Monitoring data intelligent storage system based on Internet of Things technology

Abstract

The invention discloses a monitoring data intelligent storage system based on the Internet of Things technology, relates to the technical field of monitoring data storage, and solves the technical problems that in the prior art, control cannot be carried out according to scenes, and stored data cannot be actively adjusted. Specifically, a monitoring data scene-based detection unit performs scene-based detection on a monitoring image of a real-time monitoring area, divides the monitoring image of the monitoring area into a plurality of frames of pictures, divides the pictures into moving pictures and non-moving pictures according to whether the monitoring area enters a moving subject or not, and correspondingly divides the pictures into a moving scene and a static scene; shift-static transition data and static shift transition data are collected, and regulation and control according to scenes are carried out according to data analysis; according to the method, scene-based detection is carried out on monitoring images of a real-time monitoring area, image data optimization is carried out according to different types of monitoring images, the influence degree of the monitoring images is deduced, definition compression is selectively carried out, and the data storage pressure is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring data storage, and in particular to an intelligent storage system for monitoring data based on Internet of Things technology. Background Art

[0002] Video surveillance equipment is widely used in important places. The surveillance images will be stored in various storage devices such as hard disk recorders and video storage servers. Such devices are often installed in computer rooms or equipment rooms.

[0003] However, in the prior art, the monitoring data of the monitoring area cannot be used to evaluate the quality of real-time monitoring, so it is impossible to accurately decide whether to store the current monitoring data, resulting in increased storage pressure. At the same time, it is impossible to control according to the scene, and it is impossible to actively adjust the stored data, which increases the data storage pressure. In addition, it is impossible to perform monitoring, detection and analysis according to different scenes, and it is impossible to perform monitoring regulation and storage adjustment of monitoring data.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a monitoring data intelligent storage system based on Internet of Things technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A monitoring data intelligent storage system based on Internet of Things technology, comprising a monitoring data storage platform, wherein the monitoring data storage platform is communicatively connected with a regional real-time monitoring detection unit, a monitoring data scene detection unit, a mobile scene analysis unit and a static scene analysis unit;

[0008] The regional real-time monitoring and detection unit conducts real-time monitoring and detection of the monitoring area, collects monitoring error data and monitoring efficiency data, and infers the real-time monitoring risk based on data comparison. If the risk is high, an early warning is issued, and if the risk is low, continuous monitoring is carried out;

[0009] The monitoring data scene detection unit performs scene detection on the monitoring images of the real-time monitoring area, divides the monitoring images of the monitoring area into several frames, and divides the images into moving images and non-moving images according to whether a moving subject enters the monitoring area, and divides them into moving scenes and static scenes accordingly, collects the moving-static transition data and the static-moving transition data, and performs scene-based regulation based on data analysis;

[0010] The mobile scene analysis unit analyzes the monitoring data of the monitoring area under the mobile scene, collects the mobile monitoring stable data and the mobile monitoring peak data, and performs mobile scene storage control according to the data analysis results;

[0011] The static scene analysis unit performs monitoring data analysis on the monitoring area under the static scene, collects static monitoring data and static storage data, and performs static scene storage control based on data comparison.

[0012] As a preferred embodiment of the present invention, the monitoring error data and the monitoring efficiency data are respectively the proportion of occlusion time within the acquisition time of the monitoring parameters of the mobile subject in the monitoring image when the moving trajectories of the mobile subjects in the area overlap during the use of the monitoring area, and the change distance of the moving position on the monitoring image corresponding to any moving position of the mobile subject during the use of the monitoring area.

[0013] As a preferred embodiment of the present invention, if the monitoring error data exceeds the occlusion time percentage threshold, or the monitoring efficiency data exceeds the position change distance threshold, a high-risk signal is generated; if the monitoring error data does not exceed the occlusion time percentage threshold, and the monitoring efficiency data does not exceed the position change distance threshold, a low-risk signal is generated.

[0014] As a preferred embodiment of the present invention, the moving-to-static transition data and the static-to-moving transition data are respectively the clarity deviation values ​​of the moving pictures and static pictures corresponding to adjacent set time periods when the moving atlas is transformed into the static atlas, and the number of non-moving pictures at the end of the static atlas whose clarity is consistent with the starting picture of the moving atlas when the static atlas is transformed into the moving atlas.

