A solar-powered internet of things detection device and detection method

Abstract

The application relates to the technical field of Internet of Things detection, in particular to an Internet of Things detection device and method based on solar power supply, which comprises a power supply unit, an image acquisition unit configured to acquire environmental images of the surroundings of the device, an image processing unit configured to configure the operation mode of the image processing unit as a sleep state or a working state according to the image pixel variation degree, and detect abnormal events in the surroundings based on an abnormality identification model when the image processing unit is in the working state, an adjustment analysis unit configured to determine whether to perform temperature control adjustment based on whether the power supply unit is in a deterioration state and whether the image processing unit is in a time sequence disorder state under abnormal temperature conditions, and a temperature control adjustment unit configured to determine a temperature control mode according to a solar power supply constraint state and an image processing load rate in the temperature control adjustment; and a warning unit. The application can improve the safety of the device in an outdoor extreme temperature environment.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) detection technology, and in particular to an IoT detection device and method based on solar power. Background Technology

[0002] With the deep integration of IoT and renewable energy technologies, solar-powered IoT monitoring equipment has been widely applied in professional scenarios such as outdoor environmental monitoring, security surveillance, industrial inspection, and agricultural IoT. Solar energy, as a clean and renewable energy source, combined with the low-power design of IoT devices, frees monitoring terminals from dependence on mains power cabling, enabling long-term deployment in complex environments such as remote areas, temporary locations, and hazardous zones. However, existing solar-powered IoT monitoring equipment still faces challenges in long-term operation under all-weather outdoor conditions, including a significant contradiction between miniaturization and power supply stability, difficulty in guaranteeing performance under extreme outdoor temperatures, and a power consumption conflict between continuous video monitoring and limited energy supply. These issues result in poor operational reliability, real-time performance, and accuracy of anomaly detection under all-weather conditions. Therefore, how to improve the safe operation of equipment in extreme outdoor temperatures, which pose a serious threat to its safety, is a technical problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN117353436A discloses a solar power supply system based on IoT monitoring, including: a monitoring platform, a data acquisition unit, an operation power supply unit, a line monitoring unit, a damage monitoring unit, an operation and maintenance management unit, and a power supply status unit. It collects power supply data from solar energy storage batteries and performs safety power supply monitoring and evaluation analysis to ensure solar power supply efficiency and safety. Furthermore, it uses information feedback to evaluate the power transmission data of the solar energy storage batteries, ensuring the safety of the solar energy storage battery power supply line and reducing abnormal line losses, thus improving the utilization rate of the solar energy storage battery power supply. It also comprehensively evaluates and analyzes the power supply status from both the solar energy storage battery itself and the line. However, the above technical solution has the following problems: it lacks environmental image acquisition and environmental anomaly detection functions; it does not address internal temperature regulation to ensure battery safety and processor performance under outdoor all-weather conditions; and it is difficult to achieve continuous and stable monitoring under outdoor all-weather conditions on a miniaturized self-powered terminal. Summary of the Invention

[0004] To address this, the present invention provides an IoT detection device and method based on solar power, which overcomes the problems of existing technologies lacking environmental image acquisition and environmental anomaly detection functions, not addressing internal temperature regulation to ensure battery safety and processor performance under all outdoor climate conditions, and making it difficult to achieve continuous and stable monitoring under all outdoor climate conditions on miniaturized self-powered terminals.

[0005] To achieve the above objectives, the present invention provides a solar-powered Internet of Things (IoT) detection device, comprising: The power supply unit stores the electrical energy generated by the flexible solar panels; An image acquisition unit, which is connected to the power supply unit, is used to acquire environmental images of the environment surrounding the device; An image processing unit is connected to the power supply unit and the image acquisition unit respectively, and is used to configure the operation mode of the image processing unit to be a sleep state or a working state according to the image pixel change degree, and when in the working state, it detects abnormal events in the surrounding environment based on an anomaly recognition model. The adjustment and analysis unit is connected to the power supply unit and is used to determine whether the device is under abnormal temperature conditions based on the device temperature. Under abnormal temperature conditions, it determines whether to perform temperature control adjustment based on whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. The degradation state is determined based on the risk of thermal runaway and voltage consistency deviation, and the timing disorder state is determined based on the setup time margin and the hold time margin. A temperature control unit is connected to the power supply unit, the image acquisition unit, the image processing unit, and the adjustment and analysis unit, respectively. It is used to determine whether the temperature control method is active temperature control or passive temperature control in the temperature control process based on the solar power supply constraint state and the image processing load rate. An early warning unit, which is connected to the power supply unit and the image processing unit respectively, is used to issue an early warning when an abnormal event is detected in the surrounding environment.

[0006] Furthermore, in response to the condition that the image pixel change is greater than or equal to a preset image pixel change, the image processing unit configures its operating mode to a working state. The image processing unit is configured to operate in a sleep mode when the image pixel change is less than a preset image pixel change.

[0007] Furthermore, the adjustment and analysis unit determines that the device temperature is abnormal when the device temperature is greater than the preset device temperature.

[0008] Furthermore, the adjustment and analysis unit determines to perform temperature control adjustment when the power supply unit is in a degraded state or the image processing unit is in a time-disordered state.

