Control method, device, and storage medium of camera device

Abstract

This application discloses a control method, device, and storage medium for a camera device, relating to the field of camera device management technology. The method obtains the target predicted illumination intensity by correcting the deviation between the predicted illumination of the area and the actual receivable illumination on the device side; then, it predicts the charging amount for future periods based on current battery state parameters; it predicts the power consumption for future periods based on the current device operating status and historical power consumption data; finally, it determines the predicted available power based on the difference between the predicted charging amount and the predicted power consumption, and broadcasts a status heartbeat packet to neighboring camera devices; when the predicted available power is less than a preset threshold, it determines candidate managed camera devices based on the predicted available power received from neighboring camera devices; it selects a target managed camera device from the candidate managed camera devices whose task area overlaps with its own; and based on the predicted power consumption, it sends a task management request to the target managed camera device, thereby improving the operational reliability of the camera device.

Description

Technical Field

[0001] This application relates to the field of camera equipment management technology, and in particular to control methods, devices and storage media for camera equipment. Background Technology

[0002] AOV (Always On Video) cameras typically employ an off-grid power supply architecture combining solar photovoltaic power and energy storage batteries. They are widely used in outdoor security monitoring scenarios where there is no external mains power, relying on daytime solar energy storage to support 24 / 7 standby and video recording. Current power-saving management solutions for AOV cameras generally employ fixed battery power threshold control logic. By pre-setting multiple power thresholds, when the real-time battery power drops to the corresponding threshold, control strategies such as function degradation, reducing the encoding frame rate, or even powering off and putting the entire device into sleep mode are executed in a preset sequence.

[0003] However, the threshold-triggered control mode mentioned above is prone to shutting down core functions such as motion detection and recording when the solar panel power generation efficiency is low or the energy storage supply is insufficient, resulting in the lack of security recording during critical periods and low reliability of the camera equipment.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a control method, device, and storage medium for a camera device, aiming to solve the technical problem of how to improve the operational reliability of the camera device.

[0006] To achieve the above objectives, this application proposes a control method, device, and storage medium method for a camera device, wherein the control method for the camera device includes: Based on a preset time window, historical forecast light intensity and historical ambient light intensity are obtained; The correction coefficient is determined based on the ratio between the historical ambient light intensity and the historical forecast light intensity, and the target forecast light intensity is determined by multiplying the correction coefficient by the forecast light intensity for the future period. Based on the target predicted illumination intensity and the battery state parameters corresponding to the current moment, the predicted charging amount for the future period is determined; Based on the current equipment operating status and historical power consumption, determine the predicted power consumption for future periods; Based on the predicted charging amount and the predicted power consumption, target operating parameters are determined, and the operation of the camera device is controlled based on the target operating parameters; Broadcast a status heartbeat to neighboring camera devices. The status heartbeat includes the device identifier, predicted available power, and task area. When the predicted available power is less than a preset threshold, candidate managed camera devices are determined based on the predicted available power sent by the neighboring camera devices. Select a target managed camera device from the candidate managed camera devices that has an overlapping area with its own task area; Based on the predicted power consumption, a task hosting request is sent to the target managed camera device.

[0007] In one embodiment, the step of determining a correction coefficient based on the ratio between the historical ambient light intensity and the historical forecast light intensity, and determining the target forecast light intensity based on the product of the correction coefficient and the forecast light intensity for a future period, includes: The ratio of the historical ambient light intensity to the historical predicted light intensity is determined to obtain the instantaneous correction ratio for each moment. The statistical value of the instantaneous correction ratio within the preset time window is determined to obtain the correction coefficient; The product of the correction coefficient and the predicted illumination intensity for the future time period is taken as the target predicted illumination intensity at the current time.

[0008] In one embodiment, the battery state parameters include the battery voltage and battery temperature. The step of determining the predicted charging amount for the future period based on the target predicted illumination intensity and the battery state parameters corresponding to the current time includes: The target predicted light intensity, the battery voltage, and the battery temperature are used as input parameters for a preset photovoltaic panel charging model, which represents the mapping relationship between light intensity, temperature, voltage, and photovoltaic photoelectric conversion efficiency. The charging power at each future moment is determined using the preset photovoltaic panel charging model; Starting from the current moment, the future time periods are divided according to a preset time step. The predicted charging amount for the future period is determined by multiplying the maximum charging power within the future period by the duration of the future period.

[0009] In one embodiment, before the step of using the target predicted light intensity, the battery voltage, and the battery temperature as input parameters of a preset photovoltaic panel charging model, the method further includes: Obtain the predicted rainfall probability corresponding to the predicted light intensity of the target; Based on the mapping relationship between rainfall probability and cloud transmittance, the light attenuation coefficient corresponding to the predicted rainfall probability is determined; The corrected predicted light intensity is determined by multiplying the light attenuation coefficient and the predicted light intensity, and then the corrected predicted light intensity is used as an input parameter to the preset photovoltaic panel charging model.

[0010] In one embodiment, the step of determining the predicted power consumption for future periods based on the current device operating status and historical power consumption includes: Obtain the operating parameters of each functional module of the camera device at the current moment, and construct a feature vector based on the operating parameters as the operating state of the device; Determine the similarity between the device's operating status and the historical device operating status corresponding to each historical time period, and determine the target historical time period based on the similarity. The predicted power consumption for future periods is determined based on the historical power consumption at a preset time step following the target historical period.

[0011] In one embodiment, the step of determining the target operating parameters based on the predicted charging amount and the predicted power consumption includes: Determine the cumulative value of the current battery level and the predicted charging amount, and determine the predicted available power based on the difference between the cumulative value and the predicted power consumption; Based on the predicted available power and the priority and power consumption parameters of each functional module of the camera device, the target operating parameters of each functional module at the current moment are determined.

