Image data processing method and device, electronic equipment and storage medium

Abstract

The invention provides an image data processing method and device, electronic equipment and a storage medium, and relates to the technical field of image processing and the field of financial science and technology. The method is applied to a security platform end, and comprises the following steps: storing a sample image to an archiving database, and generating an image storage path; generating image training data according to the image structured data and the image storage path, and storing the image training data to a distributed database; obtaining image training data from a distributed database according to the model training request, extracting a sample image from an archiving database according to an image storage path in the image training data, and training a preset initial image processing model according to the image training data and the sample image in a safe execution space to obtain a target image processing model; and obtaining a target image, and performing image processing on the target image through the target image processing model. According to the embodiment of the invention, model training can be carried out on the premise of ensuring data security, and the model training effect is ensured.

Description

Technical Field

[0001] The present application relates to the field of image processing technology and financial technology, and in particular to an image data processing method and device, electronic equipment, and storage medium. Background Art

[0002] Currently, many scenarios require the use of image data, which includes images and image-related data. For example, new model training scenarios in the financial technology field require image data, and existing model optimization training scenarios require image data. Models in the financial technology field can be document image processing models and face image processing models.

[0003] There are many channels for obtaining image data in related technologies. For example, one channel first obtains authorization from the data party, and then the operator obtains the image data from the data party, and then shares the image data with the developer. However, for security reasons, some image data do not want to be disclosed in plain text. For this reason, a desensitizing tool that meets the requirements is often required to desensitize the image data, and then use the desensitized image data for model training. However, the desensitization action greatly affects the quality of the image data and will have a serious impact on the model training effect. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an image data processing method and device, an electronic device, and a storage medium, which can perform model training while ensuring data security and ensure the model training effect.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes an image data processing method, which is applied to a security platform, and the method includes:

[0006] Acquire a sample image and image structured data from a service platform end; wherein the image structured data is obtained by the service platform end identifying the sample image;

[0007] Storing the sample image in a preset archiving database and generating an image storage path;

[0008] Generate image training data according to the image structured data and the image storage path, and store the image training data in a preset distributed database; wherein the image training data includes the image storage path, and the security level of the distributed database is higher than the security level of the archive database;

[0009] Receiving a model training request from a development platform end, wherein the development platform end does not have permission to access the business platform end;

[0010] Acquire the image training data from the distributed database according to the model training request, extract the sample image from the archive database according to the image storage path in the image training data, and store the image training data and the sample image in a preset secure execution space, and train a preset initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model; wherein the secure execution space does not have permission to be accessed from outside the secure execution space;

[0011] A target image is acquired, and image processing is performed on the target image using the target image processing model.

[0012] In some embodiments, the secure execution space includes a secure database and a secure host; storing the image training data and the sample image in a preset secure execution space, and training the initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model, includes:

[0013] The image training data and the sample image are stored in the security database; the security database is not accessible by hosts other than the security host;

[0014] Extracting image training data and the sample image from the security database via the security host;

[0015] The initial image processing model is trained by the security host with the image training data and the sample image to obtain the target image processing model.

[0016] In some embodiments, the training of the initial image processing model with the image training data and the sample image by the secure host to obtain the target image processing model includes:

[0017] The image processing resources are obtained from a preset resource providing host through the security host; the security host does not have the permission to be accessed by hosts other than the resource providing host, and the resource providing host does not have the permission to be accessed by hosts other than the security host;

[0018] The initial image processing model is trained by the secure host with the image processing resources, the image training data and the sample images to obtain the target image processing model.

[0019] In some embodiments, before receiving the model training request from the development platform, the method further includes:

[0020] Receive permission application requests from the development platform;

[0021] Sending the permission application request to a preset permission management terminal, which will review and approve the permission application request and send the review result to the security platform terminal; wherein the review result indicates whether the development platform terminal is authorized or not;

[0022] If the approval result indicates that the development platform is authorized, the identification of the development platform is sent to the security host, and the security host marks the development platform as authorized. The security host is used to receive the model training request from the authorized development platform.