[0015] As a preferred implementation mode of the present invention, if the moving-static transition data exceeds the maximum value of the clarity deviation threshold range, the clarity average value of the non-moving pictures corresponding to the starting time period of the static atlas when the moving atlas and the static atlas in the monitoring data are transformed is adjusted downward; if the moving-static transition data does not exceed the minimum value of the clarity deviation threshold range, the clarity of the non-moving pictures in the starting time period of the static atlas when the moving atlas and the static atlas in the monitoring data are transformed is adjusted upward, and at the same time, the number of non-moving pictures adjusted upward in the starting time period is controlled, and half of the starting time period is used as the dividing point;

[0016] If the static-to-motion transition data exceeds the threshold for the number of pictures, the number of non-moving pictures in the last period of the static atlas will be lowered in clarity, and the midpoint of the last period will be used as the dividing point; if the static-to-motion transition data does not exceed the threshold for the number of pictures, it will be directly stored without clarity adjustment.

[0017] As a preferred embodiment of the present invention, the mobile monitoring stable data and the mobile monitoring peak data are respectively the floating deviation values ​​of the mobile picture clarity corresponding to the moving process of adjacent positions of the moving subject in the mobile scene and the mobile picture clarity corresponding to the position of the moving trajectory, and the peak value of the mobile picture clarity decrease corresponding to the moving process of different moving speeds of the moving subject in the mobile scene.

[0018] As a preferred embodiment of the present invention, if the mobile monitoring stable data exceeds the clarity floating deviation threshold, or the mobile monitoring peak data exceeds the clarity degradation peak threshold, an inefficient monitoring signal is generated and the inefficient monitoring signal is sent to the monitoring data storage platform; if the mobile monitoring stable data does not exceed the clarity floating deviation threshold, and the mobile monitoring peak data does not exceed the clarity degradation peak threshold, it is inferred that the monitoring efficiency of the monitoring area in the mobile scenario is high, and an efficient monitoring signal is generated and the efficient monitoring signal is sent to the monitoring data storage platform.

[0019] As a preferred embodiment of the present invention, after receiving the inefficient monitoring signal, the monitoring data storage platform changes the specifications of the monitoring cameras in the monitoring area, detects the monitoring data collected in real time, distinguishes the moving pictures of the moving process, and if the clarity of the moving picture is lower than the required set value, compresses the current moving picture to reduce the space occupied, and if the clarity of the moving picture is higher than the required set value, stores the moving picture in the memory with the highest resolution;

[0020] After receiving the efficient monitoring signal, the monitoring data storage platform adjusts the focus of the monitoring camera according to the point position and movement process of the moving subject's moving trajectory.

[0021] As a preferred embodiment of the present invention, the static monitoring data and the static storage data are respectively the average floating span of clarity at any position in the monitoring area under the static scene, and the interval between the moment when the clarity of each position in the monitoring area under the static scene is satisfied and the starting moment of the adjacent moving atlas.

[0022] As a preferred embodiment of the present invention, if the static monitoring data exceeds the floating span mean threshold, or the static storage data exceeds the interval duration threshold, a static compression signal is generated; if the static monitoring data does not exceed the floating span mean threshold, and the static storage data does not exceed the interval duration threshold, a non-compressed storage signal is generated.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. In the present invention, real-time monitoring and detection are performed on the monitoring area, and monitoring and detection are used to infer whether there are monitoring loopholes in the current monitoring area, so as to avoid the monitoring efficiency of the monitoring area from decreasing. The monitoring efficiency of the monitoring area cannot be decreased due to the failure of early warning, which also leads to an increase in the storage intensity of the monitoring data in the monitoring area, and the inability to delete this part of the monitoring data in time, which wastes the occupied storage space;

[0025] The surveillance images of the real-time monitoring area are detected according to the scene, and the image data is optimized according to different types of surveillance images. The impact of the surveillance images is inferred and the clarity is selectively compressed to reduce the pressure of data storage.

