Online environment monitoring device

Abstract

The invention relates to the technical field of environment monitoring, particularly discloses an environment online monitoring device, and solves the problems of easy drifting of sensor data, unstable power supply and insufficient intelligence in an outdoor environment. The device comprises an environmental parameter sensing module, an energy supply and power management module, a core control and data processing module and a communication and interface module, and the communication and interface module supports at least two mainstream wired or wireless communication protocols. The device is used for data interaction with a remote monitoring center or other local equipment, data drift is corrected in real time through an integrated micro environment compensation unit, energy consumption and operation strategies are dynamically optimized in combination with sensor state diagnosis and self-adaptive sampling control, and a modular layered structure is adopted to facilitate expansion and maintenance. And the accuracy of monitoring data, the power supply stability and the intelligent level of the device are improved.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, and specifically relates to an online environmental monitoring device. Background Technology

[0002] Environmental monitoring technology is a crucial technological field for ensuring ecological security and achieving sustainable development, involving the real-time sensing and data analysis of various environmental elements such as air, water quality, and soil. Among these, online environmental monitoring devices, as the core equipment for achieving continuous and automated environmental data acquisition, directly affect the accuracy and timeliness of the monitoring data.

[0003] In existing technologies, environmental online monitoring devices typically employ fixed sensor deployments and rely on wired or wireless communication to upload collected data to a monitoring center. However, when operating in complex outdoor environments for extended periods, these devices generally face challenges such as sensor susceptibility to environmental interference leading to data drift, insufficient power supply stability affecting continuous monitoring, and a lack of local real-time verification and fault diagnosis capabilities for monitoring data.

[0004] The structural design of existing monitoring devices often fails to fully consider the needs for multi-parameter integration and modular expansion, resulting in poor equipment adaptability and high maintenance costs. In practical application scenarios where monitoring points are scattered and environmental conditions are variable, the overall reliability, energy efficiency, and intelligence level of the devices have become urgent technical challenges that restrict the improvement of the effectiveness of online environmental monitoring. Summary of the Invention

[0005] The purpose of this invention is to provide an online environmental monitoring device that can effectively solve the problems mentioned in the background art, such as the fact that the structural design of existing monitoring devices often fails to fully consider the needs of multi-parameter integration and modular expansion, resulting in poor equipment adaptability and high maintenance costs.

[0006] The specific technical solution adopted by this invention is as follows:

[0007] An online environmental monitoring device, comprising:

[0008] The environmental parameter sensing module integrates at least two different types of sensors for collecting data on environmental elements. The sensors adopt a modular packaging structure and are connected to the main body of the device through a standardized interface. The environmental parameter sensing module integrates a micro environmental compensation unit, which includes a temperature and humidity sensor and a barometric pressure sensor for real-time monitoring of the micro-environmental parameters around the sensor probe.

[0009] The power supply and management module includes a solar photovoltaic panel, an energy storage unit, and a power management circuit. The power management circuit integrates a multi-output adjustable DC-DC converter and a load switch array to provide power to the entire device and manage the charging and discharging process of the energy storage unit.

[0010] The core control and data processing module connects and coordinates the environmental parameter sensing module, the power supply and management module, and the communication and interface module. Internally, it contains a data preprocessing algorithm, a sensor status diagnosis algorithm, and adaptive sampling control logic. The data preprocessing algorithm performs noise filtering on the raw monitoring data from the environmental parameter sensing module based on a sliding time window, and uses micro-environmental parameters from the micro-environmental compensation unit to dynamically compensate and correct the filtered data through a pre-stored compensation coefficient matrix. The sensor status diagnosis algorithm runs periodically and constructs a reference feature model based on historical normal data of the sensor. It calculates the feature deviation by extracting the time domain and frequency domain features of the current sensor output data and comparing them with the reference feature model. The adaptive sampling control logic dynamically adjusts the sampling frequency and data reporting strategy according to the remaining power of the energy storage unit, the volatility of environmental data, and the priority of monitoring tasks.

[0011] The communication and interface module supports at least two mainstream wired or wireless communication protocols for data interaction between the device and a remote monitoring center or other local devices.

