WO2022142017A1

WO2022142017A1 - Image processing method and apparatus, electronic device, and storage medium

Info

Publication number: WO2022142017A1
Application number: PCT/CN2021/090589
Authority: WO
Inventors: 陈海峰; 栗鹏; 郭彤; 刘琳
Original assignee: 深圳市商汤科技有限公司
Priority date: 2020-12-30
Filing date: 2021-04-28
Publication date: 2022-07-07
Also published as: CN112651352A; CN112651352B; JP2023511642A

Abstract

An image processing method and apparatus, an electronic device, and a storage medium. The method comprises: performing image processing on an image to be processed, to obtain at least one image processing result of said image (S11); determining whether the at least one image processing result meets a determination condition of an early warning case (S12); if the at least one image processing result meets the determination condition of the early warning case, determining said image as an early warning image (S13); and generating an early warning result for the early warning case according to the early warning image (S14).

Description

Image processing method and device, electronic device and storage medium

CROSS-REFERENCE TO RELATED APPLICATIONS

This disclosure is based on the Chinese patent application with the application number of 202011606578.3, the application date of which is on December 30, 2020, and the application name is "image processing method and device, electronic equipment and storage medium", and claims the priority of the Chinese patent application, The entire content of the Chinese patent application is hereby incorporated by reference into the present application in its entirety

technical field

The present disclosure relates to the field of computer technologies, and in particular, to an image processing method and apparatus, an electronic device, and a storage medium.

Background technique

With the rapid development of mobile communication technology and the rapid popularization of intelligent hardware, a large amount of video image data is spread on the Internet every day. It is an urgent problem to supervise the large amount of video image data that spreads on the Internet every day.

SUMMARY OF THE INVENTION

The present disclosure proposes a technical solution configured to perform image processing.

According to an aspect of the embodiments of the present disclosure, an image processing method is provided, including:

Perform image processing on the image to be processed to obtain at least one image processing result of the image to be processed;

determining whether the at least one image processing result satisfies the judgment condition of the early warning case;

In the case that the at least one image processing result satisfies the judgment condition of the early warning case, determine that the to-be-processed image is an early warning image;

An early warning result for the early warning case is generated according to the early warning image.

According to an aspect of the embodiments of the present disclosure, there is provided an image processing apparatus, including:

a first processing part, configured to perform image processing on the image to be processed to obtain at least one image processing result of the image to be processed;

a first determining part, configured to determine whether the at least one image processing result satisfies a judgment condition for an early warning case;

a second determining part, configured to determine that the to-be-processed image is an early-warning image when the at least one image processing result satisfies the judgment condition of an early-warning case;

The second processing part is configured to generate an early warning result for the early warning case according to the early warning image.

According to an aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; a memory configured to store instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to The image processing method described above is performed.

According to an aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having computer program instructions stored thereon, the computer program instructions implementing the above-mentioned image processing method when executed by a processor.

According to an aspect of the present disclosure, there is provided a computer program, comprising computer-readable code, which, when the computer-readable code is executed in an electronic device and executed by a processor in the electronic device, realizes the above image Approach.

In the embodiment of the present disclosure, image processing may be performed on the image to be processed to obtain at least one image processing result of the image to be processed, and it is determined whether the at least one image processing result satisfies the judgment condition of the early warning case. In the case of the judgment condition of the early warning case, the image to be processed is determined as the early warning image, and the early warning result for the early warning case can be generated according to the early warning image. The image processing method and device, electronic device, and storage medium provided according to the embodiments of the present disclosure can realize the supervision and processing of a large amount of image data, effectively reduce labor costs, and improve the precision and efficiency of early warning.

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

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the technical solutions of the present disclosure.

FIG. 1 shows a flowchart of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 6 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 7 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 8 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 9 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 10 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

11 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 12 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 13 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 14 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 15 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 16 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 17 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 18 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 19 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 20 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 21 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure;

FIG. 22 shows a block diagram of an image processing apparatus proposed according to an embodiment of the present disclosure;

FIG. 23 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure;

FIG. 24 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.

Detailed ways

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the figures denote elements that have the same or similar functions. While various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The terms "at least one" or "at least one" herein refer to any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, and C, may mean Contains any one or more elements selected from the set of A, B, and C.

In addition, in order to better illustrate the embodiments of the present disclosure, numerous specific details are given in the following detailed description. It should be understood by those skilled in the art that the embodiments of the present disclosure may be practiced without certain specific details. In some instances, methods, means, components and circuits well known to those skilled in the art have not been described in detail so as not to obscure the subject matter of the present disclosure.

With the rapid development of communication technology and the rapid popularization of intelligent hardware devices, the Internet spreads a large amount of video image data every day. At present, video data is often managed by means of structured information analysis through the network big data monitoring and analysis platform. However, in order to hide important information, criminals often use pictures to disseminate, such as terrorism and independence, illegal reaction, pornographic violence, and the dissemination of false information. Slowly, the dissemination of text turns to pictures and videos, which increases the concealment of crimes. Sexuality, making it difficult to supervise public opinion and crack down on crimes. In response to the above problems, there are currently two main regulatory schemes for Internet dissemination of data. The first is to use a specific data collector to collect and analyze the data in the operator's Internet Data Center (IDC) computer room, upload the analysis results to the database and make an alarm, but does not mention the use of multimedia data. Data analysis technology, and its method is mainly to match the data content by formulating monitoring strategies, and does not involve the retrieval function based on the full amount of data. The second is to set up at least one image acquisition and detection master server and at least one image acquisition and detection slave server, Wireless Application Protocol (Wireless Application Protocol, WAP) service data mirror port slave Code Division Multiple Access (CDMA) or The mirrored WAP service data in the Global System for Mobile Communications (GSM) network is transmitted to the image acquisition and detection master server and the image acquisition and detection slave server. The system software architecture includes acquisition control layer, data audit layer, data management layer, Data presentation layer. The data review layer uses image recognition methods such as skin color segmentation and face recognition, but the recognition ability and scope of this method are limited to pornographic and inappropriate content images, and only for mobile CDMA and GSM networks, with a narrow application range. In view of this, how to efficiently supervise the data disseminated on the Internet is an urgent problem to be solved.

The embodiment of the present disclosure provides an image processing method, which obtains at least one image processing result of the to-be-processed image by performing image processing on the to-be-processed image, and determines whether the at least one image processing result satisfies the judgment condition of the early warning case, and then at least one image processing result When the processing result satisfies the judgment condition of the early warning case, the image to be processed is determined as the early warning image, and the early warning result for the early warning case can be generated according to the early warning image. The image processing method and device, electronic device, and storage medium provided according to the embodiments of the present disclosure can realize the supervision and processing of a large amount of image data, effectively reduce labor costs, and improve the precision and efficiency of early warning.

1 shows a flowchart of an image processing method proposed according to an embodiment of the present disclosure. The image processing method may be executed by an electronic device such as a terminal device or a server. The terminal device may be a user equipment (User Equipment, UE), a mobile device, User terminals, terminals, cellular phones, cordless phones, personal digital assistants (Personal Digital Assistant, PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc. The way to read the instruction to achieve. Alternatively, the method may be performed by a server.

As shown in Figure 1, the image processing method may include:

In S11, image processing is performed on the image to be processed to obtain at least one image processing result of the image to be processed.

In some embodiments, the images to be processed may include received real-time images and historical images stored in a database, and the images to be processed may also be obtained by performing frame extraction processing on video data.

In some embodiments, the process of performing image processing on the image to be processed to obtain at least one image processing result of the image to be processed may include: extracting preset scene elements from the image to be processed to obtain preset scene elements included in the image to be processed; Among them, the preset scene elements may be elements that the system supports to extract, such as: a specified logo, a specified flag, a specific object, a specific behavior, and the like.

In some embodiments, the process of performing image processing on the image to be processed to obtain at least one image processing result of the image to be processed may also include: extracting text content from the image to be processed to obtain text content contained in the image to be processed.

In some embodiments, the process of performing image processing on the to-be-processed image to obtain at least one image processing result of the to-be-processed image may further include: comparing the to-be-processed image with the control task, and obtaining the control result of the to-be-processed image for the control task. At least one item of the control task; wherein, the deployment task can be a task created according to at least one of the face image of the target person to be deployed and the target image to be deployed, and the deployment task can be used for real-time monitoring and control of the target person and the target person to be deployed. Images related to at least one item of target images to be deployed;

In some embodiments, the process of performing image processing on the to-be-processed image to obtain at least one image processing result of the to-be-processed image may further include: performing operations such as information extraction on the to-be-processed image to obtain source information of the to-be-processed image.

