Live broadcast early warning parameter automatic adjustment method, apparatus and device, and medium

Abstract

The invention provides a live broadcast early warning parameter automatic adjustment method and apparatus, a device and a medium. The method comprises the steps of obtaining a video stream; intercepting the video stream to obtain a key frame picture; based on the key frame picture, analyzing to obtain article information; based on the live broadcast product information and the article information, performing analysis to obtain an early warning parameter corresponding to the article information; the live broadcast product information is determined and obtained based on live broadcast products covered by the current live broadcast; adjusting a current early warning threshold value of the current live broadcast based on the early warning parameter to obtain an adjusted early warning threshold value; and carrying out monitoring and early warning on the current live broadcast by taking the adjusted early warning threshold value as a current early warning threshold value. According to the scheme, the diversification and dynamic adaptability of the live broadcast content can be improved, the real-time performance and accuracy of monitoring and early warning parameter adjustment are improved, the resource utilization rate of the live broadcast room is optimized, and the user stickiness is increased.

Description

Technical Field

[0001] The present application relates to the technical field of e-commerce live broadcast monitoring, and in particular to a method, device, equipment and medium for automatically adjusting live broadcast warning parameters. Background Art

[0002] With the rapid expansion of the e-commerce live streaming industry, live streaming content has gradually become more diverse and complex. This phenomenon has greatly enriched consumers' shopping experience, allowing them to obtain product information and make direct purchasing decisions on a dynamic and interactive platform. However, this rapid development trend has also brought new challenges to live streaming companies, especially in terms of anchor management and live streaming quality assurance.

[0003] The competitive pressure in the live broadcast industry is increasing day by day. The quality and category of live broadcast content directly affect the audience's stickiness and willingness to consume. Therefore, in order to ensure that the content of the live broadcast room meets user expectations and regulatory requirements, companies usually need to monitor the live broadcast activities of the anchor in real time. However, the existing monitoring system mainly relies on manually set parameters for regulation, which faces the following limitations: (1) Lack of real-time performance Manually adjusting monitoring parameters usually cannot be fully synchronized with the actual dynamics of the live broadcast room. Especially when there are sudden changes in the live broadcast content, the monitoring system may not be able to adjust in time, resulting in the inability to detect and deal with the problem in time.

[0004] (2) Limited accuracy Since the setting of adjustment parameters relies on human judgment, insufficient monitoring or excessive intervention may occur due to lack of experience or subjective bias. This not only affects the normal operation of the live broadcast room, but may also bring negative user experience.

[0005] (3) Single content In order to circumvent the above problems, many live broadcast companies choose to fix the categories of live broadcast rooms and reduce risks by limiting the scope of live broadcast content. However, this practice greatly limits the freedom of the anchor to display content in the live broadcast room, making the utilization efficiency of the live broadcast room lower and difficult to meet the growing diversified needs of consumers. At the same time, this category-single strategy also weakens the anchor's flexibility and innovation ability in attracting viewers, which may lead to user loss.

[0006] (4) Lack of flexibility in technical support Although the method of fixing live broadcast categories can reduce management costs and risks in the short term, in the long run, this method ignores the industry's demand for content diversity and flexibility. As consumers' requirements for live broadcast content increase, live broadcast companies need more advanced technical means to improve the diversity and quality of live broadcast content in order to cope with fierce market competition.

[0007] (5) The increasing complexity of the live streaming industry As the scale and user base of live broadcast platforms continue to expand, anchors need to display products or content of different categories in a short period of time, and the fixed category management model is difficult to adapt to such dynamic needs. This not only limits the growth potential of the platform, but also may lead to a waste of resources.

