Radio-based environment monitoring method and system

Abstract

The invention relates to the technical field of environment monitoring, and discloses a radio-based environment monitoring method and system, and the method comprises the steps: deploying an ocean buoy integrated with a plurality of parameter sensors, and detecting the water temperature, salinity, dissolved oxygen, pH value and illumination intensity in real time; transmitting the data detected in real time to a shore-based receiving station by using a radio communication module; performing drift correction and noise filtering on data transmission detected in real time; performing data fusion analysis according to the mutual relation among different data, and generating a marine environment assessment report; and abnormal early warning information is generated according to the marine environment assessment report and is fed back to a monitoring center in real time through radio. According to the invention, through integration of various parameter sensors, comprehensive monitoring of multiple parameters of the marine environment is realized, and the accuracy and reliability of monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a radio-based environmental monitoring method and system. Background Art

[0002] With the intensification of global climate change and marine environmental problems, marine environmental monitoring has become an important part of environmental protection and scientific research. Traditional marine monitoring methods rely on manual sampling or wired monitoring equipment, which have problems such as limited monitoring range, long data acquisition cycle, and high cost. In recent years, with the development of wireless communication technology, radio-based ocean buoy monitoring systems have gradually become an efficient and low-cost solution.

[0003] However, existing radio ocean buoy monitoring systems still face many challenges in terms of data transmission stability, sensor data accuracy, and energy management for long-term operation. Radio signals are susceptible to interference in the marine environment, and sensor data may be inaccurate due to drift and noise. In addition, long-term operation of ocean buoys requires a stable energy supply and self-maintenance capabilities. Therefore, there is an urgent need for an ocean buoy environmental monitoring system that can efficiently transmit data, automatically correct sensor readings, and has the ability to operate autonomously for a long time. Summary of the invention

[0004] In view of this, the present invention proposes a radio-based environmental monitoring method and system, aiming to solve the challenges of existing radio ocean buoy monitoring systems in data transmission stability, sensor data accuracy and long-term operation energy management.

[0005] The present invention proposes a radio-based environment monitoring method, comprising: Deploy an ocean buoy with integrated parameter sensors to detect water temperature, salinity, dissolved oxygen, pH value and light intensity in real time; use a radio communication module to transmit the real-time detected data to a shore-based receiving station, wherein the radio communication module adopts a radio protocol or a narrowband Internet of Things protocol; Drift correction and noise filtering of real-time detected data transmissions, including multi-point calibration based on historical data, application of Kalman filter algorithm to remove environmental noise, and dynamic adjustment of filter parameters using adaptive filters; Conduct data fusion analysis based on the relationships between different data to generate a marine environment assessment report; Abnormal warning information is generated based on the marine environment assessment report and fed back to the monitoring center in real time via radio; among them, the warning algorithms include threshold method, trend analysis method and abnormal detection algorithm based on machine learning.

[0006] Preferably, the data fusion analysis includes: Correct and obtain dissolved oxygen readings based on the effects of water temperature and salinity on dissolved oxygen solubility; Based on the regulation of photosynthesis rate by light intensity, the photosynthesis intensity is obtained by combining pH change analysis; Assess the extent of ocean acidification and eutrophication based on pH and dissolved oxygen concentration.

[0007] Preferably, the detection of dissolved oxygen is performed by an optical method or an electrochemical method, and the sensor state is monitored by wireless radio frequency identification technology, and the sensor sampling frequency is automatically adjusted to optimize the data. The dissolved oxygen correction formula is: ; Among them, DO corr Indicates the corrected dissolved oxygen value; DO raw represents the raw dissolved oxygen reading; k temp and k sal Respectively represent the temperature correction factor and salinity correction factor, T ref and S ref Indicates reference temperature and reference salinity; T meas and S meas Respectively represent the measured temperature and measured salinity; δ RF represents the data drift caused by the radio signal; δ env Indicates data drift caused by environmental noise.

[0008] Preferably, the pH value is detected using a glass electrode method or an optical pH sensor, and the electrode impedance change is detected by radio signals to determine the electrode aging or contamination state. The pH data correction formula is: ; Among them, pH corr Indicates the corrected pH; pH raw represents the original pH reading; γ represents the electrode impedance correction factor; Z RF Represents the electrode impedance for radio signal detection: Z baseline represents the reference electrode impedance; α represents the influence coefficient of dissolved oxygen on pH; DO ref Indicates the reference dissolved oxygen concentration.

[0009] Preferably, the light intensity is detected by measuring with a photodiode, and the light data is corrected for errors using the attenuation characteristics of radio signals under different light conditions. The light intensity correction formula is: ; Among them, Lux corr Indicates the corrected light intensity; Lux raw represents the original light intensity reading; β is the correction factor for light measurement due to radio signal attenuation; P RF,lossIndicates the radio signal loss power; P RF,baseline Indicates the reference radio signal power.

