Security camera fault intelligent monitoring method and system

Abstract

The invention is suitable for the technical field of equipment fault monitoring, and particularly relates to a security camera fault intelligent monitoring method and system, and the method comprises the steps: obtaining the operation power consumption data of a security camera, and determining a fault monitoring time period based on the operation power consumption data; acquiring light supplementing parameter data of the security and protection camera, constructing a light supplementing control function, and controlling a light supplementing element of the security and protection camera; a light supplementing element is controlled to supplement light, and a security and protection monitoring video is obtained; image calling is carried out from the time when light supplementing occurs in the security and protection monitoring video, a monitoring image set is constructed, pixel intensity change detection processing is carried out based on the monitoring image set, and whether a fault occurs or not is judged. According to the invention, the image change acquired by the security camera is analyzed, and whether the image change of the security camera is matched with the illumination intensity change of the light supplementing element is judged, so that whether a fault exists is judged, autonomous fault monitoring of the security camera is realized, and missing of effective data is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment fault monitoring, and particularly relates to an intelligent fault monitoring method and system for security cameras. Background Art

[0002] Security cameras, commonly known as closed-circuit television (CCTV) cameras or surveillance cameras, are video capture devices used in security and surveillance applications. They are installed in various locations such as homes, stores, banks, schools, traffic intersections, and critical infrastructure to provide real-time monitoring, record events, and deter potential abnormal behavior.

[0003] In the same area, a large number of security cameras are usually set up. However, during long-term operation, some security cameras may malfunction, and it is generally difficult to detect without manual inspection. Existing security cameras cannot actively monitor for faults, and problems such as invalid monitoring data are likely to occur. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent fault monitoring method for security cameras, aiming to solve the problem that existing security cameras cannot actively monitor for faults and are prone to problems such as invalid monitoring data.

[0005] The present invention is implemented as follows. An intelligent fault monitoring method for security cameras, the method comprising:

[0006] Obtain the operating power consumption data of the security camera, determine the fault monitoring time period based on the operating power consumption data, and perform fault monitoring within the fault monitoring time period;

[0007] Obtain the fill light parameter data of the security camera, construct a fill light control function, and control the fill light element of the security camera based on the fill light control function;

[0008] Control the fill light element to perform fill light according to a preset time interval, perform real-time data recording through the security camera, and obtain a security monitoring video;

[0009] Retrieve images from the time when fill light occurs in the security monitoring video, construct a monitoring image set, and perform pixel intensity change detection processing based on the monitoring image set to determine whether a fault has occurred.

[0010] Preferably, the step of obtaining the fill light parameter data of the security camera, constructing a fill light control function, and controlling the fill light element of the security camera based on the fill light control function specifically includes:

[0011] Obtain the fill light parameter data of the security camera, and determine the fill light type and fill light intensity range of the security camera;

[0012] Randomly obtain a set of security monitoring images, divide the security monitoring images into multiple tiles according to preset image segmentation parameters, calculate the grayscale value of each tile, and construct the tile grayscale information coordinates;

[0013] Based on the tile grayscale information coordinates, perform function fitting to obtain a fill light control function, and control the fill light element of the security camera based on the fill light control function.

[0014] Preferably, the step of controlling the fill light element to perform fill light according to a preset time interval and performing real-time data recording through the security camera to obtain a security monitoring video specifically includes:

[0015] When entering the fault monitoring time period, import a preset natural number into the fill light control function to obtain multiple sets of random values, and determine the numbers of the random values;

[0016] Calculate the span value of the fill light intensity interval, calculate the random value ratio, and determine the fill light control data according to the random value ratio and the span value of the fill light intensity interval;

[0017] Import the fill light control data into the fill light element to control the fill light element to emit light, and perform real-time data recording through the security camera to obtain a security monitoring video.

[0018] Preferably, the step of retrieving images from the time when fill light occurs in the security monitoring video, constructing a monitoring image set, and performing pixel intensity change detection processing based on the monitoring image set to determine whether a fault has occurred specifically includes:

[0019] Retrieve the fill light control data, determine the time of each fill light intensity switch, retrieve the real-time monitoring image corresponding to this time, and construct a monitoring image set;

[0020] Divide each group of real-time monitoring images in the monitoring image set into multiple picture verification areas and multiple anomaly verification areas according to a preset division method, and perform picture change verification on the multiple picture verification areas;

[0021] Statistically analyze the grayscale values of the pixels in the anomaly verification area to obtain the interval grayscale value, construct a grayscale change function, check the deviation between the grayscale change function and the fill light control function, and determine whether there is a fault.

[0022] Preferably, when it is determined that the security camera has a fault, a fault warning message is sent to the administrator, and the fault warning message at least includes the number of the security camera.

