Security camera intelligent hibernation and wake-up method and system based on energy consumption optimization

Abstract

The application relates to an energy consumption optimization-based security camera intelligent sleep-wakeup method and system, which comprises the following steps: acquiring environmental data and working state data of a security camera in a first monitoring period; calculating an environmental activity index based on illumination intensity, a moving object detection result and sound intensity; judging whether to enter a sleep mode or a wakeup mode according to a comparison result of the environmental activity index and a preset threshold; when the sleep mode is triggered, adjusting working parameters of the camera to reduce power consumption, shutting down part of function modules of an image sensor, reducing a working frequency of a processor and reducing a signal sending frequency of a wireless communication module; the scheme can realize energy consumption optimization while ensuring the effectiveness of security monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of security and artificial intelligence technology, and in particular to a security camera intelligent hibernation and wake-up method and system based on energy consumption optimization. BACKGROUND

[0002] In the field of security monitoring, security cameras are widely used in various places to ensure safety as key equipment. Traditional security cameras usually work in a continuous state, regardless of whether there are any abnormal situations in the environment, the camera remains full power operation. Under this working mode, the camera continuously collects images, processes data and transmits, resulting in a large amount of unnecessary energy consumption.

[0003] To solve this problem, some existing technical solutions attempt to optimize the energy saving of security cameras. For example, some solutions use a timed hibernation method to put the camera into a hibernation state during a preset time period to reduce power consumption. Some other solutions control the working state of the camera according to simple environmental factors such as light intensity. When the light intensity is below a certain value, the camera enters a low-power mode.

[0004] However, these solutions have obvious limitations. The timed hibernation method lacks real-time perception of the actual situation of the environment, and the camera may be in a hibernation state when the environment needs to be monitored, thereby affecting the security effect. The solution that only relies on a single environmental factor to control the state cannot fully reflect the real activity of the environment, and is prone to misjudgment. For example, in the case of low light intensity but moving objects or abnormal sounds, the camera may enter a low-power mode due to light factors, thus missing important monitoring information. SUMMARY

[0005] The main purpose of the present application is to provide a security camera intelligent hibernation and wake-up method and system based on energy consumption optimization, which can achieve energy consumption optimization while ensuring the effectiveness of security monitoring.

[0006] To achieve the above purpose, the embodiment of the present application provides a security camera intelligent hibernation and wake-up method based on energy consumption optimization, which comprises:

[0007] Obtaining environmental data and working state data of the security camera in a first monitoring period, the environmental data including light intensity, moving object detection results and sound intensity, and the working state data including real-time power consumption and running mode of the camera;

[0008] Based on the light intensity, moving object detection results and sound intensity, the environmental activity index is calculated;

[0009] Determine whether to enter a sleep mode or a wake-up mode based on a comparison result of the environment activity index with a preset first threshold and a second threshold, triggering the sleep mode if the environment activity index is less than the first threshold, and triggering the wake-up mode if the environment activity index is greater than the second threshold;

[0010] When the sleep mode is triggered, the camera's operating parameters are adjusted to reduce power consumption, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module;

[0011] When the wake-up mode is triggered, all functional modules of the camera are restored to normal working state.

[0012] Accordingly, an embodiment of the present application further provides a security camera intelligent sleep and wake-up system based on energy consumption optimization, the system comprising:

[0013] An acquisition module is used to obtain environmental data and operating status data of the security camera during the first monitoring cycle, wherein the environmental data includes light intensity, moving object detection results, and sound intensity, and the operating status data includes the real-time power consumption and operating mode of the camera;

[0014] Index calculation module, used to calculate the environmental activity index based on light intensity, moving object detection results and sound intensity;

[0015] a mode determination module, configured to determine whether to enter a sleep mode or a wake-up mode based on a comparison result of the environment activity index with a preset first threshold and a second threshold, triggering the sleep mode if the environment activity index is less than the first threshold, and triggering the wake-up mode if the environment activity index is greater than the second threshold;

[0016] A first adjustment module is used to adjust the operating parameters of the camera to reduce power consumption when the sleep mode is triggered, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module;

[0017] The second adjustment module is used to restore all functional modules of the camera to a normal working state when the wake-up mode is triggered.

[0018] In summary, by adopting the technical solution of the present application, by obtaining the environmental data and working status data of the security camera during the first monitoring cycle, it is possible to fully understand the environmental conditions and working conditions of the camera. The environmental activity index is calculated based on light intensity, moving object detection results and sound intensity, and a variety of factors that may affect the security monitoring needs are comprehensively considered, making the assessment of the environment more accurate. Whether to enter sleep mode or wake-up mode is determined based on the comparison result of the environmental activity index with the preset threshold, avoiding the limitations of traditional timed sleep or single factor control, and can flexibly adjust the working state of the camera according to the actual environmental conditions. When the sleep mode is triggered, the operating parameters of the camera are adjusted to reduce power consumption, some functional modules of the image sensor are turned off, the operating frequency of the processor is reduced, and the signal transmission frequency of the wireless communication module is reduced, effectively reducing unnecessary energy consumption. When the wake-up mode is triggered, all functional modules of the camera are restored to normal working state, ensuring that the camera can function in time when monitoring is required, and ensuring the security effect. Therefore, this method ensures the effectiveness of security monitoring while achieving energy consumption optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 Schematic diagram of a scenario of an intelligent sleep and wake-up method for a security camera based on energy consumption optimization in an embodiment of the present application;

[0021] Figure 2 A flowchart of an intelligent sleep and wake-up method for security cameras based on energy consumption optimization is provided for an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the process for calculating the environmental activity index provided in an embodiment of the present application;

[0023] Figure 4 Another schematic diagram of the process of calculating the environmental activity index provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of a power consumption adjustment process according to an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the normal state recovery process provided in an embodiment of the present application;

[0026] Figure 7A flowchart of threshold adjustment provided for the embodiments of the present application is shown in FIG. 1.

[0027] Figure 8 Another flowchart of threshold adjustment provided for the embodiments of the present application is shown in FIG. 2.

[0028] Figure 9 A flowchart of initial threshold determination provided for the embodiments of the present application is shown in FIG. 3.

[0029] Figure 10 A structural diagram of the intelligent sleep and wake-up system of the security camera based on energy consumption optimization provided for the embodiments of the present application is shown in FIG. 4.

[0030] Figure 11 A structural diagram of the computer device provided for the embodiments of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0032] The present application provides an intelligent sleep and wake-up method and system of a security camera based on energy consumption optimization, which will be described in detail below.

[0033] In the embodiments of the present application, the intelligent sleep and wake-up method of a security camera based on energy consumption optimization is a technical solution aiming to reduce the energy consumption of a security camera while ensuring the monitoring effect. The method collects and analyzes multi-dimensional data of the environment in which the security camera is located, calculates an environmental activity index, and determines whether the camera needs to enter a sleep mode or a wake-up mode based on the environmental activity index. In the sleep mode, the working parameters of the camera are adjusted to reduce power consumption; in the wake-up mode, the full functions of the camera are restored to the normal working state. In addition, the threshold setting is optimized by means of counting the number of mode switching and generating a benchmark value of the environmental activity index, so as to achieve more intelligent and efficient energy consumption management.

[0034] As shown in FIG. 1, a scene of an intelligent sleep and wake-up method of a security camera based on energy consumption optimization is provided, which mainly includes a security camera and a data processing server. The security camera and the data processing server are connected through a wireless network.

[0035] For example, in a large shopping mall, security cameras are deployed throughout the mall to monitor personnel movements, merchandise safety, and other aspects. During business hours, the mall experiences frequent traffic, requiring continuous camera monitoring to ensure safety. However, during off-hours, when fewer people are present, the need for some cameras to monitor the mall is reduced.

[0036] Each security camera in the mall collects environmental and operating status data. Environmental data includes light intensity, motion detection results, and sound intensity. For example, at mall entrances and exits, light intensity varies with the day and night. In the mall's warehouse area, motion detection results can indicate the movement of people or goods. In the mall's public areas, sound intensity can detect unusual sounds such as arguments or alarms. Operating status data includes the camera's real-time power consumption and operating mode. Real-time power consumption reflects the camera's current energy consumption, while operating modes are categorized as normal operating mode, sleep mode, and wake-up mode.

[0037] The data processing server receives environmental data and working status data from each camera and processes and analyzes them. The server calculates the environmental activity index based on light intensity, mobile object detection results, and sound intensity. For example, in the rest area of ​​a shopping mall, the light intensity is high during the day and the flow of people is relatively small. The mobile object detection results show that there is a small amount of people moving, and the sound intensity is moderate. At this time, the calculated environmental activity index is at a medium level. The server determines whether the camera enters sleep mode or wake-up mode based on the comparison results of the environmental activity index with the preset first and second thresholds. If the environmental activity index is less than the first threshold, such as in the warehouse area after the mall closes, the light intensity is low, there are no moving objects, and the sound intensity is almost zero, the server will trigger the camera to enter sleep mode; if the environmental activity index is greater than the second threshold, such as at the mall promotion event site, the light intensity is high, the flow of people is large, and the sound intensity is noisy, the server will trigger the camera to enter wake-up mode.

[0038] In sleep mode, the camera adjusts operating parameters to reduce power consumption. It shuts down some functional modules of the image sensor, such as reducing image resolution and frame rate to minimize data processing; lowering the processor's operating frequency to reduce computing resource consumption; and reducing the signal transmission frequency of the wireless communication module to reduce communication energy consumption. In wake-up mode, the camera restores all functional modules to normal operation. It first checks the current operating status of each functional module, then sequentially restores the image sensor's high-resolution mode and normal frame rate, the processor's operating frequency, and the wireless communication module's functions. After recovery, it performs a self-test to ensure that all modules are operating normally.

[0039] In addition, the data processing server will also count the number of mode switches per unit time for subsequent optimization of threshold settings. If the number of mode switches exceeds the preset upper limit of the switching frequency, it means that the current threshold setting may be unreasonable. The server will adjust the values ​​of the first and second thresholds based on the number of mode switches and the changing trend of the environmental activity index to reduce the frequency of mode switching. At the same time, based on the historical statistical analysis of environmental data, the server generates a baseline value of the environmental activity index over multiple monitoring cycles, which is used to optimize the initial settings of the first and second thresholds, making the threshold settings more consistent with the actual environmental changes in the mall.

[0040] refer to Figure 2 , Figure 2 This is a flow chart of a method for intelligent sleep and wakeup of a security camera based on energy consumption optimization provided by an embodiment of the present application. The execution subject of the method can be a computer device, which can be a single computer device or a cluster of multiple computer devices. The computer device can be a terminal device or a server. The method for intelligent sleep and wakeup of a security camera based on energy consumption optimization provided by an embodiment of the present application specifically includes:

[0041] S10: Acquire environmental data and working status data of the security camera in the first monitoring cycle, wherein the environmental data includes light intensity, moving object detection results, and sound intensity, and the working status data includes real-time power consumption and operating mode of the camera.

