A remote environment data monitoring system and method based on the Internet of Things

Abstract

This invention discloses a remote environmental data monitoring system and method based on the Internet of Things (IoT), relating to the field of IoT sensing technology. The method includes: real-time acquisition of the output voltage of a photovoltaic module; when its rate of decline exceeds a first threshold and its amplitude is less than a second threshold, a first trigger signal is generated; in response to the first trigger signal, a heating resistor is energized, and the measured rate of change of the sensor module is acquired; when the difference between the measured rate of change and a reference rate of change exceeds a preset range, a second trigger signal is generated, and a data failure flag is set; based on the presence or absence of a data failure flag, the transmitted content is dynamically locked to maintenance code or environmental data; the battery voltage is monitored, and when it falls below a third threshold, the locked transmitted content is forcibly sent, and the power supply to the sensor module is cut off. This invention achieves in-situ rights confirmation and adaptive transmission of a single data source without increasing hardware costs by establishing a logical coupling between photovoltaic power supply characteristics and thermal response characteristics.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) sensing technology, and in particular to a remote environmental data monitoring system and method based on IoT. Background Technology

[0002] In applications such as agricultural and forestry monitoring, hydrology and water conservancy, and geological disaster early warning, outdoor environmental monitoring terminals typically use solar photovoltaic modules in conjunction with batteries for power supply and operate long-term in unattended, uncontrolled environments. The core task of these terminals is to collect and upload environmental parameters in real time.

[0003] However, existing low-cost monitoring terminals generally suffer from the technical deficiency of decoupling the sensed data from the physical state of the equipment. Due to the lack of redundant backup sensors or dedicated self-testing hardware, the values ​​collected by a single sensor are semantically ambiguous under conditions of physical obstruction or contamination. For example, a significant drop in the output voltage of a photovoltaic module may be due to a natural sunset or to obstruction by foreign objects; a slow change in temperature and humidity readings may be due to stable weather conditions or to increased thermal resistance caused by dust accumulation on the probe surface.

[0004] This lack of physical reliability causes monitoring terminals to continue uploading distorted environmental data even when encountering physical faults. Furthermore, when the terminal's battery is depleted and it enters a near-death state, existing technologies typically execute a final, indiscriminate data transmission strategy. If the device is obstructed or damaged at this time, the distorted data uploaded using the remaining energy has no monitoring value and consumes communication resources intended for sending equipment maintenance alarm signals. This prevents maintenance personnel from obtaining fault information in a timely manner, increasing the difficulty and cost of remote maintenance. Summary of the Invention

[0005] This invention provides a remote environmental data monitoring system and method based on the Internet of Things, aiming to solve the technical problem that existing outdoor passive monitoring terminals cannot distinguish between changes in true environmental values ​​and physical faults of equipment when lacking external auxiliary detection hardware, thus leading to a lack of data reliability and waste of communication resources in a near-dead state.

[0006] In view of the above problems, the present invention provides a remote environmental data monitoring method based on the Internet of Things, applied to a monitoring terminal, the monitoring terminal including a photovoltaic module, a sensor module, and a battery, the method comprising the following steps: S1: Real-time acquisition of the output voltage of the photovoltaic module; when the rate of decrease of the output voltage is greater than a preset first threshold and the amplitude of the output voltage is less than a preset second threshold, a first trigger signal is generated; S2: In response to the first trigger signal, control the heating resistor to be energized to generate a constant power thermal excitation, and collect the temperature data of the sensor component during the energization period; calculate the measured rate of change of the temperature data, and compare the measured rate of change with the pre-stored benchmark rate of change; when the difference between the two exceeds the preset range, generate a second trigger signal and set the data failure flag. S3: Configure data transmission strategy: If the data failure flag exists, lock the transmission content to maintenance code; if the data failure flag does not exist, lock the transmission content to environmental data. S4: Monitor the voltage of the battery; when the voltage of the battery is lower than a preset third threshold, force the transmission strategy to be executed, send locked transmission content and then cut off the power supply to the sensor component.