[0023] In some embodiments, after generating image training data according to the image structured data and the image storage path, and storing the image training data in a preset distributed database, the method further includes:

[0024] Receive image data query requests through a preset secure cloud desktop;

[0025] Acquire the sample image from the archive database according to the image data query request, extract the image training data from the distributed database, and transmit the sample image and the image training data to the secure cloud desktop;

[0026] The sample image and the image training data are displayed through the secure cloud desktop; wherein the secure cloud desktop does not have permission for download operations.

[0027] In some embodiments, obtaining the sample image from the archive database according to the image data query request, extracting the image training data from the distributed database, and transmitting the sample image and the image training data to the secure cloud desktop includes:

[0028] Exporting the image data query request from the secure cloud desktop through a preset visual front end, and transmitting the image data query request to a preset trusted intelligent platform;

[0029] By using the trusted intelligent platform, the sample image is obtained from the archive database with the image data query request, and the image training data is extracted from the distributed database, and the sample image and the image training data are transmitted to the visualization front end;

[0030] Through the visualization front end, the sample image and the image training data are respectively desensitized, and the desensitized sample image and the desensitized image training data are transmitted to the secure cloud desktop;

[0031] Among them, the secure cloud desktop does not have the authority to communicate directly with the trusted intelligent platform.

[0032] In some embodiments, the image data query request is sent by the annotation platform;

[0033] After displaying the sample image and the image training data through the secure cloud desktop, the method further includes:

[0034] Receiving, through the secure cloud desktop, a labeling instruction for the sample image from the labeling platform;

[0035] The annotation instruction is exported from the secure cloud desktop through a preset visual front end, and transmitted to a preset trusted intelligent platform;

[0036] Annotating the sample image with the annotation instruction through the trusted intelligent platform to obtain image annotation data;

[0037] The image annotation data is stored in the distributed database through the trusted intelligent platform.

[0038] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes an image data processing device, which is applied to a security platform, and the device includes:

[0039] A data acquisition module, used to acquire a sample image and image structured data from a service platform end; wherein the image structured data is obtained by the service platform end identifying the sample image;

[0040] An image archiving module, used to store the sample image in a preset archiving database and generate an image storage path;

[0041] A data generation module, configured to generate image training data according to the image structured data and the image storage path, and store the image training data in a preset distributed database; wherein the image training data includes the image storage path, and the security level of the distributed database is higher than the security level of the archive database;

[0042] A request receiving module, used to receive a model training request from a development platform end, wherein the development platform end does not have permission to access the business platform end;

[0043] A model training module, configured to obtain the image training data from the distributed database according to the model training request, extract the sample image from the archive database according to the image storage path in the image training data, and store the image training data and the sample image in a preset secure execution space, and train a preset initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model; wherein the secure execution space does not have permission to be accessed from outside the secure execution space;

[0044] The image processing module is used to acquire a target image and perform image processing on the target image through the target image processing model.

[0045] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the image data processing method described in the first aspect when executing the computer program.

[0046] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the image data processing method described in the first aspect is implemented.

[0047] The image data processing method and device, electronic device, and storage medium proposed in the present application are constructed by first constructing a communication architecture including a business platform end, a development platform end, and a security platform end, and the development platform end is prohibited from accessing the business platform end. The business platform end is the source of data production, and the security platform end can obtain sample images and image structured data from the business platform end, and then store the sample images in an archive database, and store the image training data containing image structured data in a distributed database, and the security level of the distributed database is higher than the security level of the archive database. In this way, not only can the decoupled storage of sample images and image training data be achieved, but also the security of image training data can be guaranteed. When there is a model training demand on the development platform end, a model training request can be sent to the security platform end. The security platform end obtains image training data and sample images from the distributed database and the archive database respectively based on the model training request, and then trains the initial image processing model according to the image training data and the sample image in the secure execution space to obtain the target image processing model. Then, after obtaining the target image, the target image is processed by the target image processing model. The secure execution space does not have the authority to be accessed by the outside of the secure execution space, and has high security. In this way, the development platform does not need to obtain the target image and image training data, and can also meet the model training requirements. During the model training process, not only can the target image and image training data be used to train the model, but these data will not flow out of the secure execution space, and the data security is relatively high. Moreover, these data have not been desensitized, and the data integrity is good, so the model training effect is better. In summary, this application can perform model training under the premise of ensuring data security and ensure the model training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of an image data processing method provided by an embodiment of the present application;

