Ocean internet of things communication system and method based on crowd perception and crowd wisdom cooperation

Abstract

The present application relates to the technical field of Internet of Things communication, in particular to an ocean Internet of Things communication system and method based on group perception and group intelligence cooperation. In the present application, by adopting low-power wide-area network and satellite communication technologies, the communication coverage and reliability of the system in complex marine environments are improved, enabling real-time data transmission and immediate monitoring and response, thereby ensuring stable signal transmission in remote sea areas or deep sea environments. In combination with edge computing and deep learning algorithms, multi-dimensional and massive data can be intelligently processed and analyzed, improving data value and enabling rapid decision-making. Through group perception and collaborative decision-making, a group collaboration mechanism is formed, enabling the system to break away from the application of traditional single devices or single function modules, effectively integrating the functions of multiple sensors, achieving information sharing and optimal resource allocation, and improving the utilization efficiency of marine resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things communication, in particular to an ocean Internet of Things communication system and method based on group perception and crowd wisdom cooperation. BACKGROUND

[0002] With the continuous development of global economy, the development and utilization of marine resources are increasingly valued by countries. Internet of Things refers to connecting any object with network through information sensing device according to agreed protocol, and the object exchanges and communicates information through information transmission medium to realize intelligent identification, positioning, tracking, supervision and other functions. The marine environment is complex and changeable, and traditional marine monitoring and communication methods often cannot realize efficient and real-time information transmission and resource management, so applying Internet of Things communication technology to the management and monitoring of marine environment is the new development trend.

[0003] The prior art has the following problems in the application of ocean Internet of Things:

[0004] 1. Insufficient communication coverage: Traditional marine communication methods such as satellite communication restrict the information exchange between devices, especially in remote sea areas or deep sea environment, signal transmission is unstable and the cost is high;

[0005] 2. Data processing and decision delay: Most existing systems lack effective intelligent algorithms to process a large amount of collected marine monitoring data, resulting in decision delay and inability to respond to emergencies such as marine pollution, ship collision or climate change in time;

[0006] 3. Lack of cooperative work: Existing ocean Internet of Things systems are usually single device or single function module applications, lacking group perception and cooperative work capability, resulting in low resource utilization efficiency.

[0007] Therefore, an ocean Internet of Things communication system and method based on group perception and crowd wisdom cooperation are proposed. The present application aims to realize efficient and real-time marine monitoring and communication through group perception technology and intelligent cooperation mechanism to meet the increasing demand of marine applications. SUMMARY

[0008] The present application aims to provide an ocean Internet of Things communication system and method based on group perception and crowd wisdom cooperation to solve the problems of insufficient communication coverage, data processing and decision delay and lack of cooperative work mentioned in the background.

[0009] To achieve the above objectives, the present invention aims to provide a marine Internet of Things (IoT) communication system based on swarm perception and collective intelligence collaboration, comprising an IoT communication system and an energy management unit. The IoT communication system includes a sensor network unit, a data processing unit, a communication unit, a collaborative decision-making unit, a user interface unit, a security unit, and an energy management unit. The output terminal of the sensor network unit is electrically connected to the input terminal of the data processing unit. The output terminal of the data processing unit is electrically connected to the input terminals of the security unit and the collaborative decision-making unit, respectively. The output terminal of the collaborative decision-making unit is electrically connected to the input terminals of the communication unit and the user interface unit, respectively.

[0010] The energy management unit is used to provide energy for the Internet of Things communication system;

[0011] The sensor network unit is used to collect marine environmental characteristic data, including water quality, meteorological and sonar data, and send the characteristic data to the data processing unit;

[0012] The data processing unit receives feature data sent by the sensor network unit and performs real-time calculations and analyses based on a preset dynamic threshold model. The dynamic threshold model generates real-time indicators using the following formula: ,

[0013] in, Let be the normalized measurement value of the i-th type of sensor at time t. These are the weighting coefficients for the corresponding sensors. The rate of change of data within the time window T. The rate of change weighting coefficient;

[0014] Preset a threshold The data processing unit compares and To determine whether the current environment is abnormal, specifically:

[0015] like If so, it is determined to be an abnormal state, triggering the risk response mechanism of the collaborative decision-making unit;

[0016] like If so, it is considered a normal state and should be continuously monitored;

[0017] The communication unit is used to transmit data via LPWAN, satellite communication, or 5G communication technology.

