Intelligent visual monitoring method and system in extremely cold environment

Abstract

The invention relates to the technical field of image monitoring, in particular to an intelligent visual monitoring method and system in an extremely cold environment, and the method comprises the following steps: based on an extremely cold monitoring environment, collecting environment temperature data through a temperature sensor, measuring the wind speed and the snowfall density, and obtaining low-temperature environment state data; according to the invention, environment state data is acquired in real time, a battery charging and discharging strategy is automatically adjusted to adapt to low-temperature influence, a heating element is automatically adjusted to prevent the equipment from frosting and freezing, normal operation of functions of the monitoring equipment is ensured, and light sensitivity and exposure parameters of an image sensor are adjusted, so that the monitoring efficiency is improved. The image quality is improved, the influence of an extremely cold environment on the image quality is eliminated, the monitored data is more accurate and reliable in vision, the compression ratio is dynamically adjusted, key image data is preferentially transmitted, and the efficiency and the real-time performance of data transmission are enhanced.

Description

Technical Field

[0001] This invention relates to the field of image monitoring technology, and in particular to an intelligent visual monitoring method and system for extremely cold environments. Background Technology

[0002] Image surveillance technology primarily focuses on collecting visual data through various imaging devices for the analysis and monitoring of specific environments or objects. This technology encompasses everything from basic image acquisition and signal processing to advanced image analysis and interpretation. In practical applications, image surveillance systems include static or dynamic cameras, using algorithms to process the collected image data to identify and track targets. Image surveillance is widely used in security monitoring, industrial inspection, environmental monitoring, and many other fields, with the key being to provide real-time and effective visual information to support decision-making and operations.

[0003] Among these, the intelligent visual monitoring method for extreme cold environments focuses on developing and implementing effective monitoring systems for extremely cold climates. The monitoring system utilizes highly cold-resistant imaging equipment and algorithms adapted to extremely low temperatures to ensure the continuity and accuracy of monitoring activities. Its main applications include monitoring polar climate change, wildlife activity, and scientific research facilities in extremely cold regions. Through intelligent image analysis, the system can automatically detect and report critical information under harsh weather conditions, supporting related research and security monitoring.

[0004] Traditional monitoring methods face challenges in extreme cold environments, where equipment is susceptible to low temperatures, leading to issues such as battery performance degradation and mechanical component freezing, impacting the continuity and accuracy of visual monitoring. Especially without real-time energy management and heating strategies, monitoring can be interrupted due to rapid battery depletion or lens icing. Furthermore, traditional methods do not account for network bandwidth fluctuations in data transmission, resulting in critical data not being transmitted in a timely manner, thus limiting the application scope and effectiveness of traditional monitoring methods in extreme weather conditions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent visual monitoring method and system for extremely cold environments.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent visual monitoring method for extremely cold environments, comprising the following steps: S1: Based on the extreme cold monitoring environment, ambient temperature data is collected through temperature sensors, and wind speed and snowfall density are measured to obtain low temperature environment status data; S2: Based on the low-temperature environment status data, adjust the battery operating mode according to the battery status and real-time external temperature, and generate battery status adjustment results; S3: Based on the low-temperature environment status data and combined with the internal temperature data of the monitoring equipment, measure the temperature difference and determine whether the heating element needs to be activated. When heating is required, automatically adjust the operation of the heating element to avoid frost and freezing of the monitoring lens and generate an optimized heating strategy. S4: Based on the low-temperature environment status data and optimized heating strategy, adjust the sensitivity and exposure parameters of the image sensor, correct the image color balance, contrast and brightness, and generate image quality optimization results; S5: Based on the image quality optimization results, dynamically adjust the compression rate and the sending order of differentiated image data packets according to the current network bandwidth status to obtain data transmission information; S6: Based on the data transmission information, monitor the integrity and real-time performance of the data transmission, and detect the integrity of the received image data packets. If transmission errors and delays are detected, retransmit the data packets and generate data integrity assurance information.

[0007] As a further aspect of the present invention, the low-temperature environment status data includes external temperature readings, wind speed indicators, and snowfall density measurement results; the battery status adjustment results include adjusted discharge rate, battery temperature control parameters, and optimized charging cycle; the optimized heating strategy includes power setting values ​​for heating elements, start-up temperature thresholds, and implemented energy-saving measures; the image quality optimization results include adjusted ISO settings, exposure time, and color balance adjustment values; the data transmission information includes adjusted compression ratio, network bandwidth utilization, and data packet priority settings; and the data integrity assurance information includes the number of verified data packets, detected error types, and number of retransmissions.

[0008] As a further aspect of the present invention, based on an extreme cold monitoring environment, the specific steps for collecting ambient temperature data through a temperature sensor, measuring wind speed and snowfall density, and obtaining low-temperature environment state data are as follows: S101: Based on the extreme cold monitoring environment, temperature sensors at multiple locations are deployed to collect ambient temperature data at preset time intervals, and the data is recorded in real time to obtain ambient temperature information; S102: Based on the ambient temperature information, the monitoring area is captured by an image sensor at regular intervals. By analyzing the size of snowflakes in the continuous images and analyzing the moving speed and trajectory of the snowflakes, the wind and snow conditions are identified, and wind and snow condition data is obtained. S103: Based on the wind and snow condition data, wind speed and snowfall density are monitored in real time, and the data is summarized and analyzed to obtain wind speed and snowfall density information and generate low temperature environment condition data.

