating the processing method of obtaining an image every certain period of time.

[0144] Through the video frame extraction processing, images can be obtained one by one, that is, images obtained according to the video. And, the image processing service in the image calling service execution flow is processed to obtain the image processing result.

[0145] Of course, if the service execution process at this time is also a parallel structure, the image processing result can also be obtained in the same way as in steps S520 - S530.

[0146] In this exemplary embodiment, when the image is obtained by performing video frame extraction on a video, the service execution process can also be called for image processing, enriching the application scenarios of the service execution process. In step S520, based on the image processing information, the image is processed through the image processing service included in the service execution process corresponding to the service process identifier to obtain a service processing result.

[0147] The image processing information can be the input parameters necessary for image processing or other necessary information, and this exemplary embodiment does not make special limitations on this.

[0148] After determining the image and the image processing information, the service execution process can be called to perform image processing on both to obtain a service processing result.

[0149] It should be noted that when the image execution process is a parallel structure, the service processing result can be the processing results of at least two sub - processes. And for at least two sub - processes, the image processing information can be the same or different, and this exemplary embodiment does not make special limitations on this.

[0150] In step S530, the service processing result is aggregated to obtain an image processing result.

[0151] The aggregation process can be a way of combining the processing results of at least two sub - processes or other processing methods, and this exemplary embodiment does not make special limitations on this.

[0152] For example, when the processing result of one sub - process is result 1 and the processing result of another sub - process is result 2, they can be combined and processed in the set manner of [result 1, result 2] as the image processing result.

[0153] In this exemplary embodiment, calling the image processing service in the parallel - structured service execution process can obtain an image processing result, enriching the application methods of the service execution process and expanding the call scenarios of the service execution process.

[0154] In an optional embodiment, the download address information, the image processing information, and the category parameters are obtained through encoding processing.

[0155] The encoding method can be encoding processing using the JSON (JavaScript Object Notation) protocol or other encoding methods, and this exemplary embodiment does not make special limitations on this.

[0156] Since JSON is an ideal data exchange format, easy to read and write by humans, and also easy to parse and generate by machines, it is very commonly used in interface data development and transmission. When encoding download address information, image processing information, and category parameters using the JSON protocol encoding method, it is convenient to carry and transmit this information through business requests, providing a more convenient information transmission method for determining the service execution process.

[0157] Specifically, you can refer to Table 1 to view the content design of service call parameters:

[0158]

[0159] Among them, appid and app_sign are the identification information and signature information of the application program, that is, identity parameters; model_action is the process identifier, that is, the service execution process to be called can be determined using model_action; model_param is a parameter list, which can include download address information, image processing information, and category parameters, etc., and the download address information, image processing information, and category parameters are encoded and transmitted using the JSON protocol.

[0160] When downloading an image and calling the image processing service in the service execution process to perform image processing on the image and image processing information, the image processing service can be obtained from the path information table for calling.

[0161] In an alternative embodiment, Figure 6 shows a flowchart of the steps of a method for generating an image processing result, as Figure 6 shown, the method at least includes the following steps: In step S610, determine the image processing service included in the service execution process corresponding to the service process identifier as the service to be called, and determine the service path information of the service to be called in the path information table.

[0162] The path information table can be a table storing the service path information of the image processing service. Since there is a one-to-one correspondence between the service path information stored in the path information table and the image processing service, therefore, the image processing services in the service execution process can be determined one by one as the services to be called, and the service path information of the services to be called can be determined to be called in the order of the service execution process.

[0163] In step S620, call the service to be called according to the service path information, and perform image processing on the image through the service to be called to generate an image processing result.

[0164] In an alternative embodiment, the service path information includes the cluster identifier of the service cluster and the network path information of the image processing service. Figure 7The flowchart of steps for a method of determining a service to be invoked is shown. As Figure 7 shown, the method at least includes the following steps: In step S710, a service cluster storing the service to be invoked is determined according to a cluster identifier.

[0165] Among them, the cluster identifier may be the identifier information of a load-balanced cluster. One cluster identifier can specify a set of network path information, and one network path information specifies an image processing service. Therefore, according to the cluster identifier in the service path information, the service cluster where the image processing service in the service execution process is located, that is, the service cluster where the service to be invoked is located, can be determined.

[0166] In step S720, the service to be invoked in the service cluster is invoked according to the network path information.