[0012] In one embodiment, the control method for the camera device further includes: Upon receiving a task management request from a neighboring camera device, obtain the predicted power consumption corresponding to the neighboring camera device; The sum of the predicted power consumption and its own predicted power consumption is taken as the total power consumption. Based on its own predicted charging amount and total power consumption, the target operating parameters at the current moment are redefined.

[0013] In addition, to achieve the above objectives, this application also proposes a control device for a camera device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the camera device as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the control method for the camera device as described above.

[0015] This application provides a control method for a camera device. The method first uses historical forecast illumination intensity and historical ambient illumination intensity acquired within a preset time window, and determines a correction coefficient based on the proportional relationship between the two to correct the deviation between the regional forecast illumination and the actual receiveable illumination on the device side, thereby obtaining a target forecast illumination intensity closer to the real environment. Then, combining this target forecast illumination intensity with current battery state parameters, a preset photovoltaic panel charging model is used to accurately predict the charging amount for future periods. Simultaneously, based on the current device operating status and historical power consumption data, similarity matching is used to predict the power consumption for future periods. Finally, the difference between the predicted charging amount and the predicted power consumption is used to determine the predicted available power, which is then used to... The neighboring camera devices broadcast a status heartbeat packet, which includes their own device identifier, predicted available power, and task area. When the predicted available power is less than a preset threshold, candidate managed camera devices are determined based on the predicted available power received from neighboring camera devices. A target managed camera device with an overlapping task area is selected from the candidate managed camera devices. Based on the predicted power consumption, a task management request is sent to the target managed camera device. This enables the temporary transfer of monitoring tasks, allowing monitoring continuity to be maintained even when the device's own power is nearly exhausted, thanks to the overlapping coverage area of ​​neighboring devices. This improves the operational reliability of the camera devices in outdoor security scenarios that do not rely on external mains power. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the control method for the camera device of this application; Figure 2 A flowchart illustrating the second embodiment of the control method for the camera device of this application; Figure 3 A flowchart illustrating the control method for the camera device of this application, as provided in Embodiment 3; Figure 4 This is a schematic diagram of the hardware operating environment involved in the control method of the camera device in the embodiments of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments. It should be noted that all actions involving the acquisition of signals, information, or data in this application are performed in accordance with the relevant data protection laws and regulations of the country where the application is located, and with authorization from the owner of the corresponding device.

[0022] AOV (Always On Video) cameras typically employ an off-grid power supply architecture combining solar photovoltaic power and energy storage batteries. They are widely used in outdoor security monitoring scenarios where there is no external mains power, relying on daytime solar energy storage to support 24 / 7 standby and video recording. Current power-saving management solutions for AOV cameras generally employ fixed battery power threshold control logic. By pre-setting multiple power thresholds, when the real-time battery power drops to the corresponding threshold, control strategies such as function degradation, reducing the encoding frame rate, or even powering off and putting the entire device into sleep mode are executed in a preset sequence.

[0023] However, the threshold-triggered control mode mentioned above is prone to shutting down core functions such as motion detection and recording when the solar panel power generation efficiency is low or the energy storage supply is insufficient, resulting in the lack of security recording during critical periods and low reliability of the camera equipment.

[0024] In view of the above problems, this application proposes a control method for a camera device. This method first uses historical forecast illumination intensity and historical ambient illumination intensity acquired within a preset time window, and determines a correction coefficient based on the proportional relationship between the two to correct the deviation between the regional forecast illumination and the actual receiveable illumination on the device side, thereby obtaining a target forecast illumination intensity closer to the real environment. Then, combining this target forecast illumination intensity with current battery state parameters, a preset photovoltaic panel charging model is used to accurately predict the charging amount for future periods. Based on the difference between the predicted charging amount and the predicted power consumption, the predicted available power is determined, and a status heartbeat packet is broadcast to neighboring camera devices. The jump packet includes its own device identifier, predicted available power, and task area. When the predicted available power is less than a preset threshold, candidate managed camera devices are determined based on the predicted available power received from neighboring camera devices. A target managed camera device with an overlapping area with its own task area is selected from the candidate managed camera devices. Based on the predicted power consumption, a task management request is sent to the target managed camera device. This enables the temporary transfer of monitoring tasks, so that monitoring continuity can still be maintained by relying on the overlapping coverage area of ​​adjacent devices when its own power is about to run out, thereby improving the working reliability of the camera device in field security scenarios that do not rely on external mains power.

[0025] The following description uses the controller of a camera device as an example to illustrate this embodiment and the following embodiments.

[0026] Example 1 The first embodiment of this application provides a control method for a camera device, referring to... Figure 1 In this embodiment, the control method of the camera device includes steps S10 to S90: Step S10: Based on a preset time window, obtain historical forecast light intensity and historical ambient light intensity.

[0027] Historical forecast illumination intensity refers to the predicted solar illumination intensity data released by meteorological services or weather forecasting systems for a specific period in the past. Historical ambient illumination intensity, on the other hand, is the actual ambient solar illumination intensity data measured by photosensors installed on camera equipment during the same period in the past, reflecting the actual illumination received by the camera equipment.

[0028] For example, the controller of the camera device first defines a fixed time length as a preset time window. For instance, it could be set to the past 24 hours, the past 48 hours, or the past week. The controller accesses the camera device's internal storage or a connected cloud server to query and extract the historical forecast light intensity corresponding to each specific historical moment within the preset time window, such as every 15 minutes or hour. These historical forecast light intensities are previously downloaded and saved from meteorological services or weather forecast systems. Simultaneously, the controller reads the historical ambient light intensity recorded by the photosensor within the preset time window, corresponding to each historical forecast light intensity's historical moment. The controller timestamps the two sets of data, ensuring that each historical forecast light intensity has a corresponding historical ambient light intensity data point at the same historical moment, resulting in multiple pairs of historical forecast light intensity and historical ambient light intensity data.