[0049] Figure 2 is a flowchart of an image data processing method provided by another embodiment of the present application;

[0050] Figure 3 yes Figure 1 Flowchart of step 105 in;

[0051] Figure 4 yes Figure 3 Flow chart of step 303 in;

[0052] Figure 5 is a flowchart of an image data processing method provided by another embodiment of the present application;

[0053] Figure 6 yes Figure 5 Flowchart of step 502 in FIG.

[0054] Figure 7 is a flowchart of an image data processing method provided by another embodiment of the present application;

[0055] Figure 8 is a block diagram of the module structure of the image data processing device provided in an embodiment of the present application;

[0056] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] At present, many scenarios require the use of image data, which includes images and image-related data. There are multiple channels for obtaining image data of related technologies. Channel 1: First obtain authorization from the data party, then the operator collects data, and then shares the image data file to the developer. The disadvantages of this channel 1 are: 1. Sensitive data cannot flow out of the production environment. Unless desensitization is performed, it cannot be guaranteed that sensitive information will not be leaked; 2. There is no guarantee that there are desensitization tools that meet the requirements; 3. Desensitization actions will have a serious impact on the algorithm training effect; 4. The process is long, and there are many manual accesses, which is slow. Channel 2: Crawling on the network to obtain data. The disadvantages of this channel 2 are: 1. The quality of the acquired data is poor and insufficient to meet the requirements; 2. The scope of the crawler is limited, resulting in insufficient data richness / dimensionality; 3. Data related to special businesses cannot be obtained through crawlers, such as medical invoices, financial invoices, etc. Channel 3: Public open source data sets. The disadvantages of this channel 3 are: 1. There are authorization requirements, and the use cannot exceed the authorization scope, and the development and testing environment cannot effectively detect developers / R&D personnel, which may exceed the authorization scope.

[0061] Based on this, the main purpose of the embodiments of the present application is to propose an image data processing method and an image data processing device, an electronic device, and a storage medium, aiming to obtain and use image data with the greatest degree of freedom on the basis of ensuring the security of image data.

[0062] The image data processing method provided in the embodiment of the present application is applied to the server side, and can also be software running in the terminal or server side. The server side can be configured as 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; the software can be an application that implements the image data processing method, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: server computers, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] The embodiments of the present application provide an image data processing method and an image data processing device, an electronic device, and a storage medium, which are specifically described through the following embodiments. First, the image data processing method in the embodiments of the present application is described.

[0065] Figure 1 It is an optional flowchart of the image data processing method provided in an embodiment of the present application. The image data processing method is applied to the security platform end and may include but is not limited to steps 101 to 106.

[0066] Step 101, obtaining a sample image and image structured data from a service platform;

[0067] Step 102, storing the sample image in a preset archiving database and generating an image storage path;

[0068] Step 103, generating image training data according to the image structured data and the image storage path, and storing the image training data in a preset distributed database;

[0069] Step 104, receiving a model training request from the development platform;

[0070] Step 105, acquiring image training data from a distributed database according to a model training request, extracting sample images from an archive database according to an image storage path in the image training data, and storing the image training data and the sample images in a preset secure execution space, and training a preset initial image processing model in the secure execution space according to the image training data and the sample images to obtain a target image processing model;

[0071] Step 106, acquiring a target image, and performing image processing on the target image using a target image processing model.