[0018] The collaborative decision-making unit is used to calculate the corresponding risk level based on the judgment result of the data processing unit and the measurement value of the sensor, and generate a response command.

[0019] As a further optimized technical solution of the present invention, the user interface unit is used to provide an interface for users to connect to external terminals for use, and at the same time, it is used to transmit the data association and decision support after the collaborative decision-making unit has completed processing to the external terminal.

[0020] The security unit is used to encrypt, manage access permissions, and audit the data processed by the data processing unit.

[0021] As a further optimized technical solution of the present invention, the sensor network unit is internally provided with a water quality sensor module, a meteorological sensor module and a sonar sensor module.

[0022] The water quality sensor module is used to detect pollutants and their concentrations in seawater, acquire seawater quality data, and transmit the seawater quality data to the data processing unit.

[0023] The meteorological sensor module is used to monitor marine meteorological data, including wind speed, air temperature, and humidity data, and transmits the meteorological data to the data processing unit.

[0024] The sonar sensor module is used to monitor ocean depth and underwater obstacles, acquire water depth data and underwater obstacle location and size data, and transmit the water depth data and underwater obstacle location and size data to the data processing unit.

[0025] As a further optimized technical solution of the present invention, the normalized measurement value of the i-th type of sensor at time t The calculation steps are as follows:

[0026] Obtain the raw measurement value of the i-th type of sensor at time t from the sensor network unit. ;

[0027] Determine the maximum measurement value of this type of sensor. ;

[0028] According to the formula Calculate normalized measurement values .

[0029] As a further optimized technical solution of the present invention, the collaborative decision-making unit calculates the corresponding risk level based on the judgment result of the data processing unit and the measurement value of the sensor, and generates a response command, specifically as follows:

[0030] According to the formula This maps data from different sensors to a unified risk factor, where... For sensor weights, and according to the formula Dynamic adjustments are made, among which This is an adjustment coefficient used to amplify or suppress the effect of the rate of change;

[0031] According to the formula Calculate the probability of risk ,in This is the historical average risk value. The slope parameter controls the sensitivity of the risk curve;

[0032] According to the formula Calculate dynamic probability ,in The cumulative weighting coefficient over time is T, where T is the time window. Indicates time Rate of change of data within;

[0033] According to the formula Classify risk levels;

[0034] Commands are responded to in descending order of risk level.

[0035] As a further optimized technical solution of the present invention, the security protection unit is internally equipped with a data encryption module, an access control module, and a security audit module;

[0036] The data encryption module is used to encrypt and protect the environmental feature data inside the data processing unit using a key;

[0037] The permission management module is used to control access permissions to the system within the data processing unit.

[0038] The security audit module is used to periodically perform security checks and vulnerability assessments on the IoT communication system.

[0039] As a further optimized technical solution of the present invention, the communication unit is internally equipped with an LPWAN module, a satellite communication module and a 5G communication module;

[0040] The LPWAN module is used to enable low-power wide-area network communication to connect various devices;

[0041] The satellite communication module is used for long-distance data transmission via satellite;

[0042] The 5G communication module is used for communication and data transmission via the 5G network.

[0043] As a further optimized technical solution of the present invention, the user interface unit is internally provided with a monitoring interface module, an alarm system module and a data reporting module;

[0044] The monitoring interface module is used to provide a real-time data monitoring and visualization interface.

[0045] The alarm system module is used to notify users in a timely manner when potential risks are identified;

[0046] The data reporting module is used to generate data reports periodically for analysis and decision support.

[0047] As a further optimized technical solution of the present invention, the energy management unit is internally equipped with an energy acquisition module, an energy monitoring module, and an intelligent scheduling module;

[0048] The energy harvesting module is used to harvest natural energy to power the Internet of Things communication system. The natural energy includes solar energy, wind energy, and hydropower.

