Infrared emission method and system based on environmental perception

Abstract

The present application relates to the field of infrared monitoring, and particularly relates to an infrared emission method and system based on environment sensing, which collects multi-dimensional environment parameters of an infrared emitter to obtain environment monitoring data, analyzes the real-time working environment of the infrared emitter based on the environment monitoring data to obtain environment condition characteristics, and adjusts the working parameters of the infrared emitter according to the environment condition characteristics.

Description

Technical Field

[0001] This invention relates to the field of infrared surveillance, specifically to an infrared emission method and system based on environmental perception. Background Technology

[0002] Traditional infrared emission methods typically use fixed operating parameters for signal transmission, which cannot be adjusted in real time according to changes in the environment. However, in practical applications, the environment in which infrared transmitters operate is complex and variable. Factors such as ambient light intensity, temperature, and weather conditions can significantly affect their emission performance. Due to the lack of environmental perception and adaptive adjustment capabilities, the reliability of traditional infrared transmitters in different environments is difficult to guarantee. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an infrared emission method and system based on environmental perception, which can solve the problems in the prior art.

[0004] This invention is achieved through the following technical solution: This invention provides an infrared emission method based on environmental perception, comprising: Multi-dimensional environmental parameters are collected from the infrared transmitter to obtain environmental monitoring data; Based on the environmental monitoring data, the infrared transmitter is analyzed in real time to obtain environmental condition characteristics. The operating parameters of the infrared emitter are adjusted adaptively according to the environmental conditions.

[0005] This invention provides an environment-aware infrared emission system for implementing the environment-aware infrared emission method described in any one of the first aspects, comprising: The environmental monitoring module is used to collect multi-dimensional environmental parameters from the infrared transmitter to obtain environmental monitoring data. An environmental analysis module is used to analyze the real-time working environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics. An adaptive adjustment module is used to adaptively adjust the operating parameters of the infrared emitter according to the characteristics of the environmental conditions.

[0006] In summary, the beneficial effects of this invention are: This invention collects environmental parameters from multiple dimensions and comprehensively acquires environmental monitoring data, which can accurately reflect the working environment of the infrared transmitter. Based on this analysis, environmental condition characteristics are obtained, allowing for a deeper understanding of the impact of the environment on the equipment. Adaptive adjustments to operating parameters based on these characteristics enable the infrared transmitter to maintain good performance in different environments, improve the accuracy and stability of signal transmission, reduce false detection rates, and enhance equipment reliability. It is widely applicable to smart homes, security monitoring, and other fields, effectively improving system operating efficiency and quality. Attached Figure Description

[0007] For ease of explanation, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0008] Figure 1 This is a schematic diagram illustrating the steps of an infrared emission method based on environmental perception according to the present invention. Figure 2 This is a schematic diagram of the structure of an infrared emission system based on environmental perception according to the present invention. Detailed Implementation

[0009] All features disclosed in this specification, or all steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps.

[0010] The following is combined Figure 1-2 The present invention will be described in detail below.

[0011] like Figure 1 As shown, the present invention provides an infrared emission method based on environmental perception, comprising: S1: Collect multi-dimensional environmental parameters from the infrared transmitter to obtain environmental monitoring data; S2: Analyze the real-time working environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics; S3: Adjust the operating parameters of the infrared emitter according to the environmental conditions.

[0012] The ambient light intensity and ambient temperature of the infrared transmitter are collected by a pre-set environmental sensing module to obtain basic monitoring data composed of ambient light intensity data and ambient temperature data.

[0013] Ambient light intensity can interfere with the operation of infrared transmitters. Strong ambient light can mask infrared signals, making it difficult for receivers to accurately identify them, thus affecting the accuracy and stability of infrared transmission. For example, in outdoor environments with direct sunlight, infrared signals can be overwhelmed by strong light, causing equipment to misjudge or malfunction. Therefore, collecting ambient light intensity data helps to understand the lighting conditions around the infrared transmitter and provides a basis for subsequent parameter adjustments.

[0014] Temperature also has a significant impact on the performance of infrared emitters. Excessively high or low temperatures can alter parameters such as the emitter's power and wavelength, thus affecting its emission performance. For example, in high-temperature environments, the electronic components of the infrared emitter may overheat, leading to a decrease in emission power; while in low-temperature environments, battery performance may be affected, thus impacting the operation of the entire device. Therefore, collecting ambient temperature data can help identify the impact of temperature changes on infrared emitters in a timely manner and take appropriate measures to adjust them.

