A smart terminal security risk assessment system and method

Abstract

The present invention discloses a smart terminal security risk assessment system and method, which relates to the field of risk assessment technology and includes the following steps: continuously collecting meteorological data, geological data, and smart terminal energy data; wherein the climate data includes temperature, wind speed, and the duration of the polar day and polar night cycle; the geological data includes surface microseismic amplitude and ice layer displacement velocity; determining a demarcation point based on the duration of the polar day and polar night cycle, dividing the data time period based on the demarcation point, and increasing the energy storage capacity weight coefficient to a preset value during the polar night period. The remaining polar night days are calculated through satellite positioning and astronomical calendar, so that energy distribution has the ability to dynamically optimize in the time dimension; at the same time, light intensity sensor data is used to trigger wind speed correlation analysis, and storm precursor signals are captured under strong light conditions during the polar day period, solving the risk assessment lag problem caused by the failure to consider the impact of the polar day / polar night cycle on energy supply and meteorological disasters in the existing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and in particular to a smart terminal security risk assessment system and method. Background Art

[0002] With the continuous development of extreme environment operations such as polar scientific research and deep-sea exploration, the stable operation of intelligent terminal equipment (such as unmanned monitoring stations and automated scientific research equipment) under harsh conditions such as extreme cold, strong winds, and alternating polar day / polar night faces severe challenges. Such terminals usually need to work autonomously for a long time, and their safety is not only affected by external climate and geological conditions, but also depends on internal energy supply and microbial environment.

[0003] However, in the process of implementing the technical solutions of the embodiments of the present application, the inventors of the present application discovered that the above technology has at least the following technical problems:

[0004] Existing technologies have failed to establish a dynamic risk assessment mechanism that is compatible with the polar day / night cycle, and are unable to adjust risk assessment parameters based on the polar region's special day and night cycle; there is a lack of a comprehensive risk assessment system for the polar region's special geological conditions (such as ice layer displacement, surface micro-earthquakes) and climatic factors (such as sudden changes in wind speed). Summary of the Invention

[0005] The purpose of the present invention is to provide a smart terminal security risk assessment system and method to solve the problems raised in the above background technology.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention discloses a method for assessing security risks of smart terminals, comprising the following steps:

[0008] Continuously collect meteorological data, geological data, and smart terminal energy data at a fixed cycle;

[0009] The meteorological data include temperature, wind speed, and the duration of polar day and polar night cycles; the geological data include surface microseismic amplitude and ice displacement velocity;

[0010] Determine the demarcation point based on the duration of the polar day and polar night cycles, divide the data time periods based on the demarcation point, and increase the energy storage capacity weight coefficient to a preset value during the polar night period;

[0011] The temperature difference is calculated based on the temperature value in the current fixed period and the value in the previous fixed period, and the temperature difference is input into the constructed cracking prediction model to output the probability of ice cracking;

[0012] Output the predicted value of ice displacement speed according to ice displacement speed and ice cracking probability;

[0013] The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient.

[0014] When it is detected that the wind speed exceeds the set threshold and the foundation stability coefficient is lower than the critical value, the shell reinforcement instruction is triggered;

[0015] The terminal's safety risk value is calculated based on the foundation stability coefficient, smart terminal energy data, and ice cracking probability.

[0016] In a second aspect, the present invention discloses a smart terminal security risk assessment system, which uses the above-mentioned smart terminal security risk assessment method, including:

[0017] Data acquisition module, used to continuously collect meteorological data, geological data, and smart terminal energy data;

[0018] A data processing module is configured to determine a demarcation point based on the duration of the polar day and polar night cycles, divide the data time periods based on the demarcation point, and increase the energy storage capacity weight coefficient to a preset value during the polar night period;

[0019] Calculating a temperature difference based on the temperature value in the current fixed period and the temperature value in the previous fixed period, inputting the temperature difference into the constructed cracking prediction model, and outputting the probability of ice cracking;

[0020] Outputting a predicted value of ice layer displacement velocity according to the ice layer displacement velocity and ice layer cracking probability;

[0021] The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient.

[0022] an execution module, configured to trigger a shell reinforcement instruction when detecting that the wind speed exceeds a set threshold and the foundation stability coefficient is lower than a critical value;

[0023] The risk assessment module is used to calculate the safety risk value of the terminal based on the foundation stability coefficient, the energy data of the smart terminal, and the probability of ice cracking.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. By calculating the remaining polar night days through satellite positioning and astronomical calendar, energy distribution can be dynamically optimized in the time dimension. At the same time, light intensity sensor data is used to trigger wind speed correlation analysis to capture storm precursor signals under strong light conditions during the polar day period. This solves the risk assessment lag problem caused by the failure to consider the impact of the polar day / polar night cycle on energy supply and meteorological disasters in existing technologies, and realizes the dynamic optimization of energy storage strategies and active defense of storm warnings.