Here, the process of performing image processing on the image to be processed to obtain at least one image processing result of the image to be processed may include at least one of the above.

In S12, it is determined whether at least one image processing result satisfies the judgment condition of the early warning case.

In S13, if at least one image processing result satisfies the judgment condition of the early warning case, it is determined that the to-be-processed image is an early warning image.

In some embodiments, when creating an early warning case, a judgment condition corresponding to the early warning case may be set, for example, the judgment condition may include at least one of several preset scene elements, several text contents, and control tasks. When at least one image processing result of the to-be-processed image matches the judgment condition of any early warning case, it can be determined that at least one image processing result of the to-be-processed image satisfies the judgment condition of the early warning case, and it is determined that the to-be-processed image hits the early warning case case, which can be used as an early warning image of the early warning case.

FIG. 2 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 2 , after image processing is performed on an image to be processed, including analysis of preset scene elements, analysis of preset text content, and comparison of control tasks, an image to be processed is obtained. At least one image processing result of the processed image may include preset scene element 1, preset scene element 2, text content 1, and determining that the image to be processed hits the control task 1. Here, the judgment conditions for early warning case 1 include preset scene element 1 and control task 2, the judgment conditions for early warning case 2 include preset scene element 2, and the judgment conditions for early warning case 3 include text content 1 and text content 2. If the judgment conditions of 4 include preset scene element 3, text content 2 and control task 1, it can be determined that the image to be processed hits early warning case 1, early warning case 2, early warning case 3 and early warning case 4, respectively. As the early warning image for early warning case 1, the early warning image for early warning case 2, the early warning image for early warning case 3, and the early warning image for early warning case 4, respectively.

In step S14, an early warning result for an early warning case is generated according to the early warning image.

For example, the warning result may include the warning image and the warning case corresponding to the warning image, and the warning result may be used to notify that the to-be-processed image hits the warning case as the warning image. Exemplarily, the warning result can be pushed to a designated client for display, and the user can view the warning image corresponding to the warning result and the warning case hit by the warning image by viewing the detailed content of the warning result.

This embodiment of the present disclosure may perform image processing on the image to be processed, obtain at least one image processing result of the image to be processed, and determine whether the at least one image processing result satisfies the judgment condition of the early warning case, and if the at least one image processing result satisfies the judgment condition of the early warning case In the case of , the to-be-processed image is determined to be an early warning image, and an early warning result for an early warning case can be generated according to the early warning image. The image processing method provided according to the embodiment of the present disclosure can realize the supervision and processing of a large amount of image data, effectively reduce the labor cost, and improve the precision and efficiency of early warning.

In an embodiment of the present disclosure, the method may further include:

In response to the creation operation for the early warning case, the judgment condition of the early warning case is obtained according to the selected early warning factor.

Create an early warning case based on the judgment conditions of the early warning case.

In some embodiments, an early warning case may be created on the early warning case creation interface in response to an operation for creating an early warning case. For example, the early warning case creation interface includes a trigger control for triggering the creation of a new early warning case, and in response to a trigger operation for the early warning case, the early warning case creation interface can be displayed. You can also enter the case name, case description information, case start and close time and other information in this creation interface, and you can manually input or select from the provided early warning factors in the judgment condition creation area to select the Create an early warning factor for the judgment condition, and after selecting the early warning factor, you can also edit the selected early warning factor to create an AND or NOT logical expression based on the selected early warning factor, and then create a logical expression based on the selected early warning factor , and create a judgment condition for an early warning case.

The early warning factors may include at least one of preset scene elements, text content, and control tasks, and the selected early warning factors may be factors related to early warning cases. Exemplarily, FIG. 3 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 3 , an early warning case is first created (S01), such as creating an early warning case A involving pornography. After the case name and other information of case A, you can select at least one preset scene element related to pornography, add at least one textual content or logical expressions of multiple textual contents related to pornography, and select at least one related to pornography. A control task (actually, the logic expression can also be edited according to the selected preset scene elements, the added text content, and the selected control task), and then the judgment condition of the early warning case A is obtained, that is, the judgment condition of the early warning case is configured. (S02).

After configuring the judgment condition of the early warning case, the early warning case can be created and stored in the early warning case database in response to the triggering operation on the trigger control used to trigger the completion of the early warning case creation. Afterwards, the early warning images that meet the judgment conditions of the early warning cases can be filtered (S03), that is, the early warning images that meet the judgment conditions of the early warning cases are filtered from the images to be processed, and then the early warning results are generated according to the early warning images (S04). After the early warning result for the early warning case is generated, the user can view the corresponding early warning image according to the early warning result, and find the information of the user who sent the early warning image, etc.

In the embodiment of the present disclosure, the status of the early warning case can also be set, and the status of the early warning case includes an active state and an inactive state. For the early warning case in the active state, an early warning judgment can be performed on the received image to be processed according to the early warning case in real time. For early warning cases that are in an inactive state, no early warning judgment will be made.

In this embodiment of the present disclosure, images to be processed received in real time can be stored in a data storage area, and after an early warning case is created, it can be searched from the to-be-processed images stored in the data storage area to see if there is an image to be processed that hits an early warning case , in order to realize the supervision and processing of historical image data.

FIG. 4 shows a schematic diagram of an image processing method according to an embodiment of the present disclosure. Referring to FIG. 4 , an embodiment of the present disclosure can edit warning cases, pin warning cases, view warning results, filter warning results, and download warning results. For example, operations such as modifying or deleting an early warning case include modifying the name of the early warning case, case description information, information such as the start and closing time of the case, the judgment conditions of the early warning case, the status of the early warning case, etc.; or view the latest information through the configured rules. Early warning images, or filtering and screening early warning results according to conditions, which are not limited in this embodiment of the present disclosure.

In the embodiment of the present disclosure, the early warning factor may include at least one of preset scene elements, text content, and control tasks, and the method may further include:

Create at least one deployment database, and the target data to be deployed is stored in the deployment database.

Create at least one deployment task based on at least one deployment database.

Wherein, the deployment database includes at least one of a character deployment database and an image deployment database, the target data stored in the character deployment database includes the face image of the target person, the target data stored in the image deployment database includes target images, and the deployment tasks include human At least one of the face control task and the image control task.

The embodiment of the present disclosure can create a deployment task, which is used to supervise the deployment target, so as to filter out images associated with the deployment target from the images to be processed. The deployment task may include creating at least one of a face deployment task and an image deployment task.

Among them, the face control task is used to control the target person. With face detection, face feature extraction and storage and other technologies, the face control of the target person within a fixed range can be realized. It can be applied to fugitives, supervised personnel, etc. The application scenarios of face control can also be extended to scenarios such as comics and spoofs, with strong recognition capabilities. The image deployment task is used to control the target image. With the help of image feature extraction and storage technology, the image deployment of the target image in a fixed range can be realized, and it can be applied to the application scenarios of image deployment such as the audio and video library of terror.

In some embodiments, at least one deployment task may be created in advance, and target data to be deployed may be added to the deployment task. When creating a deployment task, you can add the name and description of the deployment task in the creation interface of the deployment task, and you can add the deployment database to be deployed as the deployment target of the deployment task in the creation interface of the deployment task. After the deployment task is created, the deployment task can be started automatically.

In this embodiment of the present disclosure, before creating a deployment task, a deployment database may be created in advance. For example, you can create the corresponding deployment database by triggering the database creation operation in the deployment database management interface. Several target data to be deployed can be added to the deployment database. For example, the target data to be deployed can be selected from several images provided by the system and added to the deployment database, or the target data to be deployed can be added to the deployment database by uploading and downloading. No specific limitation is made.

In one implementation of the embodiment of the present disclosure, operations such as deleting, modifying, querying, and searching the deployment database can also be performed. The target deployment database to be operated, and in response to the viewing operation for the target deployment database, a detailed interface of the target deployment database is displayed. In the detail interface of the target deployment database, detailed information of the target deployment database (including database construction time, name, description, etc.) and target data in the target deployment database can be displayed. The target deployment database can be modified in the details interface, including the modification of the name and description information of the target deployment database, and the addition or deletion of the target data stored in the target deployment database. Exemplarily, in this embodiment of the present disclosure, operations such as adding or deleting target data may be performed in batches.

Alternatively, after selecting the target deployment database to be operated on in the deployment database management interface, a delete operation for the target deployment database can be performed to delete the target deployment database. When deleting the target deployment database, it can be detected whether there is a deployment task targeting the target deployment database. When there is no deployment task targeting the target deployment database, the target deployment database can be deleted directly, or When there is a deployment task targeting the target deployment database, the user can be prompted through a prompt message that there is currently a deployment task targeting the target deployment database, and then in response to the user's confirmation operation on the prompt information, the target deployment database can be executed. delete operation.

