Solar camera distributed cooperative management method

Abstract

The application relates to a solar camera distributed cooperative management method based on dynamic energy distribution. The method comprises the following steps: acquiring the solar cell panel power generation efficiency, the camera working load and the environmental light intensity of each solar camera node; identifying the task priority according to the camera working load; inputting the environmental light intensity into an environmental light intensity prediction model to obtain a light prediction result, and determining the predicted available energy; determining the predicted energy demand according to the camera working load, and judging the energy supply and demand state; when the target node is in a preset condition, acquiring the energy supply and demand state of the adjacent node through distributed communication, and determining the matching result of the energy gap and the energy margin; generating energy distribution parameters and power supply mode adjustment parameters according to the task priority and the matching result, and cooperatively adjusting the power supply mode of the multiple nodes. The application can improve the problem that the power supply guarantee and the power consumption adjustment matching between the distributed solar camera nodes are insufficient.

Description

Technical Field

[0001] This application relates to the field of energy management technology for solar energy devices, and in particular to a distributed collaborative management method for solar cameras based on dynamic energy distribution. Background Technology

[0002] With the development of distributed monitoring networks, solar-powered cameras are widely deployed in outdoor roads, parks, farmland, waterways, border patrols, construction sites, and other scenarios. These devices typically rely on solar panels and energy storage units to power the cameras, communication modules, and controllers, reducing reliance on external power lines and improving the flexibility of monitoring equipment deployment.

[0003] Existing solar-powered cameras typically control their power supply based on the current battery level, current power generation status, or preset operating modes. For example, they might reduce the camera's acquisition frequency when the battery is low, maintain normal operation when sunlight is strong, or control the camera to sleep and wake up according to a fixed schedule. While this approach can reduce the power consumption of individual devices to some extent, its control is primarily based on the current state of a single node, making it difficult to fully reflect subsequent changes in sunlight, camera workload, and the energy surplus or deficit of adjacent nodes.

[0004] In a distributed monitoring network, the locations, occupancy levels, workloads, and lighting conditions of different solar-powered camera nodes may vary. For example, a target solar-powered camera node may experience a power shortage due to partial occupancy or increased workload, while adjacent solar-powered camera nodes may have energy reserves due to better lighting or lower workloads. If the power supply mode is adjusted solely based on the current state of a single node, problems such as insufficient power supply to high-priority nodes, low-priority nodes maintaining high power consumption, and incoordination between node operating states can easily occur.

[0005] Therefore, the main technical problem with existing solar camera energy management methods is that in a distributed monitoring network composed of multiple solar camera nodes, it is difficult to determine the power supply mode adjustment method that matches the node's operating status based on illumination prediction, workload, task priority, and the energy supply and demand status of adjacent nodes, resulting in insufficient matching of power supply guarantee and power consumption adjustment between different nodes. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a distributed collaborative management method for solar cameras based on dynamic energy allocation, so as to at least solve the problem that the existing solar camera energy management methods in distributed monitoring networks are difficult to determine the power supply mode adjustment method that matches the node's operating status based on illumination prediction, workload, task priority and energy supply and demand status of adjacent nodes, resulting in insufficient matching of power supply guarantee and power consumption adjustment between different nodes.

[0007] To address the aforementioned technical problems, this invention provides a distributed collaborative management method for solar-powered cameras based on dynamic energy allocation, applicable to a distributed monitoring network comprising multiple solar-powered camera nodes. The method includes: Obtain the solar panel power generation efficiency, camera workload, and ambient light intensity of each solar camera node; Based on the camera workload, identify the task priority corresponding to each solar camera node; The ambient light intensity is input into the ambient light intensity prediction model to obtain the light prediction result, and the predicted available energy of each solar camera node is determined based on the power generation efficiency of the solar panel and the light prediction result. The predicted energy demand of each solar camera node is determined based on the camera workload, and the energy supply and demand status of each solar camera node is determined based on the comparison between the predicted available energy and the predicted energy demand. When the difference between the predicted available energy and the predicted energy demand of the target solar camera node is within a preset range, or when the target solar camera node has an energy gap and the corresponding task priority meets the preset priority condition, the energy supply and demand status of the adjacent solar camera nodes is obtained through distributed communication between the solar camera nodes, and the matching result of the energy gap and energy surplus between the current node and the adjacent nodes is determined. Based on the task priority and the matching result, energy allocation parameters and power supply mode adjustment parameters are generated for adjusting the power supply mode of each solar camera node. According to the energy allocation parameters and the power supply mode adjustment parameters, the power supply mode of multiple solar camera nodes is adjusted in a coordinated manner, and the corresponding power supply mode adjustment result is output.

[0008] Compared with the prior art, the present invention has at least the following beneficial effects: This invention obtains the power generation efficiency of solar panels, the workload of cameras, and the ambient light intensity, and determines the light prediction results based on the ambient light intensity prediction model. This allows the prediction of available energy to no longer depend solely on the current power generation state, but to form a basis for energy supply judgment by combining subsequent changes in light intensity. This improves the problem of insufficient foresight when power supply control is based solely on the current node state.

[0009] This invention determines the predicted energy demand based on the camera's workload and compares the predicted available energy with the predicted energy demand to determine the energy supply and demand status. This allows the power supply mode adjustment of the solar camera node to simultaneously consider the energy supply and energy consumption status, thereby improving the matching between power supply security and workload.

[0010] When the target solar camera node is in a critical energy state or an energy deficit state and the task priority meets the preset priority conditions, the present invention obtains the energy supply and demand status of adjacent solar camera nodes through distributed communication, and generates power supply mode adjustment parameters based on the matching result of the energy deficit of the current node and the energy surplus of adjacent nodes. This enables multiple solar camera nodes to coordinate adjustments according to task priority and energy surplus / deficit, thereby improving the problem of insufficient matching of power supply guarantee and power consumption adjustment among different nodes in the distributed monitoring network. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the overall process of a distributed collaborative management method for solar cameras based on dynamic energy allocation, provided in an embodiment of this application.

[0012] Figure 2 This is a schematic diagram of the deployment structure of a solar-powered camera node in a distributed monitoring network, as provided in an embodiment of this application.

[0013] Figure 3 This is a schematic diagram illustrating a process for obtaining node status information, provided in an embodiment of this application.

[0014] Figure 4 This is a schematic diagram of a task priority acquisition process provided in an embodiment of this application.

[0015] Figure 5 This is a schematic diagram of a process for predicting available energy, provided in an embodiment of this application.

[0016] Figure 6 This is a schematic diagram of an energy supply and demand status determination process provided in an embodiment of this application.

[0017] Figure 7 This is a schematic diagram of a distributed state acquisition and surplus / shortage matching process provided in an embodiment of this application.

[0018] Figure 8 This is a schematic diagram of a power supply mode collaborative adjustment process provided in an embodiment of this application.

[0019] Figure 9 This is a schematic diagram illustrating the process of generating capability allocation parameters according to an embodiment of this application. Detailed Implementation

[0020] The present application will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are used to illustrate the technical solutions of the present application, and are not intended to limit the scope of protection of the present application. Where there is no conflict, the technical features in the following embodiments can be combined with each other.

[0021] refer to Figure 2In one embodiment, the distributed monitoring network includes multiple solar-powered camera nodes 110. These solar-powered camera nodes 110 can be deployed along roadsides, at park boundaries, in farmland, along riverbanks, at construction sites, near power transmission lines, or in other outdoor locations where continuous monitoring is required but external power supply lines are inconvenient to install. Each solar-powered camera node 110 may include a solar panel 111, a camera 112, a controller 113, an energy storage unit 114, and a communication module 115. The solar panel 111 converts ambient light into electrical energy; the energy storage unit 114 stores the electrical energy generated by the solar panel 111 and supplies power to the camera 112, controller 113, and communication module 115; the camera 112 performs tasks such as image acquisition, video acquisition, encoding, caching, and data uploading; the controller 113 acquires node status information, performs predictive processing, determines energy supply and demand status, and generates power supply mode adjustment parameters; the communication module 115 interacts with adjacent solar-powered camera nodes, edge gateways, or servers.