[0009] Furthermore, the degradation state is defined as a thermal runaway risk level greater than a preset thermal runaway risk level or a voltage consistency deviation greater than a preset voltage consistency deviation.

[0010] Furthermore, the timing disorder state refers to the setup time margin or hold time margin of the data transmitted by the image processing unit to the image acquisition unit being lower than zero.

[0011] Furthermore, the temperature control unit responds to the condition that it is under solar power supply constraint and the image processing load rate is greater than or equal to the preset image processing load rate, and determines to perform active temperature regulation. In active temperature regulation, the temperature regulation unit controls the image processing unit to switch from working state to sleep state, and controls the image acquisition unit to reduce the acquisition frame rate to a preset acquisition frame rate.

[0012] Furthermore, the temperature control unit determines to perform passive temperature regulation when it is not under solar power supply constraint or when the image processing load rate is less than the preset image processing load rate.

[0013] Furthermore, the solar power supply constraint state is when the light intensity is less than a preset light intensity or the battery power is less than a preset battery power.

[0014] This invention also provides a detection method for a solar-powered Internet of Things (IoT) detection device, comprising: Step S1: Acquire environmental images of the surrounding environment of the device through the image acquisition unit; Step S2: The image processing unit configures its operating mode to either sleep or working state based on the image pixel variation. Step S3: When the image processing unit is in working state, it detects abnormal events in the surrounding environment based on the anomaly recognition model, and issues an early warning through the early warning unit when an abnormal event is detected. Step S4: The adjustment and analysis unit determines whether the equipment is under abnormal temperature conditions based on the equipment temperature. Step S5: When it is determined that the abnormal temperature condition is being met, the adjustment and analysis unit assesses whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. Step S6: If the power supply unit is in a degraded state or the image processing unit is in a time-disordered state, the adjustment and analysis unit determines to perform temperature control adjustment. Step S7: After determining that temperature control adjustment is required, the temperature control adjustment unit determines whether the temperature control method is active temperature adjustment or passive temperature adjustment based on the solar power supply constraint state and the image processing load rate.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: In the technical solution of the present invention, the image processing unit is dynamically switched between working and dormant states by comparing the image pixel change degree with a preset threshold, thereby reducing ineffective power consumption from the source. Then, the power supply unit degradation state is characterized by thermal runaway risk degree and voltage consistency deviation, and the timing disorder state of the image processing unit is determined by combining the setup time margin and the hold time margin, thereby achieving accurate identification of fault risk under abnormal temperature conditions. Finally, based on the solar power supply constraint state and the image processing load rate, the active temperature regulation or passive temperature regulation mode is intelligently selected to achieve coordinated optimization of equipment thermal management and power supply under limited conditions.

[0016] Furthermore, this invention determines the degree of dynamic change in the surrounding environment of the device by the image pixel change degree, and configures the image processing unit to a working state or a sleep state accordingly. This avoids the image processing unit from continuously performing invalid anomaly recognition calculations when the environment is static for a long time, and significantly reduces the average power consumption of the device under unattended conditions in the field. At the same time, the image pixel change degree is taken as the larger value of the change degree corresponding to each miniature camera, and the acquisition frame rate is dynamically adjusted based on the user's demand for real-time response. This is conducive to achieving an adaptive balance between detection sensitivity and energy consumption, and extending the sustainable operating time of the device under solar power supply conditions.

[0017] Furthermore, this invention uses both thermal runaway risk and voltage consistency deviation to determine whether the power supply unit is in a deteriorated state. This allows for a comprehensive assessment of the power supply unit's health status from two dimensions: thermal safety risk and electrochemical consistency degradation. This avoids misjudgment of a single indicator leading to false or missed triggering of temperature control regulation, thereby improving the accuracy and reliability of the power supply unit's safety protection under abnormal temperature conditions.

[0018] Furthermore, by establishing and maintaining time margins to determine whether the image processing unit is in a state of timing disorder, this invention can promptly identify the risk state of potential data transmission errors when abnormal device temperature causes clock drift, signal delay, or level shift, thereby triggering temperature control intervention. This prevents the image processing unit from misidentifying or losing data due to timing disorder, ensuring the correctness and stability of the abnormal event detection function.

[0019] Furthermore, in this invention, the temperature control unit determines the temperature control method as active or passive temperature regulation based on the solar power supply constraint status and the image processing load rate. When under solar power supply constraint and with a high image processing load rate, active temperature regulation is executed by switching the image processing unit to a sleep state and reducing the frame rate of the image acquisition unit to reduce the heat load generated by the device itself, achieving cooling without additional power consumption. When not under power supply constraint or with a low image processing load rate, passive temperature regulation is executed by using a semiconductor cooling chip to achieve forced cooling, which is beneficial for quickly reducing the internal temperature of the device when the power supply is sufficient, improving the environmental adaptability and operational reliability of the device in complex outdoor environments. Attached Figure Description