[0072] Steps 101 to 106 shown in the embodiment of the present application are performed by first constructing a communication architecture including a business platform end, a development platform end and a security platform end, and the development platform end is prohibited from accessing the business platform end. The business platform end is the source of data production, and the security platform end can obtain sample images and image structured data from the business platform end, and then store the sample images in an archive database, and store the image training data containing image structured data in a distributed database, and the security level of the distributed database is higher than the security level of the archive database. In this way, not only can the decoupled storage of sample images and image training data be achieved, but also the security of image training data can be guaranteed. When there is a model training demand on the development platform end, a model training request can be sent to the security platform end. The security platform end obtains image training data and sample images from the distributed database and the archive database respectively based on the model training request, and then trains the initial image processing model according to the image training data and the sample image in the secure execution space to obtain the target image processing model. Then, after obtaining the target image, the target image is processed by the target image processing model. The secure execution space does not have the authority to be accessed from outside the secure execution space, and has high security. In this way, the development platform does not need to obtain the target image and image training data, and can also meet the model training requirements. During the model training process, not only can the target image and image training data be used to train the model, but these data will not flow out of the secure execution space, and the data security is relatively high. Moreover, these data have not been desensitized, and the data integrity is good, so the model training effect is better. In summary, this application can perform model training under the premise of ensuring data security and ensure the model training effect.

[0073] Before introducing step 101 to step 106 in detail, the communication architecture provided by the embodiment of the present application is introduced. In one embodiment, the communication architecture includes a business platform end, a development platform end and a security platform end.

[0074] The business platform end is a terminal / server end that provides business services. The business platform end may include a business service platform and a business database. Users can upload target images to the business service platform by calling the business service interface, and the business service platform calls the recognition algorithm / recognition model to recognize the target image and obtain image structured data.

[0075] The development platform end is a terminal / server end for business development. The development platform end can build an initial image processing model by writing program code. When the development platform end needs to perform model training on the initial image processing model, the three channels mentioned above can be used to obtain image-related data for model training. In an embodiment of the present application, the development platform end is prohibited from accessing the business platform end, and the development platform end can access the security platform end. For example, the development platform end generates a model training request based on the initial image processing model, and sends the model training request to the security platform end.

[0076] The security platform end is a device cluster that owns and uses image data. The security platform end can obtain image data from the business platform end, including target images and image structured data. After receiving a model training request, the security platform end can use the image data to train the initial image processing model. Specifically, in the process of using image data, image data leakage may occur, so the security platform end is provided with a secure execution space, which prohibits external access to the secure execution space. In the secure execution space, the security platform end trains the initial image processing model according to the image training data and the target image to obtain the target image processing model. The security platform end includes a trusted execution space.

[0077] In step 101 of some embodiments, the security platform obtains a sample image and image structured data from the business platform. The image structured data is obtained by the business platform identifying the sample image. The business platform has been introduced above and will not be repeated here. The sample image can be called image unstructured data. Image unstructured data is the image to be identified by algorithms such as OCR, such as document images and contract PDF images. Image structured data is the image unstructured data, which is the result in JSON format obtained after algorithm discrimination and identification, such as the identified image content.

[0078] In one embodiment, step 101 may include: according to the acquisition cycle pre-set by the security platform, each acquisition cycle passes, sending an image data acquisition request to the business platform, and receiving a sample image and image structured data sent by the business platform based on the image data acquisition request.

[0079] In step 102 of some embodiments, the security platform stores the sample image in a preset archive database and generates an image storage path. The archive database is a database that can store images. For example, the archive database is an archive network storage server, also known as an archive NAS. The image storage path indicates the path where the target image is stored in the archive database.

[0080] In step 103 of some embodiments, the security platform generates image training data based on the image structured data and the image storage path, and stores the image training data in a preset distributed database. The image training data includes the image storage path. The image training data also includes the image structured data. The security level of the distributed database is higher than that of the archive database. The distributed database can be an el ast i csearch database.