[0042] In this application, the first monitoring cycle is a pre-set specific time period for collecting and analyzing relevant data from security cameras. It is the starting stage of the entire data monitoring and processing process, and provides basic data for subsequent energy consumption optimization decisions.

[0043] Environmental data is a comprehensive collection of information reflecting the characteristics of the environment surrounding a security camera. Light intensity refers to the amount of visible light received per unit area, typically measured in lux. It reflects the brightness of the environment, and different lighting conditions can significantly affect the imaging quality of security cameras. For example, during the day, in direct sunlight outdoors, the light intensity may reach tens of thousands of lux; whereas indoors at night, the light intensity may be only a few lux. Accurately capturing light intensity data facilitates subsequent adjustment of camera operating parameters based on ambient brightness, ensuring image clarity and quality. Its technical benefit is that it provides important environmental characteristic information for calculating the environmental activity index, enabling the system to more accurately assess environmental conditions.

[0044] The result of moving object detection refers to the information obtained by identifying the presence of moving objects within the security camera's monitoring range through specific detection technologies and algorithms. This result is presented in binary form: either a moving object is present or not. For example, in a shopping mall surveillance scenario, if a customer moves within the camera's field of view, the detection result is a moving object; if the mall is closed and no one is active, the detection result is a no moving object. The moving object detection result can intuitively reflect the dynamic situation of people or objects in the environment and is one of the key indicators for determining whether the environment is active. Its technical effect is that it provides dynamic information for the calculation of the environmental activity index, helping to promptly detect abnormal activities in the environment.

[0045] Sound intensity refers to the amount of energy a sound possesses during propagation, typically measured in decibels (dB). It reflects the noise level of an environment, with different environmental scenarios producing varying degrees of sound intensity. For example, on a busy street, traffic and the clamor of people can generate high sound intensity, potentially reaching 70-80 decibels; whereas in a quiet library, the sound intensity may be only 30-40 decibels. Sound intensity data can supplement environmental activity information from the acoustic dimension. Combined with light intensity and moving object detection results, it provides a more comprehensive description of environmental conditions. The technical benefit is that it enriches the dimensionality of environmental data and improves the accuracy of the calculation of the environmental activity index.

[0046] Operating status data is a collection of information describing the security camera's operating status. Real-time power consumption refers to the current electrical energy consumed by the security camera, measured in watts (W). This value varies with the camera's operating mode and state. For example, when the camera is operating in high-resolution, high-frame-rate mode, it processes and transmits large amounts of data, resulting in relatively high real-time power consumption. However, in low-resolution, low-frame-rate mode, real-time power consumption is correspondingly lower. Real-time power consumption data provides a direct reflection of the camera's energy consumption, providing a key basis for energy optimization. Its technical benefit is that it helps identify operating states with high energy consumption, enabling targeted adjustments to operating parameters to reduce energy consumption.

[0047] The operating mode refers to the current working state of the security camera, which mainly includes normal working mode, sleep mode and wake-up mode. In normal working mode, all functional modules of the camera are in normal operating state and can collect and transmit environmental data in real time; sleep mode is a low-power state set to reduce energy consumption. In this mode, some functional modules will be turned off or the operating frequency will be reduced; wake-up mode is to restore the camera from sleep mode to normal working mode when the environmental activity increases. The operating mode data can help monitoring personnel understand the working status of the camera and provide a basis for subsequent mode switching decisions. Its technical effect is that by reasonably switching the operating mode, the working status of the camera can be dynamically adjusted according to the actual environmental conditions, thereby achieving the purpose of optimizing energy consumption.

[0048] In one embodiment, a photoresistor sensor can be used to obtain light intensity. The resistance of a photoresistor changes with light intensity. By measuring this resistance change and converting it into a corresponding voltage signal, and then using an analog-to-digital converter (ADC) to convert the analog signal into a digital signal, an accurate light intensity value can be obtained.

[0049] To detect moving objects, a motion detection algorithm based on background subtraction can be used. This algorithm first preprocesses the image captured by the camera, then compares the current frame with a pre-set background image and calculates the difference between the two. If the difference exceeds a preset threshold, it is determined to be a moving object. This algorithm is computationally simple and highly real-time, enabling rapid and accurate detection of moving objects.

[0050] To obtain sound intensity, an electret microphone sensor can be used. The electret microphone can convert sound signals into electrical signals. By amplifying and processing the electrical signals, and then converting them into digital signals through ADC, the numerical value of the sound intensity can be obtained.

[0051] To obtain real-time power consumption, a power sensor can be used. This sensor measures the camera's voltage and current and calculates real-time power consumption using the formula (power = voltage × current). This real-time power consumption data can be transmitted to the data processing unit for analysis.

[0052] The operating mode can be obtained by reading the camera's status register. The status register stores the camera's current operating mode information. By reading and parsing this register, the camera's operating mode can be obtained.

[0053] S20: Calculate the environment activity index based on the light intensity, moving object detection results, and sound intensity.

[0054] In this application, the Environmental Activity Index is a comprehensive, quantitative indicator reflecting the activity level of the security camera's surroundings. It integrates environmental data from three different dimensions: light intensity, motion detection, and sound intensity, to express the level of activity in a single numerical value. By calculating the Environmental Activity Index, complex environmental information can be simplified and quantified, providing an objective basis for subsequent sleep and wake mode determinations.

[0055] Light intensity, motion detection results, and sound intensity are the three key inputs to calculating the environmental activity index. They each reflect environmental characteristics from different perspectives. Light intensity reflects the brightness of the environment, motion detection results reflect dynamic changes in the environment, and sound intensity reflects the level of noise. These three factors are both independent and interrelated, and together determine the level of activity in the environment.

[0056] To calculate the environmental activity index, we first need to preprocess the light intensity, moving object detection results, and sound intensity to convert them into a form that can be uniformly calculated. Then, based on a specific weighting strategy, we perform a weighted summation of these three preprocessed factors to obtain the initial environmental activity index. Finally, to eliminate the interference of instantaneous fluctuations in the environmental data, we smooth and filter the initial environmental activity index to obtain the final environmental activity index.

[0057] The technical benefit is that the Environmental Activity Index quantifies complex environmental information, allowing for comparison and assessment of activity levels under different environmental conditions. This helps accurately determine whether to trigger sleep or wake mode based on the actual environmental conditions, thereby enabling intelligent energy management for security cameras.

[0058] In one embodiment, a normalization method can be used to pre-process the light intensity. The measured light intensity value is mapped to a range of 0-1. The specific calculation formula is: Normalized light intensity value = (Current light intensity - Minimum light intensity) / (Maximum light intensity - Minimum light intensity). The minimum light intensity and maximum light intensity are the minimum and maximum values ​​of light intensity in the environment obtained based on historical data statistics.

[0059] For the preprocessing of the moving object detection results, the case where there is a moving object is assigned a value of 1, and the case where there is no moving object is assigned a value of 0.

[0060] For the preprocessing of sound intensity, the normalization method is also used to map the measured value of sound intensity to the range of 0-1. The calculation formula is similar to the normalization formula of light intensity.

[0061] When determining the weight allocation strategy, we can analyze the security camera's historical environmental data and historical operating status data over the historical monitoring period. For example, if statistical analysis of a large amount of historical data reveals that moving object detection results have a greater impact on the camera's energy consumption, we can assign a higher weight to the moving object detection results.

[0062] Smoothing filtering can use a moving average filtering algorithm. This algorithm eliminates transient fluctuations by calculating the average value of the environmental activity index over a period of time. For example, the environmental activity index of the last five calculation cycles can be averaged to obtain a smoothed environmental activity index.

[0063] S30: Based on the comparison results of the environmental activity index with the preset first threshold and second threshold, determine whether to enter the sleep mode or the wake-up mode. If the environmental activity index is less than the first threshold, the sleep mode is triggered; if the environmental activity index is greater than the second threshold, the wake-up mode is triggered.

[0064] In this application, the first threshold and the second threshold are two pre-set critical values ​​used to determine whether the security camera needs to enter sleep mode or wake-up mode. The first threshold is the lower limit of the environmental activity index. When the environmental activity index is less than the first threshold, it indicates that the environment is relatively quiet and there is less activity of people and objects. At this time, sleep mode can be triggered to reduce energy consumption. The second threshold is the upper limit of the environmental activity index. When the environmental activity index is greater than the second threshold, it indicates that the environment is relatively active and there may be situations that need to be monitored. At this time, wake-up mode needs to be triggered to restore the camera to normal working state.

[0065] Comparing the environmental activity index with the first and second thresholds is a simple numerical comparison process. The comparison results clearly determine the camera's environmental state and enable appropriate mode switching. This threshold-based judgment method is simple and intuitive, enabling quick decision-making.

[0066] Its technical effect is that it dynamically adjusts the working status of the camera according to the actual environmental conditions, avoiding the camera from continuously consuming electricity when it does not need to work, while ensuring timely monitoring when the environment is active, thereby improving the energy utilization efficiency and monitoring effect of the security camera.

[0067] In one embodiment, the initial setting of the first and second thresholds can be based on historical statistical analysis of environmental data. For example, by statistically analyzing the environmental activity index over multiple historical monitoring periods, the average, maximum, and minimum values ​​are calculated. Then, using the average as the baseline value, the first threshold can be set to the baseline value minus a preset deviation value, and the second threshold can be set to the baseline value plus a preset deviation value. The size of the deviation value can be adjusted according to the actual application scenario and energy consumption optimization requirements.

[0068] In practical applications, the first and second thresholds can be dynamically adjusted based on the number of mode switches per unit time. If the number of mode switches is too frequent, it indicates that the current threshold settings may be unreasonable, and the difference between the first and second thresholds needs to be appropriately increased to reduce the frequency of mode switching and improve system stability.

[0069] S40: When the sleep mode is triggered, the operating parameters of the camera are adjusted to reduce power consumption, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module.

[0070] In this application, sleep mode is a low-power working state that a security camera enters in order to reduce energy consumption. In sleep mode, the operating parameters of the camera are adjusted to reduce unnecessary energy consumption, thereby achieving the purpose of energy saving.

[0071] Image sensors are key components in security cameras, capturing environmental image data. In normal operating mode, image sensors may offer features such as high resolution and high frame rates, but these functions consume significant power. Disabling certain image sensor functions, such as disabling high-resolution mode and switching to low-resolution mode, while also reducing the frame rate, can reduce data processing and thus lower the image sensor's power consumption.

[0072] The processor is the core component in security cameras for processing and analyzing image data. The higher the processor's operating frequency, the faster it processes data, but it also consumes more power. Lowering the processor's operating frequency and pausing non-essential background tasks can reduce the processor's workload and thus its power consumption.