[0026] 2. In the present invention, monitoring data analysis is performed on the monitoring area in the mobile scene, and the monitoring efficiency in the mobile scene is inferred to be qualified while the mobile scene monitoring analysis is performed to ensure the availability of monitoring data in the mobile scene, so as to avoid the monitoring data in the mobile scene being unable to meet the actual scene, reducing the availability of data storage ratio, and thus affecting the monitoring efficiency of the monitoring area;

[0027] Analyze the monitoring data of the monitoring area in static scenes, infer whether the monitoring data in static scenes can be compressed and stored, and compress non-moving images in static scenes without affecting the monitoring efficiency of the monitoring area, so as to alleviate the pressure of monitoring data storage in the monitoring area. It can also promote the storage efficiency of the monitoring area, ensure the monitoring duration of the monitoring area, and reduce the freeze duration caused by high memory. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0029] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 described embodiments 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 creative work are within the scope of protection of the present invention.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] See also Figure 1 As shown, a monitoring data intelligent storage system based on Internet of Things technology includes a monitoring data storage platform, which is communicatively connected to a regional real-time monitoring detection unit, a monitoring data scene detection unit, a mobile scene analysis unit, and a static scene analysis unit;

[0033] The monitoring data storage platform is used to intelligently store the monitoring data of the monitoring area. As the number of times the monitoring area is used increases, the real-time storage of the monitoring data is optimized and controlled;

[0034] The monitoring data storage platform generates a regional real-time monitoring detection signal and sends the regional real-time monitoring detection signal to the regional real-time monitoring detection unit. After receiving the regional real-time monitoring detection signal, the regional real-time monitoring detection unit performs real-time monitoring detection on the monitoring area, and infers whether there is a monitoring loophole in the current monitoring area through monitoring detection, so as to avoid the monitoring efficiency of the monitoring area from decreasing. The monitoring efficiency of the monitoring area cannot be decreased due to the failure of early warning, which also leads to an increase in the monitoring data storage intensity of the monitoring area, and the inability to delete this part of the monitoring data in time, which wastes the occupied storage space;

[0035] Obtain the percentage of occlusion time within the acquisition time of monitoring parameters of the moving subject in the monitoring image when the moving trajectories of the moving subjects in the monitoring area overlap during the use of the monitoring area, and mark the percentage of occlusion time within the acquisition time of monitoring parameters of the moving subject in the monitoring image when the moving trajectories of the moving subjects in the monitoring area overlap during the use of the monitoring area as monitoring error data, where the moving subject is the monitoring person or the monitored person, and the monitoring data is represented by parameters such as the lip movement trajectory and speech decibel of the moving subject. When the monitoring person and the monitored person are close, if the monitoring cannot monitor the lip trajectory or speech decibel and the duration exceeds the set threshold, it indicates that the monitoring area is abnormal and the data does not need to be synchronously stored;

[0036] The change distance of the moving position on the monitoring image corresponding to the arbitrary moving position of the moving subject during the use of the monitoring area is obtained, and the change distance of the moving position on the monitoring image corresponding to the arbitrary moving position of the moving subject during the use of the monitoring area is marked as the monitoring efficiency data, wherein the moving position on the image is represented by the moving subject. When monitoring at any position when the moving subject moves, the monitoring camera changes the angle accordingly to ensure that the position of the moving subject in the monitoring image is at the same position in the image of the same specification. If there is a position deviation, the angle of the monitoring camera is not changed in time, and the monitoring is inefficient;

[0037] The occlusion time ratio of the monitoring parameters of the mobile subject in the monitoring image during the use of the monitoring area when the moving trajectories of the mobile subject in the area overlap, and the change distance of the moving position on the monitoring image corresponding to any moving position of the mobile subject during the use of the monitoring area are compared with the occlusion time ratio threshold and the position change distance threshold respectively:

[0038] If the occlusion time ratio of the monitoring parameter collection time of the mobile subject in the monitoring image during the use of the monitoring area overlaps when the moving trajectories of the mobile subjects in the area are overlapped exceeds the occlusion time ratio threshold, or the change distance of the mobile position on the monitoring image corresponding to any moving position of the mobile subject during the use of the monitoring area exceeds the position change distance threshold, it is inferred that the current monitoring risk is high, a high-risk signal is generated and sent to the monitoring data storage platform;

