Internet of Things monitoring and early warning system and method for lake water quality

Abstract

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lake water quality Internet of Things monitoring and early warning, in particular to a lake water quality Internet of Things monitoring and early warning system and method. BACKGROUND

[0002] Lake water quality Internet of Things monitoring and early warning is an intelligent monitoring and early warning system that comprehensively uses Internet of Things technology, arranges various sensors such as pH value sensors, dissolved oxygen sensors, and turbidity sensors at different positions of a lake, collects water quality parameter data in real time, transmits the data to a data processing center for analysis and processing by means of a wireless network, establishes a scientific water quality evaluation model, compares it with a pre-set water quality standard and threshold, and timely issues early warning information when an abnormal water quality index is found, thereby providing data support and decision basis for lake water resource protection, water pollution prevention and control, and scientific management.

[0003] The defects of the prior art are: 1. The traditional lake water quality Internet of Things monitoring and early warning system relies on conventional water quality parameter sensors and can only obtain limited basic water quality data, thereby making it difficult to comprehensively evaluate the ecological status of the lake.

[0004] 2. The prior art lacks precise detection capability for specific pollutants in the aspect of pollutant detection, thereby failing to timely detect changes in complex pollution components.

[0005] 3. For key ecological indicators such as algal distribution and water color changes, the prior art methods either have limited monitoring range or insufficient precision, resulting in single data dimension and serious information loss in the process of lake water quality monitoring, making it difficult to accurately grasp the water quality change trend, thereby making it difficult to provide comprehensive and reliable data support for lake ecological protection work, and restricting effective management and protection of the lake ecological system. SUMMARY

[0006] The present application provides a lake water quality Internet of Things monitoring and early warning system and method to enrich data dimension, solve the problem of easy tampering and loss of traditional data storage, and ensure data integrity and reliability.

[0007] To solve the above problems, the technical solution provided by the present application is: The lake water quality Internet of Things monitoring and early warning system comprises a multi-modal intelligent sensor acquisition system, an adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interactive system, an intelligent early warning system, and a remote intelligent control and repair system, wherein: The multimodal intelligent sensor acquisition system is used to collect lake water quality and ecological data in an all-round way, providing basic data for subsequent system operation; the adaptive wireless transmission system is used to receive the data collected by the multimodal intelligent sensor acquisition system, and then intelligently select the transmission method according to the network and data conditions, and send the data to the distributed data storage system; the distributed data storage system uses blockchain technology to store data securely and ensure that the data is not tampered with and is traceable, providing a data source for the intelligent data analysis system; the intelligent data analysis system is used to predict water quality changes, identify early signs of algal blooms, and transmit the data to the intelligent data analysis system through machine learning and deep learning models. The analysis results of the energy data analysis system are used for the intelligent early warning system; the intelligent early warning system is used to issue early warnings according to real-time and predicted data according to multi-threshold rules, and intelligently classify and grade them, thereby prompting the remote intelligent control and repair system to execute corresponding instructions; the remote intelligent control and repair system is used to receive early warning information and decision-making instructions, and remotely control equipment to repair water quality, and then feed back the repaired data to the multimodal intelligent sensor acquisition system; the visual interaction system is used to intuitively present the data of the multimodal intelligent sensor acquisition system and the results of the analysis system and assist in decision-making, and at the same time transmit the decision-making instructions to the remote intelligent control and repair system.

[0008] Preferably, the multimodal intelligent sensor acquisition system includes a water quality parameter sensing unit and an innovative sensing unit; the water quality parameter sensing unit includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, an ammonia nitrogen sensor and a total phosphorus sensor; each sensor of the water quality parameter sensing unit is used to collect corresponding conventional water quality parameters; the innovative sensing unit includes a biosensor, a component containing specific microorganisms or enzyme reactions and a signal conversion component; the innovative sensing unit is used to comprehensively collect other data required by the system.