[0023] Another object of the present invention is to provide a security camera fault intelligent monitoring system, and the system includes:

[0024] A data acquisition module, configured to acquire the operating power consumption data of a security camera, determine a fault monitoring time period based on the operating power consumption data, and perform fault monitoring within the fault monitoring time period;

[0025] A function construction module, configured to acquire the fill light parameter data of a security camera, construct a fill light control function, and control the fill light element of the security camera based on the fill light control function;

[0026] A fill light data acquisition module, configured to control the fill light element to perform fill light according to a preset time interval, perform real-time data recording through the security camera, and obtain a security monitoring video;

[0027] A fault verification module, configured to retrieve images from the time when fill light occurs in the security monitoring video, construct a monitoring image set, perform pixel intensity change detection processing based on the monitoring image set, and determine whether a fault has occurred.

[0028] Preferably, the function construction module includes:

[0029] A fill light parameter acquisition unit, configured to acquire the fill light parameter data of a security camera, and determine the fill light type and fill light intensity range of the security camera;

[0030] A gray coordinate construction unit, configured to randomly acquire a set of security monitoring images, divide the security monitoring images into multiple tiles according to a preset image separation parameter, calculate the gray value of each tile, and construct a tile gray information coordinate;

[0031] An element control unit, configured to perform function fitting based on the tile gray information coordinate to obtain a fill light control function, and control the fill light element of the security camera based on the fill light control function.

[0032] Preferably, the fill light data acquisition module includes:

[0033] A function calculation unit, configured to import a preset natural number into the fill light control function when entering the fault monitoring time period, obtain multiple sets of random values, and determine the numbers of the random values;

[0034] A fill light data calculation unit, configured to calculate the span value of the fill light intensity range, calculate the random value ratio, and determine the fill light control data according to the random value ratio and the span value of the fill light intensity range;

[0035] A video recording unit, configured to import the fill light control data into the fill light element, control the fill light element to emit light, perform real-time data recording through the security camera, and obtain a security monitoring video.

[0036] Preferably, the fault verification module includes:

[0037] An image extraction unit, configured to retrieve fill light control data, determine the time for each switching of the fill light intensity, retrieve the real-time monitoring image corresponding to this time, and construct a monitoring image set;

[0038] An image division unit, configured to divide each group of real-time monitoring images in the monitoring image set into multiple frame verification areas and multiple anomaly verification areas according to a preset division method, and perform frame change verification on the multiple frame verification areas;

[0039] A fault determination unit, configured to perform grayscale value statistics on the pixels in the anomaly verification area to obtain an interval grayscale value, construct a grayscale change function, check the deviation between the grayscale change function and the fill light control function, and determine whether there is a fault.

[0040] Preferably, when it is determined that there is a fault in the security camera, a fault warning message is sent to the management personnel, and the fault warning message at least includes the number of the security camera.

[0041] An intelligent fault monitoring method for a security camera provided by the present invention can control a fill light element to generate a random light intensity by constructing a fill light control function, and then analyze the image changes collected by the security camera to determine whether the image changes of the security camera match the light intensity changes of the fill light element, so as to determine whether there is a fault, realizing the autonomous fault monitoring of the security camera and avoiding missing valid data. Description of the Drawings

[0042] Figure 1 It is a flowchart of an intelligent fault monitoring method for a security camera provided by an embodiment of the present invention;

[0043] Figure 2 It is a flowchart of obtaining the fill light parameter data of a security camera, constructing a fill light control function, and controlling the fill light element of the security camera based on the fill light control function provided by an embodiment of the present invention;

[0044] Figure 3 It is a flowchart of controlling a fill light element to perform fill light according to a preset time interval, recording real-time data through a security camera, and obtaining a security monitoring video provided by an embodiment of the present invention;

[0045] Figure 4 It is a flowchart of retrieving images from the time when fill light occurs in a security monitoring video, constructing a monitoring image set, and performing pixel intensity change detection processing based on the monitoring image set to determine whether a fault has occurred provided by an embodiment of the present invention;

[0046] Figure 5 It is an architecture diagram of an intelligent fault monitoring system for a security camera provided by an embodiment of the present invention;

[0047] Figure 6An architecture diagram of a function construction module provided by an embodiment of the present invention;

[0048] Figure 7 An architecture diagram of a supplementary light data acquisition module provided by an embodiment of the present invention;

[0049] Figure 8 An architecture diagram of a fault verification module provided by an embodiment of the present invention. Detailed implementation manners

[0050] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the present application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0052] As Figure 1 shown, it is a flowchart of a method for intelligent monitoring of security camera failures provided by an embodiment of the present invention. The method includes:

[0053] S100, obtain the operating power consumption data of the security camera, determine the fault monitoring time period based on the operating power consumption data, and perform fault monitoring within the fault monitoring time period.