[0073] The wireless communication module in a security camera transmits captured image data to a monitoring center. During normal operation, the module may continuously transmit signals, consuming significant power. Reducing the module's signal transmission frequency and disabling the beacon broadcast function can reduce power consumption.

[0074] Its technical effect is that by adjusting the camera's working parameters, it effectively reduces the camera's energy consumption, extends the equipment's battery life, and reduces operating costs without affecting the basic functions of security monitoring.

[0075] In one embodiment, the functional modules of the image sensor can be adjusted by modifying the configuration registers of the image sensor. The configuration registers store various operating parameters of the image sensor. By modifying these parameters, the high-resolution mode can be turned off and switched to the low-resolution mode, while also reducing the frame rate.

[0076] The processor's operating frequency can be adjusted by modifying the processor's clock control register. The clock control register controls the processor's clock signal frequency. By reducing the clock signal frequency, the processor's operating frequency can be lowered. Furthermore, system software can be used to suspend non-essential background tasks to reduce the processor's computational burden.

[0077] Adjusting the wireless communication module's signal transmission frequency can be achieved by modifying the module's communication protocol parameters. These parameters include information such as the signal transmission interval and frequency. By increasing the signal transmission interval, decreasing the signal transmission frequency, and disabling the beacon broadcast function, the module's power consumption can be reduced.

[0078] S50: When the wake-up mode is triggered, all functional modules of the camera are restored to normal working states.

[0079] In this application, the wake-up mode is the state in which the security camera returns to normal working mode from sleep mode. In the wake-up mode, the various functional modules of the camera need to be restored from low power consumption state to normal working state to ensure that the camera can normally collect and transmit environmental data.

[0080] Restoring all the camera's functional modules to normal operation is a step-by-step process. First, the actual operating status of each module must be tested, including the image sensor's resolution mode, the processor's operating frequency, and the wireless communication module's signal transmission frequency. Then, based on the test results, each module is restored to normal operation.

[0081] During the recovery process, care must be taken to avoid system instability caused by sudden parameter changes. For example, when restoring the processor's operating frequency, gradually increase it to the default value to avoid sudden frequency increases that could impact the system. When restoring the wireless communication module, follow a specific sequence: first enable the beacon broadcast function, then adjust the signal transmission frequency to the default value.

[0082] Finally, after completing the recovery of all functional modules, a self-test operation needs to be performed to ensure that all modules are in normal working condition. The self-test operation can be achieved by performing functional tests and data verification on each functional module.

[0083] Its technical effect is to ensure that the camera can quickly and stably return to normal working state after waking up, ensuring the continuity and reliability of security monitoring.

[0084] In one embodiment, to restore the image sensor, the configuration register of the image sensor may be read to obtain parameters such as the current resolution mode and frame rate. These parameters may then be modified to default values ​​under normal operating mode to restore the image sensor to a high-resolution mode and normal frame rate.

[0085] To recover the processor, the processor clock frequency can be gradually increased, with the magnitude of each increase adjusted based on the processor's performance and system stability. At the same time, non-essential background tasks that were previously paused can be restarted.

[0086] To restore the wireless communication module, first enable the broadcast beacon function so that other devices can recognize the presence of the camera. Then, adjust the signal transmission frequency to the default value to ensure that the wireless communication module can transmit data normally.

[0087] During the self-test operation, you can perform quality checks on the image data collected by the image sensor to check whether the image is clear and complete; perform a computing performance test on the processor to check whether the processor can process data normally; and perform a communication test on the wireless communication module to check whether data transmission is normal.

[0088] In one embodiment, reference Figure 3 , step S20 can be implemented as follows:

[0089] S201: Normalize the light intensity, the moving object detection result, and the sound intensity into dimensionless values, respectively, to obtain a normalized light intensity value, a normalized moving object detection value, and a normalized sound intensity value.

[0090] In this application, normalization is a data preprocessing method aimed at unifying data of different dimensions and ranges onto the same scale for subsequent comprehensive calculations. Light intensity is typically measured in lux, reflecting the brightness of the environment; moving object detection results are binary information, indicating the presence or absence of a moving object; and sound intensity, typically measured in decibels, reflects the noise level of the environment. These three data types differ significantly in their original form and cannot be directly calculated, thus requiring normalization.

[0091] Normalized light intensity values ​​are obtained by converting raw light intensity data to a value between 0 and 1 using a specific method. For example, in an indoor environment, light intensity may be close to 0 lux at night and reach thousands of lux during bright daylight hours. After normalization, light intensity values ​​may be close to 0 at night and close to 1 during the day, making them easier to calculate alongside other normalized data. This technical benefit allows light intensity data to be combined with other environmental factors on a unified scale, improving the accuracy of the Ambient Activity Index calculation.

[0092] The normalized moving object detection value converts the binary results of moving object detection into a calculable numerical value. Typically, the detection of a moving object is recorded as 1, and the absence of a moving object is recorded as 0. For example, in a warehouse monitoring scenario, if there are no people or goods moving, the normalized moving object detection value is 0; when a forklift is operating in the warehouse, the value becomes 1. This helps quantify dynamic changes in the environment. The technical effect is to incorporate moving object detection results into the calculation system of the environmental activity index, providing a more comprehensive reflection of the environmental status.

[0093] Normalized sound intensity values ​​are calculated by normalizing raw sound intensity data to a range of 0 to 1. In a busy factory floor, the sound intensity might reach over 80 decibels, while in a quiet library, it might be around 30 decibels. After normalization, the sound intensity value in a factory floor is closer to 1, while in a library, it's closer to 0. This technical benefit allows sound intensity data to be calculated in conjunction with other environmental factors, enriching the information dimension of the environmental activity index.

[0094] In one embodiment, light intensity normalization can be performed by first counting the minimum and maximum light intensity values ​​in the environment in historical data. Assuming the minimum value is I_min, the maximum value is I_max, and the current light intensity is I_current, then the normalized light intensity value N_light = (I_current - I_min) / (I_max - I_min). Moving object detection results are directly assigned a value of 0 or 1 based on the detection situation. Sound intensity is normalized similarly to light intensity, counting the minimum sound intensity value S_min and the maximum sound intensity value S_max. With the current sound intensity S_current, the normalized sound intensity value N_sound = (S_current - S_min) / (S_max - S_min).

[0095] S202: Perform weighted summation on the normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value to obtain an initial environment activity index.

[0096] In this application, the weighted sum is a calculation method that considers different environmental factors contributing differently to the environmental activity level. Although the normalized light intensity, moving object detection result and sound intensity are in the same scale, their importance to the environmental activity level may not be the same. For example, in some security scenarios, the appearance of moving objects may be more indicative of the activity level of the environment than the change in light intensity, so appropriate weights need to be assigned to each factor.

[0097] The initial environmental activity index is a quantitative indicator obtained by comprehensively considering the three environmental factors and their weights, which preliminarily reflects the activity level of the current environment. The allocation of weights is based on the analysis of historical data and the needs of actual application scenarios. Different weight combinations will result in different initial environmental activity indexes, which provide a key basis for subsequent judgment of whether to enter sleep or wake-up mode.

[0098] In the embodiments of this application, multiple environmental factors are integrated into a single value through weighted sum, which facilitates comparison and judgment, making the evaluation of environmental activity level more intuitive and objective.

[0099] In an embodiment, assume that the normalized light intensity value is N_light, its weight is w_light; the normalized moving object detection value is N_move, its weight is w_move; the normalized sound intensity value is N_sound, its weight is w_sound, and w_light + w_move + w_sound = 1. Then the initial environmental activity index E_initial = w_light × N_light + w_move × N_move + w_sound × N_sound. In order to determine the appropriate weights, expert evaluation method can be used, inviting experts in the security field to give weight suggestions for each factor according to experience and actual application situation; regression analysis method in machine learning can also be used to determine the weights by training the model with a large amount of historical data.

[0100] In an embodiment, referring to Figure 4 , step S202 can specifically include:

[0101] S2021: According to the historical environmental data and historical working state data of the security camera in the historical monitoring period, determine the weight distribution strategy of the light intensity, moving object detection result and sound intensity.

[0102] In this application, the historical monitoring period refers to the period of time over which the security camera performed normal monitoring. This period can be set based on actual needs, such as one month or three months. Historical environmental data can include information such as light intensity, moving object detection results, and sound intensity, which record the characteristics of the environment at different times. Historical operating status data includes the camera's real-time power consumption, reflecting the camera's energy consumption in different environments.

[0103] The weighting strategy determines the weight each environmental factor contributes to the initial environmental activity index calculation. Different environmental factors may have varying impacts on camera power consumption and environmental activity. By analyzing historical data, we can identify the relationship between each factor, camera power consumption, and environmental activity, thereby developing a reasonable weighting strategy.

[0104] Its technical effect is to make the weight distribution more in line with the actual situation, so that the initial environmental activity index can more accurately reflect the impact of environmental factors on the camera's working status and environmental activity level, and provide a more scientific basis for subsequent mode judgment.

[0105] In one embodiment, cluster analysis methods from data mining can be used. Historical environmental data and historical operating status data are clustered according to different characteristics, for example, using light intensity, mobile object detection results, and sound intensity as feature dimensions, and real-time power consumption as the result dimension. Through cluster analysis, the corresponding power consumption under different combinations of environmental characteristics is identified, and weights are determined based on the degree of impact of each factor on power consumption. For example, if a high correlation is found between mobile object detection results and power consumption, then this factor is given a higher weight in the weight allocation.

[0106] In one embodiment, step S2021 can be implemented as follows:

[0107] A: Obtain historical environmental data and historical operating status data of the security camera within a historical period, wherein the historical environmental data includes light intensity, moving object detection results and sound intensity, and the historical operating status data includes the real-time power consumption of the camera.

[0108] In this application, the historical period refers to a time period set over a period of time in the past, which can be determined based on actual needs, such as selecting data from the past month or six months. The light intensity in the historical environmental data reflects the changes in the brightness of the environment at different times, the moving object detection results reflect the dynamic situation of people or objects in the environment, and the sound intensity shows the noisiness of the environment. The real-time power consumption in the historical working status data records the power consumed by the camera under different environmental conditions.

[0109] The purpose of acquiring this historical data is to provide a foundation for subsequent analysis of the relationship between environmental factors and camera power consumption. By analyzing this data, we can understand how camera power consumption varies under varying conditions of lighting, motion, and sound intensity. This provides rich data support for determining the weighting strategy for light intensity, motion detection results, and sound intensity, making the weighting more scientific and reasonable.

[0110] In one embodiment, a dedicated storage space can be set up in the security camera's storage module to store historical data. Each time environmental data and operating status data is collected, it is stored in this space in chronological order. When historical data is needed, the data in the storage module is read and filtered according to the set historical period to select data within the corresponding time period.

