Self-adaptive control method and device for energy consumption of equipment, security and protection monitoring equipment and medium

Abstract

The invention relates to an equipment energy consumption self-adaptive control method and device, security and protection monitoring equipment and a medium. The method comprises the steps that environment prediction data, charging and discharging state data of an environment energy module in current equipment and use habit data of a camera system powered by the environment energy module are collected; based on the environment prediction data and the charging and discharging state data, the charging and discharging state of the environment energy module is controlled; determining a target working mode of the camera system based on the environment prediction data, the charging and discharging state data and the use habit data; and adjusting operation parameters of the camera system according to the target working mode, so that the energy consumption of the camera system adapts to the charging and discharging state of the environment energy module. According to the invention, adaptive balance between equipment energy consumption of the security and protection monitoring equipment and environmental energy supply can be realized, and the endurance and operation reliability of the equipment can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and in particular to a method and device for adaptive control of equipment energy consumption, and security monitoring equipment and media. Background Technology

[0002] Currently, outdoor security cameras powered by solar energy have become common products on the market. They use solar panels to convert light energy into electrical energy to charge the built-in battery, thus enabling the camera to work in environments without mains power.

[0003] Traditionally, these products rely heavily on pre-set, fixed logic. Specifically, solar panels are typically installed at a fixed angle, unable to adjust to changes in the sun's position. This results in a significant reduction in the effective light-sensitive area and low solar energy conversion efficiency during periods of low solar altitude, such as sunrise and sunset. Furthermore, camera operating modes are often based on a single battery power threshold; for example, when the battery level falls below a certain percentage, the resolution is automatically reduced or some functions are disabled. This fixed-threshold-based control logic fails to consider the changing trends in ambient lighting conditions and the varying habits of users when actually using the camera.

[0004] The aforementioned traditional technical solutions have inherent technical flaws. First, because the solar panel angle is fixed, its energy harvesting efficiency is entirely passively dependent on natural sunlight conditions, making it impossible to actively optimize energy input. In poor sunlight or during specific periods, insufficient charging can easily lead to a significant reduction in device battery life. Second, relying solely on battery level for power consumption control ignores the crucial factor of user behavior patterns. For example, for users who only need to monitor at night, the camera, which could maintain high performance during the day when there is ample sunlight, may prematurely enter low-power mode due to insufficient battery power, affecting the quality of daytime monitoring. Conversely, for users who need to check frequently throughout the day, the camera may fail to detect high demand in time, leading to excessive power consumption and depletion of power during critical periods.

[0005] Therefore, the fundamental problem with traditional methods is that their energy management strategies are static and isolated, failing to achieve dynamic coordination and intelligent adaptation among energy supply, energy storage and energy consumption, thus limiting the endurance and reliability of equipment in complex real-world environments. Summary of the Invention

[0006] The primary objective of this application is to solve at least one of the above-mentioned problems by providing a device for adaptive energy consumption control, a security monitoring device, and a medium.

[0007] To achieve the various objectives of this application, the following technical solution is adopted: An adaptive energy consumption control method for equipment, provided to meet one of the purposes of this application, includes the following steps: Collect environmental prediction data, charging and discharging status data of the current environmental energy module in the device, and usage data of the camera system powered by the environmental energy module; Based on the environmental prediction data and the charging / discharging status data, the charging / discharging status of the environmental energy module is controlled. Based on the environmental prediction data, the charging and discharging status data, and the usage habit data, the target operating mode of the camera system is determined; According to the target working mode, the operating parameters of the camera system are adjusted so that its energy consumption is adapted to the charging and discharging state of the environmental energy module.

[0008] An adaptive energy consumption control device for equipment, proposed to meet one of the objectives of this application, comprises: The data acquisition module is configured to collect environmental prediction data, charging and discharging status data of the current environmental energy module in the device, and usage habit data of the camera system powered by the environmental energy module. The power control module is configured to control the charging and discharging state of the environmental energy module based on the environmental prediction data and the charging and discharging state data. The mode determination module is configured to determine the target operating mode of the camera system based on the environmental prediction data, the charging and discharging status data, and the usage habit data. The operation control module is configured to adjust the operating parameters of the camera system according to the target working mode, so that its energy consumption is adapted to the charging and discharging state of the environmental energy module.

[0009] On another front, a security monitoring device provided to meet one of the purposes of this application includes a camera system, an environmental energy module, and a main control unit. The main control unit includes a processor and a memory. The processor calls and runs a computer program in the memory to execute the steps of the device energy consumption adaptive control method.

[0010] In another aspect, a computer-readable storage medium is provided to suit another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the described device energy consumption adaptive control method, which, when called by a computer, executes the steps included in the corresponding method.

[0011] Compared with traditional technologies, this application achieves intelligent and dynamic management of the energy consumption of security monitoring equipment by comprehensively collecting environmental prediction data, charging and discharging status data of environmental energy modules in the equipment, and usage habit data of the camera system. It can proactively adjust the charging and discharging strategy according to the availability of environmental energy, so that the energy supply matches the equipment's operating needs, thereby effectively overcoming the defects of low energy utilization efficiency and supply-demand imbalance under the traditional fixed strategy.

[0012] Specifically, through the collaborative analysis of environmental prediction data and charging / discharging status data, precise control of the charging / discharging status of the environmental energy module is achieved. This allows the energy harvesting process to adapt to changes in the external environment, significantly improving energy acquisition and storage efficiency. Simultaneously, by incorporating user habit data, the actual monitoring needs at different times can be identified, thereby formulating target operating modes that align with user behavior patterns and applying corresponding energy consumption strategies. This decision-making mechanism based on multi-dimensional data fusion ensures that the camera can fulfill its core monitoring functions while avoiding unnecessary energy consumption.

[0013] Ultimately, this application achieves a self-adaptive balance between the camera system's operating energy consumption and the power supply capacity of the environmental energy module by dynamically adjusting the operating parameters according to the target working mode. This not only significantly extends the equipment's continuous operating time in complex environments but also improves its operational reliability during peak electricity consumption periods or when energy access is limited, thereby optimizing the equipment's overall energy efficiency and practical value. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the electrical structure of the security monitoring equipment described in this application; Figure 2 This is a flowchart illustrating a typical embodiment of the device energy consumption adaptive control method of this application; Figure 3 This is a schematic block diagram of the device energy consumption adaptive control device of this application; Figure 4 This is a schematic diagram of the structure of a computer device used in this application. Detailed Implementation

[0015] The technical solution of this application will now be clearly and completely described with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0016] Figure 1An electrical block diagram of a security monitoring device according to an embodiment of this application is shown. As shown, the core of the device consists of three main parts: an environmental energy module 1, a main control unit 2, and a camera system 3. The modules interact with each other via electrical connections and a data bus, working together to achieve adaptive control of energy consumption.

[0017] The environmental energy module 1 serves as the power source for the entire device, and its main function is to capture energy from the natural environment and manage the charging and discharging of the built-in energy storage unit. This module typically includes an energy harvesting unit such as a solar panel, an energy storage unit such as a lithium-ion battery, and corresponding power management circuitry. The power management circuitry is responsible for monitoring the battery's charge and discharge status data, such as remaining charge, voltage, and current, and can execute specific charging strategies and discharge channel management according to control commands.

[0018] The main control unit 2, acting as the intelligent control hub, typically consists of a microprocessor and its peripheral circuits. This unit connects to the environmental energy module 1 and the camera system 3 via data interfaces, continuously collecting charging and discharging status data from the environmental energy module 1 and usage data from the camera system 3. It can also obtain environmental prediction data from external services via a network interface. Internally, the main control unit 2 runs a computer program based on the device energy consumption adaptive control method of this application. Through comprehensive processing and analysis of this data, it generates instructions for controlling the charging and discharging status of the environmental energy module 1, as well as control signals for adjusting the operating parameters of the camera system 3.

[0019] The camera system 3, as the main power-consuming unit, includes an image sensor, lens module, image processing chip, and necessary auxiliary function circuits. Its operating parameters, such as video resolution, recording frame rate, and the activation / deactivation of event detection functions, can be dynamically adjusted by control signals issued by the main control unit 2. The user access history recorded by this system constitutes usage habit data, which can be fed back to the main control unit 2 for decision-making.

[0020] In conclusion, Figure 1 The system architecture shown provides the hardware foundation for implementing the control method described in this application. The following sections will elaborate on various specific implementations of this application in conjunction with this architecture.

[0021] Please see Figure 2 In some embodiments, the device energy consumption adaptive control method of this application can be implemented as an application program running in the main control unit. The method includes: Step S3100: Collect environmental prediction data, charging and discharging status data of the current environmental energy module in the device, and usage data of the camera system powered by the environmental energy module; The main control unit in the security monitoring equipment of this application can be responsible for collecting and obtaining various data required for implementing the method of this application, including but not limited to environmental prediction data, charging and discharging status data, and usage habit data.