[0010] Preferably, the radio communication module has an adaptive frequency hopping function to avoid environmental interference and improve the stability of data transmission. The channel selection formula is: ; Among them, F opt represents the optimal channel frequency; f represents the candidate channel frequency; N(f) represents the noise level of frequency f; S(f) represents the signal strength of frequency f; I(f) represents the interference intensity of frequency f; λ represents the interference weight coefficient.

[0011] Preferably, the buoy system has a channel state information feedback mechanism to dynamically adjust the signal power and transmission rate according to the channel state to optimize the communication quality. The signal power adjustment formula is: ; Among them, P adj Represents the adjusted signal power; P base Indicates basic transmit power; CSI loss Indicates the current channel loss; CSI opt Indicates the channel state information under ideal channel conditions.

[0012] Preferably, the photosynthesis rate of marine phytoplankton is quantitatively analyzed by combining the changes in light intensity and pH value; the photosynthesis intensity is calculated by the following formula: ; Among them, P represents light intensity; μ represents the photosynthesis efficiency coefficient; I represents the measured light intensity; K represents the light half-saturation constant; ΔpH represents the change in pH value per unit time.

[0013] Preferably, when evaluating the degree of ocean acidification and eutrophication in combination with pH value and dissolved oxygen concentration, the acidification index and eutrophication index are calculated by the following formula: ; ; Among them, AI represents the ocean acidification index; β represents the acidification sensitivity coefficient; pHo represents the benchmark pH value; EI represents the eutrophication index; γ represents the eutrophication sensitivity coefficient; DOo represents the benchmark dissolved oxygen concentration.

[0014] The present invention also proposes a radio-based environment monitoring system, comprising: The ocean buoy body has a built-in multi-parameter sensor module and a radio communication module for real-time detection and transmission of data on water temperature, salinity, dissolved oxygen, pH value, and light intensity. The ocean buoy body has automatic calibration and remote upgrade functions; An offshore relay station is used to enhance the stability and range of signal transmission. The offshore relay station supports multi-buoy data integration and preliminary analysis functions; The shore-based receiving station is equipped with a data processing and analysis module, which is used to receive, correct, store and analyze data from buoys, generate environmental assessment reports and provide abnormal warnings. The system supports cloud platform access to achieve remote data access and sharing.

[0015] Preferably, the ocean buoy body is equipped with a solar power supply module and an energy management system, and the energy management system dynamically adjusts the sensor working mode and data transmission frequency according to real-time power and environmental conditions.

[0016] Preferably, the data processing and analysis module includes a deep learning algorithm for long-term data trend prediction and complex environment pattern recognition.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method and system for monitoring the environment of ocean buoys based on radio technology. The system realizes real-time and accurate monitoring of key environmental parameters such as water temperature, salinity, dissolved oxygen, pH value and light intensity by integrating multi-parameter sensors with radio communication modules. The advantages of the present invention include: Efficiency and stability of data transmission: The present invention adopts low-power long-distance radio protocols (such as LoRa or NB-IoT), and combines adaptive frequency hopping technology with channel state feedback mechanism to significantly reduce the impact of environmental interference on data transmission, thereby improving the stability and reliability of data transmission.

[0018] High precision of monitoring data: The present invention uses multi-point calibration, Kalman filtering, adaptive filtering and other algorithms to make fine corrections to sensor data, and combines correction formulas for parameters such as temperature and salinity to ensure the accuracy and consistency of monitoring data.

[0019] Long-term autonomous operation capability of the buoy: The system has a built-in solar power supply module and intelligent energy management system, which can dynamically adjust the working status of the sensor and communication module according to environmental conditions, thereby extending the service life of the buoy.

[0020] Multi-dimensional data fusion and analysis: This invention conducts a comprehensive analysis of data such as water temperature, salinity, dissolved oxygen, pH value and light intensity to assess environmental issues such as ocean acidification and eutrophication, and provides anomaly detection and early warning functions based on machine learning.

[0021] Buoy physical stability monitoring and correction: The present invention integrates an accelerometer and a gyroscope to monitor the drift, tilt and rotation status of the buoy, and corrects the monitoring data through radio signal interference analysis to ensure data stability and reliability in complex marine environments.

[0022] In summary, the present invention not only improves the efficiency and accuracy of marine environment monitoring, but also reduces the operation and maintenance costs of the system, providing solid technical support for marine ecosystem research and climate change monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 is a flow chart of the radio-based environmental monitoring method of the present invention; Figure 2 It is a functional block diagram of the radio-based environmental monitoring method system of the present invention. DETAILED DESCRIPTION

[0024] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] See also Figure 1 This embodiment provides a radio-based environment monitoring method, including: Deploy an ocean buoy with integrated parameter sensors to detect water temperature, salinity, dissolved oxygen, pH value and light intensity in real time; use a radio communication module to transmit the real-time detected data to a shore-based receiving station, wherein the radio communication module adopts a radio protocol or a narrowband Internet of Things protocol; Drift correction and noise filtering of real-time detected data transmissions, including multi-point calibration based on historical data, application of Kalman filter algorithm to remove environmental noise, and dynamic adjustment of filter parameters using adaptive filters; Conduct data fusion analysis based on the relationships between different data to generate a marine environment assessment report; Abnormal warning information is generated based on the marine environment assessment report and fed back to the monitoring center in real time via radio; among them, the warning algorithms include threshold method, trend analysis method and abnormal detection algorithm based on machine learning.