[0111] B: Analyze the historical environmental data and historical power consumption data to determine the extent to which light intensity, moving object detection results, and sound intensity affect camera power consumption.

[0112] In this application, the purpose of analyzing historical environmental data and historical power consumption data is to identify the intrinsic relationship between light intensity, moving object detection results, and sound intensity, and camera power consumption. Different environmental factors may have different degrees of impact on camera power consumption. For example, when the light intensity is low, the camera may need to increase the sensitivity to ensure image quality, which may increase power consumption; the presence of moving objects will cause the camera to perform more image processing, which will increase the processor burden and lead to increased power consumption; when the sound intensity is high, some additional sound processing functions may be triggered, which will also increase power consumption.

[0113] By analyzing this data, we can quantify the impact of various environmental factors on camera power consumption. The impact can be expressed as a numerical value, with larger values ​​indicating a more significant impact on power consumption.

[0114] Its technical effect is to clarify the contribution of various environmental factors to the camera's power consumption, providing a key basis for determining the weight allocation strategy, and helping to more accurately optimize the camera's energy consumption.

[0115] In one embodiment, a correlation analysis method can be used to calculate the correlation coefficients between light intensity, moving object detection results, and sound intensity, and camera power consumption. The correlation coefficient ranges from -1 to 1, with absolute values ​​closer to 1 indicating a stronger correlation. For example, if the calculated correlation coefficient between moving object detection results and camera power consumption is 0.8, it indicates that the presence of moving objects has a strong impact on camera power consumption.

[0116] C: determining a weight distribution strategy of the illumination intensity, the moving object detection result and the sound intensity according to the influence degrees.

[0117] In the present application, after determining the influence degrees of various environmental factors on the camera power consumption, a weight distribution strategy needs to be formulated according to these influence degrees. The weight distribution strategy determines the proportion of each environmental factor in the calculation of the initial environmental activity index.

[0118] Generally speaking, factors that have a greater influence on the camera power consumption should be assigned a higher weight, so that in the calculation of the initial environmental activity index, the changes of the factors will have a greater impact on the index. For example, if the moving object detection result has a greater influence on the power consumption, it is given a higher weight in the weight distribution, and when a moving object is detected, the initial environmental activity index will rise significantly, thereby more likely to trigger the wake-up mode.

[0119] The technical effect is to match the weight distribution with the actual influence of various environmental factors on the camera power consumption, so that the initial environmental activity index can more accurately reflect the influence of environmental factors on the camera power consumption, and thus achieve more effective power consumption optimization.

[0120] In an embodiment, it is assumed that the influence degree of the illumination intensity on the camera power consumption is I_light, the influence degree of the moving object detection result on the camera power consumption is I_move, and the influence degree of the sound intensity on the camera power consumption is I_sound. First, the sum of the influence degrees S = I_light + I_move + I_sound is calculated, then the weight of the illumination intensity w_light = I_light / S, the weight of the moving object detection result w_move = I_move / S, and the weight of the sound intensity w_sound = I_sound / S. These weights are applied as the weight distribution strategy in the subsequent calculation of the initial environmental activity index.

[0121] S2022: assigning corresponding weights to the normalized illumination intensity value, the normalized moving object detection value and the normalized sound intensity value according to the weight distribution strategy.

[0122] In the present application, after determining the weight distribution strategy, the corresponding weights need to be assigned to the normalized environmental data. The normalized illumination intensity value, the normalized moving object detection value and the normalized sound intensity value are already in the same scale, and by assigning weights, they can be weighted according to their respective contributions to the environmental activity in the calculation of the initial environmental activity index.

[0123] The weighting process involves applying a specific weighting strategy to each normalized data point. This ensures that the weights assigned to each environmental factor in the overall calculation are consistent with actual conditions, allowing the initial environmental activity index to more accurately reflect the true level of activity in the environment.

[0124] Its technical effect is to make the calculation of the initial environmental activity index more reasonable, fully consider the differences in the importance of different environmental factors, and provide more accurate quantitative indicators for subsequent pattern judgment.

[0125] In one embodiment, if the previous steps determine that the weight for light intensity is w_light, the weight for motion detection is w_move, and the weight for sound intensity is w_sound, then w_light is directly assigned to the normalized light intensity value, w_move to the normalized motion detection value, and w_sound to the normalized sound intensity value. These weights can be stored in the camera's configuration file and read and used each time the initial environment activity index is calculated.

[0126] S2023: Multiply the normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value by their corresponding weights to obtain weighted values.

[0127] In this application, the normalized environmental data is multiplied by the corresponding weight to reflect the importance of each factor in the calculation results. The weighted value is the result of multiplying each normalized data by the corresponding weight, which reflects the contribution of the environmental factor after considering its importance.

[0128] In this way, the influence of different environmental factors in calculating the initial environmental activity index is quantified. For example, if the weight of moving object detection results is higher, then the impact of the detected moving object on the initial environmental activity index will be greater. The technical effect is to highlight the differences in the importance of various environmental factors, making the initial environmental activity index more accurately reflect the actual activity level of the environment, providing more valuable information for subsequent pattern judgment.

[0129] In one embodiment, assuming the normalized light intensity value is N_light and its weight is w_light, the weighted value of the light intensity is V_light = w_light × N_light; the normalized moving object detection value is N_move and its weight is w_move, the weighted value of the moving object detection result is V_move = w_move × N_move; and the normalized sound intensity value is N_sound and its weight is w_sound, the weighted value of the sound intensity is V_sound = w_sound × N_sound. The camera's internal processor can perform multiplication operations to obtain the weighted values ​​of each factor.

[0130] S2024: Sum all weighted values ​​to obtain an initial environment activity index.

[0131] In this application, summing the weighted values ​​of each environmental factor is the final step in calculating the initial environmental activity index. The initial environmental activity index is an overall quantitative indicator that comprehensively considers multiple factors such as light intensity, moving object detection results, and sound intensity, and considers their respective importance.

[0132] By summing the contributions of different environmental factors, the system integrates them into a single value that intuitively reflects the current level of activity in the environment. This value is an important basis for determining whether a security camera needs to enter sleep mode or wake up mode.

[0133] Its technical effect is to combine multiple environmental factors into a numerical value that is easy to compare and judge, making it convenient to make mode switching decisions based on this index and realizing intelligent energy consumption management of security cameras.

[0134] In one embodiment, assuming that the light intensity is weighted as V_light, the moving object detection result is weighted as V_move, and the sound intensity is weighted as V_sound, then the initial environment activity index E_initial = V_light + V_move + V_sound. The processor's addition instruction can be used to perform the sum operation to obtain the initial environment activity index.

[0135] The embodiment of the present application determines a weight distribution strategy based on the historical environmental data and historical working status data of the security camera during the historical monitoring cycle, so that the weight distribution of each environmental factor is more in line with the actual situation. After assigning appropriate weights to the normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value, a weighted sum is performed to obtain the initial environmental activity index, which can more accurately reflect the contribution of each environmental factor to the environmental activity level. This weight distribution and calculation method based on historical data allows the initial environmental activity index to better reflect the true characteristics of the environment, improves the accuracy of mode judgment, and helps the security camera to switch between sleep and wake-up modes more reasonably according to the actual environmental conditions, thereby achieving more effective energy consumption management.

[0136] S203: Performing smoothing filtering on the initial environmental activity index to eliminate instantaneous fluctuation interference in the environmental data and obtain a final environmental activity index.

[0137] In this application, smoothing filtering is used to remove noise and transient fluctuations in the initial environmental activity index. Environmental data is susceptible to various accidental factors, such as a sudden change in light intensity caused by a passing vehicle or a brief noise that causes a peak in sound intensity. These transient fluctuations can destabilize the initial environmental activity index and potentially lead to misjudgments, causing the camera to frequently switch between sleep and wake modes, increasing energy consumption and system instability.

[0138] The final environmental activity index is obtained after smoothing, which more stably and accurately reflects the true level of activity in the environment. Smoothing filtering processes the initial environmental activity index over a period of time, making the index change more gradually and avoiding drastic fluctuations caused by accidental factors.

[0139] The embodiments of the present application improve the reliability of the environmental activity index, reduce the possibility of misjudgment, and make the mode switching of the security camera more reasonable, thereby effectively achieving energy consumption optimization.

[0140] In an embodiment, a moving average filtering algorithm can be used. A time window is set, for example, the latest 5 initial environment activity index values E_initial (1), E_initial (2), E_initial (3), E_initial (4), and E_initial (5) are selected, and the final environment activity index E_final = (E_initial (1) + E_initial (2) + E_initial (3) + E_initial (4) + E_initial (5)) / 5. As time goes on, the data in the time window is constantly updated, and the smoothing process is continuously carried out, so that the final environment activity index can more stably reflect the environment state.

[0141] In an embodiment, with reference to Figure 5 , step S40 can be implemented in the following way:

[0142] S401: Obtain the power consumption distribution data of each functional module of the camera at the current time, wherein the power consumption distribution data includes the power consumption proportions of the image sensor, the processor, and the wireless communication module.

[0143] In this application, the power consumption distribution data refers to the proportion of the power consumed by each functional module of the camera at the current time in the total energy consumption. The image sensor is responsible for collecting environmental images, the processor processes and analyzes the collected image data, and the wireless communication module transmits the processed data to the monitoring center. Due to the different working properties and loads of different functional modules, the power consumption will also be different.

[0144] The purpose of obtaining the power consumption distribution data is to understand the energy consumption of each functional module and find out the modules with large proportions in the total energy consumption, thereby providing a direction for subsequent energy consumption optimization. By analyzing the power consumption proportion, it can be determined which modules are the main sources of energy consumption, so as to optimize them in a targeted manner. The technical effect is to provide data basis for determining the functional modules with priority for reducing power consumption, which helps to more accurately implement energy consumption optimization measures and improve energy utilization efficiency.

[0145] In an embodiment, a plurality of current sensors can be installed in the power management module of the camera to measure the currents of the image sensor, the processor, and the wireless communication module. The working voltage of each module is known, and according to the power formula P = UI (where P is power, U is voltage, and I is current), the power consumption of each module can be calculated. The total power consumption is obtained by adding the power consumptions of each module, and then the percentage of the power consumption of each module in the total power consumption is calculated, which is the power consumption proportion of each module.

[0146] S402: Determine, based on the power consumption distribution data, the functional modules for which power consumption reduction is prioritized, and select the functional module with the highest power consumption as the main optimization target.

[0147] In this application, prioritizing functional modules for power reduction based on power consumption distribution data is a strategy based on energy consumption optimization. The functional module with the highest power consumption accounts for the largest share of total energy consumption, and optimizing it can minimize the overall energy consumption of the camera.

[0148] By selecting the functional module with the highest power consumption as the primary optimization target, you can focus resources and energy on in-depth analysis and improvement of this module, improving the efficiency and effectiveness of energy optimization. For example, if the processor accounts for the highest power consumption, you can focus on researching its operating mode and algorithm optimization to reduce its energy consumption.