[0054] In this step, obtain the operating power consumption data of the security camera. During the operation of the security camera, the operating power consumption of the security camera is different. When the picture in the monitored area does not change or the picture content is single, the operating power of the security camera will decrease. This is because the picture change is small and the data processing volume is small. Therefore, the operating power consumption of the security camera is relatively small. By recording the operating power consumption data, determine the idle time of the security camera according to the operating power consumption data. For example, if the operating power consumption of a certain security camera is the lowest between 1 am and 2 am every day, then this time period is set as the fault monitoring time period, and fault monitoring is performed within this time period.

[0055] S200, obtain the supplementary light parameter data of the security camera, construct a supplementary light control function, and control the supplementary light element of the security camera based on the supplementary light control function.

[0056] In this step, obtain the fill light parameter data of the security camera. For different security cameras, different fill light methods are adopted. Some use infrared light for fill light to collect night vision images, and some security cameras use visible light for fill light, and the security camera collects full-color images. Whichever it is, obtain the corresponding fill light parameter data. The fill light parameter data is the available fill light intensity range under the corresponding fill light method. For example, for an LED fill light, the fill light intensity range is 200 - 500 mcd. By retrieving the real-time images generated by the security camera, construct a set of fill light control functions. The parameters of the fill light control functions are randomly generated. Therefore, when using the fill light control functions to control the fill light components, the light emission intensity of the fill light components is also random.

[0057] S300, control the fill light components to perform fill light according to a preset time interval, and record real-time data through the security camera to obtain a security monitoring video.

[0058] In this step, control the fill light components to perform fill light according to a preset time interval, retrieve a preset natural number, and import the natural number as an independent variable into the fill light control function. Since the fill light control function is randomly generated, after importing the natural number into the fill light control function, the generated value is also a random value. Determine the fill light intensity of the fill light components at each moment according to the random value and the fill light intensity range, and perform fill light according to the determined fill light intensity. During this process, the security camera continuously collects data, and then the images during fill light will be recorded in the security monitoring video.

[0059] S400, retrieve images from the security monitoring video at the time of fill light, construct a monitoring image set, and perform pixel intensity change detection processing based on the monitoring image set to determine whether a failure has occurred.

[0060] In this step, retrieve images from the security monitoring video at the time of fill light. During the fill light process, the security monitoring video will contain multiple frame images (real-time monitoring images). For example, if the fill light time lasts for one second, for a security monitoring video with a frame rate of 60 frames per second, the number of frame images containing fill light is 60 frames. Randomly select one frame image and enter it into the monitoring image set. Then, under different fill light intensities, multiple real-time monitoring images will be generated. By analyzing the pixels in the real-time monitoring images, it can be determined whether there is an abnormality in the current image change to determine whether there is a failure in the security camera.

[0061] As Figure 2 shown, as a preferred embodiment of the present invention, the steps of obtaining the fill light parameter data of the security camera, constructing the fill light control function, and controlling the fill light components of the security camera based on the fill light control function specifically include:

[0062] S201. Obtain the supplementary lighting parameter data of the security camera, and determine the supplementary lighting type and the supplementary lighting intensity range of the security camera.

[0063] In this step, obtain the supplementary lighting parameter data of the security camera, determine the supplementary lighting type of the security camera, such as visible light supplementary lighting type and infrared light supplementary lighting type, and then determine the supplementary lighting intensity range under this supplementary lighting type, such as A - B mcd. Then the supplementary lighting intensity range is B - A.

[0064] S202. Randomly obtain a set of security monitoring images, divide the security monitoring images into multiple tiles according to the preset image separation parameters, calculate the grayscale value of each tile, and construct the tile grayscale information coordinates.

[0065] In this step, randomly obtain a set of security monitoring images, retrieve a set of security monitoring images obtained by real - time acquisition, divide the security monitoring images into at least four groups of tiles. For example, if the size of the security monitoring image is 1920 * 1080, it is divided into four tiles with a size of 480 * 1080. Perform grayscale processing on all tiles. Then each pixel in each tile can be characterized by a grayscale value. Calculate the sum of the grayscale values of the pixels contained in each tile, determine the number of each tile, and construct the tile grayscale information coordinates. The abscissa of the tile grayscale information coordinates is the number of the tile, and its ordinate is the sum of the grayscale values of the pixels contained in each tile, such as (1, 29378), (2, 37182), (3, 32871), and (4, 36929).

[0066] S203. Perform function fitting based on the tile grayscale information coordinates to obtain the supplementary lighting control function, and control the supplementary lighting components of the security camera based on the supplementary lighting control function.

[0067] In this step, perform function fitting based on the tile grayscale information coordinates, import the generated tile grayscale information coordinates into function fitting software, such as mathematical tools like matlab. Through fitting, a fitting function will be generated, and this function is the supplementary lighting control function. Taking (1, 29378), (2, 37182), (3, 32871), and (4, 36929) as examples, the corresponding supplementary lighting control function is y = - 50631.1 + 113543x - 28020.5x 2 - 7583.44x 3 + 2099.74x 4 , and when performing verification later, the supplementary lighting intensity at each moment is determined according to the supplementary lighting control function.