[0015] Meteorological observation data of the region where the infrared transmitter is located is obtained through a designated data platform. The meteorological observation data is then analyzed to determine the specific meteorological location of the infrared transmitter, thereby obtaining the meteorological monitoring data of the infrared transmitter.

[0016] Different geographical locations and surrounding environments have varying impacts on meteorological conditions. By acquiring specific location data of infrared transmitters and the characteristics of the surrounding environment, meteorological observation data can be analyzed and adjusted more accurately. For example, in mountainous areas, local meteorological conditions may differ significantly from those in the surrounding areas due to the terrain. In cities, surrounding environments such as buildings and trees also affect meteorological conditions. Therefore, unit-based decomposition and scene impact analysis can yield meteorological monitoring data that better reflects the actual situation.

[0017] By combining basic monitoring data with meteorological monitoring data, environmental monitoring data can be obtained. Combining basic monitoring data with meteorological monitoring data can yield comprehensive and integrated environmental monitoring data. A single data dimension cannot accurately reflect the complex environment in which the infrared transmitter is located, while multi-dimensional data integration can provide richer information, which helps to more accurately analyze the real-time working environment of the infrared transmitter, thereby providing a more reliable basis for subsequent adjustment of working parameters.

[0018] By arranging the basic monitoring data and meteorological monitoring data contained in the environmental monitoring data for each time period according to chronological order, we obtain the basic monitoring sequence and meteorological monitoring sequence in a time-aligned state. Environmental monitoring data is dynamic data that changes over time, and basic monitoring data (ambient light intensity, ambient temperature) and meteorological monitoring data (such as local weather conditions) have different values ​​at different points in time. Arranging and aligning them in chronological order clearly shows the correspondence between different types of data at the same point in time, which helps in subsequent analysis of the coordinated changes of different environmental factors over time. For example, whether the change in ambient light intensity at a specific point in time is related to meteorological conditions (such as cloud cover changes) can be intuitively observed and analyzed through time-aligned data. Many data analysis methods and models are designed based on time series data. After sorting the data by time, it is easier to apply these methods for subsequent fluctuation analysis, trend prediction, and other operations, making the analysis process more scientific and efficient.

[0019] The data fluctuation patterns of the basic monitoring sequence and the meteorological monitoring sequence are analyzed respectively. Based on the analysis results, the latest time intervals are divided on the basic monitoring sequence and the meteorological monitoring sequence. The working status of the infrared transmitter is mainly affected by recent environmental conditions. The reference value of older environmental data for the current working status is relatively small. By analyzing the data fluctuation patterns to divide the latest time intervals, we can focus on the time period with the greatest impact on the current working environment, thereby improving the pertinence and timeliness of the analysis. For example, if there is a sudden weather change (such as heavy rain) recently, this change has a more critical impact on the infrared transmitter, while the previous stable meteorological data is relatively less important.

[0020] The environment is constantly changing, and data fluctuation patterns can reflect the dynamic characteristics of the environment. By analyzing fluctuation patterns to divide time intervals, we can identify the time periods in which the environment changes significantly, thereby more accurately grasping the dynamic changes of the environment and providing a more precise time range for subsequent analysis of the impact of the environment on infrared emitters.

[0021] By analyzing the data mapping relationship between the basic monitoring sequence and the meteorological monitoring sequence in the latest time interval, several environmental monitoring characteristics in the latest time interval are obtained, which together serve as environmental condition features. There are complex interrelationships between various factors in the basic monitoring data and the meteorological monitoring data. For example, the ambient temperature changes with meteorological conditions (such as sunny or cloudy days) and ambient light intensity, and these factors jointly affect the working performance of the infrared emitter. By analyzing the data mapping relationship, the intrinsic connections and interaction mechanisms between these factors can be found, thereby obtaining more comprehensive and accurate environmental monitoring characteristics.

[0022] Environmental condition characteristics are a comprehensive description of the real-time operating environment of an infrared transmitter. They need to reflect the synergistic effects between various environmental factors. By analyzing the data mapping relationship between the basic monitoring sequence and the meteorological monitoring sequence within the latest time interval, several environmental monitoring characteristics that can represent the characteristics of the current operating environment can be extracted. These characteristics together constitute the environmental condition characteristics, providing an important basis for subsequent adjustment of the operating parameters of the infrared transmitter.