[0039] The surveillance camera issues an early warning, and the surveillance camera in the surveillance area moves to monitor the moving subjects in the surveillance area. The surveillance images completed before the early warning are sent to the administrator terminal for backup and are not stored synchronously with the surveillance data of the current surveillance area;

[0040] If the occlusion time ratio of the monitoring parameter collection time of the mobile subject in the monitoring image does not exceed the occlusion time ratio threshold when the moving trajectories of the mobile subjects in the monitoring area overlap during the use of the monitoring area, and the change distance of the mobile position on the monitoring image corresponding to any moving position of the mobile subject during the use of the monitoring area does not exceed the position change distance threshold, then it is inferred that the current monitoring risk is low, a low risk signal is generated and sent to the monitoring data storage platform;

[0041] After receiving the low-risk signal, the monitoring data storage platform generates a monitoring data scene detection signal and sends the monitoring data scene detection signal to the monitoring data scene detection unit. After receiving the monitoring data scene detection signal, the monitoring data scene detection unit performs scene detection on the monitoring image of the real-time monitoring area, optimizes the image data according to different types of monitoring images, infers the impact of the monitoring image and selectively compresses the clarity to reduce the pressure of data storage, specifically for two scenarios with and without a mobile subject;

[0042] The surveillance image of the surveillance area is divided into several frames, and the pictures are divided into moving pictures and non-moving pictures according to whether a moving subject enters the surveillance area, and are correspondingly divided into moving scenes and static scenes;

[0043] Mark the moving pictures and non-moving pictures as moving atlas and static atlas respectively according to the number of pictures; obtain the clarity deviation values ​​of the moving pictures and static pictures corresponding to the adjacent set time periods when the moving atlas is converted to the static atlas, and at the same time obtain the number of non-moving pictures whose clarity is consistent with the starting picture of the moving atlas in the last picture of the static atlas when the static atlas is converted to the moving atlas, and mark the clarity deviation values ​​of the moving pictures and static pictures corresponding to the adjacent set time periods when the moving atlas is converted to the static atlas, and the number of non-moving pictures whose clarity is consistent with the starting picture of the moving atlas in the last picture of the static atlas when the static atlas is converted to the moving atlas as moving-static transition data and static-moving transition data, and compare them with the clarity deviation threshold range and the picture quantity threshold respectively:

[0044] If the clarity deviation value of the moving pictures and static pictures corresponding to the adjacent set time periods when the moving atlas is transformed into the static atlas exceeds the maximum value of the clarity deviation threshold range, the clarity average value of the non-moving pictures corresponding to the starting time period of the static atlas when the moving atlas and the static atlas in the monitoring data are transformed will be lowered, that is, the pictures will be compressed to reduce the storage space occupied;

[0045] If the clarity deviation value of the moving pictures and static pictures corresponding to the adjacent set time periods when the moving atlas is transformed into the static atlas does not exceed the minimum value of the clarity deviation threshold range, then the clarity of the non-moving pictures in the starting time period of the static atlas when the moving atlas and the static atlas in the monitoring data are transformed is adjusted upward, and at the same time, the number of non-moving pictures adjusted upward in the starting time period is controlled, with half of the starting time period as the dividing point. The starting time period, set time period, and end time period in this application are all time period thresholds manually set by the administrator of the monitoring area according to the monitoring equipment model;

[0046] If the number of non-moving pictures at the end of the static atlas that have the same definition as the starting picture of the moving atlas exceeds the picture quantity threshold when the static atlas is converted to the moving atlas, the definition of the number of non-moving pictures at the end of the static atlas will be lowered, and the midpoint of the end of the period will be used as the dividing point to lower the definition and keep the definition unchanged;

[0047] If the number of non-moving pictures at the end of the static atlas with the same definition as the starting picture of the moving atlas does not exceed the picture quantity threshold when the static atlas is converted to the moving atlas, the pictures are directly stored without definition adjustment;

[0048] Storage is regulated according to the scene detection results, and if there is no clarity during the storage process, the start time, set time, and end time are manually adjusted if there is no clarity that does not meet the requirements.