[0009] As a further aspect of the present invention, the specific steps for adjusting the battery operating mode and generating a battery state adjustment result based on the aforementioned low-temperature environment state data, according to the battery state and real-time external temperature, are as follows: S201: Based on the low-temperature environment status data, detect the current charge and voltage of the monitoring device battery, record the battery temperature and the real-time external temperature, evaluate the battery's charging and discharging capabilities, and obtain the battery's basic state data. S202: Based on the battery basic state data, adjust the battery charging and discharging parameters according to the battery temperature and the external temperature, including adjusting the charging rate and depth of discharge, adjusting the charging and discharging strategy in low temperature environment, optimizing the energy utilization efficiency of the battery, and obtaining a battery working mode adjustment scheme. S203: Based on the battery operating mode adjustment scheme, implement the adjusted charging and discharging strategy, monitor the implementation effect of the strategy in real time, evaluate the battery's discharge performance and lifespan under low temperature conditions, and obtain the battery state adjustment result.

[0010] As a further aspect of the present invention, based on the aforementioned low-temperature environment data, combined with the internal temperature data of the monitoring device, the temperature difference is measured and it is determined whether the heating element needs to be activated. When heating is required, the operation of the heating element is automatically adjusted to avoid frost and freezing of the monitoring lens, and the steps for generating an optimized heating strategy are as follows: S301: Based on the low-temperature environment status data, the temperature inside the device is detected by a temperature sensor and compared with the external environment temperature to record the temperature difference between the inside and outside of the device, thereby obtaining a real-time temperature difference monitoring record. S302: Based on the real-time temperature difference monitoring record, the temperature difference data is compared with a preset threshold. When the temperature difference data exceeds the preset threshold, the heating element is activated and controlled to operate at a preset power to avoid frost and freezing of the monitoring lens, and the heating element start-up record is obtained. S303: Based on the heating element start-up record, the operating parameters of the heating element are adjusted according to the external environment through a fuzzy logic control algorithm to optimize heating efficiency and energy consumption, thereby obtaining an optimized heating strategy.

[0011] As a further aspect of the present invention, the fuzzy logic control algorithm is defined by the formula: Adjust the operating parameters, including To control command output, It is a proportionality constant. For the first Deviations in the rules The rate of change of ambient temperature. For the response time of the heating element, For the first The fuzzy logic member function value of each rule. For the first The weight coefficients of each rule.

[0012] As a further aspect of the present invention, the steps of adjusting the sensitivity and exposure parameters of the image sensor, correcting the image color balance, contrast, and brightness, and generating image quality optimization results based on the aforementioned low-temperature environment state data and optimized heating strategy are as follows: S401: Based on the low-temperature environment status data and optimized heating strategy, assess the current light intensity, adjust the sensitivity setting of the image sensor, increase the sensitivity under insufficient light conditions, match the monitoring environment, and obtain sensitivity adjustment data; S402: Based on the photosensitivity adjustment data, adjust the exposure time and ISO settings of the image sensor to optimize the exposure of the image in low-light environments, avoid overexposure and underexposure, and obtain the exposure parameter optimization results; S403: Based on the optimization results of the exposure parameters, adjust the color balance and contrast settings of the image, correct the color distortion that occurs under low temperature conditions, optimize the image color and contrast, and obtain the image quality optimization results.

[0013] As a further aspect of the present invention, based on the image quality optimization results, and according to the current network bandwidth status, the steps of dynamically adjusting the compression rate and adjusting the sending order of differentiated image data packets to obtain data transmission information are as follows: S501: Based on the image quality optimization results, adjust the image compression rate according to the detail richness of each region of the image to reduce the amount of data while maintaining image quality, and obtain basic compressed data; S502: Based on the compressed data, the current network bandwidth status is detected in real time, and the compression rate and compression intensity are adjusted to match network fluctuations according to the real-time changes in bandwidth, thereby optimizing the continuity of image transmission and generating adjusted compressed data. S503: Based on the adjusted compressed data, the priority of image transmission is evaluated. By analyzing the criticality of the image content, the data packets are assigned matching priorities, the transmission queue is readjusted, key image data is transmitted first, and data transmission information is generated.

[0014] As a further aspect of the present invention, based on the data transmission information, the steps of monitoring the integrity and real-time performance of data transmission, detecting the integrity of received image data packets, retransmitting data packets if transmission errors and delays are detected, and generating data integrity assurance information are specifically as follows: S601: Based on the data transmission information, monitor the speed and stability of data transmission in real time, analyze the delay and interruption frequency in the transmission, adjust the network resource allocation in real time, optimize the transmission efficiency, and obtain the transmission efficiency monitoring results. S602: Based on the transmission efficiency monitoring results, perform integrity verification on each received image data packet, identify and mark damaged and incomplete data packets, and obtain data packet integrity detection results; S603: Based on the data packet integrity detection result, retransmit the data packet, adjust the retransmission parameters, optimize the data integrity during the data transmission process, and obtain data integrity assurance information.