In an implementation of the embodiment of the present disclosure, the deployment control level can also be set for the deployment control task, for example, emergency deployment control, long-term deployment control, key deployment control and other levels are set, so as to implement the deployment control according to the deployment control level. The setting method is limited. Alternatively, you can also perform a top operation on the deployment task. For the top deployment task, you can give priority to display, including displaying the deployment results of the deployment task.

For the face control task, a face control library can be created in advance, and the face control library can store at least one of several face images and face features of at least one target person to be controlled. After the control task is deployed, the face control library can be added as the control target to be deployed in the face control task. For the image deployment task, an image deployment library can be created in advance, and the image deployment library can store at least one target image to be deployed and at least one of image features. After the image deployment task is created, the image deployment task can be The image deployment library is added as the deployment target to be deployed.

FIG. 5 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 5 , after creating a control task and performing feature extraction on the image to be processed received in real time, the extracted image features of the image to be processed may be extracted. Violent retrieval is performed in the deployment database corresponding to the deployment task, and the similarity between the image feature of the image to be processed and the feature of any target data in the deployment database is greater than the similarity threshold (the similarity threshold can be set when creating the deployment task) ), that is, when a character in the library is selected from the deployment database, it can be determined that the deployment result is that the to-be-processed image hits the deployment task. The result of deployment control, .

FIG. 6 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 6 , in the deployment process of face deployment and image deployment, it mainly involves three major sections: deployment database management, deployment tasks, and deployment result statistics. , after the to-be-processed image hits the deployment task, the deployment result for the deployment task can be generated according to the to-be-processed image, and the deployment result of the deployment task can be viewed in response to the deployment result viewing operation for the deployment task; it can also be filtered first based on conditions The control result is obtained, and the control target is further confirmed according to the to-be-processed image corresponding to the control result. Alternatively, the selected deployment result may be downloaded to a specified location in response to a download operation for the deployment result.

In this embodiment of the present disclosure, clustering processing can be performed on at least one image to be processed that hits the deployment task, so that when viewing the deployment results, the results can be viewed according to the clustering results. For example, if the control task is a face control task, after clustering, you can view the control results by character classification according to the clustering results. Or at least one to-be-processed image that hits the control task can be aggregated according to the user identity document (ID), group ID, Internet Protocol (IP) and other information of the to-be-processed image. ID, group ID, IP and other information are classified to view the deployment results. The deployment result may also be screened and viewed according to information such as time, user ID, group ID, IP, etc. The embodiment of the present disclosure does not specifically limit the viewing method of the deployment result.

In some embodiments, the target deployment task to be operated can be selected by manually entering or selecting the name of the deployment task and other information in the deployment database management interface, and in response to the viewing operation for the target deployment task, the target deployment task is displayed. The details interface of the target deployment task may include detailed information of the target deployment task (which may include information such as the name and description information of the target deployment task) and the deployment result corresponding to the target deployment task.

The detail interface of the target deployment task can include edit/modify controls. When the target deployment task changes, you can enter the creation interface of the target deployment task in response to the triggering operation for the edit/modify control to create a target deployment task in the target deployment task. Modify the target deployment task in the task creation interface. For example, if the deployment target corresponding to the target deployment task changes, the deployment database added to the target deployment task can be modified, or the deployment task name and description information can be modified. information to be modified.

The details interface of the target deployment task may also include options for the display method of the deployment results, such as: display by clustering result, display by user ID, display by group ID, display by time, custom display method, etc. In defining the display mode, the user can set the filter conditions for the deployment results to be displayed, and after the deployment results are screened according to the filter conditions, the screening results are displayed. For example, the user can set the display time range in the custom display method to display the newly added control results within this time range, or the user can set the display method in the custom display method to display according to the number of control results in the clustering results. For example, the top three clustering results are displayed.

In an embodiment of the present disclosure, the method may further include:

Obtain scene element information and scene element images;

In response to the creation operation for the preset scene element, the preset scene element is created in the preset scene management library according to the scene element information and the scene element image.

In some embodiments, the preset scene elements may be preset to-be-identified elements, such as elements such as blood, passport, ID card, bank card, etc., and the text content may include recognizable text, such as anti-inflammatory text, violence For characters such as yellow, etc., the embodiment of the present disclosure does not limit the preset scene elements and text content.

FIG. 7 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 7 , a user can add scene element information of a preset scene element to be created in the scene element creation interface, and upload the preset scene to be created. The scene element image of the element, such as "element image", "element main image". The scene element information may include information such as the training type, application scene, element name, alarm threshold, and element description of the scene element. The number of scene element images may be one or multiple. In the case where the number of scene element images is multiple, the recognition accuracy of the created preset scene elements can be improved. The number is not specifically limited.

In some embodiments, the preset scene element may be created in the preset scene management library in response to a user's creation operation on the preset scene element (eg, an operation such as a trigger operation for creating a control). Furthermore, when the user creates an early warning case, the preset scene element can be provided for the user to select, that is, the user can customize the preset scene element used to create the early warning case, which can meet the user's personalized needs and improve the creation of Flexibility in early warning cases.

In some embodiments, image processing may be performed on the image to be processed based on the preset scene elements in the preset scene management library, such as extracting the preset scene elements contained in the image to be processed. That is, after the preset scene element is created, in the image processing of the image to be processed, an extraction process for the preset scene element is added.

The embodiment of the present disclosure can also perform operations such as deletion, modification, query, search, etc. on the preset scene elements in the preset scene management library. For example, the preset scene element can be searched according to any scene element information of the preset scene element in response to the user's search operation for the preset scene element. For example, the preset scene element can be searched according to the application scene of the preset scene element or the preset scene element. search for the element name and other information, and display the searched preset scene elements in the search result display interface. And in response to the viewing operation for the selected preset scene element, the scene element information and scene element image corresponding to the preset scene element can be displayed by means of pop-up window bubbles or the like. FIG. 8 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. The display method of scene element information and scene element images corresponding to scene elements can be referred to as shown in FIG. 8 . The training types in the scene element information in the figure are training, The application scene is scene 5, the element name is element 5, and the alarm threshold is 80%; the cover of the element is a portrait.

In an implementation of the embodiment of the present disclosure, it is also possible to modify the selected preset scene elements in the search result display interface, which may include modifying scene element information and scene element images of the preset scene elements, for example: searching The result display interface may include editing/modifying controls for each preset scene element. When the preset scene element changes, you can enter the creation interface of the preset scene element in response to the trigger operation for the editing/modifying control. to modify the preset scene element in the creation interface of the preset scene element.

In still another implementation manner of the embodiment of the present disclosure, operations such as deleting preset scene elements may also be included. Exemplarily, in this embodiment of the present disclosure, operations for deleting preset scene elements may be performed in batches.

In this embodiment of the present disclosure, the activation state of the preset scene element can also be set. When the preset scene element is in the activated state, the preset scene element can be provided to the user to create an early warning case, and in the image processing process, the image to be processed Extract the preset scene element; when the preset scene element is in a non-starting state, the preset scene element cannot be provided to the user for the creation of an early warning case, and during the image processing process, the image to be processed will not be processed. Extraction of preset scene elements.

In the embodiment of the present disclosure, a user can customize the preset scene elements used for creating an early warning case, which can meet the user's personalized needs and improve the flexibility of creating an early warning case.

In this embodiment of the present disclosure, the image processing result includes at least one of preset scene elements included in the image to be processed, text content included in the image to be processed, and a deployment result of the image to be processed for the deployment task, and the at least one image is determined. Whether the processing result meets the judgment conditions for early warning cases may include:

Determine whether there is a target image processing result that matches the early warning factor corresponding to the judgment condition of the early warning case in at least one image processing result.

When there is a target image processing result that matches the warning factor corresponding to the judgment condition of the early warning case, it is determined that at least one image processing result satisfies the judgment condition of the early warning case.

In some embodiments, after image processing is performed on the image to be processed, at least one image processing result of the image to be processed can be obtained, for example, a preset scene element, text content and a hit control task corresponding to the image to be processed can be obtained. It can be determined whether there is a target image processing result that matches the early warning factor corresponding to the judgment condition of any early warning case in at least one image processing result of the image to be processed. Wherein, when the image processing result is the same as the early warning factor or when multiple image processing results satisfy the logical expressions corresponding to the multiple early warning factors, it can be determined that the image processing result is the target image processing result matching the early warning factor, so it can be determined that at least One of the image processing results satisfies the judgment condition of the early warning case.