[0008] In summary, the current technical requirements for anchor management and live broadcast quality assurance in the live broadcast industry have surpassed the traditional manual adjustment and fixed category model. In order to meet these challenges, it is urgent to develop a more intelligent and real-time monitoring and early warning system to help live broadcast companies balance content diversity and compliance, maximize the utilization of live broadcast rooms, and provide anchors with a wider content display space. This will help promote the sustainable development of the live broadcast industry and bring consumers a richer and higher-quality shopping experience. Summary of the invention

[0009] In order to overcome the defects of the above-mentioned prior art, the present application provides a method, device, equipment and medium for automatically adjusting live broadcast warning parameters, which can enhance the diversity and dynamic adaptability of live broadcast content, improve the real-time and accuracy of monitoring warning parameter adjustment, optimize the resource utilization of the live broadcast room, and increase user stickiness.

[0010] In order to solve the above technical problems, this application provides the following technical solutions: According to a first aspect of an embodiment of the present application, a method for automatically adjusting live broadcast warning parameters is provided, comprising: Get the video stream; Intercepting the video stream to obtain key frame images; Based on the key frame image, obtain item information by parsing; Based on the live broadcast product information and the item information, an early warning parameter corresponding to the item information is analyzed; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; Adjusting the current warning threshold of the current live broadcast based on the warning parameter to obtain an adjusted warning threshold; The adjusted warning threshold is used as the current warning threshold to monitor and warn the current live broadcast.

[0011] In an exemplary embodiment, the parsing and obtaining the item information based on the key frame image includes: Preprocessing the key frame image to obtain a preprocessed image; Analyzing the pre-processed image using a target detection algorithm to define an object area; Using a deep learning model to extract features from the object area to obtain object features; Inputting the item features into a classification model to identify the content of the item; The item content is parsed using an attribute recognition model to obtain the item information.

[0012] In an exemplary embodiment, the analyzing and obtaining the warning parameters corresponding to the item information based on the live product information and the item information includes: Compare and analyze the live broadcast product information and the item information to determine the live broadcast product corresponding to the item information; Based on the live broadcast product, the warning parameters are determined.

[0013] In an exemplary embodiment, adjusting the current warning threshold of the current live broadcast based on the warning parameter to obtain the adjusted warning threshold includes: Compare and analyze the warning parameter with the current warning threshold of the current live broadcast to determine whether there is a difference; In case there is a difference, the current warning threshold is adjusted to obtain the adjusted warning threshold.

[0014] In an exemplary embodiment, when there is a difference, adjusting the current warning threshold to obtain the adjusted warning threshold includes: In case of differences, determining the number of groups of the warning parameters; In the case where the warning parameter indication is a group, the current warning threshold is adjusted, and the warning parameter is used as the adjusted warning threshold.

[0015] In an exemplary embodiment, the method further comprises: In the case where the warning parameter indicates multiple groups, the values ​​of the parameters in each group are compared and analyzed, and the parameters with the smallest values ​​among the parameters of the same type form the adjusted warning threshold.

[0016] In an exemplary embodiment, the method further comprises: In the case where there is no difference, the current warning threshold is maintained to monitor and warn the current live broadcast.

[0017] According to a second aspect of an embodiment of the present application, a device for automatically adjusting live broadcast warning parameters is provided, using any one of the above-mentioned methods for automatically adjusting live broadcast warning parameters, the device comprising: Video acquisition module, used to acquire video stream; A picture capture module, used to capture the video stream to obtain a key frame picture; An information analysis module, used to analyze and obtain item information based on the key frame images; An early warning parameter determination module is used to analyze and obtain early warning parameters corresponding to the item information based on the live broadcast product information and the item information; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; An early warning threshold adjustment module, used to adjust the current early warning threshold of the current live broadcast based on the early warning parameter to obtain an adjusted early warning threshold; A monitoring and warning module is used to monitor and warn the current live broadcast by using the adjusted warning threshold as the current warning threshold.

[0018] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a method for automatically adjusting live broadcast warning parameters as described in any one of the above items.

[0019] According to the fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored. The at least one instruction or the at least one program is loaded and executed by a processor to implement a method for automatically adjusting live broadcast warning parameters as described in any one of the above items.