[0015] An intelligent visual monitoring system for extremely cold environments, the intelligent visual monitoring system for extremely cold environments being used to execute the aforementioned intelligent visual monitoring method for extremely cold environments, the system comprising: The environmental status acquisition module is based on the extreme cold monitoring environment. It collects ambient temperature data through temperature sensors, acquires images of the monitoring area, and monitors wind speed and snowfall density in real time to obtain an environmental status dataset. The battery state adjustment module analyzes the battery temperature and the real-time external temperature based on the environmental state dataset, evaluates the battery's charge and discharge capabilities, adjusts the charge and discharge parameters, and generates battery efficiency optimization information. The heating strategy optimization module detects the temperature difference between the inside and outside of the device based on the environmental status dataset, compares the temperature difference data with a preset threshold, and activates the heating element when the temperature difference exceeds the threshold, adjusts the heating time and cycle, optimizes heating efficiency and energy consumption, and generates a heating performance adjustment record. The image quality adjustment module assesses the current light intensity based on the heating efficiency adjustment record and environmental condition dataset, adjusts the sensitivity settings of the image sensor, optimizes the color balance and contrast of the image, and generates image optimization information. Based on the image optimization information, the data transmission guarantee module adjusts the compression intensity to match network fluctuations, prioritizes the transmission of key image data, and detects the integrity of received image data packets. If transmission errors and delays are detected, the data packets are retransmitted, and data integrity guarantee information is generated.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by acquiring environmental status data in real time, the battery charging and discharging strategy is automatically adjusted to adapt to the effects of low temperatures, optimizing the battery's discharge efficiency under low-temperature conditions, improving energy utilization efficiency, and ensuring the continuous operation of the equipment in extremely cold environments. By automatically adjusting the heating elements, the equipment is prevented from frosting and freezing, ensuring the normal operation of the monitoring equipment. Furthermore, the sensitivity and exposure parameters of the image sensor are adjusted, and the color balance and contrast of the image are corrected, improving image quality and eliminating the impact of extremely cold environments on image quality. This makes the monitoring data more visually accurate and reliable. The dynamically adjusted compression rate and priority transmission of key image data enhance the efficiency and real-time performance of data transmission. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flowchart of S1 of the present invention; Figure 3 This is a detailed flowchart of the S2 process of the present invention; Figure 4 This is a detailed flowchart of the S3 process of the present invention; Figure 5 This is a detailed flowchart of the S4 process of the present invention; Figure 6 This is a detailed flowchart of S5 of the present invention; Figure 7 This is a detailed flowchart of S6 of the present invention; Figure 8 This is a system flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Example 1 Please see Figure 1 This invention provides a technical solution: an intelligent visual monitoring method for extremely cold environments, comprising the following steps: S1: Based on the extreme cold monitoring environment, ambient temperature data is collected through temperature sensors, and the wind and snow conditions of the external environment are identified through image analysis. Wind speed and snowfall density are measured to obtain low temperature environment status data. S2: Based on low-temperature environment status data, adjust the battery operating mode according to the battery status and real-time external temperature, change the battery charging and discharging strategy, optimize the battery discharge efficiency under low-temperature conditions, and generate battery status adjustment results. S3: Based on low-temperature environment data and combined with the internal temperature data of the monitoring equipment, the temperature difference is measured and it is determined whether the heating element needs to be activated. When heating is required, the operation of the heating element is automatically adjusted to avoid frost and freezing of the monitoring lens, and the heating power is calculated and optimized to reduce energy consumption and generate an optimized heating strategy. S4: Based on low-temperature environment data and optimized heating strategy, adjust the sensitivity and exposure parameters of the image sensor, and correct the image color balance, contrast and brightness according to the light intensity of the low-temperature environment to match the light changes in the low-temperature environment and generate image quality optimization results. S5: Based on the image quality optimization results, the image is compressed, and the compression rate is dynamically adjusted according to the current network bandwidth status. The priority of differentiated image transmission is evaluated, the sending order of differentiated image data packets is adjusted, and key image data is transmitted first to obtain data transmission information. S6: Based on data transmission information, monitor the integrity and real-time performance of data transmission, and detect the integrity of received image data packets. If transmission errors and delays are detected, retransmit data packets, adjust the retransmission strategy, optimize data integrity, and generate data integrity assurance information.

[0021] Low-temperature environment status data includes external temperature readings, wind speed indicators, and snowfall density measurements. Battery status adjustment results include adjusted discharge rate, battery temperature control parameters, and optimized charging cycles. Optimized heating strategies include power settings for heating elements, start-up temperature thresholds, and implemented energy-saving measures. Image quality optimization results include adjusted ISO settings, exposure time, and color balance adjustment values. Data transmission information includes adjusted compression ratios, network bandwidth utilization, and data packet priority settings. Data integrity assurance information includes the number of verified data packets, detected error types, and number of retransmissions.

[0022] Please see Figure 2 Based on the extreme cold monitoring environment, the specific steps for collecting ambient temperature data through temperature sensors, identifying wind and snow conditions in the external environment through image analysis, measuring wind speed and snowfall density, and obtaining low-temperature environmental state data are as follows: S101: Based on the extreme cold monitoring environment, multiple temperature sensors are deployed to collect ambient temperature data at preset time intervals, and the data is recorded in real time to obtain ambient temperature information. The specific process is as follows: S101: Based on the extreme cold monitoring environment, multiple temperature sensors are deployed at key locations within the monitoring area to obtain temperature difference information at different altitudes. The sensors collect ambient temperature data every five minutes, and after each collection, the data is transmitted wirelessly to the central processing unit in real time. At the central processing unit, the system calibrates and verifies the received temperature data to ensure its accuracy and integrity. The calibration formula is: "Calibration result = Current temperature reading - Standard temperature reading". The processed data is recorded in a database to generate a detailed temperature change log, thus obtaining ambient temperature information.