[0020] Figure 1 This is a unit connection diagram of an IoT detection device based on solar power according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating how the image processing unit configures its operating mode based on the image pixel variation, according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating how the temperature control method is determined based on the solar power supply constraint state and image processing load rate in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an IoT detection device based on solar power according to an embodiment of the present invention; In the image: 1. Outer shell; 2. Flexible solar panel; 3. Miniature camera; 4. Semiconductor cooling chip. Figure 5 This is a flowchart of an IoT detection device based on solar power, according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] Please see Figure 1 The diagram shown is a module connection diagram of an IoT detection device based on solar power according to an embodiment of the present invention. The present invention provides an IoT detection device based on solar power, comprising: The power supply unit stores the electrical energy generated by the flexible solar panels; An image acquisition unit, which is connected to the power supply unit, is used to acquire environmental images of the environment surrounding the device; An image processing unit is connected to the power supply unit and the image acquisition unit respectively, and is used to configure the operation mode of the image processing unit to be a sleep state or a working state according to the image pixel change degree, and when in the working state, it detects abnormal events in the surrounding environment based on an anomaly recognition model. The adjustment and analysis unit is connected to the power supply unit and is used to determine whether the device is under abnormal temperature conditions based on the device temperature. Under abnormal temperature conditions, it determines whether to perform temperature control adjustment based on whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. The degradation state is determined based on the risk of thermal runaway and voltage consistency deviation, and the timing disorder state is determined based on the setup time margin and the hold time margin. A temperature control unit is connected to the power supply unit, the image acquisition unit, the image processing unit, and the adjustment and analysis unit, respectively. It is used to determine whether the temperature control method is active temperature control or passive temperature control in the temperature control process based on the solar power supply constraint state and the image processing load rate. An early warning unit, which is connected to the power supply unit and the image processing unit respectively, is used to issue an early warning when an abnormal event is detected in the surrounding environment.

[0024] Please see Figure 2 As shown, it is a flowchart of configuring the operating mode of the image processing unit according to the image pixel change degree in an embodiment of the present invention. In this embodiment of the present invention, the image processing unit configures the operating mode of the image processing unit to be working state in response to the condition that the image pixel change degree is greater than or equal to the preset image pixel change degree. The image processing unit is configured to operate in a sleep mode when the image pixel change is less than a preset image pixel change.

[0025] In this embodiment of the invention, a continuous cyclical monitoring cycle is set. The greater the user's real-time response requirement for abnormal event detection, the shorter the duration of a single monitoring cycle. Those skilled in the art can adaptively set the duration of the monitoring cycle according to the specific requirements for real-time response in actual application scenarios. This embodiment of the invention does not impose any restrictions on this.

[0026] In this embodiment of the invention, the image processing unit further includes a variability determination module, which is used to determine the variability of image pixels. The variability of image pixels is used to determine the degree of dynamic change occurring in the environment surrounding the device. Specifically, the variability of image pixels is the larger value among the variability values ​​corresponding to each miniature camera. The variability corresponding to a single camera is the standard deviation of the mean pixel value of each environmental image captured by the miniature camera within the current monitoring period, and the mean pixel value of a single environmental image is the average gray value of each pixel in the image. The acquisition frame rate of each camera within a single monitoring cycle is determined by the image processing unit based on the real-time response requirements for abnormal event detection. The higher the real-time response requirements, the larger the value of the acquisition frame rate. Those skilled in the art can adaptively set the specific value of the acquisition frame rate according to the specific real-time response requirements of the actual application scenario. This embodiment does not make specific limitations in this regard.

[0027] It should be noted that each environmental image needs to be preprocessed by the image processing unit, and the preprocessing includes, but is not limited to, noise reduction, illumination normalization and grayscale processing.

[0028] In this embodiment of the invention, the greater the image pixel change, the more intense the motion or change in the environment; the smaller the image pixel change, the more static the environment. Therefore, the greater the requirement for sensitivity in detecting abnormal events, the smaller the value of the preset image pixel change. Specifically, the image pixel change corresponding to each abnormal event detected in the historical monitoring period is statistically analyzed, the average value of the image pixel change corresponding to each abnormal event is calculated, and recorded as the preset image pixel change.

[0029] In this embodiment of the invention, the image processing unit's operating modes include a working state and a sleep state; In the aforementioned working state, at the end of the current monitoring cycle, the image processing unit uses an anomaly recognition model to identify abnormal events in each frame of environmental images acquired during the current monitoring cycle. The anomaly recognition model is a pre-trained neural network model, obtained by training on several historical environmental images containing anomalous events and their corresponding anomaly type labels. It is used for feature extraction and anomaly classification of the environmental images. In this embodiment, the anomaly recognition model may employ a convolutional neural network architecture, and the types of anomalous events it can recognize include, but are not limited to, flames, smoke, illegal intrusion, or animal entry. Model training is a common technique used by those skilled in the art. This embodiment does not limit the specific network structure, number of layers, or number of training iterations of the anomaly recognition model, as long as it can output corresponding anomaly event recognition results based on the input environmental images.

[0030] In the dormant state, the image processing unit does not need to perform abnormal event identification on the environmental images of the current monitoring period at the end of the current monitoring period.

[0031] Please continue reading. Figure 1 and Figure 2 As shown in the embodiment of the present invention, the adjustment and analysis unit determines that the device temperature is abnormal when the device temperature is greater than the preset device temperature.

