Method for automatically reducing resource occupancy rate of video surveillance decoding terminal based on mouse monitoring

Abstract

The present invention discloses a method for automatically reducing the resource occupancy rate of a video surveillance decoding end based on mouse monitoring, and the steps include: collecting the associated data of the mouse behavior of the user manipulating the video surveillance decoding end; modeling the collected associated data to obtain the probability that the resource occupancy of the video surveillance decoding end reaches the proportion that affects the normal use of other applications on the computer end; summarizing and calculating the multi-channel associated data collected in real time, and sending the summarized data and the probability obtained by the modeling calculation to the video surveillance decoding end; the video surveillance decoding end automatically releases or restarts the real-time video preview task and / or the video playback task based on the received data and according to the preset strategy. The present invention realizes that the video surveillance decoding end automatically releases or restarts its own resources according to the situation of occupying computer resources, automatically controls the occupancy rate of its own resources on the computer end within a reasonable range, and ensures that other parallel applications on the computer end can run more smoothly.

Description

Technical Field

[0001] The present invention relates to the technical field of security video monitoring, and in particular to a method for automatically reducing the resource occupancy rate of a video monitoring decoding terminal based on mouse monitoring. Background Art

[0002] In the security video surveillance scenario, the video surveillance decoder such as high-definition cameras is connected to the computer as a front-end device to receive the control signal sent by the computer to realize the control functions such as real-time monitoring screen transmission and video playback. In recent years, video surveillance systems have been widely used in production and life security. The introduction of more high-definition cameras, especially intelligent network cameras, has put forward higher requirements for network bandwidth, physical memory, CPU, GPU and other hardware resources on the video surveillance decoder, causing the video surveillance decoder to become a "big user" of user computer resources. Its excessive resource consumption has greatly squeezed the normal use of other applications.

[0003] There are two commonly used methods to reduce the resource usage of video surveillance decoding terminals:

[0004] The first is to upgrade the hardware configuration of the user's computer, but this method will increase the user's computer configuration cost.

[0005] The second is to sacrifice the use effect of the video surveillance decoding end by reducing the number of video playback paths of the video surveillance decoding end to achieve the effect of normal use compatible with other applications on the computer end. However, this method undoubtedly greatly weakens the use effect of the video surveillance decoding end. Summary of the invention

[0006] The present invention provides a method for automatically reducing the resource occupancy of a video surveillance decoding end based on mouse monitoring. According to the behavioral characteristics of a user using the video surveillance decoding end and by collecting mouse behavior related data, the method automatically analyzes the resource occupancy of the video surveillance decoding end, and automatically releases or restarts the occupied resources of the video surveillance decoding end according to the analysis results, thereby realizing the reasonable use of the video surveillance decoding end.

[0007] To achieve this object, the present invention adopts the following technical solutions:

[0008] A method for automatically reducing the resource occupancy rate of a video surveillance decoding end based on mouse monitoring is provided, the steps comprising:

[0009] S1, collecting the associated data of the user's mouse behavior of manipulating the video surveillance decoding terminal;

[0010] S2, after summarizing and calculating the collected related data, modeling the summarization data, and obtaining the probability that the resource usage of the video surveillance decoding end reaches a proportion that affects the normal use of other applications on the computer end;

[0011] S3, after summarizing and calculating the multiple channels of correlation data collected in real time, the summary data and the probability obtained by modeling calculation are sent to the video monitoring decoding end;

[0012] S4, the video surveillance decoding end automatically releases or restarts the real-time video preview task and / or the video playback task based on the received data and in accordance with a preset strategy.

[0013] Preferably, the associated data collected in step S1 includes first associated data generated by a user's mouse manipulation behavior of opening a real-time video preview, and / or second associated data generated by a user's mouse manipulation behavior of opening a video playback.

[0014] The first associated data includes any one or more of the bit rate of a single-channel real-time code stream opened by mouse control, the CPU occupancy ratio of a single-channel real-time video, the GPU occupancy ratio of a single-channel real-time video, the memory occupancy ratio of a single-channel real-time video, and the total number of real-time video previews;

[0015] The second associated data includes any one or more of the bit rate of a single-channel video stream opened by mouse control, a single-channel video CPU occupancy ratio, a single-channel video GPU occupancy ratio, a single-channel video memory occupancy ratio, and the total number of video playback channels.

