Device ai empowerment method, apparatus, electronic device, and computer storage medium

Abstract

This application discloses a method, apparatus, electronic device, and computer storage medium for AI-enabled devices. The method, applied to an AI server, includes: acquiring target parameters corresponding to a target image acquisition device; determining a target communication protocol corresponding to the target image acquisition device based on the target parameters; acquiring target video data acquired by the target image acquisition device based on the target communication protocol and the target parameters; and calling an AI model to perform event analysis on the target video data to obtain target event analysis results. This eliminates the need for image acquisition device developers to develop their own supporting AI applications. The AI ​​server enables intelligent and efficient processing of video content from various image acquisition devices, automatically and intelligently identifying and analyzing events occurring in the video using an AI model. This effectively solves the compatibility issues faced by different brands and models of image acquisition devices in AI application development and deployment, achieving efficient and low-cost AI empowerment of various image acquisition devices.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, electronic device, and computer storage medium for enabling AI in a device. Background Technology

[0002] In the current video surveillance field, with the rapid development of intelligent technologies, the function of cameras is no longer limited to simple image acquisition and transmission, but has gradually incorporated artificial intelligence (AI) technology to achieve more efficient and accurate video event analysis and processing. However, different brands and models of cameras often use different communication protocols and data formats, which leads to significant challenges in the development and deployment of AI applications. Summary of the Invention

[0003] This application provides a device, apparatus, electronic device, and computer storage medium for AI-enabled devices. It eliminates the need for image acquisition device developers to create their own supporting AI applications. By using an AI server, it enables intelligent and efficient processing of video content from various image acquisition devices. It utilizes AI models to automatically and intelligently identify and analyze events in the video, effectively solving the compatibility issues faced by different brands and models of image acquisition devices in the development and deployment of AI applications. It achieves AI-enabled capabilities for various image acquisition devices efficiently and at low cost.

[0004] In a first aspect, embodiments of this application provide a device AI-enabled method, applied to an AI server, comprising:

[0005] Obtain the target parameters corresponding to the target image acquisition device;

[0006] Based on the above target parameters, determine the target communication protocol corresponding to the target image acquisition device.

[0007] The target video data acquired by the target image acquisition device is obtained according to the target communication protocol and the target parameters described above.

[0008] The AI ​​model is invoked to perform event analysis on the target video data to obtain the target event analysis results.

[0009] In one possible implementation, before calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results, the method further includes:

[0010] The target video data collected by multiple target image acquisition devices within the target area are fused to obtain target video fused data corresponding to the target area.

[0011] The above-mentioned AI model is used to perform event analysis on the target video data, and the target event analysis results are obtained, including:

[0012] The AI ​​model is invoked to perform event analysis on the aforementioned target video fusion data, and the target event analysis results corresponding to the aforementioned target regions are obtained.

[0013] In one possible implementation, the aforementioned AI model is invoked to perform event analysis on the target video data to obtain the target event analysis results, including:

[0014] Multiple AI models are invoked to perform event analysis on the target video data collected by the aforementioned target image acquisition devices within the target area, resulting in multiple event analysis results.

[0015] By integrating the analysis results of the above multiple events, the analysis results of the target events corresponding to the above target areas are obtained.

[0016] In one possible implementation, before calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results, the method further includes:

[0017] Obtain the target function permission information corresponding to the target user terminal; the target function permission information includes the target functions that the target user terminal has the right to use and the target description information corresponding to the target functions.

[0018] The above-mentioned AI model is used to perform event analysis on the target video data, and the target event analysis results are obtained, including:

[0019] The AI ​​model is invoked to perform event analysis on the target video data based on the target description information and target event type corresponding to the target function, and to obtain the target event analysis results corresponding to the target event type.

[0020] In one possible implementation, the aforementioned target function permission information may also include at least one of the following: the target effective time period and the target usage time period of the target function corresponding to the target user terminal;

[0021] The aforementioned AI model, based on the target description information and target event type corresponding to the target function, performs event analysis on the target video data to obtain the target event analysis results corresponding to the target event type, including:

[0022] If the target acquisition time corresponding to the above target video data is within the above target valid time period and / or target usage time period, the AI ​​model is invoked to perform event analysis on the above target video data based on the target description information and target event type corresponding to the above target function, and the target event analysis results corresponding to the above target event type are obtained.

[0023] In one possible implementation, the acquisition of target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters includes:

[0024] Establish a communication connection between the target parameters and the target image acquisition device as described above;

[0025] When a communication connection is established with the aforementioned target image acquisition device, the original video data acquired by the aforementioned target image acquisition device is retrieved according to the aforementioned target communication protocol;

[0026] The original video data is converted according to the target communication protocol to obtain the target video data.

[0027] In one possible implementation, after determining the target communication protocol corresponding to the target image acquisition device based on the target parameters, the method further includes:

[0028] Receive target device control information sent by the target user terminal;

[0029] The AI ​​model is invoked to perform intent recognition on the target device control information to obtain at least one control target corresponding to the target device control information, and a target control command corresponding to the target device control information is generated based on the at least one control target.

[0030] When in communication with the target image acquisition device, the target device control command is sent to the target image acquisition device according to the target communication protocol and the target parameters, so that the target image acquisition device responds to the target device control command and executes the corresponding target control action.

[0031] In one possible implementation, after calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results, the method further includes:

[0032] Receive a target event query instruction sent by the target user terminal for the target image acquisition device; the target event query instruction carries the specified event query type;

[0033] In response to the above target event query command, the query is performed in the above target event analysis results based on the above specified event query type to obtain the corresponding specified event information;

[0034] Return the specified event information to the target user terminal so that the target user terminal can display the specified event information.

[0035] In one possible implementation, after calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results, the method further includes:

[0036] If the target event analysis results include a specified alarm event, the target alarm information corresponding to the specified alarm event is sent to the target user terminal.

