Audio and video acquisition terminal site auxiliary installation method and system

Abstract

The application relates to a kind of audio and video acquisition terminal position auxiliary installation method and system, wherein the audio and video acquisition terminal position auxiliary installation method includes: audio and video acquisition terminal receives the scoring algorithm program that platform server issues and matches target application scene;In the program is run locally in audio and video acquisition terminal and real-time analysis is carried out to self live video stream, and objective position visual angle score is generated;The score is sent to the client to guide the client user to adjust position.By the application, the problem that the result is not accurate and the feedback is not timely caused by model difference and network delay in cloud side evaluation is solved, and the pain point that evaluation standard is not unified in artificial subjective installation is solved, real-time, accurate and quantitative installation process is realized, so as to improve installation efficiency, quality and recognition accuracy of back-end AI algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of AI intelligent technology application, in particular to a method and system for assisting installation of an audio and video collection terminal. BACKGROUND

[0002] With the continuous development of artificial intelligence technology, AI technology has been widely applied and popularized in the field of audio and video monitoring, promoting the evolution of the industry from "visible" and "clear" to "understandable". Under this background, intelligent audio and video collection terminals supporting cloud management have emerged as the times require. It allows users to dynamically issue AI algorithms from the cloud side platform to the device side according to the actual application scenario, so as to perform AI inference calculation directly on the terminal, which not only reduces the continuous dependence on network bandwidth, but also improves the universality and real-time performance of AI applications.

[0003] However, under this new cloud intelligent mode, a significant technical challenge has emerged: how to effectively ensure that the image quality collected by the audio and video collection terminal meets the requirements of its end-side algorithm inference. The installation position, angle, height and other factors of the terminal device directly determine the quality of the collected image, which in turn has a decisive influence on the recognition accuracy of the end-side algorithm. At present, the common solutions mainly exist in two paths:

[0004] First, rely on the subjective experience of installation personnel and installation guide documents. This method lacks objective and unified quantitative standards, and the installation quality is difficult to be effectively guaranteed, which is easy to cause algorithm performance degradation due to improper installation.

[0005] Second, upload the pictures collected by the terminal device to the cloud side server, and evaluate the image quality by the analysis algorithm on the server side. However, this method has inherent defects: first, due to the delay caused by network transmission, real-time feedback during installation cannot be realized, which seriously affects the installation efficiency; second, and the most core problem is that in order to ensure that the end-side algorithm can run efficiently on the device with limited computing resources, the algorithm delivered to the end-side is usually a version of the original algorithm on the cloud side after optimization processing such as lightweight, quantization or pruning. This leads to the fact that the algorithm model used by the cloud side server to evaluate the image quality is different from the algorithm model actually running inside the terminal device in terms of accuracy, model structure and behavior characteristics. Therefore, the analysis and evaluation results on the cloud side cannot truly and accurately reflect the actual running effect of the end-side algorithm, and the evaluation results are inconsistent.

[0006] In summary, there is a lack of an effective evaluation method in the prior art that can evaluate the end-side image quality in real time, accurately and consistently with the performance of the business algorithm during the installation of the intelligent audio and video collection terminal. This directly leads to uneven installation quality and the algorithm recognition accuracy cannot meet the expected value, and further causes user complaints and other problems.

[0007] The present application aims to solve the defects in the prior art described above, and the technical problem to be solved is how to overcome the problem of inaccurate image quality evaluation results and non-real-time evaluation process caused by inconsistent algorithm models on the terminal side and the cloud side in the intelligent terminal mode, so as to provide a method and system capable of evaluating whether the terminal side image quality meets the requirements of the business algorithm in real time and accurately, so as to guide the installation and maintenance personnel to quickly determine the optimal installation position and ensure the best operation effect of the AI application algorithm. SUMMARY

[0008] The embodiments of the present application provide an audio and video acquisition terminal position auxiliary installation method and system, an electronic device and a storage medium, to at least solve the problem that intelligent audio and video acquisition terminals cannot evaluate whether the terminal side acquisition image quality meets the algorithm inference requirements in real time, accurately and uniformly in related technologies.

[0009] In a first aspect, the embodiments of the present application provide an audio and video acquisition terminal position auxiliary installation method applied to the audio and video acquisition terminal, including:

[0010] receiving download information issued by the platform server; the download information is obtained by the platform server analyzing the auxiliary score opening request of the client, obtaining target application scenario information, determining a target score algorithm program matched with the target application scenario information, and being generated based on the target score algorithm program;

[0011] in response to the download information, downloading and running the target score algorithm program from the platform server;

[0012] based on the running target score algorithm program, performing video image processing on the live video stream generated by the audio and video acquisition terminal, generating a position visual angle score, and sending the position visual angle score to the client for display; the position visual angle score is used to indicate the position adjustment operation of the audio and video acquisition terminal.

[0013] In some embodiments, the target score algorithm program is a score algorithm program matched with the target application scenario and determined by the platform server based on a mapping relationship; the mapping relationship is a corresponding relationship between the application scenario and the score algorithm program, which is established and stored by the platform server storing a plurality of score algorithm programs corresponding to different pre-installed application scenarios.

[0014] In some embodiments, the download information includes: a URL download address, an MD5 check code and a decompression password corresponding to the target score algorithm program; the response to the download information to download and run the target score algorithm program from the platform server includes:

[0015] Download the target scoring algorithm program package file according to the URL download address, and perform MD5 verification on the package file based on the MD5 checksum. After the verification is successful, decompress the package file using the decompression password and run the target scoring algorithm program.

[0016] In some embodiments, the step of performing video image processing on the live video stream based on the running target scoring algorithm program to generate camera viewpoint scores includes:

[0017] Based on the target scoring algorithm program being run, the live video stream is read, and the live video stream is processed by frame extraction to generate the target image after frame extraction.