[0009] Preferably, the algorithm of the multimodal intelligent sensor acquisition system includes a data acquisition module and a data fusion module, wherein: the data acquisition module specifically includes the following steps: Sa100. Preset water quality parameter change thresholds through an adaptive sampling algorithm; Sa200. When the difference between the current measured value of the water quality parameter and the measured value at the previous moment exceeds the corresponding water quality parameter change threshold, the sampling frequency is adjusted according to the frequency adjustment coefficient; The data acquisition module calibrates the clocks of the sensors of the multimodal intelligent sensor acquisition system using a periodic synchronous sampling algorithm using a network time protocol or a global positioning system, assigns a unique timestamp to each sensor, and implements synchronous sampling; The data fusion module specifically includes the following steps: Sb100. Use the weighted average fusion algorithm to set corresponding weights for the corresponding sensors according to the accuracy and reliability of different sensors; Sb200. The fusion data is obtained by multiplying the measurement value of each sensor by the corresponding weight and then dividing it by the total weight. It is expressed as follows: Wherein: M is used to represent the fused data; Used to characterize the measurement values ​​of each sensor; Used to characterize the corresponding weights of each sensor; Sb300. Using the Kalman filter fusion algorithm to solve the problem that water quality data is susceptible to noise interference, a state space model is constructed; the Kalman filter fusion algorithm includes a state equation and a measurement equation, and through the prediction and update steps, the Kalman gain is used to fuse the predicted value and the measured value to obtain the optimal estimate and suppress white noise interference; The state equation is expressed as follows: in: The system state vector used to represent time k; Used to represent the state transition matrix, which describes the state transition relationship of the system from time k-1 to time k; Used to characterize the control input matrix; Used to represent the control input vector at time k; Used to characterize the process noise vector; The measurement equation is expressed as follows: in: The measurement vector used to represent the k-th moment; Used to represent the observation matrix, which maps the system state vector to the measurement space; Used to characterize the measurement noise vector.

[0010] Preferably, the adaptive wireless transmission system includes a data preprocessing unit and a communication unit; the data preprocessing unit is used to perform preprocessing operations such as denoising, format conversion, and outlier marking on the original data; the communication unit is used to support multiple communication protocols and intelligently select the transmission method based on the network and data conditions.

[0011] Preferably, the distributed data storage system includes a blockchain network node unit and a data encryption and verification unit; the blockchain network node unit is used to build a decentralized storage network to realize distributed storage and synchronization of data; the blockchain network node unit includes various distributed node devices; the data encryption and verification unit is used to perform end-to-end encrypted transmission and storage of data; the data encryption and verification unit includes an encryption algorithm component and a hash algorithm component; the encryption algorithm component is used to encrypt data using a high-intensity encryption algorithm; the hash algorithm component is used to generate a data hash value to ensure data integrity verification.

[0012] Preferably, the intelligent data analysis system includes a machine learning algorithm unit and a deep learning model unit; the machine learning algorithm unit is used to perform data feature extraction and association analysis tasks; the deep learning model unit is used to construct a convolutional neural network or a recurrent neural network model to realize water quality trend prediction and abnormal pattern recognition.

[0013] Preferably, the visual interaction system includes a data visualization platform unit and a mobile application unit; the data visualization platform unit is used to display real-time data dashboards, historical trend analysis charts, and spatial distribution heat maps on the PC side; the mobile application unit is used to push early warning information through a mobile phone APP, support remote viewing of data and issuance of control instructions.

[0014] Preferably, the intelligent early warning system includes a threshold setting and judgment unit, an early warning information sending unit and an intelligent classification and grading unit; the threshold setting and judgment unit is used to set multi-level early warning thresholds and determine whether to issue an early warning based on real-time and predicted data; the early warning information sending unit is used to send early warning information through multiple channels; the intelligent classification and grading unit is used to automatically classify and grade early warnings based on pollutant type, concentration, and impact range factors.

[0015] Preferably, the remote intelligent control and repair system includes a remote control terminal unit and an intelligent execution device unit; the remote control terminal unit is used to receive and analyze early warning information and decision instructions, and generate equipment control signals; the remote control terminal unit includes a control server and control software; the intelligent execution device unit is used to perform repair operations according to control signals; the intelligent execution device unit includes a drug delivery device, an oxygenation device and a dredging robot.