[0007] Furthermore, in step S1, the logic for generating the first trigger signal further includes: Get the real-time clock time; Determine whether the real-time clock time is within a preset time interval; The comparison operation of the output voltage's rate of decrease and amplitude is performed only when the real-time clock time is within the preset time interval.

[0008] Further, in step S2, the formula for calculating the difference is: in, The difference, The measured rate of change of temperature is given. The benchmark rate of change, These are preset non-zero constants.

[0009] Further, in step S2, the logic for generating the second trigger signal includes: Using the difference Calculation factor The formula is: in, and This is a preset constant; Determine the factors If the threshold is less than a preset fourth threshold, then generate the second trigger signal.

[0010] Further, in step S4, cutting off the power supply to the sensor assembly specifically includes: The power switch connected to the sensor assembly is turned off; The radio frequency transmission module is controlled to transmit the content at a preset transmission power.

[0011] The present invention also provides a remote environmental data monitoring system based on the Internet of Things, comprising: Photovoltaic modules are connected to the analog-to-digital converter interface of a microcontroller; The sensor assembly includes a temperature and humidity probe and a heating resistor thermally coupled thereto, the heating resistor being connected to the output control interface of a microcontroller; The power management unit is used to collect battery voltage. The microcontroller, connected to the photovoltaic module, the sensor module and the power management unit respectively, is configured to perform the method described above.

[0012] The technical solution provided in this application has at least the following technical effects: This invention establishes a logical coupling between photovoltaic power supply characteristics, thermal response characteristics, and energy management strategies, thereby enabling in-situ self-testing and data confirmation of the physical state of the monitoring terminal without increasing the cost of additional sensor hardware.

[0013] Specifically, this invention utilizes the voltage surge characteristics of photovoltaic modules as a prerequisite for initiating self-testing, uses the thermal response characteristics of heating resistors as a physical basis for quantifying sensor cleanliness, and uses this quantification result to guide the shunt transmission strategy in the near-death state of the battery. This mechanism effectively eliminates the semantic ambiguity of a single data source in an uncontrolled environment, ensuring that the system can intelligently shield distorted environmental data and prioritize uploading equipment fault codes at the critical moment of energy depletion, thereby significantly improving the reliability of monitoring data and the efficiency of remote operation and maintenance. Attached Figure Description

[0014] Figure 1 A flowchart illustrating a remote environmental data monitoring method based on the Internet of Things (IoT) provided in an embodiment of the present invention; Figure 2 This is a system architecture diagram of a remote environmental data monitoring system based on the Internet of Things, provided as an embodiment of the present invention. Detailed Implementation

[0015] The above technical solutions will now be described in detail with reference to the accompanying drawings and specific embodiments to provide a better understanding of them. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments used only to explain the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the drawings, not all of them.

[0016] For examples, please refer toFigure 1 This invention provides a remote environmental data monitoring method based on the Internet of Things (IoT), applied to a monitoring terminal. The monitoring terminal includes a photovoltaic module, a sensor module, and a battery. The method includes the following steps: S1: Real-time acquisition of the output voltage of the photovoltaic module; when the rate of decrease of the output voltage is greater than a preset first threshold and the amplitude of the output voltage is less than a preset second threshold, a first trigger signal is generated; S2: In response to the first trigger signal, control the heating resistor to be energized to generate a constant power thermal excitation, and collect the temperature data of the sensor component during the energization period; calculate the measured rate of change of the temperature data, and compare the measured rate of change with the pre-stored benchmark rate of change; when the difference between the two exceeds the preset range, generate a second trigger signal and set the data failure flag. S3: Configure data transmission strategy: If the data failure flag exists, lock the transmission content to maintenance code; if the data failure flag does not exist, lock the transmission content to environmental data. S4: Monitor the voltage of the battery; when the voltage of the battery is lower than a preset third threshold, force the transmission strategy to be executed, send locked transmission content and then cut off the power supply to the sensor component.

