Camera running resource allocation method and device, electronic equipment and storage medium

Abstract

The application discloses a camera running resource allocation method and device, electronic equipment and storage medium, and is used for solving the technical problems that the existing running resource allocation mode consumes a large amount of resources and easily causes video stream freezing. The application comprises the following steps: receiving video streams sent by each camera; determining foreground object change information in the video streams; determining picture abnormal information and an abnormal degree corresponding to the picture abnormal information according to the foreground object change information; determining a running resource demand according to the picture abnormal information and the corresponding abnormal degree; and allocating running resources for the cameras according to the running resource demand.

Description

Technical Field

[0001] The present invention relates to the technical field of operating resource allocation, and in particular to a camera operating resource allocation method, device, electronic device and storage medium. Background Art

[0002] With the development of visual networking and home security services, more and more families are equipped with home security cameras in multiple rooms.

[0003] The current platform continuously analyzes and pushes real-time dynamic images of multiple indistinguishable code streams to users, which places high demands on network bandwidth, platform service resources such as computing power and storage, and mobile device performance. On the one hand, it consumes a lot of resources, and on the other hand, it can cause video stream lag.

[0004] Currently, the same server resources, such as computing power, storage, and network bandwidth, are uniformly allocated to each video stream from each home camera. This results in redundant resources being wasted for some video streams at the same time, resource shortages for others, and redundant resources being wasted at times for the same video stream, and resource shortages at other times. This method of continuously analyzing and delivering real-time dynamic images from multiple, uniform streams to users places high demands on network bandwidth, platform service resources such as computing power and storage, and mobile device performance. This consumes significant resources and can cause video stream lag. Summary of the Invention

[0005] The present invention provides a camera operation resource allocation method, device, electronic device and storage medium, which are used to solve the technical problems that the existing operation resource allocation method consumes large resources and easily causes video stream freezes.

[0006] The present invention provides a camera operation resource allocation method, which is applied to a server, wherein the server communicates with multiple cameras; the method comprises:

[0007] Receiving video streams sent by each of the cameras;

[0008] Determining foreground object change information in the video stream;

[0009] Determining image abnormality information and an abnormality degree corresponding to the image abnormality information according to the foreground object change information;

[0010] Determine the required amount of operating resources according to the abnormal information of the screen and the corresponding abnormality degree;

[0011] Allocate operating resources to the camera according to the operating resource demand.

[0012] Optionally, the step of determining a change in a foreground object in the video stream includes:

[0013] Decoding the video stream to obtain a decoded video stream;

[0014] identifying foreground objects in the decoded video stream;

[0015] The changes of all the foreground objects are collected to generate foreground object change information.

[0016] Optionally, the step of determining the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the foreground object change information includes:

[0017] Determine the type of each foreground object;

[0018] determining an operation mode of each type of foreground object according to the foreground object change information;

[0019] Determine user habits based on the operating modes of various types of foreground objects;

[0020] According to the operation model of each type of foreground object and the user habits, the picture abnormality information and the abnormality degree corresponding to the picture abnormality information are matched.

[0021] Optionally, the step of determining the required amount of operating resources based on the abnormal screen information and the corresponding abnormality degree includes:

[0022] Acquire first historical resource data of the operation mode at the abnormality level in the screen abnormality information;

[0023] determining a first operating resource requirement based on the first historical resource data;

[0024] Acquire second historical resource data of the user's habit of the abnormal degree in the screen abnormality information;

[0025] determining a second operating resource requirement based on the second historical resource data;

[0026] An operating resource requirement is generated according to the first operating resource requirement and the second operating resource requirement.

[0027] The present invention also provides a camera operation resource allocation device, which is applied to a server, wherein the server communicates with multiple cameras; the device comprises:

[0028] A video stream receiving module, configured to receive the video streams sent by each of the cameras;

[0029] a foreground object change information determining module, configured to determine foreground object change information in the video stream;

[0030] a picture abnormality information and abnormality degree determination module, configured to determine picture abnormality information and the abnormality degree corresponding to the picture abnormality information according to the foreground object change information;

[0031] An operating resource requirement determination module is used to determine the operating resource requirement based on the screen abnormality information and the corresponding abnormality degree;

[0032] An operating resource allocation module is used to allocate operating resources to the camera according to the operating resource demand.