[0029] Furthermore, the historical forecast light intensity within a preset time window may correspond to different values ​​at the same specific historical moment. For example, in the past week, the weather forecast on Monday showed a forecast light intensity of a1 at noon on Friday, while the weather forecast on Thursday showed a forecast light intensity of a2 at noon on Friday. In reality, the historical ambient light intensity at noon on Friday was a3.

[0030] At this point, the forecast illumination intensity corresponding to the latest historical time within the preset time window can be selected as the historical forecast illumination intensity corresponding to the historical ambient illumination intensity at the same historical time. That is, the historical forecast illumination intensity at 12 noon on Friday in the past week is a2, and the forecast illumination intensity corresponding to the same historical time is a3.

[0031] Optionally, the average value of each forecast illumination intensity corresponding to a historical moment within a preset time window can be used as the historical forecast illumination intensity corresponding to the historical ambient illumination intensity at the same historical moment. That is, the historical forecast illumination intensity at 12 noon on Friday in the past week is (a2+a2) / 2, and the forecast illumination intensity corresponding to the same historical moment is a3.

[0032] Step S20: Determine the correction coefficient based on the ratio between the historical ambient light intensity and the historical forecast light intensity, and determine the target forecast light intensity by multiplying the correction coefficient by the forecast light intensity for the future period.

[0033] It's important to note that forecasted illumination intensity is typically a regional-scale reference illumination condition for an open horizontal surface. It represents the approximate level of solar radiation or ambient light reaching the Earth's surface under average atmospheric conditions, without local obstruction. However, in real-world AOV camera deployment scenarios, the equipment is often not located in open, unobstructed areas, but rather under trees, near eaves, in the shadow of poles, on building facades, next to shrubs, or on walls or at the corners of pillars facing different directions. Furthermore, the solar panel surface may introduce additional attenuation or localized reflection gain due to dust, bird droppings, aging, installation tilt, and orientation deviations. This means that the actual illumination received by the equipment at the same time and in the same area corresponding to the forecast value may differ from the forecast illumination intensity. The correction factor is a proportional factor used to correct the deviation between the forecast illumination intensity and the actual ambient illumination intensity.

[0034] Furthermore, step S20 above includes steps S21 to S23: Step S21: Determine the ratio of the historical ambient light intensity to the historical predicted light intensity to obtain the instantaneous correction ratio corresponding to each moment.

[0035] Step S22: Determine the statistical value of each instantaneous correction ratio within the preset time window to obtain the correction coefficient.

[0036] For example, the controller calculates the instantaneous correction ratio for each historical moment by taking all data pairs within the preset time window, i.e., the ratio between the historical ambient light intensity and the historical predicted light intensity at the same historical moment. This instantaneous correction ratio reflects the degree of deviation between the actual ambient light intensity received by the camera and the predicted light intensity at the same historical moment. The controller then performs statistical processing on all calculated instantaneous correction ratios to obtain correction coefficients.

[0037] The statistical processing methods mentioned above may include: the average method, which calculates the arithmetic mean of all instantaneous correction ratios and uses this average as the final correction coefficient; the weighted average method, which assigns different weights to the instantaneous correction ratios corresponding to different historical times based on their distance from the current time, with the weight of the instantaneous correction ratios closer to the current time being higher, and then calculates the weighted average of all instantaneous correction ratios as the correction coefficient; and the median method, which takes the median of all instantaneous correction ratios as the correction coefficient to exclude the influence of extreme outliers.

[0038] Step S23: The product of the correction coefficient and the predicted illumination intensity for the future time period is used as the target predicted illumination intensity at the current time.

[0039] The forecast illumination intensity for the future period refers to the forecast illumination intensity for a future time period obtained from meteorological service departments or weather forecasting systems at the current moment. For each future moment in the future period, the controller multiplies its corresponding forecast illumination intensity with the aforementioned correction coefficient, and uses the calculated product as the target forecast illumination intensity for the current moment to predict the predicted charging amount for the future period.

[0040] Step S30: Based on the target predicted illumination intensity and the battery state parameters corresponding to the current time, determine the predicted charging amount for the future period.

[0041] The battery status parameters at the current moment include the battery's current state of charge, battery terminal voltage, battery temperature, and battery health. These battery status parameters together determine the upper limit of the charging power and charging efficiency that the battery can accept under the current conditions.

[0042] Furthermore, step S30 above includes steps S31 to S34: Step S31: The target predicted light intensity, the battery voltage, and the battery temperature are used as input parameters of a preset photovoltaic panel charging model. The preset photovoltaic panel charging model represents the mapping relationship between light intensity, temperature, voltage, and photovoltaic photoelectric conversion efficiency.

[0043] Step S32: Determine the charging power at each future moment using the preset photovoltaic panel charging model.

[0044] The preset photovoltaic panel charging model is used to describe the photoelectric conversion efficiency or output power of the photovoltaic panel in converting light energy into electrical energy under specific light intensity, battery temperature and battery voltage conditions.

[0045] Optionally, the aforementioned preset photovoltaic panel charging model can be a linear regression model. For example, the controller can acquire historical ambient light intensity, historical actual charging power, and historical battery state parameters (such as historical battery voltage and temperature) of the camera device within the same historical time period. Then, a linear regression model is established based on these parameters. Specifically, the historical battery state parameters and historical ambient light intensity are used as input variables, and the historical actual charging power is used as the output variable to determine the regression coefficients of the linear regression model. After calculating the regression coefficients, the target predicted light intensity and the battery state parameters corresponding to the current moment (i.e., the battery voltage and battery temperature) are input into the linear regression model to calculate the charging power for each future moment.

[0046] Optionally, the aforementioned preset photovoltaic panel charging model can also be a normalized power conversion model based on the nominal parameters of standard test conditions. This normalized power conversion model does not require regression coefficients from the training sample set and can calculate the charging power at each future time using the photovoltaic panel parameters described below.