[0012] Preferably, the sensor state diagnosis algorithm determines that the sensor has potential drift and starts the data compensation enhancement mode when the feature deviation exceeds a preset first threshold. When the feature deviation exceeds a higher preset second threshold, it determines that the sensor may be faulty and sends fault warning information through the communication and interface module, while recording diagnostic data in the local log.

[0013] Preferably, the adaptive sampling control logic instructs the system to enter a low-power operation mode when the energy storage unit's power level is lower than a preset low power threshold, thereby reducing the sampling rate of non-core sensors and extending the data reporting interval to prioritize the continuous operation of core monitoring functions.

[0014] Preferably, the main structure of the device adopts a layered shell design, including a protective shell, a module mounting layer and a heat dissipation substrate. The protective shell has an IP67 or higher protection rating. The module mounting layer adopts a standardized slot design with guide grooves and locking mechanisms. The heat dissipation substrate is in close contact with the core control and data processing module and the power management circuit and is in close contact with the module shell through thermal grease.

[0015] Preferably, the core control and data processing module also integrates a data fusion unit. The data fusion unit receives multi-source environmental monitoring data after preprocessing and compensation correction, and generates a comprehensive environmental quality index using a confidence-weighted data fusion algorithm. The algorithm assigns dynamic confidence weights to the data streams of each sensor. The confidence weights are calculated based on the health score output by the sensor state diagnosis algorithm and the stability index of the sensor's historical data.

[0016] Preferably, the comprehensive environmental quality index output by the data fusion unit is encapsulated together with the original calibration data of each sensor and reported through the communication and interface module.

[0017] Preferably, in addition to supporting 4G or 5G wireless communication, the communication and interface module also integrates a LoRa spread spectrum communication unit as a backup communication link. When the signal strength of the primary wireless communication link is lower than a preset threshold or the connection is interrupted, it automatically switches to the LoRa spread spectrum communication unit to transmit key environmental alarm information and device status data at a lower data rate.

[0018] Preferably, the communication and interface module also provides at least one local wired interface for device debugging, data export, or connection to a local auxiliary sensor.

[0019] Preferably, the software system of the device has embedded remote configuration and OTA upgrade functions, and authorized users can send configuration commands to the device through the remote monitoring center to dynamically modify the parameters of the adaptive sampling control logic, the threshold of the sensor state diagnosis algorithm, or the coefficients of the data preprocessing algorithm.

[0020] Preferably, the core control and data processing module can complete the online update of its own firmware and some algorithm modules without power interruption after receiving a legitimate OTA upgrade package, and perform verification and restart after the update is completed.

[0021] The technical effects achieved by this invention are as follows:

[0022] 1. By integrating a micro environmental compensation unit and combining it with the dynamic compensation and correction algorithm in the core control and data processing module, this invention can sense and counteract the interference of environmental factors on the sensor probe in real time, significantly improving the accuracy and long-term stability of monitoring data, and effectively overcoming the technical defects of traditional sensors that are prone to data drift in complex outdoor environments.

[0023] 2. This invention achieves dynamic optimal matching of energy supply and consumption through the coordinated operation of the power supply and management module and the adaptive sampling control logic. The device can intelligently adjust its operating strategy according to its own power status and environmental requirements, greatly extending the continuous working time in scenarios without a stable mains power supply, and solving the core problem of insufficient power supply stability for outdoor monitoring equipment.

[0024] 3. The built-in sensor status diagnosis algorithm and data fusion unit of this invention endow the device with localized real-time fault diagnosis and data quality assessment capabilities. This system can not only promptly identify sensor anomalies and issue alarms, but also output more reliable comprehensive environmental indicators through multi-source data fusion, improving the device's intelligence level and decision support value, and reducing complete reliance on remote central analysis.

[0025] 4. The modular structure and layered housing design of this invention make the expansion, replacement, and maintenance of sensors and functional modules extremely convenient, reducing the maintenance cost throughout the entire life cycle. Standardized interfaces and robust protective design ensure that the device can flexibly adapt to various monitoring scenarios and harsh environments, enhancing the product's versatility and reliability. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall technical architecture of the online environmental monitoring device proposed in this invention;

[0027] Figure 2 This is a schematic diagram of the core principle framework of sensor state diagnosis and adaptive sampling control in this invention. Detailed Implementation

[0028] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] Example 1

[0030] Please refer to the attached document. Figure 1 This online environmental monitoring device constitutes a complete system integrating environmental sensing, energy management, data processing, and communication. The core architecture of this system consists of four main functional parts: an environmental parameter sensing module, an energy supply and power management module, a core control and data processing module, and a communication and interface module. These modules are tightly coupled through electrical connections and a data bus, enabling fully automated operation from environmental information collection, energy acquisition and distribution, intelligent data processing to remote transmission of results.