[0017] The above steps will be explained in detail below with reference to the specific process: The execution process of the IoT-based remote environmental data monitoring method begins with the microcontroller acquiring and verifying the system's time reference. The real-time clock time, containing the current year, month, day, hour, minute, and second, is read through the microcontroller's internal bus interface. This read real-time clock time is then passed to the numerical comparison logic unit and compared with a preset time interval in memory. The preset time interval is typically configured to correspond to periods with higher local solar altitude angles, such as 10:00 AM to 2:00 PM. Only when the numerical comparison confirms that the real-time clock time strictly falls within the preset time interval does the microcontroller release the logic lock on the analog-to-digital conversion interface, allowing subsequent acquisition of electrical parameters for the photovoltaic modules. This effectively filters out interference from the gradual change in natural sunlight caused by the alternation of day and night in the time dimension.

[0018] After the analog-to-digital converter (ADC) interface is unlocked, the microcontroller initiates a precise acquisition process for the photovoltaic (PV) module's output voltage. To eliminate the clamping effect of the power management unit (PMU) on the PV module voltage during charging mode and to ensure that the acquired voltage value accurately reflects the physical shading state of the PV module, the microcontroller sends a charging pause signal to the PMU via the general-purpose output interface the instant it executes the ADC command. In response to the charging pause signal, the PMU temporarily disconnects the charging path between the PV module and the battery, forcing the PV module into a short-term open-circuit state.

[0019] During the stable window period when the photovoltaic module is in an open-circuit state, the microcontroller reads the voltage value of the analog-to-digital converter interface at a preset first sampling frequency, such as once per second. After the acquisition is completed, the charging pause signal is canceled, and the power management unit resumes normal charging operation. The acquired discrete open-circuit voltage values ​​are filled into a circular buffer queue. For the voltage values ​​of two adjacent sampling points in the buffer queue, the microcontroller performs differential operations to calculate the absolute value of the voltage change per unit time, thereby obtaining the rate of decrease of the photovoltaic module's output voltage. At the same time, the instantaneous open-circuit voltage amplitude of the photovoltaic module is also retained for use in the threshold determination logic executed in parallel.

[0020] The calculated rate of decrease and amplitude are then fed into two independent comparators. In the first comparator, the rate of decrease of the photovoltaic module's output voltage is compared with a preset first threshold, which is set to characterize the minimum voltage change rate corresponding to non-natural physical obstruction (such as instantaneous coverage by a foreign object), for example, 0.5 volts per second. In the second comparator, the amplitude of the photovoltaic module's output voltage is compared with a preset second threshold, which is set to characterize the minimum voltage level that the photovoltaic module should maintain under cloudy or low-light conditions, for example, three volts.

[0021] The outputs of the two comparators are fed into a logic AND gate. The AND gate outputs a valid level only when the rate of decrease of the photovoltaic module's output voltage is greater than a preset first threshold and the amplitude of the photovoltaic module's output voltage is less than a preset second threshold. This valid level triggers a state machine flip within the microcontroller, generating a first trigger signal in the system register. The generation of the first trigger signal signifies that the system has completed the logical switch from passive power supply monitoring to active fault diagnosis, establishing the prerequisites for initiating the subsequent heating resistor energization and temperature data acquisition process.

[0022] The generation of the first trigger signal directly initiates the standardized thermal excitation control process for the heating resistor. To eliminate the nonlinear effect of battery voltage fluctuations on the heating power and ensure the physical consistency of the thermal energy input to the sensor components during each calibration process, the microcontroller executes a constant power closed-loop control logic.

[0023] At the instant the heating resistor is energized, the power management unit (PMU) acquires the current battery supply voltage. Based on this real-time voltage value and the nominal resistance of the heating resistor, the microcontroller dynamically adjusts the duty cycle of the pulse width modulation (PWM) signal output to the power switching circuit. When the battery voltage is high, the microcontroller reduces the duty cycle to suppress overheating; when the battery voltage is low, the microcontroller automatically increases the duty cycle to compensate for power loss. Through this real-time compensation mechanism, the equivalent heat power applied to the heating resistor is strictly locked at a preset fixed value (e.g., 200 milliwatts), thus eliminating the possibility of spurious decay in the measured rate of change due to battery depletion. After the heating action continues for a preset fixed duration (e.g., five seconds), the microcontroller automatically cuts off the PWM signal output, completing one standardized heat pulse injection.