[0026] 2. This solution achieves a multi-dimensional collaborative analysis of ice layer structure changes and geological activities through a temperature-difference-driven cracking probability model, dynamic displacement velocity correction, and multi-source data vector synthesis. It can more accurately capture early signals of foundation instability and solve the problem of misjudgment of foundation stability caused by ignoring the correlation between temperature mutations and geological activities in existing technologies.

[0027] 3. This solution integrates geographic location data with astronomical calendar information to establish an energy prediction model driven by the day and night cycle, enabling the charging strategy to proactively adapt to the sudden changes in light intensity caused by the alternation of polar day and night. At the same time, it improves energy utilization efficiency through the intelligent shutdown mechanism of non-core equipment, solving the problem of unstable energy supply for smart terminals in polar environments caused by the drastic changes in the day and night cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0029] Figure 1 is a flow chart of the steps of the present invention;

[0030] Figure 2 This is a functional diagram of the system modules provided by the present invention. DETAILED DESCRIPTION

[0031] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present invention and should not be regarded as the entire invention or as a limitation or restriction of the technical solution of the present invention.

[0032] Application Overview:

[0033] In existing technologies, smart terminal devices deployed in polar research activities are exposed to extreme climatic and geological environments for long periods of time. Traditional risk assessment systems typically use fixed thresholds to determine the device's operating status. Existing methods fail to consider the cyclical impact of the polar day and night cycle on energy supply, making it impossible to dynamically adjust energy storage strategies. Furthermore, they lack analysis of the correlation between ice cracking and changes in foundation stability, resulting in delayed risk warnings. Furthermore, the abnormal proliferation of microorganisms within devices in low-temperature, high-humidity environments is not factored into the assessment system, posing a risk of equipment corrosion and circuit failure.

[0034] To address the above issues, the inventors discovered that the risk factors for smart terminals in polar environments are time-sensitive and multi-factor coupled. First, there is a temporal correlation between energy interruptions during polar night and thermodynamic changes in the ice layer, necessitating the establishment of a periodic data segmentation mechanism. Second, the displacement rate of the ice layer and the probability of cracking caused by sudden temperature changes jointly affect foundation stability, necessitating dynamic prediction through thermodynamic models. Third, the metabolic activity of microorganisms within the device has a nonlinear relationship with temperature and humidity, necessitating the establishment of a dynamic monitoring model. By integrating meteorological, geological, energy, and microbial data, it becomes necessary to construct a risk assessment framework that integrates multi-dimensional parameters.

[0035] After introducing the basic concept of the present invention, embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0036] Example 1:

[0037] See also Figure 1 A smart terminal security risk assessment method is applied to the smart terminal security risk assessment of polar unmanned scientific research stations, including the following steps:

[0038] Continuously collect meteorological data, geological data, and smart terminal energy data;

[0039] The climate data include temperature, wind speed, and the duration of polar day and polar night cycles; the geological data include surface microseismic amplitude and ice displacement velocity;

[0040] Determine a demarcation point based on the duration of the polar day and polar night cycles, divide the data time periods based on the demarcation point, and increase the energy storage capacity weight coefficient to a preset value during the polar night period;

[0041] Calculating a temperature difference based on the current period air temperature data and the previous period air temperature data, inputting the temperature difference into a constructed cracking prediction model, and outputting an ice layer cracking probability;

[0042] Outputting a predicted value of ice layer displacement velocity according to the ice layer displacement velocity and ice layer cracking probability;

[0043] The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient.

[0044] When it is detected that the wind speed exceeds a set threshold and the foundation stability coefficient is lower than a critical value, a shell reinforcement instruction is triggered;

[0045] The safety risk value of the terminal is calculated based on the foundation stability coefficient, the energy data of the smart terminal, and the probability of ice cracking.

[0046] The demarcation point division refers to dividing the data collection period into segments according to the alternation time of polar day and polar night. This can be achieved by using the astronomical calendar to calculate the threshold of the solar altitude angle change, which solves the impact of periodic environmental changes on the evaluation model.

[0047] Temperature difference calculation refers to obtaining the numerical difference between two consecutive temperature monitoring cycles. Specifically, the temperature sensor data can be processed using the differential calculation method, which provides a thermodynamic basis for predicting changes in ice structure.

[0048] Vector synthesis calculation refers to converting monitoring data of different dimensions into standardized vectors and then spatially superimposing them. Specifically, it can be achieved by combining Z-score standardization with a vector projection algorithm, which realizes the fusion evaluation of multi-dimensional geological parameters.