[0022] When collecting environmental forecast data, the main control unit periodically accesses the application programming interface (API) of an authorized third-party meteorological data service platform through its integrated network interface (such as 4G / 5G or Wi-Fi modules). The data obtained from this interface includes at least the real-time weather type (e.g., sunny, cloudy, overcast, rainy) at the equipment installation location, weather forecasts for specific future time periods (e.g., the next 6 hours or 24 hours), precise sunrise and sunset times, and real-time solar altitude and azimuth angles. This data collectively forms the basis for predicting the availability of current and near-term environmental energy (primarily solar energy). For example, the acquired "sunny" weather type and high solar altitude angle indicate favorable current solar energy collection conditions.

[0023] When collecting charging and discharging status data from the environmental energy module, the main control unit can continuously or periodically read key electrical parameters from the module's power management circuit via an analog-to-digital converter or a dedicated power management integrated circuit bus. These parameters directly reflect the energy reserve and flow status, mainly including but not limited to the remaining battery capacity, current battery voltage, and the magnitude and direction of the charging / discharging current. Based on this, the main control unit converts them into corresponding values. For example, a typical reading might be "75% remaining battery capacity, charging current +500mA," indicating that the battery is charging and has sufficient charge.

[0024] When collecting usage data from the camera system, this data originates from user interaction logs recorded during the system's operation. The main control unit can retrieve historical access records from the camera system's non-volatile memory or cloud storage. These records typically contain timestamps of each time a user remotely views live streams or playback recordings via a client application. Statistical analysis of these timestamps can create a profile of user access behavior patterns. For example, analysis might reveal that user access frequency is significantly higher between 8 PM and 11 PM than at other times, thus identifying this period as a peak time for monitoring demand.

[0025] Step S3200: Based on the environmental prediction data and the charge / discharge status data, control the charge / discharge status of the environmental energy module; Based on external environmental conditions and internal energy status, the operation of the environmental energy module can be actively and optimally controlled to establish a stable and efficient energy supply foundation. To this end, the environmental energy module can be configured at any one or two of the following levels to control its charging and discharging state: first, adjusting the physical orientation of energy harvesting units such as solar panels within the environmental energy module to maximize energy input; second, dynamically adjusting the charging and discharging strategies of energy storage units within the environmental energy module to optimize energy storage and distribution.

[0026] In one embodiment, the charging and discharging state of the environmental energy module is controlled by optimizing energy harvesting efficiency. Specifically, the main control unit analyzes the solar azimuth data in the acquired environmental prediction data, including but not limited to real-time solar altitude angle and azimuth angle. Based on this data, a built-in geometric algorithm calculates the target tracking angle required to maintain the minimum angle (i.e., near perpendicular) between the solar panel's photosensitive surface and sunlight. Subsequently, the main control unit generates corresponding control signals to drive the stepper motor or servo mechanism connected to the solar panel, causing the solar panel to rotate around the horizontal axis (adjusting the pitch angle) and / or the vertical axis (adjusting the azimuth angle) until the target tracking angle is reached. This process enables the solar panel to dynamically track sunlight, thereby entering a maximum light energy harvesting charging and discharging state, significantly improving the energy harvested per unit time, especially during periods of lower solar angle, such as morning and evening.

[0027] In another embodiment, the battery's charging and discharging strategy is further refined during or after energy harvesting optimization. The main control unit comprehensively considers the weather type (e.g., sunny, cloudy, overcast, rainy) in the environmental prediction data and the remaining battery capacity in the charging and discharging status data to determine which charging and discharging strategy should be adopted. For example, when the weather type is sunny or cloudy and the remaining battery capacity is below a first threshold (e.g., 70%), it indicates that the current charging efficiency is high and the energy storage space is sufficient, so the first charging and discharging strategy is determined. This strategy uses maximum power point tracking charging to charge the battery as quickly as possible. Conversely, when the weather type changes to overcast, or the battery capacity is above a second threshold (e.g., 85%), if continued strong charging may be inefficient or detrimental to battery life, the system switches to the second charging and discharging strategy, using pulse or trickle charging for maintenance replenishment. In extreme cases, such as rainy weather and battery levels below the third threshold (e.g., 20%), the third charging and discharging strategy is activated. This strategy actively cuts off power to non-critical loads (e.g., high-power fill lights, speakers), maintaining only minimal standby charging and core circuit operation. This extreme throttling prioritizes ensuring that the most basic monitoring functions are not interrupted due to battery depletion.

[0028] In the two embodiments above, the maximum light energy collection charging and discharging state can play the role of increasing power supply, while the adaptive charging and discharging state achieved in the latter embodiment plays the role of saving power. This realizes comprehensive intelligent control of the environmental energy module from increasing supply to saving power, so that its charging and discharging state can adapt to changes in internal and external conditions. In the more recommended embodiment, increasing supply and saving power can be implemented in parallel to serve as the best energy basis for the subsequent energy consumption management of the camera system.

[0029] Step S3300: Based on the environmental prediction data, the charging and discharging status data, and the usage habit data, determine the target operating mode of the camera system; This step applies an intelligent decision-making mechanism to fuse and analyze three types of heterogeneous information: environmental prediction data, charging and discharging status data, and usage habit data. The final output is a discrete target operating mode, which is then used to transform complex and dynamically changing environmental and status information into clear and executable operating instructions.

[0030] In one embodiment, this intelligent decision-making mechanism can be implemented through a query method based on explicit rule logic. In this embodiment, the raw input data, including environmental prediction data, charging / discharging status data, and usage habit data, is first preprocessed and feature extracted, converting it into standardized input that the decision logic can directly process. For example, continuous battery remaining power values ​​are mapped to a limited number of preset power levels such as "high," "medium," "low," and "critical"; weather types (e.g., sunny, cloudy, overcast, rainy) are mapped to "weather impact levels" that characterize their influence on charging efficiency; simultaneously, daily "peak hours" and "off-peak hours" are statistically determined based on historical usage habit data. Subsequently, the current power level, weather impact level, and whether it is during a peak hour are used as composite query keys to search a predefined pattern mapping rule table. This rule table, indexed by these conditions, explicitly specifies the target operating mode to be activated under different combinations of conditions. This method offers transparent decision-making, rapid response, and low computational resource requirements, making it highly suitable for embedded device implementation.

[0031] In another embodiment, a machine learning-based decision-making approach can be employed to achieve more complex nonlinear decisions and adaptive optimization. In this embodiment, various types of collected data are constructed into a numerical feature vector through feature engineering. This vector may contain the specific value of remaining battery power, a quantitative score calculated based on weather type and forecast, and a real-time demand intensity score derived from the current time and historical access records. This feature vector is then input into a pre-trained multi-classification model (e.g., decision tree, random forest, or lightweight neural network). The model, based on patterns learned from a large amount of historical data, directly outputs the most probable target operating mode classification result. This approach can capture more complex data relationships, possesses self-learning capabilities, and can continuously optimize decision accuracy as user habits change.

[0032] Through one or more of the aforementioned decision-making mechanisms, multi-dimensional data is fused and transformed into a clear target operating mode. This mode accurately reflects the energy consumption level that the camera system should adopt under current energy supply conditions and expected user demand, providing clear instructions for final execution control and is key to achieving adaptive energy consumption for the entire system.

[0033] Step S3400: Adjust the operating parameters of the camera system according to the target working mode so that its energy consumption is adapted to the charging and discharging state of the environmental energy module.

[0034] Based on the target working mode determined in the previous step, it can be translated into specific adjustment actions for the camera system's operating parameters, thereby achieving precise matching of energy consumption and energy supply status.

[0035] In one embodiment, adjusting the operating parameters of the camera system can be implemented as a gradient-based switching strategy using preset parameter sets. Each target operating mode corresponds to a predefined set of parameter configurations, i.e., a preset parameter set. Once the target operating mode is determined, the main control unit issues corresponding instructions to the camera system, setting its key operating parameters to the preset values ​​corresponding to that mode. This adjustment can balance monitoring performance and energy consumption at different levels. For example, when the energy supply is sufficient and user demand is high during charging and discharging, the target operating mode points to the high-performance parameter set. At this time, the camera system will operate at the highest video resolution and highest recording frame rate, and enable all event detection functions and real-time preview streams to provide the best monitoring experience. Conversely, when energy is scarce or user demand is low, it can switch to a more energy-efficient parameter set. Significant energy savings can be achieved by strategically reducing resolution and frame rate, or disabling some non-core functions, while ensuring that the most basic monitoring capabilities are maintained.