[0016] The method using the lake water quality Internet of Things monitoring and early warning system comprises the following steps: S100. Controlling the operation of the multimodal intelligent sensor acquisition system; controlling the pH sensor, the dissolved oxygen sensor, the turbidity sensor, the ammonia nitrogen sensor, and the total phosphorus sensor of the water quality parameter sensing unit to collect conventional water quality parameters; controlling the biosensor of the innovative sensing unit to detect specific pollutants by relying on the specific microorganism or enzyme reaction component and the signal conversion component; collecting data through the adaptive sampling algorithm and the periodic synchronous sampling algorithm, calculating the fused data through the weighted average fusion algorithm, and then calculating the optimal estimate using the Kalman filter fusion algorithm; S200. Control the data preprocessing unit of the adaptive wireless transmission system to perform preliminary processing on the data transmitted by the multimodal intelligent sensor acquisition system; control the communication unit to intelligently select a transmission method based on the network and data conditions, and then deliver the data to the distributed data storage system; S300. Control the distributed data storage system to store data through the distributed node devices of the blockchain network node unit and the encryption algorithm component and the hash algorithm component of the data encryption and verification unit, using blockchain technology to ensure that the data is not tampered with and is traceable, and provide a data source for the intelligent data analysis system; S400. Control the machine learning algorithm unit and the deep learning model unit of the intelligent data analysis system to analyze the data provided by the distributed data storage system, predict water quality changes, identify early signs of algal blooms, and then transmit the analysis results to the intelligent early warning system; S500. Control the threshold setting and judgment unit of the intelligent early warning system to determine whether to issue an early warning according to the multi-threshold rule based on real-time and predicted data; control the early warning information sending unit to send the early warning information; control the intelligent classification and grading unit to intelligently classify and grade the early warning information, and then control the remote intelligent control and repair system to execute corresponding instructions; S600. The control server and control software of the remote control terminal unit of the remote intelligent control and repair system receive warning information and decision instructions; then control the pharmaceutical delivery equipment, oxygenation equipment, and dredging robot remote control equipment of the control intelligent execution device unit to repair water quality, and feed back the repaired data to the multimodal intelligent sensor acquisition system; S700. Control the data visualization platform unit and the mobile application unit of the visualization interaction system to present the data of the multimodal intelligent sensor acquisition system and the results of the intelligent data analysis system and assist management personnel in making decisions, and then pass the decision instructions to the remote control and repair system.

[0017] Compared with the prior art, the present invention has the following advantages: 1. Since the multimodal intelligent sensor acquisition system of the present invention introduces biosensors and hyperspectral imaging sensors, it breaks through the limitations of traditional water quality monitoring and can accurately detect specific pollutants and macro-ecological conditions, thereby effectively enriching the data dimension and providing a new perspective and means for water quality monitoring.

[0018] 2. Since the present invention adopts a distributed data storage system and uses blockchain technology to achieve secure data storage and traceability, its distributed, encrypted and tamper-proof characteristics solve the risks of traditional data storage being easily tampered with and lost, ensuring data integrity and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the system structure of a specific embodiment of the present invention; Figure 2 It is a schematic diagram of a system implementation method of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further illustrated below with reference to specific examples. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0021] like Figure 1 、 2 As shown in the figure, the lake water quality IoT monitoring and early warning system includes a multi-modal intelligent sensor acquisition system, an adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system, and a remote intelligent control and repair system, among which: The multimodal intelligent sensor acquisition system is used to comprehensively collect lake water quality and ecological data, providing basic data for subsequent system operations. The adaptive wireless transmission system receives data collected by the multimodal intelligent sensor acquisition system, intelligently selects a transmission method based on network and data conditions, and delivers the data to the distributed data storage system. The distributed data storage system uses blockchain technology to securely store data, ensure data is tamper-proof and traceable, and provide a data source for the intelligent data analysis system. The intelligent data analysis system uses machine learning and deep learning models to predict water quality changes and identify early signs of algal blooms. The analysis results of the intelligent data analysis system are used in the intelligent early warning system. The intelligent early warning system issues warnings based on real-time and predicted data according to multi-threshold rules, and intelligently classifies and grades data, thereby prompting the remote intelligent control and repair system to execute corresponding instructions. The remote intelligent control and repair system receives warning information and decision-making instructions, remotely controls equipment to repair water quality, and then feeds the repaired data back to the multimodal intelligent sensor acquisition system. The visual interaction system is used to intuitively present the data of the multimodal intelligent sensor acquisition system and the results of the analysis system to assist in decision-making and transmit decision-making instructions to the remote intelligent control and repair system.