[0037] Secondly, embodiments of this application provide a device AI-enabled apparatus applied to an AI server, the device AI-enabled apparatus comprising:

[0038] The first acquisition module is used to acquire the target parameters corresponding to the target image acquisition device;

[0039] The determination module is used to determine the target communication protocol corresponding to the target image acquisition device based on the above target parameters.

[0040] The second acquisition module is used to acquire the target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters.

[0041] The event analysis module is used to call the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results.

[0042] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory; wherein the memory stores executable program code, and the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to execute the method steps provided by the first aspect of the embodiments of this application or any possible implementation of the first aspect.

[0043] Fourthly, embodiments of this application provide a computer storage medium storing multiple instructions adapted for loading and executing by a processor the method steps provided by the first aspect of the embodiments of this application or any possible implementation thereof.

[0044] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0045] In this embodiment, on the one hand, by acquiring the target parameters corresponding to the target image acquisition device, determining the target communication protocol corresponding to the target image acquisition device based on the target parameters, and acquiring the target video data acquired by the target image acquisition device based on the target communication protocol and the target parameters, the AI ​​server can support image acquisition devices of different brands and models, breaking down the deployment barriers of AI applications caused by differences in communication protocols and data formats, and significantly improving the compatibility and flexibility of the AI ​​system. On the other hand, by calling the AI ​​model to perform event analysis on the target video data, the developers of the image acquisition devices do not need to develop their own supporting AI applications. The AI ​​server can achieve intelligent and efficient processing of video content from various image acquisition devices, and automatically identify the target video data using the AI ​​model. Furthermore, it analyzes key events in videos, improving the efficiency and accuracy of event analysis and providing stronger technical support for the video surveillance field. This enables AI empowerment of various image acquisition devices efficiently and cost-effectively. On the other hand, compared to related technologies where AI application development for different image acquisition devices requires individual adaptation for each device, a cumbersome and time-consuming process, this application solves the problem of inconsistent communication protocols and data formats among image acquisition devices. By utilizing an AI server and a unified processing flow, it effectively solves the compatibility issues faced by different brands and models of image acquisition devices in AI application development and deployment. This greatly simplifies the development and deployment process of AI applications, reduces the development cost and time cost of AI applications for image acquisition devices, and improves the flexibility and convenience of AI technology applications in the video surveillance field. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A schematic diagram of the architecture of a device AI-enabled system provided as an exemplary embodiment of this application;

[0048] Figure 2 A flowchart illustrating a device AI-enabled method provided for an exemplary embodiment of this application;

[0049] Figure 3 A schematic diagram illustrating the process for determining target rhythm deviation information provided in an exemplary embodiment of this application;

[0050] Figure 4 A flowchart illustrating another device AI-enabled method provided as an exemplary embodiment of this application;

[0051] Figure 5 A flowchart illustrating another device AI-enabled method provided as an exemplary embodiment of this application;

[0052] Figure 6 A schematic diagram of the structure of an AI-enabled device provided as an exemplary embodiment of this application;

[0053] Figure 7 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application. Detailed Implementation

[0054] To make the features and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0056] In related technologies, camera developers need to develop separate AI applications for each type of camera. This is not only time-consuming and labor-intensive, but also makes it difficult to guarantee compatibility and interoperability between cameras of different brands and models. Furthermore, with the increasing variety of cameras, this "one-to-one" development model can no longer meet the rapidly changing market demands. Therefore, designing a solution that can uniformly process data from multiple cameras and enable AI empowerment has become a pressing technical problem.

[0057] To address the aforementioned issues, most technical solutions focus on optimizing the camera's own intelligent processing capabilities. This involves enhancing the camera's computing power and algorithm integration to enable it to directly identify and analyze video events. However, this approach not only increases the cost and complexity of the camera but also, due to limitations in the camera's hardware performance and power consumption requirements, often fails to achieve ideal results in terms of intelligent processing efficiency. Furthermore, for the large number of traditional cameras already deployed, upgrading the hardware and implementing intelligent modifications is costly and impractical.

[0058] Based on this, this application provides a device AI empowerment method. This method not only acquires target parameters corresponding to the target image acquisition device, determines the target communication protocol corresponding to the target image acquisition device based on the target parameters, and acquires target video data acquired by the target image acquisition device based on the target communication protocol and the target parameters, enabling the AI ​​server to support image acquisition devices of different brands and models. This breaks down the deployment barriers to AI applications caused by differences in communication protocols and data formats, significantly improving the compatibility and flexibility of the AI ​​system. Furthermore, by calling AI models to perform event analysis on the target video data, the method eliminates the need for image acquisition device developers to develop their own supporting AI applications. The AI ​​server can intelligently and efficiently process video content from various image acquisition devices, automatically identifying and analyzing key events in the video using AI models, improving the efficiency and accuracy of event analysis. This provides stronger technical support for the video surveillance field, efficiently and cost-effectively enabling AI empowerment of various image acquisition devices.

[0059] Please refer to the following. Figure 1 This is a schematic diagram of the architecture of a device AI-enabled system provided in an exemplary embodiment of this specification. Figure 1 As shown, the AI-enabled system of this device may include: a user terminal 110, an AI server 120, and an image acquisition device 130. Among them:

[0060] User terminal 110 can interact with AI server 120 via a network to receive messages from or send messages to AI server 120. User terminal 110 can be hardware or software. When the terminal is hardware, it can be various electronic devices, including but not limited to smartwatches, smartphones, smart displays, tablets, laptops, and desktop computers with conferencing software installed. When the terminal is software, it can be installed in the electronic devices listed above, and can be implemented as multiple software programs or software modules (e.g., to provide distributed services) or as a single software program or software module; no specific limitation is made here.