[0167] The network path information may be the routing information of the image processing service or other information, and this exemplary embodiment does not make special limitations on this. The routing information may be the storage path of the image processing service, and the corresponding image processing service, that is, the service to be invoked, can be obtained through the routing information.

[0168] Furthermore, after determining a service cluster, the service to be invoked in the service execution process can be obtained according to the network path information.

[0169] In this exemplary embodiment, the service to be invoked in the service execution process can be invoked through the service path information. The invocation process is logically rigorous and solves the load balancing problem in the storage and invocation processes, and has extremely strong practicability.

[0170] After determining the service to be invoked, the image can be subjected to image processing using the service to be invoked to generate an image processing result. And, since the amount of data of the image processing result generated during the image processing process is large, the image processing result can be stored at a specified location.

[0171] In an alternative embodiment, the image processing result is stored in a content delivery network.

[0172] Among them, the Content Delivery Network (CDN) is a new type of network construction method. It is a network overlay layer specially optimized for publishing broadband rich media on the traditional IP (Internet Protocol) network. From a broad perspective, CDN represents a network service model based on quality and order. Simply put, CDN is a strategically deployed overall system, including four elements: distributed storage, load balancing, network request redirection, and content management. Content management and global network traffic management are the core of CDN. By judging user proximity and server load, CDN ensures that content serves user requests in a very efficient manner. Generally speaking, content services are based on cache servers, also known as proxy caches, which are located at the edge of the network, only "one hop" away from users. At the same time, the proxy cache is a transparent mirror of the content provider's origin server (usually located in the data center of the CDN service provider). Such an architecture enables CDN service providers to provide the best possible experience for end users on behalf of content providers, and these users cannot tolerate any delay in request response time. According to statistics, using CDN technology can handle 70% - 95% of the content access volume of the entire website page, relieve the pressure on the server, and improve the performance and scalability of the website. Due to its high-efficiency and high-quality service quality, CDN is increasingly widely used.

[0173] It should be noted that the image processing results stored in the CDN can be the intermediate processing results of the image processing service or the final processing results of the image processing model, so as to meet the processing results in each process of the service execution process called by the business side.

[0174] In this exemplary embodiment, storing the image processing results in the content delivery network can not only meet the requirement of a large amount of image processing result data during the image processing process, but also meet the requirement of the business side to quickly obtain the image processing results, improving the response speed and success probability of business side requests.

[0175] The following makes a detailed description of the service process processing method provided in the embodiments of the present disclosure in combination with a specific application scenario.

[0176] Figure 8 The module structure diagram of the vulgar person recognition model in the application scenario is shown, as Figure 8 shown. The vulgar person recognition model includes a picture input module, a person detection module, a person vulgar classification module, a discrimination result output module, etc.

[0177] It should be noted that each functional module can provide image processing services, that is, the functional module is the external manifestation form of the service.

[0178] Figure 9 shows the module structure diagram of the pornographic anime material detection model in the application scenario, as Figure 9 shown, the functional modules included in the pornographic anime material detection model are the picture input module, the person detection module, the anime character discrimination module, the pornographic anime character discrimination module, and the discrimination result output module.

[0179] Functionally abstracting the vulgar person recognition model and the pornographic anime material detection model, it can be found that there are both the same modules in the functional modules of the two models, such as the picture input module, the person detection model, and the discrimination result output module, and there are also unique modules. For example, there is a vulgar person classification module in the vulgar person recognition model, while there are an anime character discrimination module and a pornographic anime character discrimination module in the pornographic anime material detection model.

[0180] Therefore, the vulgar person recognition model and the pornographic anime material detection model can be functionally segmented to obtain general modules and unique modules, and then deployed.

[0181] In order to avoid the explosive growth of system interfaces and maintain the generality of functional modules and the singularity of functions in the image SaaS system, a service process flow processing system can be deployed in the image SaaS system to connect the business side to the image SaaS system in a more flexible way.

[0182] The image SaaS system mainly provides image processing services for each module in the advertising placement, advertising retrieval, and advertising playback link. Especially, the image SaaS system of the news video advertising cooperation department is mainly applied to the advertising intelligent review module in advertising placement.

[0183] The main goal of the advertising intelligent review module is to use machines to replace humans to automatically review the advertisements submitted by advertisers and avoid information such as vulgar pictures, infringing trademarks, and prohibited items in the advertisements. Therefore, in the advertising intelligent review module, the image SaaS system mainly provides the corresponding processing capabilities for automatically reviewing pictures or videos in advertising materials, including judging whether the pictures or videos are vulgar, whether they contain infringing trademarks, whether they contain prohibited items, etc.