[0023] S102: Based on ambient temperature information, the monitoring area is captured by an image sensor at regular intervals. By analyzing the size of snowflakes in the continuous images, as well as the moving speed and trajectory of the snowflakes, the wind and snow conditions are identified, and the specific process for obtaining wind and snow condition data is as follows: S102: Based on ambient temperature information, an image sensor periodically captures clear images of the monitored area. By analyzing the snowflake size in consecutive images, image processing software calculates the area and boundary of each snowflake to estimate its size. Simultaneously, by tracking the positional changes of the same snowflake in adjacent images, the snowflake's movement speed and trajectory are calculated. The calculation formula is: "Snowflake movement speed = (Current position - Initial position) / Time interval," yielding wind and snow condition data, including the average snowflake size, average movement speed, and estimated wind direction and speed.

[0024] S103: Based on wind and snow condition data, the wind speed and snowfall density are monitored in real time, and the data is summarized and analyzed to obtain wind speed and snowfall density information. The specific process for generating low-temperature environment condition data is as follows: S103: Based on wind and snow condition data, deployed anemometers and snowfall density meters begin real-time monitoring of wind speed and snowfall density. Anemometers determine wind speed by measuring the velocity of airflow. Wind speed and snowfall data are aggregated and analyzed using the formula: "Snowfall Density = Cumulative Snowfall Mass / Cumulative Snowfall Area". By comparing and analyzing historical data, weather trends for the next few hours can be predicted, generating low-temperature environmental condition data.

[0025] Please see Figure 3 Based on low-temperature environmental data, and according to the battery status and real-time external temperature, the specific steps for adjusting the battery operating mode, changing the battery charging and discharging strategy, optimizing the battery's discharge efficiency under low-temperature conditions, and generating battery status adjustment results are as follows: S201: Based on low-temperature environmental state data, the current charge and voltage of the monitoring device battery are detected, the battery temperature and the real-time external temperature are recorded, the charging and discharging capabilities of the battery are evaluated, and the specific process for obtaining the basic state data of the battery is as follows. S201: Based on low-temperature environmental data, the current charge and voltage of the battery in the monitoring device are detected. A charge monitoring instrument records the battery temperature and the real-time external temperature. The charge and voltage data are recorded every minute in a data log, specifically in the format: "Charge = Current Capacity / Total Capacity 100%" and "Voltage = Current Voltage / Nominal Voltage 100%". By comparing the internal and external temperatures of the battery, the impact of temperature difference on battery performance is evaluated. The calculation formula is: "Temperature Difference = Internal Battery Temperature - Real-time External Temperature". Based on changes in temperature difference, the battery's charging and discharging capabilities are assessed, and the data is integrated into basic battery state data.

[0026] S202: Based on the battery's basic state data, and according to the battery's temperature and the external temperature, adjust the battery's charging and discharging parameters, including adjusting the charging rate and depth of discharge, adjusting the charging and discharging strategy in low-temperature environments, and optimizing the battery's energy utilization efficiency. The specific process for obtaining the battery operating mode adjustment scheme is as follows: S202: Based on the battery's basic state data, the charging and discharging parameters of the battery are adjusted according to the battery's temperature and the external temperature. The current temperature of the battery and the ambient temperature are determined, and the charging rate is adjusted accordingly. The specific adjustment formula is: "Charging rate = Standard rate - (Temperature difference / Temperature influence coefficient)". The depth of discharge is adjusted by calculating "Depth of discharge = Current discharge amount / Maximum discharge amount * 100%" to ensure the battery's optimal performance in low-temperature environments. The adjusted charging and discharging strategy includes optimizing charging time and reducing unnecessary discharge cycles, thereby improving the battery's energy utilization efficiency. This results in a battery operating mode adjustment scheme to adapt to the needs of low-temperature environments.

[0027] S203: Based on the battery operating mode adjustment scheme, implement the adjusted charging and discharging strategy, monitor the implementation effect of the strategy in real time, evaluate the battery's discharge performance and lifespan under low temperature conditions, and obtain the battery state adjustment results. The specific process is as follows: S203: Based on the battery operating mode adjustment scheme, implement the adjusted charge and discharge strategy and monitor the effect of the strategy in real time. Detailed data for each charge and discharge cycle, including charging time, discharging time, and battery temperature changes, are recorded in real time. Data analysis is used to calculate the battery's discharge performance under low-temperature conditions using the formula: "Discharge performance = Actual discharge time / Expected discharge time × 100%". Simultaneously, the battery lifespan is evaluated using the formula: "Battery lifespan = Cumulative discharge cycles / Designed discharge cycles × 100%", yielding the battery state adjustment results.

[0028] Please see Figure 4Based on low-temperature environmental data and combined with internal temperature data of the monitoring equipment, the temperature difference is measured to determine whether the heating element needs to be activated. When heating is required, the operation of the heating element is automatically adjusted to avoid frost and freezing of the monitoring lens, and the heating power is calculated and optimized to reduce energy consumption. The specific steps for generating an optimized heating strategy are as follows: S301: Based on low-temperature environment data, the temperature inside the equipment is detected by a temperature sensor and compared with the external environment temperature to record the temperature difference between the inside and outside of the equipment. The specific process for obtaining real-time temperature difference monitoring and recording is as follows: S301: Based on low-temperature environmental data, a temperature sensor detects the internal temperature of the equipment. The sensor collects data in real time and records the current temperature every minute. The detected internal temperature is compared with the external ambient temperature, and the temperature difference between the inside and outside of the equipment is recorded. The specific calculation formula is: "Temperature difference = Internal temperature of equipment - External ambient temperature". The temperature difference data is stored in the data recording system, forming a real-time temperature difference monitoring record, which is used for subsequent analysis and processing to ensure the normal operation of the equipment in low-temperature environments.