[0033] Optionally, the foreground object change information determining module includes:

[0034] A decoding submodule, configured to decode the video stream to obtain a decoded video stream;

[0035] a foreground object identification submodule, configured to identify foreground objects in the decoded video stream;

[0036] The foreground object change information collection submodule is used to collect the change conditions of all the foreground objects and generate foreground object change information.

[0037] Optionally, the image abnormality information and abnormality degree determination module includes:

[0038] A type determination submodule, used to determine the type of each foreground object;

[0039] An operation mode determination submodule, configured to determine the operation mode of each type of foreground object according to the foreground object change information;

[0040] A user habit determination submodule is used to determine user habits based on the operation mode of each type of foreground object;

[0041] The picture abnormality information and abnormality degree determination submodule is used to match the picture abnormality information and the abnormality degree corresponding to the picture abnormality information according to the operation model of each type of foreground object and the user habits.

[0042] Optionally, the operating resource requirement determination module includes:

[0043] A first historical resource data acquisition submodule is configured to acquire first historical resource data of the operation mode at the abnormality level in the screen abnormality information;

[0044] A first operating resource requirement determination submodule, configured to determine a first operating resource requirement based on the first historical resource data;

[0045] A second historical resource data acquisition submodule is configured to acquire second historical resource data of the user's habit under the abnormality level in the screen abnormality information;

[0046] A second operating resource requirement determination submodule, configured to determine a second operating resource requirement based on the second historical resource data;

[0047] The operating resource requirement generating submodule is configured to generate an operating resource requirement according to the first operating resource requirement and the second operating resource requirement.

[0048] The present invention further provides an electronic device, comprising a processor and a memory:

[0049] The memory is used to store program code and transmit the program code to the processor;

[0050] The processor is used to execute any of the above methods for allocating camera operation resources according to the instructions in the program code.

[0051] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the camera operation resource allocation method as described in any one of the above items.

[0052] It can be seen from the above technical solutions that the present invention has the following advantages: The present invention discloses a camera operation resource allocation method, which is applied to a server communicating with multiple cameras, comprising: receiving a video stream sent by each camera; determining foreground object change information in the video stream; determining image abnormality information and the degree of abnormality corresponding to the image abnormality information based on the foreground object change information; determining the operation resource demand based on the image abnormality information and the corresponding degree of abnormality; and allocating operation resources to the camera based on the operation resource demand. The present invention allocates operation resources to the camera by establishing a correlation between abnormal image information, abnormality degree, and operation resource demand. This improves the rationality of resource allocation, reduces resource waste, and avoids video stream freezes caused by insufficient operation resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart of a method for allocating camera operation resources provided by an embodiment of the present invention;

[0055] Figure 2A flowchart of a method for allocating camera operation resources provided by another embodiment of the present invention;

[0056] Figure 3 This is a structural block diagram of a camera operation resource allocation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present invention provide a camera operation resource allocation method, device, electronic device and storage medium, which are used to solve the technical problems that the existing operation resource allocation method consumes large resources and easily causes video stream freezes.

[0058] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 , Figure 1 A flowchart of the steps of a camera operation resource allocation method provided by an embodiment of the present invention.

[0060] The present invention provides a camera operation resource allocation method, which is applied to a server, wherein the server communicates with multiple cameras; the method may include the following steps:

[0061] Step 101, receiving the video stream sent by each camera;

[0062] A server is a type of computer that runs faster, handles higher loads, and is more expensive than a typical computer. A server provides computing or application services to other clients (such as PCs, smartphones, ATMs, and other terminals; in this embodiment of the present invention, a camera is used as a client) on a network. A server has high-speed CPU computing power, long-term reliable operation, strong I / O external data throughput, and improved scalability. Based on the services provided by the server, generally speaking, the server has the ability to respond to service requests, provide services, and guarantee services.