[0049] Specifically, the method uses satellite positioning and light intensity sensors to obtain real-time environmental cycle data, and automatically increases the energy storage weight during the polar night period to ensure continuous power supply to the equipment. The temperature difference data is input into the cracking prediction model constructed based on the heat conduction equation, and the output probability value reflects the fragility of the ice layer structure. The predicted value of the ice layer displacement velocity is obtained by combining real-time monitoring data and weighted calculation of the cracking probability, accurately reflecting the dynamic changes of the foundation. The normalized microseismic amplitude data and the displacement prediction value are orthogonally synthesized to generate a coefficient indicator reflecting the comprehensive stability state. When the wind speed sensor detects strong winds and the foundation coefficient is lower than the set standard, the electromagnetic locking device is automatically activated to enhance the physical protection of the terminal. Finally, the risk level is calculated through a multi-parameter weighted model to provide a quantitative basis for equipment maintenance.

[0050] Compared to existing technologies, traditional methods use static thresholds to determine risk, while this solution dynamically divides data into periods to achieve adaptive adjustment of assessment parameters. Existing systems analyze geological parameters individually, while this solution establishes a multi-parameter correlation model through vector synthesis. Conventional monitoring ignores the impact of microorganisms, while this solution predicts internal equipment risks using equations correlating temperature, humidity, and microorganisms. Furthermore, existing energy management fails to account for the unique needs of polar night periods, while this solution dynamically adjusts energy storage strategies based on period demarcation points.

[0051] Through the above technical solutions, this application realizes the dynamic monitoring and comprehensive assessment of risk factors of smart terminals in polar environments, improves the accuracy of foundation stability prediction, and ensures the timely triggering of protection mechanisms under extreme meteorological conditions. At the same time, by integrating internal microbial environmental monitoring, it effectively prevents the risk of internal corrosion of equipment and forms a comprehensive assessment system covering the external environment and internal status. The periodic data segmentation mechanism solves the problem of insufficient adaptability of traditional methods during the period of polar day and polar night, and the vector synthesis algorithm improves the fusion and analysis capabilities of multi-source heterogeneous data.

[0052] This application further proposes that the energy storage capacity weight coefficient be increased to a preset value during the polar night period, including:

[0053] During the polar night period, the geographical coordinates of the terminal are obtained by satellite positioning, and the number of remaining polar night days is calculated in combination with the astronomical calendar;

[0054] During the polar day, a light intensity sensor is used to monitor the solar radiation intensity. When the solar radiation intensity exceeds 800W / m² and lasts for 3 hours, a wind speed correlation analysis is performed:

[0055] If the wind speed during this period reaches 15m / s and the fluctuation amplitude exceeds 50% of the average value of the previous 24 hours, a storm warning signal will be generated and the electromagnetic locking device of the terminal casing will be activated 48 hours in advance.

[0056] Satellite positioning refers to the technology of obtaining the longitude and latitude coordinates of the terminal using the Beidou or GPS system. It can be implemented using a multi-band signal receiving module to accurately locate the terminal's geographical location.

[0057] The remaining polar night days refer to the remaining duration of the polar night calculated based on astronomical calendar data combined with terminal coordinates. This can be achieved through celestial orbit algorithms and provide a time benchmark for energy scheduling.

[0058] A light intensity sensor is a photoelectric conversion device used to measure solar radiation intensity, such as a silicon-based photovoltaic element. When the radiation intensity exceeds 800W / m², it indicates strong light conditions during the polar day.

[0059] Wind speed correlation analysis refers to an algorithm that compares current wind speed data with historical fluctuation values. For example, the sliding average method is used to calculate the average value of the previous 24 hours. When the instantaneous wind speed fluctuation exceeds 50%, it is determined to be an abnormal meteorological event.

[0060] An electromagnetic locking device refers to a mechanical component that enhances the structural stability of the terminal casing through electromagnetic force. For example, an electromagnetic suction cup is linked to a reinforcement frame to lock the casing joints in advance when a storm warning is triggered.

[0061] Specifically, during the polar night, the terminal coordinates are obtained in real time through the satellite positioning module, and the remaining polar night days are calculated in combination with the pre-stored astronomical calendar data, providing a time basis for the capacity allocation of the energy storage system. During the polar day period, the light intensity sensor continuously monitors the intensity of solar radiation. When the radiation value exceeds 800W / m² and lasts for 3 hours, the wind speed data analysis module is activated. This module compares the current wind speed data with the average value of the previous 24 hours. If the wind speed reaches 15m / s and the fluctuation amplitude exceeds 50% of the average, it is determined that an extreme storm event is about to occur. An early warning signal is immediately generated and the electromagnetic locking device is driven to complete the shell reinforcement within 48 hours. This process realizes the early identification and active protection of meteorological disasters through the correlation analysis of the light intensity and wind speed fluctuations during the polar day period.

[0062] Compared with existing technologies, existing risk assessment systems lack a mechanism to link the polar day / polar night cycle with energy management and meteorological disasters. They are unable to dynamically adjust energy storage strategies based on the remaining polar night days, and lack analysis of the correlation between strong sunlight and sudden changes in wind speed. This solution uses satellite positioning and astronomical calendars to calculate the remaining polar night days, enabling dynamic optimization of energy allocation over time. It also uses light intensity sensor data to trigger wind speed correlation analysis, capturing storm precursor signals during the strong sunlight conditions of the polar day period. This significantly improves the timeliness of warnings compared to traditional single wind speed threshold alarms.