[0061] Specifically, a video surveillance platform can be installed on the user terminal 110. When a user wants to enable AI for a target image acquisition device they have purchased, they can, but are not limited to, log in to the unified video surveillance platform corresponding to each image acquisition device 130 provided by the AI ​​server through their target user terminal 110, and input the target parameters corresponding to the target image acquisition device to trigger the AI ​​server to enable AI for the target image acquisition device. That is, the user terminal 110 can establish a data relationship with the network and establish a data connection relationship with the AI ​​server 120 through the network, such as sending target parameters to the AI ​​server 120 and receiving the target event analysis results corresponding to the target image acquisition device returned by the AI ​​server 120.

[0062] Image acquisition device 130 refers to hardware devices used to capture video images, such as, but not limited to, any type of camera, surveillance camera, smart door lock with camera, smart doorbell, etc. Image acquisition device 130 can acquire video data within its field of view through the camera. Simultaneously, image acquisition device 130 can establish a data relationship with a network and, through this network, establish a data connection with AI server 120, such as sending the acquired video data to AI server 120 and receiving control commands sent by AI server 120.

[0063] AI server 120 can be a server that provides AI-enabled functions for various devices, providing computing resources and storage space for the operation of the aforementioned video surveillance platform. It should be noted that AI server 120 can be either hardware or software. When AI server 120 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When AI server 120 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module; no specific limitations are made here. AI server 120 can be, but is not limited to, a hardware server, a virtual server, a cloud server, etc.

[0064] Specifically, the AI ​​server 120 can obtain the parameters corresponding to the image acquisition device 130 that needs to be AI-enabled through the network, and enable the image acquisition device 130 to be AI-enabled through the device AI-enabled method provided in this application embodiment.

[0065] The network can be a medium that provides a communication link between any two of the user terminal 110, AI server 120, and image acquisition device 130, or it can be the Internet, which includes network devices and transmission media, and is not limited thereto. The transmission media can be a wired link, such as, but not limited to, coaxial cable, fiber optic cable, and digital subscriber line (DSL), or a wireless link, such as, but not limited to, wireless fidelity (WIFI), Hypertext Transfer Protocol (HTTP), Bluetooth, and mobile device networks.

[0066] Understandably, Figure 1 The number of user terminals 110, AI servers 120, and image acquisition devices 130 in the device AI-enabled system shown is only an example. In a specific implementation, the device AI-enabled system can contain any number of user terminals 110, AI servers 120, and image acquisition devices 130.

[0067] Next, combine Figure 1 Taking the AI ​​server 120 performing device AI empowerment as an example, this application introduces a device AI empowerment method provided in an exemplary embodiment. Please refer to [link / reference] for details. Figure 2 The illustration shows a flowchart of a device AI-enabled method provided in an embodiment of this application. Figure 2 As shown, the AI-enabled method for this device can include the following steps:

[0068] S201, Obtain the target parameters corresponding to the target image acquisition device.

[0069] Specifically, the aforementioned target image acquisition device refers to the AI-enabled hardware device used to capture video images, specified for the target user. The aforementioned target parameters refer to all parameter information required to identify and connect to the target image acquisition device, such as, but not limited to, the relevant configuration and performance parameters of the target image acquisition device, including, but not limited to, at least one of the following: target model, target brand, target physical address (e.g., MAC address), target IP address, target port number, target communication interface type (e.g., Ethernet, Wi-Fi, Bluetooth, etc.), and other necessary target authentication information (e.g., target name, target password, target device identifier). The aforementioned target device identifier is a unique identifier that can characterize the identity of the target image acquisition device.

[0070] Optionally, when a target user wants the image acquisition device they purchase to have AI capabilities, they can, but are not limited to, directly log in to the video surveillance platform corresponding to the AI ​​server through their target user terminal and input target parameters such as the target device identifier of the target image acquisition device that they specify needs AI empowerment. The AI ​​server can receive the target device identifier and other target parameters input by the target user through the video surveillance platform.

[0071] Optionally, the AI ​​server may also query other configuration information of the target image acquisition device through the device management interface (e.g., but not limited to API, SDK, etc.) based on target parameters such as the target device identifier, such as resolution, frame rate, color depth, lens type (e.g., wide-angle, telephoto, etc.), video compression format, etc., to ensure that subsequent steps can be performed based on accurate target parameters and other configuration information, and avoid communication failures or data analysis errors caused by parameter or configuration mismatches.

[0072] S202, determine the target communication protocol corresponding to the target image acquisition device based on the target parameters.

[0073] Specifically, after obtaining the target parameters corresponding to the target image acquisition device, a preset communication protocol library can be searched based on the target parameters to determine the target communication protocol compatible with the target image acquisition device. The preset communication protocol library includes various communication protocols and the parameters corresponding to each image acquisition device using each communication protocol. The aforementioned communication protocols refer to the rules and conventions for data exchange between devices, such as RTSP, ONVIF, HTTP, etc.

[0074] S203: Obtain the target video data acquired by the target image acquisition device according to the target communication protocol and target parameters.

[0075] Specifically, the aforementioned target video data refers to the video stream data acquired and transmitted by the target image acquisition device. After determining the target communication protocol and target parameters corresponding to the target image acquisition device, the AI ​​server can directly send a target video request to the target image acquisition device according to the target communication protocol and target parameters, requesting the target image acquisition device to upload the acquired target video data to the AI ​​server, thereby realizing the real-time acquisition of target video data and providing a basis for subsequent event analysis.

[0076] Optionally, the aforementioned target video request carries target parameters. After receiving the target video request sent by the AI ​​server, the target image acquisition device can first verify whether the target parameters carried in the target video request are consistent with its own parameters; only if they are consistent will it respond to the target video request and send the target video data it has acquired to the AI ​​server, in order to avoid the problem of target video data leakage and ensure data security.

[0077] Optionally, such as Figure 3 As shown, the above-mentioned S203, the implementation process of obtaining the target video data acquired by the target image acquisition device according to the target communication protocol and target parameters, may include, but is not limited to:

[0078] S301, establish a communication connection between the target and the target image acquisition device based on the target parameters.