[0047] Photovoltaic panel parameters include nominal maximum power, temperature coefficient of power, fixed loss factor, reference operating voltage, and standard illuminance corresponding to standard test conditions. The nominal maximum power is denoted as... , representing the maximum power of the photovoltaic panel under standard test conditions; the temperature coefficient of power is denoted as . The variable denoted by η describes the direction and magnitude of the battery's output capability deviation relative to standard test conditions as temperature increases or decreases; the fixed loss factor denoted by η represents the degree of influence of battery hardware losses, such as DC trace losses and voltage drop losses of protection devices, on conversion efficiency; the reference operating voltage is denoted by η. This represents the expected reference operating voltage for the photovoltaic panel's MPPT (Maximum Power Point Tracking) when it is close to the standard test conditions range; the standard illuminance under standard test conditions is denoted as... .

[0048] For example, the step of determining the predicted charging power for future time periods using the above-described normalized power conversion model may include: for each future time t, the controller first calculates the target predicted illumination intensity. The ratio of relative to the standard test conditions baseline = / This ratio represents the factor by which the charging power should be amplified if the battery temperature and voltage are consistent with standard test conditions. The controller then uses the battery temperature... Determine the temperature correction factor for charging power based on the deviation from standard test conditions. : =1+ ×( T), where T represents the standard test temperature under standard test conditions. Then, based on the ratio of the predicted target illumination intensity to the standard test condition baseline, and the aforementioned temperature correction factor, the temperature at the current battery temperature is determined. Under this influence, the charging power of the battery at each future moment is = × × .

[0049] Step S33: Starting from the current time, divide the future time periods according to a preset time step.

[0050] Step S34: Determine the predicted charging amount for the future time period based on the product of the maximum charging power within the future time period and the duration of the future time period.

[0051] After determining the charging power at each future moment, starting from the current moment, each future moment is divided into several time slices of equal length according to a preset time step. For each time slice, the maximum charging power within that time slice is extracted. This maximum charging power is multiplied by the duration of the time slice to obtain the charging amount within each time slice. Subsequently, the controller iterates through all time slices within the future time period and sums up the charging amounts within all time slices.

[0052] For example, if the future time period is defined to contain N time slices, the predicted charging power of the i-th time slice is: The corresponding time slice length, i.e., the preset time step length mentioned above, is Δ. The total predicted charging amount Q for the future period can be determined by the following formula: Step S40: Based on the current device operating status and historical power consumption, determine the predicted power consumption for future periods.

[0053] Furthermore, step S40 above includes steps S41 to S43: Step S41: Obtain the operating parameters of each functional module of the camera device at the current time, and construct a feature vector based on the operating parameters as the operating state of the device.

[0054] For example, the controller acquires the operating parameters of each functional module of the camera device at the current moment, and encodes the above operating parameters into vector components according to the preset field order.

[0055] The specific operating parameters of each functional module may include the resolution, frame rate, and current working mode of the video encoding module, and the working mode includes preview, recording, snapshot, standby, and idle states; the communication connection status and signal transmission cycle of the communication module; the operating frequency and load rate of the central processing unit and digital signal processor in the scheduling module, as well as the background task execution status.

[0056] Step S42: Determine the similarity between the device operating status and the historical device operating status corresponding to each historical time period, and determine the target historical time period based on the similarity.

[0057] For example, suppose the feature vector constructed above based on the operating parameters of each functional module of the camera device is as follows: Each historical period The corresponding historical device operating status is represented by feature vectors. It indicates that it can be calculated. and Cosine similarity between them, or calculation and The similarity between the two historical periods is determined by the Euclidean distance between them, and then by the cosine similarity or Euclidean distance. The historical period corresponding to the historical equipment operating state with the highest similarity is selected as the target historical period, or the historical period corresponding to the historical equipment operating state with a similarity greater than a preset similarity threshold is selected as the target historical period.

[0058] Step S43: Determine the predicted power consumption for future periods based on the historical power consumption at a preset time step after the target historical period.

[0059] As an alternative approach to determining predicted power consumption for future periods, the end time of the target historical period is used as the time reference point. Historical power consumption corresponding to multiple consecutive preset time steps following this point is extracted along the positive direction of the time axis, i.e., the future direction. Based on the total duration of the future period and the aforementioned preset time steps, the required number of prediction steps, *m*, is determined. The historical power consumption from step 1 to step *m* after the aforementioned time reference point is determined one by one, and the historical power consumption of each step is directly mapped to the predicted power consumption of the corresponding step in the future period.

[0060] The aforementioned method for determining predicted power consumption directly uses the subsequent historical power consumption of the most similar historical period as the basis for prediction, offering significant advantages such as simple and efficient calculation and traceable results. Since it only involves similarity ranking and data mapping operations, this alternative scheme has low computational complexity and low memory consumption, making it suitable for operation in resource-constrained embedded camera devices.

[0061] As an alternative approach to determining predicted power consumption for future periods, when multiple target historical periods with high similarity to the current equipment operating state exist, a multi-sample aggregation strategy can be employed to determine the predicted power consumption. Specifically, the top k target historical periods with the highest similarity ranking are selected as candidate target historical periods, and each candidate target historical period is assigned a weight coefficient proportional to its similarity. For each time step index j in the future period, the historical power consumption corresponding to the time step index position of each candidate target historical period is extracted, and these k historical power consumptions are weighted and aggregated. The aggregation result is used as the predicted power consumption for the j-th time step of the future period.