[0022] It should be noted that the multimodal intelligent sensor acquisition system includes a water quality parameter sensing unit and an innovative sensing unit; the water quality parameter sensing unit includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, an ammonia nitrogen sensor and a total phosphorus sensor; each sensor of the water quality parameter sensing unit is used to collect the corresponding conventional water quality parameters; the innovative sensing unit includes a biosensor, a component containing specific microorganisms or enzyme reactions and a signal conversion component; the innovative sensing unit is used to comprehensively collect other data required by the system.

[0023] It should be further explained that the algorithm of the multimodal intelligent sensor acquisition system includes a data acquisition module and a data fusion module, wherein the data acquisition module specifically includes the following steps: Sa100. Pre-sets water quality parameter change thresholds through an adaptive sampling algorithm.

[0024] Sa200. When the difference between the current measured value of a water quality parameter and the measured value at the previous moment exceeds the corresponding water quality parameter change threshold, the sampling frequency is adjusted according to the frequency adjustment coefficient.

[0025] The data acquisition module uses the network time protocol or the global positioning system to calibrate the clocks of each sensor in the multimodal intelligent sensor acquisition system through a periodic synchronous sampling algorithm, assigns a unique timestamp to each sensor and realizes synchronous sampling.

[0026] The data fusion module specifically includes the following steps: Sb100. According to the difference in accuracy and reliability of different sensors, the corresponding sensor is set with a corresponding weight by using a weighted average fusion algorithm.

[0027] Sb200. The measurement value of each sensor is multiplied by the corresponding weight and then divided by the total weight to obtain the fusion data, which is expressed by formula 1: (1) Wherein: M is used to represent the fusion data; is used to represent the measurement value of each sensor; is used to represent the corresponding weight set for each sensor.

[0028] Sb300. In order to solve the problem that water quality data is easily disturbed by noise, a state space model is constructed by using Kalman filter fusion algorithm. The Kalman filter fusion algorithm includes state equation and measurement equation. Through the prediction and update steps, the optimal estimation value is obtained by using Kalman gain to fuse the predicted value and the measured value, and the white noise interference is suppressed.

[0029] The state equation is expressed by formula 2: (2) Wherein: is used to represent the system state vector at time k; is used to represent the state transition matrix, which describes the state transition relationship of the system from time k-1 to time k; is used to represent the control input matrix; is used to represent the control input vector at time k; is used to represent the process noise vector.

[0030] The measurement equation is expressed by formula 3: (3) Wherein: is used to represent the measurement vector at time k; is used to represent the observation matrix, which maps the system state vector to the measurement space; is used to represent the measurement noise vector.

[0031] In the specific embodiment, the pH value change threshold of the water quality parameter change threshold is 0.2; the dissolved oxygen concentration change threshold is 1 mg / L; the frequency adjustment coefficient is 1.5; the weight of the pH sensor of the weighted average fusion algorithm is 0.7, and the weight of the biological sensor is 0.8.

[0032] It should be further explained that the network time protocol (hereinafter referred to as NTP) communicates with the NTP server to obtain the synchronization time, allocates a unique time stamp for each sensor to realize synchronous sampling, and the code embodies the logic of obtaining the synchronization time and starting the sensor sampling.

[0033] It should be noted that the adaptive wireless transmission system comprises a data preprocessing unit and a communication unit; the data preprocessing unit is used for preprocessing operations such as denoising, format conversion, and outlier marking on raw data; and the communication unit is used to support multiple communication protocols and intelligently select transmission modes according to network and data conditions.

[0034] It should be noted that the distributed data storage system comprises a blockchain network node unit and a data encryption and verification unit; the blockchain network node unit is used to build a decentralized storage network to realize distributed storage and synchronization of data; the blockchain network node unit comprises various distributed node devices; the data encryption and verification unit is used for end-to-end encryption transmission and storage of data; the data encryption and verification unit comprises an encryption algorithm component and a hash algorithm component; the encryption algorithm component is used to encrypt data using high-strength encryption algorithms; and the hash algorithm component is used to generate data hash values to ensure data integrity verification.