[0036] In another embodiment, adjusting parameters can further reflect the refined management of functional modules. Beyond simple parameter value changes, intelligent start-stop control of the camera system's functional modules can be implemented. For example, in a target operating mode corresponding to a higher energy consumption level, continuous recording is maintained, ensuring that all event detection functions defined by AI recognition algorithms (such as human detection and vehicle detection) operate fully. In a target operating model corresponding to a lower energy consumption level, continuous recording can be switched to an energy-saving mode that triggers recording only when a corresponding movement event is detected. This may selectively disable certain computationally intensive event detection functions, or retain only the most critical event detection functions such as motion detection, thereby significantly reducing the power consumption of the image processing chip and related computing units. This dynamic scheduling of functional modules based on operating modes achieves a deeper level of control, from overall power consumption to optimization of key component power consumption.

[0037] It is easy to understand that by applying any one or any combination of the above embodiments, a dynamic balance is achieved between the energy consumption output of the camera system and the energy supply capacity represented by the environmental energy module. This ensures that the energy consumption of the camera system is always adapted to the charging and discharging state of the environmental energy module, so that the device performance can be fully utilized when energy is abundant, and intelligently manages its energy consumption according to available resources when energy is limited, prioritizing key functions. This significantly improves the device's battery life and operational reliability in complex real-world application scenarios.

[0038] As can be seen from the above embodiments, the device energy consumption adaptive control method provided in this application, through its systematic design, has achieved significant technical progress in multiple key dimensions compared with traditional technical solutions, and has solved the fundamental defects existing in traditional solutions.

[0039] First, in traditional technologies, the three systems of energy supply (charging), energy storage (battery), and energy consumption (camera operation) are relatively isolated, and their management strategies are static and fixed. This application breaks this isolation by establishing a dynamic, intelligent, and collaborative control system based on multi-dimensional data fusion. Specifically, it no longer simply reduces camera power consumption mechanically based on a single threshold of battery level, but comprehensively considers future energy input expectations (environmental prediction data), current energy storage status (charge and discharge status data), and actual energy consumption needs (usage habit data), thereby achieving a forward-looking and adaptive balance between supply and consumption. This shift from passive response to proactive intelligent regulation is the essential advancement of this application that distinguishes it from traditional technologies.

[0040] Secondly, this application demonstrates significant technological advantages in optimizing energy supply. In traditional solutions, solar panels are fixedly installed, and energy harvesting efficiency is entirely dependent on weather, resulting in substantial efficiency losses. This application introduces a dynamic light-tracking mechanism, enabling solar panels to adjust their angle in real time to maximize the light-receiving area, directly improving energy capture efficiency from the source, particularly enhancing charging capabilities under varying sunlight conditions (morning and evening hours, seasonal changes). Simultaneously, by incorporating weather forecasts for refined management of battery charging strategies, ineffective or inefficient charging attempts under adverse conditions such as cloudy or rainy days are avoided, improving charging efficiency and contributing to extended battery life. This proactive optimization of the energy supply side provides a more stable and abundant energy foundation for the equipment.

[0041] Furthermore, regarding the precision of energy consumption management, this application achieves a paradigm shift from device-centric to user-centric approaches by introducing user behavior data, i.e., usage habit data. Traditional technologies ignore users' actual usage habits and adopt a one-size-fits-all power consumption strategy, inevitably leading to excessive monitoring performance (wasting energy) in some scenarios and insufficient performance (affecting security effectiveness) in others. This application, by analyzing users' historical access patterns, can accurately identify peak and off-peak periods of monitoring demand, thereby providing high-performance guarantees when users need them and maximizing power saving when users are inactive. This energy consumption scheduling based on real needs ensures that every unit of electricity generates maximum security value, significantly extending the effective monitoring time of the device with the same battery capacity.

[0042] Ultimately, the comprehensive benefits of this application result in a qualitative leap in the device's battery life and operational reliability. By achieving dual optimization of energy supply (dynamic light tracking) and energy conservation (intelligent power scheduling), the device can better cope with complex environmental challenges such as continuous rain and varying lighting conditions. Security monitoring equipment can anticipate energy shortages and enter a low-power state in advance, or ensure full monitoring functionality when energy is plentiful, thereby significantly reducing the probability of monitoring interruptions due to power depletion. This has crucial practical value for security applications, which have extremely high requirements for continuity and reliability.

[0043] In summary, this application effectively addresses the pain points of traditional solar-powered security cameras in terms of energy efficiency, user demand adaptation, and system reliability by constructing an intelligent, collaborative, and adaptive energy consumption control system, providing users with a significantly better product experience and technical solution.

[0044] Based on any embodiment of the method in this application, controlling the charging and discharging state of the environmental energy module based on the environmental prediction data and the charging and discharging state data includes: Step S3210: Based on the solar azimuth data in the environmental prediction data, determine the target tracking angle of the solar panel in the environmental energy module; The main control unit first analyzes environmental forecast data obtained from external meteorological services, extracting key solar azimuth data. This data includes at least real-time solar altitude and solar azimuth angles. The solar altitude angle is the angle between the sun's rays and the ground plane, determining the required pitch angle of the solar panel; the solar azimuth angle is the angle between the sun's projection on the horizontal plane and true north, determining the required horizontal orientation of the solar panel. The main control unit has built-in astronomical algorithms or a pre-stored table of geographic location and time correspondences. It can combine the latitude and longitude coordinates of the installation location obtained from the security monitoring equipment's global positioning sensors with the current time of the equipment system to verify or supplement the raw solar azimuth data, ensuring data accuracy.

[0045] Based on the calculated solar altitude and azimuth angles, the main control unit determines the target tracking angle of the solar panel through geometric calculations, including calculating the pitch angle of the solar panel rotating around the horizontal axis and the azimuth angle rotating around the vertical axis. In one embodiment, the goal is to adjust the normal vector of the solar panel to be as parallel as possible to the direction of sunlight incidence, so that the photosensitive surface maintains the minimum angle with the sunlight, ideally close to perpendicular, thereby maximizing the light energy reception rate per unit area. For example, at noon, the solar altitude angle is the largest, and the calculated target pitch angle is also larger, making the panel almost horizontal towards the sky; while in the early morning or evening, the solar altitude angle is smaller, and the calculated target pitch angle is also smaller, making the panel more tilted to align with the sun near the horizon.

[0046] The specific algorithm for determining the target tracking angle can include various embodiments. In one embodiment, a precise calculation method based on spherical trigonometry is used to directly calculate the required horizontal azimuth and pitch angles based on the initial values ​​of the solar altitude angle, azimuth angle, and solar panel installation tilt angle. In another embodiment, a lookup table method can be used, where a table of solar angle changes throughout the day is pre-calculated and stored based on the geographical location and date / time. The main control unit can then quickly obtain the corresponding target angle value by directly querying the table based on the current time. This method involves less computation and has a fast response time.

[0047] Step S3220: Drive the solar panel to rotate to the target tracking angle to enter the maximum light energy collection charging and discharging state; After generating the target tracking angle, the main control unit can construct corresponding control commands and send them to the rotating mechanism connected to the solar panel via its internal drive circuit. This rotating mechanism typically includes a stepper motor or servo motor, and corresponding mechanical transmission components, such as worm gears or gear sets, to provide sufficient torque and achieve precise angle positioning. Upon receiving the commands, the drive circuit outputs corresponding pulse or voltage signals to control the stepper motor to rotate a specific angle or number of revolutions, thereby driving the solar panel to rotate around its horizontal and / or vertical axis.

[0048] To ensure the solar panel moves accurately and smoothly to the target position, in one embodiment, an angle sensor, such as an absolute encoder or potentiometer, is integrated into the rotating mechanism to provide real-time feedback on the current angle of the solar panel. The main control unit compares the actual angle fed back by the sensor with the target tracking angle and dynamically adjusts the output control signal based on the deviation until the deviation is eliminated, thereby achieving high-precision point-to-point control. In another embodiment, for applications with slightly lower precision requirements, open-loop control can be used, where the drive motor operates according to a preset number of step pulses, and its positioning accuracy depends on the manufacturing precision of the motor itself and the transmission mechanism.

[0049] Once the angle sensor confirms that the solar panel has stably reached the target tracking angle, or the motor completes the predetermined action according to the command, the main control unit determines that the solar panel has entered the maximum light energy collection and charging / discharging state. In this state, the photosensitive surface of the solar panel is aligned with or near the optimal direction of sunlight incidence, and its photoelectric conversion efficiency is significantly improved compared to fixed installation methods.