[0022] In actual deployment, the operating conditions of different solar-powered camera nodes 110 may vary. For example, a node deployed under the shade of trees on the north side of a road may be shaded in the afternoon, leading to a decrease in the power generation capacity of its solar panels 111; nodes deployed at intersections, entrances / exits, or key areas may need to maintain a high acquisition and upload frequency, resulting in a high workload for the cameras; nodes deployed in relatively less important areas may only need to acquire data intermittently or upload periodically, with lower energy consumption and a certain energy reserve. If each node adjusts its power supply mode independently based on its current power supply, high-priority nodes may reduce their frequency or go into sleep mode due to insufficient sunlight in a short period of time, while low-priority nodes may still maintain high power consumption. This application determines the power supply mode adjustment method collaboratively by predicting available energy, predicting energy demand, task priority, and the energy supply and demand status of adjacent nodes, making the power supply guarantee and power consumption adjustment among multiple nodes more consistent with the actual operating conditions.

[0023] In one embodiment, the energy allocation parameters in this application are not limited to parameters for physical power transmission between nodes, but are used to characterize the direction of protection, the direction of energy saving, the adjustment range, or the selection of operating modes for different solar camera nodes in the coordinated adjustment of power supply modes. The system can achieve distributed collaborative management by maintaining the current working mode of the target node, reducing the workload of adjacent low-priority nodes, adjusting the upload frequency, or extending the sleep cycle. This method is applicable to solar camera networks without power transmission lines between nodes, and also to distributed monitoring networks that use gateways or servers for centralized collaborative control.

[0024] refer to Figure 1 In one embodiment, the distributed collaborative management method for solar cameras based on dynamic energy allocation includes steps S10 to S60.

[0025] S10: Obtain the solar panel power generation efficiency, camera workload, and ambient light intensity of each solar camera node.

[0026] In this embodiment, the solar panel power generation efficiency is used to characterize the power generation capacity of the solar camera node during the current acquisition cycle. The camera workload is used to characterize the load level of the camera 112 during the current acquisition cycle due to its operational states such as acquisition, encoding, uploading, supplemental lighting, caching, and sleep mode. The ambient light intensity is used to characterize the lighting conditions at the location of the solar camera node. These three data points correspond to the power supply side, power consumption side, and environmental side, respectively, and are the basic inputs for subsequent predictions of available energy and predicted energy demand.

[0027] The current data acquisition cycle can be set according to actual deployment needs, such as 1 minute, 5 minutes, 10 minutes, or 30 minutes. In road monitoring scenarios, where vehicle traffic changes rapidly, the acquisition cycle can be set shorter to allow for timely adjustments to node operation modes. In farmland or river monitoring scenarios, where task changes are relatively gradual, the acquisition cycle can be set longer to reduce energy consumption caused by frequent calculations and communications. Within each acquisition cycle, the system can read the solar panel output, camera operating status, and ambient light intensity, and categorize them into node status information for the same cycle, thus ensuring that subsequent processing is based on a consistent time scale.

[0028] refer to Figure 3 In one embodiment, step S10 includes the following sub-steps.

[0029] S110, acquire the power output information of the solar panel during the current acquisition cycle.

[0030] The power generation output information may include at least one of the following: output voltage, output current, output power, and cumulative power generation of the solar panel 111 during the current sampling period. The system can acquire power generation output information through a voltage sampling circuit, current sampling circuit, or power metering chip located at the output end of the solar panel. Alternatively, the energy storage management unit can provide the controller 113 with the charging power or charging amount per unit period. For outdoor lighting environments with large output fluctuations, the system can average multiple sampled values ​​within the current sampling period to avoid distortion of the power generation output information caused by short-term changes in shadows, clouds, or reflected light.

[0031] For example, in a park boundary monitoring scenario, a solar-powered camera node sequentially collects multiple output power values ​​within a 5-minute acquisition cycle. The system can take the average output power within that cycle as the current power generation output information. If the output power momentarily decreases due to a pedestrian, foliage, or bird briefly obstructing the view, this momentary value will not directly determine the subsequent power supply mode, thereby reducing the impact of short-term interference on the node's status judgment.

[0032] S120, determine the power generation efficiency of the solar panel based on the power generation output information and the rated power generation information of the solar panel.

[0033] Rated power generation information may include the rated output power, rated power generation per cycle, or rated conversion efficiency of the solar panel 111. Solar panel power generation efficiency can be expressed as the ratio of current actual power generation capacity to rated power generation capacity. In one example, the solar panel power generation efficiency can be determined using the following formula: Eta_i = P_out_i / P_rated_i Where Eta_i represents the power generation efficiency of the solar panel of the i-th solar camera node, P_out_i represents the power output of the i-th solar camera node in the current acquisition cycle, and P_rated_i represents the rated output power of the corresponding solar panel. If the cumulative power generation per unit cycle is used, the ratio of the actual power generation in the current cycle to the rated power generation in the current cycle can also be used as the power generation efficiency of the solar panel.

[0034] For example, if the solar panel of a road monitoring node has a rated output power of 40W and the average output power during the current data collection period is 20W, then its power generation efficiency can be determined to be 0.5. This value indicates that the node's current power generation capacity is approximately half of its rated capacity. The subsequent system can combine ambient light prediction results to determine the predicted available energy for the target prediction period, rather than directly adjusting the power supply mode based solely on the current output power.

[0035] S130, acquire the working status information of the camera within the current acquisition period, and determine the workload of the camera based on the working status information.

[0036] Operating status information may include at least one of the following: acquisition frequency, video resolution, encoding load, data upload frequency, supplementary lighting status, operating duration, sleep duty cycle, and cache write status. Camera workload can be expressed as a load level or a load value. The impact of operating status information can vary depending on the application scenario. For example, in nighttime surveillance scenarios, the supplementary lighting status may have a significant impact on energy consumption; in remote mountainous communication scenarios, data upload frequency and communication module operating duration may have a significant impact on energy consumption.

[0037] For example, a monitoring node at a construction site that captures video at 15 frames per second during the day and uploads video clips every 10 minutes can be identified as having a medium workload; the same node that turns on supplementary lighting at night, captures video at 25 frames per second, and uploads it in real time can be identified as having a high workload. By converting the actual working conditions into camera workload, the system can more accurately estimate and predict energy requirements in subsequent steps.

[0038] S140, obtain the ambient light intensity at the location corresponding to the solar camera node, and use the power generation efficiency of the solar panel, the workload of the camera, and the ambient light intensity as node status information in the same acquisition cycle.

[0039] Ambient light intensity can be collected by light sensors or indirectly estimated using solar panel output power, node installation orientation, and historical calibration data. For scenarios with multiple nodes widely distributed, the system prioritizes using the ambient light intensity at each node's local location rather than uniformly using the light value at a central location, because different nodes may be affected by obstructions from buildings, trees, mountains, or equipment.

[0040] For example, among multiple solar-powered camera nodes deployed along both sides of a road, the node on the east side receives stronger sunlight in the morning, while the node on the west side may receive weaker sunlight due to building obstruction. If the system uniformly adopts the regional average light intensity, it may not accurately reflect the actual power generation capacity of each node. This application incorporates the ambient light intensity at the corresponding location of each node as part of the node's own state information, which helps improve the accuracy of subsequent predictions of available energy.

[0041] S20, based on the camera workload, identify the task priority corresponding to each solar camera node.

[0042] In this embodiment, task priority is used to indicate the importance of the monitoring task and power supply requirements of the solar-powered camera node in the current or target prediction period. Task priority can be determined based on the camera workload or by combining task execution status and operational continuity. A higher task priority indicates that the node should prioritize maintaining data acquisition, uploading, or operational continuity during power supply mode coordination adjustments.