[0026] It can be seen that this embodiment proposes an environment monitoring method based on radio technology, and the specific steps are as follows: First, deploy a series of ocean buoys that integrate multiple parameter sensors. These sensors can monitor and record key environmental indicators such as water temperature, salinity, dissolved oxygen content, pH value and light intensity in real time. Then, these real-time monitored data are transmitted to the receiving station on the shore through the radio communication module. The radio communication module here can use radio protocol or narrowband Internet of Things protocol to send and receive data. In the process of data transmission, in order to ensure the accuracy of data, it is necessary to perform drift correction and noise filtering on the real-time detected data, which includes using historical data for multi-point calibration to eliminate system errors; applying the Kalman filter algorithm to remove environmental noise and improve the purity of data; at the same time, using adaptive filters to dynamically adjust the filter parameters to adapt to the impact of environmental changes on data quality; Next, we conduct an in-depth analysis of the various environmental data collected, and use data fusion technology to analyze the interrelationships and impacts between different data to generate a comprehensive marine environmental assessment report; Finally, based on the marine environmental assessment report, corresponding abnormal warning information is generated, which will be fed back to the monitoring center in real time via radio so that timely measures can be taken. Warning algorithms include but are not limited to threshold methods, which set specific environmental parameter thresholds and issue warnings once the monitored data exceeds these thresholds; trend analysis methods, which analyze the changing trends of environmental parameters and predict possible abnormal situations; and anomaly detection algorithms based on machine learning, which use historical data to train models and automatically identify potential abnormal patterns.

[0027] It is understandable that this embodiment aims to provide an efficient and real-time marine environment monitoring solution, which realizes comprehensive monitoring through ocean buoys integrating multiple sensors and realizes rapid data transmission and processing in combination with advanced radio communication technology. This method not only improves the accuracy and timeliness of environmental monitoring, but also provides strong technical support for marine environmental protection and disaster warning. Through data fusion analysis and the generation of abnormal warning information, abnormal changes in the marine environment can be discovered in a timely manner, providing decision-making basis for relevant departments and effectively responding to marine environmental challenges.

[0028] In some embodiments of the present application, the data fusion analysis includes: Correct and obtain dissolved oxygen readings based on the effects of water temperature and salinity on dissolved oxygen solubility; Based on the regulation of photosynthesis rate by light intensity, the photosynthesis intensity is obtained by combining pH change analysis; Assess the extent of ocean acidification and eutrophication based on pH and dissolved oxygen concentration.

[0029] It can be seen that this embodiment adopts a more sophisticated processing method in the data fusion analysis stage. First, considering the influence of water temperature and salinity on dissolved oxygen solubility, the dissolved oxygen readings are corrected accordingly to obtain more accurate dissolved oxygen content information. This step is crucial for understanding the oxygen cycle and biological respiration in the ocean.

[0030] Secondly, by analyzing the regulatory effect of light intensity on the rate of photosynthesis and combining it with the change in pH, the intensity of photosynthesis in the ocean was evaluated. As a basic process in the marine ecosystem, the intensity of photosynthesis directly affects the production of organic matter and the survival of marine organisms.

[0031] Finally, the degree of ocean acidification and eutrophication was also assessed based on pH and dissolved oxygen concentration. Ocean acidification is the phenomenon that the pH value of seawater decreases due to the increase of carbon dioxide in the atmosphere, while eutrophication is the deterioration of water quality caused by excess nutrients. These two indicators are of great significance for judging the health of marine ecosystems and predicting future changes.

[0032] It is understandable that this embodiment has been deeply optimized in terms of data fusion analysis, aiming to reveal the complex changes in the marine environment from multiple angles and levels. By correcting dissolved oxygen readings, evaluating photosynthesis intensity, and analyzing the degree of ocean acidification and eutrophication, it not only improves the accuracy of data analysis, but also provides more detailed data support for the research and protection of marine ecosystems. These sophisticated data processing methods further enhance the application value of this embodiment in the field of marine environmental monitoring.

[0033] In some embodiments of the present application, the detection of dissolved oxygen is performed by an optical method or an electrochemical method, and the sensor state is monitored by wireless radio frequency identification technology, and the sensor sampling frequency is automatically adjusted to optimize the data. The dissolved oxygen correction formula is: ; Among them, DO corr Indicates the corrected dissolved oxygen value; DO raw represents the raw dissolved oxygen reading; k temp and k sal Respectively represent the temperature correction factor and salinity correction factor, T ref and S ref Indicates reference temperature and reference salinity; T meas and S measRespectively represent the measured temperature and measured salinity; δ RF represents the data drift caused by the radio signal; δ env Indicates data drift caused by environmental noise.