[0149] Its technical effect is to prioritize the optimization of functional modules with the highest power consumption, thereby quickly and effectively reducing overall energy consumption and achieving optimal energy distribution without affecting the basic functions of the camera.

[0150] S403: For the image sensor, turn off its high-resolution mode and switch to a low-resolution mode, while reducing the frame rate to reduce the amount of data processing.

[0151] In this application, while the image sensor's high-resolution mode can provide clear, detailed images, it generates a large amount of data during data acquisition and processing. This not only increases the image sensor's power consumption but also places a heavy processing burden on the subsequent processor, leading to an increase in overall energy consumption. Low-resolution mode can significantly reduce the amount of data generated while still ensuring basic monitoring needs.

[0152] Frame rate refers to the number of frames captured per second by an image sensor. A higher frame rate increases the number of images captured per unit time, and the amount of data processed increases accordingly. Lowering the frame rate reduces the number of images that need to be processed and transmitted per unit time, further reducing the amount of data processed and power consumption.

[0153] Its technical effect is to effectively reduce the energy consumption of the image sensor by adjusting the working mode and frame rate of the image sensor without seriously affecting the security monitoring effect, while also reducing the burden on the processor, thereby reducing the energy consumption of the entire camera system.

[0154] In one embodiment, mode switching and frame rate adjustment can be achieved by modifying the image sensor's internal registers. The image sensor's registers store various operating parameters. By writing specific values ​​to these registers, the parameters corresponding to the high-resolution mode can be modified to those of the low-resolution mode, and the frame rate setting can be lowered. For example, some image sensors have registers that control image resolution and frame rate. Mode and frame rate adjustment can be accomplished by sending corresponding commands to these registers through software programming.

[0155] S404: For the processor, adjust its operating frequency to the lowest supported frequency and suspend non-essential background tasks.

[0156] In this application, the processor's operating frequency is closely related to power consumption. The higher the operating frequency, the more computing tasks the processor can complete per unit time, but it also consumes more power. Adjusting the processor's operating frequency to the lowest supported frequency means minimizing processor power consumption while still meeting the camera's basic data processing requirements.

[0157] Non-essential background tasks are those that don't directly impact the security camera's core monitoring functions, such as automatic system updates and log backups. These tasks, when running in the background, consume processor resources and increase power consumption. Pausing these tasks allows the processor to focus more resources on critical monitoring data processing, reducing unnecessary energy consumption.

[0158] Its technical effect is to effectively reduce the processor's energy consumption by lowering the processor's operating frequency and suspending non-essential background tasks, thereby improving the energy utilization efficiency of the entire camera system, while not having a substantial impact on the main functions of security monitoring.

[0159] In one embodiment, the processor's operating frequency can be adjusted by modifying the processor's clock control register. The clock control register controls the processor's clock signal frequency. By changing the register settings, the clock signal frequency can be reduced, thereby reducing the processor's operating frequency. To suspend non-essential background tasks, the operating system's task management mechanism can be used to identify non-essential background processes and suspend or terminate them. For example, in a Linux-based camera, system commands can be used to stop unnecessary services and processes.

[0160] S405: For the wireless communication module, adjust its signal transmission frequency to an intermittent transmission mode, and disable the broadcast beacon function to further reduce power consumption.

[0161] In this application, the wireless communication module consumes a large amount of power during continuous signal transmission. Adjusting the signal transmission frequency to intermittent transmission mode, that is, reducing the number of signal transmissions per unit time, can significantly reduce the power consumption of the wireless communication module. In some security monitoring scenarios, real-time and continuous data transmission is not required. Intermittent transmission mode can achieve energy reduction while ensuring the necessary data transmission.

[0162] The broadcast beacon function is typically used to send information about the device's presence and related parameters to surrounding devices. In some cases, this function is not necessary for security monitoring. Disabling the broadcast beacon function can avoid unnecessary signal transmission and further reduce the energy consumption of the wireless communication module.

[0163] Its technical effect is to effectively reduce the power consumption of the wireless communication module and extend the battery life of the camera by adjusting the signal sending frequency of the wireless communication module and turning off the broadcast beacon function, without affecting the normal transmission of security monitoring data.

[0164] In one embodiment, the signal transmission frequency can be adjusted by modifying the communication protocol parameters of the wireless communication module. The communication protocol typically includes parameters such as the signal transmission interval. By modifying these parameters, the signal transmission frequency can be adjusted to an intermittent transmission mode. Disabling the broadcast beacon function can be achieved by sending specific control commands to the wireless communication module. For example, in a Wi-Fi-based camera, the broadcast beacon function can be disabled using the Wi-Fi module's configuration tool.

[0165] The embodiment of the present application first obtains the power consumption distribution data of each functional module of the camera, clarifies the proportion of each functional module in the total energy consumption, and provides an accurate basis for determining the functional modules that should be prioritized for power consumption reduction. Selecting the functional module with the highest power consumption as the main optimization target can concentrate resources for targeted optimization and improve the efficiency of energy consumption optimization. Measures such as shutting down some functions, reducing the operating frequency, and reducing the signal transmission frequency are taken for the image sensor, processor, and wireless communication module respectively. Under the premise of not affecting the basic monitoring function of the security camera, the power consumption of each functional module is effectively reduced, thereby significantly reducing the overall energy consumption of the camera and achieving effective optimization of energy consumption.

[0166] In one embodiment, reference Figure 6 , step S50 can be specifically implemented as follows:

[0167] S501: Detecting the actual working status of each functional module of the current camera, including the resolution mode of the image sensor, the operating frequency of the processor, and the signal transmission frequency of the wireless communication module.

[0168] In the present application, understanding the actual working state of each functional module of the camera is the basis for restoring its normal working state. The resolution mode of the image sensor determines the clarity and data volume of the collected image, the working frequency of the processor affects the speed and energy consumption of data processing, and the signal transmission frequency of the wireless communication module is related to the efficiency and power consumption of data transmission. When entering the sleep mode, the working state of these functional modules may have been adjusted, so accurate detection of their current state is needed when waking up in order to perform targeted recovery operations.

[0169] Detecting the actual working state of each functional module can be achieved by reading the state register of the corresponding module or querying the state information of the system. The state register stores various working parameters and state identifiers of the module, and by reading the values of these registers, the actual working state of the module can be obtained.

[0170] The technical effect is to provide a basis for subsequent accurate and orderly restoration of the normal working state of each functional module, ensuring the smooth progress of the recovery process and avoiding system instability or failure due to blind operation.

[0171] In an embodiment, for resolution mode detection of the image sensor, the configuration register of the image sensor can be read, which stores the current resolution setting information. For processor working frequency detection, the current clock frequency can be obtained by querying the clock control register of the processor to determine the working frequency. For signal transmission frequency detection of the wireless communication module, the communication protocol configuration information of the wireless communication module can be read to obtain parameters such as the current signal transmission time interval, and then determine the signal transmission frequency.

[0172] S502: According to the detection result, restore each functional module to the normal working state in sequence, and preferentially restore the high resolution mode and normal frame rate of the image sensor.

[0173] In the present application, it is necessary to perform recovery operations in sequence according to the actual working state of each functional module detected previously. The image sensor is a key component for security cameras to obtain environmental information, and restoring its high resolution mode and normal frame rate can ensure that environmental images can be collected in time and clearly, providing accurate data for subsequent monitoring and analysis. Prior restoration of the normal working state of the image sensor can ensure the real-time and effectiveness of security monitoring.

[0174] During the recovery process, performing operations in a certain order can avoid problems caused by the dependency relationship between modules. For example, after the image sensor is restored to normal, the processor can better process the collected image data, and the wireless communication module can more effectively transmit the processed data.

[0175] Its technical effect is to restore the normal working status of each functional module in an orderly manner, especially giving priority to restoring the high resolution and normal frame rate of the image sensor, thereby ensuring that the security camera can quickly return to the optimal monitoring state and improving the quality and efficiency of security monitoring.

[0176] In one embodiment, the image sensor can be restored by writing corresponding values ​​to its registers based on previously recorded normal operating parameters, switching it from low-resolution mode back to high-resolution mode and restoring the normal frame rate. During the restoration process, parameters can be set first, followed by a self-test to ensure the image sensor is functioning properly, before proceeding to the next step of restoring the processor and wireless communication module.

[0177] S503: When restoring the operating frequency of the processor, gradually increase its frequency to a default value to avoid system instability caused by frequency mutation.

[0178] In this application, sudden and significant changes in the processor's operating frequency may cause system instability because the various circuits and components within the processor need time to adapt to the frequency change. A sudden change in frequency can cause data processing errors, system freezes, and other problems. Gradually increasing the processor's operating frequency allows the processor enough time to make internal adjustments, ensuring system stability.

[0179] The default value is the recommended operating frequency for the processor under normal operating conditions. This frequency has been optimized and tested to ensure processor performance and stability. Restoring the processor's operating frequency to the default value will enable the processor to fully utilize its data processing capabilities and meet the monitoring needs of security cameras.

[0180] Its technical effect is to avoid system instability caused by frequency mutation by gradually increasing the processor's operating frequency to the default value, ensuring that the processor can work stably and efficiently, thereby ensuring the normal operation of the entire camera system.

[0181] In one embodiment, a frequency increase step size and interval can be set. For example, the processor's operating frequency can be increased by 10% each time, with each increase occurring every 100 milliseconds until the default frequency is reached. After each frequency increase, a simple system test can be performed to check whether the processor is functioning properly and ensure system stability.

[0182] S504: When restoring the function of the wireless communication module, first enable the broadcast beacon function, and then adjust the signal transmission frequency to the default value.

[0183] In this application, the broadcast beacon function allows the wireless communication module to announce its presence and related information to surrounding devices, helping to establish a stable communication connection. When restoring the wireless communication module function, first enabling the broadcast beacon function allows surrounding receiving devices to promptly identify the camera's wireless communication module and prepare for subsequent data transmission.

[0184] The default value for the signal transmission frequency is optimized to ensure efficient data transmission while minimizing energy consumption. Adjusting the signal transmission frequency to the default value will restore the wireless communication module to normal operation, ensuring accurate and timely data transmission to the monitoring center.

[0185] Its technical effect is to ensure that the wireless communication module can quickly and stably return to normal working state by first turning on the broadcast beacon function and then adjusting the signal sending frequency to the default value, thereby ensuring the reliable transmission of security monitoring data.

[0186] In one embodiment, the broadcast beacon function is enabled by sending a specific control command to the wireless communication module. Then, based on the previously recorded default signal transmission frequency parameters, the communication protocol configuration information of the wireless communication module is modified to adjust the signal transmission frequency to the default value. During this adjustment process, some communication tests can be performed to ensure that the wireless communication module is functioning properly.