[0062] For example, assuming a preset time step of 1 hour, and the future period to be predicted is the next 3 time steps (i.e., predicting the power consumption over the next 3 hours), with time steps j=1, 2, 3. Assuming the target historical time periods are sorted according to similarity, and two candidate target historical time periods are selected as time period A and time period B, then the historical power consumption corresponding to the 1st, 2nd, and 3rd time steps after time period A is found and denoted as... , and ; and the historical power consumption corresponding to the 1st, 2nd, and 3rd time steps after period B, denoted as , and For each step index j, the power consumption of the corresponding two candidate target historical time periods is weighted and averaged to obtain the predicted power consumption for the j-th time step, such as the predicted power consumption for the 1st time step. .in, and These are the weighting coefficients.

[0063] The aforementioned method for determining predicted power consumption significantly improves the robustness and generalization ability of the prediction results by introducing a multi-sample aggregation strategy. In complex and ever-changing real-world application scenarios, matching a single historical power consumption sample is prone to bias due to occasional anomalies or eigenvector quantization errors. This alternative approach utilizes weighted fusion of multiple highly similar historical samples, effectively suppressing extreme values ​​and noise interference, preventing prediction failures caused by single mismatches, and improving the accuracy of power consumption prediction.

[0064] Step S50: Based on the predicted charging amount and the predicted power consumption, determine the target operating parameters, and control the operation of the camera device based on the target operating parameters.

[0065] Furthermore, step S50 above includes steps S51 to S52: Step S51: Determine the cumulative value of the current battery power and the predicted charging amount, and determine the predicted available power based on the difference between the cumulative value and the predicted power consumption.

[0066] For example, the controller first reads the current battery level from the battery management system and calculates the cumulative value of the current battery level and the predicted charging amount to represent the upper limit of energy supply in the future period. Then, the controller subtracts the predicted power consumption from the above cumulative value to obtain the predicted available power. If the predicted available power is positive, it indicates that the energy supply is expected to be sufficient or in surplus; if the predicted available power is negative, it indicates that the energy is expected to be insufficient.

[0067] Step S52: Based on the predicted available power and the priority and power consumption parameters of each functional module of the camera device, determine the target operating parameters of each functional module at the current moment.

[0068] After determining the predicted available power, as an option for determining the target operating parameters of each functional module at the current moment, the controller can obtain the predefined priority and corresponding power consumption parameters of each functional module, and obtain the preset safety margin required for the camera equipment to maintain minimum operation. The controller subtracts the safety margin from the predicted available power to obtain the available power. Subsequently, the controller traverses each functional module in descending order of priority. For each functional module, the controller queries its power consumption parameters, which include the power consumption of each functional module in each operating mode. These operating modes may include full-performance operating mode, degraded operating mode, and shutdown mode. The configured operating parameters for full-performance operating mode, degraded operating mode, and shutdown mode are different, and the power consumption decreases sequentially from full-performance operating mode to degraded operating mode and shutdown mode. The configured operating parameters can be customized according to the application scenario.

[0069] For example, the controller first obtains the power consumption corresponding to the full-performance operation mode of the functional module. If the difference between the current remaining available power and the power consumption in the full-performance operation mode is greater than a preset safety threshold, the configuration operating parameters of the functional module in the full-performance operation mode are determined as the target operating parameters, and the available power is updated according to the power consumption in the full-performance operation mode. If the difference between the current remaining available power and the power consumption in the full-performance operation mode is less than or equal to the preset safety threshold, it means that the current remaining available power is insufficient to support the full-performance operation of the functional module. Then, the controller queries the power consumption corresponding to its degraded operation mode. If the difference between the current remaining available power and the power consumption in the degraded operation mode is greater than the preset safety threshold, the configuration operating parameters of the functional module in the degraded operation mode are determined as the target operating parameters. Conversely, if the difference between the current remaining available power and the power consumption in the degraded operation mode is still less than or equal to the preset safety threshold, the functional module is determined to be in shutdown mode.

[0070] The above-mentioned scheme for determining the target operating parameters of each functional module at the current moment can ensure that, under energy constraints, high-priority functional modules always receive the highest priority energy quota by evaluating the energy consumption budget of each functional module one by one according to priority order, thereby maximizing the continuity of core business.

[0071] After determining the predicted available power, as an alternative scheme to determine the target operating parameters of each functional module at the current moment, the power consumption parameters of each functional module can be represented in the form of a combination of operating modes. This combination of operating modes includes the configuration operating parameters of one or more functional modules and pre-stores the theoretical power consumption corresponding to that combination. For example, the combination of operating modes may include a basic monitoring mode and a high-performance alert mode. In the basic monitoring mode, the video encoding module is configured with a resolution of 1080p, corresponding to a theoretical power consumption of 150mA / h. In the high-performance alert mode, the video encoding module is configured with a resolution of 2K, and the wireless transmission module is configured with a transmission period of 1s, corresponding to a theoretical power consumption of 550mA / h.

[0072] For example, the controller first acquires multiple predefined combinations of switchable operating modes, then loads the priorities of each functional module, and determines the combination score of the operating mode combination based on the priorities and number of functional modules present in each operating mode combination. The priorities of the functional modules in the operating mode combination are directly proportional to the combination score, and the number of functional modules in the operating mode combination is also directly proportional to the combination score. Furthermore, based on the score of each operating mode combination and its corresponding theoretical power consumption, a target operating mode combination is determined, and the configured operating parameters of each functional module in the target operating combination are used as the target operating parameters. The theoretical power consumption of the target operating combination is less than the predicted available power.

[0073] The above-mentioned scheme for determining the target operating parameters of each functional module at the current moment ensures logical matching between the parameters of each functional module by combining operating modes that include multi-module configurations, thus avoiding parameter conflicts or performance bottlenecks caused by controlling a single functional module.