[0034] It can be seen that in this embodiment, optical or electrochemical methods are used for accurate measurement of dissolved oxygen. Both methods have high sensitivity and accuracy, and can reflect the dissolved oxygen content in the ocean in real time. At the same time, in order to monitor the status of the sensor and ensure the reliability of the data, wireless radio frequency identification technology is also introduced. This technology can automatically identify the identity and working status of the sensor, and automatically adjust the sampling frequency of the sensor according to the actual situation, thereby optimizing the data collection and processing process.

[0035] In the data processing stage, this embodiment also provides a specific dissolved oxygen correction formula. This formula comprehensively considers the impact of temperature, salinity, radio signals and environmental noise on the data, and corrects the original dissolved oxygen reading by introducing corresponding correction coefficients and drift amounts to obtain a more accurate dissolved oxygen value. This step is of great significance for improving the accuracy and reliability of data, and helps to more accurately assess the health of the marine environment.

[0036] It is understandable that the careful consideration and innovation of this embodiment in dissolved oxygen detection not only improves the accuracy of the data, but also reflects the pursuit of comprehensiveness and accuracy of marine environmental monitoring. Through the application of optical or electrochemical methods, combined with the introduction of wireless radio frequency identification technology, this embodiment achieves high-precision real-time monitoring of dissolved oxygen content. At the same time, the introduction of the dissolved oxygen correction formula further ensures the reliability of the data and provides a solid foundation for subsequent data analysis and abnormal warning. This series of innovative measures undoubtedly enhances the technical advantages and application prospects of this embodiment in the field of marine environmental monitoring.

[0037] In some embodiments of the present application, the pH value is detected using a glass electrode method or an optical pH sensor, and the electrode impedance change is detected by radio signals to determine the electrode aging or contamination state. The pH data correction formula is: ; Among them, pH corr Indicates the corrected pH; pH raw represents the original pH reading; γ represents the electrode impedance correction factor; Z RF Represents the electrode impedance for radio signal detection: Z baseline represents the reference electrode impedance; α represents the influence coefficient of dissolved oxygen on pH; DO ref Indicates the reference dissolved oxygen concentration.

[0038] It can be seen that this embodiment also adopts advanced technical means in the detection of pH value. Glass electrode method or optical pH sensor are two commonly used detection methods with high precision and stability, which can accurately reflect the pH value changes in the ocean. In addition, in order to monitor the state of the electrode and discover potential problems in time, this embodiment also introduces a method of detecting the impedance change of the electrode by radio signal. By detecting the impedance change of the electrode, it can be determined whether the electrode is aged or contaminated, thereby ensuring the accuracy of the pH data.

[0039] In the data processing stage, this embodiment provides a specific pH data correction formula. This formula comprehensively considers factors such as electrode impedance and the effect of dissolved oxygen on pH, and corrects the original pH reading by introducing corresponding correction coefficients and variables to obtain a more accurate pH value. This step is crucial to improving the accuracy and reliability of data, and helps to more accurately assess the pH status of the ocean and predict future changes.

[0040] Through the above series of technical means and optimization measures, this embodiment has shown significant technical advantages and application prospects in the field of marine environmental monitoring. Whether it is the detection of dissolved oxygen, the evaluation of photosynthesis intensity, or the analysis of pH value and ocean acidification and eutrophication, this embodiment has shown the characteristics of high precision, real-time and comprehensiveness. These advantages not only improve the accuracy and reliability of marine environmental monitoring, but also provide more powerful support for the research and protection of marine ecosystems.

[0041] It is understandable that the innovation of this embodiment in the field of marine environment monitoring is not limited to the detection of dissolved oxygen and pH value. In order to further enrich the monitoring content and improve the monitoring accuracy, this embodiment also involves multiple aspects such as the evaluation of photosynthesis intensity, analysis of ocean acidification and eutrophication. In terms of the evaluation of photosynthesis intensity, this embodiment adopts advanced photosynthesis rate measurement technology, which can reflect the photosynthesis status of plants in the ocean in real time. This data is of great significance for understanding the energy flow and material cycle of marine ecosystems. At the same time, in order to more comprehensively evaluate the health of the marine environment, this embodiment also introduces analytical indicators of ocean acidification and eutrophication. By monitoring parameters such as the pH value, dissolved oxygen content, and nutrient concentration of seawater, the pH status, dissolved oxygen level, and distribution of nutrients in the ocean can be comprehensively evaluated, thereby revealing potential problems and changing trends in the marine environment. The implementation of these innovative measures not only improves the technical level of this embodiment in the field of marine environment monitoring, but also provides more powerful support for the protection and sustainable utilization of marine ecosystems.

[0042] In some embodiments of the present application, the light intensity is detected by measuring with a photodiode, and the light data is corrected for errors using the attenuation characteristics of the radio signal under different light conditions. The light intensity correction formula is: ; Among them, Lux corr Indicates the corrected light intensity; Lux raw represents the original light intensity reading; β is the correction factor for light measurement due to radio signal attenuation; P RF,loss Indicates the radio signal loss power; P RF,baseline Indicates the reference radio signal power.

