WO2023065746A1

WO2023065746A1 - Algorithm application element generation method and apparatus, electronic device, computer program product and computer readable storage medium

Info

Publication number: WO2023065746A1
Application number: PCT/CN2022/107174
Authority: WO
Inventors: 胡武林; 罗春能
Original assignee: 上海商汤智能科技有限公司
Priority date: 2021-10-22
Filing date: 2022-07-21
Publication date: 2023-04-27
Also published as: CN114005055A

Abstract

Embodiments of the present disclosure disclose an algorithm application element generation method and apparatus, an electronic device, a computer program product and a computer readable storage medium, capable of improving efficiency of generating and updating an algorithm application element. The method comprises: acquiring a template configuration item and a target initial template by means of receiving an application element template creation instruction, the target initial template being a modular general processing flow template for realizing a target image processing function in a service scenario, and the template configuration item being used to define at least one function module to be realized in the general processing flow template according to a preset target demand; generating a target algorithm application element template on the basis of the template configuration item and the target initial template; if a generation instruction for the target algorithm application element template is received, acquiring at least one preset target function implementation module according to implementation configuration information in the generation instruction; and generating a target algorithm application element on the basis of the at least one target function implementation module and the target algorithm application element template.

Description

Algorithm application element generation method, device, electronic device, computer program product, and computer-readable storage medium

Cross References to Related Applications

This disclosure is based on the Chinese patent application with the application number 202111232488.7, the application date is October 22, 2021, and the application name is "Algorithm application element generation method, device, equipment and computer-readable storage medium", and requires the Chinese patent application The priority of the Chinese patent application, the entire content of the Chinese patent application is hereby incorporated into this disclosure by reference.

technical field

The present disclosure relates to computer vision technology, and in particular to a method, device, electronic equipment, computer program product, and computer-readable storage medium for generating an algorithm application element.

Background technique

Algorithm application unit is a software unit for intelligent analysis of images or videos, which can be applied to various business scenarios, such as traffic flow early warning scenarios, traffic accident road section early warning scenarios, etc. The generation process of the algorithm application element includes creating the algorithm application element and debugging the algorithm application element.

In related technologies, both creation of algorithm application elements and debugging of algorithm application elements need to be done manually by developers. For example, each time a new algorithm application element is created, an independent development process needs to be performed again, resulting in a lot of duplication. Algorithms apply meta-development work, resulting in a linear increase in development time and labor costs. Moreover, when debugging algorithm application elements in actual business scenarios, due to security and privacy requirements, it is difficult to flow back the field data of business scenarios to the development scenarios of algorithm application elements for debugging, thus lengthening the update iteration cycle of algorithm application elements. It can be seen that, in the related art, it takes a long time to generate and update the algorithm application element, so that the efficiency of generating and updating the algorithm application element is low.

Contents of the invention

Embodiments of the present disclosure at least provide a method, device, electronic device, computer program product, and computer-readable storage medium for generating algorithm application elements, which can improve the efficiency of generating and updating algorithm application elements.

The technical scheme of the embodiment of the present disclosure is realized in this way:

An embodiment of the present disclosure provides a method for generating an algorithm application element, including:

By receiving the application meta-template creation instruction, the template configuration item and the target initial template are obtained; the target initial template is a modularized general processing flow template that realizes the target image processing function in a business scenario; the template configuration item is used according to Presetting target requirements, defining at least one functional module to be realized in the general processing flow template;

Generate a target algorithm application meta-template based on the template configuration item and the target initial template;

In the case of receiving a generation instruction for the target algorithm application meta-template, according to the realization configuration information in the generation instruction, obtain at least one preset target function realization module; the realization configuration information is the at least one Implementation information corresponding to the functional module;

A target algorithm application element is generated based on the at least one target function realization module and the target algorithm application element template.

An embodiment of the present disclosure provides an algorithm application element generation device, including:

The obtaining part is configured to obtain template configuration items and target initial templates by receiving application meta-template creation instructions; the target initial templates are modularized general processing flow templates that implement target image processing functions in business scenarios; the The template configuration item is used to define at least one functional module to be implemented in the general processing flow template according to preset target requirements;

The template creation part is configured to generate a target algorithm application meta-template based on the template configuration item and the target initial template;

The generating part is configured to obtain at least one preset target function realization module according to the realization configuration information in the generation instruction when receiving the generation instruction for the target algorithm application meta-template; the realization configuration The information is the implementation information corresponding to the at least one function module; and the target algorithm application element is generated based on the at least one target function realization module and the target algorithm application element template.

An embodiment of the present disclosure provides an electronic device, including:

a memory configured to store executable algorithm application element generation instructions;

When the processor is configured to execute the executable algorithm application element generation instruction stored in the memory, implement the algorithm application element generation method provided by the embodiment of the present disclosure.

An embodiment of the present disclosure provides a computer-readable storage medium, storing executable algorithm application element generation instructions configured to cause a processor to implement the algorithm application element generation method provided by the embodiment of the present disclosure.

An embodiment of the present disclosure provides a computer program product, including a computer program or an instruction. When the computer program or instruction is executed by a processor, the above method for generating an algorithm application element is realized.

The embodiments of the present disclosure have the following technical effects: the general processing flow for intelligent processing of images or videos in actual business scenarios is abstracted into the target initial template, and according to the preset target requirements of the actual business, the template configuration items are used in the target initial template Custom configuration of the modularized general processing flow to obtain the target algorithm application meta-template corresponding to the actual business requirements; apply the meta-template for the target algorithm, and further configure the configuration information to generate the target algorithm application for realizing the actual business needs Yuan. In this way, the redundant and repeated work of independently developing algorithm application elements each time according to different business requirements is reduced, thereby reducing the generation time of algorithm application elements and improving the generation efficiency of algorithm application elements. Moreover, when debugging at the business site, the algorithm application element template or the implementation configuration information can be modified according to the field data, so as to realize the rapid update and iteration of the algorithm application element, thereby improving the update efficiency of the algorithm application element.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Description of drawings

The accompanying drawings here are incorporated into the description and constitute a part of the present description. These drawings show embodiments consistent with the present disclosure, and are used together with the description to explain the technical solution of the present disclosure.