[0031] The environmental parameter sensing module is the front-end component that directly interacts with the environment. This module integrates at least two different types of sensor units, specifically designed for synchronous or asynchronous data acquisition of atmospheric, water, or soil environmental factors. Typical configurations include, but are not limited to, atmospheric particulate matter concentration sensors, sulfur dioxide gas sensors, water pH sensors, and soil moisture sensors. These sensor units all employ independent modular packaging structures. Each sensor unit's housing is made of corrosion-resistant and chemically stable engineering plastics or metal alloys, and it achieves quick connection and locking with the device body through standardized electrical and mechanical interfaces. The standardized electrical interface is defined as a multi-pin connector containing a 5V or 3.3V DC power pin, a ground pin, and unidirectional or bidirectional data communication pins. The standardized mechanical interface uses a snap-fit ​​connector housing with a keyway to prevent mis-mating and is equipped with a manually tightened waterproof locking ring to ensure mechanical stability and IP67-level sealing performance under vibration.

[0032] Within the environmental parameter sensing module, in addition to the core monitoring sensor unit, a crucial miniature environmental compensation unit is integrated. This miniature environmental compensation unit is physically mounted as close as possible to the probes of each monitoring sensor. It contains a high-precision temperature and humidity sensor chip and a miniature barometric pressure sensor chip. The temperature and humidity sensor chip has a measurement range covering -40°C to 85°C, and the humidity measurement range covers 0% relative humidity with an accuracy of ±2% relative humidity. The miniature barometric pressure sensor has a measurement range covering 300 hPa to 1100 hPa with an accuracy better than ±1 hPa. This miniature environmental compensation unit monitors the micro-environmental temperature, humidity, and pressure parameters around the sensor probes in real time at a frequency of once per second, and transmits this micro-environmental parameter data to the core control and data processing module via an independent serial peripheral interface bus.

[0033] The core control and data processing module, serving as the central hub for the entire device's computation and control, employs a high-performance, low-power microprocessor as its main control chip. This microprocessor integrates a floating-point unit and large-capacity flash memory, and its embedded system software coordinates and drives all peripheral modules. The module communicates with other modules through a multi-channel serial peripheral interface controller, an integrated circuit bus controller, and a universal asynchronous transceiver. The core control and data processing module continuously receives two types of data streams from the environmental parameter sensing module: raw environmental monitoring data output by each monitoring sensor unit, and micro-environmental parameter data output by the micro-environmental compensation unit.

[0034] The core control and data processing module has a built-in data preprocessing algorithm. This algorithm is the first line of defense in ensuring data quality. After the data preprocessing algorithm is started, it first performs noise filtering on the input raw monitoring data based on a sliding time window. The length of the sliding time window can be configured according to the characteristics of the monitoring parameters, with a typical value of 30 seconds. Within the window, the algorithm uses a combination of median filtering and arithmetic mean filtering to remove obvious impulse interference noise and smooth random fluctuations. Specifically, the algorithm first sorts the data points within the window by numerical value, removes 10% of the data points with the maximum and minimum values, then calculates the arithmetic mean of the remaining data points, and uses this average as the filtered data output for that moment.