[0068] Such as Figure 3As shown, as a preferred embodiment of the present invention, the step of controlling the light supplement element to supplement light according to a preset time interval and performing real-time data recording through a security camera to obtain a security monitoring video specifically includes:

[0069] S301, when entering the fault monitoring time period, import a preset natural number into the light supplement control function to obtain multiple groups of random values and determine the numbers of the random values.

[0070] In this step, when entering the fault monitoring time period, import a preset natural number into the light supplement control function. The number of natural numbers is not less than the number of tiles obtained by splitting the security monitoring image. Import the above natural numbers into the corresponding light supplement control functions to obtain the corresponding number of random values, and determine the numbers of the random values according to the generation order of the random values. For example, the number of the first generated random value is 1, and the number of the nth generated random value is n.

[0071] S302, calculate the span value of the light supplement intensity interval, calculate the random value ratio, and determine the light supplement control data according to the random value ratio and the span value of the light supplement intensity interval.

[0072] In this step, calculate the span value of the light supplement intensity interval. The span value of the light supplement intensity interval is the range of the light supplement intensity that the light supplement element on the current security camera can provide. For example, 200mcd - 500mcd, 200mcd is the lowest intensity that the light supplement element can give, and 500mcd is the highest intensity that the light supplement element can give. The light supplement intensity given by the light supplement element can freely vary within 200mcd - 500mcd. Calculate the ratio of each random value. For example, there are four random values M1, M2, M3, and M4, and calculate the ratio among them M1:M2:M3:M4. Then the nth group of light supplement intensity value P is where W is the light supplement intensity range. Then the nth group of light supplement intensity value P means that when the light supplement element changes the light supplement intensity for the nth time, the light supplement intensity value will change to Enter the light supplement intensity value into the light supplement control data.

[0073] S303, import the light supplement control data into the light supplement element, control the light supplement element to emit light, and perform real-time data recording through the security camera to obtain a security monitoring video.

[0074] In this step, import the light supplement control data into the light supplement element. Then, within the subsequent fault monitoring time period, the light supplement intensity can be controlled according to the above light supplement control data, and the light supplement intensity is switched to different values at different times. The security camera continues to work and will record the real-time monitoring images at different light supplement intensity values to obtain a security monitoring video.

[0075] Such as Figure 4As shown, as a preferred embodiment of the present invention, the steps of retrieving images from the time when fill light occurs in the security monitoring video, constructing a monitoring image set, and performing pixel intensity change detection processing based on the monitoring image set to determine whether a failure has occurred specifically include:

[0076] S401, retrieve fill light control data, determine the time for each switch of the fill light intensity, retrieve the real-time monitoring image corresponding to this time, and construct a monitoring image set.

[0077] In this step, retrieve the fill light control data. The fill light control data records the time for each fill light operation. For example, at time A, the fill light intensity is a, at time B, the fill light intensity is b, and at time C, the fill light intensity is c. Then retrieve the real-time monitoring images at times A, B, and C, and store the above real-time monitoring images to obtain the monitoring image set.

[0078] S402, divide each group of real-time monitoring images in the monitoring image set into multiple picture verification areas and multiple anomaly detection areas according to a preset division method, and perform picture change verification on the multiple picture verification areas.

[0079] In this step, divide each group of real-time monitoring images in the monitoring image set into multiple picture verification areas and multiple anomaly detection areas. For example, divide the real-time monitoring image into multiple regions of equal size, continuously number each region, the regions with odd numbers are picture verification areas, and the regions with even numbers are anomaly detection areas. Perform Hough transformation on the picture verification areas to convert them into line graphs. Multiple anomaly detection areas will generate multiple line graphs. Compare the line graphs corresponding to two consecutive groups of real-time monitoring images, and determine whether the coincidence rate of the line lengths contained in the two is up to a preset value. If it reaches the preset value, it is determined that the picture change verification passes, and continue with the anomaly detection. Otherwise, if the line length coincidence rate does not reach the preset value, it is determined that there is a current picture change, stop the anomaly detection, and continue with normal picture recording.

[0080] S403, perform gray value statistics on the pixels in the anomaly detection areas to obtain the interval gray value, construct a gray change function, check the deviation between the gray change function and the fill light control function, and determine whether there is a failure.

