CN115695332A - Camera operation resource allocation method and device, electronic equipment and storage medium - Google Patents
Camera operation resource allocation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a camera running resource allocation method and device, electronic equipment and a storage medium, and aims to solve the technical problems that the existing running resource allocation mode is high in resource consumption and easy to cause video streaming blockage. The invention comprises the following steps: receiving video streams sent by the cameras; determining foreground object variation information in the video stream; determining picture abnormal information and abnormal degree corresponding to the picture abnormal information according to the foreground object change information; determining the operation resource demand according to the picture abnormal information and the corresponding abnormal degree; and allocating operating resources to the camera according to the operating resource demand.
Description
Technical Field
The present invention relates to the field of operating resource allocation technologies, and in particular, to a method and an apparatus for allocating operating resources of a camera, an electronic device, and a storage medium.
Background
With the development of video networking and housekeeping services, more and more households are respectively equipped with housekeeping cameras in a plurality of rooms.
The current platform continuously analyzes and pushes a plurality of real-time dynamic pictures of undifferentiated code streams to a user, has higher requirements on network bandwidth, platform service resources such as computational power storage and the like and mobile terminal equipment performance, consumes a large amount of resources on one hand, and causes video stream blockage on the other hand.
At present, server resources such as calculation power, storage, network bandwidth and the like are configured for each video stream of each housekeeping camera without difference, so that part of video stream resources are wasted redundantly at the same time, part of video stream resources are deficient, part of time resources of the same video stream are wasted redundantly, and part of time resources are deficient. The mode of continuously analyzing and pushing a plurality of real-time dynamic pictures without differential code streams to the user has higher requirements on network bandwidth, platform service resources such as computational power storage and the like and mobile terminal equipment performance, consumes a large amount of resources on one hand, and causes video stream blockage on the other hand.
Disclosure of Invention
The invention provides a camera running resource distribution method and device, electronic equipment and a storage medium, which are used for solving the technical problems that the existing running resource distribution mode is high in resource consumption and video streaming is easy to jam.
The invention provides a camera running resource allocation method, which is applied to a server, wherein the server is communicated with a plurality of cameras; the method comprises the following steps:
receiving video streams sent by the cameras;
determining foreground object variation information in the video stream;
determining picture abnormal information and abnormal degree corresponding to the picture abnormal information according to the foreground object change information;
determining the operation resource demand according to the picture abnormal information and the corresponding abnormal degree;
and allocating the operating resources to the camera according to the operating resource demand.
Optionally, the step of determining a foreground object variation condition in the video stream includes:
decoding the video stream to obtain a decoded video stream;
identifying a foreground object in the decoded video stream;
and collecting the change conditions of all the foreground objects to generate foreground object change information.
Optionally, the step of determining, according to the foreground object variation information, screen abnormality information and an abnormality degree corresponding to the screen abnormality information includes:
determining the type of each foreground object;
determining the operation mode of each type of foreground object according to the foreground object change information;
determining user habits according to the operation modes of the foreground objects of various types;
and matching the abnormal picture information and the abnormal degree corresponding to the abnormal picture information according to the operation model of each type of foreground object and the user habit.
Optionally, the step of determining the operation resource demand according to the screen abnormality information and the corresponding abnormality degree includes:
acquiring first historical resource data of the operation mode in the image abnormal information under the abnormal degree;
determining a first operating resource demand according to the first historical resource data;
acquiring second historical resource data of the user habit under the abnormal degree in the abnormal picture information;
determining a second operating resource demand according to the second historical resource data;
and generating the operation resource demand according to the first operation resource demand and the second operation resource demand.
The invention also provides a camera running resource distribution device, which is applied to a server, wherein the server is communicated with the plurality of cameras; the device comprises:
the video stream receiving module is used for receiving the video streams sent by the cameras;
a foreground object variation information determining module, configured to determine foreground object variation information in the video stream;
the image abnormal information and abnormal degree determining module is used for determining image abnormal information and abnormal degree corresponding to the image abnormal information according to the foreground object change information;
the running resource demand determining module is used for determining the running resource demand according to the picture abnormal information and the corresponding abnormal degree;
and the running resource allocation module is used for allocating running resources to the camera according to the running resource demand.
Optionally, the foreground object variation information determining module includes:
the decoding submodule is used for decoding the video stream to obtain a decoded video stream;
a foreground object identification sub-module for identifying foreground objects in the decoded video stream;
and the foreground object change information acquisition submodule is used for acquiring the change conditions of all the foreground objects and generating foreground object change information.