Further, it can be determined that the to-be-processed image is an early-warning image, and an early-warning result for the early-warning case is generated according to the early-warning image. Wait).

In the embodiment of the present disclosure, when creating an early warning case, the method further includes setting a weight value for the selected early warning factor, and the method may further include:

Determine the first comprehensive weight value of the early warning image for the hit early warning case according to the target image processing result of the early warning image for the hit early warning case, and the corresponding weight value of the early warning factor matched with each target image processing result in the corresponding early warning case .

According to the first comprehensive weight value of the early warning image for each hit early warning case, the second comprehensive weight value of the early warning image for all early warning cases is determined.

The warning level corresponding to the warning image is determined according to at least one of the second comprehensive weight value and the number of hit warning cases.

In some embodiments, when creating an early warning case, the weight value of each early warning factor in the early warning case may also be set. The target image processing result of the judgment condition for hitting the early warning case in the early warning image and the weight value of the early warning factor hit by the target image processing result in the judgment condition can be determined, and then the first comprehensive weight value of the early warning image for the hit early warning case can be obtained. Exemplarily, for any early warning case, each time the image to be processed hits an early warning factor in the early warning case, the weight value corresponding to the early warning factor can be accumulated to obtain the first comprehensive weight value for the early warning case.

Each early-warning case hit by the early-warning image is determined, and the first comprehensive weight value of the early-warning image for each early-warning case is accumulated to obtain a second comprehensive weight value corresponding to the early-warning case.

For example, the to-be-processed image hits the early-warning case 1 and the early-warning case 2 respectively, that is, the to-be-processed image is used as the early-warning image of the early-warning case 1 and the early-warning image of the early-warning case 2 respectively. The target image processing result of the early warning image for the early warning case 1 includes the preset scene element 1 and the control task 1. In the early warning case 1, the weight value corresponding to the scene element 1 is preset to 1, and the weight value corresponding to the control task 1 is 2. The first comprehensive weight of the image for early warning case 1 is 3. The target image processing result of the early warning image for the early warning case 2 includes the preset scene element 1 and the control task 2. In the early warning case 1, the weight value 2 corresponding to the scene element 1 is preset, and the weight value corresponding to the control task 2 is 2, then the early warning The first comprehensive weight value of the image for early warning case 2 is 4. Furthermore, the second comprehensive weight value of the early warning image for all early warning cases is 4.

In some embodiments, the early warning level corresponding to the early warning image may be determined according to at least one of the second comprehensive weight value and the number of hit early warning cases, for example: when the second comprehensive weight value is higher than the first weight threshold and hit When the number of early warning cases is higher than at least one of the first number thresholds, it can be determined that the early warning images correspond to the first warning level, or when the second comprehensive weight value is higher than the second weight threshold but smaller than the first weight preset When the number of early warning cases and hits is higher than the second number threshold but less than at least one of the first number presets, it can be determined that the warning image corresponds to the second warning level, or when the second comprehensive weight value is lower than the second When the weight threshold and the number of hit early warning cases are lower than at least one of the second number threshold, it may be determined that the early warning image corresponds to the third early warning level.

The above division of early warning levels for early warning cases is only an example of the embodiment of the present disclosure, and should not be understood as a limitation on the division method of early warning levels of early warning cases, and the embodiment of the present disclosure does not specify the division method of warning levels limited.

In an embodiment of the present disclosure, the method may further include:

In response to the viewing operation for the warning image, at least one warning level is displayed.

In response to a viewing operation for any alert level, each alert image under the alert level is displayed.

In some embodiments, the warning images may be viewed by warning level. Exemplarily, the warning level may be displayed on the warning image viewing interface in response to the viewing operation for the warning image. Taking the above example as an example, the first warning level, the second warning level and the third warning level may be displayed. Further, in response to a viewing operation for any warning level, all warning images under the warning level can be obtained, and can be sorted and displayed according to the size of the second comprehensive weight value of the warning images at the warning level.

In some embodiments, for an early-warning image at any early-warning level, trigger the early-warning image to view the detailed information of the early-warning image, including: the early-warning case hit by the early-warning image, the target image processing result of the early-warning image hitting the early-warning case, the early-warning image Information such as the source information of the image.

In an embodiment of the present disclosure, the method may further include:

In response to the viewing operation for the early warning case, each early warning image is displayed in sequence according to the first comprehensive weight value of each early warning image in the early warning case.

In some embodiments, the early-warning images under the early-warning case can be obtained corresponding to the viewing operation for any early-warning case, and can be sorted according to the first comprehensive weight value of each early-warning image, and displayed in order. So that important warning images can be displayed in front for users to view.

FIG. 9 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 9 , in the embodiment of the present disclosure, the judgment conditions of the early warning cases can be customized and configured, and the corresponding warning factors can be set according to the importance of the early warning factors in the judgment conditions. Weight, for example, configure prefabricated scene elements for the judgment conditions of early warning cases, including prefabricated scene element 1 and preset scene element 2, and set the weight values corresponding to the two preset scene elements respectively, the process of configuring text content and configuration control tasks The process is the same. 10 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 10 , after the creation of an early warning case and the configuration of the judgment conditions of the early warning case are completed, during the execution of the specific image processing method, the conditions of the early warning case are satisfied. The early warning images of the judgment conditions will be sorted according to the first comprehensive weight value from high to low, which realizes the discovery and early warning of key early warning images, and can perform subsequent processing on the early warning images according to the high and low priority, such as the fusion of early warning images. The embodiments of the present disclosure can flexibly configure the judgment conditions and weights of early warning cases according to business requirements, can adapt to rapid changes in business scenarios, can quickly create early warning cases according to new demands, and improve the adaptability and flexibility in the face of complex Internet environments .

Exemplarily, FIG. 11 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 11 , creating an early warning case includes configuring preset scene elements, configuring text content, and importing control tasks. In addition, in order to facilitate the creation of early warning cases After the management and user authority restrictions, etc., you can also divide the early warning cases into scenarios in advance, that is, select the early warning case scenarios. Exemplarily, when configuring an early warning case, you can also configure a business scenario of the early warning case. For example, the preset cases can be divided into public opinion cases, pornography-related cases, violence-related cases, important cases, common cases, etc., the embodiment of the present disclosure. There are no specific restrictions on business scenarios.

In an embodiment of the present disclosure, the real-time image has source information, and the method may further include:

For an image to be processed with at least one image processing result, an aggregation operation is performed according to the source information of the image to be processed to obtain at least one aggregation result, where the aggregation result includes a plurality of images to be processed and the images to be processed are for early warning The hit situation of the case, at least one item of source information of the multiple images to be processed in the aggregated result is the same;

Perform profile analysis according to the at least one aggregated result, and obtain at least one of at least one user profile feature and at least one user group profile feature.

For example, the image to be processed has source information, the source information may include account ID information, group ID information, IP information, etc. of the user who sent the image to be processed, and the source information may also include account ID information, group ID information, etc. , IP information and other related user identity information, such as ID number, phone number, passport number, driver's license number, driver's license number, bank card number, license plate number and other information, can be treated according to the source information of the image to be processed An aggregation operation is performed on the processed images, so that the images to be processed having at least one item of the same source information can be aggregated together to obtain at least one aggregation result.

Profile analysis may be performed on at least one aggregated result to obtain at least one of at least one user feature and at least one user group profile feature. Around information with unique identifiers, such as account ID information, ID number, phone number, etc., construct multi-dimensional user profile features of users or construct multi-dimensional user group profile features of user groups. The dimensions of profile features can be based on data sources. , algorithmic capabilities, and the addition of associated third-party information continues to expand.

For example, FIG. 12 shows a schematic diagram of an image processing method according to an embodiment of the present disclosure. Referring to FIG. 12 , the image features may include source information of the image to be processed, early warning case information associated with the image to be processed, and the image of the image to be processed. process result. For each to-be-processed image in an aggregated result, the pre-warning case information associated with the to-be-processed image and the image processing result of the to-be-processed image can be obtained, wherein the pre-warning case information may include the number of hit pre-warning cases, the scene to which the pre-warning case belongs, and the to-be-processed image. The case-involved index of the processed image for various scenes, etc., where the case-involved index for all levels of the scene can be obtained from the number of image processing results contained in the image to be processed for each type of scene, for example: preset scene elements, text content , control tasks and other information are divided into corresponding scenes, taking a pornographic scene as an example, in the case that there are 5 image processing results corresponding to pornographic scenes in the image to be processed, the case-involved index of the pornographic scene in the to-be-processed image (referred to as pornography-related scenes) index) can be determined to be 5.