FIG. 1 is a schematic diagram of an optional architecture of an algorithm application unit generating system 100 provided by an embodiment of the present disclosure;

FIG. 2 is a first schematic flow diagram of a method for generating an algorithm application element provided by an embodiment of the present disclosure;

Fig. 3 is an optional schematic flowchart II of the method for generating an algorithm application element provided by an embodiment of the present disclosure;

Fig. 4 is an optional schematic flow diagram III of the algorithm application element generation method provided by the embodiment of the present disclosure;

Fig. 5 is an optional schematic flowchart 4 of the algorithm application element generation method provided by the embodiment of the present disclosure;

FIG. 6 is an optional schematic flowchart five of the method for generating an algorithm application element provided by an embodiment of the present disclosure;

FIG. 7 is a schematic flow diagram of generating algorithm application elements in an actual application scenario provided by an embodiment of the present disclosure;

FIG. 8 is a schematic structural diagram of an algorithm application element generation device provided by an embodiment of the present disclosure;

FIG. 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with the accompanying drawings. All other embodiments obtained under the premise of creative labor belong to the protection scope of the present disclosure.

In the following description, references to "some embodiments" describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict.

Before the embodiments of the present disclosure are further described in detail, the nouns and terms involved in the embodiments of the present disclosure will be described, and the nouns and terms involved in the embodiments of the present disclosure are applicable to the following explanations.

1) 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 technique of computer science that attempts to understand the essence of intelligence and generate a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making.

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

2) Computer Vision Technology (Computer Vision, CV) Computer is actually a science that studies how to make machines "see". Further, it refers to machines that use cameras and computers instead of human eyes to identify, track and measure targets. Vision, and further image processing, so that the computer processing becomes an image that is more suitable for human observation or sent to the instrument for detection. As a scientific discipline, computer vision studies related theories and technologies, trying to build artificial intelligence systems that can obtain information from images or multidimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video analysis, video semantic understanding, video content/behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and maps It also includes common face recognition, fingerprint recognition and other biometric recognition technologies.

3) Image/video analysis refers to the analysis and processing of images collected by image acquisition equipment, so as to determine whether a specific event has occurred in the image or video. For example, by identifying and processing the video, it is analyzed whether the vehicle in the video has a traffic accident, or whether the person in the video has fallen, etc.

4) The algorithm application element is a software module for analyzing and processing images or videos, which can integrate algorithm models required for image or video analysis, as well as scheduling service components, message service components, and storage service components. Input the image or video in the specified scene into the algorithm application element, and the analysis result corresponding to the scene can be obtained. In some embodiments, it is also possible to continue to develop based on the algorithm application element, for example, continue to develop interface interaction functions, communication functions, etc. on the basis of the algorithm application element, so as to obtain a complete application software based on the algorithm application element.

At present, there are many types of algorithm application elements, and the application scenarios are also very wide, especially from the large number of customized development requirements from the security and smart city industries, and the agility of algorithm application element development is getting higher and higher. The development of the algorithm application element in the related technology is to redevelop the algorithm application element after each algorithm model is released. Exemplarily, when creating a new algorithm application element in related technologies, it is necessary to manually write each file of the algorithm application element, such as the directory structure, code files, configuration files, document files, and script files (Makefile, etc.) of the algorithm application element. content, as well as manually writing codes in code files, etc., so that the development time and labor costs of generating algorithm application elements increase linearly, reducing the generation efficiency of algorithm application elements.

Moreover, for the iteration of algorithm application elements in the application field, many application sites do not allow field data to flow back to the development organization of algorithm application elements for training iterations due to their own security considerations, and it is difficult to timely update algorithm application elements based on field data. Iterative updates lead to a very long iteration period for algorithm application elements. Moreover, even if the application site allows data backflow, according to the iterative process of the current algorithm application element, model training and algorithm update must be re-performed, and the iteration cycle is longer, which greatly reduces the update efficiency of the algorithm application element.

It can be seen from the above that in related technologies, it takes a long time to generate and update algorithm application elements, and it is impossible to quickly generate algorithm application elements and perform update iterations, thereby reducing the generation and update efficiency of algorithm application elements.

Embodiments of the present disclosure provide a method, device, electronic device, computer program product, and computer-readable storage medium for generating algorithm application elements, which can improve the efficiency of generating and updating algorithm application elements. The exemplary application of the electronic equipment provided by the embodiments of the present disclosure is described below. The electronic equipment provided by the embodiments of the present disclosure can be implemented as a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, Various types of user terminals such as personal digital assistants, dedicated messaging devices, and portable game devices) can also be implemented as servers. Hereinafter, an exemplary application in which an electronic device is implemented as a server will be explained.

Referring to FIG. 1 , FIG. 1 is a schematic diagram of an optional architecture of an algorithm application element generating system 100 provided by an embodiment of the present disclosure. The terminal 400 is connected to the server 200 through the network 300. The network 300 may be a wide area network or a local area network, or a combination of the two.

Among them, the terminal 400 belongs to a business scenario, such as an engineering developer at a project debugging site, the developer can use the terminal 400 to send an application meta template creation instruction to the server 200 according to actual business requirements;

The server 200 may be a background server, which is used to obtain template configuration items and target initial templates by receiving application meta-template creation instructions; the target initial templates are modularized general processing flow templates that realize target image processing functions in business scenarios; templates The configuration item is used to define at least one functional module to be implemented in the general processing flow template according to the preset target requirements; based on the template configuration item and the target initial template, generate the target algorithm application meta-template; after receiving the target algorithm application meta-template In the case of the generation instruction, at least one preset target function realization module is obtained according to the realization configuration information in the generation instruction; the realization configuration information is the realization information corresponding to at least one function module; based on at least one target function realization module and the target algorithm Application element template to generate target algorithm application elements.

The server 200 is further configured to send the generated target algorithm application element to the terminal 400, so that the terminal 400 analyzes and processes the video or image in the business scene by executing the target algorithm application element, and obtains a processing result; and, according to the processing result Perform further operations such as configuration update on the target algorithm application element.

In some embodiments, the server 200 can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network Cloud servers for basic cloud computing services such as cloud services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal 400 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server may be connected directly or indirectly through wired or wireless communication, which is not limited in this embodiment of the present disclosure.

In the following, the method for generating an algorithm application element provided by the embodiments of the present disclosure will be described in conjunction with the exemplary application and implementation of the electronic device provided by the embodiments of the present disclosure.

Referring to FIG. 2 , FIG. 2 is an optional schematic flowchart 1 of a method for generating an algorithm application element provided by an embodiment of the present disclosure, which will be described in conjunction with the steps shown in FIG. 2 .