[0032] In this embodiment of the invention, the device temperature is the temperature at the end of the current monitoring cycle, determined by a temperature sensor located inside the casing. In this embodiment, a higher device temperature indicates a hotter environment and a higher risk of thermal stress to the internal electronic components and power supply unit. Therefore, a greater demand for high-temperature protection of the device is required, resulting in a smaller preset device temperature value. In this embodiment, the specific value of the preset device temperature is not limited; those skilled in the art can adaptively set it within the rated operating temperature range of the power supply unit and the image processing unit based on the actual high-temperature protection requirements of the application scenario.

[0033] Specifically, the adjustment and analysis unit determines to perform temperature control adjustment when the power supply unit is in a degraded state or the image processing unit is in a time-disordered state.

[0034] Specifically, the degradation state is when the risk of thermal runaway is greater than a preset risk of thermal runaway or the voltage consistency deviation is greater than a preset voltage consistency deviation.

[0035] In this embodiment of the invention, the adjustment and analysis unit further includes a thermal runaway determination module. The thermal runaway determination module is used to determine the thermal runaway risk level, which is used to determine the probability of the power supply unit experiencing a thermal runaway accident under current environmental conditions. This provides a quantitative basis for the adjustment and analysis unit to determine whether the power supply unit is in a deteriorated state. In this embodiment, the process by which the thermal runaway determination module obtains the thermal runaway risk level includes: The thermal runaway determination module traverses the historical record database and filters out all historical records in which the device temperature is within the first preset device temperature range or the second preset device temperature range, and records them as abnormal temperature records. The thermal runaway determination module further filters out records of abnormal states of the power supply unit from the abnormal temperature records, and records them as power supply unit abnormal records. The abnormal states include, but are not limited to, voltage drop, current change and internal resistance increase. Those skilled in the art can adaptively set the determination thresholds corresponding to the above abnormal states within the rated parameter range of the power supply unit according to the specific type of the power supply unit and the sensitivity requirements of abnormal detection in the actual application scenario. The ratio of the number of abnormal records of the power supply unit to the total number of abnormal temperature records is used as the thermal runaway risk level. The thermal runaway determination module internally stores a historical record database within a preset time period. The historical record database records the device temperature value at the end of each monitoring cycle and whether the corresponding power supply unit is in an abnormal state.

[0036] It should be noted that if there are no historical records in the historical database, the thermal runaway determination module will set the thermal runaway risk level to a preset initial default value. The process of obtaining the preset initial default value includes: A preset number of thermal runaway tests will be performed on the power supply unit, and each thermal runaway test will be recorded as a simulation record. A single thermal runaway test is to continuously run the power supply unit for a preset duration under the condition that the simulated device temperature is within the first preset device temperature range or the second preset device temperature range. In this embodiment of the invention, the preset number of tests and the preset duration of operation are not specifically limited. Those skilled in the art can make adaptive settings based on factors such as the type of power supply unit, electrochemical characteristics, test standard requirements, and equipment production and testing efficiency. The simulation record of the abnormal state of the power supply unit is recorded as the abnormal simulation record; The ratio of the number of abnormal simulation records to the number of simulation records is used as the preset initial default value.

[0037] In this embodiment of the invention, a higher thermal runaway risk level indicates a higher probability of thermal runaway accidents occurring in the power supply unit under current and historical operating conditions, i.e., a greater safety risk for the power supply unit. Therefore, the greater the user's demand for power supply unit safety protection, the smaller the value of the preset thermal runaway risk level should be. This ensures that the adjustment and analysis unit can intervene in advance before the thermal runaway risk develops to a high level, initiating temperature regulation to eliminate safety hazards. In this embodiment, the preset thermal runaway risk level is empirically set based on historical data. Specifically, records of the device are collected within the most recent preset number of historical monitoring periods. The thermal runaway risk levels corresponding to each historical monitoring period in which the power supply unit operates normally, does not trigger temperature regulation operations, and the image processing unit does not exhibit temporal disorder are selected. The average value of the selected thermal runaway risk level data is calculated, and this average value is set as the preset thermal runaway risk level. The higher the user's demand for power supply unit safety protection, the larger the number of historical monitoring periods participating in the average value calculation. In this embodiment, the specific value of the preset number is not limited; users can set it themselves according to the actual application scenario's safety protection requirements and the device's operating time.

[0038] In this embodiment of the invention, the adjustment and analysis unit further includes a deviation determination module. The deviation determination module is used to determine the voltage consistency deviation, which is used to determine the degree of difference in terminal voltage between the cells in the power supply unit. In this embodiment, the process of obtaining the voltage consistency deviation includes: Obtain the terminal voltage value of each cell in the power supply unit at the end of the current monitoring cycle; The difference between the maximum and minimum terminal voltage values ​​of each cell is recorded as the voltage consistency deviation.

[0039] The power supply unit is composed of multiple battery cells connected in series. Each battery cell is an independent electrochemical energy storage unit that generates an electromotive force through an electrochemical reaction. The power supply unit also includes a battery management module, which is used to acquire the terminal voltage value of each battery cell in the power supply unit at the end of each monitoring cycle. Specifically, the battery management module collects the voltages at the positive and negative terminals of each battery cell through a voltage sampling circuit, and uses the difference between the positive and negative terminal voltages as the terminal voltage of a single battery cell.