[0035] After noise filtering, the data preprocessing algorithm immediately initiates the dynamic compensation and correction phase. This phase utilizes real-time micro-environment parameters from the micro-environment compensation unit, combined with a compensation coefficient matrix pre-stored in the microprocessor's flash memory, to precisely correct the filtered data. The compensation coefficient matrix is ​​a multi-dimensional lookup table, with its dimensions corresponding to different micro-environment temperature, humidity, and pressure ranges. Each cell stores the compensation offset and compensation gain coefficient for a specific sensor under specific micro-environment conditions. Based on the currently read micro-environment temperature, humidity, and pressure values, the algorithm determines the corresponding compensation parameters from the compensation coefficient matrix using linear interpolation or nearest neighbor lookup. Subsequently, the correction calculation is performed according to the following mathematical relationship:

[0036]

[0037] Among them, V filtered V represents the original sensor reading after noise filtering, Δ represents the compensation offset obtained from the compensation coefficient matrix, G represents the compensation gain coefficient obtained from the compensation coefficient matrix, and V represents the compensation offset obtained from the compensation coefficient matrix. corrected This is the final output environmental parameter value after dynamic compensation and correction. This compensation mechanism can effectively counteract sensor zero-point drift and sensitivity changes caused by changes in ambient temperature, as well as cross-sensitivity interference caused by changes in air pressure or humidity on certain gas sensors or particulate sensors.

[0038] Please refer to the attached document. Figure 2The core control and data processing module also incorporates a sensor status diagnostic algorithm. This algorithm runs periodically as an independent background task, with a default diagnostic cycle of one hour, but this can be modified remotely. The core objective of the sensor status diagnostic algorithm is to assess the operational health of each sensor and provide early warnings of potential faults. During the initialization phase, the algorithm collects historical data from the sensors under known normal conditions and constructs a reference feature model through feature extraction. This reference feature model includes the mean and variance characteristics of the sensor's output data in the time domain, as well as the amplitude characteristics of the dominant frequency component after Fast Fourier Transform in the frequency domain.

[0039] At the start of each diagnostic cycle, the sensor state diagnostic algorithm first extracts the time-domain and frequency-domain features of the current sensor output data stream within a window of equal duration. Time-domain feature calculation includes calculating the arithmetic mean and standard deviation of the current data window. Frequency-domain feature calculation involves performing a Fast Fourier Transform on the current data window and extracting the three highest-energy frequency components in its spectrum and their corresponding amplitude values. Subsequently, the algorithm initiates a feature comparison process, comparing the currently extracted feature vector with the baseline feature vector stored in the reference feature model. For the time-domain mean, the absolute value of its relative deviation is calculated. For the time-domain variance, the absolute value of its rate of change is calculated. For the frequency-domain dominant frequency amplitude, its Euclidean distance is calculated. Based on preset weighting coefficients, the algorithm comprehensively calculates these differences into a single feature deviation index.

[0040] The sensor condition diagnosis algorithm presets two key thresholds to guide decision-making: the first threshold is set at a feature deviation of 15, and the second threshold is set at a feature deviation of 40. When the calculated feature deviation is less than the first threshold, the algorithm determines that the sensor is in a healthy state and requires no special operation. When the feature deviation exceeds the first threshold but has not yet reached the second threshold, the algorithm determines that the sensor has a potential drift risk. At this time, the system will activate the data compensation enhancement mode. In this mode, the core control and data processing modules will dynamically adjust the parameters in the data preprocessing algorithm, such as reducing the sliding filter window to 15 seconds for faster response to changes, or temporarily adopting a more aggressive version of the compensation coefficient matrix to correct the data with stronger force.

[0041] When the characteristic deviation further increases and exceeds the second threshold, the sensor status diagnostic algorithm determines that the sensor may have experienced a hard fault or severe performance degradation. Once this determination is made, the core control and data processing module immediately sends a fault warning message to the remote monitoring center via the communication and interface module, containing the sensor identifier, fault type code, specific characteristic deviation value, and timestamp. Simultaneously, the module records detailed data of this diagnostic event in a dedicated log area of ​​its internal non-volatile memory, including the original data fragment triggered by the fault, the calculated characteristic values, and the historical characteristic deviation curve, providing data support for subsequent maintenance analysis.

[0042] The power supply and management module is the cornerstone of ensuring the long-term stable operation of the device outdoors. This module comprises three core sub-components: solar photovoltaic panels, an energy storage unit, and a power management circuit. The solar photovoltaic panels utilize monocrystalline silicon technology, with an anti-reflection coating on their surface to improve light capture efficiency, achieving a photoelectric conversion efficiency of no less than 22%. The photovoltaic panels are connected to the internal power management circuitry via weather-resistant cables. The energy storage unit typically consists of a set of lithium-ion or lithium iron phosphate batteries, with a nominal voltage of 12 volts and a capacity ranging from 20 Ah to 100 Ah depending on the application. The energy storage unit is responsible for providing power to the entire device when sunlight is insufficient or absent.