[0187] S505: After completing the recovery of all functional modules, a self-check operation is performed to ensure that all modules are in normal working condition.

[0188] In this application, after completing the recovery operation of each functional module, a self-test operation is performed to verify whether the recovery process was successful and whether each functional module can work normally. Since some unexpected situations may occur during the recovery process, such as parameter setting errors, hardware failures, etc., the self-test operation can detect and handle these problems in a timely manner.

[0189] The self-test function tests and performance checks each functional module. For example, the image sensor can be checked to see if the captured image is clear and complete; the processor can be checked to see if data processing is normal; and the wireless communication module can be checked to see if data transmission is accurate and stable.

[0190] Its technical effect is to ensure that all functional modules are in normal working condition by performing self-check operations, improve the reliability and stability of security cameras, and avoid monitoring errors or system failures caused by problems in the recovery process.

[0191] In one embodiment, a self-test program can be used to test the image sensor, processor, and wireless communication module separately. For the image sensor, a test image is captured to check whether image resolution, brightness, contrast, and other indicators meet requirements. For the processor, some simple calculation tasks are performed to check whether the calculation results are correct. For the wireless communication module, some test data is sent to check whether the receiving end can correctly receive it. If problems are found during the self-test process, the system can provide corresponding prompts and attempt to repair them.

[0192] When the wake-up mode is triggered, the embodiment of the present application can accurately understand the current status of each module by detecting the actual working status of each functional module of the current camera, providing a clear basis for subsequent recovery operations. Each functional module is restored to a normal working state in a certain order, especially giving priority to restoring the high-resolution mode and normal frame rate of the image sensor, ensuring that the security camera can quickly return to the optimal monitoring state. When restoring the functions of the processor and wireless communication module, the method of gradually increasing the frequency and turning on the functions in sequence is adopted to avoid the problem of system instability caused by sudden changes in parameters. After the recovery is completed, a self-test operation is performed to ensure that each functional module is in a normal working state, improve the reliability and stability of the system, and ensure the continuity and effectiveness of security monitoring.

[0193] In one embodiment, the method provided by the present application further includes: counting the number of mode switching times per unit time for subsequent optimization of threshold settings. Figure 7 As shown, the following steps may be specifically included:

[0194] S61: Record the time point of each triggering of the sleep mode or the wake-up mode, and calculate the time interval between two adjacent mode switches.

[0195] In this application, recording the time points when sleep mode or wake mode is triggered is used to subsequently analyze the frequency and regularity of mode switching. This time point information can reflect the changes in environmental activity at different moments and the timing of camera mode switching based on environmental changes. Calculating the time interval between two consecutive mode switches can intuitively reflect the frequency of mode switching.

[0196] Frequent mode switching can cause system instability, increase energy consumption, and potentially shorten the camera's lifespan. By recording time points and calculating intervals, we can quantify mode switching and provide data support for subsequent judgments on whether threshold settings are appropriate.

[0197] Its technical effect is to provide basic data for analyzing mode switching frequency, help discover problems in the mode switching process, and provide a basis for optimizing threshold settings and improving system stability.

[0198] In one embodiment, a time recording module can be included in the camera system. When sleep mode or wake-up mode is triggered, this module automatically records the current timestamp. When calculating the time interval between two consecutive mode switches, the time interval value can be obtained by simply subtracting the timestamp of the previous mode switch from the timestamp of the latter mode switch. These time records and calculation results can be stored in the camera's storage module for subsequent analysis.

[0199] S62: Counting the number of mode switches within a unit time according to the time interval. If the number of mode switches exceeds a preset upper limit of the switching frequency, it is determined that the current threshold setting is unreasonable.

[0200] In this application, the number of mode switches per unit time is an important indicator for measuring the frequency of mode switching. The preset upper limit of the switching frequency is a reasonable threshold set based on the actual application scenario and system performance requirements. If the number of mode switches per unit time exceeds this upper limit, it means that the camera frequently switches between sleep mode and wake mode in a short period of time. This may be due to the unreasonable settings of the first and second thresholds, causing the environmental activity index to fluctuate frequently between these two thresholds.

[0201] The purpose of determining whether the current threshold setting is unreasonable is to discover problems in a timely manner so as to adjust the threshold, reduce unnecessary mode switching, and improve system stability and energy consumption optimization effects.

[0202] Its technical effect is that by counting the number of mode switches per unit time and comparing it with the preset upper limit, problems in the threshold setting can be discovered in a timely manner, providing direction for subsequent threshold optimization.

[0203] In one embodiment, the unit time is set to one hour, and the preset upper limit for the switching frequency is set to 10. The number of mode switches within one hour is counted. If the number exceeds 10, the current threshold setting is determined to be unreasonable. A statistical program can be written into the camera system to periodically count the number of mode switches within a unit time and compare it with the preset upper limit.

[0204] S63: According to the change trend of the number of mode switching times and the environment activity index, adjust the values ​​of the first threshold and the second threshold to reduce the frequency of mode switching.

[0205] In this application, the number of mode switches reflects the frequency of mode switching under the current threshold setting, while the trend of the environmental activity index reflects the actual activity of the environment. By combining these two factors to adjust the values ​​of the first and second thresholds, the threshold settings can be more consistent with actual environmental changes and reduce unnecessary mode switching.

[0206] If the number of mode switches is too high, it means that the current threshold interval may be too small, and the environmental activity index tends to fluctuate frequently within this interval. In this case, you can appropriately increase the difference between the first and second thresholds so that a larger change in the environmental activity index is required to trigger a mode switch, thereby reducing the frequency of mode switches.

[0207] Its technical effect is to optimize the threshold setting, reduce the frequency of mode switching, and improve the stability of the system and energy consumption optimization effect by adjusting the threshold according to the number of mode switching times and the changing trend of the environmental activity index.

[0208] In one embodiment, by analyzing historical data on the number of mode switches and the environmental activity index, it is discovered that mode switching is too frequent. In this case, the first threshold is appropriately lowered and the second threshold is appropriately raised to increase the difference between the two. For example, the original first threshold was 0.2 and the second threshold was 0.8. After adjustment, the first threshold is 0.1 and the second threshold is 0.9. After the adjustment, the number of mode switches and the environmental activity index are continuously monitored, and the thresholds are further optimized based on actual conditions.

[0209] The embodiment of the present application records the time point of each triggering of sleep mode or wake-up mode and calculates the time interval between two adjacent mode switches, which can accurately count the number of mode switches per unit time. By comparing this number with the preset upper limit of the switching frequency, it is possible to promptly determine whether the current threshold setting is reasonable. If it is unreasonable, the values ​​of the first threshold and the second threshold are adjusted according to the changing trend of the number of mode switches and the environmental activity index, effectively reducing the frequency of mode switching. This series of operations avoids frequent mode switching caused by unreasonable threshold settings, improves the stability of the system, reduces unnecessary energy consumption, and realizes dynamic adjustment of energy consumption optimization of security cameras.

[0210] In one embodiment, reference Figure 8 , step S63 can be implemented as follows:

[0211] S631: Obtain the environmental activity index change curve recorded during the first monitoring period, and analyze its fluctuation frequency and amplitude.

[0212] In this application, the environmental activity index change curve can intuitively display the changes in the environmental activity index over time during the first monitoring period. The fluctuation frequency reflects the number of times the environmental activity index changes per unit time, while the fluctuation amplitude reflects the magnitude of the index change. Analyzing the fluctuation frequency and amplitude can help us understand whether the environmental activity level is stable and whether the environmental activity index changes dramatically.

[0213] If the environmental activity index fluctuates too frequently or too sharply, it may cause the camera to frequently switch between sleep and wake modes, increasing system energy consumption and instability. Analyzing these characteristics can provide important reference information for subsequent threshold adjustments.

[0214] Its technical effect is to gain an in-depth understanding of the dynamic changes of the environment by acquiring and analyzing the fluctuation frequency and amplitude of the environmental activity index change curve, providing a basis for reasonable adjustment of the threshold to reduce unnecessary mode switching.

[0215] In one embodiment, a graphing tool can be used to plot the changes in the environmental activity index recorded over time during the first monitoring period. The frequency of fluctuations can then be determined by counting the number of peaks and valleys in the curve, and the amplitude of fluctuations can be determined by calculating the difference between adjacent peaks and valleys. A data analysis module can be added to the camera system to automatically perform these calculations and analysis tasks.

[0216] S632: Determine the actual impact of the current threshold setting on camera energy consumption optimization based on the fluctuation frequency and amplitude of the environmental activity index and the number of mode switches per unit time.

[0217] In this application, the frequency and amplitude of the environmental activity index's fluctuations reflect the dynamics of the environment, while the number of mode switches per unit time reflects the frequency of mode switching. Combining these two factors allows for a comprehensive assessment of the actual impact of the current threshold setting on camera energy optimization.

[0218] If the environmental activity index fluctuates frequently and significantly, and the number of mode switches per unit time is high, it indicates that the current threshold setting may not be able to effectively respond to environmental changes, causing the camera to frequently switch modes and increase energy consumption. Conversely, if the fluctuations are small and the number of mode switches is reasonable, it indicates that the current threshold setting can achieve a certain degree of energy optimization.

[0219] Its technical effect is to accurately evaluate the effect of the current threshold setting on camera energy consumption optimization by comprehensively considering the fluctuation characteristics of the environmental activity index and the number of mode switching times, providing an accurate basis for further adjustment of the threshold.

[0220] In one embodiment, the frequency and amplitude of fluctuations in the environmental activity index, as well as the number of mode switches per unit time, are analyzed. If the frequency and amplitude of fluctuations are high, and the number of mode switches exceeds a preset upper limit, it indicates that the current threshold settings are not conducive to energy optimization and need to be adjusted. An evaluation model can be established that uses these factors as input and outputs an evaluation result of the current threshold settings' impact on energy optimization.

[0221] S633: If the number of mode switches within a unit time exceeds a preset upper limit of the switching frequency, the mode switching frequency is reduced by increasing the difference between the first threshold and the second threshold.

[0222] In this application, if the number of mode switches per unit time exceeds the preset upper limit of the switching frequency, it indicates that the current threshold setting causes the environmental activity index to fluctuate frequently between the first and second thresholds, causing the camera to frequently switch modes. Increasing the difference between the first and second thresholds can expand the stable range of the environmental activity index, requiring a larger change in the environmental activity index to trigger a mode switch.

[0223] Reducing the frequency of mode switching can reduce system energy consumption and instability, extending the life of the camera. By adjusting the threshold difference, the camera can operate in a more stable environment and improve energy optimization.

[0224] The technical effect is that by increasing the difference between the first threshold and the second threshold, the mode switching frequency is effectively reduced, and the stability of the system and the energy consumption optimization level are improved.