S101. Obtain template configuration items and target initial templates by receiving an application meta-template creation instruction; the target initial template is a modularized general processing flow template that realizes the target image processing function in a business scenario; the template configuration items are used according to preset The target requirement defines at least one functional module to be realized in the general processing flow template.

The embodiments of the present disclosure are applicable to the generation and debugging scenarios of algorithm application elements. Here, the algorithm application element is an algorithm application element used to determine whether a specific event has occurred in a business scenario. Exemplarily, generate or debug an algorithm application element used to determine whether traffic congestion occurs in a road scene, generate or debug an algorithm application element used to determine whether someone smokes in a public place, or generate or debug an algorithm application element used to determine whether a family scene Algorithms for whether someone fell in the meta and so on. The specific selection is made according to the actual situation, and the embodiment of the present disclosure makes no limitation.

In the embodiments of the present disclosure, the target initial template is an initial template used to define a target image processing function to be realized in a current business scenario among at least one preset initial template. Wherein, at least one initial template is a modularized general processing flow template corresponding to at least one image processing function. Exemplarily, when the above-mentioned image processing function is an image/video analysis function, such as identifying and processing the video, it is analyzed whether the vehicle in the video has a traffic accident, or whether the person in the video has fallen, etc. , the corresponding general processing flow may include: detection and analysis of images or video frame images to obtain detection results; detection results may include specific targets contained in images or video frame images, such as pedestrians or vehicles; attribute detection results Extraction, such as image feature extraction, obtains attribute extraction results, and encapsulates detection results and attribute extraction results as event outputs. It can be seen that for the above-mentioned general processing flow, its corresponding modularized general processing flow template can include: a detection module for the detection and analysis process, a network model for image/video analysis, and attribute extraction corresponding to the attribute extraction process module, and a template for the definition of the processing object type, the definition of the detection target, and the definition information of the attribute category name to be extracted in the general processing flow.

It should be noted that different business scenarios may correspond to different target image processing functions. For example, when the business scene is a main road in the city, the target image processing function can include image recognition of congestion, traffic accidents, etc.; Image recognition of someone having an accident, etc.

It should be noted that each modular processing flow in the initial template does not include the actual function implementation, but only the definition of the function module, the incoming and outgoing parameter information, and the execution process description of the general processing flow, such as the function module A description of the calling relationship between and so on.

In the embodiment of the present disclosure, the template configuration item may correspond to a preset target requirement, and is used to define configuration item information of at least one functional module to be implemented in the general processing flow template. In some embodiments, template configuration items can be set by a user such as a template developer based on the specified target initial template and according to preset target requirements, and an application meta-template creation instruction can be generated according to the template configuration item and the template path of the target initial template , sent to the electronic device.

In some embodiments, a management service may run on the electronic device; wherein the management service is used to receive and manage at least one initial template, and the management service may be deployed on the electronic device, or may be deployed in a wired or wireless connection with the electronic device. For other devices connected in the same way, the specific selection is made according to the actual situation, which is not limited in the embodiments of the present disclosure.

In some embodiments, when the management service is deployed on the electronic device, the electronic device may receive and archive at least one initial template by running the management service, and generate at least one template path corresponding to the at least one initial template. In this way, a user such as a template developer can specify a target initial template for generating a target algorithm application meta-template from at least one initial template by setting a template path. Here, at least one initial template may be uploaded to the management service in batches from other services in the form of a template package, or may be uploaded separately, which is not specifically limited in this embodiment of the present disclosure.

In some embodiments, an algorithm bin service may run on the electronic device; wherein, the algorithm bin service is used to generate and manage algorithm application elements. The algorithm warehouse service can receive the call of the application meta-template creation instruction through the preset creation interface, and execute the program or code that generates the algorithm application meta-template implemented in the preset creation interface. Here, the application meta-template creation instruction may be sent by other devices to the algorithm warehouse service on the electronic device. Exemplarily, the electronic device may be a background server, and the client device sends the application meta-template creation instruction to the algorithm warehouse service on the background server; the application meta-template creation instruction may also be sent to the algorithm warehouse service by other services on the electronic device , the specific selection is made according to the actual situation, which is not limited by the embodiments of the present disclosure.

In some embodiments, the electronic device can use the algorithm warehouse service to implement S101 by executing the process of S1011-S1013, as shown in FIG. 3 , as follows:

S1011. Receive an application meta-template creation instruction through a preset creation interface.

In the embodiment of the present disclosure, the electronic device may receive an application meta-template creation instruction from the preset creation interface through the running algorithm store service.

S1012. Analyze and obtain the template configuration item and the target template path from the application meta-template creation instruction.

In the embodiment of the present disclosure, the electronic device can obtain the template configuration item and the target template path from the application meta-template creation instruction by executing the program or code of the generation algorithm application meta-template implemented in the preset creation interface; here, the target template path It is the path information corresponding to the target initial template in the management service.

S1013. Obtain the target initial template according to the template path.

In the embodiment of the present disclosure, the application meta-template creation instruction includes a specified template path, and the electronic device can determine and obtain the target initial template from at least one initial template according to the template path. Here, the target initial template is a modularized general processing flow template that realizes the target image processing function in a business scenario.

Further, the target initial template may be a newly created template that does not include historical template configuration items, or may be a historical algorithm application meta-template that includes historical template configuration items, and the electronic device may create instructions based on the currently received application meta-template. The historical template configuration items in the target initial template are updated to generate a new target algorithm application meta-template, which is selected according to the actual situation, and is not limited in this embodiment of the present disclosure.

S102. Generate a target algorithm application meta-template based on the template configuration item and the target initial template.

In the embodiment of the present disclosure, since the template configuration item is aimed at the general processing flow configuration information in the target initial template, the electronic device can update or write the content of the corresponding process part in the target initial template according to the template configuration item, thereby generating The target algorithm applies a meta-template.

In some embodiments, the target initial template may include an initial process configuration template and an initial attribute configuration template; here, the initial process configuration template is used to define the general processing flow of the target image processing function, and the initial attribute configuration template is used to define the target initial template. attribute information. According to the received template configuration items, the electronic device can update or write its corresponding content items in the initial process configuration template or the initial attribute configuration template to obtain the target algorithm application meta-template. That is to say, applying the meta-template by the target algorithm also updates the target initial template of the initial process configuration template and the initial attribute configuration template.

In some embodiments, the initial process configuration template and the initial attribute configuration template can be templates in the form of independent files, or other data forms, such as data tables included in the target initial template, etc., according to actual conditions The selection is not limited by the embodiments of the present disclosure.