[0050] Step S3230: Based on the weather type in the environmental prediction data and the remaining battery power in the charge / discharge status data, determine the charge / discharge strategy of the environmental energy module; The weather type in the environmental forecast data represents the expected input of environmental energy, while the remaining battery power represents the current energy reserve status. By making a comprehensive judgment based on these two data, the optimal charging and discharging management strategy can be formulated. This enables the main control unit to intelligently manage energy storage and distribution strategies after adjusting the physical angle of the solar panels to maximize the energy input potential.

[0051] Specifically, the main control unit first analyzes and classifies the weather types in the environmental prediction data. Weather type is the most direct external factor affecting solar charging efficiency. For example, "sunny" or "cloudy" usually indicates high light intensity and stable energy input, while "overcast" or "rainy" means severely insufficient sunlight, a sharp drop in energy input, or even an interruption. Simultaneously, the main control unit continuously monitors the remaining battery charge in the charge / discharge status data, which accurately reflects the current energy level of the energy storage unit.

[0052] Based on the combination of the two types of data mentioned above, the main control unit determines the most suitable charging and discharging strategy through built-in logical judgment rules. In one implementation, this decision logic is reflected in conditional judgment based on preset thresholds. For example, when the weather type is determined to be "sunny" or "cloudy," and the remaining battery charge is below a first threshold, such as 70%, it indicates that the external conditions for efficient charging are present and the battery has sufficient charging capacity. At this time, the main control unit determines to adopt the first charging and discharging strategy, namely, enabling maximum power point tracking (MPTT) charging. This charging method can dynamically adjust the charging voltage and current, so that the solar panel always works at the maximum power output point, thereby charging the battery with the highest efficiency and achieving rapid energy replenishment.

[0053] Another implementation addresses situations where energy input is limited or the battery is nearing full charge. When the weather turns cloudy, reduced sunlight naturally decreases charging efficiency; or when the remaining battery charge exceeds a second threshold, such as 85%, it indicates that the battery's energy storage is nearing saturation. If any one or more of these conditions are met, continuing with a forceful charging method may be inefficient or even negatively impact battery life. Therefore, the main control unit will determine to switch to a second charging / discharging strategy, such as pulse charging or trickle charging. This strategy provides maintenance charging at lower power, focusing on protecting battery health and optimizing energy utilization efficiency, avoiding ineffective energy conversion.

[0054] In extreme scenarios, such as rainy weather and a battery charge level below the third threshold (e.g., 20%), the device faces the dual pressure of a near-complete power input interruption and a severe shortage of internal energy reserves. In this situation, ensuring core monitoring functions are not interrupted due to complete battery depletion becomes the primary objective. The main control unit can then activate the third charging / discharging strategy. The core of this strategy is priority management of the discharge channel, proactively cutting off power to non-critical loads in the security monitoring equipment and its imaging system, such as high-power supplementary lighting and speakers. Only the standby charging current required for minimum operation and the power supply to core circuits such as the main control unit and basic sensor circuits are retained, thus concentrating energy to ensure the most basic security monitoring capabilities.

[0055] Step S3240: Switch the charging mode or discharge channel of the environmental energy module according to the charging and discharging strategy to enter the adaptive charging and discharging state.

[0056] Based on the determined charging and discharging strategy, the main control unit generates corresponding digital or analog control signals and sends them to the power management circuit in the environmental energy module. The power management circuit, as the executor of the strategy, contains or is connected to the hardware components required to manage different charging methods and discharging channels, such as a maximum power point tracking controller, a pulse width modulation charging controller, and a multiplexer. Upon receiving the control signals, these components change their operating modes or connection states to precisely implement the predetermined strategy.

[0057] When a switching of charging methods is required, such as from regular charging to maximum power point tracking (MPPT) charging, the power management circuit activates its internal MPPT algorithm unit. This unit continuously samples the output voltage and current of the solar panel and dynamically adjusts the operating point of the power conversion circuit to ensure the solar panel always operates at its maximum power point under current sunlight conditions, thereby maximizing charging efficiency. Conversely, when the strategy requires switching to pulse or trickle charging, the power management circuit adjusts the parameters of the charging controller to replenish the battery with intermittent pulse currents or very small constant currents. This mode is particularly effective when the battery is near full charge or in low light conditions, maintaining battery health and avoiding energy waste.

[0058] When the discharge channel needs to be managed, the power management circuitry operates its integrated load switch array or power distribution unit. Specifically, for non-critical loads that need to be disconnected, the circuitry disconnects the electronic switches on the corresponding load branches, stopping the supply of power to them. These non-critical loads may include high-power components in the device that are not essential to maintaining core monitoring functions, such as high-brightness supplemental lighting or speaker amplifiers for two-way audio. Through this selective power-off, extremely limited electrical energy is prioritized and concentrated for the most critical core systems, such as the main control unit, image sensors, and their basic power supply circuitry, ensuring that even in extreme power shortages, the most basic image acquisition and recording functions can still be maintained.

[0059] The above embodiments, by concretizing the charging and discharging state control of the environmental energy module into a multi-layered, refined closed-loop control process encompassing physical attitude adjustment and electrical strategy management, deepen and solidify the inventiveness of this application. This process utilizes astronomical algorithms to dynamically determine the target tracking angle of the solar panel to maximize energy input. Then, based on intelligent judgment of weather type and remaining battery power, it formulates and executes differentiated charging and discharging strategies at the electrical level. Finally, by precisely switching charging methods and managing discharge channels, the system enters an adaptive state highly consistent with real-time internal and external conditions. This not only clearly demonstrates the coherent innovation from perception to decision-making to execution, but also, through the synergistic optimization of physical adjustment and electrical control strategies, constitutes an organic whole that dynamically responds to environmental changes and intelligently manages energy flow at both the hardware control and software algorithm levels. This fundamentally solves the problem of static and rigid energy management in traditional technologies, significantly enhancing the technological depth and practical value.

[0060] Based on any embodiment of the method in this application, the charging and discharging strategy of the environmental energy module is determined based on the weather type in the environmental prediction data and the remaining battery power in the charging and discharging state data, including any one or more of the following: Step S3231: When the weather type is sunny or cloudy and the remaining battery power is lower than the first threshold, the first charging and discharging strategy is determined, which adopts the maximum power point tracking charging method. In the embodiment corresponding to this step, the security monitoring equipment is in an ideal state with excellent energy input conditions and sufficient space for energy replenishment in the energy storage unit. The weather type here is sunny or cloudy, which directly indicates that the current and recent solar radiation intensity is high and stable, providing a reliable external guarantee for efficient photoelectric conversion. At the same time, the remaining battery power is below a first threshold, such as 70%, indicating that the battery has a large charging capacity. Based on this specific combination of conditions, the first charging and discharging strategy is determined to be adopted, namely, maximum power point tracking charging. The core of this strategy is that the power management circuit in the environmental energy module will activate its maximum power point tracking algorithm, continuously monitor the output characteristic curve of the solar panel, and dynamically adjust the equivalent load impedance of the charging circuit, so that the operating point of the solar panel is always stable near the maximum power point that it can output under the current light and temperature conditions. In this way, as much electrical energy as possible can be extracted from the solar panel to quickly charge the battery with the highest conversion efficiency, aiming to raise the battery power to a high level in a short time and reserve sufficient energy for subsequent potentially high-energy-consuming monitoring tasks.

[0061] Step S3232: When the weather type is cloudy or the remaining battery power is higher than the second threshold, the second charging and discharging strategy is determined, which adopts pulse charging or trickle charging. This step addresses two different scenarios where high-power fast charging is unsuitable. One scenario is when the weather turns cloudy, significantly reducing sunlight intensity and drastically lowering the open-circuit voltage and maximum output power of the solar panels. Forcing maximum power point tracking (MPPT) charging would result in low absolute charging power and frequent adjustments to the operating point due to light fluctuations, actually reducing efficiency. The other scenario is when the battery's remaining charge is above a second threshold, such as 85%, indicating near-full charge. Continuing with high-current charging would not only cause charging efficiency to decline but also potentially negatively impact battery cycle life due to overcharging risks. Therefore, when either of these conditions is met, a second charge / discharge strategy is activated. This strategy can be implemented as pulse charging or trickle charging. Pulse charging uses intermittent high-current pulses, measuring battery voltage changes during pulse intervals, which helps eliminate polarization and is suitable for maintenance charging when the battery is near full charge. Trickle charging uses a very small and constant current to continuously charge the battery. It is suitable for maintaining battery power or making very slow replenishments in low-light conditions such as cloudy days. Its advantages are simple circuitry, minimal damage to the battery, and better adaptability to unstable low-light input. Both methods focus on achieving gentle and efficient energy replenishment while ensuring battery safety and health.

[0062] Step S3233: When the weather type is rainy and the remaining battery power is below the third threshold, the third charging and discharging strategy is determined. This strategy cuts off the discharge channel of non-critical loads and maintains a minimum standby charging.