[0016] Preferably, the summary data includes first summary data and / or second summary data, wherein the first summary data includes any one or more of the total network downlink bandwidth of the video monitoring decoding end generated by the real-time video preview, the total CPU occupancy ratio of the real-time video preview, the total GPU occupancy ratio of the real-time video, and the total memory occupancy ratio of the real-time video.

[0017] The total network downlink bandwidth of the video monitoring decoding end generated by the real-time video preview is the sum of the bit rates of multiple real-time code streams opened simultaneously by mouse control;

[0018] The total CPU usage ratio of the real-time video preview is the comprehensive CPU usage ratio of multiple real-time videos opened simultaneously by mouse control;

[0019] The total video GPU occupancy ratio is the sum of the occupancy ratios of multiple real-time video GPUs opened simultaneously by mouse control;

[0020] The total real-time video memory occupancy ratio is the sum of the memory occupancy ratios of multiple real-time videos opened simultaneously by mouse control;

[0021] The second summary data includes any one or more of the total network downlink bandwidth of the video surveillance decoding terminal generated by video playback, the total CPU occupancy ratio of video playback, the total GPU occupancy ratio of video playback, and the total memory occupancy ratio of video playback.

[0022] The total network downlink bandwidth of the video surveillance decoding end generated by the video playback is the sum of the bit rates of the multiple video streams opened simultaneously by the mouse control;

[0023] The total CPU usage ratio of the video playback is the sum of the CPU usage ratios of multiple videos opened simultaneously by mouse control;

[0024] The total occupancy rate of the video playback GPU is the sum of the occupancy rates of multiple video recording GPUs opened simultaneously by mouse control;

[0025] The total memory usage ratio of the video playback is the sum of the memory usage ratios of the videos opened simultaneously by mouse control.

[0026] Preferably, in step S2, each data in the first summary data and / or the second summary data is modeled using Poisson distribution to obtain the probability that the resource occupancy of the video surveillance decoding end reaches a proportion that affects the normal use of other applications on the computer end.

[0027] Preferably, in step S4, the preset strategy includes a first release strategy, and the method for automatically releasing the real-time video preview task and / or the video playback task according to the first release strategy is:

[0028] Determine whether the received probability is greater than a preset probability threshold,

[0029] If yes, the real-time video preview task and / or the video playback task are automatically released;

[0030] If not, the task automatic release process will not be started.

[0031] Preferably, in step S4, the preset strategy includes a second release strategy, and the method for automatically releasing the real-time video preview task and / or the video playback task according to the second release strategy includes the steps of:

[0032] A1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data;

[0033] A2, determining whether the overall resource occupancy rate exceeds a critical threshold, wherein the critical threshold is the ratio of the resource occupancy rate of the video surveillance decoding end to affect the normal use of other applications on the computer end,

[0034] If yes, the real-time video preview task and / or the video playback task are automatically released;

[0035] If not, the task automatic release process will not be started.

[0036] Preferably, in step S4, the preset strategy includes a third release strategy, and the method for automatically releasing the real-time video preview task and / or the video playback task according to the third release strategy includes the steps of:

[0037] B1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data;

[0038] B2, determining whether the overall resource occupancy rate exceeds a critical threshold, wherein the critical threshold is the proportion of the resource occupancy rate of the video surveillance decoding end that affects the normal use of other applications on the computer end,

[0039] If yes, go to step B3;

[0040] If not, the task automatic release process will not be started;

[0041] B3, monitoring the mouse manipulation behavior and determining whether the video monitoring decoding end loses the mouse focus;

[0042] If yes, go to step B4;

[0043] If not, the task automatic release process will not be started;

[0044] B4, determining whether there is no mouse manipulation behavior within a continuous period after the video surveillance decoding end loses the mouse focus,

[0045] If yes, the real-time video preview task and / or the video playback task are automatically released;

[0046] If not, the process will be transferred to the automatic release process of the subtask.