Optionally, the screen abnormality information and abnormality degree determining module includes:
the type determining submodule is used for determining the type of each foreground object;
the operation mode determining submodule is used for determining the operation mode of each type of foreground object according to the foreground object change information;
the user habit determining submodule is used for determining user habits according to the operation modes of the foreground objects of various types;
and the image abnormal information and abnormal degree determining submodule is used for matching the image abnormal information and the abnormal degree corresponding to the image abnormal information according to the operation model of each type of foreground object and the user habit.
Optionally, the running resource demand determining module includes:
a first historical resource data acquisition submodule, configured to acquire first historical resource data of the operation mode in the screen abnormality information at the abnormality degree;
the first operating resource demand determining submodule is used for determining a first operating resource demand according to the first historical resource data;
a second historical resource data acquisition sub-module, configured to acquire second historical resource data of the user habit under the abnormal degree in the abnormal picture information;
a second operating resource demand determining submodule, configured to determine a second operating resource demand according to the second historical resource data;
and the operating resource demand generation submodule is used for generating operating resource demand according to the first operating resource demand and the second operating resource demand.
The invention also provides an electronic device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the camera operating resource allocation method according to an instruction in the program code.
The present invention also provides a computer-readable storage medium for storing program code for executing the camera operating resource allocation method as described in any one of the above.
According to the technical scheme, the invention has the following advantages: the invention discloses a camera running resource allocation method, which is applied to a server communicated with a plurality of cameras and comprises the following steps: receiving video streams sent by all cameras; determining foreground object change information in a video stream; determining picture abnormal information and abnormal degree corresponding to the picture abnormal information according to the foreground object change information; determining the operation resource demand according to the picture abnormal information and the corresponding abnormal degree; and allocating the operating resources for the camera according to the operating resource demand. The invention allocates the running resources for the camera by establishing the association relationship between the abnormal picture information, the abnormal degree and the running resource demand. Therefore, the rationality of resource allocation is improved, the resource waste is reduced, and the video stream blockage caused by insufficient running resource allocation is avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a flowchart illustrating steps of a method for allocating resources for operating a camera according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a method for allocating resources for operating a camera according to another embodiment of the present invention;
fig. 3 is a block diagram of a camera operation resource allocation apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for distributing running resources of a camera, electronic equipment and a storage medium, which are used for solving the technical problems that the existing running resource distribution mode is high in resource consumption and video streaming is easy to jam.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a method for allocating resources for operating a camera according to an embodiment of the present invention.
The invention provides a camera running resource allocation method, which is applied to a server, wherein the server is communicated with a plurality of cameras; the method may comprise the steps of:
a server is one of computers that runs faster, is more heavily loaded, and is more expensive than a regular computer. The server provides computing or application services for other clients (such as terminals of a PC, a smart phone, an ATM and the like, and the embodiment of the invention selects the camera as the client) in the network. The server has high-speed CPU computing capacity, long-time reliable operation, strong I/O external data throughput capacity and better expansibility. Generally, a server has the capability of responding to a service request, supporting a service, and guaranteeing the service according to the service provided by the server.
The camera is also called a computer camera, a computer eye, an electronic eye and the like, and is a video input device.
Video streaming refers to the transmission of video data, which can be handled as a steady and continuous stream over a network, for example. Because of the streaming, the client browser or plug-in is able to display the multimedia data before the entire file is transferred.
In the embodiment of the invention, one server can be in communication connection with a plurality of cameras so as to respectively provide resources for the plurality of cameras. The camera can send the collected video data to the server in a video streaming mode.
in the embodiment of the present invention, the video stream is a data stream composed of multiple frames of images, and according to the difference of time, different objects in the video stream may change in terms of position, posture, and the like, and these changed objects are foreground objects. And the object which remains unchanged for a long time is a background object. By identifying foreground objects in the video stream and collecting the variation condition of each foreground object in the video stream, the foreground object variation information of the whole video stream can be generated.
103, determining abnormal information of the picture and abnormal degree corresponding to the abnormal information of the picture according to the change information of the foreground object;
the abnormal information of the picture can be some behavior characteristics customized by a user, such as a dog opening a door, a large number of people coming and going in a short time and the like. The same picture abnormality information can be set to different abnormality degrees.
The generation of the abnormal picture information often causes the extra resource consumption of the camera, and the extra resource can be allocated for the camera in advance by monitoring whether the abnormal picture information occurs or not, so that the normal video acquisition operation of the camera is ensured.