[0029] S302: Based on real-time temperature difference monitoring and recording, the temperature difference data is compared with a preset threshold. When the temperature difference data exceeds the preset threshold, the heating element is activated and controlled to operate at a preset power to avoid frost and freezing of the monitoring lens. The specific process of heating element startup recording is as follows: S302: Based on real-time temperature difference monitoring and recording, the temperature difference data is compared with a preset threshold. The preset threshold is set according to the equipment's cold resistance, typically 10 degrees Celsius. When the temperature difference exceeds the preset threshold, the heating element is automatically activated. The heating element control system adjusts the heating power according to the magnitude of the temperature difference, using the formula: "Heating Power = Basic Power + (Temperature Difference - Threshold) * Power Adjustment Coefficient," where the basic power and power adjustment coefficient are preset values. The system also records the heating element's startup time and power to prevent frost and freezing of the monitoring lens, thus obtaining a heating element startup record.

[0030] S303: Based on the heating element startup record, the fuzzy logic control algorithm is used to adjust the operating parameters of the heating element according to the external environment, optimize heating efficiency and energy consumption, and obtain the specific process of the optimized heating strategy. S303: Based on the heating element startup record, fuzzy logic control adjusts the operating parameters of the heating element in real time according to the external ambient temperature and the internal temperature of the equipment. Fuzzy logic control uses temperature and energy consumption as input parameters. By adjusting heating time and heating intensity, it optimizes heating efficiency and energy consumption. Through optimization, it obtains the best heating efficiency and the lowest energy consumption, forming an optimized heating strategy to ensure the reliable and efficient operation of the equipment in low-temperature environments.

[0031] Fuzzy logic control algorithm, through the formula: Adjust the operating parameters, including The control command output represents the final adjusted power or control signal applied to the heating element, used to optimize heating efficiency and energy consumption. This is a proportional constant used to adjust the amplitude of the control output to suit the specific characteristics of the heating equipment. For the first The deviation in this rule represents the difference between the ambient temperature and the target temperature. The rate of change of ambient temperature represents the speed at which the ambient temperature changes per unit time, and is used to capture the dynamic nature of temperature changes. The heating element response time represents the time required for the heating element to go from startup to reaching a steady state, and is used to predict possible temperature changes during the heating process. For the first The fuzzy logic member function value of each rule, based on the current deviation. The calculation yields the result that describes the degree to which the deviation belongs to a certain fuzzy set. For the first The weight coefficients of each rule are determined through statistical methods based on real-time data. These coefficients are used to adjust the influence of each fuzzy rule to reflect the adaptability and efficiency of each rule under different conditions.

[0032] The specific execution process of the formula is as follows: Calculate the difference between the current ambient temperature and the set target temperature. To determine the basic size of the heating demand, and to account for possible rapid changes in ambient temperature, a temperature change rate was introduced. This helps predict potential changes in ambient temperature during the heating process, combined with the response time of the heating element. Adjusting deviation ,use The calculated deviation allows for pre-adjustment of heating settings to adapt to upcoming environmental changes, utilizing fuzzy logic member functions. Each adjusted deviation is evaluated to determine its membership degree in the fuzzy logic, thus determining the application degree of each fuzzy rule under the current circumstances. Weighting coefficients are introduced to enable the control system to more accurately reflect actual operating conditions. The coefficients are obtained by analyzing recent operational data using linear regression or other statistical analysis methods to adjust the contribution of each fuzzy rule. The outputs of all fuzzy rules are then weighted and summed, and multiplied by a proportionality constant. The final control command is calculated. Control commands are sent to the heating element to finely adjust the heating power in order to optimize energy consumption and heating efficiency during the heating process.

[0033] Please see Figure 5 The specific steps for generating image quality optimization results, based on low-temperature environment data and optimized heating strategies, are as follows: Adjusting the sensitivity and exposure parameters of the image sensor, and correcting image color balance, contrast, and brightness according to the light intensity in the low-temperature environment to match the light changes in the low-temperature environment. S401: Based on low-temperature environmental status data and optimized heating strategies, the current light intensity is assessed, the sensitivity setting of the image sensor is adjusted, and the sensitivity is increased under insufficient light conditions to match the monitoring environment. The specific process for obtaining sensitivity adjustment data is as follows: S401: Based on low-temperature environmental data and optimized heating strategies, assess the current light intensity. Use a light sensor to collect light data every minute and calculate the average light intensity. The formula is: "Average Light Intensity = (Total Light Intensity / Number of Data Collections)". Based on the calculation results, automatically adjust the image sensor's sensitivity setting. When the light intensity is below a preset threshold, for example, 200 lux, increase the sensitivity. The specific adjustment formula is: "New Sensitivity = Current Sensitivity * (Light Intensity Threshold / Actual Light Intensity)", ensuring the image sensor can match the monitoring environment under low-light conditions and obtain sensitivity adjustment data.

[0034] S402: Based on the sensitivity adjustment data, the exposure time and ISO settings of the image sensor are adjusted to optimize the exposure of the image in low light environments, avoid overexposure and underexposure, and obtain the specific process of the exposure parameter optimization results. S402: Based on the sensitivity adjustment data, the exposure time and ISO settings of the image sensor are adjusted. The current sensitivity adjustment data is read, and combined with the light intensity, a suitable exposure time and ISO value are determined. The adjustment formulas are: "Exposure time = Reference exposure time * (Expected light intensity / Actual light intensity)", and "ISO value = Reference ISO value * (Actual light intensity / Light intensity threshold)". This adjustment optimizes image exposure in low-light environments, avoiding overexposure and underexposure. By recording the parameters after each adjustment, the optimized exposure parameters are obtained.