[0049] After completing the storage control, a mobile scene analysis signal and a static scene analysis signal are generated and sent to the mobile scene analysis unit and the static scene analysis unit respectively;

[0050] After receiving the mobile scene analysis signal, the mobile scene analysis unit performs monitoring data analysis on the monitoring area under the mobile scene, infers whether the monitoring efficiency under the mobile scene is qualified, and performs mobile scene monitoring analysis to ensure the availability of monitoring data under the mobile scene, so as to avoid the monitoring data under the mobile scene failing to meet the actual scene, reducing the availability of data storage, and thus affecting the monitoring efficiency of the monitoring area;

[0051] The floating deviation value of the mobile picture clarity of the adjacent positions of the moving subject in the moving scene corresponding to the moving process and the mobile picture clarity of the position corresponding to the moving trajectory is obtained, and the peak value of the mobile picture clarity decrease of the moving process corresponding to different moving speeds of the moving subject in the moving scene is obtained, and the floating deviation value of the mobile picture clarity of the adjacent positions of the moving subject in the moving scene corresponding to the moving process and the mobile picture clarity decrease peak value of the moving process corresponding to the moving trajectory of the moving subject at different moving speeds in the mobile scene are marked as mobile monitoring stable data and mobile monitoring peak data, respectively, and compared with the clarity floating deviation threshold and the clarity decrease peak threshold, respectively:

[0052] If the floating deviation value between the clarity of the moving picture of the adjacent positions of the moving subject in the moving scene corresponding to the moving process and the clarity of the moving picture of the corresponding position of the moving trajectory exceeds the clarity floating deviation threshold, or the clarity drop peak value of the moving picture of the moving process corresponding to the different moving speeds of the moving subject in the moving scene exceeds the clarity drop peak threshold, it is inferred that the monitoring efficiency of the monitoring area in the mobile scene is low, and an inefficient monitoring signal is generated and sent to the monitoring data storage platform. After receiving the inefficient monitoring signal, the monitoring data storage platform replaces the specifications of the monitoring cameras in the monitoring area, detects the monitoring data collected in real time, and distinguishes the moving pictures of the moving process. If the clarity of the moving picture is lower than the required set value, the current moving picture is compressed to reduce the space occupancy. If the clarity of the moving picture is higher than the required set value, the moving picture is stored in the memory with the highest resolution. If the compression degree of the moving pictures of the same process is different, the corresponding image has video frame loss. Therefore, according to the browsing of the monitoring image, the administrator determines the specifications of the monitoring camera replacement;

[0053] If the floating deviation value between the clarity of the moving picture of the adjacent positions of the moving subject in the moving scene corresponding to the moving process and the clarity of the moving picture of the corresponding position of the moving trajectory does not exceed the clarity floating deviation threshold, and the clarity drop peak value of the moving picture of the moving process corresponding to different moving speeds of the moving subject in the moving scene does not exceed the clarity drop peak threshold, then it is inferred that the monitoring efficiency of the monitoring area in the mobile scene is high, and an efficient monitoring signal is generated and sent to the monitoring data storage platform. After receiving the efficient monitoring signal, the monitoring data storage platform adjusts the focus of the monitoring camera according to the point position and the moving process of the moving trajectory of the moving subject, that is, when the moving subject is at the point position of the corresponding trajectory and at the moment of the moving process, the corresponding focusing parameters are adjusted without affecting the browsing quality of the monitoring data, and the focusing parameters are focal length, focal depth and other parameters;

[0054] After receiving the static scene analysis signal, the static scene analysis unit analyzes the monitoring data of the monitoring area in the static scene, infers whether the monitoring data in the static scene can be compressed and stored, and compresses the non-moving images in the static scene without affecting the monitoring efficiency of the monitoring area, thereby alleviating the monitoring data storage pressure in the monitoring area, and promoting the storage efficiency of the monitoring area, ensuring the monitoring duration of the monitoring area, and reducing the freeze duration caused by high memory;