The monitoring of the abnormal picture information can be obtained by analyzing the foreground object change information, and if the change condition of one or some objects in the foreground object change information belongs to abnormal behavior characteristics (such as a dog opening a door), the abnormal picture information in the video stream can be judged. According to the foreground object change information, the abnormal degree of the image abnormal information can be judged. The specific abnormal degree may be set freely according to the specific situation of the abnormal information of the screen, which is not limited in the present invention.
in the embodiment of the invention, a mapping model between the picture abnormal information and the corresponding abnormal degree and the server resources can be established, and when the picture abnormal information and the corresponding abnormal degree in the video stream are acquired, the corresponding running resource demand can be matched according to the model.
In one example, the mapping model may be established according to various types of screen abnormality information in the history data and consumption amounts of running resources of corresponding abnormality degrees.
And matching to obtain corresponding operation resource demand from the mapping model by adopting the abnormal picture information obtained in real time and the corresponding abnormal degree.
It should be noted that, in order to make the operation resource demand output by the model tend to be optimal, after matching the corresponding operation resource demand, the mapping model may be continuously adaptively adjusted in combination with the actual operation resource consumption.
And 105, distributing the running resources for the camera according to the running resource demand.
After the operating resource demand is matched, the server can allocate corresponding operating resources to the camera. The operation resources may include, but are not limited to, computational resources, bandwidth resources, storage resources, etc. according to the video capture requirements.
The invention allocates the running resources for the camera by establishing the association relation between the abnormal picture information, the abnormal degree and the running resource demand. Therefore, the rationality of resource allocation is improved, the resource waste is reduced, and the video stream blockage caused by insufficient running resource allocation is avoided.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for allocating resources for camera operations according to another embodiment of the present invention. The method specifically comprises the following steps:
step 201 is the same as step 101, and reference may be specifically made to the description of step 101, which is not described herein again.
decoding refers to a process in which a recipient restores a received symbol or code to information, corresponding to an encoding process. In a computer network, a network interconnects computers through a communication network to realize resource sharing and data transmission, and when the signal form of the used communication network is different from that of a transmission device, the signal form must be converted, generally, the signal form of a transmitting party is converted into coding, and the signal form of a receiving party is converted into decoding.
In the embodiment of the present invention, after the video stream sent by the camera is obtained, the video stream may be decoded to obtain a decoded video stream.
after the video stream is decoded, a foreground object in the decoded video stream can be identified through refined pixel level segmentation, then the decoded video stream is subjected to depth filtering to remove interference, and a background area which does not need to be analyzed again is removed.
and acquiring the change conditions of all foreground objects to obtain the foreground object change information of the whole video stream.
the monitoring of the abnormal picture information can be obtained by analyzing the foreground object change information, and if the change condition of one or some objects in the foreground object change information belongs to abnormal behavior characteristics (such as a dog opening a door), the abnormal picture information in the video stream can be judged. According to the foreground object change information, the abnormal degree of the image abnormal information can be judged. The specific abnormal degree may be set freely according to the specific situation of the abnormal information of the screen, which is not limited in the present invention.
In one example, the step of determining the screen abnormality information and the abnormality degree corresponding to the screen abnormality information according to the foreground object variation information may include the following sub-steps:
s51, determining the type of each foreground object;
s52, determining the operation mode of each type of foreground object according to the foreground object change information;
s53, determining user habits according to the operation modes of the foreground objects of various types;
and S54, matching the abnormal picture information and the abnormal degree corresponding to the abnormal picture information according to the operation model of each type of foreground object and the habit of the user.
In the embodiment of the invention, the foreground objects can be different types of people, objects, pets and the like, and different foreground objects have different behavior logics, so that the behavior logics of the different types of foreground objects can be summarized according to historical data to obtain the operation modes of the different foreground objects, and the operation modes such as walking, jumping, falling and the like of people can be respectively set to be different operation modes. After the types of the foreground objects are obtained through analysis, the operation mode in which the foreground objects of the types frequently appear in the historical data can be obtained and used as the operation mode of the foreground objects of the types (the operation mode of each foreground object of the types does not need to be analyzed one by one, and therefore resource loss is reduced). After the operation mode of the foreground object is obtained, the resource consumption of the foreground object of the type can be obtained through rough analysis according to the historical resource consumption data and the number of the foreground objects of the type.
The operation mode of the foreground object may be obtained by performing adaptive learning on historical data of the camera, and a person skilled in the art may generate the operation mode in any adaptive learning manner, which is not specifically limited in the embodiment of the present invention.
After the operation mode of each foreground object is obtained, whether a scene triggering user habits appears in the video stream or not or whether the acquisition time of the video stream meets the condition that the user habits appear can be analyzed according to the operation mode of each foreground object (for example, a user can do a specific thing at 5 points every day, the user can do a series of related behaviors based on the specific thing in the next time, and the series of related behaviors are the user habits). Such as a dog when opening the door, the owner will be present, etc. Different user habits result in different resource consumption. The user habit may be obtained by performing adaptive learning on historical data of the camera, and a person skilled in the art may generate the user habit in any adaptive learning manner, which is not specifically limited in the embodiment of the present invention.