[0020] By adopting the above technical solution, the present application has the following beneficial effects: (1) Improving the diversity and dynamic adaptability of live broadcast content Through the automatic adjustment of live broadcast warning parameters, monitoring indicators can be dynamically adjusted according to the real-time status of the live broadcast room without human intervention, so that the live broadcast room can flexibly display diversified content and eliminate the limitations of fixed category management, allowing the anchor to quickly switch to display different categories of goods or content to better meet the diverse needs of consumers.

[0021] (2) Improve the real-time and accuracy of monitoring and warning parameter adjustment Through automatic adjustment of live broadcast warning parameters, the warning threshold can be quickly adjusted according to the live broadcast content, which significantly improves the response speed, prevents false warnings, and avoids delayed adjustment or misjudgment caused by lack of experience, subjective bias, fatigue, etc. during manual adjustment, thereby improving the real-time and accuracy of monitoring warning parameter adjustment.

[0022] (3) Optimize the utilization of live broadcast room resources By flexibly adjusting the warning parameters, the live broadcast room can optimize content presentation according to real-time needs, maximize the utilization rate of each live broadcast room, reduce idle time and resource waste caused by the single category of the live broadcast room, and improve the overall operational efficiency of the platform.

[0023] (4) Enhance platform compliance and risk prevention and control capabilities Automatically adjusting warning parameters can conduct real-time screening of the compliance of live content, quickly discover potential violations, and avoid the platform facing regulatory penalties due to non-compliant content. The system can dynamically identify sensitive points of different categories of live content and adjust monitoring strategies accordingly, thereby covering risky content more comprehensively.

[0024] (5) Reduce operating costs and manpower input The automated early warning and adjustment method reduces reliance on manual intervention and reduces labor costs. The systematic intelligent management enables the platform to handle larger-scale live broadcast room monitoring needs and reduces misoperation and efficiency losses caused by manual intervention.

[0025] (6) Improve user experience and increase user stickiness By ensuring the diversity and consistency of live broadcast content, consumers can get a richer and higher-quality shopping experience. The dynamic adjustment mechanism can also provide anchors with more room to play, encouraging them to innovate content formats, thereby enhancing the audience's sense of interaction and participation, and further improving user stickiness.

[0026] (7) Promoting innovation and development of the live streaming industry This method has introduced intelligent management tools to the live broadcast industry, promoted the iteration and upgrading of industry technology, and the platform can use this technical method to develop new business models, such as multi-category, cross-domain live broadcast solutions, thereby opening up more potential markets.

[0027] In summary, through the automatic adjustment of live broadcast warning parameters, the live broadcast platform can more efficiently manage and optimize the live broadcast content of the anchor, which can effectively solve the problems of insufficient real-time, accuracy and flexibility faced by the current live broadcast industry in monitoring and management, and avoid the limitations brought by the fixed category model. At the same time, this method also significantly improves the platform's monitoring capabilities and risk management level, providing technical guarantees for the long-term sustainable development of the live broadcast industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A flowchart of a method for automatically adjusting live broadcast warning parameters provided in an embodiment of the present application; Figure 2 A structural block diagram of an automatic adjustment device for live broadcast warning parameters provided in an embodiment of the present application; Figure 3 A hardware structure block diagram of an electronic device that runs a method for automatically adjusting live broadcast warning parameters provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] As used herein, "one embodiment" or "embodiment" refers to a The specific features, structures or characteristics in at least one implementation of the present application. In the description of the embodiments of the present application, it is to be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "top", "bottom", etc. is based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present application. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" can include one or more of the features explicitly or implicitly. Moreover, the terms "first", "second", etc. are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here.

[0032] See also Figure 1 , which is a flow chart of an automatic adjustment method for live broadcast warning parameters provided by an embodiment of the present application, the automatic adjustment method for live broadcast warning parameters includes: Step S1: Get video stream; Step S2: intercepting the video stream to obtain key frame images; Step S3: Based on the key frame image, analyze and obtain item information; Step S4: Based on the live broadcast product information and the item information, the warning parameters corresponding to the item information are analyzed; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; Step S5: adjusting the current warning threshold of the current live broadcast based on the warning parameter to obtain an adjusted warning threshold; Step S6: Use the adjusted warning threshold as the current warning threshold to monitor and warn the current live broadcast.