Further, the source information of all the images to be processed, the information of the hit early warning cases and the associated information of the images to be processed in the aggregation result are integrated, and at least one of the user portrait feature and the user group portrait feature corresponding to the aggregation result can be obtained. For example, if the aggregation result is obtained by aggregation based on the user account ID, the user portrait feature of the user corresponding to the user account ID can be obtained, and when the aggregation result is obtained by aggregation based on the group ID, the group can be obtained. The user group portrait feature of the group corresponding to the ID, if the aggregation result is obtained by aggregation based on the IP address, the user group portrait feature of the group corresponding to the IP address can be obtained.

The dimensions of the user portrait feature/user group portrait feature may be added or deleted according to specific application scenarios. User portrait feature/user group portrait feature can comprehensively describe the related information and features of the user/user group, and provide important indicators and basis for later warning analysis and judgment.

In an embodiment of the present disclosure, the method may further include:

According to at least one of the early warning information, at least one user portrait feature, and at least one user group portrait feature, at least one of the key personnel and the key group is determined; wherein, the early warning information includes source information of each early warning image and the number of early warning cases hit by the warning image.

For example, key control personnel can be determined according to at least one user profile feature. Exemplarily, the user profile feature shows that the user/user group is involved in a large number of cases, or the user's case involvement index for various scenarios exceeds a threshold, etc. It can be determined that the user is the key control personnel. Alternatively, key groups to be controlled may be determined according to at least one user group portrait feature. Exemplarily, the user group portrait feature shows that the user group is involved in a large number of cases, or the user group's case involvement index for various scenarios exceeds a threshold. etc., it can be determined that the user group is the key control group. Alternatively, key control personnel can be determined according to the source information of each early warning image and the number of early warning cases hit by the early warning image. Exemplarily, when the cumulative number of early warning cases hit by the early warning image exceeds the threshold, the source information of the early warning image can be determined. The user information of the user who spreads the warning image is obtained, and the user is determined as the key control personnel.

After determining at least one of the key control personnel and the key control group, the monitoring and statistics of the image data sent by at least one of the key control personnel and the key control group may be prioritized, or priority may be given to the key control personnel and key control groups. The warning information corresponding to at least one item in the control crowd is displayed accordingly.

FIG. 13 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 13 , in response to a viewing operation for at least one of the key personnel and the crowd, the key personnel and the crowd can be viewed. At least one of the cases involved in the case and check at least one of the user identity information of the key control personnel and key control groups, etc.

In an embodiment of the present disclosure, the method may further include:

In response to the retrieval operation for the to-be-retrieved image, retrieval is performed in the first database to obtain a retrieval result of the to-be-retrieved image, where the retrieval result of the to-be-retrieved image includes a first image that matches the to-be-retrieved image , wherein the first database includes the image to be processed and the image features of the image to be processed;

Traceability analysis is performed according to the sending time of the first image to obtain the dissemination information of the to-be-retrieved image, where the dissemination information includes the source user information that sent the to-be-retrieved image, the dissemination track information of the to-be-retrieved image, the at least one item of audience user information of the image to be processed.

For example, FIG. 14 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 14 , after image feature extraction is performed on a received image to be processed, the image to be processed and the image of the to-be-processed image can be processed in real time. The features are stored in the first database, and the source information of the image to be processed is stored in the source database. The embodiments of the present disclosure support the functions of viewing, creating, editing, and deleting the first database.

When there is a retrieval requirement, the first image that meets the requirement can be retrieved from the first database. Exemplarily, FIG. 15 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 15 , in response to a retrieval operation for an image to be processed, image features of the image to be processed can be extracted, The feature invocation retrieval algorithm performs retrieval in the first database to obtain a first image whose image features match those of the image to be processed, wherein the retrieval may use distributed retrieval, which is not described in detail in this embodiment of the present disclosure.

In the embodiment of the present disclosure, image features with powerful representation capabilities can be extracted through an image depth feature extraction algorithm, and the to-be-processed image and the image features of the to-be-processed image are stored in an image database. The image to be retrieved can be imported by calling the Application Programming Interface (API) interface, and the image features of the image to be retrieved can be extracted and compared with the image features of the image to be processed in the image database to determine that the similarity satisfies the similarity. The image to be processed with the threshold value is used as the first image matching the image to be retrieved.

Among them, the retrieval of the image to be retrieved can be divided into original image retrieval and approximate retrieval. The similarity threshold in the original image retrieval is higher than the similarity threshold in the approximate retrieval, and the homologous image of the image to be retrieved can be found through the original image retrieval. , similar images of the image to be retrieved can be retrieved through approximate retrieval. In order to be able to realize the correct matching of compressed, edited and tampered images in the first database storing large-scale image data, and return the matched images and their related information.

Exemplarily, the first database can be divided into a face database and an image database, wherein the images to be processed stored in the image database are non-face images, the images to be processed stored in the face database are face images, and the images to be retrieved are non-face images. When a face image is used, the image search is performed in the image database. When the to-be-retrieved image is a face image, the face search can be performed in the face database.

Here, searching for pictures by image is to search for the existence of the specified target from the historical picture data in the image database, and searching for pictures by face is to search for the existence of the specified face from the face database. After the retrieval is completed, clustering can be performed from the dimensions of ID, group, IP, etc. according to business requirements, so as to realize the sending trajectory analysis of the images to be retrieved, and obtain the corresponding dissemination information.

For example, the first images can be sorted according to the sending time of the first images to determine the first image with the earliest sending time. According to the first image with the earliest sending time, the source user information of the image to be retrieved can be obtained, for example: sending The first image with the earliest time is image 1. It can be determined that the user who sent image 1 is the source user, and the account ID, group ID, IP address and other information of the image 1 can be obtained. and other information, the information of the source user is searched from at least one of the user portrait feature and the user group portrait feature as the source user information.

Image information of each first image (which may include information of the user who sent the first image) can be acquired, and the image information of each first image can be displayed in sequence, so as to obtain the propagation track information of the image to be retrieved. Alternatively, the audience user information of the to-be-retrieved image can also be analyzed according to the propagation track information of the to-be-retrieved image.

In an embodiment of the present disclosure, the method may further include:

The second database is searched according to the keyword, and a retrieval result for the keyword is obtained, and the retrieval result for the keyword includes at least one second image, source information of the second image or image processing the result matches the keyword;

Wherein, the second database includes the image to be processed and at least one image processing result of the image to be processed.

For example, FIG. 16 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 16 , after image processing is performed on the received image to be processed, the image to be processed and the image of the to-be-processed image may be processed in real time. Information such as the result and the source data of the image to be processed are stored in the second database, and the source information is stored in the source database. Specifically, it may include recognizing simplified and traditional Chinese characters and English characters in the image to be processed through a powerful image text recognition algorithm, and storing the recognized text information as an image recognition result in the second database. In this embodiment of the present disclosure, functions of viewing, creating, editing, and deleting the second database are supported.

FIG. 17 is a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 17 , after the image to be processed is parsed from the preset text content, the obtained keywords or an AND-or-not logical expression composed of keywords can be used. The corresponding search is performed in the second database in the form of a formula, and the second image that matches the keyword or the keyword or non-equivalent logical expression is searched, so as to realize the conversion of clues from text to image, and realize the three-dimensional and rich clues. It is of great significance for the research and judgment work such as the expansion and concatenation of clues.

Here, search by text is to retrieve the existence of relevant text from historical image data, and according to business requirements, the retrieved second images can be clustered from dimensions such as ID, group, IP, etc., to realize the sending track of the image analyze.

This embodiment of the present disclosure can also perform offline data analysis processing on the images to be processed, and after batch uploading the images to be processed, configure parsing rules in the image processing configuration interface for the images to be processed, so as to parse the images to be processed according to the parsing rules and generate corresponding The parsing results are obtained to achieve targeted data review and clue discovery, and the parsing results can be displayed in response to the display operation for the parsing results, as shown in FIG. 18 and FIG. 19 , and FIG. 18 shows an embodiment according to the present disclosure. Schematic diagram of the proposed image processing method; FIG. 19 shows a schematic diagram of the proposed image processing method according to an embodiment of the present disclosure.