In some embodiments, the template configuration item includes: a template function configuration item and a template attribute configuration item; wherein, the template function configuration item is used to define module information of at least one functional module; the template attribute configuration item is used to configure the target algorithm to be generated Attribute information of the application meta template.

In some embodiments, the template attribute configuration item at least includes: at least one of template name and template version, and the electronic device can set the name, version, author, and applicable scenario for the generated target algorithm application meta-template through the template attribute configuration item. Attribute information such as type, software and hardware platform information, instruction information, etc.

It should be noted that, in the embodiments of the present disclosure, each functional module of at least one functional module may correspond to at least one function realization module, and the function realization module may be a pre-implemented and compiled software unit, which may be implemented by calling or loading, etc. The code execution method directly realizes the corresponding function. The module information may be information corresponding to the function realization module, such as module name, function type or software version and other information. The module information here does not include the specific implementation information of the function realization module, such as the acquisition address or call interface of the function module. According to the module information contained in the template function configuration item, among at least one functional module to be implemented in the target initial template, the electronic device can specify which functions or configured modules are used to realize the modularized general processing flow in the target initial template, and realize the Definition of at least one functional module to be implemented in the general processing flow template.

In the embodiments of the present disclosure, a function module with the same definition may correspond to at least one function realization module, for example, a function module with the same name may correspond to a function realization module realized in at least one way or at least one software version. Through the template configuration item, the electronic device can preliminarily define the functional modules used to realize the general processing flow, so as to obtain the target algorithm application meta-template. In the case of receiving a generation instruction for the target algorithm application meta-template, according to the implementation configuration information in the generation instruction, further designated target function realization modules for each defined function module are obtained correspondingly.

In some embodiments, at least one function realization module may be pre-stored in the electronic device, or may be stored in other external storage spaces accessible by the electronic device. The specific selection is made according to the actual situation, and the embodiment of the present disclosure makes no limitation.

In some embodiments, based on FIG. 3, the electronic device can implement S102 by performing the process of S1021-S1022, as shown in FIG. 4, as follows:

S1021. According to the template function configuration item, update the initial process configuration template with the preset template syntax to obtain the target process configuration template.

In the embodiment of the present disclosure, the electronic device can update the initial function configuration items included in the initial process configuration template, such as the definition of function modules, to the ones described in the template function configuration items according to the template function configuration items and the preset template syntax. The module information, that is, the information of the function realization module, obtains the target process configuration template.

It should be noted that the target process configuration template is based on the modularized general processing process implemented by the target initial template, and further defines the corresponding configuration information of the functional modules contained in it, but does not contain the specific implementation data of the functional modules, and needs to be added The corresponding data processing functions, such as image processing functions, can be realized only after the data is specifically realized.

In some embodiments, the template function configuration item at least includes: at least one of target algorithm application model information and algorithm application type; wherein, the target algorithm application model information may be an algorithm model for realizing the target image processing function, such as a computer-based The neural network model of vision technology; the algorithm application type is used to represent the processing object type applicable to the target image processing function, and may include at least one of video type and image type.

In some embodiments, the target algorithm application model can be a deep learning model trained using images or videos in business scenarios, for example, a convolutional neural network (Convolutional Neural Network, CNN) trained using images or videos in business scenarios ) model, or a shallow machine model trained using images or videos of business scenarios, for example, a Support Vector Machine (Support Vector Machine, SVM) This disclosure is not limited here.

S1022. According to the template attribute configuration item, update the initial attribute configuration template to obtain the target attribute configuration template, thereby obtaining the target algorithm application meta-template.

In the embodiment of the present disclosure, the electronic device can update the initial attribute configuration items included in the initial attribute configuration template according to the template attribute configuration items to obtain the target attribute configuration template, and according to the updated target process configuration template and target attribute configuration template, Get the target algorithm application meta-template.

In some embodiments, the preset template syntax may be golang template syntax, and the electronic device may parse out the template path and template configuration information from the application meta-template creation instruction calling the preset creation interface through the preset creation interface; here, Template configuration information can include template name, algorithm application type, template version and template default model definition. The electronic device may obtain the target initial template from the management service according to the template path. Exemplarily, the target initial template may include a pipeline configuration file as an initial process configuration template, and a spec file as an initial property configuration template. The electronic device defines the algorithm application type and template default model as template function configuration items, writes the algorithm application type and template default model into the initial function configuration item in the pipeline configuration file with golang template syntax, and uses the template name and template version as templates The attribute configuration item updates the initial attribute configuration item in the spec file, such as the initial definition of the template name and version, so as to obtain the corresponding target algorithm application meta-template.

It can be understood that the algorithm application meta-template is the realization of the function to be completed, such as the process framework for the realization of the algorithm model to be completed or the realization of the function module to be completed, that is, the algorithm application meta-template is an empty algorithm application element, which needs to be realized by The functional modules and generating algorithm application elements can analyze the image or video of the business scene.

It should be noted that the target algorithm application unit can be used to intelligently process the image or video of the business scene to realize the target image processing function, such as a software unit for determining whether a specific event occurs in the business scene, which is the ultimate goal of the embodiments of the present disclosure .

In some embodiments, the electronic device can also maintain and manage the generated at least one algorithm application element template through the algorithm warehouse service, for example, perform operations such as traversal search or deletion processing on the at least one algorithm application element.

S103. In the case of receiving a generation instruction for the target algorithm application meta-template, according to the realization configuration information in the generation instruction, obtain at least one preset target function realization module; the realization configuration information is the realization information corresponding to at least one function module .

In the embodiment of the present disclosure, after the electronic device generates the target algorithm application meta-template, it can archive the target algorithm application meta-template through the algorithm warehouse service, for example, save it in a preset database. In this way, in actual application scenarios, the same type of algorithm application meta-requirements can reuse the same algorithm application meta-template to generate corresponding target algorithm application meta-templates based on the implementation configuration information of their respective requirements, so as to improve the reusability of target algorithm application meta-templates .

In the embodiment of the present disclosure, the electronic device can assign a corresponding template identifier to the generated algorithm application meta-template through the algorithm warehouse service, so that the algorithm application meta-template can be called according to the template identifier. In some embodiments, the template identifier may be a template ID, or other types of identification information, which is selected according to actual conditions, and is not limited in this embodiment of the present disclosure.