[0023] Based on the various environmental monitoring characteristics included in the environmental condition features, the parameters of the pre-built digital model of the working environment are adjusted. The pre-built digital model of the working environment is based on general conditions or historical data, while the actual environmental conditions are constantly changing. The environmental condition features contain various real-time information about the current environment. Adjusting the parameters of the digital model according to these features enables the model to more accurately reflect the actual working environment of the infrared transmitter, providing a more reliable basis for subsequent analysis. Different environmental monitoring characteristics will have different effects on the infrared transmitter. By adjusting the model parameters, these influencing factors can be incorporated into the model, thereby improving the model's fit to the actual environment and prediction accuracy, making the subsequent model-based analysis results more credible.

[0024] The impact of environmental conditions on the infrared transmitter is analyzed using a digital model of the working environment after parameter adjustment. This analysis reveals the environmental impact patterns of these environmental conditions on the transmitter. The influence of environmental conditions on the infrared transmitter is complex, involving multiple aspects such as transmission power, transmission angle, and signal stability. Analyzing the working environment using the digital model after parameter adjustment allows for a deeper understanding of how environmental factors affect the performance of the infrared transmitter, clarifying the specific mechanisms and methods of environmental impact, and providing a basis for developing effective adaptation strategies. Analyzing the environmental impact patterns can quantify the impact of environmental factors on the infrared transmitter, such as determining the magnitude of changes in transmission power and the degree of signal attenuation under different environmental conditions. These quantified results help to more accurately assess the degree of environmental impact on the infrared transmitter, providing specific reference indicators for subsequent adjustments.

[0025] An adaptive adjustment algorithm, pre-deployed to analyze environmental impact patterns, generates an adaptive operating strategy for the infrared transmitter. This algorithm, pre-designed based on the transmitter's operating principles and environmental change patterns, automatically generates corresponding strategies based on environmental impact patterns. This automated approach allows for rapid and accurate responses to environmental changes, avoiding the lag and subjectivity of manual intervention, and improving system response speed and adjustment efficiency. The adaptive operating strategy is tailored to current environmental conditions, aiming to ensure optimal performance of the infrared transmitter under various environments. The strategy generated by the adaptive adjustment algorithm comprehensively considers various environmental factors and the transmitter's operational requirements, thereby optimizing operating parameters and improving the transmitter's stability and reliability.

[0026] The infrared transmitter's operating parameters are adjusted adaptively according to the aforementioned working adaptation strategy. This allows the device to adapt to constantly changing environmental conditions, ensuring normal operation in various environments. For example, in environments with strong light, the transmission power can be appropriately increased to ensure accurate signal reception; in low-temperature environments, certain parameters can be adjusted to improve the device's cold resistance. Reasonable adjustment of operating parameters can improve the infrared transmitter's working efficiency and quality, reduce misoperation and malfunctions. By adjusting parameters in a timely manner according to environmental changes, the infrared transmitter can always be in optimal working condition, thereby improving the overall system's operating efficiency and reliability.

[0027] In one embodiment of the present invention, the step of acquiring multi-dimensional environmental parameters from an infrared emitter to obtain environmental monitoring data includes: S11: The ambient light intensity and ambient temperature of the infrared transmitter are collected by the pre-set environmental sensing module to obtain basic monitoring data composed of ambient light intensity data and ambient temperature data. S12: Obtain meteorological observation data of the area where the infrared transmitter is located through the designated data platform, and perform specific meteorological analysis on the meteorological observation data of the infrared transmitter location to obtain the meteorological monitoring data of the infrared transmitter; S13: Combine the basic monitoring data with the meteorological monitoring data to obtain environmental monitoring data.

[0028] The light sensor in the pre-set environmental sensing module starts working, measuring the ambient light intensity around the infrared emitter at regular time intervals (e.g., per second, per minute, the specific interval can be set according to actual needs). After each measurement, the light intensity value is recorded, forming a series of ambient light intensity data. Similarly, the temperature sensor in the environmental sensing module measures the temperature of the environment around the infrared emitter at set time intervals, and the measured temperature value is recorded, forming ambient temperature data. These ambient light intensity data and ambient temperature data together constitute the basic monitoring data.