[0040] In this embodiment of the invention, a larger voltage consistency deviation indicates a greater difference in terminal voltage between the cells in the power supply unit. This means a more severe differentiation in the state of charge, internal resistance, and aging degree of each cell. An increase in voltage consistency deviation signifies that some cells have already deteriorated significantly, leading to a decrease in the overall charging and discharging efficiency of the battery pack. Furthermore, under abnormal equipment temperature conditions, cells with higher voltage pose a risk of overcharging, while cells with lower voltage pose a risk of over-discharging. Therefore, the greater the user's demand for safety protection of the power supply unit, the smaller the value of the preset voltage consistency deviation threshold should be. This ensures that the adjustment and analysis unit can determine that the power supply unit is in a deteriorated state when the voltage consistency deviation is still at a low level, and initiate temperature regulation in advance to protect the power supply unit. The preset voltage consistency deviation is empirically set based on historical data. Specifically, voltage consistency deviation records of the device are collected over several recent historical monitoring periods. The voltage consistency deviations corresponding to each historical monitoring period where the power supply unit is operating normally, has not triggered temperature regulation operations, and the image processing unit has not experienced temporal disorder are selected. The arithmetic mean of the selected voltage consistency deviations is calculated and recorded as the preset voltage consistency deviation.

[0041] Specifically, the timing disorder state refers to the image processing unit's setup time margin or hold time margin for the data transmitted by the image acquisition unit being lower than zero.

[0042] In this embodiment of the invention, the adjustment analysis unit further includes a margin determination module, which is used to determine the setup time margin and hold time margin of the image processing unit for the data transmitted by the image acquisition unit; In this embodiment of the invention, the image acquisition unit sends a data signal to the image processing unit in each clock cycle. The data receiving register inside the image processing unit samples and latches the data line at the sampling clock edge. To ensure correct sampling, the data signal must arrive before the sampling clock edge and remain stable, so that the register of the image processing unit has sufficient time to read and latch the data.

[0043] In this embodiment, the setup time margin is used to determine the redundancy time required to determine the actual arrival time of the data signal relative to the minimum setup time required by the sampling clock edge. The process of the margin determination module obtaining the setup time margin includes: The margin determination module obtains the actual length of time that the data signal output by the image acquisition unit remains in a valid state before the sampling clock edge arrives, and records it as the actual setup time; wherein, the valid state is when the voltage value of the data signal is less than a low level threshold or greater than a high level threshold, and the low level threshold is less than the high level threshold; The margin determination module reads the minimum setup time requirement value pre-stored inside the image processing unit; wherein, the minimum setup time requirement value is the minimum time length during which the input data of the image processing unit's data receiving register must remain stable and valid before the sampling clock edge arrives, i.e., in a valid state. Its value is determined by the manufacturing process and electrical characteristics of the image processing unit, and is pre-stored in the register of the image processing unit. It is an inherent parameter for the normal operation of the image processing unit. The margin determination module calculates the difference between the actual establishment time and the minimum establishment time requirement, and records the difference as the establishment time margin.

[0044] In this embodiment, the voltage value of the data signal is the potential difference between the electrical signal output by the image acquisition unit on its data output pin and the signal ground GND, measured in volts. In digital circuit transmission, the data signal's logic state is represented by its voltage level. When the image acquisition unit outputs logic 0, the voltage value on its data output pin is pulled down below a low-level threshold; when it outputs logic 1, the voltage value on its data output pin is pulled up above a high-level threshold. In the valid state, the data signal has stabilized at a defined logic level and can be correctly identified as logic 0 or logic 1 by the image processing unit.

[0045] In this embodiment, a larger voltage value of the data signal indicates a higher amplitude of the high-level logic state corresponding to the data signal, meaning a stronger high-level driving capability, better signal quality, stronger anti-interference capability, and more reliable data transmission. Conversely, a smaller voltage value of the data signal indicates a lower amplitude of the low-level logic state corresponding to the data signal, meaning a stronger low-level pull-down capability, better signal quality, and stronger anti-interference capability. Therefore, the greater the demand for reliable data signal transmission and anti-interference capability, the smaller the low-level threshold and the larger the high-level threshold, ensuring a wider range for determining high and low levels in the data signal, enabling the image processing unit to more reliably identify logic 0 and logic 1. Specifically, the process of obtaining the low-level threshold and the high-level threshold includes: Multiple sets of test data frames transmitted between the image acquisition unit and the image processing unit are collected. Each test data frame includes the actual voltage value of the data signal and its corresponding correct logic state identifier. Set multiple combinations of level thresholds with different values. Each combination contains a low level threshold and a high level threshold, and the low level threshold is always less than the corresponding high level threshold. For each set of level threshold combinations, traverse each test data frame and compare the data signal voltage value with the set of level threshold combinations: if the voltage value is less than the low level threshold or greater than the high level threshold, it is determined to be a valid state; if it is between the two, it is determined to be an invalid state. The judgment result is compared with the expected judgment result corresponding to the correct logic state identifier of each test data frame. The proportion of frames with consistent judgments to the total number of frames is counted as the judgment accuracy under this level threshold combination. The low-level threshold and high-level threshold corresponding to the one with the highest judgment accuracy are taken as the low-level threshold and the high-level threshold.