In some embodiments, the algorithm warehouse service can pre-implement a preset generation interface for generating algorithm application elements according to the algorithm application element template. The algorithm warehouse service receives the generation of the target algorithm application meta template through the preset generation interface. Instructions, such as the generation instruction corresponding to the template ID of the target algorithm application meta-template, the implementation configuration information can be parsed from the generation instruction, and according to the implementation configuration information, at least one function implementation module corresponding to each function module can be obtained. The target function realization module specified for each function module, so as to obtain at least one preset target function realization module. Exemplarily, the implementation configuration information may be an address or link of an algorithm model or a functional component specified by a developer for a business scenario, or a call interface function of a pre-implemented function, and the like. The electronic device can obtain the algorithm application model or functional component according to the resolved address or link, or realize the corresponding function call by calling the interface function. Here, the target function realization module is a function module that has been pre-realized.

In some embodiments, the implementation configuration information includes: process configuration information; wherein, the process configuration information represents call information corresponding to a pre-implemented function implementation module. The electronic device can obtain the corresponding function realization module through the process configuration information and call or load it into the algorithm application meta-template.

In some embodiments, the implementation configuration information includes: at least one of the plug-in configuration information and the model path; based on the above-mentioned FIG. 2-FIG. 4, as shown in FIG. Realized as follows:

S1031. According to the model path, determine the target algorithm application model as at least one target function realization module.

In the embodiment of the present disclosure, the electronic device may obtain a target algorithm application model from at least one pre-implemented algorithm application model according to the model path in the process configuration information, as at least one target function realization module.

Here, the pre-implemented at least one algorithm application model can be a trained neural network model, or a neural network model to be trained, and the subsequent training and model effect improvement will be carried out in the process of iteration and debugging of the target algorithm application element, specifically The selection is made according to the actual situation, and is not limited by the embodiments of the present disclosure.

S1032. Determine the target function plug-in as at least one target function realization module according to the plug-in configuration information.

In the embodiment of the present disclosure, the electronic device can parse out the plug-in configuration information from the generation instruction, and obtain the target function plug-in from at least one pre-implemented function plug-in according to the plug-in configuration information as at least one target function realization module. Here, the target function plug-in is a plug-in module that pre-completes the function implementation defined by the template function configuration item. The target plug-in can be a software development kit (Software Development Kit, SDK) packaged according to the pre-trained functional model, which is selected according to the actual situation, and is not limited in the embodiments of the present disclosure.

In some embodiments, there may be at least one plug-in configuration information, and correspondingly, there may be at least one target function plug-in.

In some embodiments, the plug-in configuration information may be the call path information of the functional plug-in; it may also be information such as link information, or name, version, etc., which are selected according to actual conditions, and are not limited in this embodiment of the present disclosure.

In some embodiments, when the target image processing function is a single-frame detection image processing function, the target function plug-in may include: at least one of an image detection plug-in and an image attribute extraction plug-in.

It should be noted that, in the embodiment of the present disclosure, S1031 and S1032 are parallel method processes, and in actual application, one or more of them may be selected for execution according to the actual situation, and specifically selected according to the actual situation. Not limited.

In some embodiments, the process configuration information may also include invocation information of other types of function realization modules, such as invocation information of pre-implemented script files, etc., which are selected according to actual conditions, and are not limited in this embodiment of the present disclosure.

In some embodiments, the process configuration information may further include: at least one of the detection target type of the image detection plug-in, and the attribute information to be extracted corresponding to the image attribute extraction plug-in. Here, the image detection plug-in may be a functional plug-in including a neural network model for multi-target detection, and the detection target type may be used to specify a detection target among at least one preset detection target object that the neural network model for multi-target detection can detect. One or more types of detection target objects. The image attribute extraction plug-in can be a pre-implemented SDK for extracting at least one image attribute information from the detection result. The attribute information to be extracted can be used to extract at least one attribute information from the attribute extraction plug-in. Specify One or more attribute information.

It can be understood that the electronic device can further instantiate and customize the target algorithm application element by configuring the detection target type corresponding to the image detection plug-in and the attribute information to be extracted corresponding to the image attribute extraction plug-in, thereby improving the generation of the target algorithm application. Yuan flexibility.

In some embodiments, the implementation configuration information further includes: attribute configuration information; here, the attribute configuration information is used to configure the attribute information of the algorithm application element to be generated. In some embodiments, the attribute configuration information at least includes: at least one of an application meta name, an application meta version, an application meta description, and an algorithm scenario type. Alternatively, the attribute configuration information may also include author, algorithm scene type label, hardware platform information, authorization information, etc., which are selected according to actual conditions, and are not limited in this embodiment of the present disclosure.

S104. Generate a target algorithm application element based on at least one target function realization module and a target algorithm application element template.

In the embodiments of the present disclosure, the electronic device can render the template function configuration items in the target process configuration template in the target algorithm application meta-template according to at least one acquired target function realization module, and render each configuration content in the template function configuration items item, rendered as the realization data in the corresponding target function realization module, so as to complete the realization of each function module defined by the target algorithm application element template, and obtain the target algorithm application element capable of analyzing the image or video of the business scene.

In some embodiments, based on FIG. 4, as shown in FIG. 6, S1041 may be implemented through S1041-S1042, which will be described in conjunction with each step.

S1041. Call at least one functional interface corresponding to at least one target function realization module, and obtain at least one implementation data returned by the at least one functional interface when at least one functional interface is called successfully.

In some embodiments, the at least one target function realization module acquired by the electronic device may be a callable function interface of a prepackaged target function realization module. The electronic device can call at least one functional interface corresponding to at least one target function realization module, and obtain at least one implementation data returned by the at least one functional interface to the electronic device when at least one functional interface is called successfully. Wherein, at least one piece of implementation data is used to actually realize at least one function module defined in the target algorithm application meta-template. In some embodiments, at least one implementation data may be executable data.

S1042. Use at least one realization data to render the template function configuration item to obtain the target algorithm application element.

In the embodiment of the present disclosure, the electronic device can use each of at least one realization data to render the corresponding content configuration items in the module function configuration items, so as to update the target process configuration template and complete the adjustment of each function configuration item in the template. The function realization of the function module defined by the content configuration item. When the function realization of each function module defined in the target algorithm application element template is completed, the target algorithm application element is obtained.

In some embodiments, when the attribute configuration information is also included in the implementation configuration information, the electronic device may also update the template attribute configuration items in the target attribute configuration module in the target algorithm application meta-template according to the attribute configuration information.