[0081] In this step, the gray values of the pixels in the abnormal inspection area are statistically analyzed, and the abnormal inspection area is gray-processed and converted into a grayscale image. At this time, the gray values of all the pixels included in the grayscale image are statistically analyzed, that is, the interval gray value corresponding to the grayscale image is obtained. In one test, the monitored image set will include multiple groups of real-time monitored images. For example, it includes four groups of real-time monitored images, namely the first real-time monitored image, the second real-time monitored image, the third real-time monitored image, and the fourth real-time monitored image. Then, the interval gray values An in the first real-time monitored image, the interval gray values Bn in the second real-time monitored image, the interval gray values Cn in the third real-time monitored image, and the interval gray values Dn in the fourth real-time monitored image are used to construct the gray value coordinates (m, An), where m is the number of the real-time monitored image, and An is the nth interval gray value. For example, (1, A2 = 3872) indicates that the second interval gray value corresponding to the first real-time monitored image is 3872. According to the multiple groups of gray value coordinates corresponding to the interval gray values with the same number, a gray change function is constructed. For example, (1, A2 = 3872), (2, B2 = 2978), (3, C2 = 5082), and (4, D2 = 4823), and the gray change function is y = 9706.38 - 8097.37x + 1874.51x 2 +530.926x 3 -142.443x 4 , calculate the deviation within the fitting interval segment of the gray change function and the supplementary light control function, that is, the deviation within the independent variable interval [1, 4]. Specifically, sample the independent variable interval according to a preset step size. For example, 0.1 is the sampling step size. Starting from 1, sample once every 0.1 interval to obtain multiple sampling points Pi, calculate the deviation ∑(f1(Pi) - f2(Pi)), calculate the sum of all deviations. If the sum exceeds the preset range, it is determined that there is a picture abnormality, otherwise, it is determined that the security camera is normal. When it is determined that the security camera has a fault, a fault warning message is sent to the management personnel, and the fault warning message at least includes the number of the security camera.

[0082] As Figure 5 shown, a security camera fault intelligent monitoring system provided by an embodiment of the present invention includes:

[0083] A data acquisition module 100, configured to acquire the operating power consumption data of the security camera, determine the fault monitoring time period based on the operating power consumption data, and perform fault monitoring during the fault monitoring time period.

[0084] In this system, the data acquisition module 100 acquires the operating power consumption data of the security camera. During the operation of the security camera, the operating power consumption of the security camera is different. When there is no change in the picture in the monitored area or the picture content is single, the operating power of the security camera will decrease. This is because the picture change is small and the data processing volume is small. Therefore, the operating power consumption of the security camera is relatively small. By recording the operating power consumption data, the idle time of the security camera is determined according to the operating power consumption data. For example, if the operating power consumption of a certain security camera is the lowest between 1 am and 2 am every day, then this time period is set as the fault monitoring time period, and fault monitoring is carried out during this time period.

[0085] The function construction module 200 is used to acquire the fill light parameter data of the security camera, construct a fill light control function, and control the fill light element of the security camera based on the fill light control function.

[0086] In this system, the function construction module 200 acquires the fill light parameter data of the security camera. For different security cameras, different fill light methods are adopted. Some use infrared light for fill light to collect night vision images, and some security cameras use visible light for fill light, and the security camera collects full-color images. In either case, the corresponding fill light parameter data is acquired. The fill light parameter data is the available fill light intensity range under the corresponding fill light method. For example, for an LED fill light, the fill light intensity range is 200 - 500 mcd. By retrieving the real-time image generated by the security camera, a set of fill light control functions is constructed. The parameters of the fill light control function are randomly generated. Therefore, when using the fill light control function to control the fill light element, the light emission intensity of the fill light element is also random.

[0087] The fill light data acquisition module 300 is used to control the fill light element to perform fill light according to a preset time interval, and perform real-time data recording through the security camera to obtain a security monitoring video.

[0088] In this system, the fill light data acquisition module 300 controls the fill light element to perform fill light according to a preset time interval, retrieves a preset natural number, and imports the natural number as an independent variable into the fill light control function. Since the fill light control function is randomly generated, after importing the natural number into the fill light control function, the generated value is also a random value. According to this random value and the fill light intensity range, the fill light intensity of the fill light element at each moment is determined, and fill light is performed according to the determined fill light intensity. During this process, the security camera performs continuous data acquisition, so that the image during fill light will be recorded in the security monitoring video.

[0089] The fault verification module 400 is used to retrieve images from the time when fill light occurs in the security monitoring video, construct a monitoring image set, and perform pixel intensity change detection processing based on the monitoring image set to determine whether a fault has occurred.

[0090] In this system, the fault verification module 400 retrieves images from the time when supplementary lighting occurs in the security monitoring video. During the process of supplementary lighting, the security monitoring video will contain multiple frame images (real-time monitoring images). For example, if the supplementary lighting time lasts for one second and the security monitoring video has a frame rate of 60 frames per second, the number of frame images containing supplementary lighting is 60 frames. Randomly select one frame image and input it into the monitoring image set. Then, under different supplementary lighting intensities, multiple real-time monitoring images will be generated. By analyzing the pixels in the real-time monitoring images, it can be determined whether there is an abnormality in the current image change to determine whether there is a fault in the security camera.

[0091] As Figure 6 shown, as a preferred embodiment of the present invention, the function construction module 200 includes:

[0092] The supplementary lighting parameter acquisition unit 201 is used to acquire the supplementary lighting parameter data of the security camera, and determine the supplementary lighting type and the supplementary lighting intensity range of the security camera.