[0047] Preferably, the subtask automatic release process is as follows: the video surveillance decoding end identifies the subtask with the largest resource consumption from the real-time video preview task and / or the video playback task, and then automatically releases the subtask.

[0048] Preferably, in step S4, the preset strategy includes a first restart strategy, and the method for automatically restarting the real-time video preview task and / or the video playback task according to the first restart strategy is:

[0049] After the real-time video preview task and / or the video playback task are automatically released in step B4, if the video surveillance decoding end regains the mouse focus, the real-time video preview task and / or the video playback task are automatically restarted.

[0050] Preferably, in step S4, the preset strategy includes a second restart strategy, and the method for restarting the subtask according to the second restart strategy is:

[0051] After the subtask is automatically released, if the video decoding end regains the mouse focus, the subtask is automatically restarted.

[0052] Preferably, the probability includes a first probability that real-time preview triggers a critical threshold, or a second probability that video playback triggers the critical threshold, or a third probability that real-time preview and video playback trigger the critical threshold together, and the critical threshold is the proportion of resource occupancy at the video surveillance decoding end that affects the normal use of other applications on the computer end.

[0053] The present invention automatically analyzes the resource occupancy of the video surveillance decoding end according to the behavioral characteristics of the user using the video surveillance decoding end and by collecting mouse behavior related data, so that the video surveillance decoding end automatically releases or restarts its own resources according to the situation of occupying computer resources, and automatically controls the occupancy rate of its own resources on the computer end within a reasonable range, ensuring that other parallel applications on the computer end can run more smoothly. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 It is a diagram of the implementation steps of the method for automatically reducing the resource occupancy rate of the video monitoring decoding end based on mouse monitoring provided by an embodiment of the present invention;

[0056] Figure 2 It is a flow chart of performing real-time video preview or video playback by monitoring the mouse focus of the video surveillance decoding terminal and collecting the associated data of the mouse control behavior;

[0057] Figure 3 It is a flow chart of using Poisson distribution modeling to obtain the probability of real-time preview triggering the critical threshold and the probability of video playback triggering the critical threshold;

[0058] Figure 4 It is a schematic diagram of sending summary data and probability data to the video surveillance decoding end;

[0059] Figure 5 The figure is a flowchart of automatically releasing or restarting a task at a video surveillance decoding end. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0061] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0062] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention 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. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as a limitation on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0063] In the description of the present invention, unless otherwise clearly specified and limited, if the term "connection" or the like appears to indicate the connection relationship between components, the term should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components or the interaction relationship between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring provided in an embodiment of the present invention includes the following four technical contents:

[0065] First, we collect data related to the user's mouse behavior when manipulating the video surveillance decoder. Considering that the video surveillance decoder usually occupies too many resources in the two business scenarios of multi-channel real-time video preview and multi-channel video playback, data collection is also targeted at these two business scenarios.

[0066] The associated data includes first associated data generated by a user's mouse manipulation behavior of opening a real-time video preview (referred to as the mouse focus of the real-time video preview behavior), and / or second associated data generated by a mouse manipulation behavior of opening a video playback (referred to as the mouse focus of the video playback behavior), wherein the first associated data includes: the bit rate of a single-channel real-time stream opened by mouse manipulation, the CPU occupancy ratio of a single-channel real-time video, the GPU occupancy ratio of a single-channel real-time video, the memory occupancy ratio of a single-channel real-time video, and the total number of real-time video previews;

[0067] The second associated data includes the bit rate of the single-channel video stream opened by mouse control, the CPU occupancy ratio of the single-channel video, the GPU occupancy ratio of the single-channel video, the memory occupancy ratio of the single-channel video, and the total number of video playback channels.

[0068] The method flow for collecting the first associated data and the second associated data is as follows: Figure 2 shown.