[0063] Through the above technical solution, this application solves the problem of delayed risk assessment caused by the failure to consider the impact of the polar day / polar night cycle on energy supply and meteorological disasters in the existing technology, realizes the dynamic optimization of energy storage strategies and active defense of storm warnings, and ensures the operational stability of terminal equipment in extreme environments.

[0064] The present application further proposes that the calculation of foundation stability coefficient includes:

[0065] Calculating a temperature difference based on the current period air temperature data and the previous period air temperature data, inputting the temperature difference into a cracking prediction model constructed based on thermodynamic principles, and outputting an ice layer cracking probability;

[0066] According to the ice layer displacement velocity and the ice layer cracking probability, a probability weighted average calculation is used to obtain a predicted value of the ice layer displacement velocity;

[0067] The surface microseismic amplitude data are standardized and vector composited with the predicted ice layer displacement velocity to obtain the foundation stability coefficient.

[0068] Temperature difference calculation refers to obtaining temperature data from two consecutive monitoring periods through a temperature sensor and performing a difference calculation. This can be achieved by using a difference algorithm or a sliding window mean comparison to reflect the severity of ice layer temperature changes.

[0069] The cracking prediction model is a mathematical relationship established based on the thermodynamic expansion coefficient and material fatigue theory. It can be implemented using finite element simulation or empirical formula fitting. It is used to quantify the probability of damage to ice structure caused by temperature changes, predict the risk of structural damage to polar ice due to temperature changes, and provide key parameters for foundation stability assessment (such as combining it with ice displacement velocity to correct displacement prediction values).

[0070] By analyzing the impact of temperature changes on ice structure, we can quantify the likelihood of ice cracking caused by sudden temperature changes. Specifically, the greater the temperature difference, the more significant the thermal stress within the ice, and the higher the risk of cracking.

[0071] By inputting the current cycle temperature value, the previous cycle temperature value, and ice layer material parameters, the cracking prediction model outputs the ice layer cracking probability (the value range is [0,1], and the higher the value, the higher the cracking risk);

[0072] The predicted ice displacement velocity is a dynamic weighted result that combines real-time displacement monitoring data with the probability of cracking. This can be achieved using linear regression or a probability density weighted algorithm to correct for the potential risk of accelerated displacement due to ice cracking.

[0073] Normalization processing refers to converting microseismic amplitude data of different dimensions into dimensionless values ​​of a unified scale. Specifically, it can be achieved by using Z-score normalization or range method to eliminate the impact of data scale differences on synthetic calculations.

[0074] Vector synthesis calculation refers to the vector superposition of the standardized microseismic amplitude and the displacement velocity prediction value in the spatial direction. Specifically, it can be implemented using vector addition or projection decomposition algorithm in the Euclidean space coordinate system, and is used to comprehensively evaluate the stability of the foundation in three-dimensional space.

[0075] Specifically, in polar environments, ice temperature and geological activity have a decisive influence on the foundation stability of smart terminals. By periodically collecting air temperature data, the temperature difference between adjacent periods is calculated to reflect the trend of thermal stress changes. This data is then input into a cracking prediction model based on thermodynamic principles. For example, by using an equation relating the thermal expansion coefficient to ice thickness, the probability of ice cracking under temperature differences is output. Subsequently, the real-time monitored ice displacement velocity is combined with the cracking probability, for example, using probability-weighted averaging or Bayesian inference methods to predict the potential accelerated displacement of the ice layer. Simultaneously, the surface microseismic amplitude data is normalized, for example, by removing dimensional differences to bring it into the same order of magnitude as the predicted displacement velocity. Finally, the normalized microseismic amplitude data is vector-synthesized with the predicted displacement velocity, for example, through vector superposition in a spatial coordinate system, to obtain a comprehensive coefficient reflecting the overall stability of the foundation.

[0076] Compared to existing technologies, traditional methods typically rely solely on single-sensor data to assess foundation conditions, such as analyzing only temperature or displacement velocity, while ignoring the impact of multiple coupled factors on stability. This approach, by using a temperature-differential-driven cracking probability model, dynamic displacement velocity correction, and multi-source data vector synthesis, enables a multi-dimensional, coordinated analysis of ice layer structural changes and geological activity, enabling more accurate detection of early signs of foundation instability.

[0077] Through the above technical solution, this application solves the problem of misjudging foundation stability caused by ignoring the correlation between temperature changes and geological activity in the existing technology. By integrating thermodynamic models with multi-source data synthesis calculations, it can effectively identify the composite foundation risks caused by ice cracking and microseismic activity, thereby providing a more reliable basis for decision-making on the casing reinforcement of smart terminals and avoiding device overturning or structural damage caused by assessment errors.