[0043] Task priority is not necessarily equivalent to workload. For example, a node with a high workload may be performing a long-term routine inspection; another node with a medium workload may still require a high power supply even if it is located at a park entrance or in a high-security area. To ensure that task priorities have a clear source, this embodiment extracts the task execution status and working continuity status from the camera workload and determines the load level and task priority accordingly. In practical applications, configuration can also be combined with node deployment location or management policies, but this configuration does not change the basic process of identifying task priorities based on camera workload in this application.

[0044] refer to Figure 4 In one embodiment, step S20 includes the following sub-steps.

[0045] S210, extract the task execution status and working continuity status of the camera from the camera workload.

[0046] Task execution status can include real-time monitoring status, periodic inspection status, event logging status, low-power standby status, and data retransmission status. Work continuity status can include the time the current task has been running, the expected duration within the target prediction period, or the remaining execution time in the current working mode. Task execution status reflects what task the node is currently executing, while work continuity status reflects the ongoing time pressure on that task.

[0047] For example, in an agricultural monitoring scenario, a node might take low-frequency images of crop growth during the day, its task execution status being periodic inspection, and its working duration being short-term operation. In contrast, a node at the park entrance might be in real-time monitoring mode, requiring continuous video streaming, and its working duration is long-term operation. Even when both are in working mode, their task priorities and energy requirements differ.

[0048] S220, determine the load level of the corresponding solar camera node based on the task execution status and the working continuity status.

[0049] Load levels can be categorized as low, medium, and high load, or by numerical levels. The system can use both task execution status and operational continuity as criteria for judgment. For example, nodes that collect and upload data in real time and run continuously for more than a preset time can be classified as high load level; nodes that collect data periodically and upload data intermittently can be classified as medium load level; and nodes in standby or hibernation mode can be classified as low load level.

[0050] In practical applications, the system can set a load level judgment table. This judgment table can be configured according to device power consumption and business needs, and is not the only limitation. For example, when the sampling frequency is higher than a first frequency threshold, the upload frequency is higher than a first upload threshold, and the duration exceeds a first duration threshold, it is determined to be a high load level; when the sampling frequency is lower than a second frequency threshold and the sleep duty cycle is high, it is determined to be a low load level.

[0051] S230, determine the task priority corresponding to the solar camera node according to the load level.

[0052] The system can determine task priorities based on load levels. For example, high load levels correspond to high task priorities, medium load levels correspond to medium task priorities, and low load levels correspond to low task priorities. In some embodiments, the system can also set higher base priorities for nodes located in key monitoring locations, but this base priority can be used as a configuration parameter for mapping load levels to task priorities, rather than replacing the workload of the cameras.

[0053] For example, in a road monitoring scenario, intersection nodes are in a state of continuous data acquisition and real-time uploading, which the system can determine as high task priority; mid-section road nodes only perform periodic image acquisition, which the system can determine as medium task priority; and parking area edge nodes are in a standby or low-frequency inspection state, which the system can determine as low task priority. This task priority will affect the subsequent power supply guarantee level and the selection of nodes to be reduced in power consumption.

[0054] S240, the task priority is used as a constraint condition for the subsequent generation of energy allocation parameters and power supply mode adjustment parameters.

[0055] When generating energy allocation parameters, the system can prioritize ensuring the operational continuity of high-priority nodes. For example, if a high-priority node has an energy deficit, while adjacent low-priority nodes have energy reserves or can reduce their workload, the system can maintain the current operating mode of the high-priority node and reduce the data acquisition or upload frequency of the low-priority node. In this way, task priority is not just a label, but a constraint that actually participates in the coordinated adjustment of power supply modes.

[0056] S30, input the ambient light intensity into the ambient light intensity prediction model to obtain the light prediction result, and determine the predicted available energy of each solar camera node based on the power generation efficiency of the solar panel and the light prediction result.

[0057] The power supply capacity of solar-powered camera nodes depends not only on their current power generation status but also on changes in sunlight during the target prediction period. Controlling power solely based on current output may not be adaptable to subsequent reductions or increases in sunlight. For example, if a node is currently experiencing strong sunlight but is about to enter shadow during the prediction period, maintaining high power consumption could lead to a decrease in energy storage. Conversely, if a node is currently generating less power due to temporary cloud cover but sunlight is about to recover during the prediction period, immediately reducing the data collection frequency could result in an unnecessary decrease in monitoring capability.

[0058] Therefore, the system inputs the ambient light intensity into the ambient light intensity prediction model to obtain the light prediction results for the target prediction period, and combines this with the solar panel power generation efficiency to determine the predicted available energy. This predicted available energy is then compared with the predicted energy demand to determine the energy supply and demand status.

[0059] refer to Figure 5 In one embodiment, step S30 includes the following sub-steps.

[0060] S310, input the ambient light intensity within the current acquisition period into the ambient light intensity prediction model to obtain the light prediction result within the target prediction period.

[0061] The ambient light intensity prediction model can be executed by controller 113, edge gateway, or server. This model can receive the ambient light intensity within the current acquisition period, or it can receive ambient light intensity sequences from multiple historical acquisition periods. The light prediction results output by the model can include the average predicted light intensity within the target prediction period, the predicted light intensity at multiple times, the light change trend, or the cumulative light amount.

[0062] In one optional implementation, the ambient light intensity prediction model can be a time-series prediction model based on historical light intensity sequences. In another optional implementation, the ambient light intensity prediction model can be a pre-calibrated empirical prediction rule, such as determining the light changes within the target prediction period based on the current light intensity, the current time period, and recent light change trends. If device resources are limited in the deployment scenario, the model can use lightweight rules; if the edge gateway or server has strong computing power, the model can use a more complex prediction model. The above model structures are all optional implementations and do not limit this application to using a specific model.

[0063] For example, a solar-powered camera node deployed on the west side of the park's road gradually becomes affected by building shadows after 4 PM each day. The system can predict a continuous decrease in sunlight intensity over the next 30 minutes based on historical records of light intensity changes and the current light intensity. This light prediction result will be used to determine the node's predicted available energy, rather than simply using the current higher power generation capacity.

[0064] S320, determine the predicted light intensity change information within the target prediction period based on the light prediction result.

[0065] Predicted light intensity variation information can represent how light intensity changes over time within a target prediction period. This can include average predicted light intensity, cumulative predicted light intensity, upward trend in light intensity, downward trend in light intensity, and the amplitude of light intensity fluctuations. The system can select a suitable quantization value for calculating the predicted power generation based on the light intensity prediction results.

[0066] For example, if the target prediction period is the next 30 minutes, and the ambient light intensity prediction model outputs predicted light intensities for the next six moments, the system can calculate the average of these six predicted values ​​as the predicted light intensity change information. If the model outputs a decreasing light intensity trend, the system can estimate the average light intensity within the target prediction period based on the current light intensity and the rate of decrease. For solar power generation estimation, predicted light intensity change information reflects the energy supply capacity within the target prediction period better than a single current light value.

[0067] S330, Based on the predicted light intensity change information and the power generation efficiency of the solar panel, determine the predicted power generation within the target prediction period.

[0068] The system can determine the predicted power generation based on predicted changes in light intensity, solar panel power generation efficiency, and the target prediction period length. In one example, the following formula can be used: E_gen_i=Eta_i*L_pred_i*T_pred*K_i Where E_gen_i represents the predicted power generation of the i-th solar camera node within the target prediction period, Eta_i represents the power generation efficiency of the solar panel of the i-th solar camera node, L_pred_i represents the irradiance quantization value corresponding to the predicted light intensity change information within the target prediction period, T_pred represents the length of the target prediction period, and K_i represents the conversion factor related to the solar panel specifications.

[0069] The above formula is an example calculation method. In practical applications, K_i can be determined based on the solar panel area, rated power, installation angle, or historical power generation calibration data. If the system has already established a mapping relationship between irradiance and power generation using historical data, the predicted power generation can also be obtained directly by looking up the table based on the predicted irradiance change information and power generation efficiency.