[0043] It can be seen that this embodiment also adopts innovative technical means in the detection of light intensity. As a commonly used light measurement element, photodiode has the characteristics of high sensitivity and fast response, and can reflect the changes in light intensity in real time. However, in practical applications, the measurement of light data is often affected by many factors, such as environmental noise, equipment aging, etc., which may cause errors in the measurement results. In order to reduce this error and improve the accuracy of light data, this embodiment introduces the attenuation characteristics of radio signals under different lighting conditions to correct the errors of light data. By measuring the loss power of the radio signal and comparing it with the reference radio signal power, the correction coefficient can be calculated to correct the original light intensity reading to obtain a more accurate light intensity value. This step is of great significance for improving the accuracy and reliability of light data, and helps to more accurately assess the light conditions of the ocean and predict future changes.

[0044] It is understandable that the technological innovation and comprehensive monitoring capabilities demonstrated by this embodiment in the field of marine environmental monitoring not only improve the accuracy and reliability of monitoring, but also provide more abundant data support for the research and protection of marine ecosystems. By introducing radio signal detection technology, this embodiment successfully realizes real-time monitoring and error correction of multiple key parameters such as light intensity and pH value, effectively improving the accuracy and stability of monitoring data. The implementation of these innovative measures has not only brought new technological breakthroughs to the field of marine environmental monitoring, but also provided a more powerful scientific basis for the sustainable utilization and protection of marine ecosystems. At the same time, the multiple aspects involved in this embodiment, such as photosynthesis intensity assessment, ocean acidification and eutrophication degree analysis, have further enriched the content of marine environmental monitoring and provided a more comprehensive perspective for a comprehensive understanding of the health status of the marine environment.

[0045] In some embodiments of the present application, the radio communication module has an adaptive frequency hopping function to avoid environmental interference and improve the stability of data transmission. The channel selection formula is: ; Among them, F opt represents the optimal channel frequency; f represents the candidate channel frequency; N(f) represents the noise level of frequency f; S(f) represents the signal strength of frequency f; I(f) represents the interference intensity of frequency f; λ represents the interference weight coefficient.

[0046] It can be seen that this embodiment has also made in-depth innovations and optimizations in radio communication. In order to ensure the stability and reliability of data transmission, this embodiment introduces an adaptive frequency hopping function. This function can automatically select the optimal channel frequency according to the actual situation, thereby effectively avoiding environmental interference and improving the quality and efficiency of data transmission. In order to achieve this goal, this embodiment provides a specific channel selection formula. The formula comprehensively considers multiple factors such as the noise level, signal strength and interference strength of the candidate channel. By introducing the corresponding weight coefficient, each candidate channel is comprehensively evaluated to finally determine the optimal channel frequency. This step is of great significance for improving the stability and reliability of data transmission, and helps to ensure the real-time transmission and accurate reception of monitoring data. Through this series of innovative measures and optimization means, this embodiment has demonstrated outstanding technical strength and application prospects in the field of marine environment monitoring. Whether it is the detection of dissolved oxygen, pH value, light intensity, or the introduction of radio communication technology, this embodiment has shown the characteristics of high precision, real-time and comprehensiveness. These advantages not only improve the accuracy and reliability of marine environmental monitoring, but also provide more powerful support for the research and protection of marine ecosystems.

[0047] It is understandable that the innovation of this embodiment in the field of marine environment monitoring is not only reflected in the detection of light intensity and the optimization of data transmission, but also in the real-time monitoring and comprehensive analysis of multiple key parameters. Through real-time monitoring of parameters such as light intensity, pH value, dissolved oxygen, and in-depth analysis of the relationship between these parameters, this embodiment can more comprehensively understand the health of the marine environment and provide more accurate data support for the research and protection of marine ecosystems.

[0048] In addition, this embodiment also has powerful data processing and analysis capabilities. Through real-time processing and analysis of monitoring data, abnormal conditions in the marine environment can be discovered in a timely manner, providing timely warnings and decision-making support for relevant departments. At the same time, this embodiment can also predict the changing trend of the marine environment based on historical data and real-time monitoring data, providing a scientific basis for the sustainable use and protection of the marine ecosystem.

[0049] In summary, this embodiment has demonstrated outstanding technical strength and application prospects in the field of marine environment monitoring. By introducing a variety of technical means such as photodiodes and radio signal detection technology, and combining innovative measures such as adaptive frequency hopping function, this embodiment realizes real-time monitoring and error correction of multiple key parameters, and improves the accuracy and reliability of monitoring data. These advantages not only bring new technological breakthroughs to the field of marine environment monitoring, but also provide more powerful support for the research and protection of marine ecosystems.

[0050] In some embodiments of the present application, the buoy system has a channel state information feedback mechanism, which dynamically adjusts the signal power and transmission rate according to the channel state to optimize the communication quality. The signal power adjustment formula is: ; Among them, P adj Represents the adjusted signal power; P base Indicates basic transmit power; CSI loss Indicates the current channel loss; CSI opt Indicates the channel state information under ideal channel conditions.