[0035] It should be noted that the intelligent data analysis system comprises a machine learning algorithm unit and a deep learning model unit; the machine learning algorithm unit is used to perform data feature extraction and correlation analysis tasks; and the deep learning model unit is used to build a convolutional neural network or recurrent neural network model to realize water quality trend prediction and abnormal pattern recognition.

[0036] It should be noted that the visual interactive system comprises a data visualization platform unit and a mobile application unit; the data visualization platform unit is used to display real-time data dashboards, historical trend analysis graphs, and spatial distribution heat maps on a PC; and the mobile application unit is used to push warning information through a mobile phone APP, support remote data viewing, and issue control instructions.

[0037] It should be noted that the intelligent early warning system comprises a threshold setting and judgment unit, a warning information sending unit, and an intelligent classification and grading unit; the threshold setting and judgment unit is used to set multiple levels of early warning thresholds and determine whether to issue a warning according to real-time and predicted data; the warning information sending unit is used to send warning information through multiple channels; and the intelligent classification and grading unit is used to automatically classify and grade warnings according to pollutant types, concentrations, and influence range factors.

[0038] It should be noted that the remote intelligent control and repair system comprises a remote control terminal unit and an intelligent execution device unit; the remote control terminal unit is used to receive, analyze, and generate device control signals based on warning information and decision instructions; the remote control terminal unit comprises a control server and control software; and the intelligent execution device unit is used to perform repair operations according to control signals; the intelligent execution device unit comprises a reagent injection device, an oxygenation device, and a dredging robot.

[0039] The method of using the lake water quality Internet of Things monitoring and early warning system includes the following steps: S100. Control the operation of the multimodal intelligent sensor acquisition system; control the pH sensor, dissolved oxygen sensor, turbidity sensor, ammonia nitrogen sensor, and total phosphorus sensor of the water quality parameter sensing unit to collect conventional water quality parameters; control the biosensor of the innovative sensing unit to detect specific pollutants by relying on specific microorganisms or enzyme reaction components and signal conversion components; collect data through an adaptive sampling algorithm and a periodic synchronous sampling algorithm, calculate the fused data through a weighted average fusion algorithm, and then use the Kalman filter fusion algorithm to calculate the optimal estimate.

[0040] S200. Control the data preprocessing unit of the adaptive wireless transmission system to perform preliminary processing on the data transmitted by the multimodal intelligent sensor acquisition system; control the communication unit to intelligently select the transmission method based on the network and data conditions, and then send the data to the distributed data storage system.

[0041] S300. Control the distributed data storage system to store data through the distributed node devices of the blockchain network node unit and the encryption algorithm components and hash algorithm components of the data encryption and verification unit, use blockchain technology to ensure that the data is not tampered with and is traceable, and provide a data source for the intelligent data analysis system.

[0042] S400. Control the machine learning algorithm unit and deep learning model unit of the intelligent data analysis system to analyze the data provided by the distributed data storage system, predict water quality changes, identify early signs of algal bloom, and then transmit the analysis results to the intelligent early warning system.

[0043] S500. Control the threshold setting and judgment unit of the intelligent early warning system to determine whether to issue an early warning based on real-time and predicted data and multi-threshold rules (such as blue, yellow, orange, and red warnings); control the early warning information sending unit to send the early warning information; control the intelligent classification and grading unit to intelligently classify and grade the early warning information, and then control the remote intelligent control and repair system to execute corresponding instructions.

[0044] S600. The control server and control software of the remote control terminal unit of the remote intelligent control and repair system receive warning information and decision instructions; then control the intelligent execution equipment unit to release the pharmaceutical equipment, oxygenation equipment, and remote control equipment of the dredging robot to repair the water quality, and feed back the repaired data to the multimodal intelligent sensor acquisition system; S700. The data visualization platform unit and mobile application unit of the control visualization interaction system present the data of the multimodal intelligent sensor acquisition system and the results of the intelligent data analysis system to assist managers in decision-making, and then pass the decision instructions to the remote control and repair system.