[0070] For example, if a farmland monitoring node has a power generation efficiency of 0.6 during the current data collection period and predicts a high average light intensity in the next hour, its predicted power generation is sufficient. Conversely, another node, while having the same current power generation efficiency, predicts entering a shaded area in the next hour, resulting in a lower predicted power generation. The system can thus distinguish between two nodes with similar current states but different subsequent power supply capabilities.

[0071] S340, the predicted power generation is determined as the predicted available energy of the corresponding solar camera node within the target prediction period.

[0072] Predicted available energy represents the predicted energy available for use by the solar-powered camera nodes within the target prediction period. In some implementations, the predicted available energy can be directly derived from the predicted power generation; in other implementations, the predicted available energy can be modified by combining the current available power of the energy storage unit 114. For example, when the current power of the energy storage unit 114 is high, the predicted power generation and a portion of the available stored energy can be used together as the predicted available energy; when the power of the energy storage unit 114 is low, the proportion of energy available for the camera workload can be reduced to preserve the basic operating energy of the controller and communication module.

[0073] For example, if the current energy storage unit at a road intersection node has a low charge, but the predicted sunlight intensity for the next 30 minutes is strong, the system can determine that its predicted available energy is in a recoverable state. Conversely, if the current energy storage unit at a park boundary node has a sufficient charge, but the sunlight intensity for the next 30 minutes is significantly reduced, the system can consider its predicted available energy insufficient to support high-power consumption modes. This predicted available energy will serve as the input for determining the energy supply and demand status in step S40.

[0074] S40, determine the predicted energy demand of each solar camera node based on the camera workload, and determine the energy supply and demand status of each solar camera node based on the comparison result between the predicted available energy and the predicted energy demand.

[0075] After determining the predicted available energy, the system determines the predicted energy demand based on the camera workload and compares the predicted available energy with the predicted energy demand. This comparison result is used to determine the energy supply and demand status of the solar-powered camera nodes within the target prediction period and serves as the basis for subsequent distributed status acquisition, surplus / deficit matching, and power supply mode adjustment.

[0076] In existing single-node energy-saving control methods, the system may only reduce power consumption when the battery level is below a threshold, or only enter a low-power mode when the current power generation is low. This approach cannot fully reflect the supply and demand relationship within the target prediction period. This application, by comparing predicted available energy and predicted energy demand, can identify risks in advance before a node actually loses power or its power level becomes too low, and provides a basis for distributed coordinated adjustment.

[0077] refer to Figure 6 In one embodiment, step S40 includes the following sub-steps.

[0078] S410, determine the working mode and working duration of the corresponding solar camera node within the target prediction period based on the camera workload.

[0079] The operating mode can include at least one of the following: normal acquisition mode, low-power acquisition mode, continuous upload mode, intermittent upload mode, sleep mode, and supplementary lighting mode. The operating duration indicates the expected duration of the corresponding operating mode within the target prediction period. The system can determine the operating mode and operating duration based on the current camera workload, task execution status, task priority, and historical operating patterns.

[0080] For example, if a node at the entrance of a park is currently in real-time video uploading mode and has a high task priority, the system can predict that the node will need to maintain normal acquisition and continuous uploading mode for the next 30 minutes; if a node at the edge of a farmland is currently in periodic shooting mode, the system can predict that it will only need to be briefly awakened for shooting and intermittently uploaded for the next 30 minutes; if a node at night has its supplementary lighting on, its working mode within the target prediction period should also include supplementary lighting mode.

[0081] S420, determine the predicted power consumption within the target prediction period based on the operating mode and the operating duration.

[0082] Different operating modes correspond to different power consumption per unit time. The system can pre-store the mapping relationship between operating modes and power consumption per unit time, and calculate and predict power consumption based on the operating duration. For example, the power consumption per unit time in normal acquisition mode is higher than that in low-power acquisition mode, the communication power consumption in continuous upload mode is higher than that in intermittent upload mode, and the power consumption in supplementary lighting mode is higher than that in ordinary daytime acquisition mode.

[0083] In one example, the predicted power consumption can be calculated using the following formula: E_need_i=sum(P_mode_i_j*T_mode_i_j) Where E_need_i represents the predicted power consumption of the i-th solar camera node within the target prediction period, P_mode_i_j represents the power consumption per unit time of the i-th solar camera node in the j-th working mode, and T_mode_i_j represents the duration of the j-th working mode within the target prediction period.

[0084] For example, if a road node is expected to continuously collect data for 30 minutes and upload video for 20 minutes over the next 30 minutes, the system can calculate the power consumption for data collection and the power consumption for video upload separately, and then sum them to obtain the predicted power consumption. If the node also needs to turn on supplementary lighting at night, the power consumption of the supplementary lighting can also be included in the calculation as a working mode power consumption.

[0085] S430, the predicted power consumption is determined as the predicted energy demand, and the energy difference between the predicted available energy and the predicted energy demand is calculated.

[0086] The predicted energy demand represents the energy required for a node to maintain its projected operating mode within the target prediction period. The system compares the predicted available energy with the predicted energy demand to obtain the energy difference. In one example, the following formula can be used: DeltaE_i=E_avail_i-E_need_i Where DeltaE_i represents the energy difference of the i-th solar camera node, E_avail_i represents the predicted available energy, and E_need_i represents the predicted energy demand.

[0087] If DeltaE_i is significantly greater than 0, it indicates that the predicted available energy can cover the predicted energy demand, and the node may have an energy surplus. If DeltaE_i is significantly less than 0, it indicates that the predicted available energy is insufficient to support the predicted energy demand, and the node may have an energy deficit. If DeltaE_i is close to 0 or within a preset range, it indicates that the node is in a critical supply-demand state. This energy difference is the basis for subsequent state classification, triggering distributed communication, and surplus-deficit matching.

[0088] S440, determine the energy supply and demand status of the solar camera node based on the energy difference.

[0089] Energy supply and demand status can include energy surplus status, energy deficit status, and energy criticality status. This status classification is used to subsequently determine whether to request status updates from adjacent nodes and how to perform coordinated adjustments to power supply modes.

[0090] For example, if a target node's predicted available energy is slightly higher than its predicted energy requirement, but the difference is small, the node may enter an energy-deficient state if the actual future light intensity decreases slightly. The system classifies this as an energy critical state and uses subsequent steps to check if adjacent nodes have any energy reserves. As another example, if a high-priority node's predicted available energy is significantly lower than its predicted energy requirement, the system classifies this as an energy deficit state and, when the task priority condition is met, requests the status of adjacent nodes to determine if the target node's operation can be guaranteed by reducing the load on low-priority nodes.

[0091] In one embodiment, reference Figure 7, step S440 includes the following sub-steps.

[0092] S441, when the energy difference is greater than a first preset threshold, determine that the solar camera node is in an energy surplus state, and determine the part exceeding the first preset threshold as the energy surplus.

[0093] The first preset threshold T1 is used to distinguish between the energy surplus state and the energy critical state. When DeltaE_i > T1, it can be considered that the predicted available energy has a certain surplus relative to the predicted energy demand. The system can determine the part exceeding the first preset threshold as the energy surplus: E_surplus_i = DeltaE_i - T1, when DeltaE_i > T1 Where, E_surplus_i represents the energy surplus of the i-th solar camera node, and T1 represents the first preset threshold.

[0094] For example, if the predicted available energy of a certain adjacent node in the next 30 minutes is 120 units and the predicted energy demand is 80 units, and if the first preset threshold is 10 units, then its energy difference is 40 units, and the part exceeding the first preset threshold is 30 units. This node can be determined to have an energy surplus. Subsequently, this node can be used as an adjacent surplus node in the surplus and deficit matching.