[0051] It can be seen that this embodiment also makes innovative designs in optimizing the communication quality of the buoy system. As a key device for marine environment monitoring, the stability of the communication quality of the buoy directly affects the real-time and accuracy of the monitoring data. In order to ensure the communication quality between the buoy and the data center, this embodiment introduces a channel state information feedback mechanism. This mechanism can monitor the state information of the channel in real time, including key parameters such as channel loss and signal strength, and dynamically adjust the signal power and transmission rate based on this information. In order to achieve this goal, this embodiment provides a specific signal power adjustment formula. The formula comprehensively considers multiple factors such as basic transmission power, current channel loss, and channel state information under ideal channel conditions, and determines the optimal signal power through corresponding calculations and adjustments. This step is of great significance for improving communication quality and ensuring real-time transmission and accurate reception of data. Through this innovative design, this embodiment has achieved remarkable results in optimizing the communication quality of the buoy system, providing a strong guarantee for the real-time and accuracy of marine environment monitoring data.

[0052] It is understandable that the innovative efforts made by this embodiment in the field of marine environment monitoring are not limited to the above aspects. In order to ensure that the buoy system can work continuously and stably in a complex and changeable marine environment, this embodiment also comprehensively considers and optimizes the structural design, energy management and data security of the buoy. In terms of structural design, the buoy adopts high-strength and corrosion-resistant materials to ensure that it can maintain its integrity and stability in harsh marine environments. At the same time, the shape design of the buoy has also been carefully considered to reduce water flow resistance and improve its floating stability and durability at sea. In terms of energy management, this embodiment adopts high-efficiency and long-life energy systems, such as solar panels and energy storage batteries, to ensure that the buoy can continuously provide a stable power supply for the monitoring equipment. In addition, in order to ensure the security and integrity of the monitoring data, this embodiment also adopts advanced data encryption technology and transmission protocols to prevent the data from being stolen or tampered with during transmission. The implementation of these innovative measures not only further improves the reliability and stability of the buoy system, but also provides a more solid guarantee for the accuracy and security of marine environment monitoring data. In summary, the technological innovation and comprehensive optimization demonstrated by this embodiment in the field of marine environmental monitoring not only improve the accuracy and reliability of monitoring, but also provide richer data support and scientific basis for the research and protection of marine ecosystems.

[0053] In some embodiments of the present application, the photosynthesis rate of marine phytoplankton is quantitatively analyzed by combining light intensity and pH value changes; the photosynthesis intensity is calculated by the following formula: ; Among them, P represents light intensity; μ represents the photosynthesis efficiency coefficient; I represents the measured light intensity; K represents the light half-saturation constant; ΔpH represents the change in pH value per unit time.

[0054] It can be seen that this embodiment proposes a more in-depth and detailed analysis method in terms of marine environmental monitoring. Photosynthesis is an important part of the life activities of marine phytoplankton. Its rate not only reflects the growth status of phytoplankton in the marine ecosystem, but is also one of the important indicators for assessing the health status of the marine environment. In order to more accurately understand the photosynthesis rate of marine phytoplankton, this embodiment combines the changes in light intensity and pH value for quantitative analysis. This method comprehensively considers the impact of light intensity on photosynthesis and the changes in pH value during photosynthesis. By introducing the corresponding calculation formula, the photosynthesis intensity can be calculated more accurately. This step is of great significance for in-depth understanding of the growth status of marine phytoplankton and the health status of the marine environment, and helps to provide more accurate data support for the research and protection of marine ecosystems. Through this innovative analysis method, this embodiment once again demonstrated its outstanding technical strength and application prospects in the field of marine environmental monitoring.

[0055] In some embodiments of the present application, when the degree of ocean acidification and eutrophication is assessed by combining pH value and dissolved oxygen concentration, the acidification index and eutrophication index are calculated by the following formula: ; ; Among them, AI represents the ocean acidification index; β represents the acidification sensitivity coefficient; pHo represents the benchmark pH value; EI represents the eutrophication index; γ represents the eutrophication sensitivity coefficient; DOo represents the benchmark dissolved oxygen concentration.

[0056] It can be seen that this embodiment proposes a more comprehensive and scientific assessment method in terms of marine environmental monitoring. Ocean acidification and eutrophication are two major issues facing the current marine environment, posing a serious threat to the health and stability of the marine ecosystem. In order to more accurately assess the degree of acidification and eutrophication of the ocean, this embodiment conducts an in-depth analysis in combination with changes in pH and dissolved oxygen concentration. This method comprehensively considers the two key indicators of pH and dissolved oxygen concentration, and by introducing the corresponding calculation formulas, it can calculate the ocean acidification index and eutrophication index respectively. This step is of great significance for a comprehensive understanding and assessment of the health status of the marine environment, helps to promptly discover and solve problems in the marine environment, and provides a scientific basis for the protection and restoration of the marine ecosystem. Through this innovative assessment method, this embodiment once again proves its leading position and technical strength in the field of marine environmental monitoring, and has made important contributions to the protection and sustainable development of the marine environment.