[0081] In one embodiment, the process of storing the image training data in a preset distributed database may include: encrypting the image training data using a data encryption algorithm to obtain the image encrypted training data; and storing the image encrypted training data in the distributed database. The subsequent steps require first obtaining the image encrypted training data from the distributed database, and then decrypting the image encrypted training data using a data decryption algorithm to obtain the image training data. The data encryption algorithm and the data decryption algorithm are a pair of symmetric encryption and decryption algorithms.

[0082] In one embodiment, referring to Figure 2 Before step 104, the image data processing method provided in the embodiment of the present application may further include:

[0083] Step 201, receiving a permission application request from a development platform;

[0084] Step 202: Send the permission application request to a preset permission management terminal, which will review and approve the permission application request and send the review result to the security platform terminal;

[0085] Step 203: If the approval result indicates that the development platform is authorized, the identification of the development platform is sent to the security host, and the security host marks the development platform as authorized. The security host is used to receive model training requests from the authorized development platform.

[0086] The permission management end is a terminal / server end used to approve permission application requests. The approval result can indicate whether the development platform end is authorized or not. For example, when a user needs to obtain model training permissions, the development platform end sends a permission application request to the permission management end. The permission management end will send the permission application request to the direct supervisor, department head, and information security department for approval. After approval, the development platform end where the user is located obtains the corresponding permissions.

[0087] The benefit of the above-mentioned embodiment of steps 201 to 203 is that, with the help of the permission management end, the security platform end can mark whether the development platform end has the model training permission, thereby realizing secure communication interconnection.

[0088] In step 104 of some embodiments, the security platform receives a model training request from the development platform. The development platform does not have access to the business platform. The development platform cannot obtain sample images and image structured data from the business platform. The model training request refers to a request for model training.

[0089] In step 105 of some embodiments, the security platform obtains image training data from a distributed database according to a model training request, extracts sample images from an archive database according to an image storage path in the image training data, and stores the image training data and sample images in a preset secure execution space. The security platform trains a preset initial image processing model according to the image training data and sample images in the secure execution space to obtain a target image processing model. The secure execution space does not have permission to be accessed from outside the secure execution space.

[0090] The secure execution space includes a secure database and a secure host. The secure database does not have access permissions from hosts other than the secure host. Figure 3 , step 105 may include:

[0091] Step 301, storing image training data and sample images in a secure database;

[0092] Step 302, extracting image training data and sample images from a security database through a security host;

[0093] Step 303: Train the initial image processing model with the image training data and sample images through the security host to obtain the target image processing model.

[0094] Trusted execution space, also known as trusted execution environment, is a hardware-based secure computing solution. Trusted execution space builds a secure area in the server cluster through software and hardware methods to ensure that the programs and data loaded inside are protected in terms of confidentiality and integrity. A secure database refers to a container that can store data, such as a network storage server, also known as a safe house NAS. A secure host refers to a device that can access a secure database and perform model training, also known as a safe house server.

[0095] The benefit of the above-mentioned embodiment of step 301 to step 303 is that model training can be performed on the basis of ensuring data security, and the security host can access the security host at any time, which is more flexible.

[0096] In one embodiment, referring to Figure 4 , step 203 may include:

[0097] Step 401, obtaining image processing resources from a preset resource providing host through a security host;

[0098] Step 402: Train the initial image processing model using image processing resources, image training data and sample images through a secure host to obtain a target image processing model.

[0099] Specifically, the resource providing host refers to a host that provides image processing related resources. The resources provided by the resource providing host may include collective P IPY sources, collective YUM sources, collective image repositories, etc. The secure host does not have the permission to be accessed by hosts other than the resource providing host. The resource providing host does not have the permission to be accessed by hosts other than the secure host.

[0100] The benefit of the embodiment of steps 401 to 402 is that the security host is allowed to obtain the required resources from the resource providing host, but the security host and the resource providing host are not allowed to access other hosts, thereby reducing the possibility of data leakage while being able to obtain the required resources.