[0049] The energy monitoring module is used to collect real-time usage data of various natural energy sources and monitor this real-time usage data. When an abnormal situation is detected or the energy consumption exceeds the preset threshold, an alarm is issued.

[0050] The intelligent scheduling module is used to obtain energy usage information, issue task scheduling instructions to each unit based on this energy usage information, and generate task execution instructions based on these task scheduling instructions.

[0051] This invention also provides a marine Internet of Things (IoT) communication method based on swarm perception and collective intelligence collaboration, comprising the following steps:

[0052] S1. Data Acquisition: The water quality sensor module, meteorological sensor module, and sonar sensor module in the sensor network unit are activated. Each sensor module monitors marine environmental characteristic data in real time. The water quality sensor collects pollutants and their concentrations in seawater, the meteorological sensor monitors wind speed, air temperature, and humidity, and the sonar sensor acquires data on water depth and underwater obstacles. Then, the collected characteristic data are transmitted to the data processing unit.

[0053] S2. Data Processing: The data processing unit receives the feature data sent by the sensor network unit and performs real-time calculation and analysis based on the preset dynamic threshold model.

[0054] S3, Decision Support: The data processing unit transmits the analyzed data to both the security unit and the collaborative decision-making unit. The collaborative decision-making unit calculates the corresponding risk level based on the judgment results of the data processing unit and the measurement values ​​of the sensors, and generates response instructions.

[0055] S4. Communication and Information Output: Transmit the data processed by the collaborative decision-making unit to the communication unit, select an appropriate communication method for data transmission, and transmit the data to an external terminal.

[0056] S5. User Interaction: Users access the system through the user interface unit, including a graphical monitoring interface, to view key environmental data and system status in real time. When potential risks are identified, the alarm system module promptly notifies the user, allowing the user to respond quickly. At the same time, the data reporting module generates data reports regularly to provide users with environmental analysis and decision support.

[0057] S6. Energy Management: The energy management unit continuously monitors the system's energy usage. The energy acquisition module collects energy from nature, the energy monitoring module records and analyzes energy usage data, and issues alarms when the data exceeds preset thresholds. At the same time, the intelligent scheduling module issues task scheduling instructions based on energy usage.

[0058] S7. Security Assurance: The security assurance unit encrypts the data output by the data processing unit to protect information security. At the same time, the access control module verifies the user's access rights to the system. In addition, the security audit module runs regularly to check for system vulnerabilities and issue security assessment results.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. In this invention, by adopting technologies such as low-power wide area network and satellite communication, the communication coverage and reliability of the system in complex marine environments are improved, enabling data to be transmitted in real time and realizing immediate monitoring and response, thereby ensuring stable signal transmission in remote sea areas or deep-sea environments.

[0061] 2. In this invention, by setting up collaborative innovation between the data processing unit and the collaborative decision-making unit, the real-time performance, reliability, and intelligence level of the system are significantly improved, and environmental status indicators are dynamically generated and combined with preset thresholds. This enables accurate determination of abnormal states.

[0062] 3. In this invention, by setting up a collaborative decision-making unit, the environmental risk level can be dynamically assessed, thereby enabling the system to respond to instructions according to the risk level. This allows the system to handle high-risk events more promptly. Simultaneously, by dynamically adjusting sensor weights, the impact of environmental changes on decision-making is amplified, and the risk probability is corrected by incorporating the time accumulation effect, thus improving the system's predictive ability for unexpected events. Attached Figure Description

[0063] Figure 1 This is a block diagram illustrating the overall principle of the marine Internet of Things communication system based on swarm perception and collective intelligence collaboration of the present invention.

[0064] Figure 2 This is a schematic diagram of the internal structure of the sensor network unit, data processing unit, collaborative decision-making unit, and security unit of the present invention.

[0065] Figure 3 This is a schematic diagram illustrating the internal workings of the user interface unit, communication unit, and collaborative decision-making unit of the present invention.