[0043] The power management circuit is the intelligent control core of the power supply and management module, and its main body is a highly integrated power management integrated circuit. This circuit integrates a multi-output adjustable DC-DC converter and a load switch array composed of metal-oxide-semiconductor field-effect transistors. The DC-DC converter is responsible for stably converting the fluctuating DC power generated by the solar photovoltaic panel or the DC power output from the energy storage unit into the 3.3V DC power required by the core control and data processing module, the 5V DC power required by the environmental parameter sensing module, and various voltage levels required by other peripheral chips. The load switch array is controlled by the digital input / output ports of the core control and data processing module, and can independently connect or disconnect the power path to each functional module, achieving fine-grained power consumption management.

[0044] The power management circuit is also responsible for the precise management of the charging and discharging process of the energy storage unit. It monitors the terminal voltage and charging / discharging current of the energy storage unit in real time, and adopts a three-stage charging algorithm: first, constant current charging; then, when the voltage reaches the set value, it switches to constant voltage charging; and finally, it enters the float charging state. The circuit has a built-in battery capacity calculation model, which uses a combination of coulomb counting and voltage lookup table method to estimate the remaining percentage of the energy storage unit's capacity in real time, and periodically reports this capacity value to the core control and data processing module via the integrated circuit bus.

[0045] The adaptive sampling control logic within the core control and data processing module works closely with the power management circuit to achieve dynamic optimization of energy allocation. The adaptive sampling control logic receives remaining power information from the power management circuit and analyzes the volatility of current environmental data from the environmental parameter sensing module. Data volatility is quantified by calculating the standard deviation of a specific environmental parameter over the last 5 minutes. The system also pre-stores a monitoring task priority table, which defines which sensors are considered core sensors and which are non-core sensors under different monitoring targets.

[0046] The adaptive sampling control logic dynamically decides and outputs control commands based on the above input information. These commands mainly adjust two aspects: the sampling frequency of the environmental parameter sensing module and the data reporting strategy of the communication and interface module. For example, when the remaining power of the energy storage unit is higher than 70% and the environmental data fluctuates significantly, the logic will instruct the environmental parameter sensing module to operate at a higher sampling frequency, such as sampling once every 10 seconds, and instruct the communication and interface module to report a complete data packet once every 5 minutes. When the remaining power of the energy storage unit drops to between 30% and 70%, the logic will appropriately reduce the sampling frequency to once every 30 seconds and extend the data reporting interval to once every 15 minutes.

[0047] When the remaining power of the energy storage unit reported by the power management circuit falls below a preset low power threshold, such as 30%, the adaptive sampling control logic immediately instructs the system to enter a low-power operation mode. In this mode, the logic first cuts off power to non-core sensors via the load switch array. Then, it instructs the remaining core sensors to operate at the minimum necessary sampling frequency, such as sampling once every 2 minutes. Simultaneously, it significantly extends the data reporting interval of the communication and interface modules, for example, adjusting it to report only the most critical environmental parameters and device status information once per hour. The core objective of this strategy is to minimize overall system power consumption, prioritize the continuous operation of core monitoring functions, and extend the device's survival time under conditions without external charging as much as possible.

[0048] The main structure of the device employs a meticulously designed layered shell to meet multiple requirements for protection, installation, and heat dissipation. From the outside in, the shell consists of three main layers: a protective outer shell, a module mounting layer, and a heat dissipation substrate. The protective outer shell is injection molded from high-strength polycarbonate composite material, achieving an overall IP67 protection rating, completely preventing dust intrusion and temporary immersion in pressurized water. The top of the shell features a dedicated area for mounting solar photovoltaic panels; this area's surface is optically polished and coated with an anti-reflective film to maximize light transmittance. Multiple louvered ventilation holes are located on the sides of the shell, with waterproof and breathable membranes inside to ensure heat dissipation while preventing liquid water intrusion.