In some embodiments, the user can create an offline analysis task, including creating the task name of the offline analysis task, etc. After creating the offline analysis task, the data to be analyzed can be uploaded to the offline analysis task, wherein the data to be analyzed can be a plurality of pending analysis tasks. Processes compressed packages of images or individual video files. The parsing rules for offline parsing tasks can be configured in the image processing configuration interface, which can include configuring the preset scene elements to be recognized, the text content to be recognized, the target object to be tracked (the face image of the target person or the target image) and the corresponding at least one of the deployment tasks. In response to the triggering operation for the parsing operation, parsing the data to be parsed according to the parsing rules, and obtaining parsing results, all parsing results can be displayed, or the parsing results can be filtered according to the display conditions configured by the user. The analysis results are displayed.

The embodiments of the present disclosure can perform statistical analysis based on massive image data and structured early warning data (including early warning cases and early warning results corresponding to early warning cases, deployment tasks, and deployment results corresponding to control tasks), and then display the overall situation of macro data. Among them, the overall situation of macro data covers basic video and picture processing data volume display, alarm trend analysis, alarm proportion, alarm type, alarm number, etc., and the overall situation display interface of macro data can refer to FIG. 20 , which shows an example according to the present disclosure Schematic diagram of the proposed image processing method.

In order to make those skilled in the art better understand the embodiments of the present disclosure, the following describes the embodiments of the present disclosure through specific examples.

FIG. 21 shows a schematic diagram of an image processing method proposed according to an embodiment of the present disclosure. Referring to FIG. 21 , an early warning case, a face control task, and an image control can be configured in response to a user's configuration operation on a user interface (UI) interface. Task.

After performing data preprocessing on the accessed data source, that is, the data to be parsed (for example, performing operations such as data deduplication, format conversion, frame extraction decoding, etc.), the to-be-processed image is obtained. Image processing may be performed on the image to be processed to obtain an image processing result of the image to be processed, which may specifically include performing preset scene element analysis on the image to be processed to obtain a preset scene element label of the image to be processed; performing text content analysis on the image to be processed to obtain The text content label of the image to be processed; after the face feature extraction, the face control tasks are compared to obtain the face control tasks hit by the to-be-processed images; after the image features are extracted, the image control tasks are compared to obtain the images hit by the to-be-processed images Controlling tasks. After each image processing result of the to-be-processed image is fused, the to-be-processed image and the image processing result of the to-be-processed image are stored in structured data and stored in a structure database.

Early warning analysis can be performed on the images to be processed and the image processing results of the to-be-processed images stored in the structural database to determine the early warning cases hit by the to-be-processed images, generate early warning results for the early warning cases, and store the early warning results in the structural database. , and push the warning results to the UI for display. The detailed content of the warning result can be read from the structure database for display in response to the viewing operation for the warning result. It is also possible to aggregate the warning results or images to be processed, and store the aggregated results in the structure database.

Dynamic portrait processing can be performed according to image processing results, warning results, control results, etc., to obtain at least one of user portrait characteristics and user group portrait characteristics, which can be used to determine and supervise at least one of key users and key user groups. item.

After the face feature extraction is performed on the image to be processed, the image to be processed and the face image features of the image to be processed can be stored in the database in real time, and the face clustering operation can be performed on the stored face image. When there is a need to search for images by face, retrieval can be performed in the face database according to the face image features of the images to be retrieved, so as to obtain homologous images or similar images of the images to be retrieved. Similarly, after the image features are extracted from the images to be processed, the images to be processed and the image features of the images to be processed can be stored in the database in real time, and the image clustering operation can be performed on the stored images. When there is a need to search for an image by image, the image database can be retrieved according to the image characteristics of the image to be retrieved, so as to obtain a homologous image or a similar image of the image to be retrieved.

When there is a need to search for images by text, the structure database can be searched according to the keyword to obtain an image matching the keyword, and the image processing result of the image matching the keyword includes the keyword.

The embodiments of the present disclosure provide a complete view analysis solution for massive video image data disseminated in Internet scenarios, and the main analysis algorithms provided include trademark (logotype, LOGO), flag, specific object, specific behavior, face, optical character recognition ( Optical Character Recognition, OCR), text semantic recognition and other algorithms to analyze the video image data accordingly. By configuring early warning cases, configuring face control tasks and image control tasks, and searching for pictures by text, pictures, faces, dynamic portraits, and offline data analysis, accurate and extensive information screening can be achieved, forming a powerful The view structure analysis capability engine, and then realize perfect pre-event and post-event early warning research and judgment, which can help Internet cyberspace governance, provide strong technical support for Internet police affairs, and promote the co-construction, co-governance and sharing of cyberspace.

The embodiments of the present disclosure can implement identification, early warning and monitoring for bad content and harmful information in current social networking, short video, live broadcast and other scenarios.

It can be understood that the above-mentioned method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle and logic. Those skilled in the art can understand that, in the above method of the specific embodiment, the specific execution order of each step should be determined by its function and possible internal logic.

In addition, the embodiments of the present disclosure also provide image processing apparatuses, electronic devices, computer-readable storage media, and programs, all of which can be used to implement any image processing method provided by the embodiments of the present disclosure, and the corresponding technical solutions and descriptions and refer to the methods Some of the corresponding records will not be repeated.

FIG. 22 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure. As shown in FIG. 22 , the apparatus includes:

The first processing part 221 can be configured to perform image processing on the image to be processed to obtain at least one image processing result of the image to be processed;

The first determining part 222 can be configured to determine whether the at least one image processing result satisfies the judgment condition of the early warning case;

The second determination part 223 may be configured to determine that the image to be processed is an early warning image when the at least one image processing result satisfies the judgment condition of an early warning case;

The second processing part 224 may be configured to generate a warning result for the warning case according to the warning image.

In this way, the embodiment of the present disclosure can perform image processing on the image to be processed, obtain at least one image processing result of the image to be processed, and determine whether the at least one image processing result satisfies the judgment condition of the early warning case. When the processing result satisfies the judgment condition of the early warning case, the image to be processed is determined as the early warning image, and the early warning result for the early warning case can be generated according to the early warning image. The image processing device provided according to the embodiment of the present disclosure can realize the supervision and processing of a large amount of image data, effectively reduce the labor cost, and improve the precision and efficiency of early warning.

In some embodiments, the apparatus may further include:

The third processing part can be configured to obtain the judgment condition of the early warning case according to the selected early warning factor in response to the creation operation for the early warning case;

The first creation part may be configured to create the early warning case according to the judgment condition of the early warning case.

In some embodiments, the early warning factor may include at least one of preset scene elements, text content, and deployment tasks, and the apparatus may further include:

The second creation part can be configured to create at least one deployment and control database, where the target data to be deployed and controlled is stored in the deployment and control database;

The third creation part can be configured to create at least one deployment task according to the at least one deployment database;

Wherein, the deployment database may include at least one of a character deployment database and an image deployment database, the target data stored in the character deployment database may include a face image of a target person, and the target data stored in the image deployment database A target image may be included, and the deployment task includes at least one of a face deployment task and an image deployment task.

In some embodiments, the apparatus may further include:

The acquisition part can be configured to acquire scene element information and scene element images;

The fourth creation part may be configured to create the preset scene element in the preset scene management library according to the scene element information and the scene element image in response to the creation operation for the preset scene element.

In some embodiments, the image processing result may include preset scene elements included in the to-be-processed image, text content included in the to-be-processed image, and a deployment result of the to-be-processed image for the deployment task At least one of the first determining part 222 can also be configured to: determine whether there is a target image processing target matching the early warning factor corresponding to the judgment condition of the early warning case in the at least one image processing result Result: In the case that there is a target image processing result matching the early warning factor corresponding to the judgment condition of the early warning case, it is determined that the at least one image processing result satisfies the judgment condition of the early warning case.

In some embodiments, when creating the early warning case, it can also include setting a weight value for the selected early warning factor, and the device can also include:

The third determining part can be configured to determine the early warning according to the target image processing result of the early warning image for the hit early warning case, and the corresponding weight value of the early warning factor matched with each target image processing result in the corresponding early warning case the first comprehensive weight value of the image for the hit warning case;

The fourth determination part may be configured to determine the second comprehensive weight value of the early warning image for all early warning cases according to the first comprehensive weight value of the early warning image for each hit early warning case;

The fifth determining part may be configured to determine the warning level corresponding to the warning image according to at least one of the second comprehensive weight value and the number of hit warning cases.

In some embodiments, the apparatus may further include:

The first display part may be configured to display each early warning image in sequence according to the first comprehensive weight value of each early warning image in the early warning case in response to the viewing operation for the early warning case.

In some embodiments, the apparatus may further include:

The second display part can be configured to display at least one warning level in response to the viewing operation for the warning image;

The third display part may be configured to display each warning image under the warning level in response to a viewing operation for any warning level.