[0046] In this embodiment, the hold time margin is used to determine the redundancy time for the data signal to remain stable after the sampling clock edge arrives. The process of the margin determination module obtaining the hold time margin includes: The margin determination module obtains the actual length of time that the data signal output by the image acquisition unit remains valid after the sampling clock edge arrives, and records it as the actual holding time. The margin determination module reads the minimum hold time requirement value pre-stored inside the image processing unit. The minimum hold time requirement value is the minimum time length for which the input data of the image processing unit's data receiving register must remain stable and valid after the sampling clock edge arrives. Its value is pre-stored in the register of the image processing unit. The margin determination module calculates the difference between the actual holding time and the minimum holding time requirement, and records the difference as the holding time margin.

[0047] In this embodiment of the invention, the process by which the margin determination module determines the timing state of the image processing unit after acquiring the setup time margin and the hold time margin includes: When the setup time margin or the hold time margin is lower than zero, the margin determination module determines that the image processing unit is currently in a state of temporal disorder. If the time elapsed before the sampling clock edge arrives in the data output by the image acquisition unit is greater than or equal to the minimum setup time required by the image processing unit, then the setup time margin is greater than or equal to zero, and the data can be sampled correctly; if the time elapsed is less than the required minimum setup time, then the setup time margin is negative, indicating that the sampling clock edge has arrived before the data has stabilized, and the image processing unit may latch into an incorrect data value. If the data output by the image acquisition unit remains valid for a period of time after the sampling clock edge that is greater than or equal to the minimum hold time required by the image processing unit, then the hold time margin is greater than or equal to zero, and the data can be correctly latched. If the hold time is less than the required minimum hold time, then the hold time margin is negative, indicating that the data expires too quickly after the sampling clock edge, and the image processing unit may also latch an incorrect data value.

[0048] It is understandable that when the device temperature is abnormal, it may cause timing disorder in the setup time margin and hold time margin to fall below zero. Specifically, the abnormal device temperature causes the frequency of the clock circuit inside the image processing unit or the image acquisition unit to drift, causing the position of the sampling clock edge relative to the data valid window to shift. The abnormal temperature also causes changes in the signal propagation delay on the data transmission path, causing the time when the data signal arrives at the image processing unit to deviate. Under abnormal temperature conditions, the output voltage fluctuation of the power supply unit intensifies, causing the level threshold of the data interface to shift, thereby affecting the timing characteristics.

[0049] Please see Figure 3 As shown, it is a flowchart of an embodiment of the present invention for determining the temperature control method based on the solar power supply constraint state and the image processing load rate. In this embodiment of the present invention, the temperature control adjustment unit responds to the condition that it is in the solar power supply constraint state and the image processing load rate is greater than or equal to the preset image processing load rate, and determines to perform active temperature adjustment. In active temperature regulation, the temperature regulation unit controls the image processing unit to switch from working state to sleep state, and controls the image acquisition unit to reduce the acquisition frame rate to a preset acquisition frame rate.

[0050] In this embodiment of the invention, the temperature control adjustment unit further includes an image processing load rate determination module. The image processing load rate determination module is used to determine the image processing load rate, which is used to determine the backlog of data to be processed by the image processing unit at the current moment. The image processing load rate is the number of environmental images that have not yet been detected based on the anomaly recognition model at the end of the current monitoring cycle. In this embodiment of the invention, a higher image processing load rate indicates a larger backlog of image frames to be processed by the image processing unit. The image processing unit needs to operate at a higher load in subsequent moments to complete the processing of the backlogged data. The resulting power consumption and heat accumulation will further increase the temperature inside the casing. Therefore, the greater the demand for temperature control response speed, the larger the preset image processing load rate should be, allowing for a greater lead time to trigger active temperature regulation before the image processing unit experiences a significant temperature rise due to task backlog. In this embodiment, the preset image processing load rate is determined through simulation. Several candidate image processing load rate thresholds are collected. Active temperature regulation simulation tests are performed on devices with image processing load rates greater than or equal to these candidate thresholds. The internal temperature drop value of the device is used as the evaluation index. The average value of the candidate thresholds whose internal temperature drop values ​​within a preset time period are greater than the preset internal temperature drop value is recorded as the preset image processing load rate. The specific values ​​of the preset time period and the preset internal temperature drop value can be set by the user according to the actual application scenario's requirements for cooling effect; this embodiment does not impose specific limitations on this.

[0051] In this embodiment of the invention, the active temperature regulation process includes: When the temperature regulation module determines to perform active temperature regulation, the temperature regulation module sends control commands to the image processing unit and the image acquisition unit respectively; For the image processing unit, the temperature regulation module controls it to switch from the current working state to the sleep state. In the sleep state, the image processing unit stops detecting and recognizing abnormal events in the surrounding environment based on the anomaly recognition model, so as to reduce the power consumption of the image processing unit and reduce the heat generated during its operation. For the image acquisition unit, the temperature regulation module controls it to reduce the current acquisition frame rate to a preset acquisition frame rate. The preset acquisition frame rate is lower than the initial acquisition frame rate. In this embodiment, the greater the user's demand for heat control, the smaller the value of the preset acquisition frame rate. This embodiment does not specifically limit the value of the preset acquisition frame rate; the user can set it themselves. It is understood that when the image acquisition unit performs image acquisition at the preset acquisition frame rate, the number of image frames acquired per unit time decreases, and the data reading, transmission, and processing load of the image acquisition unit itself decreases accordingly, thereby reducing the heat generation of the image acquisition unit. At the end of the next monitoring cycle or when the device temperature drops below the preset device temperature, the temperature adjustment module determines to stop active temperature adjustment, controls the image processing unit to resume from the sleep state to the working state, and controls the image acquisition unit to resume the initial acquisition frame rate, so that the device resumes normal working mode.