[0049] The module mounting layer, located inside the protective housing, is a metal backplate with precision guide grooves and a mechanical locking mechanism. The size and shape of the guide grooves are precisely matched to the connectors of each sensor and communication module, ensuring that the modules can only be inserted in the correct orientation. The locking mechanism uses a spring-assisted lever-type latch; a clear click sound indicates that the module is mechanically locked when inserted into place. This design allows field personnel to quickly replace and upgrade sensors or communication modules without tools, greatly simplifying the maintenance process.

[0050] The heat sink is the innermost layer of the layered housing design. It is made of high thermal conductivity aluminum or copper alloy and is 3 mm thick. The heat sink makes close contact with the metal casing of the core control and data processing module and the heat dissipation pads of high-power components in the power management circuit via thermal grease. The core control and data processing module generates heat during high-speed operation, and the DC-DC converter in the power management circuit also generates heat due to power loss during energy conversion. This heat is efficiently transferred to the heat sink via thermal grease, and then dissipated to the external environment through convection between the heat sink and the internal air, as well as heat conduction with the protective casing. This ensures that the core components operate within a suitable temperature range, preventing performance degradation or damage due to overheating.

[0051] The core control and data processing module also integrates an advanced data fusion unit. This unit receives multi-source environmental monitoring data from a data preprocessing algorithm, which has already undergone noise filtering and dynamic compensation correction. For example, in an air quality monitoring scenario, it may simultaneously receive PM2.5 concentration values ​​from a particulate matter sensor, NO2 concentration values ​​from a nitrogen dioxide sensor, and O3 concentration values ​​from an ozone sensor. The data fusion unit employs a confidence-weighted data fusion algorithm to generate a more reliable and comprehensive environmental quality index.

[0052] The core of this algorithm lies in dynamically assigning a confidence weight to each input data stream. The confidence weight is calculated based on two main factors: one is the sensor's health score, periodically output by the sensor state diagnosis algorithm. This health score is an inverse mapping function of feature deviation; the higher the feature deviation, the lower the health score. The other factor is the stability index of the sensor's historical data, obtained by calculating the coefficient of variation of the sensor's output data over the past 24 hours. The data fusion unit, according to a predefined weight calculation formula, combines the health score and the stability index to calculate a dynamic confidence weight value between 0 and 1 for each sensor data stream.

[0053] Subsequently, the data fusion unit performs fusion calculations. For the environmental quality index to be generated, if it is defined as a weighted average of the various sub-indices, the fusion calculation process is as follows: First, the calibrated data values ​​of each sensor are normalized to a unified index scale. Then, they are weighted and summed using their corresponding dynamic confidence weights to obtain a comprehensive environmental quality index. This comprehensive environmental quality index, along with the original calibrated data from each sensor, their respective confidence weights, and device status information, is packaged into a standard format data frame, ready to be reported to the remote monitoring center via the communication and interface module. This fusion output not only provides a macroscopic assessment of the environmental condition but also preserves the traceability of the original data and indicates the credibility of each data source.

[0054] The communication and interface module is responsible for data exchange between the device and the outside world. This module is designed to support at least two mainstream wired or wireless communication protocols to ensure connection reliability. For wireless communication, the module integrates a high-performance 4G or 5G cellular communication module as the primary communication link. This module supports TCP or UDP network protocols and can establish a secure socket connection with the remote monitoring center server, enabling high-speed data upload and command reception.

[0055] Considering potential cellular network coverage blind spots or weak signal areas in outdoor environments, the communication and interface module also integrates a LoRa spread spectrum communication unit as a backup communication link. The LoRa unit operates in a specific unlicensed frequency band, featuring long transmission distance and strong penetration capabilities, but with a lower data transmission rate. The communication and interface module internally runs a link status monitoring algorithm, continuously monitoring the received signal strength indicator (RSS) of the primary 4G or 5G link. When the RSS indicator remains below a preset threshold (e.g., -100 dBm) for 10 consecutive seconds, or when a complete network connection interruption is detected, the module automatically triggers a communication link switch. The system immediately attempts to transmit critical environmental alarm information, basic device status data, and a minimum number of core sensor readings to any potential LoRa gateways or relay nodes via the LoRa spread spectrum communication unit at a pre-configured low data rate. This dual-link design ensures that the device can maintain minimum data connectivity and alarm capabilities even under extremely harsh communication conditions.