[0057] See also Figure 2 This embodiment also proposes a radio-based environment monitoring system, including: The ocean buoy body has a built-in multi-parameter sensor module and a radio communication module for real-time detection and transmission of data on water temperature, salinity, dissolved oxygen, pH value, and light intensity. The ocean buoy body has automatic calibration and remote upgrade functions; An offshore relay station is used to enhance the stability and range of signal transmission. The offshore relay station supports multi-buoy data integration and preliminary analysis functions; The shore-based receiving station is equipped with a data processing and analysis module, which is used to receive, correct, store and analyze data from buoys, generate environmental assessment reports and provide abnormal warnings. The system supports cloud platform access to achieve remote data access and sharing.

[0058] It can be seen that this embodiment has also made comprehensive innovations and optimizations in the design of the marine environment monitoring system. As the core part of the system, the marine buoy body integrates a multi-parameter sensor module and a radio communication module, which can detect and transmit data of multiple key parameters such as water temperature, salinity, dissolved oxygen, pH value and light intensity in real time. This design not only improves the comprehensiveness and accuracy of the monitoring data, but also enables the system to more comprehensively reflect the status of the marine environment. At the same time, the marine buoy body also has automatic calibration and remote upgrade functions, which can automatically calibrate the sensor to ensure the accuracy of the data, and through the remote upgrade function, it can achieve continuous optimization and upgrading of the system function. As a bridge for signal transmission, the offshore relay station can enhance the stability and range of signal transmission, and ensure that the data can be stably and reliably transmitted to the shore-based receiving station. In addition, the offshore relay station also supports multi-buoy data integration and preliminary analysis functions, which can integrate and preliminarily analyze data from multiple buoys, and provide more comprehensive data support for subsequent in-depth research and decision-making. As the data processing and analysis center of the system, the shore-based receiving station is equipped with a data processing and analysis module, which can receive, correct, store and analyze data from the buoy, generate environmental assessment reports and provide abnormal warnings. This design not only improves the efficiency of data processing and analysis, but also can promptly detect abnormal conditions in the marine environment, and provide timely warnings and decision-making support for relevant departments. At the same time, the system also supports cloud platform access to achieve remote data access and sharing, so that monitoring data can be more conveniently obtained and used. Through these innovative designs and optimization methods, this embodiment has demonstrated outstanding technical strength and application prospects in the field of marine environmental monitoring. Whether it is the design of the buoy system, the configuration of the relay station or the function of the shore-based receiving station, it has shown the characteristics of high precision, real-time and comprehensiveness. These advantages not only improve the accuracy and reliability of marine environmental monitoring, but also provide more powerful support for the research and protection of marine ecosystems.

[0059] In some embodiments of the present application, the ocean buoy body is equipped with a solar power supply module and an energy management system, and the energy management system dynamically adjusts the sensor working mode and data transmission frequency according to real-time power and environmental conditions.

[0060] It is understandable that the innovative design of energy management in this embodiment also reflects the high attention paid to the stability and durability of the buoy system. As the main energy source of the buoy, the solar power supply module uses solar energy, a clean and renewable energy, to provide the buoy with long-lasting and environmentally friendly power support. This design not only reduces the dependence on traditional energy and reduces operating costs, but also helps to protect the environment and conforms to the concept of sustainable development. The introduction of the energy management system further improves the energy utilization efficiency. The system can intelligently adjust the working mode and data transmission frequency of the sensor according to the real-time power and environmental conditions. When the power is sufficient and the environmental conditions are good, the sensor can collect data at a higher frequency to ensure the real-time and comprehensiveness of the data; when the power is insufficient or the environmental conditions are bad, the system will automatically reduce the collection frequency of the sensor, extend the working time of the buoy, and ensure the continuity and stability of the data. This intelligent adjustment mechanism not only improves the energy utilization efficiency, but also ensures that the buoy can maintain the best working state in different environments. Through these innovative designs and optimization methods, this embodiment once again demonstrates its outstanding technical strength and application prospects in the field of marine environmental monitoring.

[0061] In some embodiments of the present application, the data processing and analysis module includes a deep learning algorithm for long-term data trend prediction and complex environment pattern recognition.

[0062] It is understandable that the innovations in data processing and analysis of this embodiment are also of great significance. The introduction of deep learning algorithms marks a new step in the data processing and analysis capabilities of the marine environment monitoring system. This algorithm can accurately predict the changing trends of data by learning and analyzing long-term data, and provide early warning and prediction for changes in the marine environment. At the same time, the deep learning algorithm also has the ability to recognize complex environmental patterns, and can extract key information from a large amount of monitoring data and identify complex patterns and laws in the marine environment. This function not only improves the accuracy and efficiency of data analysis, but also provides more abundant data support and scientific basis for in-depth research on marine ecosystems. Through these innovative designs and optimization methods, this embodiment has once again demonstrated its leading technical level and broad application prospects in the field of marine environment monitoring.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A radio-based environmental monitoring method, characterized in that: include: Deploy ocean buoys with integrated sensors for several parameters to monitor water temperature, salinity, dissolved oxygen, pH and light intensity in real time; Transmitting the real-time detected data to a shore-based receiving station using a radio communication module, wherein the radio communication module adopts a radio protocol or a narrowband Internet of Things protocol; Drift correction and noise filtering of real-time detected data transmissions, including multi-point calibration based on historical data, application of Kalman filter algorithm to remove environmental noise, and dynamic adjustment of filter parameters using adaptive filters; Conduct data fusion analysis based on the relationships between different data to generate a marine environment assessment report; Abnormal warning information is generated based on the marine environment assessment report and fed back to the monitoring center in real time via radio; among them, the warning algorithms include threshold method, trend analysis method and abnormal detection algorithm based on machine learning.