[0078] This application further proposes that the smart terminal energy data includes:

[0079] Before the polar night begins, the current latitude and longitude of the smart terminal is obtained through polar satellite positioning, and the remaining days are calculated based on the polar night cycle data;

[0080] Obtaining the remaining power of the smart terminal, and inputting the remaining days and the remaining power of the smart terminal into a pre-built calculation model to output the number of safe operation days;

[0081] During the polar day period, the light sensor monitors the daily effective sunlight duration in real time. When the sunlight duration exceeds 8 hours, the smart terminal is charged at maximum power; when the sunlight duration is less than 4 hours, it switches to low-power discharge mode and shuts down non-core devices.

[0082] Among them, polar satellite positioning refers to the technology of obtaining the terminal's geographic location coordinates through the polar orbit satellite system. It can be specifically implemented using the polar enhancement module of the Beidou satellite navigation system, which is used to accurately locate the coordinates of the device within the polar circle.

[0083] The remaining days calculation refers to an algorithm that calculates the duration of the polar night based on astronomical calendar data. Specifically, it can be implemented by combining a solar altitude angle model with historical meteorological data to predict the length of the polar night that the equipment will soon face.

[0084] The calculation model refers to a mathematical relationship that relates energy supply to operating requirements. Specifically, a linear regression model can be used to integrate the device power consumption curve and the battery capacity attenuation coefficient to evaluate the terminal's sustainable operation capability during the polar night.

[0085] A light sensor refers to a photoelectric conversion device that monitors the intensity of solar radiation. It can be implemented using a silicon photocell array combined with a light intensity calibration module to identify the effective charging period during the polar day.

[0086] Specifically, at the beginning of the polar night period, after determining the geographical coordinates of the terminal through satellite positioning, the remaining polar night days are calculated in combination with astronomical calendar data. The remaining days and the current remaining power of the terminal are input into the calculation model, which outputs the number of safe days for sustainable operation of the terminal based on the average power consumption of the device and the battery capacity attenuation characteristics. If the number of safe days is lower than the remaining polar night days, the low power mode is triggered in advance. During the operation phase of the polar day period, the effective light duration is continuously monitored through the light sensor: when the effective light duration in a single day exceeds 8 hours, the maximum power charging mode is started to quickly replenish energy; when the effective light duration is less than 4 hours, it automatically switches to low-power discharge mode and shuts down non-core equipment such as the data transmission module, giving priority to basic monitoring functions.

[0087] Compared to existing technologies, traditional energy management methods rely solely on simple estimates of remaining power, failing to consider the dynamic impact of the polar region's unique diurnal cycle on energy supply and demand. This solution integrates geographic location data with astronomical calendar information to establish a diurnal cycle-driven energy forecasting model. This enables charging strategies to proactively adapt to sudden changes in light intensity caused by the alternation between polar day and night, while also improving energy efficiency through intelligent shutdown of non-core devices.

[0088] Through the above technical solution, this application effectively solves the problem of unstable energy supply for smart terminals in polar environments caused by drastic changes in the day and night cycle. By accurately matching the remaining days with the power consumption of the equipment, it avoids accidental power outages during the polar night. At the same time, it uses the light intensity to dynamically adjust the charging power, maximizes energy reserves while ensuring the safe operation of the equipment, and significantly reduces the risk of operation interruption of polar scientific research equipment due to insufficient energy.

[0089] The present application further proposes a method for assessing the security risks of smart terminals, in which the continuous collection of meteorological data, geological data, and smart terminal energy data includes: obtaining the internal data of the smart terminal, the internal data including the terminal's internal microbial data, temperature data, and humidity data; synchronously inputting the humidity data, temperature data, and microbial data into a constructed prediction model, which outputs a microbial proliferation prediction curve within a specific time window in the future by establishing a dynamic correlation equation among the three; judging whether the microbial proliferation data is greater than the safety value based on the microbial proliferation prediction curve, and if so, starting a cyclic disinfection instruction.

[0090] Among them, the terminal's internal microbial data refers to the total number of colonies or the activity parameters of specific corrosive bacteria in the closed environment of the smart terminal. It can be detected through optical sensors or bioelectrochemical sensors to reflect the potential threat of the internal microbial environment of the device to the terminal material.

[0091] Among them, the dynamic correlation equation refers to a mathematical model established based on the nonlinear relationship between humidity, temperature and microbial proliferation rate. It can be implemented by multivariate regression algorithm or neural network model training to quantify the microbial proliferation trend under different temperature and humidity conditions.

[0092] Among them, the cyclic disinfection instruction refers to the operation of sterilizing the inside of the equipment by triggering an ultraviolet lamp or an ozone generator. It can be implemented by using a timing pulse control or a concentration threshold interlock mechanism to inhibit circuit corrosion or insulation failure caused by excessive microbial reproduction.