[0225] In one embodiment, assume that the first threshold is 0.3 and the second threshold is 0.7, and the number of mode switches per unit time exceeds a preset upper limit. In this case, the first threshold can be adjusted to 0.2 and the second threshold can be adjusted to 0.8, thereby increasing the difference between the two. After the adjustment, a larger change in the environmental activity index is required to trigger the sleep or wake mode, thereby reducing unnecessary mode switches. A threshold adjustment mechanism can be set in the camera control system. When it is detected that the number of mode switches exceeds the upper limit, the threshold difference is automatically increased according to the preset rules.

[0226] S634: When adjusting the threshold, the threshold setting within the time period with smaller fluctuations in the environmental activity index is prioritized to ensure that the triggering conditions for the sleep mode and the wake-up mode are more stable.

[0227] In this application, periods of minimal environmental activity index fluctuation indicate a relatively stable environment, and the threshold settings during these periods are likely to be more consistent with the actual conditions of the environment. Prioritizing the threshold settings for these periods avoids disrupting the original stable trigger conditions due to excessive threshold adjustments. Blindly adjusting the threshold without considering environmental stability may result in unnecessary mode switching even in an otherwise stable environment.

[0228] Ensuring stable trigger conditions for sleep and wake modes improves system reliability and optimizes energy consumption. Stable trigger conditions allow the camera to switch modes more accurately based on actual environmental changes, rather than being disrupted by accidental fluctuations.

[0229] Its technical effect is to make the threshold adjustment more scientific and reasonable by giving priority to retaining the threshold setting of the stable time period, ensuring the stability of the trigger conditions of the sleep and wake-up modes, thereby improving the overall performance of the system.

[0230] In one embodiment, historical data for the environmental activity index is analyzed to identify time periods with minimal fluctuations. For example, between 2:00 AM and 4:00 AM daily, the environmental activity index fluctuates minimally. When adjusting thresholds, the threshold settings for this time period are kept constant as much as possible, and only the thresholds for other time periods with larger fluctuations are adjusted. This strategy can be implemented by setting time intervals and corresponding threshold rules in the system.

[0231] S635: Apply the adjusted threshold to the next monitoring cycle, and verify the optimization effect by monitoring the number of mode switches and changes in the environment activity index in real time. If the optimization effect does not meet expectations, further adjust the threshold until the performance indicators are met.

[0232] In this application, the adjusted threshold is applied to the next monitoring cycle to verify the actual effect of the threshold adjustment. Real-time monitoring of changes in the number of mode switches and the environmental activity index can intuitively reflect the effectiveness of the threshold adjustment. If the number of mode switches decreases and the fluctuations in the environmental activity index can more reasonably trigger the sleep and wake modes, it indicates that the optimization is effective; otherwise, it indicates that further adjustment of the threshold is needed.

[0233] Continuously adjusting thresholds until performance indicators are met is an iterative optimization process. Through continuous monitoring and adjustment, threshold settings can be optimized, effectively optimizing security camera energy consumption. Performance indicators can be set based on actual application requirements, such as mode switching frequency and energy consumption reduction ratio.

[0234] Its technical effect is to ensure that the threshold setting can adapt to environmental changes through real-time verification and continuous adjustment of the threshold, achieve the expected energy consumption optimization effect, and improve the stability and reliability of the system.

[0235] In one embodiment, performance indicators are set as mode switching no more than five times per unit time and energy consumption reduced by 20%. The adjusted threshold is applied to the next monitoring cycle, and the number of mode switches and energy consumption are monitored in real time. If the number of mode switches still exceeds five or the energy consumption reduction does not reach 20%, the threshold is adjusted again based on further analysis of the monitoring data. An automatic optimization module can be included in the camera system to automatically adjust the threshold based on preset performance indicators and real-time monitoring data.

[0236] The embodiment of the present application obtains the environmental activity index change curve recorded in the first monitoring cycle, analyzes its fluctuation frequency and amplitude, and combines the number of mode switches per unit time to comprehensively evaluate the actual impact of the current threshold setting on the camera energy consumption optimization. If the number of mode switches per unit time exceeds the preset upper limit, the difference between the first threshold and the second threshold is increased to reduce the mode switching frequency. When adjusting the threshold, the threshold setting within the time period with smaller fluctuations in the environmental activity index is retained first to ensure that the triggering conditions of the sleep mode and wake-up mode are more stable. The adjusted threshold is applied to the next monitoring cycle and the optimization effect is verified in real time. If it does not meet expectations, it is further adjusted. Through this iterative optimization method, the threshold setting is continuously adapted to environmental changes, ultimately achieving more accurate energy consumption optimization and improving the overall performance of the system.

[0237] In one embodiment, the method provided by the present application may further include generating a baseline value of the environmental activity index within multiple monitoring periods based on historical statistical analysis of environmental data, for optimizing the initial setting of the first threshold and the second threshold. Figure 9 Before obtaining the first threshold and the second threshold in the first monitoring period, the following steps may be further included:

[0238] S701: Determine a multi-monitoring period range to be counted, where the multi-monitoring period includes at least three historical monitoring periods before a first monitoring period.

[0239] In this application, the purpose of determining the range of multiple monitoring periods is to obtain sufficient historical data to calculate the baseline value of the environmental activity index. At least three historical monitoring periods before the first monitoring period are selected because fewer monitoring periods may not accurately reflect the long-term changes in the environment, while three or more monitoring periods can provide a richer data sample, making the calculated baseline value more representative and stable.

[0240] By conducting statistical analysis of data from multiple monitoring cycles, we can reduce the impact of random factors and better understand the overall trend of environmental activity. Different monitoring cycles may be affected by factors such as season, weather, and time of day. Combining data from multiple cycles can more comprehensively consider these factors.

[0241] Its technical effect is to provide a sufficient data basis for the subsequent generation of environmental activity index benchmark values, so that the benchmark values ​​can more accurately reflect the long-term activity level of the environment and provide a more reliable basis for the initial setting of thresholds.

[0242] In one embodiment, the specific range of multiple monitoring periods can be determined based on actual circumstances. For example, if the first monitoring period is the current week, the previous four weeks can be selected as the multiple monitoring periods to be counted. A data filtering function can be set up in the camera's storage system to automatically filter out corresponding historical monitoring period data based on the time range.

[0243] S702: Obtain statistical data of the environmental activity index in each historical monitoring period, including the average value, maximum value, minimum value, and standard deviation.

[0244] In this application, the statistical data obtained from the environmental activity index can describe the environmental activity during each historical monitoring period from multiple perspectives. The average value reflects the overall level of environmental activity during that period; the maximum and minimum values ​​show the fluctuation range of environmental activity; and the standard deviation measures the dispersion of the data, that is, the fluctuation of the environmental activity index around the average value.

[0245] These statistics provide detailed information for the subsequent calculation of the baseline value of the environmental activity index. By analyzing this data, we can understand the stability and changing trends of environmental activity, thereby more reasonably determining the baseline value.

[0246] Its technical effect is to enrich the description of historical environmental activity, provide comprehensive data support for the accurate calculation of the baseline value of the environmental activity index, and help optimize the initial setting of the threshold.

[0247] In one embodiment, a data statistics program can be written to process the environmental activity index data for each historical monitoring period. The program can automatically calculate the average, maximum, minimum, and standard deviation, and store the results in a database. For example, given a month's worth of historical monitoring period data, the program can quickly calculate various statistical data for the environmental activity index for that month.

[0248] S703: Calculate a baseline value of the environment activity index based on the statistical data in the multiple monitoring periods, where the baseline value is a weighted average of the average values ​​of the environment activity index in all monitoring periods.

[0249] In this application, the baseline value is calculated by taking a weighted average of the average values ​​of the environmental activity index across all monitoring periods. This is to comprehensively consider the importance of different monitoring periods. Different monitoring periods may have different representations of overall environmental activity due to factors such as season and special events. By weighted averaging, more representative monitoring periods can be given higher weights, making the baseline value more reflective of the actual level of activity in the environment.

[0250] The baseline value is a comprehensive reference indicator that represents the average level of environmental activity over multiple monitoring periods. Setting thresholds based on the baseline value can better reflect the actual environment and avoid unreasonable threshold settings due to the specificity of a single monitoring period.

[0251] Its technical effect is to improve the accuracy and representativeness of the benchmark value by calculating the weighted average, provide a more scientific basis for the initial setting of the threshold, and help achieve more reasonable energy consumption optimization.

[0252] In one embodiment, assume that three historical monitoring periods are selected, and their average environmental activity index values ​​are A1, A2, and A3, respectively. The corresponding weights are w1, w2, and w3, respectively, and w1 + w2 + w3 = 1. Then, the baseline value of the environmental activity index, B, = w1 × A1 + w2 × A2 + w3 × A3. The weights can be determined based on factors such as the season of the monitoring period and data quality. For example, a higher weight can be assigned to a monitoring period with higher data quality that better represents normal environmental conditions.

[0253] S704: According to the baseline value and in combination with the maximum and minimum values ​​of the environmental activity index, an initial range of a first threshold and a second threshold is set, wherein the first threshold is the baseline value minus a preset deviation value, and the second threshold is the baseline value plus a preset deviation value.

[0254] In this application, the initial threshold range is set based on the baseline value and the maximum and minimum values ​​of the environmental activity index. This is to ensure that the threshold can adapt to environmental changes while maintaining a certain degree of stability. The preset deviation value is a value determined based on actual application scenarios and experience, and it takes into account the possible fluctuation range of the environmental activity index.

[0255] The initial range of the first and second thresholds provides a reasonable interval for subsequent mode determination. By setting the deviation value appropriately, we can avoid setting the threshold too strict or too loose, so that the camera can accurately switch modes under appropriate environmental conditions.

[0256] Its technical effect is to set the initial threshold range based on the benchmark value and the preset deviation value, making the threshold setting more scientific and reasonable, improving the accuracy of pattern judgment, and laying the foundation for achieving energy consumption optimization of security cameras.

[0257] In one embodiment, assume that the baseline value of the environmental activity index is B and the preset deviation value is D. Then, the first threshold value T1 = B - D, and the second threshold value T2 = B + D. The deviation value D can be determined based on the fluctuation of historical data and actual application requirements. For example, if the environmental activity index fluctuates widely, the deviation value can be appropriately increased; otherwise, the deviation value can be reduced.

[0258] S705: Determine a first threshold and a second threshold of the first monitoring period according to the initial range.

[0259] In this application, determining the first and second thresholds for the first monitoring period based on the initial range is a process of applying the above calculations and settings to actual monitoring. The selection of specific thresholds within the initial range can be based on practical factors such as the current season, time period, and environmental characteristics.

[0260] Determining appropriate first and second thresholds allows the camera to more accurately switch modes based on the level of activity within the first monitoring cycle, optimizing energy consumption. Reasonable threshold settings can prevent the camera from frequently switching modes or entering sleep or wakeup mode under inappropriate circumstances.