[0063] This step defines an extreme scenario facing an energy crisis. In this case, the weather is rainy, meaning the solar panels can barely generate any effective power input, essentially cutting off the energy source. Simultaneously, the battery's remaining charge is below a third threshold, such as 20%, indicating that energy storage is about to be depleted, and the system's operation is precarious. Under this dual pressure, the primary goal shifts from efficient charging or normal operation to maintaining core monitoring functions uninterrupted at all costs, i.e., achieving maximum endurance. Therefore, the third charge / discharge strategy is initiated. The core action of this strategy is intelligent management of the discharge channels, specifically by proactively cutting off power to a series of non-critical loads. These non-critical loads include, but are not limited to, components in the device that consume high power but are not essential for maintaining basic monitoring records, such as high-power infrared lights, speakers, and high-brightness status indicators. By operating the load switch array in the power management circuit, the discharge channels flowing to these loads are disconnected. At the same time, only a minimum standby charging current is maintained, and power is supplied to the most critical circuit units, such as the main control unit, the minimized operating system of the image sensor, and the necessary circuitry for triggering event recording. This strategy employs extreme energy conservation measures, dedicating every precious ounce of electricity to ensuring basic security functions such as motion detection-triggered recording, thereby maximizing the system's uptime until the weather improves or external intervention is implemented.

[0064] The embodiments obtained by flexibly combining the above steps, through precise condition judgment, select the most targeted charging and discharging management strategies for the environmental energy module in different scenarios, demonstrating the intelligent adaptive control capability of the entire chain from energy input, storage to distribution of the technical solution of this application.

[0065] Based on any embodiment of the method in this application, the target operating mode of the camera system is determined based on the environmental prediction data, the charge / discharge state data, and the usage habit data, including: Step S3310: Map the remaining battery power in the charge / discharge status data to a predefined power level, whereby the power level characterizes the power supply capability of the environmental energy module. To facilitate decision-making, continuous remaining battery power values ​​can be discretized and categorized into a finite number of preset power levels. This transforms precise values ​​representing energy reserves into qualitative indicators that are easier for decision-making logic to process. The power level classification is based on the battery's ability to support the device's continuous operation time, i.e., its power supply capacity. For example, a specific mapping rule could be: when the remaining battery power is greater than or equal to 70%, it is mapped to a "high" power level, indicating sufficient energy reserves to support long-term high-performance operation; when the power is between 40% and 70%, it is mapped to a "medium" power level, indicating adequate energy reserves but requiring attention to power consumption control; when the power is between 20% and 40%, it is mapped to a "low" power level, indicating tight energy reserves and the need to significantly reduce power consumption to extend battery life; and when the power is below 20%, it is mapped to a "critical" power level, indicating that energy is about to run out and extreme power-saving measures must be taken. This classification simplifies the complex power status into clear levels, providing clear input for subsequent decision-making.

[0066] Step S3320: Map the weather type in the environmental prediction data to a predefined weather impact level, wherein the weather impact level is used to characterize the charging efficiency of the environmental energy module. Similarly, weather types in environmental forecast data can be quantitatively mapped to predefined weather impact levels. These levels qualitatively characterize the expected charging efficiency of the environmental energy module over a future period. The mapping rules are based on the typical impact of different weather types on solar irradiance. For example, "sunny" is mapped to a "high" weather impact level, indicating extremely high charging efficiency; "partly cloudy" is mapped to a "medium" weather impact level, indicating moderate but potentially fluctuating charging efficiency; "overcast" is mapped to a "low" weather impact level, indicating very low charging efficiency; and "rainy" is mapped to an "extremely low" weather impact level, indicating virtually no effective charging. This transforms abstract meteorological information into a direct assessment of expected energy input.

[0067] Step S3330: Based on the user access time information of the camera system in the usage habit data, statistically determine the time period distribution data reflecting the number of times the user accesses the system in different time periods; For usage habit data generated during the historical use of security monitoring equipment, time pattern characteristics can be extracted. Usage habit data consists of a series of historical timestamp records generated by users accessing the camera system of security monitoring equipment. By statistically analyzing these timestamps, such as calculating the average number of visits or total visit duration for each hour of the day over the past week or month, a time-period distribution data reflecting user behavior patterns can be generated. This distribution data reveals the periodic fluctuations in user monitoring needs. For example, the analysis results may show that 8 PM to 11 PM is a high-frequency period for user visits, while 2 AM to 5 AM is a low-frequency period. Time-period distribution data is not limited to distinguishing between peak and off-peak periods, but rather includes a more detailed distribution dataset, providing richer evidence for subsequent judgments.

[0068] Step S3340: Using the power level, the weather impact level, the time period distribution data, and the current system time as input conditions, the corresponding target working mode is output through a preset decision engine.

[0069] The standardized information generated in the aforementioned steps, including power level, weather impact level, and time period distribution data, along with the current system time of the security monitoring equipment, are sent to the preset decision engine for comprehensive calculation, which can output a definite target working mode.

[0070] The decision engine is a concrete implementation of the intelligent decision-making mechanism applied in this application, and is a software module that executes multi-condition decision-making logic. Its operation can have different embodiments. In one embodiment, the decision engine is a rule-based system containing a pattern mapping rule table. This rule table uses the power level, weather impact level, and time period category (such as peak or off-peak period) determined by the system's current time and time period distribution data as joint query keys to directly map to the corresponding target working mode. For example, the rule can stipulate that when the power level is "high," the weather impact level is "high," and the current period is a peak time, the output target working mode is the first working mode. In another embodiment, the decision engine can be a pre-trained multi-class machine learning model, such as a decision tree or random forest. In this case, input conditions, such as the specific power value corresponding to the power level, the weather type quantification score corresponding to the weather impact level, and the real-time demand intensity score corresponding to the time period category, are constructed into a numerical feature vector. The model directly outputs the most probable target working mode classification based on this vector.

[0071] As can be seen, in the above embodiments, the decision engine replaces the traditional single power threshold judgment method by comprehensively considering three key dimensions: energy supply capacity, expected energy input, and real-time user demand, thereby realizing intelligent and precise decision-making for the camera system's working mode.

[0072] Based on any embodiment of the method in this application, the power level, the weather impact level, the time period distribution data, and the current system time are used as input conditions, and a corresponding target working mode is output through a preset decision engine, including: Step S3341: Based on the current time and the time period distribution data, determine the current time period category, which includes peak time periods and off-peak time periods; The time-period distribution data is organized at the hourly or half-hourly granularity to reflect the average frequency or total duration of user access to the camera system within different time periods. Based on this data, preset rules are used to determine the time period category to which the current time belongs, such as peak or off-peak periods. In one specific implementation, an access frequency threshold can be set. The historical access frequency corresponding to the current time is compared with this threshold. If the average access frequency for the current time period is higher than the threshold, it is determined that the current period is a peak period, indicating that the user's monitoring demand is strong at this time; conversely, it is determined to be an off-peak period, indicating that the user's demand is low. In another implementation, a relative ranking method can be used, for example, defining the consecutive hours with the highest access frequency in a day (such as 7 pm to 10 pm) as peak periods, and the rest of the time as off-peak periods. This method directly bases on the historical patterns of user behavior, transforming abstract time-period distribution data into clear labels that the decision engine can understand.

[0073] Step S3342: Using the power level, the weather impact level, and the time period category as composite query conditions, query the predefined mode mapping rule table, wherein the mode mapping rule table defines the mapping relationship between the power level, the weather impact level, and the time period category and each target working mode; The power level, weather impact level, and time period category determined in the previous step are combined as a composite query condition. This condition constitutes a multi-dimensional decision index. This index is then queried and matched within a predefined schema mapping rule table. This rule table is essentially a lookup table stored in system memory or non-volatile storage, designed with power level, weather impact level, and time period category as a composite primary key. Each row in the table explicitly specifies the target operating mode to be output when these three conditions are in a specific combination. For example, a record in the rule table might be defined as: when the power level is "high," the weather impact level is "high," and the time period category is "peak hours," the corresponding target operating mode is "first operating mode." This rule means that under conditions of sufficient energy reserves, high expected charging efficiency, and strong user demand, the operating mode with the highest performance should be selected. Through this explicit definition, the rule table solidifies the complex multi-factor decision-making logic, making the decision-making process transparent, deterministic, and highly efficient.

[0074] Step S3343: Output the target working mode obtained from the pattern mapping rule table.

[0075] Once a record that perfectly matches the current composite query condition is successfully found in the mode mapping rule table, the target operating mode corresponding to that record is locked as the output result. This target operating mode is a specific instruction code or enumeration value that directly corresponds to the specific set of operating parameters that the camera system needs to switch to in the next stage. For example, the target operating mode obtained from the query might be "second operating mode". This output result will be passed to the subsequent operation control module as the direct basis for adjusting operating parameters such as camera system resolution, frame rate, and function on / off states.