[0055] The average floating span of clarity at any position in the monitoring area under static scenes is obtained, and the interval between the moment when clarity is satisfied at each position in the monitoring area under static scenes and the start moment of the adjacent moving atlas is obtained. The average floating span of clarity at any position in the monitoring area under static scenes and the interval between the moment when clarity is satisfied at each position in the monitoring area under static scenes and the start moment of the adjacent moving atlas are marked as static monitoring data and static storage data, respectively, and compared with the floating span average threshold and the interval duration threshold, respectively:

[0056] If the floating span mean of the clarity at any position in the monitoring area under a static scene exceeds the floating span mean threshold, or the interval between the moment when the clarity at each position in the monitoring area under a static scene is satisfied and the start moment of the adjacent moving atlas exceeds the interval duration threshold, it is inferred that the static scene monitoring is inefficient, and a static compression signal is generated and sent to the monitoring data storage platform. After receiving the static compression signal, the monitoring data storage platform compresses and stores the non-moving pictures whose corresponding clarity is not satisfied in the static scene, adjusts the angle of the monitoring camera in the monitoring area, and controls the monitoring period when no moving subject enters in the static scene.

[0057] If the floating span mean of the clarity at any position in the monitoring area under the static scene does not exceed the floating span mean threshold, and the interval between the moment when the clarity of each position in the monitoring area under the static scene is satisfied and the start moment of the adjacent moving atlas does not exceed the interval duration threshold, it is inferred that the static scene monitoring is efficient, and an uncompressed storage signal is generated and sent to the monitoring data storage platform. The monitoring data storage platform sets a storage period, and image compression is performed if the storage period is exceeded.

[0058] When the present invention is in use, the regional real-time monitoring detection unit performs real-time monitoring detection on the monitoring area, collects monitoring error data and monitoring efficiency data, and infers real-time monitoring risks based on data comparison; the monitoring data scene detection unit performs scene detection on the monitoring image of the real-time monitoring area, collects motion-static transition data and static-motion transition data, and performs scene-based regulation based on data analysis; the mobile scene analysis unit performs monitoring data analysis on the monitoring area under the mobile scene, collects mobile monitoring stable data and mobile monitoring peak data, and performs mobile scene storage control based on the data analysis results; the static scene analysis unit performs monitoring data analysis on the monitoring area under the static scene, collects static monitoring data and static storage data, and performs static scene storage control based on data comparison.

[0059] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A monitoring data intelligent storage system based on Internet of Things technology, characterized in that: It includes a monitoring data storage platform, which is communicatively connected with a regional real-time monitoring detection unit, a monitoring data scene detection unit, a mobile scene analysis unit, and a static scene analysis unit; The regional real-time monitoring and detection unit conducts real-time monitoring and detection of the monitoring area, collects monitoring error data and monitoring efficiency data, and infers the real-time monitoring risk based on data comparison. If the risk is high, an early warning is issued, and if the risk is low, continuous monitoring is carried out; The monitoring data scene detection unit performs scene detection on the monitoring images of the real-time monitoring area, divides the monitoring images of the monitoring area into several frames, and divides the images into moving images and non-moving images according to whether a moving subject enters the monitoring area, and divides them into moving scenes and static scenes accordingly, collects the moving-static transition data and the static-moving transition data, and performs scene-based regulation based on data analysis; The mobile scene analysis unit analyzes the monitoring data of the monitoring area under the mobile scene, collects the mobile monitoring stable data and the mobile monitoring peak data, and performs mobile scene storage control according to the data analysis results; The static scene analysis unit performs monitoring data analysis on the monitoring area under the static scene, collects static monitoring data and static storage data, and performs static scene storage control based on data comparison.

2. According to claim 1, a monitoring data intelligent storage system based on Internet of Things technology is characterized in that: The monitoring error data and monitoring efficiency data are respectively the proportion of occlusion time within the acquisition time of the monitoring parameters of the mobile subject in the monitoring image when the moving trajectories of the mobile subjects in the area overlap during the use of the monitoring area, and the change distance of the moving position on the monitoring image corresponding to any moving position of the mobile subject during the use of the monitoring area.