[0018] The target image is subjected to image quality analysis to generate the camera position perspective score.

[0019] In some embodiments, the step of performing image quality analysis on the target image and generating the camera angle score includes:

[0020] Based on the running target scoring algorithm program, conditional factors in the target image are identified, and the camera position perspective score is derived by reasoning and analysis based on the conditional factors; wherein, the conditional factors include one or more of the following camera position information data: the angle between the optical axis of the audio-visual acquisition terminal and the horizontal plane, the installation height of the audio-visual acquisition terminal relative to the horizontal ground, the horizontal distance between the audio-visual acquisition terminal and the preset detection area reference point, and the angle between the projection of the optical axis of the audio-visual acquisition terminal on the horizontal plane and the preset detection area reference normal.

[0021] In some embodiments, sending the camera angle score to the client for display includes: calling the OSD interface of the chip built into the audio and video acquisition terminal to overlay the camera angle score onto the live video stream.

[0022] In some embodiments, after sending the camera viewpoint score to the client for display, the method further includes:

[0023] Upon receiving a signal from the client user that the camera position does not meet the standard based on the camera position viewpoint score input, a command to continue adjusting the camera position is generated;

[0024] In response to the position continuous adjustment instruction, a new live video stream is generated based on the client user readjusted position information, a new position visual angle score is generated by performing video image processing on the new live video stream based on the target scoring algorithm program, and the position adjustment is generated until a signal is received that the client user confirms that the position visual angle score meets the standard.

[0025] In some embodiments, the method further includes:

[0026] receiving a secondary scoring closing request initiated by the client;

[0027] In response to the secondary scoring closing request, a picture with the position visual angle score is intercepted based on the target scoring algorithm program, and the picture with the position visual angle score is reported to the platform server for storage.

[0028] In some embodiments, the method further includes:

[0029] based on the secondary scoring closing request, deleting the target scoring algorithm program running on the audio and video collection terminal.

[0030] In a second aspect, the embodiments of the present application provide an audio and video collection terminal position auxiliary installation system, comprising an audio and video collection terminal, a platform server and a client; the audio and video collection terminal is connected with the platform server and the client respectively;

[0031] The audio and video collection terminal is configured to execute the audio and video collection terminal position auxiliary installation method as described in the first aspect.

[0032] In a third aspect, the embodiments of the present application provide an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the audio and video collection terminal position auxiliary installation method as described in the first aspect.

[0033] In a fourth aspect, the embodiments of the present application provide a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the audio and video collection terminal position auxiliary installation method as described in the first aspect.

[0034] Compared with the related art, the audio and video acquisition terminal site auxiliary installation scheme provided by the embodiment of the application takes the audio and video acquisition terminal as an execution subject, directly performs real-time video image processing on the live video stream by receiving and running the scoring algorithm program matched with the target application scene and issued by the platform server, generates objective site perspective scoring data locally, and sends the site perspective scoring data to the client for display, so that the client user adjusts the site of the audio and video acquisition terminal according to the real-time displayed site perspective score until the site perspective score reaches the preset index. The scheme solves the problems of non-real-time, inaccuracy and non-uniform standard of end-side image quality evaluation, realizes that the installation and maintenance personnel can quickly adjust and determine the optimal installation site according to real-time and accurate visual scoring, and thus greatly improves the installation efficiency and quality and guarantees the end-side AI algorithm recognition effect.

[0035] The details of one or more embodiments of the application are presented in the following drawings and description to make other features, objects and advantages of the application more apparent. BRIEF DESCRIPTION OF DRAWINGS

[0036] The drawings described herein are intended to provide further understanding of the application, form a part of the application, and serve to explain the application without constituting an improper limitation of the application. In the drawings:

[0037] Figure 1 is a hardware block diagram of an audio and video acquisition terminal site auxiliary installation method according to an embodiment of the application;

[0038] Figure 2 is a flowchart of an audio and video acquisition terminal site auxiliary installation method according to an embodiment of the application;

[0039] Figure 3 is an installation flowchart of an audio and video acquisition terminal site auxiliary installation method according to an embodiment of the application;

[0040] Figure 4 is an AI camera starting scoring algorithm program sub-flowchart according to an embodiment of the application;

[0041] Figure 5 is a live video stream scoring superposition sub-flowchart according to an embodiment of the application;

[0042] Figure 6 is a picture quality analysis element diagram for a passenger flow statistics scene according to an embodiment of the application;

[0043] Figure 7 is a best site finding flowchart according to an embodiment of the application;

[0044] Figure 8is a structural block diagram of an auxiliary installation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort fall within the scope of the present application. In addition, it should be understood that although the efforts made in this development process can be complex and lengthy, some design, manufacture or production changes made on the basis of the technical content disclosed in the present application by those of ordinary skill in the art related to the content disclosed in the present application are only routine technical means and should not be understood as insufficient disclosure of the present application.

[0046] In the present application, the term "embodiment" means that the specific features, structures or properties described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0047] Unless otherwise defined, the technical terms or scientific terms involved in the present application should be understood as the usual meaning understood by those of ordinary skill in the art to which the present application belongs. The terms "one", "a", "an", "the", and the like similar words involved in the present application do not represent quantity limitation, but can represent singular or plural. The terms "include", "contain", "have", and any variations thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but can also include steps or units not listed, or can also include other steps or units inherent to these processes, methods, products or devices. The terms "connected", "connected", "coupled" and the like similar words involved in the present application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "multiple" involved in the present application means greater than or equal to two. The term "and / or" describes the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. The terms "first", "second", "third" and the like involved in the present application are only to distinguish similar objects, and do not represent a specific order for the objects.