After the operation models and the user habits of the foreground objects of various types are obtained, the image abnormal information and the abnormal degree corresponding to the image abnormal information can be matched according to the mapping model.
in the embodiment of the invention, a mapping model between the picture abnormal information and the corresponding abnormal degree and the server resources can be established, and when the picture abnormal information and the corresponding abnormal degree in the video stream are acquired, the corresponding running resource demand can be matched according to the model.
In one example, the mapping model may be established according to various types of screen abnormality information in the history data and consumption amounts of running resources of corresponding abnormality degrees.
And matching to obtain corresponding operation resource demand from the mapping model by adopting the abnormal picture information obtained in real time and the corresponding abnormal degree.
In one example, the step of determining the operation resource demand according to the screen abnormality information and the corresponding abnormality degree may include the following sub-steps:
s61, acquiring first historical resource data of the operation mode in the image abnormal information under the abnormal degree;
s62, determining a first operation resource demand according to the first historical resource data;
s63, acquiring second historical resource data of the user habit in the abnormal picture information under the abnormal degree;
s64, determining a second operation resource demand according to the second historical resource data;
and S65, generating the operation resource demand according to the first operation resource demand and the second operation resource demand.
In the embodiment of the present invention, the mapping model may match the corresponding first operating resource demand according to the operating mode. The first running resource demand is obtained by learning the mapping model according to the first historical resource data of the camera under the abnormal degree in the self-adaptive learning process. The first historical resource data is the actual resource consumption in the video stream acquired by the operation mode at different moments.
The mapping model may also match corresponding second operating resource demands according to user habits. And the second running resource demand is obtained by learning the mapping model according to the second historical resource data of the camera under the abnormal degree in the self-adaptive learning process. The second historical resource data is the actual resource consumption in the video stream acquired by the user at different times.
And step 207, distributing the running resources for the camera according to the running resource demand.
After the running resource demand is matched, the server can allocate corresponding running resources to the camera. The operation resources may include, but are not limited to, computational resources, bandwidth resources, storage resources, etc. according to the video capture requirements.
The invention allocates the running resources for the camera by establishing the association relationship between the abnormal picture information, the abnormal degree and the running resource demand. Therefore, the rationality of resource allocation is improved, the resource waste is reduced, and the video stream blockage caused by insufficient running resource allocation is avoided.
Referring to fig. 3, fig. 3 is a block diagram of a camera operation resource allocation apparatus according to an embodiment of the present invention.
The embodiment of the invention provides a camera running resource distribution device, which is applied to a server, wherein the server is communicated with a plurality of cameras; the device comprises:
a video stream receiving module 301, configured to receive video streams sent by the cameras;
a foreground object variation information determining module 302, configured to determine foreground object variation information in a video stream;
a picture abnormal information and abnormal degree determining module 303, configured to determine, according to the foreground object change information, picture abnormal information and an abnormal degree corresponding to the picture abnormal information;
an operating resource demand determining module 304, configured to determine an operating resource demand according to the screen abnormality information and the corresponding abnormality degree;
and the running resource allocation module 305 is configured to allocate running resources to the cameras according to the running resource demand.
In this embodiment of the present invention, the foreground object variation information determining module 302 includes:
the decoding submodule is used for decoding the video stream to obtain a decoded video stream;
a foreground object identification submodule for identifying a foreground object in the decoded video stream;
and the foreground object change information acquisition submodule is used for acquiring the change conditions of all foreground objects and generating foreground object change information.
In this embodiment of the present invention, the module 303 for determining abnormal screen information and abnormal degree includes:
the type determining submodule is used for determining the type of each foreground object;
the operation mode determining submodule is used for determining the operation modes of the foreground objects of various types according to the foreground object change information;
the user habit determining submodule is used for determining user habits according to the operation modes of the foreground objects of various types;
and the image abnormal information and abnormal degree determining submodule is used for matching the image abnormal information and the abnormal degree corresponding to the image abnormal information according to the operation model and the user habit of each type of foreground object.
In this embodiment of the present invention, the running resource demand determining module 304 includes:
the first historical resource data acquisition submodule is used for acquiring first historical resource data of the operation mode in the image abnormal information under the abnormal degree;
the first operating resource demand determining submodule is used for determining a first operating resource demand according to the first historical resource data;
the second historical resource data acquisition submodule is used for acquiring second historical resource data of the user habit in the abnormal picture information under the abnormal degree;
the second running resource demand determining submodule is used for determining a second running resource demand according to second historical resource data;
and the operating resource demand generation submodule is used for generating operating resource demand according to the first operating resource demand and the second operating resource demand.