[0066] Figure 4 This is a schematic diagram of the steps of the marine Internet of Things communication method based on swarm perception and collective intelligence collaboration in this invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] In one specific embodiment, such as Figure 1 As shown, the marine Internet of Things (IoT) communication system based on swarm perception and collective intelligence collaboration includes an IoT communication system and an energy management unit. The IoT communication system includes a sensor network unit, a data processing unit, a communication unit, a collaborative decision-making unit, a user interface unit, a security unit, and an energy management unit.

[0069] The energy management unit is used to collect natural energy to power the IoT communication system. It also collects real-time usage data of various natural energy sources and generates corresponding task scheduling instructions based on this data, thereby ensuring the normal operation of all units and modules. Internally, the energy management unit includes an energy acquisition module, an energy monitoring module, and an intelligent scheduling module.

[0070] like Figure 2 and Figure 3As shown, the sensor network unit collects marine environmental characteristic data and sends it to the data processing unit. The sensor network unit internally includes a water quality sensor module, a meteorological sensor module, and a sonar sensor module. The data processing unit processes the characteristic data sent by the sensor network unit and performs real-time analysis. The data processing unit internally includes an edge computing module, a data fusion module, and an intelligent algorithm module. The communication unit transmits data via LPWAN, satellite communication, or 5G communication technologies. The communication unit internally includes an LPWAN module, a satellite communication module, and a 5G communication module. The collaborative decision-making unit utilizes swarm intelligence algorithms for data association and decision support. The collaborative decision-making unit internally includes a swarm intelligence algorithm module, a task allocation module, and a risk assessment module. The user interface unit provides an interface for users to connect to external terminals and transmits the data association and decision support processed by the collaborative decision-making unit to these external terminals. The user interface unit internally includes a monitoring interface module, an alarm system module, and a data reporting module. The security unit encrypts, manages access permissions, and performs security audits on the data processed by the data processing unit. The security unit internally includes a data encryption module, an access control module, and a security audit module.

[0071] like Figure 4 As shown, in practical applications, to address the problems of marine environmental degradation and overexploitation of resources, this marine IoT communication system based on collective perception and collaborative intelligence was implemented in a specific sea area. Its aim is to monitor marine water quality, weather, and ecological conditions in real time, and to ensure data security and reliability. The overall operation steps of the system are as follows:

[0072] Step 1: Data Acquisition. The water quality sensor module, meteorological sensor module, and sonar sensor module in the sensor network unit are activated. Each sensor module monitors marine environmental characteristics in real time. Water quality sensors are deployed in the sea area to monitor the concentration of pollutants (such as heavy metals, oils, and suspended solids) in seawater and transmit the water quality data to the data processing unit via data cables. For example, if the concentration of heavy metals at a certain location exceeds the standard, the data is transmitted to the data processing unit. Meteorological sensors are set up on the marine platform to collect meteorological data such as wind speed, temperature, and humidity in real time and transmit it to the data processing unit. This provides accurate weather forecast data before an approaching storm. Simultaneously, sonar sensors are installed underwater to monitor the water depth and underwater obstacles in the surrounding waters in real time, such as shipwrecks or marine life, acquiring data on their location and size, and transmitting this data to the data processing unit.

[0073] The second step is data processing: The data processing unit receives the feature data sent by the sensor network unit and uses the edge computing module to perform preliminary processing and filtering on the data from the sensor network unit to keep the data concise and efficient. The data after preliminary processing enters the data fusion module. The data fusion module can integrate various data from water quality, meteorology and sonar sensors to generate a comprehensive environmental analysis report. At the same time, the intelligent algorithm module implements machine learning technology to predict and recognize patterns in the fused data in real time in order to discover potential environmental changes.

[0074] The data processing unit receives feature data sent by the sensor network unit and performs real-time calculations and analyses based on a preset dynamic threshold model. The dynamic threshold model generates real-time indicators using the following formula: ,

[0075] in, Let be the normalized measurement value of the i-th type of sensor at time t. These are the weighting coefficients for the corresponding sensors. The rate of change of data within the time window T. The rate of change weighting coefficient;

[0076] Preset a threshold The data processing unit compares and To determine whether the current environment is abnormal, specifically:

[0077] like If so, it is determined to be an abnormal state, triggering the risk response mechanism of the collaborative decision-making unit;

[0078] like If so, it is considered a normal state and should be continuously monitored;

[0079] The communication unit is used to transmit data via LPWAN, satellite communication, or 5G communication technology.