It can be understood that in the embodiments of the present disclosure, the electronic device abstracts the general processing flow for intelligently processing images or videos of business scenarios into an initial template, and according to the preset target requirements of the actual business, initializes the target through template configuration items. The modularized general processing flow in the template is customized and configured to obtain the target algorithm application meta-template corresponding to the actual business requirements; the target algorithm application meta-template is further configured to realize the configuration information, and the target for realizing the actual business demand can be generated Algorithmic application meta. In this way, the redundant and repeated work of independently developing algorithm application elements each time according to different business requirements is reduced, thereby reducing the generation time of algorithm application elements and improving the generation efficiency of algorithm application elements. Moreover, when debugging at the business site, the algorithm application element template or the implementation configuration information can be modified according to the field data, so as to realize the rapid update and iteration of the algorithm application element, thereby improving the update efficiency of the algorithm application element.

In some embodiments, after the electronic device generates the target algorithm application element, it can use the target algorithm application element to process the target image function. The electronic device can use the detection plug-in in the target algorithm application element to detect the service Perform target detection on the image or video in the scene to obtain the target detection result; use the attribute extraction plug-in to perform attribute extraction on the target detection result according to the attribute information to be extracted, and obtain the attribute extraction result; call the algorithm function model based on the target detection result and the attribute extraction result , get the processing result, and the processing result is used to determine whether a specific event occurs in the business scenario.

In some embodiments, the electronic device can further debug the obtained target algorithm application element according to the on-site data generated in the business scenario, to optimize the processing result of the algorithm application element, so as to meet the corresponding requirements of the business scenario and ensure that Perform accurate analysis on images or videos of business scenarios.

The electronic device can update at least one of the target algorithm application element template and the target algorithm application element according to the image or video in the business scene to obtain the updated algorithm application element; and use the updated algorithm application element to update the image or target algorithm application element in the business scene The video is processed, and the updated processing result is obtained.

It can be understood that in the embodiments of the present disclosure, the electronic device can use the rapidly generated algorithm application element to perform image processing, and can further update the algorithm application element on-site according to the image processing result, thereby improving the application convenience of the algorithm application element and update efficiency.

In the following, based on FIG. 7 , an electronic device implemented as a background server will be taken as an example. The implementation process of the embodiments of the present disclosure in actual application scenarios is introduced.

S201. The development client uploads the template package to the management service of the background server.

In S201, the development client may be a development device connected to the background server; the template package is equivalent to at least one initial template. The management service may be a ModelManager service.

S202. The background server generates a template path according to the template package through the management service.

In S202, the background server receives the template package uploaded by the development client through the preset service interface of the management service, and generates a corresponding template path for each initial template included in the template package. Its execution process is consistent with the description of the corresponding process in S101, and will not be repeated here.

S203. The development client sends an application meta template creation instruction to the algorithm warehouse service in the background server by calling the creation template interface of the algorithm warehouse service, and specifies the template name, algorithm application type, template version, and template path in the application meta template creation instruction , template default model.

In S203, the background server also runs the algorithm warehouse service, and the creation template interface of the algorithm warehouse service is equivalent to the preset creation interface in the above embodiment. The execution process of S203 is consistent with the above-mentioned process description of S1011, and will not be repeated here. .

S204. The algorithm warehouse service on the background server generates a target template and obtains a template ID of the target template according to the application meta-template creation instruction.

In S204, when the algorithm warehouse service receives the application meta template creation instruction through the preset creation interface, according to the template path specified in the application meta template creation instruction, obtain the target initial template from at least one initial template maintained by the management service . Here, the target initial template can contain pipeline configuration files and spec configuration files. The algorithm warehouse service updates the pipeline configuration file in the target initial template with the preset golang template syntax according to the algorithm application type, template path, and template default model in the application meta-template creation instruction; and, according to the application meta-template creation instruction The target template name and template version in , update the spec configuration file in the target initial template to obtain the target template. Here, the target template is equivalent to the target algorithm application meta-template.

Here, after the background server generates the target template, it can store the target template and the corresponding template ID in the preset database, so that the template in the preset database can be called later by the template ID to generate an algorithm application element.

S205. The development client sends a generation instruction to the algorithm warehouse server in the background server by calling the generation template interface of the algorithm warehouse service, specifying and carrying the template ID, the name of the algorithm application element, the algorithm application type, version, and description in the generation instruction , author, algorithm scene type label, model path, detection plug-in, attribute plug-in, algorithm application meta-type, detection target type, and the name of the attribute type to be filtered are used as parameters for calling the generated template interface.

In S205, the template ID is used to specify the target template, the name of the algorithm application element, the algorithm application type, version, description, author, and algorithm scene type tags belong to the above attribute configuration information, model path, detection plug-in, attribute extraction plug-in, algorithm application Metatype, detection target type, and attribute type name to be filtered belong to the above-mentioned process configuration information. Here, the generating template interface is equivalent to the preset generating interface.

S206. The algorithm warehouse service on the background server generates the target algorithm application element according to the generation instruction.

In S206, when the algorithm warehouse service on the background server receives the generation instruction through the generation template interface, it determines and obtains the target template from the preset database according to the template ID passed in by the generation instruction in the generation template interface, And according to the model path specified by the generation command, download the corresponding pre-trained target neural network model; according to the detection plug-in and attribute extraction plug-in specified by the generation command, determine the pre-packaged detection plug-in corresponding to the detection plug-in from the preset implementation module library SDK, and the attribute extraction SDK corresponding to the attribute extraction plug-in. In addition, the algorithm warehouse service can obtain the callable functional interfaces corresponding to the detection SDK and attribute extraction SDK.

In S206, the algorithm warehouse service can render and update according to the target neural network model and the template default model defined in the pipeline configuration file of the target template; and, according to the detection target type specified in the generation instruction, call the corresponding functional interface of the detection SDK; And according to the type name of the attribute to be filtered specified in the generation instruction; call the function interface corresponding to the attribute extraction SDK. In the case that the function interfaces of the detection SDK and the attribute extraction SDK are called successfully, the corresponding implementation data of the detection SDK and the attribute extraction SDK are obtained. The algorithm warehouse service then uses the corresponding implementation data of the detection SDK and attribute extraction SDK, as well as the algorithm application metatype in the generated command, to render and update the pipeline configuration file in the target template.

In S206, the algorithm warehouse service also re-renders and updates the spec configuration file of the target template according to the attribute configuration information such as the name of the algorithm application element specified in the generation instruction, the algorithm application type, version, description, author, and algorithm scene type label , and finally get the updated pipeline configuration file and the updated spec configuration file, so as to complete the rendering of the target template and obtain the target algorithm application element.