An embodiment of the present invention further provides an electronic device, where the device includes a processor and a memory:
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is used for executing the camera running resource allocation method according to the embodiment of the invention according to the instructions in the program codes.
The embodiment of the invention also provides a computer-readable storage medium, which is used for storing the program code, and the program code is used for executing the camera running resource allocation method of the embodiment of the invention.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal apparatus that comprises the element.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The camera running resource allocation method is applied to a server, and the server is communicated with a plurality of cameras; the method comprises the following steps:
receiving video streams sent by the cameras;
determining foreground object variation information in the video stream;
determining picture abnormal information and abnormal degree corresponding to the picture abnormal information according to the foreground object change information;
determining the operation resource demand according to the picture abnormal information and the corresponding abnormal degree;
and allocating the operating resources to the camera according to the operating resource demand.
2. The method of claim 1, wherein the step of determining foreground object variation in the video stream comprises:
decoding the video stream to obtain a decoded video stream;
identifying a foreground object in the decoded video stream;
and collecting the change conditions of all the foreground objects to generate foreground object change information.
3. The method according to claim 2, wherein the step of determining the screen abnormality information and the abnormality degree corresponding to the screen abnormality information based on the foreground object variation information includes:
determining the type of each foreground object;
determining the operation mode of each type of foreground object according to the foreground object change information;
determining user habits according to the operation modes of the foreground objects of various types;
and matching the abnormal picture information and the abnormal degree corresponding to the abnormal picture information according to the operation model of each type of foreground object and the user habit.
4. The method according to claims 1-3, wherein the step of determining the operating resource demand based on the screen anomaly information and the corresponding anomaly level comprises:
acquiring first historical resource data of the operation mode in the image abnormal information under the abnormal degree;
determining a first operating resource demand according to the first historical resource data;
acquiring second historical resource data of the user habit under the abnormal degree in the abnormal picture information;
determining a second operating resource demand according to the second historical resource data;
and generating the operation resource demand according to the first operation resource demand and the second operation resource demand.
5. The camera running resource distribution device is applied to a server, and the server is communicated with a plurality of cameras; the device comprises:
the video stream receiving module is used for receiving the video streams sent by the cameras;
a foreground object variation information determining module, configured to determine foreground object variation information in the video stream;
the image abnormal information and abnormal degree determining module is used for determining image abnormal information and abnormal degree corresponding to the image abnormal information according to the foreground object change information;
the running resource demand determining module is used for determining the running resource demand according to the picture abnormal information and the corresponding abnormal degree;
and the running resource allocation module is used for allocating running resources to the camera according to the running resource demand.
6. The apparatus of claim 5, wherein the foreground object variation information determining module comprises:
the decoding submodule is used for decoding the video stream to obtain a decoded video stream;
a foreground object identification sub-module for identifying foreground objects in the decoded video stream;
and the foreground object change information acquisition submodule is used for acquiring the change conditions of all the foreground objects and generating foreground object change information.
7. The apparatus of claim 6, wherein the screen abnormality information and abnormality degree determination module comprises:
the type determining submodule is used for determining the type of each foreground object;
the operation mode determining submodule is used for determining the operation mode of each type of foreground object according to the foreground object change information;
the user habit determining submodule is used for determining user habits according to the operation modes of the foreground objects of various types;
and the image abnormal information and abnormal degree determining submodule is used for matching the image abnormal information and the abnormal degree corresponding to the image abnormal information according to the operation model of each type of foreground object and the user habit.
8. The apparatus of claims 5-7, wherein the operational resource demand determination module comprises:
a first historical resource data acquisition submodule, configured to acquire first historical resource data of the operation mode in the screen abnormality information at the abnormality degree;
the first operating resource demand determining submodule is used for determining a first operating resource demand according to the first historical resource data;
a second historical resource data obtaining submodule, configured to obtain second historical resource data of the user habit in the image abnormal information at the abnormal degree;
a second operating resource demand determining submodule, configured to determine a second operating resource demand according to the second historical resource data;
and the operating resource demand generation submodule is used for generating operating resource demand according to the first operating resource demand and the second operating resource demand.
9. An electronic device, comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the camera head operation resource allocation method according to any one of claims 1 to 4 according to instructions in the program code.
10. A computer-readable storage medium for storing program code for executing the camera head operation resource allocation method according to any one of claims 1 to 4.
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