[0080] The normalized measurement value of the i-th type of sensor at time t The calculation steps are as follows:

[0081] Obtain the raw measurement value of the i-th type of sensor at time t from the sensor network unit. ;

[0082] Determine the maximum measurement value of this type of sensor. ;

[0083] According to the formula Calculate normalized measurement values .

[0084] The third step, decision support, involves the data processing unit simultaneously transmitting the analyzed data to both the security unit and the collaborative decision-making unit. The collaborative decision-making unit utilizes swarm intelligence algorithms to perform correlation analysis on the collected data, providing intelligent decision support. For example, when a water quality sensor detects pollution, the system can quickly analyze its impact range and provide corresponding remediation suggestions. Based on the decision analysis results, the task allocation module, combined with environmental characteristic data, automatically assigns cleanup tasks to relevant management departments. Meanwhile, the risk assessment module continuously monitors environmental changes, assesses potential risks, and guides decision-making in a timely manner.

[0085] The collaborative decision-making unit calculates the corresponding risk level based on the judgment result of the data processing unit and the measurement value of the sensor, and generates a response command, specifically:

[0086] According to the formula This maps data from different sensors to a unified risk factor, where... For sensor weights, and according to the formula Dynamic adjustments are made, among which This is an adjustment coefficient used to amplify or suppress the effect of the rate of change;

[0087] According to the formula Calculate the probability of risk ,in This is the historical average risk value. The slope parameter controls the sensitivity of the risk curve;

[0088] According to the formula Calculate dynamic probability ,in The cumulative weighting coefficient over time is T, where T is the time window. Indicates time Rate of change of data within;

[0089] According to the formula Classify risk levels;

[0090] Step 4, Communication and Information Output: The data processed by the collaborative decision-making unit is transmitted to the communication unit, which is equipped with an LPWAN module for low-power connection with near-shore network equipment; for long-distance data transmission, a satellite communication module is used; and in scenarios with large data volumes, a 5G communication module is enabled to ensure real-time data transmission and high efficiency.

[0091] Step 5, User Interaction: Users access the system through the user interface unit, including a graphical monitoring interface, to view key environmental data and system status in real time. Various monitoring data are also displayed in real time. Users can intuitively view the marine conditions on the visual interface, and the alarm system module can immediately notify users when danger is detected, allowing users to respond quickly and prevent potential environmental hazards. At the same time, the data reporting module regularly generates monitoring and analysis reports to support subsequent scientific decision-making.

[0092] Step 6, Energy Management: The energy management unit continuously monitors the system's energy usage. The energy harvesting module collects energy from nature, such as through solar panels and wind turbines, to power the entire system. The energy monitoring module monitors energy consumption in real time and issues an early warning if usage exceeds preset values, ensuring or reducing energy competition between devices. At the same time, the intelligent scheduling module rationally allocates work tasks based on device usage to optimize energy use.

[0093] Step 7, Security Assurance: The security assurance unit encrypts the data output from the data processing unit to ensure data security during transmission and prevent malicious tampering. At the same time, the access control module controls the system's access permissions to ensure that only authorized users can access sensitive data. In addition, the security audit module regularly checks the system's security status and assesses security vulnerabilities.

[0094] Example: On a certain day, a water quality sensor detected that the concentration of heavy metals exceeded the safety threshold during monitoring. It immediately generated a data packet and transmitted it to the data processing unit through the communication unit.

[0095] The edge computing module of the data processing unit performs preliminary processing on the water quality data. Subsequently, the data passes through the data fusion module and is fused and analyzed together with wind speed and temperature data from meteorological sensors to produce a comprehensive report on the marine environment of the area.