[0069] In the second aspect, the data generated by the collected mouse manipulation behavior of the user using the video surveillance decoding terminal is modeled. The user usually has a relatively fixed period of time when using the video surveillance decoding terminal every day. The associated data generated by the mouse operation behavior during this period is random. Based on the characteristics of the associated data generation with fixed period and random data, the present invention adopts Poisson distribution for modeling. The modeling method is:

[0070] If the first associated data is collected, the total network downlink bandwidth of the video monitoring decoding end generated by the real-time video preview (the sum of the bit rates of multiple real-time streams opened simultaneously by mouse control), the total CPU occupancy ratio of the real-time video preview (the sum of the CPU occupancy ratios of multiple real-time videos opened simultaneously), the total GPU occupancy ratio of the real-time video (the sum of the GPU occupancy ratios of multiple real-time videos opened simultaneously), and the total memory occupancy ratio of the real-time video (the sum of the memory occupancy ratios of multiple real-time videos opened simultaneously) are modeled respectively;

[0071] If the collected data is the second related data, the total network downlink bandwidth (the sum of the bit rates of multiple video streams opened simultaneously by mouse control), the total CPU occupancy ratio (the sum of the CPU occupancy ratios of multiple videos opened simultaneously), the total GPU occupancy ratio (the sum of the GPU occupancy ratios of multiple videos opened simultaneously), and the total memory occupancy ratio (the sum of the memory occupancy ratios of the videos opened simultaneously) of the video surveillance decoding end generated by the video playback are modeled respectively.

[0072] The modeling method is: through Poisson distribution modeling, the probability of the resource usage of the video surveillance decoding end reaching the proportion that affects the normal use of other applications (hereinafter referred to as the "critical threshold") is obtained. The probability function of Poisson distribution modeling is:

[0073]

[0074] The probability distribution of the random variable X is P(X=k), where k represents the value of the variable, and λ is the average usage of the resources of the video surveillance decoding end when the "critical threshold" is triggered. From this, the probability P(X) of triggering the "critical threshold" when the current video surveillance decoding end resource usage is X can be calculated.

[0075] The value of X is k=0, 1, 2, ..., which is the rounded value of the resource usage of the current video surveillance decoding end.

[0076] After the modeling is successful, the first probability of real-time preview triggering the critical threshold, the second probability of video playback triggering the critical threshold, and the third probability of real-time preview and video playback combined triggering the critical threshold are obtained. The critical threshold is the proportion of video surveillance decoding end resource usage that affects the normal use of other applications on the computer end.

[0077] The method flow of using Poisson distribution modeling to obtain the probability of real-time preview triggering the critical threshold or the probability of video playback triggering the critical threshold is as follows: Figure 3 shown.

[0078] The total downlink bandwidth of the video surveillance decoding end network generated by real-time preview or video playback is calculated using the following formula:

[0079] ∑sum=B1+B2+B3+…

[0080] Wherein, ∑sum represents the total downstream bandwidth of the network;

[0081] B1, B2, and B3 represent the bit rates of the corresponding single-channel real-time video stream or recorded video stream.

[0082] In the third aspect, after summarizing and calculating the associated data collected in real time, the summarized data and the probability obtained by modeling calculation (including the first probability and / or the second probability and / or the third probability) are sent to the video surveillance decoding end, specifically:

[0083] If the mouse control behavior is a real-time video preview behavior, the first associated data generated by the real-time collected video preview is summarized and calculated to obtain first summary data which is transmitted to the video monitoring decoding end. The first summary data includes the total network downlink bandwidth, the total CPU occupancy ratio, the total GPU occupancy ratio, and the total memory occupancy ratio of the video monitoring decoding end generated by the real-time video preview.

[0084] The total network downlink bandwidth of the video surveillance decoding end generated by the real-time video preview is the sum of the bit rates of multiple real-time streams opened simultaneously by mouse control;

[0085] The total CPU usage ratio of real-time video preview is the comprehensive CPU usage ratio of multiple real-time videos opened simultaneously by mouse control;

[0086] The total video GPU usage ratio is the sum of the usage ratios of multiple real-time video GPUs opened simultaneously by mouse control;

[0087] The total real-time video memory occupancy ratio is the sum of the memory occupancy ratios of multiple real-time videos opened simultaneously by mouse control; if the mouse control behavior is a video playback behavior, the second associated data generated by the video playback is summarized and calculated to obtain second summary data that is transmitted to the video surveillance decoding end, and the second summary data includes the total network downlink bandwidth, the total CPU occupancy ratio, the total GPU occupancy ratio, and the total memory occupancy ratio of the video surveillance decoding end generated by the video playback;

[0088] The total downlink network bandwidth of the video surveillance decoding end generated by the video playback is the sum of the bit rates of the multiple video streams opened simultaneously by the mouse control;

[0089] The total CPU usage of video playback is the sum of the CPU usage of multiple video channels opened simultaneously by mouse control.