[0063] A camera, also known as a computer camera, computer eye, electronic eye, etc., is a video input device.

[0064] Video streaming refers to the transmission of video data, i.e., it can be processed as a steady and continuous stream over a network. Because of the streaming, the client browser or plug-in can display the multimedia data before the entire file is transmitted.

[0065] In an embodiment of the present invention, a server can be connected to multiple cameras for communication, so as to provide resources for the multiple cameras. The cameras can send the collected video data to the server in the form of video streams.

[0066] Step 102, determining foreground object change information in the video stream;

[0067] In the embodiments of the present invention, a video stream is a data stream consisting of multiple frames of images. Depending on the time of day, different objects in the video stream may experience changes in position, posture, and other aspects. These objects that change are considered foreground objects. Objects that remain unchanged for a long period of time are considered background objects. By identifying foreground objects in the video stream and capturing the changes in each foreground object in the video stream, foreground object change information for the entire video stream can be generated.

[0068] Step 103, determining the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the foreground object change information;

[0069] Abnormal screen information can be user-defined behavior characteristics, such as a dog scratching at the door, a large number of people coming and going in a short period of time, etc. The same abnormal screen information can be set with different abnormality levels.

[0070] The generation of abnormal image information often leads to additional resource consumption of the camera. By monitoring whether abnormal image information occurs, additional resources can be allocated to the camera in advance, thereby ensuring the normal video acquisition operation of the camera.

[0071] Monitoring of abnormal image information can be obtained by analyzing foreground object change information. If the change of one or more objects in the foreground object change information exhibits abnormal behavior characteristics (such as a puppy scratching at the door), it can be determined that abnormal image information exists in the video stream. Based on the foreground object change information, the degree of abnormality of the abnormal image information can also be determined. The specific degree of abnormality can be freely set according to the specific circumstances of the abnormal image information, and the present invention does not impose specific limitations on this.

[0072] Step 104, determining the required amount of operating resources based on the abnormal information of the screen and the corresponding abnormality degree;

[0073] In an embodiment of the present invention, a mapping model between image abnormality information and the corresponding abnormality degree and server resources can be established. When the image abnormality information and the corresponding abnormality degree in the video stream are obtained, the corresponding operating resource requirements can be matched according to the model.

[0074] In one example, a mapping model may be established based on various types of screen abnormality information in historical data and the consumption of operating resources corresponding to the abnormality levels.

[0075] Then, the abnormal image information and the corresponding abnormality degree obtained in real time are used to match the corresponding operating resource requirements from the mapping model.

[0076] It should be noted that in order to make the operating resource demand output by the model tend to be optimal, after matching the corresponding operating resource demand, the mapping model can be continuously and adaptively adjusted in combination with the actual operating resource consumption.

[0077] Step 105: Allocate operating resources to the camera according to the operating resource requirements.

[0078] After matching the required operating resources, the server can allocate the corresponding operating resources to the camera. Depending on the needs of video capture, operating resources may include but are not limited to computing resources, bandwidth resources, storage resources, etc.

[0079] The present invention allocates operating resources to cameras by establishing a correlation between abnormal image information, abnormality level, and operating resource requirements. This improves the rationality of resource allocation, reduces resource waste, and avoids video stream freezes caused by insufficient operating resource allocation.

[0080] See also Figure 2 , Figure 2 This is a flowchart of a method for allocating camera operation resources provided in another embodiment of the present invention. Specifically, the method may include the following steps:

[0081] Step 201, receiving the video stream sent by each camera;

[0082] Step 201 is the same as step 101. For details, please refer to the description of step 101 and will not be repeated here.

[0083] Step 202: Decode the video stream to obtain a decoded video stream.

[0084] Decoding is the process by which the receiver converts received symbols or codes into information, corresponding to the encoding process. In computer networks, computers are interconnected through communication networks to achieve resource sharing and data transmission. When the signal format of the communication network differs from that of the transmission equipment, signal conversion must be performed. Generally, the signal conversion performed by the sender is called encoding, and the signal conversion performed by the receiver is called decoding.