[0056] In addition to wireless communication capabilities, the communication and interface module also provides at least one local wired interface, typically a waterproof RS485 or Ethernet interface. This local wired interface can be used for parameter configuration during equipment installation and commissioning, and can also be used in special circumstances to directly export historical data stored internally by the device via a local connection. Furthermore, this interface can also be used to connect local auxiliary sensors or actuators, expanding the device's functional range.

[0057] The device's entire software system integrates remote configuration and over-the-air (OTA) upgrade capabilities, granting it exceptionally high flexibility in remote management. Authorized users can send encrypted and authenticated configuration commands to designated devices via the management platform of the remote monitoring center. These commands can dynamically modify various parameters within the device, such as the low-battery threshold and the time window length for data volatility assessment in the adaptive sampling control logic; the values ​​of the first and second thresholds in the sensor state diagnostic algorithm; and the length of the sliding time window or specific coefficient values ​​in the compensation coefficient matrix in the data preprocessing algorithm.

[0058] For more complex software updates, the system supports a complete over-the-air (OTA) update protocol. The remote monitoring center can package a new firmware version or a specific algorithm module, generate a digitally signed OTA update package, and send it to the target device. Upon receiving the OTA update package, the core control and data processing module first verifies the legitimacy of its digital signature to ensure the update source is trustworthy. After successful verification, the module backs up the currently running firmware to a designated storage area and then securely writes the new firmware or algorithm module to the update area of ​​the program memory. The entire writing process is performed without power interruption. After writing is complete, the module calculates the checksum of the new firmware and compares it with the expected value. Once confirmed, the system automatically restarts and loads the new firmware version, completing the entire online update process. This mechanism enables the device to continuously evolve, fixing potential defects or adding new features without human intervention on-site.

[0059] Example 2

[0060] This embodiment focuses on describing the targeted implementation scheme of the environmental parameter sensing module and data fusion unit of the online environmental monitoring device in a specific water quality monitoring application scenario, as well as the special operation strategy of the power supply and management module under continuous rainy weather.

[0061] In water quality monitoring applications, the sensor unit configuration of the environmental parameter sensing module focuses on acquiring key water parameters. A typical configuration includes a pH sensor using a glass electrode method, a total dissolved solids sensor based on conductivity, a novel dissolved oxygen sensor based on optical fluorescence, and a turbidity sensor using a diffused light method to measure water turbidity. All these sensor units utilize a pressure-resistant and waterproof enclosure unique to this embodiment, achieving an IP68 protection rating, enabling long-term immersion at a depth of 5 meters underwater. The sensor probe materials have been specially treated to address corrosive ions that may be present in the water; for example, the pH sensor's glass electrode uses a low-impedance lithium-ion glass membrane, and the metal components are made of titanium alloy to resist corrosion from seawater or industrial wastewater.

[0062] The miniature environmental compensation unit plays a crucial role in this scenario. In addition to conventional temperature, humidity, and air pressure monitoring, this unit integrates a miniature water temperature sensor with a measurement accuracy of ±0.1 degrees Celsius. Since water pH and dissolved oxygen measurements are highly sensitive to temperature, this water temperature parameter is directly used for high-precision temperature compensation of the sensor data. The pre-stored compensation coefficient matrix in the core control and data processing module is also specifically optimized for water quality parameters, containing precise compensation coefficients for each water quality parameter sensor under different water temperatures and ambient air temperatures.

[0063] When generating a comprehensive environmental quality index in this scenario, the data fusion unit also adjusted its algorithm strategy. Given the often interrelated relationships among water quality parameters—for example, dissolved oxygen content is closely related to water temperature and water disturbance—a simple weighted average may not be sufficient to reflect the true water quality. Therefore, this embodiment introduces a lightweight multivariate regression model into the data fusion algorithm. This model uses compensated and corrected total dissolved solids, turbidity, and water temperature as input features to predict a theoretical dissolved oxygen reference value. The data fusion unit then compares the actually measured dissolved oxygen value with the model-predicted reference value; the magnitude of the deviation serves as an important factor in adjusting the confidence weight of the dissolved oxygen sensor data.