[0048] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. Taking running on a terminal as an example, Figure 1 This is a hardware structure block diagram of a terminal according to an embodiment of the audio / video acquisition terminal auxiliary installation method of this application. Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0049] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the audio / video acquisition terminal assisted installation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0050] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0051] This embodiment provides a method for assisted installation of an audio / video acquisition terminal. Figure 2is a flowchart of a site assisting installation method of an audio-video collecting terminal according to an embodiment of the present application, as shown in Figure 2 The flowchart includes the following steps:

[0052] In step S201, download information issued by a platform server is received; the download information is obtained by the platform server analyzing an auxiliary scoring opening request of a client, obtaining target application scenario information, determining a target scoring algorithm program matched with the target application scenario information, and generating the download information based on the target scoring algorithm program.

[0053] Specifically, a user selects a pre-installed target application scenario through a client and issues an auxiliary scoring opening request. After receiving the opening request, the platform server analyzes the target application scenario information selected by the user and determines a target scoring algorithm program matched with the target application scenario. The platform server issues relevant download information of the target scoring algorithm program to an audio-video collecting terminal, and the audio-video collecting terminal receives the download information.

[0054] The audio-video collecting terminal generally refers to an embedded intelligent device integrating an image sensor, an audio collector, a computing processing unit, and a network communication module, which is used for collecting environmental audio-video information and performing local or cloud intelligent analysis, such as an AI camera in a smart network camera, a pan-tilt camera, a smart video doorbell, a driving recorder and a vehicle-mounted monitoring terminal, an unmanned aerial vehicle (UAV), a cruise robot, a smart door lock / smart home device with a camera, etc.

[0055] The client refers to an application program or a software module running on a user terminal device and providing a human-computer interaction interface for the user. The client is configured to send an auxiliary scoring opening request to the platform server and receive and display site view scoring data from the platform server. For example, when the AI camera is taken as an embodiment, the client can be a mobile phone APP.

[0056] In step S202, the target scoring algorithm program is downloaded and run from the platform server in response to the download information.

[0057] Specifically, the audio-video collecting terminal downloads and runs the target scoring algorithm program according to the download information issued by the platform server.

[0058] Through the above S201, S202 steps, the platform server opens the request according to the auxiliary score issued by the client and the selected target application scene, and downloads the target score algorithm program information to the audio and video collection terminal; the terminal downloads and runs the target score algorithm program according to the information, which realizes the "on-demand supply" of the score algorithm program. Through the accurate matching and scene-based distribution of the platform side, the problem that the traditional terminal pre-installed fixed evaluation tool cannot flexibly adapt to diversified on-site scenes is effectively solved, not only realizing the precision and specialization of the evaluation process - ensuring that the score algorithm distributed to the terminal is optimized for "passenger flow statistics" "vehicle identification" and other scenes, and the evaluation standard and business demand are highly consistent, but also realizing the on-demand allocation and efficient use of terminal resources. In addition, the terminal does not need to pre-install all algorithms, which significantly saves storage space, temporarily loads and runs specific algorithms only during installation and debugging, and deletes them after completion, which also ensures that the terminal runs in a pure and efficient state in daily business, and its computing power and storage resources are concentrated for core business functions.

[0059] Step S203, based on the target score algorithm program running, video image processing is performed on the live video stream generated by the audio and video collection terminal to generate a camera angle score, and the camera angle score is sent to the client for display; the above camera angle score is used to indicate the camera adjustment operation of the audio and video collection terminal.

[0060] Specifically, after the target score algorithm program runs, the audio and video collection terminal acquires the live video stream based on the target score algorithm program, performs video image processing on the live video stream, generates a camera angle score, and sends the camera angle score to the client for display, and the client user adjusts the camera of the audio and video collection terminal according to the displayed camera angle score.

[0061] This step runs the score algorithm program matching the target application scene on the audio and video collection terminal locally, analyzes the live video stream generated by itself in real time and generates an objective camera angle score, which solves the problems of non-uniform standards caused by relying on human experience in traditional installation process, and evaluation delay caused by cloud-side analysis and evaluation result distortion caused by cloud / terminal algorithm model difference. Compared with the way of subjective judgment of installation and maintenance personnel or the need to transmit the picture to the cloud for asynchronous and offline analysis in the traditional technology, this scheme realizes the real-time, objective quantification and precision of the installation quality evaluation on the terminal side, which brings the beneficial effects that the installation and maintenance personnel can quickly locate the optimal installation camera according to the real-time changing score, thereby greatly improving the installation efficiency and quality, and ensuring the best operation effect of the terminal-side AI business algorithm from the source.

[0062] The following preferred AI camera is taken as an example to specifically introduce the scheme and its implementation mode, Figure 3is a schematic diagram of an installation process of a site auxiliary installation method of an audio and video acquisition terminal according to an embodiment of the present application, as shown in Figure 3 As described above, the specific installation work flow is as follows:

[0063] S301, installation starts.

[0064] Specifically, the client user selects a preset target application scenario through the client APP, issues an auxiliary scoring start request, and starts the AI camera site installation process.

[0065] S302, start the scoring algorithm program sub-process.

[0066] Specifically, in response to the auxiliary scoring start request, the platform server identifies the preset target application scenario information selected by the client, matches the target scoring algorithm program corresponding to the target application scenario by calling the mapping relationship, and downloads the target scoring algorithm program related information to the AI camera; the AI camera downloads and runs the target scoring algorithm program through the download information.

[0067] S303, site perspective score display sub-process.

[0068] Specifically, the AI camera generates a live video stream based on the running target scoring algorithm program, processes the video image, generates a site perspective score, and sends the site perspective score to the client for display.

[0069] S304, find the best site sub-process.

[0070] Specifically, the client user confirms whether the current installation site meets the preset requirements according to the site perspective score displayed on the client, and if not, continues to adjust the AI camera site to find the best site.

[0071] S305, installation ends.