[0045] It should be noted that the working principle of the present invention is: The working principle of the lake water quality Internet of Things monitoring and early warning system is a comprehensive process that integrates data collection, transmission, storage, analysis, early warning, control and repair, and visual interaction.

[0046] Specifically, the system first collects the basic water quality parameters of the lake in real time through the water quality parameter sensing units (including pH sensors, dissolved oxygen sensors, turbidity sensors, ammonia nitrogen sensors and total phosphorus sensors) in the multimodal intelligent sensor acquisition system, and accurately detects specific pollutants through innovative sensing units (including biosensors, built-in specific microorganisms or enzyme reaction components and signal conversion components). At the same time, it uses an adaptive sampling algorithm to dynamically adjust the sampling frequency according to the preset water quality parameter change threshold, and a periodic synchronous sampling algorithm to calibrate the sensor clock with the help of the network time protocol or the global positioning system to ensure synchronous sampling of each sensor. The collected data is then processed by a data fusion algorithm, including a weighted average fusion algorithm based on sensor accuracy and reliability. The reliability distribution weights are calculated to fuse the data, and the Kalman filter fusion algorithm is used to build a state space model to reduce noise interference and obtain the optimal estimate. The processed data is then subjected to pre-processing operations such as denoising, format conversion, and outlier marking by the data pre-processing unit of the adaptive wireless transmission system. The communication unit then intelligently selects transmission methods such as LoRa or 5G according to the network conditions to efficiently deliver the data to the distributed data storage system. The system uses blockchain network node units (including servers, storage hard drives, and blockchain software) to build a decentralized storage network, and uses data encryption and verification units (including encryption algorithm components and hash algorithm components) to perform end-to-end encrypted transmission and storage of data to ensure that the data is not tampered with and is traceable, providing a basis for intelligent data analysis systems. The system provides a reliable data source, the intelligent data analysis system performs data feature extraction and association analysis tasks through the machine learning algorithm unit, and the deep learning model unit constructs a convolutional neural network or a recurrent neural network model to predict water quality trends and identify abnormal patterns, thereby predicting water quality changes and identifying early signs of algal blooms. The intelligent early warning system, based on the results of the intelligent data analysis system, determines whether to issue an early warning according to multi-threshold rules (such as blue, yellow, orange, and red warnings) based on real-time and predicted data through the threshold setting and judgment unit. The early warning information sending unit sends early warning information through multiple channels such as text messages and emails. The intelligent classification and grading unit automatically classifies and grades early warnings according to factors such as pollutant type, concentration, and impact range, prompting The remote intelligent control and repair system operates. The remote control terminal unit (including the control server and control software) of the remote intelligent control and repair system receives warning information and decision-making instructions, controls the intelligent execution equipment unit (including the drug delivery equipment, oxygenation equipment and dredging robot) to remotely control the equipment to repair the water quality. The repaired data is fed back to the multimodal intelligent sensor acquisition system to form a closed loop. Finally, the visual interactive system displays the real-time data dashboard, historical trend analysis chart, spatial distribution heat map, etc. on the PC side through the data visualization platform unit, and pushes warning information through the mobile phone APP through the mobile application unit, supports remote viewing of data and issuing control instructions, and intuitively presents the data of the acquisition system and the results of the analysis system to assist management personnel in making decisions.The decision instructions are then transmitted to the remote control and restoration system to achieve comprehensive monitoring and precise management of lake water quality.

[0047] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0048] The above description of the disclosed embodiments is intended to enable any person skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments presented herein but is intended to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0049] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