[0096] The collaborative decision-making unit receives the analysis results in real time, uses swarm intelligence algorithms to determine the pollution risk in the area, and assesses the potential impact through the risk assessment module. If the pollution risk is confirmed, the task allocation module automatically assigns the environmental cleanup task to the appropriate environmental protection organization.

[0097] Through the user interface unit, relevant management personnel can view alarms and environmental reports on the monitoring interface module. After confirming the severity of the problem, they can immediately take remedial measures. At the same time, the data reporting module generates an execution report of the remediation work.

[0098] The security unit conducts regular audits of data monitoring and user permissions to ensure the operational security of the system.

[0099] Throughout the system's operation, the energy management unit continuously collects solar and wind energy for power supply, ensuring the normal operation of all equipment and the rational management of energy consumption.

[0100] The entire system can achieve efficient and real-time marine monitoring and communication through swarm sensing technology and intelligent collaboration mechanisms to meet the growing needs of marine applications.

[0101] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An ocean Internet of Things communication system based on crowd perception and crowd intelligence collaboration, characterized in that, The application relates to an Internet of Things communication system and an energy management unit, wherein the Internet of Things communication system comprises a sensor network unit, a data processing unit, a communication unit, a collaborative decision unit, a user interface unit, a security guarantee unit and an energy management unit; the output end of the sensor network unit is electrically connected with the input end of the data processing unit; the output end of the data processing unit is electrically connected with the input end of the security guarantee unit and the collaborative decision unit; the output end of the collaborative decision unit is electrically connected with the input end of the communication unit and the user interface unit; the energy management unit is used for providing energy for the Internet of Things communication system; the sensor network unit is used for collecting marine environment characteristic data, including water quality, weather and sonar data, and transmitting the characteristic data to the data processing unit; the communication unit is used for transmitting data through LPWAN, satellite communication or 5G communication technology; the collaborative decision unit is used for calculating corresponding risk levels according to the determination results of the data processing unit and the measurement values of the sensors and generating response instructions; the collaborative decision unit calculates corresponding risk levels according to the determination results of the data processing unit and the measurement values of the sensors and generates response instructions, specifically as follows: the instructions are responded in the order from high to low according to the risk levels; the user interface unit is used for providing an interface for a user to connect an external terminal for use, and is used for transmitting the data association and decision support processed by the collaborative decision unit to the external terminal; the security guarantee unit is used for encrypting, permission managing and security auditing the data processed by the data processing unit. The sensor network unit is internally provided with a water quality sensor module, a weather sensor module and a sonar sensor module; the water quality sensor module is used for detecting pollutants and their concentrations in seawater, acquiring seawater quality data and transmitting the seawater quality data to the data processing unit; the weather sensor module is used for monitoring marine weather data, the weather data including wind speed, air temperature and humidity data and transmitting the weather data to the data processing unit; the sonar sensor module is used for monitoring the water depth and underwater obstacles of the sea, acquiring water depth data and underwater obstacle position and size data and transmitting the water depth data and underwater obstacle position and size data to the data processing unit. The security guarantee unit is internally provided with a data encryption module, a permission management module and a security audit module; the data encryption module is used for encrypting and protecting the environment characteristic data in the data processing unit through a key; the permission management module is used for permission controlling the system access in the data processing unit; the security audit module is used for periodically checking the security of the Internet of Things communication system and evaluating the vulnerabilities. The data processing unit is used for receiving characteristic data sent by the sensor network unit and performing real-time calculation and analysis based on a preset dynamic threshold model, and the dynamic threshold model generates a real-time index through the following formula: , wherein, is the normalized measurement value of the i-th sensor at time t, is the weight coefficient of the corresponding sensor, is the data rate of change within the time window T, is the rate of change weight coefficient; A threshold is preset , the data processing unit determines whether the current environment state is abnormal by comparing and , specifically: If then the abnormal state is determined and the risk response mechanism of the collaborative decision unit is triggered; If then the normal state is determined and the monitoring is continued; The communication unit is internally provided with an LPWAN module, a satellite