[0074] The above steps achieve proactive and adaptive control of equipment operation status through illumination correction and power supply and demand prediction. This application calculates illumination correction coefficients based on historical forecast and actual illumination data within a preset time window, correcting future forecast illumination intensity to obtain a target forecast illumination intensity that closely matches the actual on-site conditions. This effectively eliminates illumination forecast deviations and improves power generation prediction accuracy. Furthermore, it combines the target forecast illumination intensity with battery status parameters to predict future charging volume, and simultaneously predicts future power consumption based on the current equipment operating status and historical power consumption data. Finally, it dynamically determines and implements control based on the power supply and demand relationship. Compared to traditional passive control methods with fixed thresholds, this embodiment can predict changes in power supply and demand in advance, proactively optimize equipment operating parameters, avoid forced shutdown of core security functions in low-light scenarios, effectively prevent the loss of critical recordings, and significantly improve the power supply stability and reliability of off-grid camera equipment for field security monitoring.

[0075] Step S60: Broadcast a status heartbeat packet to neighboring camera devices. The status heartbeat packet includes the device identifier, predicted available power, and task area.

[0076] A status heartbeat is a data frame periodically broadcast by a camera device via its wireless communication module. It is used to inform neighboring camera devices of its current critical status information, including device identifier, heartbeat generation timestamp, device online status, device operating status, installation location, battery level, battery temperature, predicted available battery power, and task area. The task area refers to the geographical range or field of view covered by the camera device, which can be represented as a set of coordinate points, a rectangular boundary, a polygon, or a sector, and is stored in the camera device's configuration file.

[0077] For example, the controller reads the device identifier of its own camera device from local storage. Next, it obtains its latest predicted available battery power. Then, it reads a preset task area from the device configuration file, such as a list of rectangular vertex coordinates or the center and radius of a circle. At least the device identifier, predicted available battery power, and task area are encapsulated into a status heartbeat packet, along with a heartbeat generation timestamp. Afterward, the controller controls the wireless communication module to broadcast the status heartbeat packet to a shared communication channel, where all neighboring camera devices within communication range can receive and parse the status heartbeat packet.

[0078] Step S70: When the predicted available power is less than a preset threshold, a candidate managed camera device is determined based on the predicted available power received from the neighboring camera devices.

[0079] The controller's background task continuously monitors the wireless communication channel. Whenever it receives a status heartbeat packet from a neighboring camera device, it parses the device identifier, predicted available battery power, task area, and heartbeat reception timestamp. In each detection cycle, such as after each heartbeat broadcast, it compares its current predicted available battery power with a preset threshold, such as 20% of the total battery capacity. If its predicted available battery power is greater than or equal to the preset threshold, it does not execute subsequent steps and continues normal monitoring; if it is less than the preset threshold, it enters the managed device decision process. The controller iterates through all the latest status heartbeat packets sent by neighboring cameras in its local cache, and selects neighboring cameras with predicted available battery power greater than the preset available battery power threshold as candidate managed cameras based on the predicted available battery power sent by the neighboring cameras.

[0080] Optionally, in addition to the predicted available power, the controller can also extract other auxiliary information from the status heartbeat packets of neighboring cameras, including: the time difference between the heartbeat reception timestamp and the current time, and the battery temperature recorded in the status heartbeat packet. The controller multiplies the predicted available power, time difference, and battery temperature by preset weighting coefficients and then performs a weighted sum to obtain a status score for each neighboring camera. The predicted available power is positively correlated with the status score; the time difference is negatively correlated with the status score, meaning the older the status information of the neighboring camera, the lower the status score; and the greater the deviation of the battery temperature from the optimal operating temperature, the lower the status score. Finally, the controller selects camera devices with status scores greater than a preset status score threshold from all neighboring camera devices as candidate managed camera devices.

[0081] Step S80: Select a target managed camera device from the candidate managed camera devices that has an overlapping area with its own task area.

[0082] After identifying candidate managed camera devices, the controller iterates through these devices, extracting their task regions from their cached status heartbeat packets. Then, it performs a spatial intersection check between the task regions of the candidate managed camera devices and its own task region.

[0083] For example, if both task areas are represented by rectangles, it is determined whether the projection intervals of the two rectangles intersect in the longitude and latitude directions. If they intersect in both longitude and latitude directions, an overlapping area is determined to exist. If one or both task areas are represented by polygons, a polygon intersection algorithm, such as the separating axis theorem or the scan line method, is used to determine whether the two polygons have a common set of points. If both task areas are represented by circles, the spherical distance between the centers of the two circles is calculated. If this distance is less than the sum of the radii of the two circles, an overlapping area is determined to exist.

[0084] After identifying candidate managed camera devices that overlap with their own task area, calculate the area of ​​the overlapping area between the task area of ​​each candidate managed camera device and its own task area. Select the candidate managed camera device with the largest overlapping area as the target managed camera device. Alternatively, sort the candidate managed camera devices in descending order of overlapping area and select the first preset number of candidate managed camera devices as the target managed camera device.

[0085] Step S90: Based on the predicted power consumption, send a task hosting request to the target managed camera device.

[0086] After determining the target managed camera device and its own predicted power consumption, a task hosting request is sent to the target managed camera device. The task hosting request includes at least its own device identifier, the target managed camera device's device identifier, predicted power consumption, its own task area, and the request's validity period.

[0087] For example, upon receiving a task hosting request, the target managed camera device first performs an admission judgment based on the source device identifier (i.e., the device identifier of the camera device sending the task hosting request), the overlapping area, and the request validity period. The admission judgment includes verifying its current battery level, temperature, and the number of tasks already hosted to determine if it can accept new tasks. If admission is successful, the target managed camera device can configure the overlapping area as a temporary additional area of ​​interest within its optical coverage area and redetermine the operating parameters related to the additional area of ​​interest according to a preset operating parameter adjustment strategy. For example, it may appropriately increase the encoding frame rate and bitrate lower limit during event triggering, shorten the frame interval, or increase the capture frequency to improve the detection sensitivity and image quality of the overlapping area during the hosting period. Upon reaching the request validity period or receiving a notification of return from the source device, the original operating parameters are restored, and the additional area of ​​interest configuration is cleared.