[0184] Figure 10 shows the schematic structural diagram of the service process flow processing system in the application scenario, as Figure 10 shown, the service process flow system includes a configuration center 1010, a service cluster module 1020, a service process flow processing module 1030 of the image SaaS system, and a business side 1040.

[0185] When each image processing service goes online, it registers the corresponding service path information in the configuration center 1010. The service process flow module 1030 of the image SaaS system subscribes to this service path information for subsequent calls. Specifically, after the service cluster module 1020 goes online, it sends a request to the configuration center 1010 and sends the cluster identifier of its service cluster and the network path information of the image processing service to the configuration center 1010. Further, the configuration center 1010 writes the cluster identifier of the service cluster and the network path information of the image processing service into the path information table and provides it for the service process flow module 1030 of the image SaaS system to subscribe to.

[0186] The service process flow module 1030 of the image SaaS system monitors this path information table. When it monitors that new cluster identifiers of service clusters and network path information of image processing services are written into this path information table, it determines the image processing model of the image processing service corresponding to the cluster identifier of the service cluster and the network path information of the image processing service, and performs service process flow processing on the image processing service according to the service process information in the image processing model to obtain a service execution process, which is published to the configuration center 1010. When the service process flow module 1030 of the image SaaS system subscribes to this service execution process, it can load this service execution process into memory for calling.

[0187] The business end 1040 sends a business request to the service process flow module 1030 of the image SaaS system and carries service call parameters to determine the service execution process to be called. The service process flow module 1030 of the image SaaS system sequentially calls the image processing services in the service execution process to obtain an image processing result.

[0188] Moreover, when this service execution process is a parallel structure, it also aggregates the service processing results of each sub-process to obtain an image processing result and returns it to the business end 1040.

[0189] Figure 11 The structural schematic diagram of the service process flow processing flow of the vulgar person recognition model with a parallel chain structure is shown as Figure 11 As shown, this vulgar person recognition model is a parallel chain structure, including a picture input service, a person detection service, a person vulgarity classification service, a key part detection service, a key part vulgarity classification service, and a discrimination result output service.

[0190] This service process flow processing flow can also be encoded and processed using the JSON protocol, and the encoding method is as follows:

[0191]

[0192]

[0193] When the service execution process is in a chain structure, a network graph of the service execution process can be gradually generated. Then, when each image processing service is accessed, only the corresponding service execution process needs to be published to the configuration center using the JSON encoding protocol, and the service process handling module of the image SaaS system will subscribe to this information to load it into memory and call the service execution process.

[0194] Specifically, the service path information corresponding to the vulgar person recognition model is as follows:

[0195]

[0196] Each image processing service of the vulgar person recognition model corresponds to a cluster identifier. For example, the cluster identifier of the picture input service is 1981633:65536, the cluster identifier of the person detection model service is 1528065:720896, the cluster identifier of the key part detection service is 1570305:983040, the cluster identifier of the person vulgar classification service is 1528065:655360, and the cluster identifier of the key part vulgar classification service is 1528065:589824. This cluster identifier corresponds to a set of network path information and a service port, that is, a set of service clusters.

[0197] When the service process handling module of the image SaaS system calls an image processing service, it will obtain a set of network path information and a service port through the cluster identifier, and send the service request to the corresponding service instance.

[0198] Based on the above application scenarios, it can be seen that the service process handling method provided by the embodiments of the present disclosure divides the image processing service according to function segmentation, ensuring the uniqueness of the image processing function while ensuring the reusability of the image processing service. Further, the service process handling of the image processing service obtained by function segmentation saves at least 50% of the labor cost and at least 30% of the development resources, and also greatly reduces the maintenance cost. At the same time, various image processing systems can be accessed more flexibly, improving the efficiency of accessing the image processing system.

[0199] It should be noted that although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0200] The following introduces the device embodiments of the present disclosure, which can be used to execute the service process flow processing method in the above embodiments of the present disclosure. For details not disclosed in the device embodiments of the present disclosure, please refer to the embodiments of the service process flow processing method above of the present disclosure.

[0201] Figure 12 Schematically shows a structural block diagram of a service process flow processing device in some embodiments of the present disclosure, as Figure 12 shown, the service process flow processing device 1200 mainly may include: a function segmentation module 1210, a service determination module 1220, an information acquisition module 1230, and a process processing module 1240.