3. According to claim 2, a monitoring data intelligent storage system based on Internet of Things technology is characterized in that: If the monitoring error data exceeds the occlusion time ratio threshold, or the monitoring efficiency data exceeds the position change distance threshold, a high-risk signal is generated; If the monitoring error data does not exceed the occlusion time ratio threshold, and the monitoring efficiency data does not exceed the position change distance threshold, a low risk signal is generated.

4. According to the monitoring data intelligent storage system based on Internet of Things technology as described in claim 1, it is characterized in that: The moving-static transition data and the static-moving transition data are respectively the clarity deviation values ​​of the moving pictures and static pictures corresponding to adjacent set time periods when the moving atlas is transformed into the static atlas, and the number of non-moving pictures at the end of the static atlas whose clarity is consistent with the starting picture of the moving atlas when the static atlas is transformed into the moving atlas.

5. According to claim 4, a monitoring data intelligent storage system based on Internet of Things technology is characterized in that: If the moving-static transition data exceeds the maximum value of the definition deviation threshold range, the definition average of the non-moving pictures corresponding to the starting period of the static atlas when the moving atlas and the static atlas in the monitoring data are transitioned is lowered; If the moving-static transition data does not exceed the minimum value of the definition deviation threshold range, the definition of the non-moving pictures in the starting period of the static atlas when the moving atlas and the static atlas in the monitoring data are transitioned is increased, and the number of non-moving pictures increased in the starting period is controlled, and half of the starting period is used as the dividing point; If the static-to-motion transition data exceeds the threshold of the number of images, the number of non-moving images at the end of the static atlas will be reduced in clarity, and the midpoint of the end of the period will be used as the dividing point; If the static-motion transition data does not exceed the threshold of the number of pictures, the data is directly stored without clarity adjustment.

6. The monitoring data intelligent storage system based on Internet of Things technology according to claim 1 is characterized in that: The mobile monitoring stable data and mobile monitoring peak data are respectively the floating deviation value of the mobile picture clarity corresponding to the moving process of adjacent positions of the moving subject in the mobile scene and the mobile picture clarity corresponding to the position of the moving trajectory, and the peak value of the mobile picture clarity decrease corresponding to the moving process of different moving speeds of the moving subject in the mobile scene.

7. The monitoring data intelligent storage system based on Internet of Things technology according to claim 6 is characterized in that: If the mobile monitoring stable data exceeds the clarity floating deviation threshold, or the mobile monitoring peak data exceeds the clarity drop peak threshold, an inefficient monitoring signal is generated and sent to the monitoring data storage platform; If the mobile monitoring stable data does not exceed the clarity floating deviation threshold, and the mobile monitoring peak data does not exceed the clarity drop peak threshold, it is inferred that the monitoring efficiency of the monitoring area in the mobile scenario is high, and an efficient monitoring signal is generated and sent to the monitoring data storage platform.

8. The monitoring data intelligent storage system based on Internet of Things technology according to claim 7 is characterized in that: After receiving the inefficient monitoring signal, the monitoring data storage platform will replace the specifications of the monitoring cameras in the monitoring area, detect the monitoring data collected in real time, and distinguish the moving pictures in the moving process. If the clarity of the moving picture is lower than the required set value, the current moving picture will be compressed to reduce the space occupied. If the clarity of the moving picture is higher than the required set value, the moving picture will be stored in the memory with the highest resolution; After receiving the efficient monitoring signal, the monitoring data storage platform adjusts the focus of the monitoring camera according to the point position and movement process of the moving subject's moving trajectory.

9. The monitoring data intelligent storage system based on Internet of Things technology according to claim 1 is characterized in that: The static monitoring data and static storage data are respectively the average floating span of clarity at any position in the monitoring area under static scenes and the interval between the moment when clarity at each position in the monitoring area under static scenes is satisfied and the start moment of the adjacent moving atlas.

10. The monitoring data intelligent storage system based on Internet of Things technology according to claim 9 is characterized in that: If the static monitoring data exceeds the floating span mean threshold, or the static storage data exceeds the interval duration threshold, a static compression signal is generated; If the static monitoring data does not exceed the floating span mean threshold and the static storage data does not exceed the interval duration threshold, a non-compressed storage signal is generated.

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