communication module and a 5G communication module; the LPWAN module is used for realizing low-power wide-area network communication to connect various devices; the satellite communication module is used for long-distance data transmission through a satellite; the 5G communication module is used for communication and data transmission through a 5G network. ​ ​ According to the formula mapping different sensor data into a uniform risk factor, wherein is a sensor weight, and according to the formula is dynamically adjusted, wherein is an adjustment factor for amplifying or suppressing the influence of the rate of change; According to the formula The risk probability is calculated where is the historical average risk value, is a slope parameter, controlling the sensitivity of the risk curve; According to the formula The dynamic probability is calculated where is the time cumulative weight coefficient, T is the time window, represents the data change rate within the time ; According to the formula , the risk level is divided; ​ 2.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 1, wherein, ​ ​ 3.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 1, wherein, ​ ​ ​ ​ 4.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 3, wherein, the normalized measurement value of the ith class of sensors at time t the calculation step is: obtaining raw measurements of the ith class of sensors at time t from the sensor network unit ; determining the maximum measurement value of the sensor of this class ; The normalized measurement value is calculated according to the formula . 5.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 1, wherein, ​ ​ ​ ​ 6.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 1, wherein, ​ ​ ​ ​ 7.The crowd-sensing and crowd-intelligence collaborative based Internet of Underwater Things communication system of claim 1, wherein, The internal of the user interface unit is provided with a monitoring interface module, an alarm system module and a data reporting module; The monitoring interface module is used for providing real-time data monitoring and visual operation interface; The alarm system module is used for timely informing the user when potential risks are identified; The data reporting module is used for generating data reports periodically for analysis and decision support. 8.The ocean Internet of Things communication system based on crowd perception and crowd wisdom collaboration of claim 1, wherein, The internal of the energy management unit is provided with an energy collection module, an energy monitoring module and an intelligent scheduling module; The energy collection module is used for collecting natural energy for the Internet of Things communication system, including solar energy, wind energy and water energy; The energy monitoring module is used for collecting real-time use data of various natural energy, and monitoring these real-time use data, and issuing an alarm when detecting abnormal conditions or energy consumption exceeding a preset threshold; The intelligent scheduling module is used for obtaining energy use conditions, issuing task scheduling instructions for each unit based on these energy use conditions, and generating task execution instructions according to these task scheduling instructions. 9.The method of claim 1-8, wherein, The following steps are included: S1, data collection: start the water quality sensor module, weather sensor module and sonar sensor module in the sensor network unit, each sensor module monitors the marine environmental characteristic data in real time, the water quality sensor collects pollutants and their concentrations in seawater, the weather sensor monitors wind speed, air temperature and humidity, and the sonar sensor obtains water depth and underwater obstacle data, and then transmits the collected characteristic data to the internal of the data processing unit; S2, data processing: the data processing unit receives the characteristic data sent by the sensor network unit, and performs real-time calculation and analysis based on a preset dynamic threshold model; S3, decision support: the data processing unit transmits the analyzed data to the safety guarantee unit and the collaborative decision unit at the same time, the collaborative decision unit calculates the corresponding risk level according to the determination result of the data processing unit and the measurement value of the sensor, and generates a response instruction; S4, communication and information output: transmit the data processed by the collaborative decision unit to the communication unit, select appropriate communication mode for data transmission, and transmit the data to the external terminal; S5, user interaction: the user accesses the system through the user interface unit, including a graphical monitoring interface, real-time viewing of key environmental data and system status, when potential risks are identified, the alarm system module timely informs the user, so that the user can quickly respond, and at the same time, the data reporting module generates data reports periodically to provide environmental analysis and decision support for the user; S6, energy management: the energy management unit continuously monitors the energy use conditions of the system, the energy collection module collects energy from nature, the energy monitoring module records and analyzes energy use data, and alarms when the preset threshold is exceeded, and the intelligent scheduling module issues task scheduling instructions according to the energy use conditions; S7, safety guarantee: the safety guarantee unit encrypts the data output by the data processing unit to protect information security, the permission management module verifies the user's access rights to the system, and in addition, the security audit module runs periodically to check system vulnerabilities and issue security evaluation results.

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