[0095] S442, when the energy difference is less than a second preset threshold, determine that the solar camera node is in an energy gap state, and determine the part lower than the second preset threshold as the energy gap.

[0096] The second preset threshold T2 is used to distinguish between the energy gap state and the energy critical state. When DeltaE_i < T2, it can be considered that the predicted available energy of the node is not sufficient to support the predicted energy demand. The system can determine the part lower than the second preset threshold as the energy gap: E_gap_i = T2 - DeltaE_i, when DeltaE_i < T2 Where, E_gap_i represents the energy gap of the i-th solar camera node, and T2 represents the second preset threshold.

[0097] For example, if the predicted available energy of a node at the entrance of a certain park in the next 30 minutes is 50 units and the predicted energy demand is 85 units, and if the second preset threshold is 0, then its energy difference is -35 units, and the energy gap is 35 units. If the task priority of this node is relatively high, the system will generate a second status acquisition instruction carrying the energy gap and task priority in the subsequent steps.

[0098] S443, if the energy difference is between the second preset threshold and the first preset threshold, determine that the solar camera node is in an energy critical state.

[0099] When T2 <= DeltaE_i <= T1, the system determines that the node is in an energy critical state. An energy critical state indicates that the node's current predicted supply and demand relationship is unstable; it is neither suitable to directly assume sufficient margin nor a severe shortage. In this situation, if the system immediately reduces the node's workload, it may cause an unnecessary decrease in monitoring capabilities; if the system maintains its current mode, energy shortages may occur if illumination continues to decrease or the load increases. Therefore, an energy critical state is suitable for triggering the status acquisition of adjacent nodes to supplement the judgment.

[0100] For example, if a node in the middle of a road is predicted to have 90 units of available energy and a predicted energy demand of 85 units in the next 30 minutes, with a first preset threshold of 10 units and a second preset threshold of 0 units, then its energy difference is 5 units, which is within the critical range. The system can request energy supply and demand status feedback from neighboring nodes. If neighboring low-priority nodes have a large energy reserve, the system can maintain the current operating mode of that node; if surrounding nodes have no energy reserve, the system can appropriately reduce the power consumption of low-priority nodes.

[0101] S444, the energy surplus state, the energy deficit state, or the energy critical state are taken as the energy supply and demand state, and the energy surplus or the energy deficit is taken as the input for subsequent matching.

[0102] The system stores energy supply and demand status as part of the node status and can send it to neighboring nodes or gateways in distributed communication. Energy surplus and energy deficit are direct inputs for subsequent surplus-deficit matching. By retaining both status and values, the system can determine which state a node belongs to and compare the relative sizes of surplus and deficit among different nodes.

[0103] S50, when the difference between the predicted available energy and the predicted energy demand of the target solar camera node is within a preset range, or when the target solar camera node has an energy gap and the corresponding task priority meets the preset priority condition, the energy supply and demand status of the adjacent solar camera nodes is obtained through distributed communication between the solar camera nodes, and the matching result of the energy gap and energy surplus between the current node and the adjacent nodes is determined.

[0104] When the target solar camera node is in a critical energy state, or when there is an energy gap and the task priority meets the preset priority conditions, the system obtains the energy supply and demand status of adjacent solar camera nodes through distributed communication. The obtained energy supply and demand status of adjacent nodes is used to match the energy gap of the target node to determine the matching result between the current node and its adjacent nodes. The system may not obtain the status of adjacent nodes in all cases, but may trigger status acquisition only when the above conditions are met to reduce unnecessary communication overhead.

[0105] In real-world scenarios, the target solar-powered camera node can be any node that needs to perform a power supply mode determination. Adjacent solar-powered camera nodes can be nodes that are geographically close to the target node, have stable communication connections, are located in the same monitoring area, or are managed by the same gateway. For example, among nodes deployed along a road, adjacent nodes can be cameras that are adjacent to each other; among nodes deployed at the boundary of a park, adjacent nodes can be other cameras within the same section.

[0106] refer to Figure 8 In one embodiment, step S50 includes the following sub-steps.

[0107] S510, when the target solar camera node is in the energy critical state, a first state acquisition instruction is generated to confirm the energy reserve of adjacent nodes.

[0108] The first-state acquisition command is mainly used for supplementary judgments in critical states. When the target node is in an energy critical state, the system cannot reliably determine whether its workload should be reduced. If adjacent nodes have energy reserves, the system may tend to maintain the target node's current operating mode; if neither adjacent node has energy reserves, the system may reduce the power consumption of low-priority nodes.

[0109] For example, if the predicted supply-demand difference at a certain park perimeter wall node is in the critical range, and the node's task priority is medium, the system generates a first-state acquisition command, requesting feedback on the energy supply and demand status from the two adjacent nodes. If one of the adjacent nodes is in an energy surplus state and has a low task priority, the system can identify that adjacent node as a node to be reduced in energy consumption in subsequent steps, without immediately reducing the monitoring capability of the target node.

[0110] S520, when the target solar camera node is in the energy gap state and the corresponding task priority meets the preset priority condition, a second state acquisition instruction carrying the energy gap and the task priority is generated.

[0111] The second-state acquisition command is used for collaborative processing of high-priority gap nodes. Compared to the first-state acquisition command, the second-state acquisition command carries the energy gap and task priority, enabling adjacent nodes or gateways to know the gap size and guarantee requirements of the target node. The preset priority condition can be that the task priority reaches high priority, or reaches medium-high priority or above. This condition can be set according to actual monitoring needs.

[0112] For example, the main gate node in the park is responsible for monitoring key entrances and exits, and has a high task priority. Due to continuous cloudy weather and real-time data uploads, this node is in a state of energy deficit. The system generates a second-state acquisition command, carrying an energy deficit of 35 units and high task priority information. After receiving the command, adjacent nodes report their own energy reserves and task priorities. Based on this, the system determines whether to reduce the load of adjacent low-priority nodes to ensure the continued operation of the main gate node.

[0113] S530, in response to the first state acquisition instruction or the second state acquisition instruction, acquires the energy supply and demand status of adjacent solar camera nodes through distributed communication.

[0114] Distributed communication can be achieved through direct communication between nodes, or through edge gateways or server forwarding. Information fed back by neighboring nodes can include energy supply and demand status, energy surplus, energy deficit, task priority, power supply assurance level, or current operating mode. The system can filter valid feedback based on communication quality, node distance, and status update time.

[0115] For example, a target node sends a status acquisition command to its two adjacent nodes. If one node fails to respond in time due to poor communication quality, the system can use only the energy supply and demand status reported by the other node for matching; if multiple adjacent nodes report status, the system can prioritize the node with the larger energy reserve and lower task priority as the target for subsequent coordinated adjustment.

[0116] S540, the energy supply and demand status of the adjacent solar camera nodes is obtained as input to determine the matching result.

[0117] The system compares the energy deficit of the target node with the energy surplus of adjacent nodes to obtain a matching result. This matching result is used to generate subsequent energy allocation parameters. It should be noted that the matching result in this application is used to determine the coordinated adjustment method of the power supply mode, and does not require adjacent nodes to actually transmit electrical energy to the target node. For example, if an adjacent node has an energy surplus, it can indicate that the adjacent node has the conditions to reduce its own power consumption or maintain low load operation in the current cycle, thereby enabling the system as a whole to prioritize the target node.

[0118] In one embodiment, determining the matching result in step S50 includes the following sub-steps.

[0119] L1 determines the energy gap of the target solar camera node based on the energy supply and demand status of the target solar camera node.

[0120] When the target node is in an energy deficit state, the system can directly use the energy deficit obtained in step S442. When the target node is in an energy critical state, the system can use its proximity to the second preset threshold as the basis for judging potential deficits, or simply mark it as a node that needs confirmation from adjacent nodes. This processing method can adapt to scheduling strategies under different scenarios.

[0121] L2 determines the energy reserve of the adjacent solar camera nodes based on the energy supply and demand status of the adjacent solar camera nodes.