[0093] Specifically, in polar low-temperature environments, condensation may form inside the casing of smart terminals due to temperature differences, leading to increased humidity. At the same time, the heat generated by the operation of the equipment may cause local temperature fluctuations. By synchronously collecting temperature, humidity, and microbial data, the predictive model can analyze the dynamic relationship between the three. For example, when the temperature is between -10°C and 5°C and the humidity exceeds 70%, the model will predict the exponential proliferation of psychrophilic bacteria. Furthermore, if the proliferation curve output by the model exceeds the preset threshold within the next 72-hour window, the system will automatically start the disinfection program, such as releasing short-wave ultraviolet rays during the device's dormant period, and continuing the disinfection for 30 minutes to maintain the microbial concentration within a safe range.

[0094] Compared to existing technologies, existing risk assessment systems typically focus only on external environmental parameters and fail to consider the abnormal growth of microorganisms within equipment in the unique polar climate. This solution proactively predicts internal corrosion risks in equipment by establishing a correlation model between temperature, humidity, and microorganisms, addressing the lack of environmental monitoring in existing technologies.

[0095] Through the above technical solution, this application can effectively identify the circuit corrosion risk caused by microbial proliferation inside the smart terminal, and avoid equipment short circuit or material degradation problems caused by uncontrolled bacterial flora through dynamic prediction and automatic disinfection mechanism, thereby significantly improving the long-term operation reliability of smart terminals in polar environments.

[0096] This application further proposes that calculating the security risk value of the terminal includes:

[0097] The foundation stability coefficient, smart terminal energy data, and ice cracking probability are input into a pre-built risk assessment model to output a safety risk value of the terminal.

[0098] Among them, the foundation stability coefficient refers to a quantitative indicator that reflects the stability of the surface structure where the smart terminal is located. It can be achieved through vector synthesis calculation of the surface microseismic amplitude and the predicted value of the ice layer displacement velocity. This indicator is used to evaluate the comprehensive impact of ice layer displacement and geological activities on the physical support structure of the terminal.

[0099] Smart terminal energy data refers to a set of dynamic parameters that reflect the operating status of the device's power supply system. Specifically, it may include parameters such as energy storage capacity, remaining power, and charging and discharging power. It is used to evaluate the supporting role of energy supply on the terminal's continuous operation capability in extreme environments.

[0100] The probability of ice cracking refers to the numerical possibility of ice structure damage predicted based on temperature changes. Specifically, it can be dynamically predicted by inputting temperature difference data into a thermodynamic model. This parameter is used to identify the sudden risk of ice cracking to the terminal foundation.

[0101] Specifically, the foundation stability coefficient is calculated by integrating standardized surface microseismic amplitude data and predicted ice displacement velocities to form a multidimensional vector. This is then synthesized, using, for example, Euclidean distance or weighted average algorithms, to obtain a quantitative result reflecting the comprehensive stability of the foundation. Smart terminal energy data collects changes in energy storage capacity and charge / discharge efficiency during the polar day / night cycle, combined with a remaining operating days prediction model to assess the sustainability of the energy system under extreme light conditions. The ice cracking probability is determined by constructing a heat conduction equation based on periodic temperature data. This equation uses continuous temperature difference inputs to predict the internal stress distribution of the ice layer and output a cracking risk level. These three parameters are simultaneously input into a risk assessment model (a systematic analytical tool used to identify, quantify, and manage potential risks. A risk assessment model, for example, employs multi-factor linear regression or neural network algorithms to dynamically generate a safety risk value, which represents the comprehensive probability of terminal failure or damage within a specific time period).

[0102] The risk assessment model integrates multi-dimensional risk parameters (foundation stability coefficient, energy data, ice cracking probability) to quantify the comprehensive safety risks of smart terminals in polar environments, and implement risk level classification and dynamic response.

[0103] By inputting foundation stability, energy system status, and ice structure risk, the model constructs a multi-layer perceptron. The input layer is standardized parameters, and the hidden layer extracts features through a nonlinear activation function. The model then outputs a safety risk value: a quantitative risk level (such as a range of 0 to 1, where the higher the value, the higher the risk).

[0104] Compared to existing technologies, traditional approaches typically assess single risk factors in isolation, such as monitoring only wind speed or power consumption, without considering the synergistic impact of geological activity and energy status. This solution integrates three dynamic parameters: foundation stability coefficient, energy data, and ice cracking probability, to construct a multidimensional risk assessment system. This system accurately captures complex risk scenarios in polar environments, such as sudden temperature changes causing ice displacement and accompanied by energy supply shortages. This addresses the existing technology's inability to identify cross-cutting risk factors.