[0033] In an optional embodiment, the video stream in the above step S1 can be obtained through a camera device in the live broadcast room, or through a live broadcast platform.

[0034] In an optional embodiment, the above step S2 may include: Use FFmpeg: Use the FFmpeg command line tool to extract key frames using specific filter options, and intercept the video stream based on the key frames to obtain the key frame images; or, Image content-based method: directly analyze the image content of the video stream, select frames with significant features as key frames, and intercept the video stream based on the key frames to obtain key frame images; or, Motion analysis-based method: extract key frames by analyzing the motion information in the video, and intercept the video stream based on the key frames to obtain key frame images; or, Key frame detection algorithm based on trajectory curve point density features: extract key frames by analyzing the distribution characteristics of video frames on the time axis, and intercept the video stream based on the key frames to obtain key frame images; or, Clustering-based method: First, cluster the video frames, then select one or more representative frames in each cluster as key frames, and intercept the video stream based on the key frames to obtain the key frame images; or, Sampling-based method: randomly extract video frames or randomly extract them within a specified time interval as key frames, and intercept the video stream based on the key frames to obtain key frame images.

[0035] In an optional embodiment, the above step S3 may include: Preprocessing the key frame image to obtain a preprocessed image; Analyze the pre-processed image using the object detection algorithm to frame the object area; Use the deep learning model to extract features from the object area and obtain the object features; Input item features into the classification model to identify the content of the item; Use the attribute recognition model to parse the item content and obtain the item information.

[0036] Specifically, the key frame images are preprocessed, which may include adjusting brightness, contrast, and denoising. The preprocessed images are analyzed using target detection algorithms, such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), or Faster R-CNN (Faster Region-based Convolutional Neural Networks), to identify all possible object locations in the image and provide bounding boxes of the objects in the image. The detected bounding boxes are further adjusted using techniques such as non-maximum suppression (NMS) to frame the object area. Deep learning models such as convolutional neural networks (CNN) are used to extract features from each detected object area, convert the image information of the object into a feature vector that can be used by the classifier, and input the extracted feature vector into a trained classification model, such as ResNet, Inception, etc. The specific category of the object is predicted based on the feature vector, that is, the content of the object is identified. The attribute recognition model is used to parse the content of the object and further identify the object information.

[0037] In an optional embodiment, the above step S4 may include: Compare and analyze the live broadcast product information and the item information to determine the live broadcast product corresponding to the item information; Determine warning parameters based on live broadcast products.

[0038] Specifically, the live broadcast product information includes the picture information, text information, category, and corresponding warning parameters of the live broadcast products covered by the current live broadcast; Comparing and analyzing the live product information and the item information can be performed by comparing and analyzing the image information of the live product with the item images in the item information, or by comparing and analyzing the text information of the live product with the text description of the item in the item information obtained by parsing the attribute recognition model.

[0039] In an optional embodiment, the above step S5 may include: Compare and analyze the warning parameters with the current warning threshold of the current live broadcast to determine whether there is a difference; When there is a difference, the current warning threshold is adjusted to obtain the adjusted warning threshold; when there is no difference, the current warning threshold is maintained to monitor and warn the current live broadcast.

[0040] Specifically, when there is a difference, adjusting the current warning threshold to obtain the adjusted warning threshold may include: In case of discrepancies, determine the number of groups of warning parameters; When the warning parameters are indicated as a group, the current warning threshold is adjusted and the warning parameters are used as the adjusted warning threshold; when the warning parameters are indicated as multiple groups, the values ​​of the parameters in each group are compared and analyzed, and the adjusted warning threshold is composed of the parameters with the smallest values ​​among the parameters of the same type.

[0041] That is, if the host displays multiple items, the item information parsed from the key frame image will also be multiple. At this time, based on the live product information and item information, the warning parameters corresponding to the item information are analyzed to be multiple groups. Since the warning thresholds corresponding to multiple items are different, the values ​​of each group of parameters are compared and analyzed, and the adjusted warning threshold is composed of the parameters with the smallest values ​​among the same type of parameters.