[0079] Specifically, the AI ​​server can establish a stable communication link with the target image acquisition device through the corresponding network communication library or API (such as Socket programming, HTTP requests, etc.) based on the target parameters corresponding to the target image acquisition device. This provides a communication foundation for subsequent video data acquisition and processing, ensuring that data can be effectively transmitted from the target image acquisition device to the AI ​​server, providing the necessary input for subsequent data processing and analysis.

[0080] S302, when in communication connection with the target image acquisition device, retrieves the raw video data acquired by the target image acquisition device according to the target communication protocol.

[0081] Specifically, after determining the target communication protocol used by the target image acquisition device, the AI ​​server can first configure the corresponding communication parameters according to the requirements of the target communication protocol, such as the target port number, encryption method, and authentication mechanism, to ensure that the AI ​​server can correctly communicate with the target image acquisition device and effectively transmit video data. Then, while maintaining a communication connection with the target image acquisition device, the AI ​​server sends a corresponding target video retrieval command to the target image acquisition device according to the target communication protocol to retrieve the raw video data that the target image acquisition device is currently acquiring or has already stored. The aforementioned target communication protocol refers to a set of rules and conventions used for data transmission and exchange between the target image acquisition device and the data receiving end (e.g., the AI ​​server), defining the data format, transmission method, and control commands. The aforementioned raw video data refers to the video data directly generated by the target image acquisition device without any processing, typically containing information such as original pixel values, frame rate, and resolution. The AI ​​server's direct retrieval of the raw video data acquired by the target image acquisition device can preserve image information to the maximum extent, providing greater flexibility and accuracy for subsequent data processing, while also avoiding data loss or distortion problems that may be introduced by intermediate conversion.

[0082] S303 performs protocol conversion on the original video data according to the target communication protocol to obtain the target video data.

[0083] Specifically, after the AI ​​server retrieves the raw video data collected by the target image acquisition device, it can perform protocol conversion on the raw video data according to the target communication protocol. That is, the raw video data is converted into target video data in a target format that the AI ​​server can understand, so as to meet the needs of the AI ​​server for subsequent data analysis. In this way, the protocol conversion ensures that the target video data meets the requirements of subsequent processing or application, enhances data compatibility, and improves the overall performance and user experience of the AI ​​server in AI empowerment.

[0084] Please continue to refer to the following. Figure 2 ,like Figure 2 As shown, in step S203 above, after obtaining the target video data acquired by the target image acquisition device according to the target communication protocol and target parameters, the AI-enabled method of the device may also include, but is not limited to:

[0085] S204, invoke the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results.

[0086] Specifically, after retrieving the target video data, it can be input into a pre-trained AI model for event analysis, outputting target event analysis results. This automated analysis by the AI ​​model empowers the target image acquisition device, improving its intelligence, accuracy, and efficiency in event analysis, while reducing the cost of manual monitoring. The aforementioned AI model is trained using artificial intelligence algorithms and can perform specific event analysis tasks, such as, but not limited to, analysis of absenteeism, fire incidents, smoking incidents, failure to wear helmets, failure to wear masks, illegal parking incidents, and custom event analysis.

[0087] In this embodiment, on the one hand, by acquiring the target parameters corresponding to the target image acquisition device, determining the target communication protocol corresponding to the target image acquisition device based on the target parameters, and acquiring the target video data acquired by the target image acquisition device based on the target communication protocol and the target parameters, the AI ​​server can support image acquisition devices of different brands and models, breaking down the deployment barriers of AI applications caused by differences in communication protocols and data formats, and significantly improving the compatibility and flexibility of the AI ​​system. On the other hand, by calling the AI ​​model to perform event analysis on the target video data, the developers of the image acquisition devices do not need to develop their own supporting AI applications. The AI ​​server can achieve intelligent and efficient processing of video content from various image acquisition devices, and automatically identify the target video data using the AI ​​model. Furthermore, it analyzes key events in videos, improving the efficiency and accuracy of event analysis and providing stronger technical support for the video surveillance field. This enables AI empowerment of various image acquisition devices efficiently and cost-effectively. On the other hand, compared to related technologies where AI application development for different image acquisition devices requires individual adaptation for each device, a cumbersome and time-consuming process, this application solves the problem of inconsistent communication protocols and data formats among image acquisition devices. By utilizing an AI server and a unified processing flow, it effectively solves the compatibility issues faced by different brands and models of image acquisition devices in AI application development and deployment. This greatly simplifies the development and deployment process of AI applications, reduces the development cost and time cost of AI applications for image acquisition devices, and improves the flexibility and convenience of AI technology applications in the video surveillance field.

[0088] In some possible embodiments, when a target user installs multiple target image acquisition devices within a target area, to facilitate the user's management of the target area, they can simultaneously or sequentially input the target parameters of the multiple target image acquisition devices installed within the target area, as well as the target area information corresponding to the installation area of ​​each target image acquisition device, such as, but not limited to, the target area name and target area location, into the video surveillance platform. After the AI ​​server obtains the target parameters and target area information corresponding to each of the multiple target image acquisition devices, it can associate the target image acquisition devices belonging to the same target area, and retrieve the target video data collected by each of the multiple target image acquisition devices within the target area according to their respective target communication protocols and target parameters. Then, it fuses the target video data collected by each of the multiple target image acquisition devices within the target area to obtain the target video fusion data corresponding to the target area. The fusion process of the aforementioned target video data may include, but is not limited to, aligning the target video frames acquired by each target image acquisition device to the same timeline using timestamp information; extracting feature points, such as corner points and edge points, from each target video frame; using a feature matching algorithm to find the correspondence between different target video frames; calculating a spatial transformation matrix based on the feature matching results; and applying the spatial transformation matrix to align each target video frame to the same spatial coordinate system. Finally, performing pixel-level and feature-level fusion on the aligned target video frames, and stitching the fused target video frames together to form complete target video fusion data. After obtaining the target video fusion data corresponding to the aforementioned target region, an AI model can be called to perform event analysis on the target video fusion data to obtain the target event analysis results corresponding to the target region. The target event analysis results corresponding to the aforementioned target region may include, but are not limited to, all target event information occurring within the target region, such as, but not limited to, smoking events occurring within the target region and the time of occurrence of smoking events, illegal parking events occurring within the target region and the time of occurrence of illegal parking events, and information on the illegally parked vehicles, etc.