[0202] The function segmentation module 1210 is configured to perform function segmentation processing on an image function model to obtain at least two image processing services; the service determination module 1220 is configured to determine an image processing service among at least two image processing services and query an image processing model including the image processing service; wherein, the image processing model includes the image function model; the information acquisition module 1230 is configured to acquire service process information of the image processing service in the image processing model; the process processing module 1240 is configured to perform service process flow processing on the image processing service according to the service process information to obtain a service execution process of the image processing model.

[0203] In some embodiments of the present disclosure, the service process flow processing device further includes:

[0204] A path storage module, configured to store the image processing service in a service cluster to obtain service path information of the image processing service, and store the service path information in a path information table.

[0205] In some embodiments of the present disclosure, the service determination module includes: an information monitoring sub-module, configured to determine an image processing model including the image processing service storing the service path information when it monitors that the path information table stores the service path information.

[0206] In some embodiments of the present disclosure, the service process flow processing device further includes: an identification generation module, configured to generate an execution process identification corresponding to the service execution process for calling the service execution process.

[0207] In some embodiments of the present disclosure, the service process flow processing device further includes: a parameter receiving module, configured to receive service call parameters; wherein, the service call parameters include a service process identification;

[0208] A process determination module, configured to determine a service execution process corresponding to a service process identifier according to an execution process identifier, and perform image processing on an image through the service execution process corresponding to the service process identifier to generate an image processing result.

[0209] In some embodiments of the present disclosure, the process determination module includes: an object download sub-module, configured to download an image according to download address information;

[0210] A service processing sub-module, configured to perform image processing on the image through an image processing service included in the service execution process corresponding to the service process identifier based on image processing information to obtain a service processing result;

[0211] A result summarization sub-module, configured to perform summarization processing on the service processing result to obtain an image processing result.

[0212] In some embodiments of the present disclosure, the image includes an image obtained by performing video frame extraction processing on a video, and the source of the image is determined by a category parameter in a service call parameter.

[0213] In some embodiments of the present disclosure, the download address information, the image processing information, and the category parameter are obtained through encoding processing.

[0214] In some embodiments of the present disclosure, the process determination module includes: an authentication processing sub-module, configured to perform authentication processing on an identity parameter to obtain an authentication processing result;

[0215] An authentication success sub-module, configured to determine a service execution process corresponding to the service process identifier according to the execution process identifier based on the authentication processing result.

[0216] In some embodiments of the present disclosure, the process determination module includes: a path determination sub-module, configured to determine an image processing service included in the service execution process corresponding to the service process identifier as a service to be called, and determine service path information of the service to be called in a path information table;

[0217] A path call sub-module, configured to call the service to be called according to the service path information, and perform image processing on the image through the service to be called to generate an image processing result.

[0218] In some embodiments of the present disclosure, the path call sub-module includes: a cluster determination unit, configured to determine a service cluster storing the service to be called according to a cluster identifier;

[0219] A service call unit, configured to call the service to be called in the service cluster according to network path information.

[0220] In some embodiments of the present disclosure, the service process handling device further includes: a result storage sub-module configured to store the image processing result in a content delivery network.

[0221] The specific details of the service process handling device provided in each embodiment of the present disclosure have been described in detail in the corresponding method embodiments, and thus will not be elaborated herein.

[0222] Figure 13 The structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0223] It should be noted that Figure 13 The computer system 1300 of the electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0224] As Figure 13 shown, the computer system 1300 includes a central processing unit (CPU) 1301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1302 or the program loaded from the storage section 1308 into the random access memory (RAM) 1303. In the RAM 1303, various programs and data required for system operation are also stored. The CPU 1301, ROM 1302, and RAM 1303 are connected to each other via a bus 1304. The input / output (I / O) interface 1305 is also connected to the bus 1304.

[0225] The following components are connected to the I / O interface 1305: an input section 1306 including a keyboard, a mouse, etc.; an output section 1307 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1308 including a hard disk, etc.; and a communication section 1309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1309 performs communication processing via a network such as the Internet. A drive 1310 is also connected to the I / O interface 1305 as needed. A removable medium 1311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1310 as needed so that a computer program read from it can be installed into the storage section 1308 as needed.