[0105] Through the above technical solution, this application achieves comprehensive risk perception of the polar intelligent terminal operating environment. In particular, during the alternation of polar day and polar night, it can simultaneously monitor three key indicators: changes in foundation stability, energy system status, and ice layer structure risks. Through parameter fusion calculation, it can accurately quantify complex security threats. This method effectively avoids the problem of delayed protective measures caused by a single risk assessment. For example, when accelerated ice displacement and insufficient energy storage occur simultaneously, a dual response strategy of reinforcement and energy allocation can be triggered in advance to ensure the continued stable operation of the terminal in extreme environments.

[0106] This application further proposes that the calculation of the security risk value of the terminal further includes:

[0107] After the security risk value is calculated, the current risk level is determined by comparing it with the preset classification threshold range, and the corresponding control strategy is executed.

[0108] Among them, the safety risk value refers to a quantitative indicator generated through a comprehensive assessment of the foundation stability coefficient, smart terminal energy data and ice cracking probability. It can be implemented using a weighted fusion algorithm to characterize the comprehensive degree of danger of the terminal's current environment and operating status.

[0109] Among them, the hierarchical control strategy refers to triggering different levels of equipment protection measures according to the preset threshold range of the safety risk value. It can be implemented through a preset instruction mapping table. For example, in the low-risk range, only the energy distribution mode is adjusted, and in the high-risk range, the physical reinforcement device and the emergency disinfection system are activated, thereby achieving precise matching of risk response measures.

[0110] Specifically, once the safety risk value is calculated, the system determines the current risk level by comparing it with the preset grading threshold intervals. For example, when the risk value is in the first interval, energy management strategies are prioritized to extend equipment life. When the risk value jumps to the second interval, the shell reinforcement and microbial disinfection procedures are simultaneously initiated. If the risk value exceeds the critical value of the third interval, a satellite alarm signal is immediately triggered and non-essential loads are cut off. This grading mechanism can address equipment corrosion problems caused by abnormal microbial proliferation during the polar night period. When the risk value reaches the corresponding threshold, a cyclic disinfection command is immediately initiated, and the shell protection level is dynamically adjusted based on the decline in the foundation stability coefficient.

[0111] Compared to existing technologies, existing risk assessment systems typically use a single threshold to trigger fixed protective measures, failing to dynamically respond to the combined risks of microbial proliferation and energy shortages in polar environments. This solution, by using a hierarchical control strategy, can match differentiated response measures to different risk stages. For example, during the initial stages of microbial proliferation, only localized disinfection is initiated to avoid premature energy consumption, while simultaneously implementing multiple protective actions when the risk of ice cracking increases.

[0112] Through the above-mentioned technical solution, this application solves the problem in the existing technology that the risk assessment mechanism is rigid and cannot effectively deal with the multi-factor coupling risks in the special polar environment, and realizes the precise matching of equipment protection measures and real-time risk levels. In particular, during the stage of abnormal microbial proliferation, disinfection operations can be carried out in a timely manner to avoid equipment circuit failure due to corrosion, and at the same time, redundant operations that reduce energy consumption are reduced through a hierarchical strategy.

[0113] Example 2:

[0114] See also Figure 1 , a smart terminal security risk assessment system, using the above-mentioned smart terminal security risk assessment method, including: a data acquisition module for continuously collecting meteorological data, geological data, and smart terminal energy data;

[0115] A data processing module is configured to determine a demarcation point based on the duration of the polar day and polar night cycles, divide the data time periods based on the demarcation point, and increase the energy storage capacity weight coefficient to a preset value during the polar night period;

[0116] Calculating a temperature difference based on the current period air temperature data and the previous period air temperature data, inputting the temperature difference into a constructed cracking prediction model, and outputting an ice layer cracking probability;

[0117] Outputting a predicted value of ice layer displacement velocity according to the ice layer displacement velocity and ice layer cracking probability;

[0118] The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient.

[0119] an execution module, configured to trigger a shell reinforcement instruction when detecting that the wind speed exceeds a set threshold and the foundation stability coefficient is lower than a critical value;

[0120] The risk assessment module is used to calculate the safety risk value of the terminal based on the foundation stability coefficient, the energy data of the smart terminal, and the probability of ice cracking.

[0121] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0122] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart terminal security risk assessment method, applied to the smart terminal security risk assessment of polar unmanned scientific research stations, characterized by: The following steps are involved: Meteorological data, geological data, and smart terminal energy data are continuously collected at fixed intervals, and the internal microbial environment is monitored by calculating the microbial proliferation prediction curve within a specific future time window; The meteorological data include temperature, wind speed, and the duration of polar day and polar night cycles; the geological data include surface microseismic amplitude and ice displacement velocity; Determine the demarcation point based on the duration of the polar day and polar night cycle, divide the data time period based on the demarcation point, increase the energy storage capacity weight coefficient to a preset value during the polar night period, and calculate the remaining polar night days through satellite positioning and astronomical calendar; During the polar day, the light sensor monitors the effective daily sunlight duration in real time and implements different charging strategies for smart terminals. The temperature difference is calculated based on the temperature value in the current fixed period and the value in the previous fixed period, and the temperature difference is input into the constructed cracking prediction model to output the probability of ice cracking; Output the predicted value of ice displacement speed according to ice displacement speed and ice cracking probability; The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient. When it is detected that the wind speed exceeds the set threshold and the foundation stability coefficient is lower than the critical value, the shell reinforcement instruction is triggered; The terminal's safety risk value is calculated based on the foundation stability coefficient, smart terminal energy data, and ice cracking probability.