[0089] In some possible embodiments, after the AI ​​server retrieves target video data collected by multiple target image acquisition devices that are related (e.g., belong to the same user) within the target area, it can, but is not limited to, directly call multiple AI models to perform event analysis on the target video data collected by each of the multiple target image acquisition devices within the target area, obtaining multiple event analysis results. Then, the above multiple event analysis results are integrated to obtain the target event analysis result corresponding to the target area. The above multiple AI models can, but are not limited to, AI models provided by multiple edge devices that are communicatively connected to the AI ​​server. In this embodiment of the application, using multiple edge AI models to perform event analysis on the target video data collected by each of the multiple target image acquisition devices within the target area can improve the efficiency of event analysis and avoid the problem of low efficiency of AI server's AI empowerment due to too much target video data to be analyzed.

[0090] In some possible embodiments, before calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results in S204, the target function permission information of the target user terminal corresponding to the target image acquisition device can be obtained first. The target function permission information may include, but is not limited to, the target functions that the target user terminal has access to, and the target description information corresponding to the target functions. The target functions may include, but are not limited to, at least one of the following: custom detection functions, fire detection functions, absenteeism detection functions, smoking detection functions, helmet-less detection functions, mask-less detection functions, license plate recognition functions, and vehicle illegal parking behavior recognition functions. The custom detection functions are user-defined functions that need to be detected, such as, but not limited to, detecting the appearance of a person wearing red clothes in the target video data, crying detection, etc. The target description information corresponding to the target functions may include, but is not limited to, the target event type that needs to be detected for the target function, the pre-set target start time, and the target end time. The process of calling the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results in S204 above may include, but is not limited to: calling the AI ​​model to perform event analysis on the target video data according to the target description information and target event type corresponding to the target function, and obtaining the target event analysis results corresponding to the target event type. For example, but not limited to, using the target description information and target event type corresponding to the target function that the target user has permission to use as prompt words for the AI ​​model, so as to assist the AI ​​model in performing targeted event analysis based on the permissions and needs of the target user, and to perform targeted device AI empowerment based on user needs.

[0091] Optionally, the aforementioned target function permission information may also include, but is not limited to, at least one of the following: the target effective time period and the target usage time period corresponding to the target function on the target user terminal. The aforementioned target effective time period can be the time period during which the target user terminal has the permission to use the target function, and the aforementioned target usage time period can be the time period for the target user terminal to use the target function pre-set. The aforementioned process of calling the AI ​​model to perform event analysis on the target video data based on the target description information and target event type corresponding to the target function, and obtaining the target event analysis result corresponding to the target event type, may include, but is not limited to: the AI ​​model will only be called when the target acquisition time corresponding to the target video data is within the target effective time period and / or the target usage time period. Based on the target description information and event type corresponding to the target function, the AI ​​model will perform event analysis on the target video data and obtain the target event analysis result corresponding to the target event type. This can protect the interests of developers through the target effective time period, or avoid the problem of wasting AI resources due to situations such as off-duty detection during weekends or holidays.

[0092] Please refer to the following. Figure 4 The example illustrates a flowchart of another device AI-enabled method provided in an embodiment of this application. Figure 4 As shown, the AI-enabled method for this device can include the following steps:

[0093] S401, Obtain the target parameters corresponding to the target image acquisition device.

[0094] Specifically, S401 is the same as S201, and will not be repeated here.

[0095] S402, determine the target communication protocol corresponding to the target image acquisition device based on the target parameters.

[0096] Specifically, S402 is the same as S202, and will not be repeated here.

[0097] S403 receives target device control information sent by the target user terminal.

[0098] Specifically, when a target user wants to remotely control their target image acquisition device through a video surveillance platform on their target user terminal, they can input the corresponding target device control information on the video surveillance platform through the target user terminal. Then, the AI ​​server can receive the target device control information input by the target user terminal. The aforementioned target device control information can be target control text, target control voice, or information carried in the target control operation input by the target user; this application embodiment does not limit this.

[0099] S404, invoke the AI ​​model to perform intent recognition on the target device control information, obtain at least one control target corresponding to the target device control information, and generate target control instructions corresponding to the target device control information based on at least one control target.

[0100] Specifically, after receiving the target device control information sent by the target user terminal, the AI ​​model can be directly invoked to perform intent recognition on the target device control information in order to obtain at least one control target in the target device control information, such as controlling the target image acquisition device to turn off, or controlling the target image acquisition device to rotate the target angle, and automatically generating the target control command corresponding to the target device control information based on at least one control target.

[0101] It is understood that the target control instructions corresponding to the aforementioned target device control information may include one or more, and may be generated based on the target user's control requirements in the target device control information, such as, but not limited to, the number of control targets. This application embodiment does not limit this.

[0102] S405, when in communication connection with the target image acquisition device, sends a target device control command to the target image acquisition device according to the target communication protocol and target parameters, so that the target image acquisition device responds to the target device control command and executes the corresponding target control action.