[0072] Specifically, when the site perspective score displayed on the client meets the preset requirements, that is, the best site is found, the client user fixes and installs the AI camera, and the entire auxiliary installation process ends.

[0073] By the above steps, the score algorithm program accurately matched with the target application scene and delivered by the platform server is received and run by the audio and video collection terminal, and the video stream generated by itself is analyzed and scored locally in real time, which solves the technical problem that it is difficult to evaluate the real-time, accurate and consistent performance of the business algorithm of the end-side collected image quality under the cloud management mode. Compared with the existing technology which relies on artificial experience to cause inconsistent installation standards, and relies on cloud-side analysis to cause distorted evaluation results due to algorithm model differences and network delay, the present scheme enables installation and maintenance personnel to quickly complete the adjustment and optimization of the site based on real-time and objective visual scoring data, greatly improving the installation efficiency and quality, and fundamentally ensuring the best running effect of the end-side AI business algorithm.

[0074] In some embodiments, the target score algorithm program is determined by the platform server calling a mapping relationship, and the mapping relationship is stored by the platform server to store a plurality of score algorithm programs corresponding to different pre-installed application scenes, and to establish and store the corresponding relationship between the application scene and the score algorithm program.

[0075] Specifically, a plurality of pre-set different application scenes and their corresponding score algorithm programs are uploaded to the platform server, the platform server receives and establishes the mapping relationship thereof, and stores the pre-set application scene, score algorithm program and mapping relationship; when receiving the auxiliary scoring function opening request issued by the client, the above platform server confirms the matching target score algorithm program according to the pre-installed target application scene information selected by the user, and delivers the download information of the target score algorithm program to the audio and video collection terminal.

[0076] The present scheme can be realized by the metadata management function of the platform server, and its core lies in constructing an intelligent "scene-algorithm" matching system, which can be realized by the following process:

[0077] Firstly, the algorithm metadata management system is constructed and the mapping relationship between the scene and the algorithm is established. Specifically, the following methods can be used: the administrator uploads the developed different scoring algorithm program packages to the object storage through the Web management interface, such as passenger_flow.zip for passenger flow statistics, face_recognition.zip for face recognition, etc.; at the same time, a mapping relationship table is created in the metadata database of the platform, for example, algorithm_mapping_table is created in MySQL, which at least contains scene_id (application scenario ID), scene_name (application scenario name), algorithm_version (algorithm version), algorithm_url (algorithm package address in object storage), algorithm_md5 (MD5 check code of algorithm package), Status (status, such as 1 enabled, 0 disabled) and other fields, which are used to store the corresponding relationship between the scene and the algorithm.

[0078] Then, the intelligent matching of the scene and the algorithm is realized based on the mapping relationship. Exemplarily, the following process can be used: when the platform server receives the auxiliary scoring start request sent by the client containing scene_id or scene_name, the matching process is triggered; the application program of the platform server accurately locates the download and verification information of the target scoring algorithm program completely matched with the target application scenario by executing the database query operation, for example, using Java Spring Boot service to execute “SELECT algorithm_url, algorithm_md5 FROM algorithm_mapping_table WHERE scene_name = 'passenger_flow' AND status = 1”, so as to complete the intelligent matching of “scene-algorithm”.

[0079] Through the scheme, the problem of mismatching between the scoring algorithm program and the specific application scenario is solved. Without the mapping relationship, the system cannot intelligently assign appropriate evaluation tools for different AI tasks (such as passenger flow statistics / vehicle recognition), resulting in unprofessional and inaccurate evaluation results. The scheme ensures that the scoring algorithm issued to the terminal is optimized for the current scene, and its evaluation dimensions and parameter settings (such as identification height, angle, distance and other factors) completely meet the business requirements, greatly improving the accuracy and effectiveness of the scoring results. In addition, when a new scoring algorithm is added or updated, the mapping relationship only needs to be updated in the background of the platform server, without the need for firmware upgrade of a large number of terminals, which reduces the system maintenance cost and improves the operation efficiency.

[0080] Taking the AI camera as an example, the following steps are taken to implement the scheme: Figure 4The specific content of the scheme is described in detail. Figure 4 is a sub-flow diagram of an AI camera starting scoring algorithm program according to an embodiment of the present application, as Figure 4 The specific process is as follows:

[0081] The client user selects the pre-installed target application scenario information through the client (such as an APP) and issues an auxiliary scoring opening request. After the platform server receives the auxiliary scoring opening request containing the pre-installed target application scenario information, it matches the corresponding target scoring algorithm based on the pre-set mapping relationship between the multiple different application scenarios, scoring algorithm programs and their mapping relationship in the platform server, and downloads the target scoring algorithm program to the AI camera. The AI camera downloads and runs the target scoring algorithm program based on the above download information.

[0082] In the present scheme, the mapping relationship between the application scenarios and the scoring algorithm programs is established and maintained, realizing intelligent management and accurate matching of the scoring algorithm programs. This mechanism solves the problem that the scoring tools in the prior art are disconnected from the specific installation scenarios and cannot flexibly adapt to diversified AI application requirements. Compared with the traditional scheme, the present scheme can automatically match the optimal evaluation algorithm according to the target scenario specified by the client user, ensuring the accuracy and professionalism of the scoring results and greatly improving the intelligent level and operation efficiency of the installation evaluation.

[0083] In some embodiments, the download information includes: a URL download address corresponding to the target scoring algorithm program, an MD5 check code and a decompression password; and the downloading and running the target scoring algorithm program from the platform server in response to the download information includes:

[0084] According to the URL download address, the package file of the target scoring algorithm program is downloaded, and the MD5 check code is used for MD5 check on the package file. After the check is passed, the package file is decompressed via the decompression password, and the target scoring algorithm program is run.