[0088] The above steps further introduce a low-power hosting mechanism for neighborhood collaboration: the camera periodically broadcasts a status heartbeat packet containing the device identifier, predicted available power, and task area to the neighborhood; when its predicted available power is lower than a preset threshold, the controller filters candidate hosting camera devices based on the received status information of neighboring devices; then, from the candidate devices, it selects a target hosting camera device that overlaps with its own task area through spatial intersection judgment; finally, it sends a task hosting request to the target device based on the predicted power consumption, realizing the temporary transfer of the monitoring task, so that when its own power is about to run out, it can still maintain the continuity of monitoring by relying on the overlapping coverage area of ​​adjacent devices, thereby improving the reliability of the camera device and the persistence of the detection task in the field security scenario that does not rely on external mains power.

[0089] Example 2 Based on the above embodiment one, referring to Figure 2In this embodiment, before step S31, the control method of the camera device includes steps S35 to S37: Step S35: Obtain the predicted rainfall probability corresponding to the predicted light intensity of the target.

[0090] Step S36: Determine the light attenuation coefficient corresponding to the predicted rainfall probability based on the mapping relationship between rainfall probability and cloud transmittance.

[0091] Forecasted precipitation probability is the probability of precipitation given in a weather forecast that is time-aligned with the target forecast light intensity. Cloud transmittance is the ability of a cloud layer to transmit both direct and diffuse light reaching the Earth's surface; the higher the precipitation probability, the thicker the cloud layer, and the lower the cloud transmittance.

[0092] For example, after obtaining the target forecast light intensity, the controller can obtain the forecast rainfall probability for the same future time period as the target forecast light intensity, and determine the light attenuation coefficient corresponding to the forecast rainfall probability for the future time period according to the preset mapping relationship between rainfall probability and cloud transmittance.

[0093] As an alternative method for determining the light attenuation coefficient, the controller obtains a predefined rainfall probability-cloud transmittance mapping table. This mapping table divides continuous rainfall probabilities into several discrete intervals and assigns a fixed cloud transmittance value to each interval. Specifically, the controller compares the forecast rainfall probability for each future time period with each interval in the rainfall probability-transmittance mapping table to determine the interval to which the forecast rainfall probability belongs. Once the interval is determined, the controller directly obtains the preset cloud transmittance for that interval as the mapping result and uses this cloud transmittance as the light attenuation coefficient.

[0094] As an alternative approach to determining the light attenuation coefficient, the controller acquires a predefined set of rainfall probability-cloud transmittance feature points. This set contains several key feature points, each including a specific rainfall probability threshold and its corresponding cloud transmittance value. The controller first obtains the predicted rainfall probability for each future time period. Then, the controller iterates through the feature point set, locating the interval between two adjacent feature points containing the predicted rainfall probability. Based on the relative position of the predicted rainfall probability within the interval formed by these two adjacent feature points, the controller performs linear interpolation calculations. For example, the cloud transmittance corresponding to two adjacent feature points can be weighted and fused according to the ratio of the predicted rainfall probability to the distance between the two adjacent feature points, thus obtaining the cloud transmittance corresponding to the predicted rainfall probability. Finally, the light attenuation coefficient is determined based on the cloud transmittance, where cloud transmittance is inversely proportional to the light attenuation coefficient.

[0095] Step S37: Determine the corrected predicted light intensity based on the product of the light attenuation coefficient and the predicted light intensity, and input the corrected predicted light intensity as an input parameter into the preset photovoltaic panel charging model.

[0096] Before substituting the predicted light intensity into the photovoltaic panel charging model calculation, a light attenuation coefficient matching the predicted rainfall probability is superimposed to correct the light intensity value. This converts the light penetration loss caused by rain and simultaneous thick clouds into a quantifiable value for inclusion in the photovoltaic input conditions. Instead of simply using the overall weather conditions of the area to determine the actual light received by the camera equipment, this reduces the problem of overestimating the light intensity value. In rainy or cloudy weather, the corrected light intensity value more closely matches the actual amount of light energy received by the photovoltaic panel. The calculated predicted charging amount is more accurate, and the predicted available power is more in line with reality. This avoids energy dispatching starting high-load operation modes based on overestimated charging estimates, reducing the possibility of unexpected shutdowns of the camera equipment due to insufficient power, and effectively improving the power supply stability of the camera equipment.

[0097] Example 3 Based on the above embodiments one and two, referring to Figure 3 In this embodiment, the control method of the camera device includes steps S100~S130: Step S100: When a task management request is received from a neighboring camera device, the predicted power consumption of the neighboring camera device is obtained.

[0098] Step S120: The sum of the predicted power consumption and its own predicted power consumption is taken as the total power consumption value.

[0099] Step S130: Based on the predicted charging amount and the total power consumption, redetermine the target operating parameters for the current moment.

[0100] For example, the controller obtains its own predicted charging amount and its own predicted power consumption, parses the predicted power consumption of the neighboring camera devices from the received managed request, takes the sum of the predicted power consumption of the neighboring camera devices and its own predicted power consumption as the total power consumption value, and re-determines the target operating parameters at the previous time based on the new total power consumption value and its own predicted charging amount. The step of re-determining the target operating parameters is the same as step S50 above, and will not be described in detail here.

[0101] Understandably, after taking on a hosting task, the camera equipment considers the additional energy consumption brought by the hosting task as part of the total energy consumption. By reallocating the limited energy budget, it can extend its working time to ensure long-term detection of overlapping areas. This avoids blind spots in the monitoring area originally handled by the camera equipment that initiated the hosting request due to premature shutdown, thereby improving the stability of the camera equipment's monitoring work.