[0052] In this embodiment, through the aforementioned control strategy, the active temperature regulation addresses the issue at the source of the device's workload, simultaneously reducing the operating load of the image processing unit and the image acquisition unit. This reduces heat generation inside the casing, thereby lowering the internal temperature. This method achieves cooling without consuming additional electrical energy, making it particularly suitable for scenarios with limited solar power or insufficient battery power. Under limited power supply conditions, it prioritizes preventing damage to the core hardware of the device from overheating, while extending the device's continuous operating time.

[0053] It should be noted that during the active temperature regulation process, the image acquisition unit continues to acquire environmental images of the surrounding environment at the preset acquisition frame rate and caches them. The image processing unit does not perform real-time abnormal event identification in the sleep state. After the device exits the active temperature regulation mode and the image processing unit resumes working, the cached historical environmental images can be retrospectively processed and analyzed to ensure that no key abnormal event information is missed during the active temperature regulation period.

[0054] Specifically, the temperature control unit determines to perform passive temperature regulation when it is not under solar power supply constraint or when the image processing load rate is less than the preset image processing load rate.

[0055] In this embodiment of the invention, when performing passive temperature regulation, the semiconductor cooling chip is energized, and passive temperature regulation is stopped when the device temperature drops below a preset device temperature. In this embodiment, when the semiconductor cooling chip is energized, its cold end absorbs the heat inside the shell and pumps the heat to the external environment through its hot end, thereby cooling the space inside the shell.

[0056] Specifically, the solar power supply constraint state is when the light intensity is less than a preset light intensity or the battery power is less than a preset battery power.

[0057] In this embodiment of the invention, the light intensity is determined by a light sensor disposed on the outer surface of the housing. The power supply unit further includes a battery management chip, which is used to determine the battery charge. The battery charge is the ratio of the current remaining charge of the power supply unit to its rated capacity, expressed as a percentage.

[0058] In this embodiment of the invention, a higher light intensity indicates more abundant ambient light, resulting in a stronger power generation capacity of the flexible solar panel and a greater power supply to the device. Conversely, a lower light intensity indicates insufficient ambient light, leading to a weaker power generation capacity of the flexible solar panel and a more limited external power supply to the device. A lower battery capacity indicates less stored energy in the power supply unit, a shorter operating time relying on its own stored energy, and a higher dependence on solar energy for charging. Therefore, the greater the demand for continuous operation or reliable power supply, the lower the preset light intensity and the higher the preset battery capacity.

[0059] In this embodiment, the preset light intensity and the preset battery level are determined through simulation: Collect several candidate preset light intensity values ​​and several candidate preset battery power values, and combine each candidate preset light intensity value with each candidate preset battery power value to obtain multiple sets of candidate parameter combinations; Under different ambient light conditions and different initial battery charge conditions, passive temperature regulation simulation tests were performed on devices not under solar power supply constraints, with each set of candidate parameter combinations as the boundary. Using the continuous operating time of the equipment and the number of power outages as evaluation indicators, the simulation test results under each combination of candidate parameters were recorded. Based on all candidate parameter combinations in the simulation results where the continuous operating time of the device is greater than or equal to the preset operating time threshold and the number of power outages is less than or equal to the preset number of outages threshold, calculate the average value of the candidate preset light intensity value and the average value of the candidate preset battery power value in each candidate parameter combination, and record them as the preset light intensity and the preset battery power, respectively.

[0060] Both the preset runtime threshold and the preset interruption number threshold can be set by the user according to the actual application scenario's requirements for device battery life and power supply reliability. This embodiment does not impose specific limitations on this.

[0061] Please see Figure 4 As shown, this is a schematic diagram of the structure of an IoT detection device based on solar power according to an embodiment of the present invention. In this embodiment, the device further includes: The outer casing 1 has a flexible solar panel 2 on its outer surface; A semiconductor cooling chip 4 is embedded in the wall of the outer casing 1 and is connected to the power supply unit and the temperature control unit respectively. Its cold end is located inside the outer casing 1 and is used to exchange heat with the air or heating device inside the outer casing 1 to absorb heat. Its hot end is located outside the outer casing 1 and is used to release heat to the external environment. A temperature sensor is disposed inside the housing 1 to obtain the device temperature.

[0062] In this embodiment of the invention, the image acquisition unit includes two micro-hole cameras 3, which are disposed on the outer surface of the housing 1; the power supply unit, image processing unit, adjustment and analysis unit, temperature control adjustment unit and early warning unit are all disposed inside the housing 1.