[0085] Specifically, the download information issued by the platform server includes the URL download address corresponding to the target scoring algorithm program, the MD5 check code and the decompression password. When the audio and video collection terminal receives the download information, the package file of the target scoring algorithm program is downloaded according to the URL download address in the download information, and the MD5 check code is used for MD5 check on the package file. After the check is passed, the package file is decompressed via the decompression password, and the target scoring algorithm program is run. If the download fails, a download failure response is returned to the platform server.

[0086] This solution addresses potential issues such as file corruption, tampering, and secure access during the download and transmission of the scoring algorithm program by introducing standard security verification mechanisms (MD5 checksum) and access control mechanisms (decompression password). This ensures the integrity and reliability of the scoring algorithm program obtained by the audio and video acquisition terminal, further improving the overall reliability, security, and stability of the solution.

[0087] In some embodiments, the step of performing video image processing on the live video stream based on the running target scoring algorithm program to generate camera viewpoint scores includes:

[0088] Based on the target scoring algorithm program being run, the live video stream is read, and the live video stream is processed by frame extraction to generate the target image after frame extraction.

[0089] The target image is subjected to image quality analysis to generate the camera position perspective score.

[0090] Specifically, after the target scoring algorithm program runs, it obtains live video stream data by calling the streaming interface of the built-in chip of the audio and video acquisition terminal, extracts frames from the live video stream data at a fixed frequency to generate target images, and generates camera angle scores by performing image quality analysis on the target images.

[0091] Continuing with the example of AI cameras, combined with Figure 5 The process will be explained in detail. Figure 5 This is a schematic diagram of the scoring superimposed on the sub-stream of the live video stream according to an embodiment of this application. The specific process is as follows:

[0092] The scoring algorithm program within the AI ​​camera starts up. It obtains the live video stream data by calling the streaming interface of the AI ​​camera's built-in chip, extracts frames from the live video stream at a fixed frequency to obtain the current frame (i.e., the target image), evaluates the quality of the current frame, generates a camera position perspective score, and overlays this score onto the camera's live video stream so that the client user can see the current camera position perspective score on their client's live stream. The scoring algorithm program does not stop at this point but waits for a very short time interval (e.g., 1 second if the frame extraction frequency is 1Hz) before acquiring new video stream frames again. During this process, the client user may have adjusted their camera position, causing changes in the live video stream data. The process then returns to the step of extracting frames at a fixed frequency, analyzes the new live video stream, and generates a new camera position perspective score, repeating this cycle continuously.

[0093] In the scheme, by adopting frame extraction processing technology on the terminal side, the analysis of continuous video stream is converted into static analysis of key image frames, solving the technical problem that high real-time AI video analysis is difficult to realize on embedded devices with limited computing resources. Compared with the traditional scheme, which either cannot process video stream in real time due to insufficient terminal computing power, or introduces delay and model difference due to dependence on cloud analysis, the scheme improves the efficiency and real-time performance of terminal local processing, enabling client users to obtain instant and smooth score feedback, while ensuring the stable operation of the terminal main business, achieving a balance between efficiency and accuracy under limited resources.

[0094] In some embodiments, the picture quality analysis of the target picture to generate the camera angle score includes:

[0095] Based on the running target score algorithm program, a condition factor in the target picture is identified, and the camera angle score is inferred and analyzed according to the condition factor; wherein the condition factor includes one or more of the following camera information data: the included angle between the optical axis of the audio and video acquisition terminal and the horizontal plane, the installation height of the audio and video acquisition terminal relative to the horizontal ground, the horizontal distance between the audio and video acquisition terminal and the preset detection area reference point, and the included angle between the projection of the optical axis of the audio and video acquisition terminal on the horizontal plane and the preset detection area reference normal.

[0096] Specifically, the running target score algorithm program analyzes the picture quality of the target picture, and the process is as follows: identifying the condition factor of the target picture, and inferring and analyzing the camera angle score according to the condition factor. Wherein the condition factor includes one or more of the camera information, which are respectively: the included angle α between the optical axis of the audio and video acquisition terminal and the horizontal plane, the installation height H of the audio and video acquisition terminal relative to the horizontal ground, the horizontal distance L between the audio and video acquisition terminal and the preset detection area reference point, and the included angle β between the projection of the optical axis of the audio and video acquisition terminal on the horizontal plane and the preset detection area reference normal.

[0097] Taking an AI camera as an example, assuming that the target application scenario is passenger flow statistics, and the preset detection area is the door range in the scene, combined with Figure 6 The content of the scheme is specifically explained.

[0098] Figure 6 is a picture quality analysis element for passenger flow statistics scene according to the embodiments of the present application, like Figure 6In this context, α is the elevation angle of the AI ​​camera, H is the deployment height of the AI ​​camera relative to the ground, the preset detection area is the doorway, the reference point of the preset detection area is the center point of the threshold line, L is the horizontal distance from the AI ​​camera to the doorway, and β is the horizontal angle between the camera axis and the vertical line of the door. The above-mentioned conditional factors are identified from the extracted target image using a target scoring algorithm program. These conditional factors are then evaluated by the target scoring algorithm program to obtain the camera position perspective score. In specific implementation, based on experience in passenger flow statistics scenarios, it is generally recommended that the value range of each factor be 25°≤α≤40°, 2.5m≤H≤3.5m, 5m≤L≤8m, and -25°≤β≤25°. The optimal value of each conditional factor will be influenced by the interactions between the factors.

[0099] As can be seen from the above process, the implementation of this solution relies on computer vision technology and a dedicated AI algorithm model. Its core is to infer the pose and geometric relationship of the audio / video acquisition terminal in three-dimensional space from a two-dimensional image. Specifically, this can be achieved through the following steps:

[0100] First, a pre-trained AI model can be used to identify key visual elements and other information for calculating geometric parameters from the target image obtained by frame extraction. For example, an object detection model can be used to identify specific objects in the target image, such as "doors," "people," "vehicles," and "shelves," as a reference benchmark for calculation; models such as YOLO, SSD, or Faster R-CNN can be selected. Alternatively, a semantic segmentation model can be used to accurately identify regional features such as the outline of a "door" and the position of the horizon. Or, a keypoint detection model can be used to locate key points of specific objects in the target image, such as the four corners of a door, the top of a person's head, and the bottom of their feet.