[0085] In the embodiment of the present invention, after the video stream sent by the camera is acquired, the video stream may be decoded to obtain a decoded video stream.

[0086] Step 203, identifying foreground objects in the decoded video stream;

[0087] After decoding the video stream, the foreground objects in the decoded video stream can be identified through refined pixel-level segmentation, and then the decoded video stream can be deeply filtered to remove interference and eliminate background areas that do not need to be analyzed.

[0088] Step 204: collecting changes of all foreground objects and generating foreground object change information;

[0089] By collecting the changes of all foreground objects, the foreground object change information of the entire video stream can be obtained.

[0090] Step 205, determining the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the foreground object change information;

[0091] Monitoring of abnormal image information can be obtained by analyzing foreground object change information. If the change of one or more objects in the foreground object change information exhibits abnormal behavior characteristics (such as a puppy scratching at the door), it can be determined that abnormal image information exists in the video stream. Based on the foreground object change information, the degree of abnormality of the abnormal image information can also be determined. The specific degree of abnormality can be freely set according to the specific circumstances of the abnormal image information, and the present invention does not impose specific limitations on this.

[0092] In one example, the step of determining the image abnormality information and the abnormality degree corresponding to the image abnormality information based on the foreground object change information may include the following sub-steps:

[0093] S51, determining the type of each foreground object;

[0094] S52, determining the operation mode of each type of foreground object according to the foreground object change information;

[0095] S53, determining user habits based on the operation modes of various types of foreground objects;

[0096] S54 , matching the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the operation model of each type of foreground object and the user's habits.

[0097] In an embodiment of the present invention, foreground objects can be of different types, such as people, objects, and pets. Different foreground objects have different behavioral logics. Therefore, based on historical data, the behavioral logics of different types of foreground objects can be summarized to obtain the operating modes of different foreground objects. For example, a person's walking, jumping, falling, etc. can be set to different operating modes. After analyzing and obtaining the type of each foreground object, the operating mode of the foreground object of that type that frequently appears in the historical data can be obtained and used as the operating mode of the foreground object of that type (there is no need to analyze the operating mode of each foreground object of that type one by one to reduce resource loss). After obtaining the operating mode of the foreground object, the resource consumption of the foreground object of that type can be roughly analyzed based on the historical resource consumption data and the number of foreground objects of that type.

[0098] The operating mode of the foreground object can be obtained by adaptively learning the historical data of the camera. Those skilled in the art can generate the operating mode by any adaptive learning method, and the embodiment of the present invention does not specifically limit this.

[0099] After obtaining the operating mode of each foreground object, it is possible to analyze whether there are scenes in the video stream that trigger user habits, or whether the acquisition time of the video stream is consistent with the occurrence of user habits (e.g., the user will do specific things at 5 o'clock every day, and in the following time, the user will do a series of related behaviors based on the specific things. This series of related behaviors is the user habit). For example, when a puppy scratches the door, the owner will appear, etc. Different user habits will result in different resource consumption. Among them, user habits can be obtained by adaptively learning the historical data of the camera. Those skilled in the art can generate user habits through any adaptive learning method, and the embodiments of the present invention do not specifically limit this.

[0100] After obtaining the operation models and user habits of various types of foreground objects, the abnormal image information and the abnormality degree corresponding to the abnormal image information can be matched according to the mapping model.

[0101] Step 206, determining the required amount of operating resources based on the screen abnormality information and the corresponding abnormality degree;

[0102] In an embodiment of the present invention, a mapping model between image abnormality information and the corresponding abnormality degree and server resources can be established. When the image abnormality information and the corresponding abnormality degree in the video stream are obtained, the corresponding operating resource requirements can be matched according to the model.

[0103] In one example, a mapping model may be established based on various types of screen abnormality information in historical data and the consumption of operating resources corresponding to the abnormality levels.

[0104] Then, the abnormal image information and the corresponding abnormality degree obtained in real time are used to match the corresponding operating resource requirements from the mapping model.