[0035] S403: Based on the exposure parameter optimization results, adjust the color balance and contrast settings of the image, correct the color distortion that occurs under low temperature conditions, optimize the image color and contrast, and obtain the image quality optimization results. The specific process is as follows: S403: Based on the exposure parameter optimization results, adjust the color balance and contrast settings of the image. Using image processing algorithms, calculate the degree of color distortion for each image using the formula: "Color Distortion = Original Color Value - Desired Color Value". Based on the calculation results, the system automatically adjusts the color balance and contrast using the formulas: "New Color Value = Original Color Value + Color Correction Coefficient" and "New Contrast Ratio = Original Contrast Ratio * Contrast Adjustment Coefficient". This adjustment corrects color distortion under low-temperature conditions, optimizes the image's color and contrast, and ultimately yields an optimized image quality result.

[0036] Please see Figure 6 Based on the image quality optimization results, the images are compressed, and the compression rate is dynamically adjusted according to the current network bandwidth. The priority of differentiated image transmission is evaluated, and the sending order of differentiated image data packets is adjusted, prioritizing the transmission of key image data. The specific steps for obtaining data transmission information are as follows: S501: Based on the image quality optimization results, the compression rate of the image is adjusted according to the detail richness of each region of the image to reduce the amount of data and maintain the image quality. The specific process for obtaining the basic compressed data is as follows: S501: Based on the image quality optimization results, image processing software is used to analyze the detail richness of each region of the image and calculate the detail richness index for each region. The formula is: "Detail Richness Index = Number of Edge Pixels / Total Number of Pixels in the Region". Based on the calculation results, the system automatically adjusts the image compression ratio. The specific compression adjustment formula is: "Compression Ratio = Baseline Compression Ratio - (Detail Richness Index * Compression Adjustment Coefficient)", ensuring that while maintaining image quality, the data volume is minimized. The processed image data is stored as base compressed data for processing and transmission.

[0037] S502: Based on compressed data, the current network bandwidth status is detected in real time. According to the real-time changes in bandwidth, the compression rate and compression intensity are adjusted to match network fluctuations, optimize the continuity of image transmission, and generate adjusted compressed data. The specific process is as follows: S502: Based on compressed data, it detects the current network bandwidth status in real time, using a bandwidth monitoring tool to detect and record the network bandwidth. The network bandwidth detection formula is: "Current bandwidth = Data transmission rate / Time". Based on real-time bandwidth changes, it dynamically adjusts the image compression ratio, specifically using the formula: "Adjusted compression ratio = Base compression ratio * (Base bandwidth / Current bandwidth)". This adjustment matches network fluctuations, optimizes the continuity of image transmission, and generates adjusted compressed data by recording the compression intensity after each adjustment, ensuring stable image transmission under different bandwidth conditions.

[0038] S503: Based on the adjusted compressed data, the priority of image transmission is evaluated. By analyzing the criticality of the image content, the data packets are assigned matching priorities, the transmission queue is readjusted, and key image data is transmitted first. The specific process for generating data transmission information is as follows: S503: Based on the adjusted compressed data, the priority of image transmission is evaluated, the criticality of image content is analyzed, and a criticality index is calculated using the formula: "Criticality Index = Number of pixels in important areas / Total number of pixels". Based on the criticality index, data packets are calibrated, and the priority of the transmission queue is adjusted. The specific priority adjustment formula is: "Priority = Baseline Priority + (Criticality Index * Priority Adjustment Coefficient)". This prioritizes the transmission of critical image data, ensuring the rapid delivery of important information. The transmission queue records are adjusted, and data transmission information is generated for use in actual image transmission.

[0039] Please see Figure 7 Based on data transmission information, the system monitors the integrity and real-time performance of data transmission, detects the integrity of received image data packets, and retransmits data packets if transmission errors or delays are detected. The retransmission strategy is adjusted to optimize data integrity, and the specific steps for generating data integrity assurance information are as follows: S601: Based on data transmission information, the specific process of real-time monitoring of data transmission speed and stability, analyzing latency and interruption frequency during transmission, adjusting network resource allocation in real time, optimizing transmission efficiency, and obtaining transmission efficiency monitoring results is as follows: S601: Based on data transmission information, it monitors the speed and stability of data transmission in real time, using network monitoring tools to collect transmission speed and latency data. The transmission speed calculation formula is: "Transmission speed = Amount of data transmitted / Time interval", and the latency calculation formula is: "Latency = Data transmission time - Data reception time". By analyzing this data, it identifies the latency and interruption frequency during transmission. The specific analysis formula is: "Interruption frequency = Number of interruptions / Total transmission time". Based on the analysis results, it adjusts network resource allocation in real time, optimizing bandwidth and data routing to improve transmission efficiency. The adjustment process is recorded and saved, generating transmission efficiency monitoring results.

[0040] S602: Based on the transmission efficiency monitoring results, perform integrity verification on each received image data packet, identify and mark damaged and incomplete data packets, and obtain the data packet integrity detection results. The specific process is as follows: S602: Based on transmission efficiency monitoring results, perform integrity verification on each received image data packet, calculate the checksum of each data packet using the formula: "Checksum = Sum of all bytes in the data packet", and compare it with the expected checksum. If a checksum discrepancy is found, the data packet is marked as corrupt or incomplete. Analyze the proportion of corrupt data packets using the formula: "Corruption rate = Number of corrupt data packets / Total number of data packets". This allows for the identification and marking of all corrupt and incomplete data packets, generating data packet integrity detection results for subsequent processing.