[0101] After extracting the recognition results containing the aforementioned key visual elements, conditional factors can be calculated based on these results. Specifically, based on the extracted visual elements, various installation parameters can be calculated by combining camera intrinsic parameters (such as focal length and distortion coefficients) and known physical dimensions; that is, the aforementioned conditional factors are obtained. The calculation process for each of the main installation parameters is illustrated below:

[0102] Calculate the pitch angle α (the angle between the optical axis of the AI ​​camera and the horizontal plane): The horizon or horizontal structure (such as the top line of a door frame or the eaves) can be identified through semantic segmentation or edge detection, and the angle between it and the horizontal midline of the image can be calculated.

[0103] Calculate the installation height H: first detect the position of the pedestrian as the height reference, then locate the head and foot pixel coordinates (y_head, y_foot), combine the known height H_person and focal length f, and estimate according to the pinhole camera model: H ≈ (H_person × f) / (y_foot - y_head).

[0104] Calculate the horizontal distance L (the horizontal distance between the AI camera and the reference point of the preset detection area): identify the bottom edge of the detection area (such as a door), measure its pixel width W_pixel, and according to the actual physical width W_real and the focal length f, estimate according to the principle of similar triangles: L ≈ (W_real × f) / W_pixel.

[0105] Calculate the horizontal angle β (the angle between the optical axis of the AI camera and the normal of the preset detection area): identify the center point of the detection area in the image (x_door, y_door), compare it with the horizontal offset of the image center point (x_center, y_center), combine the image width of the current frame image_width and the horizontal field of view angle FOV_h of the audio and video acquisition terminal to estimate: β ≈ [(x_door - x_center) / image_width] × FOV_h. If the center of the door coincides with the center of the image, then β = 0°; if the center of the door is to the left, then β is negative (or positive, depending on the definition of the coordinate system). The horizontal field of view angle FOV_h refers to the maximum angle range that the audio and video acquisition terminal can see in the horizontal direction, which is a fixed optical characteristic parameter of the audio and video acquisition terminal itself and can be obtained through the application programming interface provided by it; the image width of the current frame image_width can be obtained by calling the interface provided by the camera chip SDK to read the resolution information of the current video stream by the scoring algorithm program.

[0106] Next, based on the above parameters, the shooting angle score is inferred and analyzed. Among them, the calculated condition factor is input into the preset scoring model to generate the final score. Specifically, a scoring model can be constructed based on a rule base or a scoring function, such as setting an ideal range and weight for each parameter, and the final score is calculated as Score = w1 x f(a) + w2 x f(H) + w3 x f(L) + w4 x f(b), where w is the weight, and f is the scoring function of each parameter. Alternatively, a model can be constructed based on machine learning, and a large number of labeled samples are used to train a regression model to directly map the condition factor to the comprehensive score, such as collecting images and their corresponding expert scores under different shooting angles, and training XGBoost or a simple neural network using a large amount of collected data to obtain the trained model. When the above condition factor is input into the trained model, the comprehensive score score analyzed by the model can be directly obtained.

[0107] Through the above steps, the present scheme introduces an objective parameterized evaluation method based on computer vision, which identifies condition factors from target pictures and generates shooting angle scores based on condition factors. Compared with the traditional installation, which completely depends on the experience of installation personnel and has no objective standard, and the problem that the focus of manual judgment (such as whether the picture is "beautiful" or "covers all") does not meet the requirements of the back-end AI algorithm (such as strict requirements for picture angle and target proportion), resulting in substandard algorithm accuracy after installation, the present scheme accurately measures a series of quantifiable physical parameters such as height H, pitch angle a, distance L, and horizontal angle b, achieving the objectification and unification of installation standards.

[0108] At the same time, after using the present scheme, the score can no longer be a simple "pass / fail" signal, but a diagnostic result containing rich information, such as if the score is low, the system can clearly indicate which parameter (such as the b angle) caused the problem. At this time, the customer single user can see the prompt "horizontal angle deviation is large -10 points", so as to adjust the camera left / right in a targeted manner, reducing the number of blind debugging, shortening the installation time, and improving the work efficiency.

[0109] Furthermore, these condition factors (a, H, L, b) are key influencing factors that have been verified to be strongly related to the recognition accuracy of the back-end AI algorithm. Optimizing these parameters is to directly optimize the input quality of the algorithm, thereby ensuring that the installation effect is highly consistent with the requirements of the AI business algorithm, such as ensuring that the pitch angle a is within the optimal range to avoid excessive deformation of the target object, and ensuring that the horizontal angle b is not too large to avoid the target being blocked. This ensures the best operation effect of the algorithm from the source, and reduces the subsequent misrecognition rate and user complaint rate.

[0110] In addition, the scheme can also provide data basis for installation quality management and traceability. The final parameter score and key factor value can be recorded every time the installation is performed, forming a digital archive. For example, the administrator can remotely check the installation report and see, for example, "the installation score is 95 points, a = 25°, height H = 3.1m, L = 6m, angle β = 5°". The transparency, measurability and auditability of the installation process are realized, and a solid data foundation is provided for service quality assessment and problem troubleshooting.

[0111] In summary, the scheme solves the core technical problem of relying on subjective experience and lacking unified quantitative standard in the installation process of the audio and video acquisition terminal, so that the installation quality is measurable, optimizable and traceable.