In some embodiments, the image to be processed has source information, and the apparatus may further include:

The aggregation part can be configured to perform an aggregation operation on an image to be processed with at least one image processing result according to the source information of the image to be processed to obtain at least one aggregation result, and the aggregation result may include a plurality of images to be processed and The to-be-processed image is for the hit situation of the early warning case, and at least one item of source information of the multiple to-be-processed images in the aggregation result is the same;

The portrait part may be configured to perform portrait analysis according to the at least one aggregation result, to obtain at least one of at least one user portrait feature and at least one user group portrait feature.

In some embodiments, the apparatus may further include:

The sixth determining part can be configured to determine at least one of the key personnel and the key crowd according to at least one of the early warning information, at least one user profile feature, and at least one user group profile feature; wherein the early warning The information may include source information of each warning image and the number of warning cases hit by the warning image.

In some embodiments, the apparatus may further include:

The first retrieval part can be configured to perform retrieval in the first database in response to a retrieval operation for the image to be retrieved, and obtain a retrieval result of the image to be retrieved, where the retrieval result of the image to be retrieved includes a retrieval result related to the image to be retrieved. Retrieving a first image that matches an image, wherein the first database includes the image to be processed and the image features of the image to be processed;

The analysis part can be configured to perform traceability analysis according to the sending time of the first image, and obtain the dissemination information of the image to be retrieved, where the dissemination information includes the source user information that sent the image to be retrieved, the image to be retrieved At least one of the propagation track information of the image to be retrieved and the audience user information of the image to be retrieved.

In some embodiments, the apparatus may further include:

The second retrieval part is configured to perform a search in the second database according to the keyword to obtain a retrieval result for the keyword, the retrieval result for the keyword includes at least one second image, the second image The source information of the image or the image processing result matches the keyword; wherein, the second database may include the to-be-processed image and at least one image processing result of the to-be-processed image.

In some embodiments, the functions or modules included in the apparatuses provided in the embodiments of the present disclosure may be used to execute the methods described in the above method embodiments. For specific implementation, reference may be made to the descriptions of the above method embodiments. For brevity, here No longer.

Embodiments of the present disclosure further provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the foregoing method is implemented. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.

Embodiments of the present disclosure also provide a computer program product, including computer-readable codes. When the computer-readable codes are run on a device, a processor in the device executes the image processing method for implementing the image processing method provided by any of the above embodiments. instruction.

Embodiments of the present disclosure further provide another computer program product for storing computer-readable instructions, which, when executed, cause the computer to perform the operations of the image processing method provided by any of the foregoing embodiments.

An embodiment of the present disclosure provides a computer program, including computer-readable code, which implements the above-mentioned image processing method when the computer-readable code runs in an electronic device and is executed by a processor in the electronic device .

The electronic device may be provided as a terminal, server or other form of device.

FIG. 23 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc. terminal.

23, an electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814 , and the communication component 816 .

The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 can include one or more processors 820 to execute instructions to perform all or some of the steps of the methods described above. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

Memory 804 is configured to store various types of data to support operation at electronic device 800 . Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like. Memory 804 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.

Power supply assembly 806 provides power to various components of electronic device 800 . Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800 .

Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). In the case where the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action. In some embodiments, the multimedia component 808 includes at least one of a front-facing camera and a rear-facing camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, at least one of the front camera and the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.

Audio component 810 is configured to at least one of output and input audio signals. For example, audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when electronic device 800 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 804 or transmitted via communication component 816 . In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.

Sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of electronic device 800 . For example, the sensor assembly 814 can detect the on/off state of the electronic device 800, the relative positioning of the components, such as the display and the keypad of the electronic device 800, the sensor assembly 814 can also detect the electronic device 800 or one of the electronic device 800 Changes in the position of components, presence or absence of user contact with the electronic device 800 , orientation or acceleration/deceleration of the electronic device 800 and changes in the temperature of the electronic device 800 . Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. Sensor assembly 814 may also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as wireless network (WiFi), second generation mobile communication technology (2G) or third generation mobile communication technology (3G), or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmed gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.

In an exemplary embodiment, a non-volatile computer-readable storage medium, such as a memory 804 comprising computer program instructions executable by the processor 820 of the electronic device 800 to perform the above method is also provided.

FIG. 24 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server. 24, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource, represented by memory 1932, for storing instructions executable by the processing component 1922, such as applications. An application program stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Additionally, the processing component 1922 is configured to execute instructions to perform the above-described methods.

The electronic device 1900 may also include a power supply assembly 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input output (I/O) interface 1958 . The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as a Microsoft server operating system (Windows ServerTM), a graphical user interface based operating system (Mac OS XTM) introduced by Apple, a multi-user multi-process computer operating system (UnixTM). ), Free and Open Source Unix-like Operating System (LinuxTM), Open Source Unix-like Operating System (FreeBSDTM) or similar.

In an exemplary embodiment, a computer-readable storage medium, which may be a volatile storage medium or a non-volatile storage medium, is also provided. For example, the memory 1932 includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to implement the image processing method described above.

The present disclosure may be at least one of systems, methods and computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present disclosure.

A computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above. Computer-readable storage media, as used herein, are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to at least one of an external computer or external storage device over a network such as the Internet, a local area network, a wide area network, and a wireless network . The network may include at least one of copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .

Computer program instructions for carrying out operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages. Source or object code, written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect). In some embodiments, custom electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can be personalized by utilizing state information of computer readable program instructions. Computer readable program instructions are executed to implement various aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference to at least one of flowchart illustrations and block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that at least one of each block of the flowchart illustrations and block diagrams, and at least one of combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in at least one of means for implementing the functions/acts specified in one or more blocks of the flowchart and block diagrams. These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause at least one of computers, programmable data processing apparatus and other devices to operate in a particular manner, so that the computer readable storage medium of the instructions The medium then includes an article of manufacture including at least one of the instructions to implement the various aspects of the functions/acts specified in one or more blocks of the flowchart and block diagrams.

Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement at least one of the functions/acts specified in one or more blocks of the flowchart and block diagrams.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that at least one of each block of the block diagrams and flowchart illustrations, and at least one of combinations of blocks in the block diagrams and flowchart illustrations, can be used with special-purpose It is implemented in a hardware-based system, or can be implemented in a combination of dedicated hardware and computer instructions.

The computer program product can be specifically implemented by hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.

Various embodiments of the present disclosure have been described above, and the foregoing descriptions are exemplary, not exhaustive, and not limiting of the disclosed embodiments. Numerous modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the various embodiments, the practical application or improvement over the technology in the marketplace, or to enable others of ordinary skill in the art to understand the various embodiments disclosed herein.

Industrial Applicability

In the embodiment of the present disclosure, by performing image processing on the image to be processed, at least one image processing result of the image to be processed is obtained; it is determined whether at least one image processing result satisfies the judgment condition of the early warning case; if the at least one image processing result satisfies the judgment of the early warning case If the conditions are met, the image to be processed is determined as an early warning image; the early warning result for the early warning case is generated according to the early warning image, which effectively improves the precision and efficiency of the early warning.

Claims

An image processing method, comprising:

Perform image processing on the image to be processed to obtain at least one image processing result of the image to be processed;

determining whether the at least one image processing result satisfies the judgment condition of the early warning case;

In the case that the at least one image processing result satisfies the judgment condition of the early warning case, determining that the to-be-processed image is an early warning image;

An early warning result for the early warning case is generated according to the early warning image.
The method of claim 1, wherein the method further comprises:

In response to the creation operation for the early warning case, according to the selected early warning factor, the judgment condition of the early warning case is obtained;

The early warning case is created according to the judgment condition of the early warning case.
The method according to claim 2, wherein the early warning factor can include at least one of preset scene elements, text content, and control tasks, and the method further comprises:

creating at least one deployment and control database, where target data to be deployed and controlled is stored in the deployment and control database;

creating at least one deployment task according to the at least one deployment database;

Wherein, the deployment database includes at least one of a character deployment database and an image deployment database, the target data stored in the character deployment database includes a face image of the target person, and the target data stored in the image deployment database includes the target image, and the control task includes at least one of a face control task and an image control task.
The method of claim 3, wherein the method further comprises:

Obtain scene element information and scene element images;

In response to a creation operation for a preset scene element, the preset scene element is created in a preset scene management library according to the scene element information and the scene element image.
The method according to claim 3, wherein the image processing result includes preset scene elements included in the to-be-processed image, text content included in the to-be-processed image, and the to-be-processed image for the layout control At least one of the control results of the task, the determining whether the at least one image processing result satisfies the judgment condition of the early warning case, including:

determining whether there is a target image processing result that matches the warning factor corresponding to the judgment condition of the warning case in the at least one image processing result;