[0076] The above embodiments introduce a decision-making mechanism based on table lookup. The entire reasoning process does not involve complex numerical calculations and features fast response speed, low computational resource consumption, and clear and traceable decision logic. It is very suitable for embedded security devices with limited computing power to achieve real-time and reliable adaptive energy consumption control.

[0077] Based on any embodiment of the method in this application, the power level, the weather impact level, the time period distribution, and the current time are used as input conditions, and a corresponding target working mode is output through a preset decision engine, including: Step S334A: Encode the input conditions into a numerical feature vector, the numerical feature vector including: the specific value of the remaining battery power, the quantitative score corresponding to the weather impact level, and the real-time demand intensity score determined based on the current time and the time period distribution; To enable reasoning using machine learning models, input conditions of different types and dimensions can be transformed into a unified numerical feature vector suitable for machine learning model processing. Feature engineering is then used to extract and construct the features most effective for classification decisions. Accordingly, firstly, the specific value of the remaining battery power is directly used as one dimension of the feature vector, providing a continuous and accurate measure of energy reserves. Secondly, the discrete categorical variable of weather impact level (e.g., high, medium, low) can also be quantified. One implementation is to assign a predefined score to each level, for example, quantifying "high" as 3 points, "medium" as 2 points, and "low" as 1 point. This quantification transforms qualitative weather impact into comparable numerical values. Finally, a real-time demand intensity score is calculated based on the current time and time period distribution data. For example, the average access frequency of the current time period (e.g., 8 PM to 9 PM) in historical time period distribution data can be calculated, and this frequency value can be normalized to the range of 0 to 1 as the real-time demand intensity score. The higher the score, the stronger the user's monitoring demand at the current moment. Through the above processing, all input conditions are encoded into a structured numerical feature vector, such as a three-dimensional vector containing [battery percentage, weather intensity score, real-time demand intensity score], to prepare for model inference.

[0078] Step S334B: Input the numerical feature vector into a pre-trained multi-classification model, and the multi-classification model determines the corresponding target working mode. The multi-classification model is a decision tree model, random forest model, or neural network model trained with historical data.

[0079] After determining the numerical feature vector, a pre-trained multi-classification model is used to process the numerical feature vector and output the classification result of the target operating mode. The multi-classification model acts as a decision engine, which is a machine learning algorithm that can classify input data into multiple predetermined categories. In this application, the model is obtained through supervised training using historical data. The training process first requires collecting a large number of training samples. Each sample contains a numerical feature vector at a historical moment (i.e., the remaining battery power, weather intensity score, and real-time demand intensity score at that time) and a label of the target operating mode that has been proven to be optimal at that moment by expert rules or actual verification, such as the first, second, third, and fourth operating modes. Subsequently, the selected model is trained using these labeled samples. The internal parameters of the model are continuously adjusted through optimization algorithms such as gradient descent to minimize the error between the model's predicted output and the true label, for example, to below a preset threshold. After sufficient training, the model can learn the complex and potential mapping relationship between input features and the optimal operating mode.

[0080] Regarding model selection, there are several implementation methods. One approach is to use a decision tree model, which makes decisions based on a series of if-then rules based on feature values; the model structure is intuitive and easy to understand. Another approach is to use a random forest model, which determines the final classification result by constructing multiple decision trees and having them collectively vote. This ensemble learning method typically achieves higher accuracy and robustness and is less prone to overfitting. A third approach is to use a neural network model, especially a simple multilayer perceptron, which can fit complex decision boundaries through multilayer nonlinear transformations and has a strong ability to capture deep interactions between features. When new numerical feature vectors are input into these trained models, the models will calculate based on their learned knowledge and output the target working mode category with the highest probability.

[0081] The decision engine implementation based on machine learning models in the above embodiments has the advantage of automatically learning complex and nonlinear decision-making patterns from data compared to rule-based methods. It possesses stronger adaptability and potential optimization space, making it particularly suitable for scenarios where user behavior patterns are complex and variable, or where the relationship between environmental factors and energy consumption is difficult to exhaustively enumerate with simple rules. The model training process ensures that its decision-making is based on data driven by historical best practices, making the entire energy consumption adaptive control system more intelligent.

[0082] Based on any embodiment of the method in this application, the operating parameters of the camera system are adjusted according to the target working mode, including any one or more of the following: Step S3410: When the target working mode is the first working mode, control the camera system to run with the first preset parameter group, the first preset parameter group includes the highest video resolution, the highest recording frame rate, enabling all event detection functions and real-time preview stream; When the target operating mode is the first operating mode, it means that the current operating conditions are considered optimal. This decision is typically based on sufficient power supply from the environmental energy module, high expected charging efficiency, and peak user monitoring demand. In this mode, the core control objective is to maximize the monitoring performance and user experience of the camera system, rather than prioritizing energy saving. The main control unit will issue instructions to the camera system, configuring it to operate with the first preset parameter group.

[0083] The first set of preset parameters aims to enable the highest level of image quality and functional completeness. For video streaming, the camera system is set to operate at the highest video resolution supported by the hardware, such as 1080p Full HD or higher 2K or 4K resolution, to ensure clear details in the captured images. Simultaneously, the recording frame rate is set to the highest value, such as 30 frames per second or higher, to ensure smooth, ghosting-free video recording, which is crucial for accurately recording fast-moving objects. Real-time preview streaming is enabled and maintains high bandwidth transmission, allowing users to obtain low-latency, high-quality live streaming footage when remotely viewing through a client application.

[0084] At the intelligent function level, the first preset parameter group requires all available event detection functions to be enabled. This includes, but is not limited to, basic motion detection, precise human detection, vehicle detection, and even more advanced AI recognition algorithms such as pet detection and package detection. The computing units of all these functions are activated, recording detected events and instantly pushing rich notification information to the user. In addition, auxiliary functions related to high performance can also be in a ready state, such as full-color night vision mode and high-power supplementary lights on standby, to meet the needs of various complex monitoring scenarios.

[0085] This parameter configuration is designed to fully utilize the current abundant energy supply, providing users with top-tier security services without any compromises. It is suitable for typical scenarios with efficient energy acquisition, ample battery power, and high user activity, such as during sunny daytime periods when users frequently check the system, ensuring maximum security monitoring effectiveness. This mode reflects the design principle of prioritizing core functional performance when energy is plentiful.

[0086] Step S3420: When the target working mode is the second working mode, control the camera system to operate with the second preset parameter group. The second preset parameter group includes maintaining the highest video resolution, reducing the recording frame rate, and maintaining some core event detection functions. When the target operating mode is the second operating mode, it indicates that the current operating conditions are considered good, but an energy efficiency optimization strategy needs to be introduced. This judgment is usually based on the fact that the power supply capacity of the environmental energy module is adequate but not extremely abundant, or that the charging efficiency is expected to fluctuate to some extent, or that user monitoring demand has dropped from a peak to a stable stage. In this mode, the control objective is to achieve significant energy-saving effects by selectively adjusting parameters while maintaining the core monitoring image quality, thus balancing performance and power consumption. The main control unit will issue instructions to the camera system to configure it to operate with the second preset parameter group.

[0087] The core strategy of the second preset parameter group is to maintain key performance indicators while reducing resource consumption of non-critical functions. Regarding image quality, the camera system continues to maintain the same maximum video resolution as the first operating mode, such as 1080P Full HD. This ensures that the clarity and detail capture capability of the recorded video are not affected, maintaining core monitoring value. Energy saving is mainly achieved through dynamic adjustments. The recording frame rate can be actively reduced, for example, from 30 frames per second to 15 frames per second. This adjustment significantly reduces the amount of video data that needs to be processed and stored per unit of time, thereby reducing the load and power consumption of the image processing chip and storage system. Meanwhile, for general monitoring scenarios, a frame rate of 15 frames per second still provides sufficiently coherent video recording.

[0088] At the intelligent function level, the second preset parameter group implements fine-grained function scheduling. By keeping some core event detection functions enabled, such as continuing to run high-value and relatively efficient AI algorithms like human or vehicle detection, the ability to perceive critical security threats is ensured. However, some non-core event detection functions that are more computationally complex or not essential to the current scenario can be considered for shutdown, such as disabling vehicle detection in residential areas at night or pet detection during specific time periods. This selective shutdown further reduces the energy consumption of the computing unit. The real-time preview streaming function is generally kept on, but its bitrate can be reduced to accommodate possible bandwidth or processing power adjustments.