In some embodiments, after the algorithm warehouse service generates the target algorithm application element, it may also save the target algorithm application element and assign a corresponding application element identifier for subsequent invocation of the target algorithm application element. The target algorithm application element can be used to realize the target image processing function in business scenarios.

From the above, it can be seen that the electronic device can generate an algorithm application meta-template according to the target initial template and template configuration information, and then generate a target algorithm application meta-template according to the implementation configuration information and the target algorithm application meta-template, so that developers do not need to manually generate algorithm applications completely by writing codes elements, and manually debug the generated algorithm application elements, which improves the generation and update efficiency of algorithm application elements.

The present disclosure also provides an algorithm application element generation device, and FIG. 8 is a schematic structural diagram of the algorithm application element generation device provided by an embodiment of the present disclosure; as shown in FIG. 8 , the algorithm application element generation device 1 includes:

The obtaining part 11 is configured to obtain template configuration items and target initial templates by receiving application meta-template creation instructions; the target initial templates are modularized general processing flow templates that realize target image processing functions in business scenarios; The template configuration item is used to define at least one functional module to be implemented in the general processing flow template according to preset target requirements;

The template creation part 12 is configured to generate a target algorithm application meta-template based on the template configuration item and the target initial template;

The generating part 13 is configured to obtain at least one preset target function realization module according to the realization configuration information in the generation instruction when receiving the generation instruction for the target algorithm application meta-template; the realization The configuration information is the implementation information corresponding to the at least one function module; and the target algorithm application element is generated based on the at least one target function realization module and the target algorithm application element template.

In some embodiments, the acquisition part 11 is further configured to receive the application meta-template creation instruction through a preset creation interface; from the application meta-template creation instruction, parse and obtain the template configuration item and target Template path; the target template path is path information corresponding to the target initial template in the management service; according to the template path, the target initial template is obtained.

In some embodiments, the template configuration items include: template function configuration items and template attribute configuration items; the target initial template includes: an initial process configuration template and an initial attribute configuration template; the template creation part is further configured to According to the template function configuration item, update the initial process configuration template with the preset template syntax to obtain the target process configuration template; the template function configuration item includes the configuration information of the function realization module; according to the template attribute The configuration item is to update the initial attribute configuration template to obtain the target attribute configuration template, thereby obtaining the target algorithm application meta-template; the template attribute configuration item is used to configure the attribute information of the target algorithm application meta-template.

In some embodiments, the template function configuration items include at least:

At least one of target algorithm application model information and algorithm application type; wherein, the algorithm application type includes at least one of video type and image type;

The template attribute configuration items at least include:

At least one of template name and template version.

In some embodiments, the implementation configuration information includes: process configuration information; the process configuration information includes: at least one of plug-in configuration information and a model path; the generating part is further configured to, according to the model path, Determine the target algorithm application model as the at least one target function realization module; and/or, according to the plug-in configuration information, determine the pre-realized target function plug-in as the at least one target function realization module; the target function plug-in is used for Realize the functions defined by the template function configuration items.

In some embodiments, the target function plug-in includes: at least one of an image detection plug-in and an image attribute extraction plug-in; the process configuration information further includes:

At least one of the detection target type corresponding to the image detection plug-in, and at least one of the attribute information to be extracted corresponding to the image attribute extraction plug-in.

In some embodiments, the algorithm application element generating device further includes a receiving module configured to obtain at least one preset target function realization module according to the realization configuration information in the generation instruction , receiving a generation instruction for applying a meta-template for the target algorithm through a preset generation interface.

In some embodiments, the generating part 13 is further configured to render the template function configuration items in the target process configuration template according to the at least one target function realization module, so as to obtain the target algorithm application element .

In some embodiments, the generation part 13 is further configured to call at least one function interface corresponding to the at least one target function realization module, and if the call of the at least one function interface is successful, obtain the At least one implementation data returned by at least one functional interface; using the at least one implementation data to render the template function configuration item to obtain the target algorithm application element.

In some embodiments, the implementation configuration information further includes: attribute configuration information; the generating part is further configured to update template attribute configuration items in the target attribute configuration template according to the attribute configuration information; the attribute configuration The information is used to configure the attributes of the algorithm application element; the attribute configuration information at least includes: at least one of the application element name, the application element version, the application element description, and the algorithm scene type.

In some embodiments, the target algorithm application meta-template is stored in a preset database; the algorithm application meta-generating device further includes a management part configured to respond to the management of the target algorithm application meta-template An instruction to search or delete the target algorithm application meta-template by traversing the algorithm application meta-template in the preset database.

It should be noted that the description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

The embodiment of the present disclosure also provides an electronic device. FIG. 9 is a schematic structural diagram of the electronic device provided by the embodiment of the present disclosure. As shown in FIG. 9 , the electronic device 2 includes: a memory 22 and a processor 23, wherein the memory 22 and the processing The device 23 is connected through a communication bus 24; the memory 22 is used to store executable algorithm application element generation instructions; the processor 23 is used to implement the method provided by the embodiment of the present disclosure when executing the executable computer program stored in the memory 22, for example , the algorithm application element generation method provided by the embodiment of the present disclosure.

The embodiment of the present disclosure provides a computer-readable storage medium, which stores an executable algorithm application element generation instruction, which is used to cause the processor 23 to implement the method provided by the embodiment of the present disclosure, for example, the algorithm provided by the embodiment of the present disclosure Applied meta generation method.

An embodiment of the present disclosure provides a computer program product, including a computer program or an instruction. When the computer program or instruction is executed by a processor, the above method for generating an algorithm application element is implemented.

In some embodiments of the present disclosure, the computer readable storage is transferred to a computer readable storage medium which may be volatile or nonvolatile. The computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; it may also be various devices including one or any combination of the above memories.

In some embodiments of the present disclosure, executable algorithm application element generation instructions may be in the form of programs, software, software modules, scripts or codes, in any form of programming language (including compiled or interpreted language, or declarative or procedural language) and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

As an example, executable algorithm application meta-generated instructions may, but do not necessarily correspond to files in a file system, may be stored as part of a file that holds other programs or data, for example, in a hypertext markup language (HTML, Hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple cooperating files (for example, in a file that stores one or more modules, subroutines, or code sections file).

As an example, the executable algorithm application element generation instructions can be deployed to be executed on one computing device, or on multiple computing devices located at one site, or, alternatively, at multiple sites and interconnected by a communication network. Execution on multiple computing devices.