[0090] The total GPU usage ratio for video playback is the sum of the GPU usage ratios of multiple video recording channels opened simultaneously by mouse control.

[0091] The total memory usage of video playback is the sum of the memory usage of videos opened simultaneously by mouse control.

[0092] In addition, the probabilities obtained by Poisson distribution modeling (including the first probability of real-time preview triggering the critical threshold, the second probability of video playback triggering the critical threshold, and the third probability of real-time preview and video playback combining to trigger the critical threshold) are passed to the video surveillance decoding end.

[0093] Please refer to the flowchart for sending summary data and probability data to the video surveillance decoding end. Figure 4 .

[0094] In the fourth aspect, the video surveillance decoding end performs analysis and calculation based on the received data and in accordance with the preset strategy, and automatically releases or restarts the real-time video preview task and / or the video playback task according to the analysis result. The preset strategy includes a first release strategy, and the method for automatically releasing the real-time video preview task and / or the video playback task according to the first release strategy is:

[0095] Determine whether the received probability is greater than the preset probability threshold,

[0096] If yes, the real-time video preview task and / or video playback task are automatically released, and the released tasks are recorded locally for backup;

[0097] If not, the task automatic release process will not be started.

[0098] The preset strategy also includes a second release strategy. The method for automatically releasing the real-time video preview task and / or the video playback task according to the second release strategy includes the following steps:

[0099] A1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data (including the first summary data and / or the second summary data).

[0100] The overall resource usage is calculated as the percentage of "received data resource consumption / total machine resources".

[0101] A2, determine whether the overall resource usage exceeds the critical threshold. The critical threshold is the ratio of the video surveillance decoding end resource usage to affect the normal use of other applications on the computer.

[0102] If yes, the real-time video preview task and / or video playback task are automatically released, and the released tasks are recorded locally for backup;

[0103] If not, the task automatic release process will not be started.

[0104] The preset strategy also includes a third release strategy. The method for automatically releasing the real-time video preview task and / or the video playback task according to the third release strategy includes the following steps:

[0105] B1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data;

[0106] B2, determine whether the overall resource usage exceeds the critical threshold. The critical threshold is the ratio of the resource usage of the video surveillance decoding end to affect the normal use of other applications on the computer.

[0107] If yes, go to step B3;

[0108] If not, the task automatic release process will not be started;

[0109] B3, monitor the mouse control behavior and determine whether the video surveillance decoding end loses the mouse focus;

[0110] If yes, go to step B4;

[0111] If not, the task automatic release process will not be started;

[0112] B4, determine whether there is no mouse control behavior within a continuous period of time after the video surveillance decoding end loses the mouse focus,

[0113] If yes, the real-time video preview task and / or video playback task are automatically released, and the released tasks are recorded locally for backup;

[0114] If not, the process will be transferred to the automatic release process of the subtask.

[0115] The automatic release process of subtasks is as follows: the video surveillance decoding end identifies the subtask with the largest resource consumption from the real-time video preview task and / or the video playback task, and then automatically releases the subtask, and records the released subtask to the local for backup.

[0116] The preset strategy also includes a first restart strategy. The method for automatically restarting the real-time video preview task and / or the video playback task according to the first restart strategy is:

[0117] After the real-time video preview task and / or the video playback task is automatically released in step B4, if the video surveillance decoding end regains the mouse focus, the local backup real-time video preview task and / or the video playback task is automatically restarted.

[0118] The preset strategy includes a second restart strategy. The method for automatically restarting the real-time video preview task and / or the video playback task according to the second restart strategy is:

[0119] After the subtask is automatically released, if the video decoding end regains the mouse focus, the local backup subtask will be automatically restarted.

[0120] For the process of automatically releasing or restarting tasks on the video surveillance decoding end, please refer to Figure 5 .