[0042] Among them, the types of parameters include face parameters, picture brightness parameters, sound parameters, object parameters, etc.

[0043] It can be seen from the above technical solutions of the embodiments of the present application that, in the embodiments of the present application, the live broadcast warning parameters are automatically adjusted so that the live broadcast platform can more efficiently manage and optimize the live broadcast content of the anchor, which can effectively solve the problems of insufficient real-time, accuracy and flexibility faced by the current live broadcast industry in monitoring and management, and avoid the limitations brought by the fixed category model. At the same time, the method also significantly improves the monitoring ability and risk management level of the platform, providing technical guarantee for the long-term sustainable development of the live broadcast industry.

[0044] Corresponding to the automatic adjustment method of the live broadcast warning parameters provided in the above-mentioned embodiment, the embodiment of the present application also provides an automatic adjustment device for the live broadcast warning parameters. Since the automatic adjustment device for the live broadcast warning parameters provided in the embodiment of the present application corresponds to the automatic adjustment method for the live broadcast warning parameters provided in the above-mentioned embodiment, the implementation method of the aforementioned automatic adjustment method for the live broadcast warning parameters is also applicable to the automatic adjustment device for the live broadcast warning parameters provided in this embodiment, and will not be described in detail in this embodiment.

[0045] See also Figure 2 , which is a structural block diagram of an automatic adjustment device for live broadcast warning parameters provided by an embodiment of the present application; the device includes: 01: Video acquisition module, used to obtain video stream; 02: Screen capture module, used to capture the video stream and obtain key frame images; 03: Information parsing module, used to parse and obtain item information based on key frame images; 04: Warning parameter determination module, used to analyze and obtain warning parameters corresponding to the item information based on the live broadcast product information and item information; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; 05: Warning threshold adjustment module, used to adjust the current warning threshold of the current live broadcast based on the warning parameters to obtain the adjusted warning threshold; 06: Monitoring and warning module, used to use the adjusted warning threshold as the current warning threshold to monitor and warn the current live broadcast.

[0046] It should be noted that the device provided in the above embodiment, when implementing its functions, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0047] The automatic adjustment device for live broadcast warning parameters in the embodiment of the present application can enable the live broadcast platform to more efficiently manage and optimize the live broadcast content of the anchor through automatic adjustment of the live broadcast warning parameters, which can effectively solve the problems of insufficient real-time, accuracy and flexibility faced by the current live broadcast industry in monitoring and management, and avoid the limitations brought by the fixed category model. At the same time, the method also significantly improves the monitoring ability and risk management level of the platform, providing technical guarantee for the long-term sustainable development of the live broadcast industry.

[0048] An embodiment of the present application also provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the automatic adjustment method of live broadcast warning parameters provided in the above method embodiment.

[0049] The memory can be used to store software programs and modules. The processor executes various functional applications and realizes high-level autonomous driving by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, application programs required for functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0050] The method embodiments provided in the embodiments of the present application may be executed in a computer terminal, a server or a similar computing device, that is, the above-mentioned electronic device may include a computer terminal, a server or a similar computing device. Figure 3 is a hardware structure block diagram of an electronic device for running a method for automatically adjusting live broadcast warning parameters provided in an embodiment of the present application, such as Figure 3 As shown, the internal structure of the electronic device may include but is not limited to: a processor, a network interface and a memory. The processor, network interface and memory in the electronic device may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0051] Among them, the processor (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device. The network interface may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). Memory is a memory device in an electronic device for storing programs and data. It is understandable that the memory here can be a high-speed RAM storage device or a non-volatile memory device, such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor. The memory provides a storage space, which stores the operating system of the electronic device, which may include but is not limited to: Windows system (an operating system), Linux (an operating system), Android (Android, a mobile operating system) system, IOS (a mobile operating system) system, etc., and this application does not limit this; and, in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and these instructions may be one or more computer programs (including program codes). In the embodiment of this specification, the processor loads and executes one or more instructions stored in the memory to implement the automatic adjustment method of the live warning parameters provided in the above method embodiment.