[0102] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the control method of the camera device of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0103] This application provides a control device for a camera device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the control method of the camera device in the first embodiment described above.

[0104] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a control device suitable for implementing the camera device of the present application embodiments. Figure 4 The control device of the camera shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0105] like Figure 4As shown, the control device of the camera equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 1002 or a program loaded from the storage device 1003 into the random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the camera equipment's control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the camera equipment's control device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a camera equipment control device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0106] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0107] The camera control device provided in this application, employing the camera control method described in the above embodiments, can solve the technical problem of how to improve the operational reliability of the camera device. Compared with the prior art, the beneficial effects of the camera control device provided in this application are the same as those of the camera control method provided in the above embodiments, and other technical features in the camera control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0108] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0110] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the control method of the camera device in the above embodiments.

[0111] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0112] The aforementioned computer-readable storage medium may be included in the control device of the camera equipment; or it may exist independently and not assembled into the control device of the camera equipment.

[0113] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the control device of the camera equipment, enable the control device to write computer program code for performing the operations of this application in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, or as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0115] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0116] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the control method of the above-described camera device, thereby solving the technical problem of how to improve the operational reliability of the camera device. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the control method of the camera device provided in the above embodiments, and will not be repeated here.

[0117] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the control method for the camera device as described above.

[0118] The computer program product provided in this application can solve the technical problem of how to improve the operational reliability of camera equipment. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the camera equipment control method provided in the above embodiments, and will not be repeated here.

[0119] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for a camera device, characterized in that, The control method for the camera equipment includes: Based on a preset time window, historical forecast light intensity and historical ambient light intensity are obtained; The correction coefficient is determined based on the ratio between the historical ambient light intensity and the historical forecast light intensity, and the target forecast light intensity is determined by multiplying the correction coefficient by the forecast light intensity for the future period. Based on the target predicted illumination intensity and the battery state parameters corresponding to the current moment, the predicted charging amount for the future period is determined; Based on the current equipment operating status and historical power consumption, determine the predicted power consumption for future periods; Based on the predicted charging amount and the predicted power consumption, target operating parameters are determined, and the operation of the camera device is controlled based on the target operating parameters; Broadcast a status heartbeat to neighboring camera devices. The status heartbeat includes the device identifier, predicted available power, and task area. When the predicted available power is less than a preset threshold, candidate managed camera devices are determined based on the predicted available power sent by the neighboring camera devices. Select a target managed camera device from the candidate managed camera devices that has an overlapping area with its own task area; Based on the predicted power consumption, a task hosting request is sent to the target managed camera device.

2. The control method for the camera device as described in claim 1, characterized in that, The step of determining a correction coefficient based on the ratio between the historical ambient light intensity and the historical forecast light intensity, and determining the target forecast light intensity based on the product of the correction coefficient and the forecast light intensity for the future period, includes: The ratio of the historical ambient light intensity to the historical predicted light intensity is determined to obtain the instantaneous correction ratio for each moment. The statistical value of the instantaneous correction ratio within the preset time window is determined to obtain the correction coefficient; The product of the correction coefficient and the predicted illumination intensity for the future time period is taken as the target predicted illumination intensity at the current time.

3. The control method for the camera device as described in claim 1, characterized in that, The battery state parameters include the battery voltage and battery temperature. The step of determining the predicted charging amount for the future period based on the target predicted illumination intensity and the battery state parameters corresponding to the current time includes: The target predicted light intensity, the battery voltage, and the battery temperature are used as input parameters for a preset photovoltaic panel charging model, which represents the mapping relationship between light intensity, temperature, voltage, and photovoltaic photoelectric conversion efficiency. The charging power at each future moment is determined using the preset photovoltaic panel charging model; Starting from the current moment, the future time periods are divided according to a preset time step. The predicted charging amount for the future period is determined by multiplying the maximum charging power within the future period by the duration of the future period.

4. The control method for the camera device as described in claim 3, characterized in that, Before the step of using the target predicted light intensity, the battery voltage, and the battery temperature as input parameters for a preset photovoltaic panel charging model, the method further includes: Obtain the predicted rainfall probability corresponding to the predicted light intensity of the target; Based on the mapping relationship between rainfall probability and cloud transmittance, the light attenuation coefficient corresponding to the predicted rainfall probability is determined; The corrected predicted light intensity is determined by multiplying the light attenuation coefficient and the predicted light intensity, and then the corrected predicted light intensity is used as an input parameter to the preset photovoltaic panel charging model.

5. The control method for the camera device as described in claim 1, characterized in that, The step of determining the predicted power consumption for future periods based on the current device operating status and historical power consumption includes: Obtain the operating parameters of each functional module of the camera device at the current moment, and construct a feature vector based on the operating parameters as the operating state of the device; Determine the similarity between the device's operating status and the historical device operating status corresponding to each historical time period, and determine the target historical time period based on the similarity. The predicted power consumption for future periods is determined based on the historical power consumption at a preset time step following the target historical period.

6. The control method for the camera device as described in claim 1, characterized in that, The step of determining the target operating parameters based on the predicted charging amount and the predicted power consumption includes: Determine the cumulative value of the current battery level and the predicted charging amount, and determine the predicted available power based on the difference between the cumulative value and the predicted power consumption; Based on the predicted available power and the priority and power consumption parameters of each functional module of the camera device, the target operating parameters of each functional module at the current moment are determined.

7. The control method for the camera device as described in claim 1, characterized in that, The control method for the camera device also includes: Upon receiving a task management request from a neighboring camera device, obtain the predicted power consumption corresponding to the neighboring camera device; The sum of the predicted power consumption and its own predicted power consumption is taken as the total power consumption. Based on its own predicted charging amount and total power consumption, the target operating parameters at the current moment are redefined.

8. A camera device, characterized in that, The camera device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the camera device as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method for the camera device as described in any one of claims 1 to 7.

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