[0093] In this module, the supplementary lighting parameter acquisition unit 201 acquires the supplementary lighting parameter data of the security camera, determines the supplementary lighting type of the security camera, such as visible light supplementary lighting type and infrared light supplementary lighting type, and then determines the supplementary lighting intensity range under this supplementary lighting type, such as A - B mcd. Then the supplementary lighting intensity range is B - A.

[0094] The grayscale coordinate construction unit 202 is used to randomly acquire a set of security monitoring images, divide the security monitoring images into multiple tiles according to the preset image separation parameters, calculate the grayscale value of each tile, and construct the tile grayscale information coordinates.

[0095] In this module, the grayscale coordinate construction unit 202 randomly acquires a set of security monitoring images, retrieves a set of security monitoring images obtained by real-time acquisition, divides the security monitoring images into at least four groups of tiles. For example, if the size of the security monitoring image is 1920 * 1080, it is divided into four tiles with a size of 480 * 1080. Perform grayscale processing on all tiles. Then, each pixel in each tile can be characterized by a grayscale value. Calculate the sum of the grayscale values of the pixels contained in each tile, determine the number of each tile, and construct the tile grayscale information coordinates. The abscissa of the tile grayscale information coordinates is the number of the tile, and its ordinate is the sum of the grayscale values of the pixels contained in each tile, such as (1, 29378), (2, 37182), (3, 32871), and (4, 36929).

[0096] The component control unit 203 is used to perform function fitting based on the tile grayscale information coordinates to obtain a supplementary lighting control function, and control the supplementary lighting components of the security camera based on the supplementary lighting control function.

[0097] In this module, the component control unit 203 performs function fitting based on the tile grayscale information coordinates, imports the generated tile grayscale information coordinates into function fitting software such as mathematical tools like Matlab. Through fitting, a fitting function will be generated, and this function is the fill light control function. Taking (1, 29378), (2, 37182), (3, 32871), and (4, 36929) as examples, the corresponding fill light control function is y = -50631.1 + 113543x - 28020.5x 2 - 7583.44x 3 + 2099.74x 4 , and during subsequent verification, the fill light intensity at each moment is determined according to the fill light control function.

[0098] As Figure 7 shown, as a preferred embodiment of the present invention, the fill light data acquisition module 300 includes:

[0099] A function calculation unit 301, which is used to import a preset natural number into the fill light control function when entering the fault monitoring time period, obtain multiple sets of random values, and determine the numbers of the random values.

[0100] In this module, when the function calculation unit 301 enters the fault monitoring time period, it imports a preset natural number into the fill light control function. The number of natural numbers is not less than the number of tiles obtained by splitting the security monitoring image. The above natural numbers are imported into the corresponding fill light control function, thereby obtaining the corresponding number of random values. The numbers of the random values are determined according to the generation order of the random values. For example, the number of the first generated random value is 1, and the number of the nth generated random value is n.

[0101] A fill light data calculation unit 302, which is used to calculate the span value of the fill light intensity interval, calculate the random value ratio, and determine the fill light control data according to the random value ratio and the span value of the fill light intensity interval.

[0102] In this module, the fill light data calculation unit 302 calculates the span value of the fill light intensity interval. The span value of the fill light intensity interval is the range of the fill light intensity that the fill light element on the current security camera can provide. For example, 200 mcd - 500 mcd, 200 mcd is the lowest intensity that the fill light element can give, and 500 mcd is the highest intensity that the fill light element can give. The fill light intensity given by the fill light element can freely vary within 200 mcd - 500 mcd. Calculate the ratio of each random value. For example, there are four random values M1, M2, M3, and M4, and calculate the ratio M1:M2:M3:M4 among them. Then the nth group of fill light intensity value P is Where W is the supplementary light intensity range, then the nth group of supplementary light intensity values P means that when the supplementary light intensity is changed for the nth time by the supplementary light element, the supplementary light intensity value will be changed to Enter the supplementary light intensity value into the supplementary light control data.

[0103] The video recording unit 303 is used to import the supplementary light control data into the supplementary light element, control the supplementary light element to emit light, and perform real-time data recording through the security camera to obtain the security monitoring video.

[0104] In this module, the video recording unit 303 imports the supplementary light control data into the supplementary light element. Then, during the subsequent fault monitoring period, the supplementary light intensity can be controlled according to the above-mentioned supplementary light control data, and different supplementary light intensities can be switched at different times. The security camera will continue to work and record the real-time monitoring images under different supplementary light intensity values to obtain the security monitoring video.

[0105] Such as Figure 8 As shown, as a preferred embodiment of the present invention, the fault verification module 400 includes:

[0106] The image extraction unit 401 is used to retrieve the supplementary light control data, determine the time for each switch of the supplementary light intensity, retrieve the real-time monitoring image corresponding to this time, and construct a monitoring image set.