[0041] S603: Based on the data packet integrity detection results, retransmit the data packet, adjust the retransmission parameters, optimize the data integrity during data transmission, and obtain the specific process of data integrity assurance information. S603: Based on the packet integrity detection results, packet retransmission is performed, and retransmission parameters are adjusted. The retransmission control formula is: "Number of retransmissions = Base number of retransmissions + (Damage ratio * Retransmission adjustment coefficient)" to ensure data integrity during data transmission. By monitoring the retransmission effect in real time, the retransmission strategy is optimized, and the retransmission interval and maximum number of retransmissions are adjusted. These adjustments ensure that data integrity and transmission efficiency are maintained even when network conditions change. All retransmission operations and adjustment parameters are recorded, generating data integrity assurance information for continuous network transmission optimization.

[0042] Please see Figure 8 An intelligent visual monitoring system for extremely cold environments, used to execute the aforementioned intelligent visual monitoring method for extremely cold environments, the system comprising: The environmental status acquisition module is based on the extreme cold monitoring environment. It collects ambient temperature data through temperature sensors, acquires images of the monitoring area, and monitors wind speed and snowfall density in real time to obtain an environmental status dataset. The battery state adjustment module analyzes the battery temperature and the real-time external temperature based on the environmental state dataset, evaluates the battery's charge and discharge capabilities, adjusts the charge and discharge parameters, and generates battery efficiency optimization information. The heating strategy optimization module detects the temperature difference between the inside and outside of the device based on the environmental status dataset, compares the temperature difference data with a preset threshold, and activates the heating element when the temperature difference exceeds the threshold, adjusts the heating time and cycle, optimizes heating efficiency and energy consumption, and generates a heating performance adjustment record. The image quality adjustment module assesses the current light intensity based on the heating efficiency adjustment record and environmental condition dataset, adjusts the sensitivity settings of the image sensor, optimizes the color balance and contrast of the image, and generates image optimization information. The data transmission assurance module adjusts the compression intensity to match network fluctuations based on image optimization information, prioritizes the transmission of critical image data, and detects the integrity of received image data packets. If transmission errors and delays are detected, the data packets are retransmitted, the retransmission strategy is optimized, and data integrity assurance information is generated.

[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent visual monitoring method for extremely cold environments, characterized in that, Includes the following steps: Based on the extreme cold monitoring environment, ambient temperature data is collected through temperature sensors, and wind speed and snowfall density are measured to obtain low temperature environment status data. Based on the aforementioned low-temperature environment status data, the battery operating mode is adjusted according to the battery status and real-time external temperature, generating a battery status adjustment result. Based on the low-temperature environment data and combined with the internal temperature data of the monitoring equipment, the temperature difference is measured and it is determined whether the heating element needs to be activated. When heating is required, the operation of the heating element is automatically adjusted to avoid frost and freezing of the monitoring lens and to generate an optimized heating strategy. Based on the low-temperature environment data and optimized heating strategy, the sensitivity and exposure parameters of the image sensor are adjusted to correct the image color balance, contrast and brightness, and generate image quality optimization results. Based on the image quality optimization results, the compression rate is dynamically adjusted according to the current network bandwidth status, and the sending order of differentiated image data packets is adjusted to obtain data transmission information. Based on the data transmission information, the integrity and real-time performance of data transmission are monitored, and the integrity of received image data packets is detected. If transmission errors and delays are detected, data packets are retransmitted, and data integrity assurance information is generated.

2. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, The low-temperature environment status data includes external temperature readings, wind speed indicators, and snowfall density measurement results. The battery status adjustment results include adjusted discharge rate, battery temperature control parameters, and optimized charging cycles. The optimized heating strategy includes power settings for heating elements, start-up temperature thresholds, and implemented energy-saving measures. The image quality optimization results include adjusted ISO settings, exposure time, and color balance adjustment values. The data transmission information includes adjusted compression ratios, network bandwidth utilization, and data packet priority settings. The data integrity assurance information includes the number of verified data packets, detected error types, and number of retransmissions.

3. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the extreme cold monitoring environment, the specific steps for obtaining low-temperature environmental state data by collecting ambient temperature data through temperature sensors and measuring wind speed and snowfall density are as follows: Based on the extreme cold monitoring environment, temperature sensors at multiple locations are deployed to collect ambient temperature data at preset time intervals, and the data is recorded in real time to obtain ambient temperature information. Based on the ambient temperature information, the monitoring area is captured by an image sensor at regular intervals. By analyzing the size of snowflakes in the continuous images, as well as the moving speed and trajectory of the snowflakes, the wind and snow conditions are identified, and wind and snow condition data is obtained. Based on the wind and snow condition data, wind speed and snowfall density are monitored in real time, and the data is summarized and analyzed to obtain wind speed and snowfall density information, and generate low temperature environment condition data.

4. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the aforementioned low-temperature environment data, the steps for adjusting the battery operating mode and generating battery status adjustment results according to the battery status and real-time external temperature are as follows: Based on the low-temperature environment status data, the current charge and voltage of the monitoring device battery are detected, the battery temperature and the real-time external temperature are recorded, the battery's charging and discharging capabilities are evaluated, and the battery's basic state data is obtained. Based on the battery's basic state data, the battery's charging and discharging parameters are adjusted according to the battery's temperature and the external temperature. This includes adjusting the charging rate and depth of discharge, adjusting the charging and discharging strategy in low-temperature environments, and optimizing the battery's energy utilization efficiency to obtain a battery operating mode adjustment scheme. Based on the battery operating mode adjustment scheme, the adjusted charging and discharging strategy is implemented, and the implementation effect of the strategy is monitored in real time. The discharge performance and life of the battery under low temperature conditions are evaluated to obtain the battery state adjustment result.

5. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the aforementioned low-temperature environment data, combined with the internal temperature data of the monitoring equipment, the temperature difference is measured to determine whether the heating element needs to be activated. When heating is required, the operation of the heating element is automatically adjusted to avoid frost and freezing of the monitoring lens. The specific steps for generating an optimized heating strategy are as follows: Based on the low-temperature environment data, the internal temperature of the device is detected by a temperature sensor and compared with the external ambient temperature to record the temperature difference between the inside and outside of the device, thus obtaining a real-time temperature difference monitoring record. Based on the real-time temperature difference monitoring record, the temperature difference data is compared with a preset threshold. When the temperature difference data exceeds the preset threshold, the heating element is activated and controlled to operate at a preset power to avoid frost and freezing of the monitoring lens, thus obtaining the heating element start-up record. Based on the heating element startup record, the operating parameters of the heating element are adjusted according to the external environment through a fuzzy logic control algorithm to optimize heating efficiency and energy consumption, thereby obtaining an optimized heating strategy.

6. The intelligent visual monitoring method for extreme cold environments according to claim 5, characterized in that, The fuzzy logic control algorithm is based on the formula: Adjust the operating parameters, including To control command output, It is a proportionality constant. For the first Deviations in the rules The rate of change of ambient temperature. For the response time of the heating element, For the first The fuzzy logic member function value of each rule. For the first The weight coefficients of each rule.

7. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the aforementioned low-temperature environment data and optimized heating strategy, the steps for adjusting the sensitivity and exposure parameters of the image sensor, correcting image color balance, contrast, and brightness, and generating image quality optimization results are as follows: Based on the low-temperature environment status data and optimized heating strategy, the current light intensity is evaluated, the sensitivity setting of the image sensor is adjusted, the sensitivity is increased under insufficient light conditions, and the sensitivity is matched to the monitoring environment to obtain sensitivity adjustment data. Based on the photosensitivity adjustment data, the exposure time and ISO settings of the image sensor are adjusted to optimize the exposure of the image in low-light environments, avoid overexposure and underexposure, and obtain the optimized exposure parameters. Based on the optimization results of the exposure parameters, the color balance and contrast settings of the image are adjusted to correct the color distortion that occurs under low temperature conditions, optimize the image color and contrast, and obtain the image quality optimization results.

8. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the image quality optimization results, and according to the current network bandwidth status, the steps of dynamically adjusting the compression rate and adjusting the sending order of differentiated image data packets to obtain data transmission information are as follows: Based on the image quality optimization results, the compression ratio of the image is adjusted according to the detail richness of each region of the image to reduce the amount of data while maintaining image quality, thus obtaining basic compressed data. Based on the compressed data, the current network bandwidth status is detected in real time. According to the real-time changes in bandwidth, the compression rate and compression intensity are adjusted to match network fluctuations, optimize the continuity of image transmission, and generate adjusted compressed data. Based on the adjusted compressed data, the priority of image transmission is evaluated. By analyzing the criticality of the image content, the data packets are assigned matching priorities, the transmission queue is readjusted, key image data is transmitted first, and data transmission information is generated.

9. The intelligent visual monitoring method for extreme cold environments according to claim 1, characterized in that, Based on the data transmission information, the steps of monitoring the integrity and real-time performance of data transmission, detecting the integrity of received image data packets, retransmitting data packets if transmission errors or delays are detected, and generating data integrity assurance information are as follows: Based on the data transmission information, the speed and stability of data transmission are monitored in real time, the frequency of delay and interruption during transmission is analyzed, network resource allocation is adjusted in real time, transmission efficiency is optimized, and transmission efficiency monitoring results are obtained. Based on the transmission efficiency monitoring results, integrity checks are performed on each received image data packet, and damaged and incomplete data packets are identified and marked to obtain data packet integrity detection results; Based on the data packet integrity detection results, data packets are retransmitted, and retransmission parameters are adjusted to optimize data integrity during data transmission, thereby obtaining data integrity assurance information.

10. An intelligent visual monitoring system for extremely cold environments, characterized in that, The intelligent visual monitoring method for extreme cold environments according to any one of claims 1-9, the system comprising: The environmental status acquisition module is based on the extreme cold monitoring environment. It collects ambient temperature data through temperature sensors, acquires images of the monitoring area, and monitors wind speed and snowfall density in real time to obtain an environmental status dataset. The battery state adjustment module analyzes the battery temperature and the real-time external temperature based on the environmental state dataset, evaluates the battery's charge and discharge capabilities, adjusts the charge and discharge parameters, and generates battery efficiency optimization information. The heating strategy optimization module detects the temperature difference between the inside and outside of the device based on the environmental status dataset, compares the temperature difference data with a preset threshold, and activates the heating element when the temperature difference exceeds the threshold, adjusts the heating time and cycle, optimizes heating efficiency and energy consumption, and generates a heating performance adjustment record. The image quality adjustment module assesses the current light intensity based on the heating efficiency adjustment record and environmental condition dataset, adjusts the sensitivity settings of the image sensor, optimizes the color balance and contrast of the image, and generates image optimization information. Based on the image optimization information, the data transmission guarantee module adjusts the compression intensity to match network fluctuations, prioritizes the transmission of key image data, and detects the integrity of received image data packets. If transmission errors and delays are detected, the data packets are retransmitted, and data integrity guarantee information is generated.

Images