[0121] In summary, the method for automatically reducing the resource occupancy rate of the video monitoring decoding end based on mouse monitoring provided by the embodiment of the present invention is as follows: Figure 1 As shown, the steps include:

[0122] S1, collecting the associated data of the user's mouse behavior of manipulating the video surveillance decoding terminal;

[0123] S2, modeling the collected correlation data to obtain the probability that the resource usage of the video surveillance decoding end reaches a proportion that affects the normal use of other applications on the computer end;

[0124] S3, after summarizing the multi-channel correlation data collected in real time, the summary data and the probability obtained by modeling calculation are sent to the video surveillance decoding end; the summary calculation is to sum up the bit rates of the multi-channel real-time code streams opened simultaneously by mouse control to obtain the total network downlink bandwidth generated by the real-time preview; the summary calculation obtains the total CPU occupancy ratio of the video preview; the summary calculation obtains the total GPU occupancy ratio of the real-time video; the summary calculation obtains the total memory occupancy ratio of the real-time video; the summary calculation obtains the total network downlink bandwidth of the video surveillance decoding end generated by the video playback; the summary calculation obtains the total CPU occupancy ratio of the video playback; the summary calculation obtains the total GPU occupancy ratio of the video playback; the summary calculation obtains the total memory occupancy ratio of the video playback;

[0125] S4, the video surveillance decoding end automatically releases or restarts the real-time video preview task and / or the video playback task based on the received data and in accordance with a preset strategy.

[0126] The present invention realizes that the video surveillance decoding end automatically releases or restarts its own resources according to the situation of occupying computer resources through the technical solution provided above, and automatically controls the occupancy rate of its own resources on the computer end within a reasonable range, ensuring that other parallel applications on the computer end can run more smoothly.

[0127] It should be noted that the above specific implementations are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art should understand that various modifications, equivalent substitutions, changes, etc. can be made to the present invention. However, as long as these changes do not deviate from the spirit of the present invention, they should be within the scope of protection of the present invention. In addition, some terms used in the specification and claims of this application are not restrictive, but are only for the convenience of description.

Claims

1. A method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring, characterized in that the steps include: S1, collecting the associated data of the user's mouse behavior of manipulating the video surveillance decoding terminal; S2, after summarizing and calculating the collected related data, modeling the summarization data, and obtaining the probability that the resource usage of the video surveillance decoding end reaches a proportion that affects the normal use of other applications on the computer end; S3, after summarizing and calculating the multiple channels of correlation data collected in real time, the summary data and the probability obtained by modeling calculation are sent to the video monitoring decoding end; S4, the video surveillance decoding end automatically releases or restarts the real-time video preview task and / or the video playback task based on the received data and according to a preset strategy; In step S4, the preset strategy includes a first release strategy, a second release strategy, or a third release strategy. When the method of automatically releasing the real-time video preview task and / or the video playback task according to the first release strategy is: Determine whether the received probability is greater than a preset probability threshold, If yes, the real-time video preview task and / or the video playback task are automatically released; If not, the task automatic release process will not be started; The method for automatically releasing the real-time video preview task and / or the video playback task according to the second release strategy comprises the following steps: A1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data; A2, determining whether the overall resource occupancy rate exceeds a critical threshold, wherein the critical threshold is the ratio of the resource occupancy rate of the video surveillance decoding end to affect the normal use of other applications on the computer end, If yes, the real-time video preview task and / or the video playback task are automatically released; If not, the task automatic release process will not be started; The method for automatically releasing the real-time video preview task and / or the video playback task according to the third release strategy comprises the following steps: B1, calculating the overall resource occupancy rate of the computer by the video surveillance decoding end according to the received data; B2, determining whether the overall resource occupancy rate exceeds a critical threshold, wherein the critical threshold is the proportion of the resource occupancy rate of the video surveillance decoding end that affects the normal use of other applications on the computer end, If yes, go to step B3; If not, the task automatic release process will not be started; B3, monitoring the mouse manipulation behavior and determining whether the video monitoring decoding end loses the mouse focus; If yes, go to step B4; If not, the task automatic release process will not be started; B4, determining whether there is no mouse manipulation behavior within a continuous period after the video surveillance decoding end loses the mouse focus, If yes, the real-time video preview task and / or the video playback task are automatically released; If not, the process will be transferred to the automatic release process of the subtask.

2. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 1, characterized in that: The associated data collected in step S1 includes first associated data generated by a user's mouse manipulation behavior of opening a real-time video preview, and / or second associated data generated by a user's mouse manipulation behavior of opening a video playback. The first associated data includes any one or more of the bit rate of a single-channel real-time code stream opened by mouse control, the CPU occupancy ratio of a single-channel real-time video, the GPU occupancy ratio of a single-channel real-time video, the memory occupancy ratio of a single-channel real-time video, and the total number of real-time video previews; The second associated data includes any one or more of the bit rate of a single-channel video stream opened by mouse control, a single-channel video CPU occupancy ratio, a single-channel video GPU occupancy ratio, a single-channel video memory occupancy ratio, and the total number of video playback channels.

3. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 2, characterized in that: The summary data includes first summary data and / or second summary data, wherein the first summary data includes any one or more of the total network downlink bandwidth of the video monitoring decoding end generated by the real-time video preview, the total CPU occupancy ratio of the real-time video preview, the total GPU occupancy ratio of the real-time video, and the total memory occupancy ratio of the real-time video, The total network downlink bandwidth of the video monitoring decoding end generated by the real-time video preview is the sum of the bit rates of multiple real-time code streams opened simultaneously by mouse control; The total CPU usage ratio of the real-time video preview is the comprehensive CPU usage ratio of multiple real-time videos opened simultaneously by mouse control; The total video GPU occupancy ratio is the sum of the occupancy ratios of multiple real-time video GPUs opened simultaneously by mouse control; The total real-time video memory occupancy ratio is the sum of the memory occupancy ratios of multiple real-time videos opened simultaneously by mouse control; The second summary data includes any one or more of the total network downlink bandwidth of the video surveillance decoding terminal generated by video playback, the total CPU occupancy ratio of video playback, the total GPU occupancy ratio of video playback, and the total memory occupancy ratio of video playback. The total network downlink bandwidth of the video surveillance decoding end generated by the video playback is the sum of the bit rates of the multiple video streams opened simultaneously by the mouse control; The total CPU usage ratio of the video playback is the sum of the CPU usage ratios of multiple videos opened simultaneously by mouse control; The total occupancy rate of the video playback GPU is the sum of the occupancy rates of multiple video recording GPUs opened simultaneously by mouse control; The total memory usage ratio of the video playback is the sum of the memory usage ratios of the videos opened simultaneously by mouse control.

4. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 3, characterized in that: In step S2, each data in the first summary data and / or the second summary data is modeled using Poisson distribution to obtain the probability that the resource occupancy of the video surveillance decoding end reaches a proportion that affects the normal use of other applications on the computer end.

5. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 1, characterized in that: When the third release strategy is executed, the subtask automatic release process is: the video surveillance decoding end identifies the subtask with the largest resource consumption from the real-time video preview task and / or the video playback task, and then automatically releases the subtask.

6. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 1, characterized in that: When the third release strategy is executed, in step S4, the preset strategy includes a first restart strategy, and the method for automatically restarting the real-time video preview task and / or the video playback task according to the first restart strategy is: After the real-time video preview task and / or the video playback task are automatically released in step B4, if the video surveillance decoding end regains the mouse focus, the real-time video preview task and / or the video playback task are automatically restarted.

7. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to claim 6, characterized in that: When the third release strategy is executed, in step S4, the preset strategy includes a second restart strategy, and the method for automatically restarting the subtask according to the second restart strategy is: After the subtask is automatically released, if the video surveillance decoding end regains the mouse focus, the subtask is automatically restarted.

8. The method for automatically reducing the resource occupancy rate of a video surveillance decoding terminal based on mouse monitoring according to any one of claims 1 to 7, characterized in that: The probability includes a first probability that real-time preview triggers a critical threshold, or a second probability that video playback triggers the critical threshold, or a third probability that real-time preview and video playback trigger the critical threshold together, and the critical threshold is the proportion of resource occupancy at the video surveillance decoding end that affects the normal use of other applications on the computer end.

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