[0107] In this module, the image extraction unit 401 retrieves the supplementary light control data. The supplementary light control data records the time for each supplementary light. For example, at time A, the supplementary light intensity is a, at time B, the supplementary light intensity is b, and at time C, the supplementary light intensity is c. Then, retrieve the real-time monitoring images at times A, B, and C, and store the above real-time monitoring images to obtain the monitoring image set.

[0108] The image division unit 402 is used to divide each group of real-time monitoring images in the monitoring image set into multiple picture verification areas and multiple anomaly verification areas according to a preset division method, and perform picture change verification on the multiple picture verification areas.

[0109] In this module, the image division unit 402 divides each set of real-time monitoring images in the monitoring image set into multiple picture verification areas and multiple anomaly detection areas according to a preset division method. For example, the real-time monitoring images are divided into multiple regions of equal size, and each region is consecutively numbered. The regions with odd numbers are picture verification areas, and the regions with even numbers are anomaly detection areas. The Hough transform is performed on the picture verification areas to convert them into line graphs. Multiple anomaly detection areas will generate multiple line graphs. By comparing the line graphs corresponding to two consecutive sets of real-time monitoring images, it is determined whether the coincidence rate of the line lengths contained in the two reaches a preset value. If it reaches the preset value, it is determined that the picture change verification passes, and the anomaly detection continues. Otherwise, if the line length coincidence rate does not reach the preset value, it is determined that there is a current picture change, the anomaly detection is stopped, and the normal picture recording continues.

[0110] The fault determination unit 403 is used to statistically analyze the gray values of the pixels in the anomaly detection areas to obtain the interval gray values, construct a gray value change function, check the deviation between the gray value change function and the fill light control function, and determine whether there is a fault.

[0111] In this module, the fault determination unit 403 statistically analyzes the gray values of the pixels in the anomaly detection areas, performs gray processing on the anomaly detection areas, and converts them into gray images. At this time, the gray values of all the pixels contained in the gray images are statistically analyzed, that is, the interval gray values corresponding to the gray images are obtained. In one test, the monitoring image set will contain multiple sets of real-time monitoring images. For example, it contains four sets of real-time monitoring images, namely the first real-time monitoring image, the second real-time monitoring image, the third real-time monitoring image, and the fourth real-time monitoring image. Then, the interval gray values An in the first real-time monitoring image, the interval gray values Bn in the second real-time monitoring image, the interval gray values Cn in the third real-time monitoring image, and the interval gray values Dn in the fourth real-time monitoring image. A gray value coordinate (m, An) is constructed, where m is the number of the real-time monitoring image, and An is the nth interval gray value. For example, (1, A2 = 3872) indicates that the second interval gray value corresponding to the first real-time monitoring image is 3872. According to the multiple gray value coordinates corresponding to the interval gray values with the same number, a gray value change function is constructed. For example, (1, A2 = 3872), (2, B2 = 2978), (3, C2 = 5082), and (4, D2 = 4823), and the gray value change function is y = 9706.38 - 8097.37x + 1874.51x 2 +530.926x 3 -142.443x 4, calculate the deviation within the fitting interval segment of the grayscale change function and the fill light control function, that is, the deviation within the independent variable interval [1, 4]. Specifically, sample the independent variable interval according to a preset step size. For example, 0.1 is the sampling step size. Starting from 1, sample once every 0.1 interval to obtain multiple sampling points Pi, calculate the deviation ∑(f1(Pi) - f2(Pi)), calculate the sum of all deviations. If the sum exceeds the preset range, it is determined that there is an abnormal picture; otherwise, it is determined that the security camera is normal. When it is determined that the security camera has a fault, send a fault warning message to the administrator. The fault warning message at least includes the number of the security camera.

[0112] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps do not necessarily need to be executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily need to be executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0113] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0115] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0116] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A security camera fault intelligent monitoring method, characterized in that: The method comprises: Obtaining operating power consumption data of the security camera, determining a fault monitoring time period based on the operating power consumption data, and performing fault monitoring within the fault monitoring time period; Obtain fill light parameter data of the security camera, construct a fill light control function, and control the fill light element of the security camera based on the fill light control function; The fill light element is controlled to fill light according to the preset time interval, and the real-time data is recorded by the security camera to obtain the security monitoring video; The image is retrieved from the time when fill light occurs in the security monitoring video, a monitoring image set is constructed, and pixel intensity change detection processing is performed based on the monitoring image set to determine whether a fault has occurred.

2. The intelligent monitoring method for security camera failure according to claim 1, characterized in that: The steps of obtaining fill light parameter data of the security camera, constructing a fill light control function, and controlling the fill light element of the security camera based on the fill light control function specifically include: Obtain the fill light parameter data of the security camera, and determine the fill light type and fill light intensity range of the security camera; A set of security monitoring images is randomly obtained, and the security monitoring images are divided into multiple blocks according to the preset image segmentation parameters, and the gray value of each block is calculated to construct the block gray information coordinates; Function fitting is performed based on the grayscale information coordinates of the image blocks to obtain a fill light control function, and the fill light element of the security camera is controlled based on the fill light control function.