2. The radio-based environmental monitoring method according to claim 1, characterized in that: The data fusion analysis includes: Correct and obtain dissolved oxygen readings based on the effects of water temperature and salinity on dissolved oxygen solubility; Based on the regulation of photosynthesis rate by light intensity, the photosynthesis intensity is obtained by combining pH change analysis; Assess the extent of ocean acidification and eutrophication based on pH and dissolved oxygen concentration.

3. The radio-based environmental monitoring method according to claim 2, characterized in that: The dissolved oxygen detection is performed by optical or electrochemical methods, and the sensor status is monitored by wireless radio frequency identification technology, and the sensor sampling frequency is automatically adjusted to optimize the data. The dissolved oxygen correction formula is: ； Among them, DO corr Indicates the corrected dissolved oxygen value; DO raw represents the raw dissolved oxygen reading; k temp and k sal Respectively represent the temperature correction factor and salinity correction factor, T ref and S ref Indicates reference temperature and reference salinity; T meas and S meas Respectively represent the measured temperature and measured salinity; δ RF represents the data drift caused by the radio signal; δ env Indicates data drift caused by environmental noise.

4. The radio-based environmental monitoring method according to claim 3, characterized in that: The pH value is detected by using a glass electrode method or an optical pH sensor, and the electrode impedance change is detected by radio signals to determine the electrode aging or contamination state. The pH data correction formula is: ； Among them, pH corr Indicates the corrected pH; pH raw represents the original pH reading; γ represents the electrode impedance correction factor; Z RF Represents the electrode impedance for radio signal detection: Z baseline represents the reference electrode impedance; α represents the influence coefficient of dissolved oxygen on pH; DO ref Indicates the reference dissolved oxygen concentration.

5. The radio-based environmental monitoring method according to claim 4, characterized in that: The light intensity is detected by measuring with a photodiode, and the light data is corrected for errors using the attenuation characteristics of radio signals under different light conditions. The light intensity correction formula is: ； Among them, Lux corr Indicates the corrected light intensity; Lux raw represents the original light intensity reading; β is the correction factor for light measurement due to radio signal attenuation; P RF,loss Indicates the radio signal loss power; P RF,baseline Indicates the reference radio signal power.

6. The radio-based environmental monitoring method according to claim 5, characterized in that: The radio communication module has an adaptive frequency hopping function to avoid environmental interference and improve the stability of data transmission. The channel selection formula is: ； Among them, F opt represents the optimal channel frequency; f represents the candidate channel frequency; N(f) represents the noise level of frequency f; S(f) represents the signal strength of frequency f; I(f) represents the interference intensity of frequency f; λ represents the interference weight coefficient.

7. The radio-based environmental monitoring method according to claim 6, characterized in that: The buoy system has a channel status information feedback mechanism, which dynamically adjusts the signal power and transmission rate according to the channel status to optimize the communication quality. The signal power adjustment formula is: ； Among them, P adj Represents the adjusted signal power; P base Indicates basic transmit power; CSI loss Indicates the current channel loss; CSI opt Indicates the channel state information under ideal channel conditions.

8. A radio-based environment monitoring system, used to implement the radio-based environment monitoring method according to any one of claims 1 to 7, characterized in that: include: The ocean buoy body has a built-in multi-parameter sensor module and a radio communication module for real-time detection and transmission of data on water temperature, salinity, dissolved oxygen, pH value, and light intensity. The ocean buoy body has automatic calibration and remote upgrade functions; An offshore relay station is used to enhance the stability and range of signal transmission. The offshore relay station supports multi-buoy data integration and preliminary analysis functions; The shore-based receiving station is equipped with a data processing and analysis module, which is used to receive, correct, store and analyze data from buoys, generate environmental assessment reports and provide abnormal warnings. The system supports cloud platform access to achieve remote data access and sharing.

9. The radio-based environment monitoring system according to claim 8, characterized in that: The ocean buoy body is equipped with a solar power supply module and an energy management system, and the energy management system dynamically adjusts the sensor working mode and data transmission frequency according to real-time power and environmental conditions.

10. The radio-based environment monitoring system according to claim 8, characterized in that: The data processing and analysis module includes a deep learning algorithm for long-term data trend prediction and complex environment pattern recognition.

Images