[0024] At the initial moment of energizing the heating resistor, the sensor simultaneously enters a high-frequency data acquisition mode. The analog-to-digital converter inside the sensor continuously reads the temperature values ​​on the surface of the sensing probe at a preset second sampling frequency, such as 10 Hz. The data acquisition process covers both the energizing and heating phases of the heating resistor and the natural cooling phase after power-off. The acquired discrete temperature values ​​are stored sequentially in the microcontroller's random access memory, forming a time-temperature curve containing complete thermal response characteristics. For the heating segment of this time-temperature curve, the microcontroller calls a least-squares linear regression algorithm subroutine to perform fitting calculations on the discrete temperature data points. The slope value extracted through the regression calculation quantifies the sensor's temperature rise per unit time; this slope value constitutes the defined measured temperature change rate.

[0025] To determine the physical state of the calculated measured temperature change rate, a pre-stored reference change rate is retrieved from the microcontroller's non-volatile memory and entered into the computation unit. The reference change rate is measured under ideal conditions—a clean, undamaged surface and free from external solar thermal interference—during the sensor's factory calibration phase, by applying the same thermal excitation. The value of the reference change rate serves as a theoretical anchor point for measuring the current thermal damping state, providing a standardized minuend for subsequent difference calculations.

[0026] It should be noted that, in order to eliminate the physical influence of ambient base temperature on the sensor's thermal conductivity and specific heat capacity, and to ensure the consistency of the thermal damping calibration algorithm under different climatic conditions, the pre-stored reference rate of change is stored in the microcontroller's non-volatile memory using a multidimensional lookup table data structure. This multidimensional lookup table establishes a non-linear mapping relationship between the ambient base temperature and the reference rate of change.

[0027] Before the heating resistor is energized to generate thermal excitation, the current ambient temperature collected by the sensor is read by the microcontroller and used as an index key. The microcontroller uses this index key to search a multidimensional lookup table and extracts the temperature-compensated baseline rate of change that matches the current environmental conditions. If the current ambient temperature lies between two nodes in the lookup table, the microcontroller uses a linear interpolation algorithm to calculate the precise baseline rate of change value. The temperature-compensated baseline rate of change represents the baseline rate of change that the sensor should produce in response to standard thermal excitation under the current ambient temperature and with a clean, undamaged surface, thus providing an environmentally adaptable standardized minuend for subsequent difference calculations.

[0028] It should be noted that the physical mechanism of this invention for detecting contamination using a heating resistor is based on the thermal damping effect. When the probe surface of the sensor assembly is clean, the heat generated by the heating resistor can be transferred to the temperature-sensing element and the surrounding air with a fixed thermal conductivity, exhibiting a standard thermal response curve. However, when the probe surface is covered with mud, bird droppings, or thick layers of ash, the contaminant layer constitutes additional thermal resistance and heat capacity. On the one hand, the insulating effect of the contaminant layer hinders the dissipation of heat to the ambient air, which may lead to localized heat accumulation (increased measured rate of change); on the other hand, if the contaminant (such as wet mud) has a large specific heat capacity, it will absorb the initial heat, causing a lag in the heat flux received by the temperature-sensing element (decreased measured rate of change). Regardless of the form of deviation, as long as the difference between the measured rate of change and the baseline rate of change exceeds the tolerance range defined by the Hill equation, it can be physically determined that there is a non-gaseous covering on the probe surface.

[0029] After the measured rate of temperature change and the baseline rate of temperature change are extracted, they are immediately sent to the arithmetic logic unit of the microcontroller to perform difference calculation. To ensure the numerical stability of the mathematical operation under any physical boundary conditions while quantifying the degree of deviation between the two, a regularization formula incorporating a safety basis parameter is used in the difference calculation process. This difference... The specific expression of the regularization formula is as follows: In the formula, This represents the measured rate of change of temperature, which is collected and calculated in real time. Represents the pre-stored benchmark rate of change. The non-zero constant is a preset value. As an addend to the denominator, the non-zero constant ensures that even under extreme physical conditions where the base rate of change approaches zero, the result of the division operation remains bounded and computable. This prevents the microcontroller from resetting due to a floating-point division-by-zero error, and also plays a role in smoothing and suppressing the background computational noise that may be introduced under low thermal excitation conditions.