Claims

1. An environmental on-line monitoring device, characterized by, The device comprises: an environmental parameter sensing module integrated with at least two different types of sensors for data collection of environmental elements, the sensors are modularly packaged and connected to the device body through standardized interfaces, the environmental parameter sensing module is internally integrated with a miniature environmental compensation unit, which contains a temperature and humidity sensor and a barometric pressure sensor for real-time monitoring of the micro-environmental parameters around the sensor probe; a power supply and power management module containing a solar photovoltaic panel, an energy storage unit, and a power management circuit, the power management circuit is integrated with a multi-output adjustable DC-DC converter and a load switch array for providing power to the entire device and managing the charging and discharging process of the energy storage unit; a core control and data processing module that connects and coordinates the environmental parameter sensing module, the power supply and power management module, and the communication and interface module, which internally solidifies data preprocessing algorithms, sensor state diagnosis algorithms, and adaptive sampling control logic, the data preprocessing algorithms perform noise filtering on the original monitoring data from the environmental parameter sensing module based on a sliding time window, and use the micro-environmental parameters from the miniature environmental compensation unit to dynamically compensate and correct the filtered data through a pre-stored compensation coefficient matrix, the sensor state diagnosis algorithm runs periodically and builds a reference feature model based on historical normal data of the sensor, calculates the feature deviation by extracting the time and frequency domain features of the current sensor output data and comparing them with the reference feature model, the adaptive sampling control logic dynamically adjusts the sampling frequency and data reporting strategy based on the remaining power of the energy storage unit, the volatility of environmental data, and the priority of monitoring tasks; a communication and interface module that supports at least two mainstream wired or wireless communication protocols for data interaction between the device and the remote monitoring center or other local devices.

2. The device according to claim 1, wherein, The sensor state diagnosis algorithm determines that the sensor has potential drift when the feature deviation exceeds the pre-set first threshold and starts the data compensation reinforcement mode, and determines that the sensor may have a fault when the feature deviation exceeds the higher pre-set second threshold and sends a fault warning message through the communication and interface module, while recording the diagnosis data in the local log.

3. The device according to claim 1, wherein, The adaptive sampling control logic instructs the system to enter a low-power running mode when the energy storage unit's power is below the pre-set low-power threshold, reducing the sampling rate of non-core sensors and extending the data reporting interval to prioritize the continuous operation of core monitoring functions.

4. The device according to claim 1, wherein, The main structure of the device adopts a layered shell design, including a protective shell, a module installation layer, and a heat dissipation substrate, the protective shell has an IP67 or above protection level, the module installation layer uses a standardized slot design with guide grooves and locking mechanisms, and the heat dissipation substrate is in close contact with the core control and data processing module and the power management circuit through thermal grease and the module shell.

5. The device according to claim 1, wherein, The core control and data processing module is also integrated with a data fusion unit, which receives the pre-processed and compensated multi-source environmental monitoring data, and generates a comprehensive environmental quality index using a data fusion algorithm based on confidence weighting. The algorithm assigns a dynamic confidence weight to the data stream of each sensor, and the calculation of the confidence weight is based on the health score output by the sensor state diagnosis algorithm and the stability index of the historical data of the sensor.

6. The device according to claim 5, wherein, The comprehensive environmental quality index output by the data fusion unit is packaged together with the original corrected data of each sensor and reported through the communication and interface module.

7. The device according to claim 1, wherein In addition to supporting 4G or 5G wireless communication, the communication and interface module is also integrated with a LoRa spread spectrum communication unit as a backup communication link. When the signal strength of the primary wireless communication link is below a preset threshold or the connection is interrupted, it automatically switches to the LoRa spread spectrum communication unit to transmit critical environmental warning information and device status data at a lower data rate.

8. The device according to claim 7, wherein, The communication and interface module also provides at least one local wired interface for device debugging, data export, or connecting local auxiliary sensors.

9. The device according to claim 1, wherein, The software system of the device solidifies remote configuration and OTA upgrade functions, allowing authorized users to send configuration instructions to the device through a remote monitoring center to dynamically modify the parameters of the adaptive sampling control logic, the thresholds of the sensor state diagnosis algorithm, or the coefficients of the data preprocessing algorithm.

10. The device according to claim 9, wherein After receiving a legitimate OTA upgrade package, the core control and data processing module can complete the online update of its firmware and part of the algorithm modules without power loss, and perform verification and restart after the update is completed.

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