[0105] In one example, the step of determining the required amount of operating resources based on the abnormality information of the screen and the corresponding abnormality degree may include the following sub-steps:

[0106] S61, obtaining first historical resource data of the abnormal degree of the operation mode in the screen abnormality information;

[0107] S62, determining a first operating resource requirement based on the first historical resource data;

[0108] S63, obtaining second historical resource data of the user's habits at an abnormal level in the screen abnormality information;

[0109] S64, determining a second operating resource requirement based on the second historical resource data;

[0110] S65: Generate an operating resource requirement according to the first operating resource requirement and the second operating resource requirement.

[0111] In an embodiment of the present invention, the mapping model can match the corresponding first operating resource demand based on the operating mode. The first operating resource demand is learned by the mapping model during the adaptive learning process based on the first historical resource data of the camera under the degree of abnormality. The first historical resource data is the actual resource consumption in the video stream collected at different times during the operating mode.

[0112] The mapping model can also match the corresponding second operating resource requirements based on user habits. This second operating resource requirement is learned by the mapping model during the adaptive learning process based on the second historical resource data of the camera under different abnormality levels. This second historical resource data is the actual resource consumption of the video stream collected at different times based on the user's habits.

[0113] Step 207: Allocate operating resources to the camera according to the operating resource requirements.

[0114] After matching the required operating resources, the server can allocate the corresponding operating resources to the camera. Depending on the needs of video capture, operating resources may include but are not limited to computing resources, bandwidth resources, storage resources, etc.

[0115] The present invention allocates operating resources to cameras by establishing a correlation between abnormal image information, abnormality level, and operating resource requirements. This improves the rationality of resource allocation, reduces resource waste, and avoids video stream freezes caused by insufficient operating resource allocation.

[0116] See also Figure 3 , Figure 3 This is a structural block diagram of a camera operation resource allocation device provided by an embodiment of the present invention.

[0117] An embodiment of the present invention provides a camera operation resource allocation device, which is applied to a server that communicates with multiple cameras. The device includes:

[0118] The video stream receiving module 301 is used to receive the video stream sent by each camera;

[0119] a foreground object change information determination module 302, configured to determine foreground object change information in a video stream;

[0120] The image abnormality information and abnormality degree determination module 303 is used to determine the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the foreground object change information;

[0121] The operating resource requirement determination module 304 is used to determine the operating resource requirement based on the screen abnormality information and the corresponding abnormality degree;

[0122] The operating resource allocation module 305 is used to allocate operating resources to the camera according to the operating resource demand.

[0123] In the embodiment of the present invention, the foreground object change information determining module 302 includes:

[0124] The decoding submodule is used to decode the video stream to obtain a decoded video stream;

[0125] a foreground object recognition submodule, for identifying foreground objects in the decoded video stream;

[0126] The foreground object change information collection submodule is used to collect the changes of all foreground objects and generate foreground object change information.

[0127] In the embodiment of the present invention, the image abnormality information and abnormality degree determination module 303 includes:

[0128] A type determination submodule, used to determine the type of each foreground object;

[0129] An operation mode determination submodule is used to determine the operation mode of each type of foreground object according to the foreground object change information;

[0130] A user habit determination submodule is used to determine user habits based on the operation mode of each type of foreground object;

[0131] The picture abnormality information and abnormality degree determination submodule is used to match the picture abnormality information and the abnormality degree corresponding to the picture abnormality information according to the operation model of each type of foreground object and user habits.

[0132] In an embodiment of the present invention, running the resource requirement determination module 304 includes:

[0133] A first historical resource data acquisition submodule is used to acquire first historical resource data of the operating mode under abnormality in the screen abnormality information;

[0134] A first operation resource requirement determination submodule, configured to determine a first operation resource requirement based on first historical resource data;

[0135] The second historical resource data acquisition submodule is used to acquire the second historical resource data of the user's habits at an abnormal level in the screen abnormality information;

[0136] A second operation resource requirement determination submodule, configured to determine a second operation resource requirement based on second historical resource data;

[0137] The operating resource requirement generating submodule is configured to generate an operating resource requirement according to the first operating resource requirement and the second operating resource requirement.