[0030] The calculated difference This is then substituted as an independent variable into a nonlinear health assessment model to generate dimensionless factors. This nonlinear mapping process is mathematically expressed using the Hill equation: In the formula, and These are all preset constant parameters. A factor is assigned a value greater than or equal to 2. The step response characteristics for changes in the difference. Before the difference reaches a constant... Before the critical point is determined, the higher-order power operation keeps the value of the factor close to 1, which reflects the algorithm's tolerance for slight thermal resistance fluctuations on the sensor surface; once the difference increases and exceeds the critical point, the value of the factor will drop rapidly at a power-law rate, realizing the sensitive interception and identification of severe thermal resistance anomalies.

[0031] The factor calculated through nonlinear mapping is then sent to a numerical comparator for threshold determination. The comparator compares the factor with a preset fourth threshold (e.g., 0.3) in the register. When the comparison shows that the factor value is less than the preset fourth threshold, the microcontroller determines that the current thermal damping characteristics have exceeded the system's tolerance range, and then generates a second trigger signal in the interrupt control register or status flag, thereby completing the physical conversion from numerical calculation results to logic control signals.

[0032] After the thermal damping response verification is completed, the microcontroller immediately configures the system's transmission strategy based on the verification results. The microcontroller checks whether a data failure flag (i.e., the second trigger signal) exists in the status register. If the flag exists, the microcontroller points the payload pointer to the preset maintenance code and disables the environmental sensor reading interface to prevent erroneous data from entering the transmission buffer; if the flag does not exist, the microcontroller points the payload pointer to the latest environmental data buffer. This locking action is independent of the battery voltage state, ensuring that the system will not output distorted environmental parameters at any time if contamination is detected, thus fundamentally eliminating data ambiguity.

[0033] The power management unit continuously monitors the battery voltage. When the voltage drops to a preset third threshold (e.g., 3.3V), a non-maskable interrupt is triggered. The interrupt service routine does not re-acquire or evaluate data; instead, it directly executes the previously locked transmission strategy. The RF module is woken up and transmits the payload (maintenance code or environmental data) pointed to by the pointer at a preset power. After data transmission is complete, the microcontroller immediately outputs a power-down control signal, physically cutting off the power supply circuit to the sensor components and controlling the system to enter an ultra-low-power deep sleep mode. In this mode, the system only retains the ultra-low-power wake-up interrupt for the power management unit or the photovoltaic voltage monitoring circuit. When the photovoltaic module voltage recovers or the battery voltage returns to a safe threshold, the system will automatically reset and wake up, re-entering the normal monitoring cycle.

[0034] Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides a remote environmental data monitoring system based on the Internet of Things (IoT), which is used to perform the above-described method. The system includes: Photovoltaic modules are connected to the analog-to-digital converter interface of a microcontroller; The sensor assembly includes a temperature and humidity probe and a heating resistor thermally coupled thereto, the heating resistor being connected to the output control interface of the microcontroller; The power management unit is used to collect battery voltage. The microcontroller, connected to the photovoltaic module, sensor module and power management unit respectively, is configured to execute the above-described IoT-based remote environmental data monitoring method.

[0035] Specifically, this relies on a specific circuit topology connection between the photovoltaic module and the microcontroller. The positive output terminal of the photovoltaic module is designed with two parallel current paths on the circuit board. The first path is directly connected to the charging input terminal of the power management unit (Power Management Unit) to transfer the electrical energy converted from solar energy into the battery for storage. The second path is connected in series to a voltage divider network composed of precision resistors, with the intermediate node of the voltage divider network physically connected to the analog-to-digital converter (ADC) interface of the microcontroller. Through this parallel current-shading circuit topology, while the output voltage of the photovoltaic module is clamped or regulated by the Power Management Unit for charging, its original voltage fluctuation characteristics can also be digitally acquired by the microcontroller in real time. This circuit design eliminates the hardware requirement for a separate light sensor or shading detection switch at the physical level, giving the photovoltaic module the dual functional attributes of a system charging power source and a physical shading detection sensor.