[0050] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A lake water quality Internet of Things monitoring and early warning system, characterized by: It includes a multi-modal intelligent sensor acquisition system, an adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system, and a remote intelligent control and repair system, including: The multimodal intelligent sensor acquisition system is used to collect lake water quality and ecological data in an all-round way, providing basic data for subsequent system operation; the adaptive wireless transmission system is used to receive the data collected by the multimodal intelligent sensor acquisition system, and then intelligently select the transmission method according to the network and data conditions, and send the data to the distributed data storage system; the distributed data storage system uses blockchain technology to store data securely and ensure that the data is not tampered with and is traceable, providing a data source for the intelligent data analysis system; the intelligent data analysis system is used to predict water quality changes, identify early signs of algal blooms, and transmit the data to the intelligent data analysis system through machine learning and deep learning models. The analysis results of the energy data analysis system are used for the intelligent early warning system; the intelligent early warning system is used to issue early warnings according to real-time and predicted data according to multi-threshold rules, and intelligently classify and grade them, thereby prompting the remote intelligent control and repair system to execute corresponding instructions; the remote intelligent control and repair system is used to receive early warning information and decision-making instructions, and remotely control equipment to repair water quality, and then feed back the repaired data to the multimodal intelligent sensor acquisition system; the visual interaction system is used to intuitively present the data of the multimodal intelligent sensor acquisition system and the results of the analysis system and assist in decision-making, and at the same time transmit the decision-making instructions to the remote intelligent control and repair system.

2. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The multimodal intelligent sensor acquisition system includes a water quality parameter sensing unit and an innovative sensing unit; the water quality parameter sensing unit includes a pH sensor, a dissolved oxygen sensor, a turbidity sensor, an ammonia nitrogen sensor and a total phosphorus sensor; each sensor of the water quality parameter sensing unit is used to collect corresponding conventional water quality parameters; the innovative sensing unit includes a biosensor, a component containing specific microorganisms or enzyme reactions and a signal conversion component; the innovative sensing unit is used to comprehensively collect other data required by the system.

3. The lake water quality Internet of Things monitoring and early warning system according to claim 2 is characterized by: The algorithm of the multimodal intelligent sensor acquisition system includes a data acquisition module and a data fusion module, wherein the data acquisition module specifically includes the following steps: Sa100. Preset water quality parameter change thresholds through an adaptive sampling algorithm; Sa200. When the difference between the current measured value of the water quality parameter and the measured value at the previous moment exceeds the corresponding water quality parameter change threshold, the sampling frequency is adjusted according to the frequency adjustment coefficient; The data acquisition module calibrates the clocks of the sensors of the multimodal intelligent sensor acquisition system using a periodic synchronous sampling algorithm using a network time protocol or a global positioning system, assigns a unique timestamp to each sensor, and implements synchronous sampling; The data fusion module specifically includes the following steps: Sb100. Use the weighted average fusion algorithm to set corresponding weights for the corresponding sensors according to the accuracy and reliability of different sensors; Sb200. The fusion data is obtained by multiplying the measurement value of each sensor by the corresponding weight and then dividing it by the total weight. It is expressed as follows: Wherein: M is used to represent the fused data; Used to characterize the measurement values ​​of each sensor; Used to characterize the corresponding weights of each sensor; Sb300. Using the Kalman filter fusion algorithm to solve the problem that water quality data is susceptible to noise interference, a state space model is constructed; the Kalman filter fusion algorithm includes a state equation and a measurement equation, and through the prediction and update steps, the Kalman gain is used to fuse the predicted value and the measured value to obtain the optimal estimate and suppress white noise interference; The state equation is expressed as follows: in: The system state vector used to represent time k; Used to represent the state transition matrix, which describes the state transition relationship of the system from time k-1 to time k; Used to characterize the control input matrix; Used to represent the control input vector at time k; Used to characterize the process noise vector; The measurement equation is expressed as follows: in: The measurement vector used to represent the k-th moment; Used to represent the observation matrix, which maps the system state vector to the measurement space; Used to characterize the measurement noise vector.

4. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The adaptive wireless transmission system includes a data preprocessing unit and a communication unit; the data preprocessing unit is used to perform preprocessing operations such as denoising, format conversion, and outlier marking on the original data; the communication unit is used to support multiple communication protocols and intelligently select the transmission method based on the network and data conditions.

5. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized in that: The distributed data storage system includes a blockchain network node unit and a data encryption and verification unit; the blockchain network node unit is used to build a decentralized storage network to realize distributed storage and synchronization of data; the blockchain network node unit includes various distributed node devices; the data encryption and verification unit is used to perform end-to-end encrypted transmission and storage of data; the data encryption and verification unit includes an encryption algorithm component and a hash algorithm component; the encryption algorithm component is used to encrypt data using a high-intensity encryption algorithm; the hash algorithm component is used to generate a data hash value to ensure data integrity verification.

6. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The intelligent data analysis system includes a machine learning algorithm unit and a deep learning model unit; the machine learning algorithm unit is used to perform data feature extraction and association analysis tasks; the deep learning model unit is used to construct a convolutional neural network or a recurrent neural network model to realize water quality trend prediction and abnormal pattern recognition.

7. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The visual interaction system includes a data visualization platform unit and a mobile application unit; the data visualization platform unit is used to display real-time data dashboards, historical trend analysis charts, and spatial distribution heat maps on the PC side; the mobile application unit is used to push early warning information through a mobile phone APP, support remote data viewing and issue control instructions.

8. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The intelligent early warning system includes a threshold setting and judgment unit, an early warning information sending unit and an intelligent classification and grading unit; the threshold setting and judgment unit is used to set multi-level early warning thresholds and determine whether to issue an early warning based on real-time and predicted data; the early warning information sending unit is used to send early warning information through multiple channels; the intelligent classification and grading unit is used to automatically classify and grade early warnings based on pollutant type, concentration, and impact range factors.

9. The lake water quality Internet of Things monitoring and early warning system according to claim 1 is characterized by: The remote intelligent control and repair system includes a remote control terminal unit and an intelligent execution device unit; the remote control terminal unit is used to receive and analyze early warning information and decision instructions, and generate equipment control signals; the remote control terminal unit includes a control server and control software; the intelligent execution device unit is used to perform repair operations according to the control signal; the intelligent execution device unit includes a drug delivery device, an oxygenation device and a dredging robot.

10. A method utilizing the lake water quality Internet of Things monitoring and early warning system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S100. Controlling the operation of the multimodal intelligent sensor acquisition system; controlling the pH sensor, the dissolved oxygen sensor, the turbidity sensor, the ammonia nitrogen sensor, and the total phosphorus sensor of the water quality parameter sensing unit to collect conventional water quality parameters; controlling the biosensor of the innovative sensing unit to detect specific pollutants by relying on the specific microorganism or enzyme reaction component and the signal conversion component; collecting data through the adaptive sampling algorithm and the periodic synchronous sampling algorithm, calculating the fused data through the weighted average fusion algorithm, and then calculating the optimal estimate using the Kalman filter fusion algorithm; S200. Control the data preprocessing unit of the adaptive wireless transmission system to perform preliminary processing on the data transmitted by the multimodal intelligent sensor acquisition system; control the communication unit to intelligently select a transmission method based on the network and data conditions, and then deliver the data to the distributed data storage system; S300. Control the distributed data storage system to store data through the distributed node devices of the blockchain network node unit and the encryption algorithm component and the hash algorithm component of the data encryption and verification unit, using blockchain technology to ensure that the data is not tampered with and is traceable, and provide a data source for the intelligent data analysis system; S400. Control the machine learning algorithm unit and the deep learning model unit of the intelligent data analysis system to analyze the data provided by the distributed data storage system, predict water quality changes, identify early signs of algal blooms, and then transmit the analysis results to the intelligent early warning system; S500. Control the threshold setting and judgment unit of the intelligent early warning system to determine whether to issue an early warning according to the multi-threshold rule based on real-time and predicted data; control the early warning information sending unit to send the early warning information; control the intelligent classification and grading unit to intelligently classify and grade the early warning information, and then control the remote intelligent control and repair system to execute corresponding instructions; S600. The control server and control software of the remote control terminal unit of the remote intelligent control and repair system receive warning information and decision instructions; Then, the medicine delivery equipment, oxygenation equipment, and dredging robot remote control equipment of the control intelligent execution device unit are controlled to repair the water quality, and the repaired data is fed back to the multi-modal intelligent sensor acquisition system; S700. Control the data visualization platform unit and the mobile application unit of the visualization interaction system to present the data of the multimodal intelligent sensor acquisition system and the results of the intelligent data analysis system and assist management personnel in making decisions, and then pass the decision instructions to the remote control and repair system.

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