[0122] When adjacent nodes are in a state of energy surplus, the system can use the energy surplus obtained in step S441. When there are multiple adjacent nodes, the system can calculate the energy surplus of each adjacent node separately, or it can aggregate the energy surplus of multiple adjacent nodes. When selecting nodes to reduce energy consumption, the system can prioritize adjacent nodes with larger energy surplus and lower task priority.

[0123] For example, if the target node has a 35-unit energy deficit, adjacent node A has a 40-unit energy surplus and a low task priority, and adjacent node B has a 20-unit energy surplus but a high task priority, then the system can prioritize selecting adjacent node A to participate in the matching process, rather than prioritizing reducing the workload of adjacent node B.

[0124] L3, if the energy margin is not less than the energy gap, determines that the node and its neighboring nodes are perfectly matched.

[0125] A perfect match means that the energy margin of adjacent nodes is sufficient to cover the coordinated adjustment needs corresponding to the energy gap of the target node. For example, if the energy gap of the target node is 30 units and the energy margin of adjacent nodes is 45 units, then the system can be determined to be perfectly matched. In this case, the system can maintain or improve the power supply guarantee level of the target node and appropriately reduce the power consumption of adjacent nodes according to task priority.

[0126] L4 determines that the node and its neighboring nodes are partially matched when the energy margin is less than the energy gap but greater than zero.

[0127] Partial matching indicates that adjacent nodes have some energy margin, but it is insufficient to fully cover the collaborative needs corresponding to the energy gap of the target node. For example, if the target node's energy gap is 50 units and the adjacent nodes have an energy margin of 20 units, the system can determine that it is a partial match. In this case, the system can maintain the power supply guarantee level of the target node while reducing the power consumption level of lower priority nodes to alleviate the overall energy pressure.

[0128] L5, if there is no energy reserve in the adjacent solar camera node, determines that there is a mismatch between this node and the adjacent node.

[0129] A mismatch indicates that adjacent nodes cannot provide sufficient energy margin for collaborative decision-making. For example, if the target node has an energy deficit, while surrounding nodes are either in a critical or deficit state, the system determines this as a mismatch. In this case, the system can prioritize reducing the power consumption level of lower-priority nodes to prevent the distributed monitoring network as a whole from continuously operating at high power consumption.

[0130] S60, based on the task priority and the matching result, generate energy allocation parameters and power supply mode adjustment parameters for adjusting the power supply mode of each solar camera node, and perform coordinated adjustment of the power supply mode of multiple solar camera nodes according to the energy allocation parameters and the power supply mode adjustment parameters, and output the corresponding power supply mode adjustment result.

[0131] After obtaining the task priority and matching results, the system generates energy allocation parameters and power supply mode adjustment parameters. The energy allocation parameters indicate the direction of power supply protection or power consumption reduction for each solar camera node in the coordinated adjustment of power supply modes. The power supply mode adjustment parameters control the operating mode, power consumption level, or operating cycle of the corresponding node. The system applies the power supply mode adjustment parameters to the corresponding solar camera node to adjust the node's power supply mode.

[0132] refer to Figure 9 In one embodiment, step S60 includes the following sub-steps.

[0133] S610, determine the power supply guarantee level of each solar camera node according to the task priority.

[0134] Power supply assurance level indicates the degree to which the system guarantees the continuity of node operation during power supply mode coordination adjustments. High task priority nodes correspond to high power supply assurance levels, medium task priority nodes correspond to medium power supply assurance levels, and low task priority nodes correspond to low power supply assurance levels. The power supply assurance level can affect whether the node maintains its current operating mode, whether its power consumption level is reduced, and whether it is designated as a node to be reduced in power consumption.

[0135] For example, nodes at the main entrance of the park, road intersections, or key areas can have a higher power supply guarantee level due to their higher task priority; nodes in the middle of ordinary roads or low-frequency inspection nodes can have a lower power supply guarantee level. When the distributed network is under energy pressure, the system prioritizes maintaining the working mode of nodes with high guarantee levels and prioritizes reducing the power consumption of nodes with low guarantee levels.

[0136] S620, if the matching result is a perfect match, generate energy distribution parameters for maintaining or improving the power supply guarantee level of the target solar camera node.

[0137] When the matching result is a perfect match, it indicates that there is sufficient energy margin among adjacent nodes to support coordinated adjustment. The system can generate energy allocation parameters to maintain the target node's current operating mode or, when necessary, increase its power supply guarantee level. Increasing the power supply guarantee level here can manifest as maintaining normal acquisition frequency, maintaining normal upload frequency, avoiding entering low-power mode, or shortening sleep time, rather than necessarily increasing physical power consumption.

[0138] For example, if the target node is the park entrance node with an energy deficit of 25 units, and adjacent low-priority nodes have a 40-unit energy surplus, the system can determine a perfect match and generate energy allocation parameters. This allows the target node to continue real-time data acquisition and normal uploading, while simultaneously implementing slight energy reduction measures for adjacent low-priority nodes, such as reducing their upload frequency.

[0139] S630, if the matching result is a partial match, generate energy allocation parameters for maintaining the power supply guarantee level of the target solar camera node and reducing the power consumption level of the low-priority solar camera node.

[0140] When the matching result is a partial match, it indicates that adjacent nodes have energy reserves, but these reserves are insufficient to completely cover the energy gap of the target node. The system can maintain the power supply guarantee level of the target node while implementing more explicit power reduction control for low-priority nodes. For example, reducing the sampling frequency, shortening the real-time upload duration, or extending the sleep cycle. This strategy is suitable for situations where high-priority target nodes need to be guaranteed, but the overall network energy reserves are limited.

[0141] For example, the target node is an accident-prone intersection with high task priority and an energy deficit of 50 units; the adjacent ordinary road segment nodes have a surplus of 20 units. The system determines it to be a partial match and maintains normal data collection at the target node, while adjusting the data upload of the adjacent ordinary road segment nodes from real-time to periodic, thereby reducing overall energy consumption.

[0142] S640, if the matching result is a mismatch, generate energy allocation parameters for reducing the power consumption level of low-priority solar camera nodes.

[0143] When the matching result is a mismatch, it means that there is no energy margin available for matching among adjacent nodes. In this case, the system can reduce the power consumption level of low-priority nodes, thereby reducing the overall energy pressure on the network. If the target node is a high-task-priority node, the system can still maintain its current operating mode as much as possible; if the target node itself has a low priority, the system can also reduce its unnecessary workload according to the actual strategy.

[0144] For example, during periods of continuous rain, multiple nodes may be in a state of insufficient energy or near-critical operation. The system can prioritize powering high-priority nodes such as those at park entrances and major road intersections, while reducing the data collection frequency of nodes in ordinary areas or extending their sleep cycles. This approach prevents low-priority nodes from consuming excessive energy and affecting the overall network stability.

[0145] S650, determine the power supply mode adjustment parameters corresponding to each solar camera node according to the energy distribution parameters, adjust the power supply mode of the corresponding solar camera node according to the power supply mode adjustment parameters, and output the power supply mode adjustment result.

[0146] The power supply mode adjustment parameters may include the acquisition frequency adjustment value, data upload frequency adjustment value, sleep cycle adjustment value, power consumption level adjustment value, and operating mode hold command or operating mode switching command. The system sends the power supply mode adjustment parameters to the controller 113 of the corresponding node, and the controller 113 adjusts the operating status of the camera 112, communication module 115, or other power-consuming components. The power supply mode adjustment result may include node identifier, operating mode before adjustment, operating mode after adjustment, adjustment time, power supply guarantee level, and power consumption level.

[0147] In one embodiment, step S650 includes the following sub-steps.

[0148] H1, based on the energy allocation parameters, determines the target solar camera node to be protected and the adjacent solar camera nodes to have their energy consumption reduced from multiple solar camera nodes.