[0063] Please see Figure 5 The diagram shows a flowchart of a solar-powered IoT detection method according to an embodiment of the present invention. The present invention also provides a solar-powered IoT detection method, comprising: Step S1: Acquire environmental images of the surrounding environment of the device through the image acquisition unit; Step S2: The image processing unit configures its operating mode to either sleep or working state based on the image pixel variation. Step S3: When the image processing unit is in working state, it detects abnormal events in the surrounding environment based on the anomaly recognition model, and issues an early warning through the early warning unit when an abnormal event is detected. Step S4: The adjustment and analysis unit determines whether the equipment is under abnormal temperature conditions based on the equipment temperature. Step S5: When it is determined that the abnormal temperature condition is being met, the adjustment and analysis unit assesses whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. Step S6: If the power supply unit is in a degraded state or the image processing unit is in a timing disorder state, the adjustment and analysis unit determines to perform temperature control adjustment. Step S7: After determining that temperature control adjustment is required, the temperature control adjustment unit determines whether the temperature control method is active temperature adjustment or passive temperature adjustment based on the solar power supply constraint state and the image processing load rate.

[0064] In this embodiment of the invention, in step S6, if the power supply unit is not in a degraded state and the image processing unit is not in a timing disorder state, then return to step S4 to continue monitoring; otherwise, determine to perform temperature control adjustment and proceed to step S7.

[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A solar-powered Internet of Things (IoT) detection device, characterized in that, include: The power supply unit stores the electrical energy generated by the flexible solar panels; An image acquisition unit, which is connected to the power supply unit, is used to acquire environmental images of the environment surrounding the device; An image processing unit is connected to the power supply unit and the image acquisition unit respectively, and is used to configure the operation mode of the image processing unit to be a sleep state or a working state according to the image pixel change degree, and when in the working state, it detects abnormal events in the surrounding environment based on an anomaly recognition model. The adjustment and analysis unit is connected to the power supply unit and is used to determine whether the device is under abnormal temperature conditions based on the device temperature. Under abnormal temperature conditions, it determines whether to perform temperature control adjustment based on whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. The degradation state is determined based on the risk of thermal runaway and voltage consistency deviation, and the timing disorder state is determined based on the setup time margin and the hold time margin. A temperature control unit is connected to the power supply unit, the image acquisition unit, the image processing unit, and the adjustment and analysis unit, respectively. It is used to determine whether the temperature control method is active temperature control or passive temperature control based on the solar power supply constraint state and the image processing load rate during temperature control. An early warning unit, which is connected to the power supply unit and the image processing unit respectively, is used to issue an early warning when an abnormal event is detected in the surrounding environment.

2. The solar-powered IoT detection device according to claim 1, characterized in that, The image processing unit is configured to operate in a working state when the image pixel change is greater than or equal to a preset image pixel change. The image processing unit is configured to operate in a sleep mode when the image pixel change is less than a preset image pixel change.

3. The solar-powered IoT detection device according to claim 1, characterized in that, The adjustment and analysis unit determines that the device temperature is abnormal when the device temperature is higher than the preset device temperature.

4. The solar-powered IoT detection device according to claim 3, characterized in that, The adjustment and analysis unit determines to perform temperature control adjustment when the power supply unit is in a degraded state or the image processing unit is in a time-disordered state.

5. The solar-powered IoT detection device according to claim 4, characterized in that, The degradation state is defined as either a thermal runaway risk level greater than a preset thermal runaway risk level or a voltage consistency deviation greater than a preset voltage consistency deviation.

6. The solar-powered IoT detection device according to claim 4, characterized in that, The timing disorder state refers to the image processing unit's setup time margin or hold time margin for the data transmitted by the image acquisition unit being lower than zero.

7. The solar-powered IoT detection device according to claim 1, characterized in that, The temperature control unit responds to the condition that it is under solar power supply constraint and the image processing load rate is greater than or equal to the preset image processing load rate, and determines to perform active temperature adjustment. In active temperature regulation, the temperature regulation unit controls the image processing unit to switch from working state to sleep state, and controls the image acquisition unit to reduce the acquisition frame rate to a preset acquisition frame rate.

8. The solar-powered IoT detection device according to claim 7, characterized in that, The temperature control unit determines to perform passive temperature regulation when it is not under solar power supply constraint or when the image processing load rate is less than the preset image processing load rate.

9. The solar-powered IoT detection device according to claim 8, characterized in that, The solar power supply constraint state is when the light intensity is less than the preset light intensity or the battery power is less than the preset battery power.

10. A detection method using a solar-powered Internet of Things (IoT) detection device according to any one of claims 1 to 9, characterized in that, include: Step S1: Acquire environmental images of the surrounding environment of the device through the image acquisition unit; Step S2: The image processing unit configures its operating mode to either sleep or working state based on the image pixel variation. Step S3: When the image processing unit is in working state, it detects abnormal events in the surrounding environment based on the anomaly recognition model, and issues an early warning through the early warning unit when an abnormal event is detected. Step S4: The adjustment and analysis unit determines whether the equipment is under abnormal temperature conditions based on the equipment temperature. Step S5: When it is determined that the abnormal temperature condition is being met, the adjustment and analysis unit assesses whether the power supply unit is in a degraded state and whether the image processing unit is in a time-disordered state. Step S6: If the power supply unit is in a degraded state or the image processing unit is in a timing disorder state, the adjustment and analysis unit determines to perform temperature control adjustment. Step S7: After determining that temperature control adjustment is required, the temperature control adjustment unit determines whether the temperature control method is active temperature adjustment or passive temperature adjustment based on the solar power supply constraint state and the image processing load rate.

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