[0029] Ambient light intensity can interfere with the signal reception and transmission of infrared transmitters. Strong ambient light can cause infrared receivers to misinterpret signals, leading to errors in infrared communication. For example, in direct sunlight outdoors, infrared signals can be obscured by strong light, making it difficult for the receiver to accurately identify them. Therefore, collecting ambient light intensity data can help the system understand the extent to which current ambient light affects the infrared transmitter, allowing for subsequent adjustments. Temperature has a significant impact on the performance of infrared transmitters. Excessively high or low temperatures can alter parameters such as the transmitter's transmission power and wavelength, thus affecting its transmission performance. For instance, in high-temperature environments, the electronic components of the infrared transmitter may overheat, leading to a decrease in transmission power; while in low-temperature environments, battery performance may be affected, consequently impacting the operation of the entire device. Therefore, collecting ambient temperature data helps to promptly detect the impact of temperature changes on the infrared transmitter and take appropriate compensatory measures.

[0030] The system connects to a designated data platform via a network. This platform is typically provided by a professional meteorological data service agency or a government meteorological department. The system sends a request to the platform to obtain meteorological observation data for the area where the infrared transmitter is located. This data includes weather conditions (such as sunny, cloudy, rainy, etc.), wind speed, wind direction, air pressure, humidity, and other information. The system obtains the specific geographical location information of the infrared transmitter through the Global Positioning System (GPS) or other positioning technologies. At the same time, it obtains the characteristics of its surrounding scene through image recognition technology, Geographic Information System (GIS), and other means, such as whether there are tall buildings, mountains, or bodies of water nearby.

[0031] Based on the specific location data of the infrared transmitter, the large-scale meteorological observation data obtained from the data platform is broken down into smaller units. For example, the meteorological data of the entire city is divided into smaller units such as blocks and residential areas to determine the specific unit area where the infrared transmitter is located. This yields specific meteorological performance data for that unit area. Combined with the characteristics of the surrounding environment, the impact of the environment on the weather is analyzed. For example, the presence of many tall buildings nearby may affect wind speed and direction; proximity to water may result in relatively higher humidity. Based on this impact analysis, the specific meteorological performance data is adjusted to obtain meteorological monitoring data that better reflects the actual working environment of the infrared transmitter.

[0032] Meteorological conditions such as rainfall, snowfall, strong winds, and humidity have multifaceted effects on infrared emission. Rainfall and snowfall scatter and absorb infrared signals, reducing transmission distance and intensity. Acquiring meteorological observation data provides a more comprehensive understanding of the environmental conditions surrounding the infrared transmitter. Different geographical locations and surrounding environments have varying impacts on meteorological conditions. By obtaining specific location data of the infrared transmitter and characteristics of its surrounding environment, meteorological observation data can be analyzed and adjusted more accurately. For example, in mountainous areas, due to topography, local meteorological conditions may differ significantly from those of the surrounding areas. In cities, buildings, trees, and other surrounding elements also influence meteorological conditions. Therefore, unit-based analysis and scene impact analysis can yield meteorological monitoring data that better reflects the actual situation.

[0033] By integrating the collected basic monitoring data (ambient light intensity data and ambient temperature data) with the parsed meteorological monitoring data, these data can be stored in the same database or data structure and arranged according to time order or other rules to form complete environmental monitoring data. Single-dimensional data cannot fully reflect the complex environment in which the infrared transmitter is located. Integrating basic monitoring data and meteorological monitoring data can yield more complete and comprehensive environmental monitoring data. Such data can provide richer information for subsequent real-time operating environment analysis of the infrared transmitter, help to more accurately assess the impact of the environment on the infrared transmitter, and thus formulate more reasonable operating parameter adjustment strategies.

[0034] In one embodiment of the present invention, the step of performing specific meteorological analysis on the meteorological observation data to obtain meteorological monitoring data of the infrared transmitter at the location of the infrared transmitter includes: S121: Obtain the specific location data of the infrared transmitter's setting location and surrounding scene features; S122: The meteorological observation data is decomposed into units based on the specific positioning data to obtain the specific meteorological performance data of the unit area where the infrared transmitter is located; S123: Analyze the impact of the scene on the weather based on the surrounding scene characteristics, and adjust the specific meteorological performance data to obtain the meteorological monitoring data of the infrared transmitter.