[0149] The target node to be protected is typically a node in an energy deficit or critical energy state with a high task priority. Adjacent nodes to be reduced in energy consumption are typically nodes with lower task priority, energy reserves, or whose workload can be reduced. By identifying the nodes to be protected and the nodes to be reduced in energy consumption, the system can avoid performing the same adjustments on all nodes, thus achieving differentiated control.

[0150] For example, among five nodes deployed along a road, the third node is located at an intersection and has a high task priority, while the second and fourth nodes are ordinary road segment nodes. If the third node has an energy shortage, while the second node has an energy surplus and a low priority, the system can designate the third node as the node to be guaranteed and the second node as the node to have its energy consumption reduced.

[0151] H2, when the power supply guarantee level of the target solar camera node is higher than that of the adjacent solar camera node, generates a first power supply mode adjustment parameter for maintaining the current working mode of the target solar camera node.

[0152] The first power supply mode adjustment parameters are used for the target node. Its function can be to maintain the target node's current acquisition frequency, current upload frequency, or current operating mode. For example, when the target node is performing a high-priority real-time monitoring task, the system can avoid switching it to a low-power mode and instead reduce overall energy consumption by adjusting adjacent low-priority nodes.

[0153] For example, the park entrance node is acquiring and uploading video at 25 frames per second in real time, and its power supply guarantee level is higher than that of the adjacent perimeter wall node. The system generates the first power supply mode adjustment parameters to ensure that the entrance node continues to maintain its current acquisition and upload status. These parameters may include control options such as "maintain current acquisition frequency," "maintain current upload cycle," and "do not enter sleep mode."

[0154] H3 generates a second power supply mode adjustment parameter for reducing the workload of the adjacent solar camera nodes, wherein the reduction of workload includes at least one of reducing the acquisition frequency, reducing the data upload frequency, or extending the sleep cycle.

[0155] The second power supply mode adjusts parameters for adjacent low-priority nodes. Reducing workload can be achieved in several ways. For example, lowering the acquisition frequency can reduce the video frame rate from 25 frames per second to 15 frames per second or lower; lowering the data upload frequency can adjust real-time uploads to uploads every few minutes; extending the sleep cycle can increase the node's sleep time within the target prediction period. These methods can be implemented individually or in combination.

[0156] For example, in a farmland monitoring scenario, a certain adjacent node is only used for periodic observation of crop growth, and its task priority is low. The system can adjust the node's data collection frequency from once every 5 minutes to once every 10 minutes, and change the data upload from real-time upload to centralized upload. For nodes on ordinary road sections, the system can reduce their video frame rate while retaining necessary monitoring capabilities. For nodes in low-risk boundary areas, the system can extend their sleep cycle to reduce power consumption.

[0157] H4, adjust the power supply mode of the target solar camera node according to the first power supply mode adjustment parameter, and adjust the power supply mode of the adjacent solar camera node according to the second power supply mode adjustment parameter.

[0158] The system sends the first and second power supply mode adjustment parameters to the corresponding nodes. The target node maintains its current operating mode according to the first power supply mode adjustment parameters, while adjacent nodes reduce their workload according to the second power supply mode adjustment parameters. After adjustment, each node can return its adjusted operating status, such as the current acquisition frequency, upload frequency, sleep cycle, power consumption level, and energy storage unit capacity. The system can use these operating statuses as input for the next acquisition cycle, ensuring that subsequent power supply mode adjustments continue to be based on the latest node status.

[0159] For example, in a continuous cloudy day scenario, the system reduces the upload frequency of adjacent low-priority nodes within a cycle. In the next acquisition cycle, if the lighting recovers and the energy margin of adjacent nodes increases, the system can gradually restore their upload frequency; if the lighting continues to decrease, the system can further extend the sleep cycle of low-priority nodes. Through periodic acquisition, prediction, judgment, matching, and adjustment, the distributed monitoring network can operate dynamically according to changes in lighting and load.

[0160] In summary, the distributed collaborative management method for solar cameras based on dynamic energy allocation provided by this invention incorporates the power supply status, energy consumption status, ambient light changes, and inter-node collaborative status of solar camera nodes into a single processing procedure. The system first acquires the solar panel power generation efficiency, camera workload, and ambient light intensity of each solar camera node. Then, based on an ambient light intensity prediction model, it determines the light prediction results for the target prediction period and, combined with the solar panel power generation efficiency, determines the predicted available energy. Simultaneously, the system determines the predicted energy demand based on the camera workload and judges the energy supply and demand status by comparing the predicted available energy with the predicted energy demand. Therefore, the system can simultaneously evaluate the node's operational capability within the target prediction period from both the power supply and energy consumption sides, avoiding the judgment lag caused by power supply control based solely on current power consumption or current power generation status.

[0161] Building upon the aforementioned supply and demand forecasting, this invention further introduces task priority and the energy supply and demand status of adjacent nodes as the basis for coordinated adjustment. When the difference between the predicted available energy and the predicted energy demand of the target solar camera node is within a preset range, or when the target solar camera node has an energy gap and the corresponding task priority meets a preset priority condition, the system obtains the energy supply and demand status of adjacent solar camera nodes through distributed communication and determines the matching result based on the energy gap of the target node and the energy surplus of adjacent nodes. This processing enables the system to no longer perform isolated energy reduction control on a single node, but rather to determine the basis for coordinated adjustment of the power supply mode based on the energy surplus and deficit relationship between the target node and adjacent nodes, thereby improving the coordination of energy management among nodes in the distributed monitoring network.

[0162] This invention generates energy allocation parameters and power supply mode adjustment parameters based on task priority and matching results, and applies them to multiple solar camera nodes. For target nodes with high task priority and energy shortages or in a critical energy state, the system can maintain their current operating mode or improve their power supply guarantee level. For adjacent nodes with lower task priority and lower power consumption, the system can reduce their acquisition frequency, reduce data upload frequency, or extend their sleep cycle. Therefore, power supply mode adjustment is no longer a single-node power reduction process under a fixed threshold, but a differentiated control based on predicted supply and demand, task priority, and the surplus / deficit matching results of adjacent nodes, enabling the continuous operation capability of high-priority nodes and the power consumption control of low-priority nodes to be mutually matched.

[0163] Through the aforementioned complete processing, this invention improves the foresight and targeting of power supply mode adjustments in distributed monitoring scenarios characterized by changes in illumination, partial occlusion, differences in task load, and uneven node energy states. The system utilizes illumination prediction results to anticipate energy supply changes within the target prediction period, uses camera workload to reflect energy consumption demands within the target prediction period, uses task priority to determine the power supply guarantee sequence for different nodes, and uses the energy supply and demand status of adjacent nodes to determine the range of coordinated power supply mode adjustments. This addresses the problems in existing technologies where control is based solely on the current state of a single node, resulting in insufficient power supply guarantees for high-priority nodes, continuous high power consumption for low-priority nodes, and incoordination between node operating states.

[0164] Furthermore, the energy allocation parameters in this invention characterize the direction of protection, the direction of energy saving, or the adjustment range of each solar camera node in the coordinated adjustment of power supply modes, without requiring physical power transmission between nodes. Therefore, this solution is applicable to solar camera networks without inter-node power transmission lines, and can also achieve coordinated management through inter-node communication, gateways, or servers. The power supply mode adjustment parameters can directly affect the camera node's acquisition frequency, data upload frequency, sleep cycle, or power consumption level, enabling the technical solution to be implemented in specific device control actions, thus possessing good engineering feasibility.

[0165] During periodic operation, the adjusted operating status of the solar camera nodes can also serve as input for the next acquisition cycle. As ambient light intensity, solar panel power generation efficiency, camera workload, and the energy supply and demand status of adjacent nodes change, the system can continuously update the predicted available energy, predicted energy demand, matching results, and power supply mode adjustment parameters. Therefore, this invention enables a distributed solar camera network to maintain dynamic adaptation under complex lighting conditions and different workloads, improving the reliability of continuous operation and collaborative management of multiple solar camera nodes.