[0112] In some embodiments, the sending the shooting angle score to the client for display includes: calling a chip OSD interface built in the audio and video acquisition terminal to superimpose the shooting angle score on the live video stream.

[0113] Specifically, after generating the shooting angle score, the target score algorithm program running above calls a new product OSD interface built in the audio and video acquisition terminal to superimpose the shooting angle score on the live video stream, so that the client user can see the real-time shooting angle score on the client.

[0114] The scheme solves the problem of how to present the score information most directly and most timely to the client user by calling the OSD API through the SDK provided by the terminal chip manufacturer and passing in the score text and display position parameters, avoiding the client user switching between the terminal screen and the client App for viewing, so that the user can directly see the current shooting angle score from the picture of the live video stream, improving the convenience and efficiency of operation.

[0115] In some embodiments, after the shooting angle score is sent to the client for display, the method further includes:

[0116] In the case where the client user inputs a shooting position non-compliance signal based on the shooting angle score, a shooting position continuous adjustment instruction is generated;

[0117] In response to the shooting position continuous adjustment instruction, a new live video stream is generated based on the shooting position information adjusted by the client user, the new live video stream is processed by video image based on the target score algorithm program running, a new shooting angle score is generated, and a shooting position adjustment compliance result is generated until a signal that the shooting angle score is compliant is received from the client user.

[0118] Specifically, taking the AI camera as an example, combining Figure 7The content of the present solution is described in detail.

[0119] Figure 7 is a schematic diagram of a process of finding the best position according to the embodiments of the present application, as Figure 7 shown, the target scoring algorithm program runs, and the scoring is started; the client user adjusts the AI camera position, the AI camera generates a live video stream, the target scoring algorithm program running generates a position visual angle score based on the live video stream, and the position visual angle score is superimposed into the live video stream; the user observes the score value on the live picture; the user confirms whether the above-mentioned displayed score value meets the preset standard (such as the highest score appears, or is greater than the preset score value, for example, greater than 85 points); if the position visual angle score has met the standard, the camera position is determined, fixed installation is performed, and the installation process is ended; if the position visual angle score does not meet the preset standard, the camera position is continuously adjusted, a new live video stream is generated, the target scoring algorithm program generates a new position visual angle score based on the new live video stream, and it is continuously determined whether the new position visual angle score meets the preset standard; if yes, the camera position is determined, fixed installation is performed, and the installation process is ended; if no, the camera position is continuously adjusted until the newly generated position visual angle score meets the preset standard, the camera position is determined, fixed installation is performed, and the position installation process is ended.

[0120] The present solution solves the problem of how to guide the installation and maintenance personnel to continuously optimize after single scoring until the best installation position is found through a closed loop adjustment process of continuously adjusting the position, regenerating the video stream and scoring until the score meets the standard, clearly defines the complete workflow of human-computer interaction, ensures that the installation work can effectively converge to the optimal result, and improves the practicality and integrity of the solution.

[0121] In some embodiments, the method further includes:

[0122] receiving a secondary scoring closing request initiated by the client;

[0123] in response to the secondary scoring closing request, based on the target scoring algorithm program running, intercepting a picture with the position visual angle score, and reporting the picture with the position visual angle score to the platform server for storage.

[0124] Specifically, the client initiates a secondary scoring closing request to the audio and video acquisition terminal, in response to the secondary scoring closing request, the audio and video acquisition terminal intercepts a picture with the current position visual angle score based on the target scoring algorithm program running, and reports the picture to the platform server for storage, which is used for subsequent evaluation of the installation work quality.

[0125] The scheme provides visual evidence for installation work quality through screenshot reporting and retention, facilitates subsequent audit and acceptance, and enhances the traceability of installation work.

[0126] In some embodiments, the method further includes:

[0127] Based on the auxiliary score closing request, deleting the target score algorithm program running on the audio and video collection terminal.

[0128] Specifically, when receiving the auxiliary score closing request sent by the client, the audio and video collection terminal performs a deletion operation on the target score algorithm program running in it, and uninstalls the target score algorithm program.

[0129] In the scheme, by automatically deleting the score algorithm program on the terminal after the installation of the auxiliary is completed, the problem that the temporary function module occupies the limited storage and computing resources of the terminal for a long time is effectively solved. Compared with the method in the traditional scheme that the function program is persisted after installation and may affect the performance and stability of the device main business, the scheme significantly improves the utilization efficiency of terminal resources and the system running stability, ensures the purity and safety of the core business function of the device, embodies the "use and go" non-invasive design concept, and provides a complete closed loop for dynamic management and on-demand use of cloud applications.

[0130] It should be noted that the steps shown in the above flow or the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0131] The embodiment also provides an audio and video collection terminal site auxiliary installation system, which is used to implement the above-mentioned embodiments and preferred embodiments, and details are not repeated. As used below, the terms "module", "unit", "sub-unit" and the like can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware implementation is also possible and conceived.

[0132] Figure 8 is a structural block diagram of an audio and video collection terminal site auxiliary installation system according to an embodiment of the application, as Figure 8 shown, the apparatus includes a client 810, an audio and video collection terminal 820, and a platform server 830;

[0133] The audio and video collection terminal 820 is connected with the platform server 830 and the client 810 respectively;

[0134] The audio and video collection terminal 820 is configured to execute any of the above audio and video collection terminal auxiliary installation methods.

[0135] It should be noted that each of the above modules can be a functional module or a program module, and can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor, or each of the above modules can be located in different processors in any combination. In this embodiment, specific examples can refer to the examples described in the above embodiments and optional implementation manners, which will not be described herein.

[0136] The embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to perform the steps in any of the above method embodiments.

[0137] Optionally, the electronic device can further include a transmission device and an input and output device, wherein the transmission device is connected with the processor, and the input and output device is connected with the processor.