[0035] If the infrared transmitter is equipped with a Global Positioning System (GPS) module, the system can directly read the transmitter's latitude and longitude coordinates from the module to obtain accurate positioning data. If it is not equipped with a GPS module, network positioning technology, such as base station positioning or Wi-Fi positioning, can be used to estimate the transmitter's approximate location by interacting with nearby communication base stations or Wi-Fi hotspots and using signal strength and related algorithms.

[0036] Images of the surrounding environment are captured using cameras installed near the infrared transmitter or integrated into the device. Image recognition algorithms are then used to identify objects in the images, such as tall buildings, mountains, trees, and bodies of water, and to determine their location, size, and distribution. A GIS database is then queried to obtain detailed geographic information about the location, including topography and land use type (e.g., commercial area, residential area, industrial area). Combined with the location data, scene feature information related to the vicinity of the infrared transmitter is extracted.

[0037] Different geographical locations have different meteorological conditions. Even within the same city, meteorological conditions may vary in different areas. By obtaining specific location data from infrared transmitters, it is possible to accurately match them with specific areas in meteorological data, thereby obtaining meteorological information that is more consistent with the actual situation. Surrounding scene characteristics have a significant impact on meteorological conditions. For example, factors such as topography, buildings, and water bodies can change airflow, temperature distribution, and humidity changes. Understanding the characteristics of the surrounding scene helps to analyze the impact of these factors on meteorology and provides a basis for subsequent data adjustments.

[0038] Meteorological observation data covering a large area (such as a city or region) is obtained from a designated data platform. This data typically includes information from multiple meteorological monitoring stations and covers meteorological elements such as temperature, humidity, wind speed, wind direction, and air pressure. The acquired meteorological data is then divided into units based on geographical regions. For example, a city may be divided into several blocks or grid units, each with specific geographical boundaries.

[0039] Based on the specific location data of the infrared transmitter, the unit area where it is located is determined. Data from the meteorological monitoring station corresponding to the unit area is extracted from the meteorological data, or the specific meteorological performance data of the unit area, including the values ​​of meteorological elements such as temperature, humidity, and wind speed, is estimated by combining data from adjacent stations using interpolation algorithms.

[0040] Large-scale meteorological observation data is usually based on the average or comprehensive data of multiple monitoring stations. This may not be accurate enough for a specific location. By breaking down the data into units, meteorological data can be refined into smaller geographical units, making the obtained meteorological performance data more reflective of the actual meteorological conditions at the location of the infrared transmitter. Dividing the meteorological data into units facilitates data storage, management and analysis. In subsequent processing, the data of each unit area can be analyzed and adjusted independently, improving processing efficiency and accuracy.

[0041] If there are mountains nearby, they will block airflow, causing changes in wind speed and direction. On the windward slope, rising air currents may occur, bringing precipitation; while on the leeward slope, the air descends, and the weather is relatively dry. Based on the characteristics of the mountains, such as their height, orientation, and slope, we can analyze their impact on wind speed, direction, and precipitation. High-rise buildings in cities can create an "urban canyon" effect, affecting wind speed and direction. The layout and density of buildings can also affect the local temperature and humidity distribution. For example, dense buildings can obstruct air circulation, leading to localized increases in temperature and decreases in humidity. By analyzing the characteristics of buildings, such as their height, spacing, and orientation, we can assess their impact on meteorological elements.

[0042] If there are lakes, rivers, or other bodies of water nearby, their presence increases air humidity and regulates local temperature. During the day, the water absorbs heat, making the surrounding area relatively cooler; at night, the water releases heat, making the surrounding area relatively warmer. The impact of the water on humidity and temperature is analyzed based on factors such as the area, depth, and distance of the water. Based on the results of this scene impact analysis, specific meteorological data are adjusted. For example, if the analysis shows that surrounding buildings reduce wind speed by 20%, the wind speed data for that unit area is multiplied by 0.8 for correction; if the water increases humidity by 10%, the humidity data is added by 10%. The adjusted data is the meteorological monitoring data from the infrared transmitter.

[0043] Specific meteorological data is obtained based on meteorological monitoring stations or interpolation algorithms, without considering the impact of surrounding environments on the weather. By conducting scene impact analysis and adjusting the data, the meteorological monitoring data can more accurately reflect the actual meteorological environment in which the infrared transmitter is located. This provides a more accurate basis for subsequent adjustments to the infrared transmitter's operating parameters. Accurate meteorological monitoring data helps the system better understand the impact of the environment on the infrared transmitter, thereby formulating more reasonable operating parameter adjustment strategies. For example, in cases of high wind speed, the transmission power of the infrared transmitter can be appropriately increased to ensure stable signal transmission; in environments with high humidity, moisture-proof measures can be taken to improve the reliability of the equipment.