The above descriptions are merely examples of the present disclosure, and are not intended to limit the protection scope of the present disclosure. Any modifications, equivalent replacements and improvements made within the spirit and scope of the present disclosure are included in the protection scope of the present disclosure.

Industrial Applicability

The algorithm application element generation method, device, electronic equipment, computer program product, and computer-readable storage medium provided by the embodiments of the present disclosure abstract the general processing flow for intelligently processing images or videos of actual business scenarios into target initial templates, and According to the preset target requirements of the actual business, the template configuration items are used to customize the modularized general processing flow in the target initial template to obtain the target algorithm application meta-template corresponding to the actual business needs; for the target algorithm application meta-template, further By configuring and implementing the configuration information, the target algorithm application element used to realize the actual business requirements can be generated. In this way, the redundant and repeated work of independently developing algorithm application elements each time according to different business requirements is reduced, thereby reducing the generation time of algorithm application elements and improving the generation efficiency of algorithm application elements. Moreover, when debugging at the business site, the algorithm application element template or the implementation configuration information can be modified according to the field data, so as to realize the rapid update and iteration of the algorithm application element, thereby improving the update efficiency of the algorithm application element.

Claims

An algorithmic application meta-generation method comprising:

By receiving the application meta-template creation instruction, the template configuration item and the target initial template are obtained; the target initial template is a modularized general processing flow template that realizes the target image processing function in a business scenario; the template configuration item is used according to Presetting target requirements, defining at least one functional module to be realized in the general processing flow template;

Generate a target algorithm application meta-template based on the template configuration item and the target initial template;

In the case of receiving a generation instruction for the target algorithm application meta-template, according to the realization configuration information in the generation instruction, obtain at least one preset target function realization module; the realization configuration information is the at least one Implementation information corresponding to the functional module;

A target algorithm application element is generated based on the at least one target function realization module and the target algorithm application element template.
The method according to claim 1, wherein said obtaining template configuration items and target initial templates by receiving an application meta-template creation instruction includes:

receiving the application meta-template creation instruction through a preset creation interface;

From the application meta-template creation instruction, analyze and obtain the template configuration item and the target template path; the target template path is the path information corresponding to the target initial template in the management service;

According to the template path, the target initial template is obtained.
The method according to claim 1 or 2, wherein the template configuration items include: template function configuration items and template attribute configuration items; the target initial template includes: an initial process configuration template and an initial attribute configuration template;

The generating a target algorithm application meta-template based on the template configuration item and the target initial template includes:

According to the template function configuration item, update the initial process configuration template with the preset template syntax to obtain a target process configuration template; the template function configuration item includes configuration information of the function realization module;

According to the template attribute configuration item, update the initial attribute configuration template to obtain the target attribute configuration template, thereby obtaining the target algorithm application meta-template; the template attribute configuration item is used to configure the attributes of the target algorithm application meta-template information.
The method according to claim 3, wherein the template function configuration items at least include:

At least one of target algorithm application model information and algorithm application type; wherein, the algorithm application type includes at least one of video type and image type;

The template attribute configuration items at least include:

At least one of template name and template version.
The method according to claim 1, 2 or 4, wherein the implementation configuration information includes: process configuration information; the process configuration information includes: at least one of plug-in configuration information and model paths;

The obtaining at least one preset target function realization module according to the realization configuration information in the generation instruction includes:

According to the model path, determine the target algorithm application model as the at least one target function realization module; and/or,

According to the plug-in configuration information, determine the target function plug-in as the at least one target function realization module; the target function plug-in is used to realize the function defined by the template function configuration item.
The method according to claim 5, wherein the target function plug-in includes: at least one of an image detection plug-in and an image attribute extraction plug-in; the process configuration information further includes:

At least one of the detection target type corresponding to the image detection plug-in, and at least one of the attribute information to be extracted corresponding to the image attribute extraction plug-in.
The method according to claim 1, 2, 4 or 6, wherein, before obtaining at least one preset target function realization module according to the realization configuration information in the generation instruction, the method further comprises:

A generation instruction for applying a meta-template for the target algorithm is received through a preset generation interface.
The method according to claim 3, wherein said generating a target algorithm application element based on said at least one target function realization module and said target algorithm application element template comprises:

According to the at least one target function realization module, the template function configuration items in the target process configuration template are rendered to obtain the target algorithm application element.
The method according to claim 8, wherein, according to the at least one target function realization module, the template function configuration item in the target process configuration template is rendered to obtain the target algorithm application element, comprising:

Call at least one functional interface corresponding to the at least one target function realization module, and obtain at least one implementation data returned by the at least one functional interface if the at least one functional interface call is successful;

Render the template function configuration item by using the at least one realization data to obtain the target algorithm application element.
The method according to claim 8 or 9, wherein the implementation configuration information further comprises: attribute configuration information; the method further comprises:

According to the attribute configuration information, update the template attribute configuration item in the target attribute configuration template; the attribute configuration information is used to configure the attributes of the algorithm application element; the attribute configuration information includes at least: application element name, application At least one of meta version, application meta description, and algorithm scenario type.
The method according to claim 1, 2, 4, 6, 8 or 9, wherein the target algorithm application meta-template is stored in a preset database; the method further comprises:

In response to the management instruction for the target algorithm application meta-template, the target algorithm application meta-template is searched or deleted by traversing the algorithm application meta-template in the preset database.
An algorithm application element generating device, comprising:

The obtaining part is configured to obtain template configuration items and target initial templates by receiving application meta-template creation instructions; the target initial templates are modularized general processing flow templates that realize target image processing functions in business scenarios; the described The template configuration item is used to define at least one functional module to be implemented in the general processing flow template according to preset target requirements;

The template creation part is configured to generate a target algorithm application meta-template based on the template configuration item and the target initial template;

The generating part is configured to obtain at least one preset target function realization module according to the realization configuration information in the generation instruction when receiving the generation instruction for the target algorithm application meta-template; the realization configuration The information is implementation information corresponding to the at least one function module; based on the at least one target function realization module and the target algorithm application element template, a target algorithm application element is generated.
An electronic device comprising:

a memory configured to store executable algorithm application element generation instructions;

The processor is configured to implement the method according to any one of claims 1 to 11 when executing the executable algorithm application element generation instruction stored in the memory.
A computer-readable storage medium, storing executable algorithm application element generation instructions configured to cause a processor to implement the method described in any one of claims 1 to 11 when executed.
A computer program product, comprising computer programs or instructions, which, when executed by a processor, implement the method of any one of claims 1 to 11.