[0261] Its technical effect is to determine the threshold of the first monitoring cycle according to the initial range, so that the security camera can work more reasonably in the initial stage, thereby improving the stability of the system and the energy consumption optimization effect.

[0262] In one embodiment, within the initial range [T1, T2], adjustments are made based on the current season and time of day. For example, during late night hours when activity is low, the first threshold can be appropriately raised and the second threshold lowered to make it easier for the camera to enter sleep mode. A threshold selection module can be incorporated into the camera's control system to automatically select appropriate thresholds based on various practical factors.

[0263] The embodiment of the present application determines the range of multiple monitoring cycles to be counted, obtains the statistical data of the environmental activity index in each historical monitoring cycle, and calculates the baseline value of the environmental activity index. The baseline value comprehensively considers the environmental conditions of multiple monitoring cycles and is more representative and stable. The initial range of the first threshold and the second threshold is set according to the baseline value combined with the maximum and minimum values ​​of the environmental activity index, so that the threshold setting is more scientific and reasonable. The first threshold and the second threshold of the first monitoring cycle are determined according to the initial range, which provides a reasonable basis for the mode judgment of the security camera in the initial stage, helps the camera to switch between sleep and wake-up modes more accurately according to the actual environmental conditions, realizes energy consumption optimization in the initial stage, and lays a good foundation for subsequent continuous optimization.

[0264] Accordingly, in order to better implement the above method, the embodiment of the present application also provides a security camera intelligent sleep and wake-up system 80 based on energy consumption optimization, wherein, Figure 10 As shown, the security camera intelligent sleep and wake-up system 80 based on energy consumption optimization includes:

[0265] Acquisition module 801, configured to acquire environmental data and operating status data of the security camera during a first monitoring period, wherein the environmental data includes light intensity, moving object detection results, and sound intensity, and the operating status data includes the real-time power consumption and operating mode of the camera;

[0266] An index calculation module 802 is configured to calculate an environment activity index based on light intensity, moving object detection results, and sound intensity;

[0267] A mode determination module 803 is configured to determine whether to enter a sleep mode or a wake mode based on a comparison result of the environment activity index with a preset first threshold and a second threshold. If the environment activity index is less than the first threshold, the sleep mode is triggered; if the environment activity index is greater than the second threshold, the wake mode is triggered.

[0268] A first adjustment module 804 is configured to adjust operating parameters of the camera to reduce power consumption when the sleep mode is triggered, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module;

[0269] The second adjustment module 805 is configured to restore all functional modules of the camera to a normal working state when the wake-up mode is triggered.

[0270] The implementation of each of the above modules can be specifically referred to the above method embodiments, which will not be described in detail here. The technical effects achieved by each module and device can be referred to the description of the above method embodiments.

[0271] It should be noted that, in specific implementations, the above modules can be arbitrarily combined, integrated into one or more modules, or implemented as independent entities. Furthermore, the above modules can be implemented in the form of hardware or software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, a magnetic disk, or an optical disk, etc.

[0272] like Figure 11As shown, an embodiment of the present application further provides a computer device 10, which includes a processor 901 and a memory 902, wherein the memory 902 stores a computer program, and when the computer program is executed by the processor 901, the processor 901 performs the steps of any of the methods described above.

[0273] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0274] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A security camera intelligent sleep and wake-up method based on energy consumption optimization, characterized in that: The following steps are involved: Obtaining environmental data and operating status data of the security camera during a first monitoring cycle, wherein the environmental data includes light intensity, moving object detection results, and sound intensity, and the operating status data includes real-time power consumption and operating mode of the camera; Calculate the environmental activity index based on light intensity, moving object detection results and sound intensity; Determine whether to enter a sleep mode or a wake-up mode based on a comparison result of the environment activity index with a preset first threshold and a second threshold, triggering the sleep mode if the environment activity index is less than the first threshold, and triggering the wake-up mode if the environment activity index is greater than the second threshold; When the sleep mode is triggered, the camera's operating parameters are adjusted to reduce power consumption, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module; When the wake-up mode is triggered, all functional modules of the camera are restored to normal working state; Adjusting the camera's operating parameters to reduce power consumption includes the following steps: Obtain power consumption distribution data of each functional module of the camera, including the power consumption proportion of the image sensor, processor, and wireless communication module; According to the power consumption distribution data, determine the functional modules with the highest power consumption reduction priority, and select the functional modules with the highest power consumption as the main optimization targets; For image sensors, turn off their high-resolution mode and switch to low-resolution mode, while reducing the frame rate to reduce data processing; For the processor, adjust its operating frequency to the lowest supported frequency and suspend non-essential background tasks; For the wireless communication module, its signal transmission frequency is adjusted to intermittent transmission mode, and the broadcast beacon function is turned off to further reduce power consumption.

2. The method according to claim 1, characterized in that Calculate the environment activity index based on light intensity, moving object detection results, and sound intensity, including the following steps: Normalizing the light intensity, moving object detection results, and sound intensity to dimensionless values ​​to obtain normalized light intensity values, normalized moving object detection values, and normalized sound intensity values; The normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value are weighted and summed to obtain the initial environment activity index; The initial environmental activity index is smoothed and filtered to eliminate instantaneous fluctuation interference in the environmental data to obtain a final environmental activity index.

3. The method according to claim 2, characterized in that The normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value are weighted and summed to obtain the initial environment activity index, including the following steps: Determine the weight distribution strategy for light intensity, moving object detection results, and sound intensity based on the historical environmental data and historical working status data of the security camera during the historical monitoring period; assigning corresponding weights to the normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value according to the weight assignment strategy; Multiply the normalized light intensity value, the normalized moving object detection value, and the normalized sound intensity value by their corresponding weights to obtain weighted values; Sum up all weighted values ​​to get the initial environment activity index.

4. The method according to claim 3, characterized in that Based on the historical environmental data and historical working status data of the security camera during the historical monitoring period, a weight distribution strategy for light intensity, moving object detection results, and sound intensity is determined, including the following steps: Obtaining historical environmental data and historical operating status data of the security camera within a historical period, wherein the historical environmental data includes light intensity, moving object detection results, and sound intensity, and the historical operating status data includes the real-time power consumption of the camera; Analyze the historical environmental data and the historical power consumption data to determine the degree to which light intensity, moving object detection results, and sound intensity affect camera power consumption; A weight distribution strategy for light intensity, moving object detection results, and sound intensity is determined according to the degree of influence.

5. The method according to claim 1, wherein Restoring all functional modules of the camera to normal working state includes the following steps: Detect the actual working status of each functional module of the current camera, including the resolution mode of the image sensor, the operating frequency of the processor, and the signal transmission frequency of the wireless communication module; Based on the test results, each functional module is restored to normal working state in turn, with priority given to restoring the high-resolution mode and normal frame rate of the image sensor; When restoring the processor's operating frequency, gradually increase its frequency to the default value to avoid system instability caused by frequency mutations; When restoring the wireless communication module, first enable the beacon broadcast function, then adjust the signal transmission frequency to the default value; After all functional modules are restored, perform a self-test to ensure that all modules are in normal working condition.

6. The method according to claim 2, characterized in that The method further comprises the following steps: Record the time point of each triggering of sleep mode or wake-up mode, and calculate the time interval between two adjacent mode switches; Counting the number of mode switches within a unit time according to the time interval, and if the number of mode switches exceeds a preset upper limit of the switching frequency, determining that the current threshold setting is unreasonable; According to the changing trends of the number of mode switching times and the environmental activity index, the values ​​of the first threshold and the second threshold are adjusted to reduce the frequency of mode switching.

7. The method according to claim 6, characterized in that Adjusting the values ​​of the first threshold and the second threshold according to the changing trends of the mode switching times and the environment activity index includes the following steps: Obtain the environmental activity index change curve recorded during the first monitoring period and analyze its fluctuation frequency and amplitude; Determine the actual impact of the current threshold setting on camera energy consumption optimization based on the fluctuation frequency and amplitude of the environmental activity index and the number of mode switches per unit time; If the number of mode switches per unit time exceeds a preset upper limit of the switching frequency, the mode switching frequency is reduced by increasing the difference between the first threshold and the second threshold; When adjusting the threshold, prioritize retaining the threshold settings for time periods with less fluctuation in the environmental activity index to ensure more stable triggering conditions for sleep mode and wake-up mode.

8. The method according to any one of claims 2 to 7, characterized in that The method further comprises the following steps: Determine a multi-monitoring period range to be counted, where the multi-monitoring period includes at least three historical monitoring periods before the first monitoring period; Obtain statistical data on the environmental activity index within each historical monitoring period, including the average, maximum, minimum, and standard deviation; Calculating a baseline value of the environmental activity index based on the statistical data over the multiple monitoring periods, where the baseline value is a weighted average of the average values ​​of the environmental activity index over all monitoring periods; According to the reference value, combined with the maximum and minimum values ​​of the environmental activity index, initial ranges of a first threshold and a second threshold are set, wherein the first threshold is the reference value minus a preset deviation value, and the second threshold is the reference value plus the preset deviation value; A first threshold and a second threshold of the first monitoring period are determined according to the initial range.

9. An intelligent sleep and wake-up system for security cameras based on energy consumption optimization, characterized in that: The system comprises: An acquisition module is used to obtain environmental data and operating status data of the security camera during the first monitoring cycle, wherein the environmental data includes light intensity, moving object detection results, and sound intensity, and the operating status data includes the real-time power consumption and operating mode of the camera; Index calculation module, used to calculate the environmental activity index based on light intensity, moving object detection results and sound intensity; a mode determination module, configured to determine whether to enter a sleep mode or a wake-up mode based on a comparison result of the environment activity index with a preset first threshold and a second threshold, triggering the sleep mode if the environment activity index is less than the first threshold, and triggering the wake-up mode if the environment activity index is greater than the second threshold; A first adjustment module is used to adjust the operating parameters of the camera to reduce power consumption when the sleep mode is triggered, including shutting down some functional modules of the image sensor, reducing the operating frequency of the processor, and reducing the signal transmission frequency of the wireless communication module; The second adjustment module is used to restore all functional modules of the camera to a normal working state when the wake-up mode is triggered; The first adjustment module is specifically used to: obtain power consumption distribution data of each functional module of the camera, wherein the power consumption distribution data includes the power consumption proportion of the image sensor, the processor and the wireless communication module; According to the power consumption distribution data, determine the functional modules with the highest power consumption reduction priority, and select the functional modules with the highest power consumption as the main optimization targets; For image sensors, turn off their high-resolution mode and switch to low-resolution mode, while reducing the frame rate to reduce data processing; For the processor, adjust its operating frequency to the lowest supported frequency and suspend non-essential background tasks; For the wireless communication module, its signal transmission frequency is adjusted to intermittent transmission mode, and the broadcast beacon function is turned off to further reduce power consumption.

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