[0101] In one embodiment, the process of training the initial image processing model with the image training data and the sample image to obtain the target image processing model may include: inputting the sample image into the initial image processing model for image processing to obtain the sample image processing data; calculating the loss data according to the sample image processing data and the image training data to obtain the target loss data; adjusting the parameters of the initial image processing model according to the target loss data to obtain the target image processing model. For example, the image training data includes the real image category of the sample image, and the sample image processing data includes the predicted image category of the sample image. The target loss data can be calculated as the target category loss data according to the real image category and the predicted image category. The parameters of the initial image processing model can be adjusted according to the target category loss data to obtain the target image processing model as the target image classification model.

[0102] The above-mentioned loss data can be calculated using error functions such as mean square error loss function and similarity function.

[0103] The security platform includes a security cloud desktop. In one embodiment, after step 103, refer to Figure 5 The image data processing method provided in this embodiment may further include:

[0104] Step 501, receiving an image data query request through a preset secure cloud desktop;

[0105] Step 502, obtaining a sample image from the archive database according to the image data query request, extracting image training data from the distributed database, and transmitting the sample image and the image training data to the secure cloud desktop;

[0106] Step 503: Display sample images and image training data via a secure cloud desktop.

[0107] Specifically, the secure cloud desktop does not have the permission to download operations. The secure cloud desktop refers to the webssh resource pool. The data and text on the cloud desktop cannot be downloaded or copied to the local computer. User behavior is monitored and data can be manually cleared and reset regularly.

[0108] The benefit of the embodiment of the above steps 501 to 503 is that when there is an image data query request, data can be displayed through a secure cloud desktop that does not allow download operations, reducing the risk of data leakage and ensuring data security.

[0109] The security platform also includes a visualization front end and a trusted intelligent platform. Figure 6 , step 502 may include:

[0110] Step 601, exporting an image data query request from a secure cloud desktop through a preset visual front end, and transmitting the image data query request to a preset trusted intelligent platform;

[0111] Step 602, through the trusted intelligent platform, with an image data query request, obtain a sample image from the archive database, extract image training data from the distributed database, and transmit the sample image and image training data to the visualization front end;

[0112] Step 603, through the visualization front end, the sample image and the image training data are respectively desensitized, and the desensitized sample image and the desensitized image training data are transmitted to the secure cloud desktop.

[0113] Specifically, the secure cloud desktop does not have the authority to communicate directly with the trusted intelligent platform. The visualization front end and the trusted intelligent platform are both processing units that can perform corresponding tasks. The visualization front end is used as a data forwarding intermediate station between the secure cloud desktop and the trusted intelligent platform. The visualization front end is pre-set with desensitization rules, which can be used to desensitize sample images and image training data.

[0114] The benefit of the above-mentioned embodiment of step 601 to step 603 is that it prevents sensitive information from leaking out, thereby further improving data security.

[0115] The image data query request is sent by the annotation platform. Figure 7 After step 503, the image data processing method provided by this embodiment may further include:

[0116] Step 701, receiving a labeling instruction for a sample image from a labeling platform via a secure cloud desktop;

[0117] Step 702, exporting the annotation instructions from the secure cloud desktop through the preset visual front end, and transmitting them to the preset trusted intelligent platform;

[0118] Step 703, annotating the sample image with the annotation instruction through the trusted intelligent platform to obtain image annotation data;

[0119] Step 704: Store the image annotation data in a distributed database through a trusted intelligent platform.

[0120] Specifically, the annotation platform refers to the terminal / server used by the annotation personnel. The annotation personnel can perform data annotation operations on the secure cloud desktop through the annotation platform, making data annotation convenient and efficient.

[0121] The benefit of the above-mentioned embodiment of step 701 to step 704 is that the sample image can be annotated again based on the secure cloud desktop, which has high flexibility and security.