[0138] An embodiment of the present invention further provides an electronic device, the device including a processor and a memory:

[0139] The memory is used to store program codes and transmit the program codes to the processor;

[0140] The processor is used to execute the camera operation resource allocation method according to the instructions in the program code.

[0141] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the camera operation resource allocation method of the embodiment of the present invention.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0144] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0149] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0150] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A camera operation resource allocation method, characterized in that: Applied to a server, the server communicates with multiple cameras; the method includes: Receiving video streams sent by each of the cameras; Determining foreground object change information in the video stream; Determining image abnormality information and an abnormality degree corresponding to the image abnormality information according to the foreground object change information; Determining the operating resource demand according to the screen abnormality information and the corresponding abnormality degree includes: obtaining first historical resource data of the operating mode under the abnormality degree in the screen abnormality information; determining a first operating resource requirement based on the first historical resource data; Acquire second historical resource data of the user's habit of the abnormal degree in the abnormal screen information; determining a second operating resource requirement based on the second historical resource data; generating an operating resource requirement according to the first operating resource requirement and the second operating resource requirement; Allocate operating resources to the camera according to the operating resource demand.

2. The method according to claim 1, characterized in that The step of determining a change in a foreground object in the video stream comprises: Decoding the video stream to obtain a decoded video stream; identifying foreground objects in the decoded video stream; The changes of all the foreground objects are collected to generate foreground object change information.

3. The method according to claim 2, characterized in that The step of determining the image abnormality information and the abnormality degree corresponding to the image abnormality information according to the foreground object change information includes: Determine the type of each foreground object; determining an operation mode of each type of foreground object according to the foreground object change information; Determine user habits based on the operating modes of various types of foreground objects; According to the operation mode of each type of foreground object and the user habits, the screen abnormality information and the abnormality degree corresponding to the screen abnormality information are matched.

4. A camera operation resource allocation device, characterized in that: Applied to a server, the server communicating with a plurality of cameras; The device comprises: A video stream receiving module, configured to receive the video streams sent by each of the cameras; a foreground object change information determining module, configured to determine foreground object change information in the video stream; a picture abnormality information and abnormality degree determination module, configured to determine picture abnormality information and the abnormality degree corresponding to the picture abnormality information according to the foreground object change information; An operating resource requirement determination module is configured to determine an operating resource requirement based on the screen abnormality information and the corresponding abnormality degree, including: a first historical resource data acquisition submodule, configured to acquire first historical resource data of the operating mode in the screen abnormality information at the abnormality degree; a first operating resource requirement determination submodule, configured to determine a first operating resource requirement based on the first historical resource data; a second historical resource data acquisition submodule, configured to acquire second historical resource data of the user habits in the screen abnormality information at the abnormality degree; a second operating resource requirement determination submodule, configured to determine a second operating resource requirement based on the second historical resource data; and an operating resource requirement generation submodule, configured to generate an operating resource requirement based on the first operating resource requirement and the second operating resource requirement. An operating resource allocation module is used to allocate operating resources to the camera according to the operating resource demand.

5. The device according to claim 4, characterized in that The foreground object change information determination module includes: A decoding submodule, configured to decode the video stream to obtain a decoded video stream; a foreground object identification submodule, configured to identify foreground objects in the decoded video stream; The foreground object change information collection submodule is used to collect the change conditions of all the foreground objects and generate foreground object change information.

6. The device according to claim 5, characterized in that The image abnormality information and abnormality degree determination module includes: A type determination submodule, used to determine the type of each foreground object; An operation mode determination submodule, configured to determine the operation mode of each type of foreground object according to the foreground object change information; A user habit determination submodule is used to determine user habits based on the operation mode of each type of foreground object; The picture abnormality information and abnormality degree determination submodule is used to match the picture abnormality information and the abnormality degree corresponding to the picture abnormality information according to the operation mode of each type of foreground object and the user habits.

7. An electronic device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the camera operation resource allocation method described in any one of claims 1-3 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the camera operation resource allocation method according to any one of claims 1 to 3.

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