[0103] Specifically, when in communication with the target image acquisition device, the AI ​​server can send target device control commands to the target image acquisition device according to the target communication protocol and target parameters, so that the target image acquisition device responds to the target device control commands and executes the corresponding target control actions. Users can achieve remote AI control of various image acquisition devices through the video monitoring platform provided by the AI ​​server without the need for developers of each image acquisition device to develop corresponding online applications. At the same time, it can also be compatible with remote AI control of image acquisition devices previously purchased by users that do not have supporting online applications, ensuring the practicality and compatibility of device AI-enabled remote control.

[0104] Please refer to the following. Figure 5 The example illustrates a flowchart of another device AI-enabled method provided in an embodiment of this application. Figure 5 As shown, the AI-enabled method for this device can include the following steps:

[0105] S501, Obtain the target parameters corresponding to the target image acquisition device.

[0106] Specifically, S501 is the same as S201, and will not be repeated here.

[0107] S502, determine the target communication protocol corresponding to the target image acquisition device based on the target parameters.

[0108] Specifically, S502 is the same as S202, and will not be repeated here.

[0109] S503 acquires target video data collected by the target image acquisition device according to the target communication protocol and target parameters.

[0110] Specifically, S503 is the same as S203, and will not be repeated here.

[0111] S504 calls the AI ​​model to perform event analysis on the target video data and obtains the target event analysis results.

[0112] Specifically, S504 is the same as S204, and will not be repeated here.

[0113] S505 receives a target event query command sent by the target user terminal for the target image acquisition device, the target event query command carrying the specified event query type.

[0114] Specifically, when a target user wishes to view events recorded by a specific image acquisition device, they can trigger a target event viewing command on the video surveillance platform via their client. This command is transmitted over the network to the AI ​​server and may, but is not limited to, carry the specified event type the user wishes to view, such as, but not limited to, intrusion incidents or smoking incidents. The AI ​​server can receive the target event viewing command for the specific image acquisition device from the target user's client via the network.

[0115] S506, in response to the target event query command, queries the target event analysis results based on the specified event query type to obtain the corresponding specified event information.

[0116] Specifically, after receiving a target event query instruction, the AI ​​server can respond to the instruction by querying the corresponding specified event information in the target event analysis results according to the specified event query type. The specified event information may include, but is not limited to, the occurrence time, video clips, image frames, event summaries, etc. of the specified event corresponding to the specified event query type. Thus, through accurate event query type matching and fast query, the information that users care about can be efficiently extracted from a large number of target event analysis results, reducing the interference of irrelevant data and improving the accuracy and efficiency of information retrieval.

[0117] S507 returns specified event information to the target user terminal so that the target user terminal can display the specified event information.

[0118] Specifically, after obtaining the specified event information desired by the target user, the system can, but is not limited to, encapsulate the specified event information into a format suitable for transmission (such as, but not limited to, JSON, XML, or binary data) and send it back to the target user's terminal via the network. Upon receiving the specified event information, the user terminal can parse it and present it to the target user in, for example, but not limited to, a list, timeline, video playback, or image display. This allows the target user to intuitively understand the event details they care about. Whether for security monitoring, event review, or other purposes, it provides immediate and specific information feedback, enhancing the practicality of AI-enabled devices and user satisfaction.

[0119] In some possible embodiments, after calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results in S204, it may also include, but is not limited to: if the target event analysis results include a specified alarm event (such as, but not limited to, an intrusion event or smoking event pre-set by the target user), sending the target alarm information corresponding to the specified alarm event to the target user terminal with the permission to use the target image acquisition device, so as to promptly inform the user after the specified alarm event is analyzed, thereby improving the user experience and the practicality and intelligence of the device's AI empowerment.

[0120] Please refer to the following. Figure 6 The illustration shows a schematic diagram of the structure of a device AI-enabled device provided in an embodiment of this application. Figure 6 As shown, the AI-enabled device 600 may include:

[0121] The first acquisition module 610 is used to acquire the target parameters corresponding to the target image acquisition device;

[0122] The determining module 620 is used to determine the target communication protocol corresponding to the target image acquisition device based on the above target parameters.

[0123] The second acquisition module 630 is used to acquire the target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters.

[0124] The event analysis module 640 is used to call the AI ​​model to perform event analysis on the above target video data and obtain the target event analysis results.

[0125] In one possible implementation, the aforementioned AI-enabled device 600 further includes:

[0126] The fusion module is used to fuse the target video data collected by multiple target image acquisition devices within the target area to obtain target video fusion data corresponding to the target area.

[0127] The aforementioned event analysis module 640 is specifically used for:

[0128] The AI ​​model is invoked to perform event analysis on the aforementioned target video fusion data, and the target event analysis results corresponding to the aforementioned target regions are obtained.

[0129] In one possible implementation, the event analysis module 640 described above is specifically used for:

[0130] Multiple AI models are invoked to perform event analysis on the target video data collected by the aforementioned target image acquisition devices within the target area, respectively, to obtain multiple event analysis results; the above multiple event analysis results are integrated to obtain the target event analysis results corresponding to the aforementioned target area.

[0131] In one possible implementation, the aforementioned AI-enabled device 600 further includes:

[0132] The third acquisition module is used to acquire the target function permission information corresponding to the target user terminal; the target function permission information includes the target functions that the target user terminal has the right to use and the target description information corresponding to the target functions.

[0133] The event analysis module 640 is specifically used to: call the AI ​​model, perform event analysis on the target video data according to the target description information and target event type corresponding to the target function, and obtain the target event analysis results corresponding to the target event type.

[0134] In one possible implementation, the aforementioned target function permission information may also include at least one of the following: the target effective time period and the target usage time period of the target function corresponding to the target user terminal;

[0135] The event analysis module 640 is specifically used to: when the target acquisition time corresponding to the target video data is within the target effective time period and / or target usage time period, call the AI ​​model to perform event analysis on the target video data according to the target description information and target event type corresponding to the target function, and obtain the target event analysis result corresponding to the target event type.

[0136] In one possible implementation, the second acquisition module 630 includes:

[0137] A communication unit is used to establish a communication connection with the target image acquisition device based on the aforementioned target parameters.