[0122] See also Figure 8 The embodiment of the present application also provides an image data processing device, which can implement the above-mentioned image data processing method. Figure 8 The module structure block diagram of the image data processing device provided in the embodiment of the present application includes: a data acquisition module 801, an image archiving module 802, a data generation module 803, a request receiving module 804, a model training module 805 and an image processing module 806. Among them, the data acquisition module 801 is used to obtain sample images and image structured data from the business platform end; wherein the image structured data is obtained by the business platform end through recognition of the sample image; the image archiving module 802 is used to store the sample image in a preset archiving database and generate an image storage path; the data generation module 803 is used to generate image training data according to the image structured data and the image storage path, and store the image training data in a preset distributed database; wherein the image training data includes the image storage path, and the security level of the distributed database is higher than the security level of the archiving database; the request receiving module 804 is used to receive the model training data from the development platform end; Request, wherein the development platform end does not have the authority to access the business platform end; a model training module 805, used to obtain image training data from the distributed database according to the model training request, extract sample images from the archive database according to the image storage path in the image training data, and store the image training data and the sample images in a preset secure execution space, and in the secure execution space, train a preset initial image processing model according to the image training data and the sample images to obtain a target image processing model; wherein the secure execution space does not have the authority to be accessed from outside the secure execution space; an image processing module 806, used to obtain a target image, and perform image processing on the target image through the target image processing model.

[0123] In another embodiment, the image data processing device also includes: a permission approval module, which is specifically used to: receive a permission application request from a development platform end; send the permission application request to a preset permission management end, and the permission management end approves the permission application request and sends the approval result to the security platform end; wherein the approval result indicates whether the development platform end is authorized or not; if the approval result indicates that the development platform end is authorized, the identification of the development platform end is sent to the security host, and the security host marks the development platform end as authorized, and the security host is used to receive the model training request from the authorized development platform end.

[0124] In another embodiment, the image data processing device also includes: a data query module, which is specifically used to: receive image data query requests through a preset secure cloud desktop; obtain sample images from an archive database according to the image data query request, and extract image training data from a distributed database, and transmit the sample images and image training data to the secure cloud desktop; display the sample images and image training data through the secure cloud desktop; wherein the secure cloud desktop does not have the authority for download operations.

[0125] In another embodiment, the image data processing device also includes: a data annotation module, which is specifically used to: receive annotation instructions for sample images from an annotation platform through a secure cloud desktop; export annotation instructions from the secure cloud desktop through a preset visual front end, and transmit them to a preset trusted intelligent platform; annotate sample images with annotation instructions through the trusted intelligent platform to obtain image annotation data; and store the image annotation data in a distributed database through the trusted intelligent platform.

[0126] It should be noted that the specific implementation of the image data processing device is basically the same as the specific embodiment of the above-mentioned image data processing method, and will not be described in detail here.

[0127] The embodiment of the present application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned image data processing method. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0128] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0129] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0130] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the image data processing method of the embodiment of this application;

[0131] Input / output interface 903, used to implement information input and output;

[0132] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);

[0133] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0134] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0135] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned image data processing method.

[0136] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0138] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0139] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0141] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0142] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0146] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0147] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for processing image data, characterized in that: Applied to the security platform, the method includes: Acquire a sample image and image structured data from a service platform end; wherein the image structured data is obtained by the service platform end identifying the sample image; Storing the sample image in a preset archiving database and generating an image storage path; Generate image training data according to the image structured data and the image storage path, and store the image training data in a preset distributed database; wherein the image training data includes the image storage path, and the security level of the distributed database is higher than the security level of the archive database; Receiving a model training request from a development platform end, wherein the development platform end does not have permission to access the business platform end; Acquire the image training data from the distributed database according to the model training request, extract the sample image from the archive database according to the image storage path in the image training data, and store the image training data and the sample image in a preset secure execution space, and train a preset initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model; wherein the secure execution space does not have permission to be accessed from outside the secure execution space; A target image is acquired, and image processing is performed on the target image using the target image processing model.