3. The intelligent monitoring method for security camera failure according to claim 1, characterized in that: The step of controlling the fill light element to perform fill light according to a preset time interval, and recording real-time data through a security camera to obtain a security monitoring video specifically includes: When entering the fault monitoring time period, a preset natural number is introduced into the fill light control function to obtain multiple groups of random value numbers, and the numbers of the random value numbers are determined; Calculate the fill light intensity interval span value, calculate the random value ratio, and determine the fill light control data according to the random value ratio and the fill light intensity interval span value; The fill light control data is imported into the fill light element to control the fill light element to emit light, and the real-time data is recorded through the security camera to obtain the security monitoring video.

4. The intelligent monitoring method for security camera failure according to claim 3 is characterized in that: The step of retrieving images from the time when fill light occurs in the security monitoring video, constructing a monitoring image set, performing pixel intensity change detection processing based on the monitoring image set, and determining whether a fault occurs specifically includes: Retrieve fill light control data, determine the time of switching fill light intensity each time, retrieve the real-time monitoring image corresponding to the time, and construct a monitoring image set; Each group of real-time monitoring images in the monitoring image set is divided into a plurality of image verification areas and a plurality of abnormality inspection areas according to a preset division method, and image change inspection is performed on the plurality of image verification areas; The grayscale values ​​of the pixels in the abnormal inspection area are counted to obtain the interval grayscale value, a grayscale change function is constructed, the deviation between the grayscale change function and the fill light control function is checked, and whether there is a fault is determined.

5. The intelligent monitoring method for security camera failure according to claim 1, characterized in that: When it is determined that the security camera has a fault, a fault warning message is sent to a management personnel, wherein the fault warning message at least includes the serial number of the security camera.

6. An intelligent monitoring system for security camera failures, characterized in that: The system comprises: A data acquisition module, used to acquire the operating power consumption data of the security camera, determine the fault monitoring time period based on the operating power consumption data, and perform fault monitoring within the fault monitoring time period; A function building module, used for obtaining fill light parameter data of a security camera, building a fill light control function, and controlling the fill light element of the security camera based on the fill light control function; The fill light data acquisition module is used to control the fill light element to perform fill light according to a preset time interval, and to record real-time data through a security camera to obtain a security monitoring video; The fault detection module is used to retrieve images from the time when fill light occurs in the security monitoring video, build a monitoring image set, perform pixel intensity change detection processing based on the monitoring image set, and determine whether a fault occurs.

7. The intelligent security camera fault monitoring system according to claim 6, characterized in that: The function building module includes: A fill light parameter acquisition unit, used to acquire fill light parameter data of a security camera, and determine the fill light type and fill light intensity range of the security camera; A grayscale coordinate construction unit is used to randomly obtain a group of security monitoring images, divide the security monitoring images into multiple blocks according to preset image segmentation parameters, calculate the grayscale value of each block, and construct the block grayscale information coordinates; The component control unit is used to perform function fitting based on the block grayscale information coordinates to obtain a fill light control function, and control the fill light component of the security camera based on the fill light control function.

8. The intelligent monitoring system for security camera failure according to claim 6, characterized in that: The fill light data acquisition module comprises: A function calculation unit, used for importing a preset natural number into a fill light control function to obtain multiple groups of random value numbers and determine the numbering of the random value numbers when entering a fault monitoring time period; A fill light data calculation unit, used to calculate the fill light intensity interval span value, calculate the random value ratio, and determine the fill light control data according to the random value ratio and the fill light intensity interval span value; The video recording unit is used to import the fill light control data into the fill light element, control the fill light element to emit light, and record the real-time data through the security camera to obtain the security monitoring video.

9. The intelligent security camera fault monitoring system according to claim 8, characterized in that: The fault checking module comprises: An image extraction unit is used to retrieve fill light control data, determine the time of each switch of fill light intensity, retrieve the real-time monitoring image corresponding to the time, and construct a monitoring image set; An image division unit is used to divide each group of real-time monitoring images in the monitoring image set into a plurality of image verification areas and a plurality of abnormality inspection areas according to a preset division method, and perform image change inspection on the plurality of image verification areas; The fault determination unit is used to perform grayscale value statistics on the pixels in the abnormal inspection area, obtain the interval grayscale value, construct a grayscale change function, check the deviation between the grayscale change function and the fill light control function, and determine whether there is a fault.

10. The intelligent monitoring system for security camera failure according to claim 6, characterized in that: When it is determined that the security camera has a fault, a fault warning message is sent to a management personnel, wherein the fault warning message at least includes the serial number of the security camera.

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