[0138] The retrieval unit is used to retrieve the original video data acquired by the target image acquisition device according to the target communication protocol when the device is in a communication connection with the target image acquisition device.

[0139] The protocol conversion unit is used to convert the original video data according to the target communication protocol to obtain the target video data.

[0140] In one possible implementation, the aforementioned AI-enabled device 600 further includes:

[0141] The first receiving module is used to receive target device control information sent by the target user terminal;

[0142] The AI ​​module is used to call the AI ​​model to perform intent recognition on the target device control information, obtain at least one control target corresponding to the target device control information, and generate a target control instruction corresponding to the target device control information based on the at least one control target.

[0143] The first sending module is configured to, when in a communication connection with the target image acquisition device, send the target device control command to the target image acquisition device according to the target communication protocol and the target parameters, so that the target image acquisition device responds to the target device control command and executes the corresponding target control action.

[0144] In one possible implementation, the aforementioned AI-enabled device 600 further includes:

[0145] The second receiving module is used to receive a target event query instruction sent by the target user terminal for the target image acquisition device; the target event query instruction carries a specified event query type.

[0146] The query module is used to respond to the above target event query command, and to query the above target event analysis results based on the specified event query type to obtain the corresponding specified event information;

[0147] The second sending module is used to return the specified event information to the target user terminal so that the target user terminal can display the specified event information.

[0148] In one possible implementation, the aforementioned AI-enabled device 600 further includes:

[0149] The third sending module is used to send the target alarm information corresponding to the specified alarm event to the target user terminal when the target event analysis results include the specified alarm event.

[0150] The division of modules in the above-described AI-enabled device is for illustrative purposes only. In other embodiments, the AI-enabled device can be divided into different modules as needed to complete all or part of the functions of the above-described AI-enabled device. The implementation of each module in the AI-enabled device provided in the embodiments of this specification can be in the form of a computer program. This computer program can run on an AI server. The program modules constituted by this computer program can be stored in the memory of the AI ​​server. When the computer program is executed by a processor, it implements all or part of the steps of the AI-enabled device method described in the embodiments of this specification.

[0151] Please refer to the following. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this specification. Figure 7 As shown, the electronic device 700 may include: at least one processor 710, at least one communication bus 720, user interface 730, at least one network interface 740, and memory 710.

[0152] The communication bus 720 can be used to realize the connection and communication of the above components.

[0153] The user interface 730 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0154] The network interface 740 may optionally include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0155] The processor 710 may include one or more processing cores. The processor 710 connects to various parts within the electronic device 700 using various interfaces and lines. It executes instructions, programs, code sets, or instruction sets stored in the memory 710, and calls data stored in the memory 710 to perform various functions and process data within the routing device 700. Optionally, the processor 710 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 710 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 710 and may be implemented as a separate chip.

[0156] The memory 710 may include RAM or ROM. Optionally, the memory 710 may include a non-transitory computer-readable medium. The memory 710 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 710 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as event analysis, video retrieval, AI empowerment, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 710 may also be at least one storage device located remotely from the aforementioned processor 710. Figure 7 As shown, the memory 710, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0157] In some possible embodiments, the electronic device 700 may be as described above. Figure 6 The AI-enabled device 600 shown has a processor 710 that can call program instructions stored in the memory 710 and specifically perform the following operations:

[0158] Obtain the target parameters corresponding to the target image acquisition device; determine the target communication protocol corresponding to the target image acquisition device based on the target parameters; obtain the target video data acquired by the target image acquisition device based on the target communication protocol and the target parameters; call the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results.

[0159] In some possible embodiments, before the processor 710 executes the above-mentioned invocation of the AI ​​model to perform event analysis on the target video data and obtains the target event analysis result, it is also used to perform:

[0160] The target video data collected by multiple target image acquisition devices within the target area are fused to obtain the target video fused data corresponding to the target area.

[0161] When the processor 710 executes the above-mentioned call to the AI ​​model to perform event analysis on the target video data and obtain the target event analysis result, it is specifically used to perform: call the AI ​​model to perform event analysis on the above-mentioned target video fusion data and obtain the target event analysis result corresponding to the above-mentioned target region.

[0162] In some possible embodiments, when the processor 710 executes the above-mentioned call to the AI ​​model to perform event analysis on the target video data and obtains the target event analysis results, it is specifically used to perform:

[0163] Multiple AI models are invoked to perform event analysis on the target video data collected by the aforementioned target image acquisition devices within the target area, respectively, to obtain multiple event analysis results; the above multiple event analysis results are integrated to obtain the target event analysis results corresponding to the aforementioned target area.

[0164] In some possible embodiments, before the processor 710 executes the above-mentioned invocation of the AI ​​model to perform event analysis on the target video data and obtains the target event analysis result, it is also used to perform:

[0165] Obtain the target function permission information corresponding to the target user terminal; the target function permission information includes the target functions that the target user terminal has the right to use and the target description information corresponding to the target functions.

[0166] When the processor 710 executes the above-mentioned call to the AI ​​model to perform event analysis on the target video data and obtain the target event analysis result, it specifically performs the following: calls the AI ​​model to perform event analysis on the target video data according to the target description information and target event type corresponding to the target function, and obtains the target event analysis result corresponding to the target event type.

[0167] In some possible embodiments, the target function permission information may further include at least one of the following: the target effective time period and the target usage time period of the target function corresponding to the target user terminal;

[0168] When the processor 710 executes the above-mentioned call to the AI ​​model, performs event analysis on the target video data based on the target description information and target event type corresponding to the target function, and obtains the target event analysis result corresponding to the target event type, it is specifically used to execute:

[0169] If the target acquisition time corresponding to the above target video data is within the above target valid time period and / or target usage time period, the AI ​​model is invoked to perform event analysis on the above target video data based on the target description information and target event type corresponding to the above target function, and the target event analysis results corresponding to the above target event type are obtained.