[0226] In particular, according to embodiments of the present disclosure, the processes described in each method flowchart can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 1309 and / or installed from the removable medium 1311. When the computer program is executed by the central processing unit (CPU) 1301, various functions defined in the system of the present application are performed.

[0227] It should be noted that the computer-readable medium shown in the embodiments of the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0228] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the above-mentioned module, segment of a program, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0229] It should be noted that although several modules or units of devices for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0230] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0231] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed herein.

[0232] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A service process handling method, characterized in that, The method includes: Performing functional segmentation processing on an image function model to obtain at least two image processing services; Storing the image processing services in a service cluster to obtain service path information of the image processing services, and storing the service path information in a path information table; the at least two image processing services include general image processing services; Determining an image processing service among the at least two image processing services, and querying an image processing model including the image processing service; wherein, the image processing model includes the image function model; Obtaining service process information of the image processing service in the image processing model; Performing service processification processing on the image processing service according to the service process information to obtain a service execution process of the image processing model; Generating an execution process identifier corresponding to the service execution process for calling the service execution process; Receiving service call parameters; wherein, the service call parameters include a service process identifier; Determining the service execution process corresponding to the service process identifier according to the execution process identifier, and performing image processing on the image through the service execution process corresponding to the service process identifier to generate an image processing result.

2. The service process handling method according to claim 1, wherein The querying the image processing model including the image processing service includes: When it is monitored that the path information table stores the service path information, determining the image processing model of the image processing service storing the service path information.

3. The service process flow processing method according to claim 1, characterized in that The service call parameters further include download address information and image processing information, and the performing image processing on the image through the service execution process corresponding to the service process identifier to generate an image processing result includes: Downloading an image according to the download address information; Based on the image processing information, performing image processing on the image through the image processing services included in the service execution process corresponding to the service process identifier to obtain a service processing result; Performing summary processing on the service processing result to obtain an image processing result.

4. The service process flow processing method according to claim 3, wherein The image includes an image obtained by performing video frame extraction processing on a video, and the source of the image is determined by a category parameter in the service call parameters.

5. The service process flow processing method according to claim 4, characterized in that, The download address information, the image processing information, and the category parameter are obtained through encoding processing.

6. The service process flow processing method according to claim 1, characterized in that The service call parameters include identity parameters; The determining the service execution process corresponding to the service process identifier according to the execution process identifier includes: Performing authentication processing on the identity parameters to obtain an authentication processing result; Based on the authentication processing result, determining the service execution process corresponding to the service process identifier according to the execution process identifier.

7. The service process processing method according to claim 1, wherein The performing image processing on the image through the service execution process corresponding to the service process identifier to generate an image processing result includes: Determining the image processing service included in the service execution process corresponding to the service process identifier as a service to be called, and determining the service path information of the service to be called in the path information table; Call the service to be called according to the service path information, and perform image processing on the image through the service to be called to generate an image processing result.

8. The service process flow processing method according to claim 7, characterized in that, The service path information includes the cluster identifier of the service cluster and the network path information of the image processing service; The calling of the service to be called according to the service path information includes: Determine the service cluster storing the service to be called according to the cluster identifier; Call the service to be called in the service cluster according to the network path information.

9. The service process flow processing method according to claim 1, wherein, The method further includes: Store the image processing result in a content delivery network.

10. A service process handling device, characterized in that, The apparatus includes: A function segmentation module configured to perform function segmentation processing on an image function model to obtain at least two image processing services; store the image processing services in a service cluster to obtain service path information of the image processing services, and store the service path information in a path information table; the at least two image processing services include a general image processing service; A service determination module configured to determine an image processing service from the at least two image processing services and query an image processing model including the image processing service; wherein the image processing model includes the image function model; An information acquisition module configured to acquire service process information of the image processing service in the image processing model; A process processing module configured to perform service processification processing on the image processing service according to the service process information to obtain a service execution process of the image processing model; generate an execution process identifier corresponding to the service execution process for calling the service execution process; receive service call parameters; wherein the service call parameters include a service process identifier; determine the service execution process corresponding to the service process identifier according to the execution process identifier, and perform image processing on the image through the service execution process corresponding to the service process identifier to generate an image processing result.

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the service processification processing method according to any one of claims 1 to 9.

12. An electronic device, characterized in that, Including: A processor; And A memory for storing executable instructions of the processor; Wherein the processor is configured to execute the service processification processing method according to any one of claims 1 to 9 by executing the executable instructions.

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