[0138] Optionally, in the embodiment, the processor can be configured to execute the following steps through the computer program:

[0139] S1, receiving download information issued by the platform server; the download information is obtained by the platform server analyzing an auxiliary score opening request of a client, obtaining target application scenario information, determining a target score algorithm program matched with the target application scenario information, and generating based on the target score algorithm program;

[0140] S2, in response to the download information, downloading and running the target score algorithm program to the platform server;

[0141] S3, based on the running target score algorithm program, performing video image processing on the live video stream generated by the audio and video collection terminal, generating a shooting angle score, and sending the shooting angle score to the client for display; the shooting angle score is used to indicate a shooting angle adjustment operation of the audio and video collection terminal.

[0142] It should be noted that specific examples in the embodiment can refer to examples described in the above embodiments and optional implementation manners, which will not be described herein.

[0143] In addition, in combination with the audio and video acquisition terminal site auxiliary installation method in the above embodiments, an application embodiment can provide a storage medium for implementation. The storage medium stores a computer program; the computer program is executed by a processor to implement any one of the audio and video acquisition terminal site auxiliary installation methods in the above embodiments.

[0144] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0145] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to a memory, database or other medium used in the embodiments provided by the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (Read-Only Memory, ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), a ferroelectric memory (Ferroelectric Random Access Memory, FRAM), a phase change memory (Phase Change Memory, PCM), a graphene memory, etc. The volatile memory can include a random access memory (Random Access Memory, RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0146] Those skilled in the art should understand that each technical feature of the above-described embodiments can be combined arbitrarily, and for the sake of brevity, each technical feature of the above-described embodiments is not described in all possible combinations, however, as long as the combinations of the technical features do not exist, it should be considered that it is within the scope of the description.

[0147] The above-described embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it should not be understood as a limitation on the patent scope of the application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An audio and video acquisition terminal site auxiliary installation method, characterized by, The application is applied to the audio and video collection terminal, and comprises the following steps: Receiving download information issued by a platform server; the download information is obtained by the platform server analyzing an auxiliary score opening request of a client, obtaining target application scenario information, determining a target score algorithm program matched with the target application scenario information, and generating the target score algorithm program based on the target score algorithm program; In response to the download information, downloading and running the target score algorithm program from the platform server; Based on the running target score algorithm program, video image processing is performed on a live video stream generated by the audio and video collection terminal to generate a camera angle score, including: based on the running target score algorithm program, reading the live video stream and performing frame extraction processing on the live video stream to generate a target picture after frame extraction processing; based on the running target score algorithm program, identifying a condition factor in the target picture, and inferring and analyzing the condition factor to obtain the camera angle score; wherein the condition factor includes one or more of the following camera information data: an included angle between an optical axis of the audio and video collection terminal and a horizontal plane, an installation height of the audio and video collection terminal relative to a horizontal ground, a horizontal distance between the audio and video collection terminal and a preset detection area reference point, and an included angle between a projection of the optical axis of the audio and video collection terminal on a horizontal plane and a preset detection area reference normal line; wherein the included angle between the projection of the optical axis of the audio and video collection terminal on the horizontal plane and the preset detection area reference normal line is estimated by identifying a center point of the preset detection area in the target picture, comparing a horizontal offset with the center point of the target picture, and combining the image width of the current frame and the horizontal field of view angle of the audio and video collection terminal; The camera angle score is sent to the client for display; the camera angle score is used to indicate a camera adjustment operation on the audio and video collection terminal; Receiving an auxiliary score closing request initiated by the client; Based on the auxiliary score closing request, deleting the target score algorithm program running on the audio and video collection terminal.

2. The audio and video acquisition terminal installation site auxiliary method of claim 1, wherein, The target score algorithm program is a score algorithm program matched with the target application scenario determined by the platform server based on a mapping relationship; the mapping relationship is a corresponding relationship between the application scenario and the score algorithm program established and stored by the platform server by storing a plurality of score algorithm programs corresponding to different pre-installed application scenarios.

3. The audio and video acquisition terminal installation site auxiliary method of claim 1, wherein, The download information includes a URL download address, an MD5 check code and a decompression password corresponding to the target score algorithm program; the downloading and running of the target score algorithm program from the platform server in response to the download information comprises: Downloading a package file of the target score algorithm program according to the URL download address, performing MD5 check on the package file based on the MD5 check code, decompressing the package file via the decompression password after the check is passed, and running the target score algorithm program.

4. The audio and video acquisition terminal installation site auxiliary method of claim 1, wherein, The sending of the camera angle score to the client for display comprises: calling a chip OSD interface built in the audio and video acquisition terminal to superimpose the camera angle score on the live video stream.

5. The audio and video acquisition terminal installation site auxiliary method of claim 1, wherein, After the camera angle score is sent to the client for display, the method further comprises: In the case that a camera non-standard signal input by a client user based on the camera angle score is received, a camera continuous adjustment instruction is generated; In response to the camera continuous adjustment instruction, a new live video stream is generated based on the camera information adjusted by the client user, video image processing is performed on the new live video stream based on the target score algorithm program, a new camera angle score is generated, and a camera adjustment standard result is generated until a signal confirming that the camera angle score meets the standard is received from the client user.

6. The audio and video acquisition terminal installation site auxiliary method according to any one of claims 1 to 5, characterized in that, The method further comprises: In response to the auxiliary score closing request, a picture with the camera angle score is intercepted based on the target score algorithm program, and the picture with the camera angle score is reported to the platform server for storage.

7. An audio and video acquisition terminal site auxiliary installation system, characterized by, The system comprises an audio and video acquisition terminal, a platform server and a client; the audio and video acquisition terminal is connected with the platform server and the client respectively; The audio and video acquisition terminal is used to execute the camera auxiliary installation method of the audio and video acquisition terminal in any one of claims 1 to 6.

Images