[0036] The integrated structure design of the sensor components focuses on the effective transfer and coupling of the thermal field. The temperature and humidity probe and the heating resistor are closely mounted on the same thermally conductive printed circuit board area, or the heating resistor is directly integrated into the temperature and humidity probe's package housing. The physical distance between them is strictly controlled to minimize air thermal resistance. The control terminal of the heating resistor is connected to the microcontroller's output control interface via a power drive circuit. This physical layout of shared substrate or close-range packaging establishes a low thermal resistance conduction path from the heating resistor to the temperature and humidity probe. When the microcontroller drives the heating resistor to heat up through the output control interface, the generated heat flow can quickly and controllably cover the sensing interface of the temperature and humidity probe, ensuring that the temperature and humidity probe can not only respond to thermal changes in the external environment, but also sensitively capture changes in thermal damping caused by surface contamination, realizing the dual functional multiplexing of a single probe between environmental parameter acquisition and cleanliness detection.

[0037] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote environmental data monitoring method based on the Internet of Things, applied to a monitoring terminal, the monitoring terminal comprising a photovoltaic module, a sensor module, and a battery, characterized in that, The method includes the following steps: S1: Real-time acquisition of the output voltage of the photovoltaic module; when the rate of decrease of the output voltage is greater than a preset first threshold and the amplitude of the output voltage is less than a preset second threshold, a first trigger signal is generated; S2: In response to the first trigger signal, control the heating resistor to be energized to generate a constant power thermal excitation, and collect the temperature data of the sensor component during the energization period; calculate the measured rate of change of the temperature data, and compare the measured rate of change with the pre-stored benchmark rate of change; when the difference between the two exceeds the preset range, generate a second trigger signal and set the data failure flag. S3: Configure data transmission strategy: If the data failure flag exists, lock the transmission content to maintenance code; if the data failure flag does not exist, lock the transmission content to environmental data. S4: Monitor the voltage of the battery; when the voltage of the battery is lower than a preset third threshold, force the transmission strategy to be executed, send locked transmission content and then cut off the power supply to the sensor component.

2. The remote environmental data monitoring method based on the Internet of Things according to claim 1, characterized in that, In step S1, the logic for generating the first trigger signal further includes: Get the real-time clock time; Determine whether the real-time clock time is within a preset time interval; The comparison operation of the output voltage's rate of decrease and amplitude is performed only when the real-time clock time is within the preset time interval.

3. The remote environmental data monitoring method based on the Internet of Things according to claim 1, characterized in that, In step S2, the formula for calculating the difference is: in, The difference, The measured rate of change of temperature is given. The benchmark rate of change, These are preset non-zero constants.

4. The remote environmental data monitoring method based on the Internet of Things according to claim 3, characterized in that, In step S2, the logic for generating the second trigger signal includes: Using the difference Calculation factor The formula is: in, and This is a preset constant; Determine the factor If the threshold is less than a preset fourth threshold, then generate the second trigger signal.

5. The remote environmental data monitoring method based on the Internet of Things according to claim 1, characterized in that, In step S4, cutting off the power supply to the sensor assembly specifically includes: The power switch connected to the sensor assembly is turned off; The radio frequency transmission module is controlled to transmit the content at a preset transmission power.

6. A remote environmental data monitoring system based on the Internet of Things, characterized in that, include: Photovoltaic modules are connected to the analog-to-digital converter interface of a microcontroller; The sensor assembly includes a temperature and humidity probe and a heating resistor thermally coupled thereto, the heating resistor being connected to the output control interface of a microcontroller; The power management unit is used to collect battery voltage; A microcontroller, connected to the photovoltaic module, the sensor module and the power management unit respectively, is configured to perform the method as described in any one of claims 1 to 5.

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