[0052] An embodiment of the present application also provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the automatic adjustment method of live broadcast warning parameters provided in the method embodiment.

[0053] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0054] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. And the above describes a specific embodiment of this specification. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-small sample image classification and parallel processing are also possible or may be advantageous.

[0055] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0056] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0057] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for automatically adjusting live broadcast warning parameters, characterized in that: include: Get the video stream; Intercepting the video stream to obtain key frame images; Based on the key frame image, obtain item information by parsing; Based on the live broadcast product information and the item information, an early warning parameter corresponding to the item information is analyzed; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; Adjusting the current warning threshold of the current live broadcast based on the warning parameter to obtain an adjusted warning threshold; The adjusted warning threshold is used as the current warning threshold to monitor and warn the current live broadcast.

2. The automatic adjustment method of live broadcast warning parameters according to claim 1 is characterized in that: The step of parsing and obtaining item information based on the key frame image includes: Preprocessing the key frame image to obtain a preprocessed image; Analyzing the pre-processed image using a target detection algorithm to define an object area; Using a deep learning model to extract features from the object area to obtain object features; Inputting the item features into a classification model to identify the content of the item; The item content is parsed using an attribute recognition model to obtain the item information.

3. The automatic adjustment method of live broadcast warning parameters according to claim 1 is characterized in that: The analyzing and obtaining the warning parameters corresponding to the item information based on the live product information and the item information includes: Compare and analyze the live broadcast product information and the item information to determine the live broadcast product corresponding to the item information; Based on the live broadcast product, the warning parameters are determined.

4. A method for automatically adjusting live broadcast warning parameters according to any one of claims 1 to 3, characterized in that: The step of adjusting the current warning threshold of the current live broadcast based on the warning parameter to obtain the adjusted warning threshold comprises: Compare and analyze the warning parameter with the current warning threshold of the current live broadcast to determine whether there is a difference; In case there is a difference, the current warning threshold is adjusted to obtain the adjusted warning threshold.

5. The automatic adjustment method of live broadcast warning parameters according to claim 4 is characterized in that: In the case where there is a difference, adjusting the current warning threshold to obtain the adjusted warning threshold includes: In case of differences, determining the number of groups of the warning parameters; In the case where the warning parameter indication is a group, the current warning threshold is adjusted, and the warning parameter is used as the adjusted warning threshold.

6. The automatic adjustment method of live broadcast warning parameters according to claim 5 is characterized in that: The method further comprises: In the case where the warning parameter indicates multiple groups, the values ​​of the parameters in each group are compared and analyzed, and the parameters with the smallest values ​​among the parameters of the same type form the adjusted warning threshold.

7. The automatic adjustment method of live broadcast warning parameters according to claim 6 is characterized in that: The method further comprises: In the case where there is no difference, the current warning threshold is maintained to monitor and warn the current live broadcast.

8. An automatic adjustment device for live broadcast warning parameters, using an automatic adjustment method for live broadcast warning parameters as claimed in any one of claims 1 to 7, characterized in that: The device comprises: Video acquisition module, used to acquire video stream; A picture capture module, used to capture the video stream to obtain a key frame picture; An information analysis module, used to analyze and obtain item information based on the key frame images; An early warning parameter determination module is used to analyze and obtain early warning parameters corresponding to the item information based on the live broadcast product information and the item information; the live broadcast product information is determined based on the live broadcast products covered by the current live broadcast; An early warning threshold adjustment module, used to adjust the current early warning threshold of the current live broadcast based on the early warning parameter to obtain an adjusted early warning threshold; A monitoring and warning module is used to monitor and warn the current live broadcast by using the adjusted warning threshold as the current warning threshold.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement an automatic adjustment method for live broadcast warning parameters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement a method for automatically adjusting live broadcast warning parameters as described in any one of claims 1 to 7.

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