When there is a target image processing result matching the warning factor corresponding to the judgment condition of the early warning case, it is determined that the at least one image processing result satisfies the judgment condition of the early warning case.
The method according to claim 5, wherein when creating the early warning case, it further comprises setting a weight value for the selected early warning factor, and the method further comprises:

According to the target image processing result of the early warning image for the hit early warning case, and the corresponding weight value of the early warning factor matched with each target image processing result in the corresponding early warning case, the early warning image for the hit early warning case is determined. The first comprehensive weight value of ;

According to the first comprehensive weight value of the early warning image for each hit early warning case, determine the second comprehensive weight value of the early warning image for all early warning cases;

An early warning level corresponding to the early warning image is determined according to at least one of the second comprehensive weight value and the number of hit early warning cases.
The method of claim 6, wherein the method further comprises:

In response to the viewing operation for the early warning case, each early warning image is displayed in sequence according to the first comprehensive weight value of each early warning image in the early warning case.
The method of claim 6, wherein the method further comprises:

in response to a viewing operation for the warning image, displaying at least one warning level;

In response to a viewing operation for any alert level, each alert image under the alert level is displayed.
The method of any one of claims 1 to 8, wherein the image to be processed has source information, the method further comprising:

For an image to be processed with at least one image processing result, an aggregation operation is performed according to the source information of the image to be processed to obtain at least one aggregation result, where the aggregation result includes a plurality of images to be processed and the images to be processed are for early warning The hit situation of the case, at least one item of source information of the multiple images to be processed in the aggregated result is the same;

Perform profile analysis according to the at least one aggregated result, and obtain at least one of at least one user profile feature and at least one user group profile feature.
The method of claim 9, wherein the method further comprises:

According to at least one of the early warning information, at least one user portrait feature, and at least one user group portrait feature, at least one of the key personnel and the key group is determined; wherein, the early warning information includes source information of each early warning image and the number of early warning cases hit by the warning image.
The method of any one of claims 1 to 10, wherein the method further comprises:

In response to the retrieval operation for the image to be retrieved, perform retrieval in the first database to obtain a retrieval result of the image to be retrieved, and the retrieval result of the image to be retrieved includes a first image matching the image to be retrieved , wherein the first database includes the image to be processed and the image features of the image to be processed;

Traceability analysis is performed according to the sending time of the first image to obtain the dissemination information of the to-be-retrieved image, where the dissemination information includes the source user information that sent the to-be-retrieved image, the dissemination track information of the to-be-retrieved image, the at least one item of audience user information describing the image to be retrieved.
The method of claim 11, wherein the method further comprises:

The second database is searched according to the keyword, and a retrieval result for the keyword is obtained, and the retrieval result for the keyword includes at least one second image, source information of the second image or image processing the result matches the keyword;

Wherein, the second database includes the image to be processed and at least one image processing result of the image to be processed.
An image processing device, comprising:

a first processing part, configured to perform image processing on the image to be processed to obtain at least one image processing result of the image to be processed;

a first determining part, configured to determine whether the at least one image processing result satisfies a judgment condition for an early warning case;

a second determining part, configured to determine that the to-be-processed image is an early-warning image when the at least one image processing result satisfies the judgment condition of an early-warning case;

The second processing part is configured to generate an early warning result for the early warning case according to the early warning image.
The image processing apparatus according to claim 13, wherein the image processing apparatus further comprises;

The third processing part is configured to, in response to the creation operation for the early warning case, obtain the judgment condition of the early warning case according to the selected early warning factor;

The first creating part is configured to create the early warning case according to the judgment condition of the early warning case.
The image processing apparatus according to claim 14, wherein the early warning factor can include at least one of preset scene elements, text content, and control tasks; the image processing apparatus further comprises:

The second creation part is configured to create at least one deployment and control database, where the target data to be deployed and controlled is stored in the deployment and control database;

The third creation part is configured to create at least one deployment task according to the at least one deployment database;

Wherein, the deployment database includes at least one of a character deployment database and an image deployment database, the target data stored in the character deployment database includes a face image of the target person, and the target data stored in the image deployment database includes the target image, and the control task includes at least one of a face control task and an image control task.
The image processing apparatus according to claim 15, wherein the image processing apparatus further comprises:

The acquisition part is configured to acquire scene element information and scene element images;

The fourth creation part is configured to create the preset scene element in the preset scene management library according to the scene element information and the scene element image in response to the creation operation for the preset scene element.
The image processing apparatus according to claim 15, wherein the image processing result includes preset scene elements included in the image to be processed, text content included in the image to be processed, at least one of the deployment results of the deployment task,

The first determining part is specifically configured to determine whether there is a target image processing result matching the early warning factor corresponding to the judgment condition of the early warning case in the at least one image processing result; In the case of the target image processing result that matches the early warning factor corresponding to the judgment condition, it is determined that the at least one image processing result satisfies the judgment condition of the early warning case.
The image processing apparatus according to claim 17, wherein, when creating the early warning case, the image processing apparatus further comprises:

The third determination part is configured to determine the early warning image according to the target image processing result of the early warning image for the hit early warning case, and the corresponding weight value of the early warning factor matched with each target image processing result in the corresponding early warning case the first comprehensive weight value for the hit early warning case;

a fourth determining part, configured to determine a second comprehensive weight value of the early warning image for all early warning cases according to the first comprehensive weight value of the early warning image for each hit early warning case;

The fifth determination part is configured to determine the early warning level corresponding to the early warning image according to at least one of the second comprehensive weight value and the number of hit early warning cases.
The image processing apparatus according to claim 18, wherein the image processing apparatus further comprises:

The first display part is configured to display each early warning image in order according to the first comprehensive weight value of each early warning image in the early warning case in response to the viewing operation for the early warning case.
The image processing apparatus according to claim 18, wherein the image processing apparatus further comprises:

The second display part is configured to display each early warning image in sequence according to the first comprehensive weight value of each early warning image in the early warning case in response to the viewing operation for the early warning case.

The third display part is configured to, in response to a viewing operation for any warning level, display each warning image under the warning level.
The image processing apparatus according to any one of claims 13 to 20, wherein the image processing apparatus further comprises:

The aggregation part is configured to perform an aggregation operation on images to be processed with at least one image processing result according to the source information of the image to be processed to obtain at least one aggregation result, where the aggregation result includes a plurality of images to be processed and the The hit situation of the to-be-processed image for the early warning case, and at least one item of source information of the multiple to-be-processed images in the aggregation result is the same;

The portrait part is configured to perform portrait analysis according to the at least one aggregation result, and obtain at least one of at least one user portrait feature and at least one user group portrait feature.
The image processing apparatus according to claim 21, wherein the image processing apparatus further comprises:

The sixth determination part is configured to determine at least one of the key personnel and the key crowd according to at least one of the early warning information, at least one user profile feature, and at least one user group profile feature; wherein the early warning information It includes the source information of each early warning image and the number of early warning cases hit by the early warning image.
The image processing apparatus according to any one of claims 13 to 22, wherein the image processing apparatus further comprises:

The first retrieval part is configured to perform retrieval in the first database in response to the retrieval operation for the image to be retrieved, and obtain the retrieval result of the image to be retrieved, and the retrieval result of the image to be retrieved includes the retrieval result of the image to be retrieved and the image to be retrieved. a first image matched with an image, wherein the first database includes the image to be processed and the image features of the image to be processed;

The analysis part is configured to perform traceability analysis according to the sending time of the first image, and obtain the dissemination information of the to-be-retrieved image, the dissemination information including the source user information that sent the to-be-retrieved image, the information of the to-be-retrieved image. At least one item of dissemination track information and audience user information of the to-be-retrieved image.
The image processing apparatus according to claim 23, wherein the image processing apparatus further comprises:

The second retrieval part is configured to perform a search in the second database according to the keyword to obtain a retrieval result for the keyword, the retrieval result for the keyword includes at least one second image, the second image The source information of the image or the image processing result matches the keyword;

Wherein, the second database includes the image to be processed and at least one image processing result of the image to be processed.
An electronic device comprising:

a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any one of claims 1 to 12.
A computer-readable storage medium having computer program instructions stored thereon, the computer program instructions implementing the method of any one of claims 1 to 12 when executed by a processor.
A computer program, comprising computer-readable code that, when the computer-readable code runs in an electronic device and is executed by a processor in the electronic device, realizes a right when executed by a processor in the electronic device The method of any one of claims 1 to 12.