[0170] In some possible embodiments, when the processor 710 executes the above-mentioned method of obtaining target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters, it is specifically used to perform the following: establishing a communication connection with the target image acquisition device according to the target parameters; retrieving the original video data acquired by the target image acquisition device according to the target communication protocol while in communication connection with the target image acquisition device; and performing protocol conversion on the original video data according to the target communication protocol to obtain target video data.

[0171] In some possible embodiments, after the processor 710 executes the above-described target communication protocol corresponding to the target image acquisition device based on the target parameters, it is further configured to: receive target device control information sent by the target user terminal; call the above-described AI model to perform intent recognition on the target device control information to obtain at least one control target corresponding to the target device control information, and generate a target control instruction corresponding to the target device control information based on the at least one control target; and, when in a communication connection with the target image acquisition device, send the target device control instruction to the target image acquisition device according to the target communication protocol and the target parameters, so that the target image acquisition device responds to the target device control instruction and executes a corresponding target control action.

[0172] In some possible embodiments, after the processor 710 executes the above-mentioned call to the AI ​​model to perform event analysis on the target video data and obtains the target event analysis results, it is further used to execute:

[0173] The system receives a target event query instruction sent by the target user terminal for the target image acquisition device; the target event query instruction carries a specified event query type; in response to the target event query instruction, the system queries the target event analysis results based on the specified event query type to obtain the corresponding specified event information; and returns the specified event information to the target user terminal so that the target user terminal can display the specified event information.

[0174] In some possible embodiments, after the processor 710 executes the above-mentioned call to the AI ​​model to perform event analysis on the target video data and obtains the target event analysis results, it is further used to execute:

[0175] If the target event analysis results include a specified alarm event, the target alarm information corresponding to the specified alarm event is sent to the target user terminal.

[0176] This application also provides a computer storage medium storing instructions that, when run on a computer or processor, cause the computer or processor to execute one or more steps of any of the above methods. If the constituent modules of the above-described AI-enabled device are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned storage medium.

[0177] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0178] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0179] The above-described embodiments are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made by those skilled in the art to the technical solutions of this application without departing from the spirit of this application should fall within the protection scope defined by the claims of this application.

Claims

1. A method for enabling AI in devices, characterized in that, Applied to an AI server, the method includes: Obtain the target parameters corresponding to the target image acquisition device; The target communication protocol corresponding to the target image acquisition device is determined based on the target parameters; The target video data acquired by the target image acquisition device is obtained according to the target communication protocol and the target parameters. The AI ​​model is invoked to perform event analysis on the target video data, and the target event analysis results are obtained.

2. The method as described in claim 1, characterized in that, Before calling the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results, the method further includes: The target video data collected by multiple target image acquisition devices within the target area are fused to obtain target video fused data corresponding to the target area. The step of calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results includes: An AI model is invoked to perform event analysis on the target video fusion data to obtain the target event analysis results corresponding to the target region.

3. The method as described in claim 1, characterized in that, The step of calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results includes: Multiple AI models are invoked to perform event analysis on the target video data collected by multiple target image acquisition devices within the target area, resulting in multiple event analysis results. The analysis results of the multiple events are integrated to obtain the target event analysis results corresponding to the target area.

4. The method as described in claim 1, characterized in that, Before calling the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results, the method further includes: Obtain the target function permission information corresponding to the target user terminal; the target function permission information includes the target functions that the target user terminal has the right to use and the target description information corresponding to the target functions; The step of calling the AI ​​model to perform event analysis on the target video data and obtaining the target event analysis results includes: The AI ​​model is invoked to perform event analysis on the target video data based on the target description information and target event type corresponding to the target function, and to obtain the target event analysis result corresponding to the target event type.

5. The method as described in claim 4, characterized in that, The target function permission information also includes at least one of the following: the target effective time period and the target usage time period of the target function corresponding to the target user terminal; The AI ​​model is invoked to perform event analysis on the target video data based on the target description information and target event type corresponding to the target function, and to obtain the target event analysis results corresponding to the target event type, including: If the target acquisition time corresponding to the target video data is within the target valid time period and / or target usage time period, the AI ​​model is invoked to perform event analysis on the target video data based on the target description information and target event type corresponding to the target function, and to obtain the target event analysis result corresponding to the target event type.

6. The method as described in claim 1, characterized in that, The step of obtaining the target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters includes: A communication connection is established between the target parameters and the target image acquisition device; When in communication connection with the target image acquisition device, the original video data acquired by the target image acquisition device is retrieved according to the target communication protocol; The original video data is converted according to the target communication protocol to obtain the target video data.

7. The method as described in claim 1, characterized in that, After determining the target communication protocol corresponding to the target image acquisition device based on the target parameters, the method further includes: Receive target device control information sent by the target user terminal; The AI ​​model is invoked to perform intent recognition on the target device control information to obtain at least one control target corresponding to the target device control information, and a target control command corresponding to the target device control information is generated based on the at least one control target. When in communication with the target image acquisition device, the target device control command is sent to the target image acquisition device according to the target communication protocol and the target parameters, so that the target image acquisition device responds to the target device control command and executes the corresponding target control action.

8. An AI-enabled device for equipment, characterized in that, The AI-enabled device, applied to AI servers, includes: The first acquisition module is used to acquire the target parameters corresponding to the target image acquisition device; The determining module is used to determine the target communication protocol corresponding to the target image acquisition device based on the target parameters; The second acquisition module is used to acquire target video data acquired by the target image acquisition device according to the target communication protocol and the target parameters. The event analysis module is used to call the AI ​​model to perform event analysis on the target video data and obtain the target event analysis results.

9. An electronic device, characterized in that, include: A processor and a memory; wherein the memory stores executable program code, and the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for performing the method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as described in any one of claims 1-7.

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