[0166] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application are still within the scope of this application.

Claims

1. A distributed collaborative management method for solar-powered cameras based on dynamic energy allocation, characterized in that, The method, applied to a distributed monitoring network comprising multiple solar-powered camera nodes, includes: Obtain the solar panel power generation efficiency, camera workload, and ambient light intensity of each solar camera node; Based on the camera workload, identify the task priority corresponding to each solar camera node; The ambient light intensity is input into the ambient light intensity prediction model to obtain the light prediction result, and the predicted available energy of each solar camera node is determined based on the power generation efficiency of the solar panel and the light prediction result. The predicted energy demand of each solar camera node is determined based on the camera workload, and the energy supply and demand status of each solar camera node is determined based on the comparison between the predicted available energy and the predicted energy demand. When the difference between the predicted available energy and the predicted energy demand of the target solar camera node is within a preset range, or when the target solar camera node has an energy gap and the corresponding task priority meets the preset priority condition, the energy supply and demand status of the adjacent solar camera nodes is obtained through distributed communication between the solar camera nodes, and the matching result of the energy gap and energy surplus between the current node and the adjacent nodes is determined. Based on the task priority and the matching result, energy allocation parameters and power supply mode adjustment parameters are generated for adjusting the power supply mode of each solar camera node. According to the energy allocation parameters and the power supply mode adjustment parameters, the power supply mode of multiple solar camera nodes is adjusted in a coordinated manner, and the corresponding power supply mode adjustment result is output.

2. The method according to claim 1, characterized in that, The acquisition of the solar panel power generation efficiency, camera workload, and ambient light intensity for each solar camera node includes: Obtain the power output information of the solar panel during the current data collection period; The power generation efficiency of the solar panel is determined based on the power output information and the rated power generation information of the solar panel. Acquire the working status information of the camera within the current acquisition period, and determine the workload of the camera based on the working status information; The ambient light intensity at the location corresponding to the solar camera node is obtained, and the power generation efficiency of the solar panel, the workload of the camera, and the ambient light intensity are used as node status information in the same acquisition cycle.

3. The method according to claim 1, characterized in that, The step of identifying the task priority corresponding to each solar camera node based on the camera workload includes: Extract the task execution status and working continuity status of the camera from the camera workload; The load level of the corresponding solar camera node is determined based on the task execution status and the working continuity status. Determine the task priority corresponding to the solar camera node according to the load level; The task priority is used as a constraint for the subsequent generation of energy allocation parameters and power supply mode adjustment parameters.

4. The method according to claim 1, characterized in that, The step of inputting the ambient light intensity into the ambient light intensity prediction model to obtain the light prediction result, and determining the predicted available energy of each solar camera node based on the solar panel power generation efficiency and the light prediction result, includes: Input the ambient light intensity within the current collection period into the ambient light intensity prediction model to obtain the light prediction result within the target prediction period; Based on the illumination prediction results, determine the predicted illumination intensity change information within the target prediction period; Based on the predicted light intensity change information and the power generation efficiency of the solar panel, the predicted power generation within the target prediction period is determined; The predicted power generation is determined as the predicted available energy of the corresponding solar camera node within the target prediction period.

5. The method according to claim 4, characterized in that, The step of determining the predicted energy demand of each solar camera node based on the camera workload, and determining the energy supply and demand status of each solar camera node based on the comparison between the predicted available energy and the predicted energy demand, includes: The working mode and duration of the corresponding solar camera node within the target prediction period are determined based on the camera workload. The predicted power consumption within the target prediction period is determined based on the operating mode and the operating duration. The predicted power consumption is determined as the predicted energy demand, and the energy difference between the predicted available energy and the predicted energy demand is calculated. The energy supply and demand status of the solar camera node is determined based on the energy difference.

6. The method according to claim 5, characterized in that, The step of determining the energy supply and demand status of the solar camera node based on the energy difference includes: If the energy difference is greater than a first preset threshold, the solar camera node is determined to be in an energy surplus state, and the portion exceeding the first preset threshold is determined to be the energy surplus. If the energy difference is less than the second preset threshold, the solar camera node is determined to be in an energy deficit state, and the portion below the second preset threshold is determined to be an energy deficit. If the energy difference is between the second preset threshold and the first preset threshold, the solar camera node is determined to be in an energy critical state. The energy surplus state, the energy deficit state, or the energy critical state are taken as the energy supply and demand state.

7. The method according to claim 6, characterized in that, When the difference between the predicted available energy and the predicted energy demand of the target solar camera node is within a preset range, or when the target solar camera node has an energy gap and the corresponding task priority meets a preset priority condition, the energy supply and demand status of adjacent solar camera nodes is obtained through distributed communication between solar camera nodes, including: When the target solar camera node is in the energy critical state, a first state acquisition command is generated to confirm the energy reserve of adjacent nodes. When the target solar camera node is in the energy gap state and the corresponding task priority meets the preset priority condition, a second state acquisition instruction carrying the energy gap and the task priority is generated. In response to the first state acquisition instruction or the second state acquisition instruction, the energy supply and demand status of adjacent solar camera nodes is acquired through distributed communication. The energy supply and demand status of adjacent solar camera nodes is used as input to determine the matching result.

8. The method according to claim 7, characterized in that, The determination of the matching results of energy gap and energy margin between this node and its neighboring nodes includes: The energy gap of the target solar camera node is determined based on the energy supply and demand status of the target solar camera node; The energy reserve of the adjacent solar camera nodes is determined based on the energy supply and demand status of the adjacent solar camera nodes. If the energy margin is not less than the energy gap, the node is determined to be a perfect match with its neighboring nodes. If the energy margin is less than the energy gap but greater than zero, it is determined that there is a partial match between this node and its neighboring nodes. If there is no energy reserve between the adjacent solar camera nodes, it is determined that there is a mismatch between this node and the adjacent node.

9. The method according to claim 8, characterized in that, The process involves generating energy allocation parameters and power supply mode adjustment parameters for adjusting the power supply mode of each solar camera node based on the task priority and the matching result, and then coordinating the power supply mode adjustment of multiple solar camera nodes according to the energy allocation parameters and the power supply mode adjustment parameters, outputting the corresponding power supply mode adjustment result, including: The power supply guarantee level of each solar camera node is determined based on the task priority. If the matching result is a perfect match, energy allocation parameters are generated to maintain or improve the power supply guarantee level of the target solar camera node. In the case where the matching result is a partial match, energy allocation parameters are generated to maintain the power supply guarantee level of the target solar camera node and reduce the power consumption level of low-priority solar camera nodes. If the matching result is a mismatch, energy allocation parameters are generated to reduce the power consumption level of low-priority solar camera nodes; The power supply mode adjustment parameters for each solar camera node are determined based on the energy distribution parameters, and the power supply mode of the corresponding solar camera node is adjusted according to the power supply mode adjustment parameters, and the power supply mode adjustment result is output.

10. The method according to claim 9, characterized in that, The step of determining the power supply mode adjustment parameters for each solar camera node based on the energy distribution parameters, and adjusting the power supply mode of the corresponding solar camera node according to the power supply mode adjustment parameters, includes: Based on the energy allocation parameters, the target solar camera node to be protected and the adjacent solar camera nodes to have their energy consumption reduced are determined from multiple solar camera nodes. If the power supply guarantee level of the target solar camera node is higher than that of the adjacent solar camera node, a first power supply mode adjustment parameter is generated to maintain the current working mode of the target solar camera node. A second power supply mode adjustment parameter is generated to reduce the workload of the adjacent solar camera nodes, wherein reducing the workload includes at least one of reducing the acquisition frequency, reducing the data upload frequency, or extending the sleep cycle; The power supply mode of the target solar camera node is adjusted according to the first power supply mode adjustment parameter, and the power supply mode of the adjacent solar camera node is adjusted according to the second power supply mode adjustment parameter.

Images