2. The method according to claim 1, characterized in that: The secure execution space includes a secure database and a secure host; The storing the image training data and the sample image into a preset secure execution space, and training the initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model, comprises: The image training data and the sample image are stored in the security database; the security database is not accessible by hosts other than the security host; Extracting image training data and the sample image from the security database via the security host; The initial image processing model is trained by the security host with the image training data and the sample image to obtain the target image processing model.

3. The method according to claim 2, characterized in that The step of training the initial image processing model with the image training data and the sample image through the security host to obtain the target image processing model includes: The image processing resources are obtained from a preset resource providing host through the security host; the security host does not have the permission to be accessed by hosts other than the resource providing host, and the resource providing host does not have the permission to be accessed by hosts other than the security host; The initial image processing model is trained by the secure host with the image processing resources, the image training data and the sample images to obtain the target image processing model.

4. The method according to claim 2, characterized in that: Before receiving the model training request from the development platform, the method further includes: Receive permission application requests from the development platform; Sending the permission application request to a preset permission management terminal, which will review and approve the permission application request and send the review result to the security platform terminal; wherein the review result indicates whether the development platform terminal is authorized or not; If the approval result indicates that the development platform is authorized, the identification of the development platform is sent to the security host, and the security host marks the development platform as authorized. The security host is used to receive the model training request from the authorized development platform.

5. The method according to any one of claims 1 to 4, characterized in that: After generating image training data according to the image structured data and the image storage path, and storing the image training data in a preset distributed database, the method further includes: Receive image data query requests through a preset secure cloud desktop; Acquire the sample image from the archive database according to the image data query request, extract the image training data from the distributed database, and transmit the sample image and the image training data to the secure cloud desktop; The sample image and the image training data are displayed through the secure cloud desktop; wherein the secure cloud desktop does not have permission for download operations.

6. The method according to claim 5, characterized in that The acquiring the sample image from the archive database according to the image data query request, extracting the image training data from the distributed database, and transmitting the sample image and the image training data to the secure cloud desktop includes: Exporting the image data query request from the secure cloud desktop through a preset visual front end, and transmitting the image data query request to a preset trusted intelligent platform; By using the trusted intelligent platform, the sample image is obtained from the archive database with the image data query request, and the image training data is extracted from the distributed database, and the sample image and the image training data are transmitted to the visualization front end; Through the visualization front end, the sample image and the image training data are respectively desensitized, and the desensitized sample image and the desensitized image training data are transmitted to the secure cloud desktop; Among them, the secure cloud desktop does not have the authority to communicate directly with the trusted intelligent platform.

7. The method according to claim 5, characterized in that The image data query request is sent by the annotation platform end; After displaying the sample image and the image training data through the secure cloud desktop, the method further includes: Receiving, through the secure cloud desktop, a labeling instruction for the sample image from the labeling platform; The annotation instruction is exported from the secure cloud desktop through a preset visual front end, and transmitted to a preset trusted intelligent platform; Annotating the sample image with the annotation instruction through the trusted intelligent platform to obtain image annotation data; The image annotation data is stored in the distributed database through the trusted intelligent platform.

8. An image data processing device, characterized in that: Applied to a security platform, the device comprises: A data acquisition module, used to acquire a sample image and image structured data from a service platform end; wherein the image structured data is obtained by the service platform end identifying the sample image; An image archiving module, used to store the sample image in a preset archiving database and generate an image storage path; A data generation module, configured to generate image training data according to the image structured data and the image storage path, and store the image training data in a preset distributed database; wherein the image training data includes the image storage path, and the security level of the distributed database is higher than the security level of the archive database; A request receiving module, used to receive a model training request from a development platform end, wherein the development platform end does not have permission to access the business platform end; A model training module, configured to obtain the image training data from the distributed database according to the model training request, extract the sample image from the archive database according to the image storage path in the image training data, and store the image training data and the sample image in a preset secure execution space, and train a preset initial image processing model in the secure execution space according to the image training data and the sample image to obtain a target image processing model; wherein the secure execution space does not have permission to be accessed from outside the secure execution space; The image processing module is used to acquire a target image and perform image processing on the target image through the target image processing model.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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