[0089] This parameter configuration aims to achieve an optimal balance between performance and power consumption. It is suitable for scenarios with good energy conditions but requiring proactive energy efficiency management, or for situations where user demand declines from peak levels, such as during the daytime in cloudy weather or after the evening peak. This mode embodies an intelligent strategy of actively managing energy consumption while ensuring core monitoring effectiveness.

[0090] Step S3430: When the target working mode is the third working mode, control the camera system to run with the third preset parameter group. The third preset parameter group includes reducing the video resolution, switching to the motion-triggered recording mode, and disabling some non-core event detection functions. When the target operating mode is the third operating mode, it indicates that the current operating environment has entered a state of resource strain. This judgment is usually based on a significant decrease in the power supply capacity of the environmental energy module to a low level, and / or the expected continued low charging efficiency, such as during consecutive cloudy days, and / or when the user is experiencing a low period of monitoring demand. In this mode, the core control objective shifts to implementing a more proactive energy-saving strategy to maximize the equipment's battery life while ensuring uninterrupted basic monitoring functions. The main control unit will issue instructions to the camera system, configuring it to operate with the third preset parameter group.

[0091] The core feature of the third preset parameter group lies in its systematic reduction of performance specifications and shift in operating strategy to achieve significant power savings. Regarding image quality, the camera system can proactively reduce video resolution, for example, from 1080P Full HD to 720P Standard Definition. This adjustment directly reduces the amount of data acquired by the image sensor and the pressure on subsequent encoding, processing, and storage, resulting in considerable energy savings. More importantly, the recording strategy is fundamentally switched from continuous recording to motion-triggered recording. In this mode, the camera system is normally in a low-power standby state, running only basic motion detection algorithms; it only automatically wakes up and starts recording for a period of time when a moving object is detected in the frame, and then returns to standby after the event ends. This event-based triggering mechanism avoids unnecessary recording in empty scenes and is one of the most effective means of energy saving.

[0092] At the intelligent function level, the third preset parameter group can adopt a more stringent function pruning strategy. The camera system will disable some non-core event detection functions, such as retaining basic motion detection to trigger recording, but disabling all computationally intensive advanced AI recognition functions, such as human detection and vehicle detection. This means that the camera system can still record all movement, but will no longer perform real-time intelligent classification and filtering. The real-time preview streaming function may be set to start on demand, that is, only temporarily activated when the user actively initiates a viewing request, rather than continuously consuming resources. These measures together reduce the operating power consumption of the camera system to a low level.

[0093] This parameter configuration is an effective solution to energy shortages. It is suitable for scenarios where battery power is at a low to medium level, charging is difficult due to insufficient light, or users are inactive for extended periods, such as on rainy afternoons or late at night. This mode reflects the camera system's priority in ensuring that events are recorded—the core security requirement—when resources are limited, while simultaneously achieving intelligent adaptability for extreme energy saving through deep optimization.

[0094] Step S3440: When the target working mode is the fourth working mode, control the camera system to run with the fourth preset parameter group. The fourth preset parameter group includes turning off the real-time preview stream, performing motion-triggered recording at the lowest resolution and lowest frame rate, and turning off all event detection functions.

[0095] When the target operating mode is the fourth operating mode, it indicates that the system is in an emergency state of extreme energy crisis. This judgment is usually based on the fact that the power supply capacity of the environmental energy module has dropped to a critical level, and the charging efficiency is expected to be extremely low, such as during continuous rainy weather, when energy input is almost interrupted. In this mode, the sole control objective of the camera system is to take all possible extreme throttling measures to maintain the bare minimum monitoring function with minimal energy consumption, preventing complete shutdown due to depletion of power, thereby maximizing its lifespan. The main control unit will issue the most stringent instructions to the camera system, configuring it to operate with the fourth preset parameter group.

[0096] The core principle of the fourth preset parameter group is to sacrifice almost all non-core functions, retaining only the absolute minimum power consumption necessary to maintain the basic recording capabilities of the camera system. First, the real-time preview stream function is completely disabled. Users can no longer remotely view the live feed via a client, eliminating the enormous power consumption associated with maintaining continuous video streaming. Second, the recording function is compressed to its limits. Furthermore, the lowest supported video resolution (e.g., 480P or lower) and the lowest frame rate (e.g., 5-10 frames per second) are used, strictly adhering to motion-triggered recording. This means the camera system is in deep sleep most of the time, retaining only the most basic circuit power to maintain the operation of the motion detection sensor.

[0097] At the functional level, all event detection functions, including basic motion detection and all advanced AI recognition functions, are forcibly disabled. The camera system also ceases to perform any intelligent analysis; its core logic is simplified to: recording is triggered by a dedicated, extremely low-power infrared sensor or pixel change detection circuit. Once triggered, the camera system briefly wakes from sleep mode, records a short video at the lowest quality, and then quickly returns to sleep mode. This design ensures that, even in extreme cases, the camera system can still physically record significant changes in the monitored area.

[0098] This parameter configuration is the camera system's last resort in the face of survival crises. It is suitable for extreme scenarios where the battery is about to run out and there is no effective power replenishment. This mode embodies the last line of defense in the system design. Through extreme functional reduction and energy consumption control, it prioritizes ensuring that the equipment does not go completely offline, buying valuable time to wait for the weather to improve or for external intervention. Thus, even under the most severe conditions, it upholds the core value of security equipment.

[0099] Through the four-level gradient operating modes and their corresponding preset parameter sets defined in the above steps, this application constructs a refined and coherent energy consumption adaptive control system. The core contribution of this system lies in its complete transcendence of traditional simple on / off power management based on a single power threshold. This system dynamically and precisely couples the operating parameters of the camera system with the environmental energy state and user needs, achieving a smooth transition and seamless switching from maximizing performance to ensuring extreme survivability. Each operating mode is not an isolated setting but rather an organic link in a complete response spectrum, collectively ensuring that the device's energy consumption strategy under any operating condition is the optimal solution for performance, battery life, and reliability under the current conditions. This systematic, multi-dimensional perception-based gradient control method not only solves the rigidity problem of static strategies but also significantly improves the overall energy efficiency and practical value of security monitoring equipment in complex real-world environments through its inherent adaptability and predictability.

[0100] Please see Figure 3 This invention provides an adaptive energy consumption control device for equipment, which is a functional embodiment of the adaptive energy consumption control method of this application. The device includes a data acquisition module 3100, a power control module 3200, a mode determination module 3300, and an operation control module 3400. The data acquisition module 3100 is configured to acquire environmental prediction data, charging / discharging status data of the environmental energy module in the current equipment, and usage habit data of the camera system powered by the environmental energy module. The power control module 3200 is configured to control the charging / discharging status of the environmental energy module based on the environmental prediction data and the charging / discharging status data. The mode determination module 3300 is configured to determine the target operating mode of the camera system based on the environmental prediction data, the charging / discharging status data, and the usage habit data. The operation control module 3400 is configured to adjust the operating parameters of the camera system according to the target operating mode, so that its energy consumption is adapted to the charging / discharging status of the environmental energy module.

[0101] Based on any embodiment of the device in this application, the power control module 3200 includes: an angle determination module, configured to determine the target tracking angle of the solar panel in the environmental energy module based on the solar azimuth data in the environmental prediction data; an angle tracking module, configured to drive the solar panel to rotate to the target tracking angle to enter the maximum light energy collection charging and discharging state; a strategy determination module, configured to determine the charging and discharging strategy of the environmental energy module based on the weather type in the environmental prediction data and the remaining battery power in the charging and discharging state data; and a strategy application module, configured to switch the charging mode or discharging channel of the environmental energy module according to the charging and discharging strategy to enter the adaptive charging and discharging state.

[0102] Based on any embodiment of the device in this application, the strategy determination module includes any one or more of the following: a first strategy module, configured to determine a first charge-discharge strategy when the weather type is sunny or cloudy and the remaining battery power is below a first threshold, wherein the strategy adopts maximum power point tracking charging; a second strategy module, configured to determine a second charge-discharge strategy when the weather type is overcast or the remaining battery power is above a second threshold, wherein the strategy adopts pulse charging or trickle charging; and a third strategy module, configured to determine a third charge-discharge strategy when the weather type is rainy and the remaining battery power is below a third threshold, wherein the strategy cuts off the discharge channel of non-critical loads and maintains minimal standby charging.

[0103] Based on any embodiment of the device in this application, the mode determination module 3300 includes: a power mapping module, configured to map the remaining battery power in the charge / discharge state data to a predefined power level, the power level being used to characterize the power supply capability of the environmental energy module; a weather mapping module, configured to map the weather type in the environmental prediction data to a predefined weather impact level, the weather impact level being used to characterize the charging efficiency of the environmental energy module; a time period statistics module, configured to statistically determine time period distribution data reflecting the number of times a user accesses the camera system in different time periods based on the user access time information in the usage habit data; and an engine decision module, configured to take the power level, the weather impact level, the time period distribution data, and the current system time as input conditions, and output the corresponding target working mode through a preset decision engine.