2. The method for intelligent terminal security risk assessment according to claim 1, characterized in that: Increasing the energy storage capacity weight coefficient to a preset value during the polar night period includes: During the polar night period, the geographical coordinates of the terminal are obtained by satellite positioning, and the number of remaining polar night days is calculated in combination with the astronomical calendar; During the polar day, a light intensity sensor is used to monitor the solar radiation intensity. When the solar radiation intensity exceeds 800W / m² and lasts for 3 hours, a wind speed correlation analysis is performed: If the wind speed during this period reaches 15m / s and the fluctuation amplitude exceeds 50% of the average value of the previous 24 hours, a storm warning signal will be generated and the electromagnetic locking device of the terminal casing will be activated 48 hours in advance.

3. The method for intelligent terminal security risk assessment according to claim 1, wherein: The foundation stability coefficient obtained includes: The temperature difference is calculated based on the temperature data of the current period and the temperature data of the previous period, and the temperature difference is input into the cracking prediction model built based on thermodynamic principles to output the probability of ice cracking; According to the ice displacement velocity and ice cracking probability, the predicted value of ice displacement velocity is obtained by probability weighted average calculation; The surface microseismic amplitude data are standardized and vector composited with the predicted ice layer displacement velocity to obtain the foundation stability coefficient.

4. The method for intelligent terminal security risk assessment according to claim 1, wherein: During the polar day, the light sensor monitors the effective daily sunlight duration in real time and implements different charging strategies for smart terminals, including: Before the polar night begins, the current latitude and longitude of the smart terminal is obtained through polar satellite positioning, and the remaining days are calculated based on the polar night cycle data; Obtaining the remaining power of the smart terminal, and inputting the remaining days and the remaining power of the smart terminal into a pre-built calculation model to output the number of safe operation days; During the polar day period, the light sensor monitors the daily effective sunlight duration in real time. When the sunlight duration exceeds 8 hours, the smart terminal is charged at maximum power; when the sunlight duration is less than 4 hours, it switches to low-power discharge mode and shuts down non-core devices.

5. The method for intelligent terminal security risk assessment according to claim 1, wherein: Monitoring of the internal microbial environment by calculating the predicted curve of microbial proliferation within a specific time window in the future includes: Acquiring internal data of the smart terminal, wherein the internal data includes microbial data, temperature data, and humidity data inside the terminal; The humidity data, temperature data and microbial data are simultaneously input into a constructed prediction model, which outputs a microbial proliferation prediction curve within a specific future time window by establishing a dynamic correlation equation between the three; According to the microbial proliferation prediction curve, it is determined whether the microbial proliferation data is greater than the safety value, and if so, a cyclic disinfection instruction is started.

6. The method for intelligent terminal security risk assessment according to claim 1, characterized in that: Calculating the security risk value of the terminal includes: The foundation stability coefficient, smart terminal energy data, and ice cracking probability are input into a pre-built risk assessment model to output a safety risk value of the terminal.

7. The method for intelligent terminal security risk assessment according to claim 1, characterized in that: After calculating the security risk value of the terminal, the method further includes: After the security risk value is calculated, the current risk level is determined by comparing it with the preset classification threshold range, and the corresponding control strategy is executed.

8. A smart terminal security risk assessment system, characterized by: A smart terminal security risk assessment method according to any one of claims 1 to 7 is used, wherein the smart terminal security risk assessment system comprises: Data acquisition module, used to continuously collect meteorological data, geological data, and smart terminal energy data; A data processing module is configured to determine a demarcation point based on the duration of the polar day and polar night cycles, divide the data time periods based on the demarcation point, and increase the energy storage capacity weight coefficient to a preset value during the polar night period; Calculating a temperature difference based on the temperature value in the current fixed period and the temperature value in the previous fixed period, inputting the temperature difference into the constructed cracking prediction model, and outputting the probability of ice cracking; Outputting a predicted value of ice layer displacement velocity according to the ice layer displacement velocity and ice layer cracking probability; The surface microseismic amplitude data is standardized and vector-synthesized with the predicted ice displacement velocity to obtain the foundation stability coefficient. an execution module, configured to trigger a shell reinforcement instruction when detecting that the wind speed exceeds a set threshold and the foundation stability coefficient is lower than a critical value; The risk assessment module is used to calculate the safety risk value of the terminal based on the foundation stability coefficient, the energy data of the smart terminal, and the probability of ice cracking.

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