[0104] Based on any embodiment of the device in this application, the engine decision module includes: a peak-valley judgment module, configured to determine the current time period category based on the current time and the time period distribution data, wherein the time period category includes peak time periods and off-peak time periods; a rule mapping module, configured to query a predefined mode mapping rule table using the power level, the weather impact level, and the time period category as composite query conditions, wherein the mode mapping rule table defines the mapping relationship between the power level, the weather impact level, and the time period category and each target working mode; and a mode output module, configured to output the target working mode obtained from the mode mapping rule table.

[0105] Based on any embodiment of the device in this application, the engine decision module includes: a vector encoding module, configured to encode the input conditions into a numerical feature vector, the numerical feature vector including: the specific value of the remaining battery power, the quantitative score corresponding to the weather impact level, and the real-time demand intensity score determined based on the current time and the time period distribution; and a model decision module, configured to input the numerical feature vector into a pre-trained multi-classification model, and the multi-classification model determines the corresponding target working mode, wherein the multi-classification model is a decision tree model, a random forest model, or a neural network model trained with historical data.

[0106] Based on any embodiment of the device in this application, the operation control module 3400 includes any one or more of the following: a first control module, configured to control the camera system to operate with a first preset parameter group when the target working mode is the first working mode, the first preset parameter group including the highest video resolution, the highest recording frame rate, enabling all event detection functions and real-time preview stream; a second control module, configured to control the camera system to operate with a second preset parameter group when the target working mode is the second working mode, the second preset parameter group including maintaining the highest video resolution, reducing the recording frame rate, and maintaining some core event detection functions; a third control module, configured to control the camera system to operate with a third preset parameter group when the target working mode is the third working mode, the third preset parameter group including reducing the video resolution, switching to motion-triggered recording mode, and disabling some non-core event detection functions; and a fourth control module, configured to control the camera system to operate with a fourth preset parameter group when the target working mode is the fourth working mode, the fourth preset parameter group including disabling the real-time preview stream, performing motion-triggered recording at the lowest resolution and lowest frame rate, and disabling all event detection functions.

[0107] To address the aforementioned technical problems, embodiments of this application also provide a computer device that can be used to implement security monitoring equipment. For example... Figure 4The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, a network interface, and various communication components connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, they enable the processor to implement a device energy consumption adaptive control method. The processor of the computer device provides computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When these computer-readable instructions are executed by the processor, they enable the processor to execute the device energy consumption adaptive control method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] In this embodiment, the processor is used to execute... Figure 3 The system defines the specific functions of each module and its sub-modules. The memory stores the program code and various data required to execute these modules or sub-modules. The network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / sub-modules in the device's energy consumption adaptive control device. The server can call the server's program code and data to execute the functions of all sub-modules.

[0109] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the device power consumption adaptive control method of any embodiment of this application.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0111] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those in the open-source operations, methods, and processes of this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0112] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for adaptive control of device energy consumption, characterized in that, The method comprises: collecting environmental prediction data, current device environment energy module charging and discharging state data, and camera system usage habit data powered by the environment energy module; controlling the charging and discharging state of the environment energy module based on the environmental prediction data and the charging and discharging state data; determining the target working mode of the camera system based on the environmental prediction data, the charging and discharging state data, and the usage habit data; adjusting the operating parameters of the camera system according to the target working mode, so that the energy consumption of the camera system is adapted to the charging and discharging state of the environment energy module.

2. The method of claim 1, wherein, controlling the charging and discharging state of the environment energy module based on the environmental prediction data and the charging and discharging state data, comprising: determining the target tracking angle of the solar panel in the environment energy module based on the solar azimuth data in the environmental prediction data; driving the solar panel to rotate to the target tracking angle to enter the maximum light energy collection charging and discharging state; determining the charging and discharging strategy of the environment energy module based on the weather type in the environmental prediction data and the battery remaining capacity in the charging and discharging state data; switching the charging mode or discharging channel of the environment energy module according to the charging and discharging strategy to enter the adaptive charging and discharging state.

3. The method of claim 2, wherein the device energy adaptive control is characterized by, determining the charging and discharging strategy of the environment energy module based on the weather type in the environmental prediction data and the battery remaining capacity in the charging and discharging state data, comprising any one or any combination of the following: when the weather type is sunny or cloudy, and the battery remaining capacity is lower than the first threshold, determining the first charging and discharging strategy, which adopts the maximum power point tracking charging mode; when the weather type is overcast or the battery remaining capacity is higher than the second threshold, determining the second charging and discharging strategy, which adopts the pulse or trickle charging mode; when the weather type is rainy, and the battery remaining capacity is lower than the third threshold, determining the third charging and discharging strategy, which cuts off the discharging channel of non-critical loads and maintains the minimum standby charging.

4. The method of claim 1, wherein the device energy adaptive control is characterized by, determining the target working mode of the camera system based on the environmental prediction data, the charging and discharging state data, and the usage habit data, comprising: mapping the battery remaining capacity in the charging and discharging state data to a predefined power level, which is used to represent the power supply capacity of the environment energy module; mapping the weather type in the environmental prediction data to a predefined weather influence level, which is used to represent the charging efficiency of the environment energy module; statistically determining the time period distribution data reflecting the user access times in different time periods according to the time information of the user accessing the camera system in the usage habit data; taking the power level, the weather influence level, the time period distribution data, and the current system time as input conditions, and outputting the corresponding target working mode through a preset decision engine.

5. The method of claim 4, wherein the device energy adaptive control is characterized by, taking the power level, the weather influence level, the time period distribution data, and the current system time as input conditions, and outputting the corresponding target working mode through a preset decision engine, comprising: determining a time period category to which the current time belongs based on the current time and the time period distribution data, the time period category including a peak time period and a valley time period; querying a predefined mode mapping rule table based on the power level, the weather influence level and the time period category as a composite query condition, wherein the mode mapping rule table defines a mapping relationship between the power level, the weather influence level and the time period category and each target working mode; outputting a target working mode obtained by querying the mode mapping rule table.

6. The method of claim 4, wherein the device energy adaptive control is characterized by, outputting a target working mode corresponding to the input condition through a preset decision engine, including: encoding the input condition into a numerical feature vector, the numerical feature vector including a specific numerical value of the battery remaining power, a quantified score corresponding to the weather influence level, and a real-time demand intensity score determined based on the current time and the time period distribution; inputting the numerical feature vector into a pre-trained multi-classification model to determine the corresponding target working mode, wherein the multi-classification model is a decision tree model, a random forest model or a neural network model trained through historical data.

7. The method according to any one of claims 1 to 6, wherein, adjusting the running parameters of the camera system according to the target working mode, including any one or any combination of the following: when the target working mode is a first working mode, controlling the camera system to run with a first preset parameter group, the first preset parameter group including a highest video resolution, a highest video frame rate, turning on all event detection functions and a real-time preview stream; when the target working mode is a second working mode, controlling the camera system to run with a second preset parameter group, the second preset parameter group including maintaining the highest video resolution, reducing the video frame rate and maintaining part of the core event detection function; when the target working mode is a third working mode, controlling the camera system to run with a third preset parameter group, the third preset parameter group including reducing the video resolution, switching to a mobile trigger recording mode and turning off part of the non-core event detection function; when the target working mode is a fourth working mode, controlling the camera system to run with a fourth preset parameter group, the fourth preset parameter group including turning off the real-time preview stream, performing mobile trigger recording at the lowest resolution and the lowest frame rate, and turning off all event detection functions.

8. A device power consumption self-adaptive control apparatus, characterized in that, including: a data acquisition module configured to acquire environmental prediction data, charging and discharging state data of an environmental energy module in a current device, and usage habit data of a camera system powered by the environmental energy module; a power control module configured to control the charging and discharging state of the environmental energy module based on the environmental prediction data and the charging and discharging state data; a mode determination module configured to determine a target working mode of the camera system based on the environmental prediction data, the charging and discharging state data and the usage habit data; The operation control module is configured to adjust operation parameters of the camera system according to the target operation mode, so that the energy consumption of the camera system is adapted to the charge-discharge state of the environmental energy module.

9. A security monitoring device comprising a camera system, an environmental energy module and a master control unit, the master control unit comprising a processor and a memory, characterized in that, The processor invokes a computer program stored in the memory to execute the steps of the device energy consumption self-adaptive control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program stored in the form of computer readable instructions and realized according to the method of any one of claims 1 to 7 is invoked by a computer to execute the steps included in the corresponding method.

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