[0044] In one embodiment of the present invention, the step of analyzing the real-time operating environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics includes: S21: Arrange the basic monitoring data and meteorological monitoring data contained in the environmental monitoring data of each time period according to the time order to obtain the basic monitoring sequence and meteorological monitoring sequence in a time-aligned state; S22: Analyze the data fluctuation patterns of the basic monitoring sequence and the meteorological monitoring sequence respectively, so as to divide the latest time interval on the basic monitoring sequence and the meteorological monitoring sequence according to the analysis results; S23: Analyze the data mapping relationship between the basic monitoring sequence and the meteorological monitoring sequence in the latest time interval to obtain several environmental monitoring characteristics in the latest time interval, which are used together as environmental condition features.

[0045] Basic monitoring data (including ambient light intensity data and ambient temperature data) and meteorological monitoring data (such as wind speed, humidity, air pressure, etc.) are extracted from environmental monitoring data. Based on the timestamp of the data record, the basic monitoring data and meteorological monitoring data are sorted in ascending order to ensure that the basic monitoring data and meteorological monitoring data at the same time point correspond in their respective sequences, forming a basic monitoring sequence and a meteorological monitoring sequence that are in a time-aligned state.

[0046] Environmental monitoring data is dynamic data that changes over time. Different types of data (basic monitoring data and meteorological monitoring data) are collected at different points in time. By sorting and aligning the data by time, we can ensure that different dimensions of data at the same point in time can be compared and analyzed, making the data consistent and comparable. This helps to accurately observe the state of different environmental factors at the same moment and provides a reliable data foundation for subsequent analysis. Time series analysis is an important method for studying the laws of data change over time. After arranging the data into a sequence according to time, various time series analysis techniques, such as trend analysis and periodic analysis, can be easily applied to better understand the dynamic characteristics of environmental factors.

[0047] For each data dimension (such as ambient light intensity, temperature, wind speed, etc.) in the basic monitoring sequence and meteorological monitoring sequence, its mean, standard deviation, variance and other statistics are calculated. By observing the changes in these statistics, the fluctuation characteristics of the data are identified. For example, a larger standard deviation indicates more drastic data fluctuations. Time series analysis methods such as moving average and autoregressive integral moving average (ARIMA) are used to model and analyze each data dimension. Through parameter and residual analysis of the model, the periodicity, trend and seasonality of data fluctuation patterns are captured. Based on the results of fluctuation pattern analysis, the time nodes when the data changes significantly are determined. The latest time period with relatively consistent data fluctuation characteristics is selected as the latest time interval. A certain threshold can be set. When the change of the data statistics or model parameters exceeds the threshold, it is considered that the data has changed significantly. This is used to divide the time interval.

[0048] The operating status of an infrared transmitter is primarily influenced by recent environmental conditions; older environmental data offers relatively little reference value for the current operating status. By dividing the data into recent time intervals, we can focus on the periods that have the greatest impact on the current operating environment, improving the relevance and timeliness of the analysis. For example, if a sudden weather change (such as a heavy rain) occurs recently, this change may have a more critical impact on the infrared transmitter, while previous stable weather data is relatively less important. The environment is constantly changing, and data fluctuation patterns can reflect its dynamic characteristics. By analyzing these fluctuation patterns to divide the time intervals, we can identify periods of significant environmental change, thereby more accurately grasping the dynamic patterns of environmental change and providing a more precise time range for subsequent analysis of the environment's impact on the infrared transmitter.

[0049] Within the latest time interval, correlation analysis is performed on the various data dimensions in the basic monitoring sequence and meteorological monitoring sequence. Methods such as Pearson correlation coefficient and Spearman correlation coefficient can be used to calculate the correlation between different data dimensions. For example, the correlation between ambient light intensity and wind speed can be analyzed to determine whether there is a synergistic relationship between the two. Methods such as Granger causality test are used to further analyze the causal relationship between various data dimensions to determine which changes in data dimensions are caused by changes in other data dimensions, thereby revealing the inherent causal mechanism between environmental factors. Based on the results of correlation and causal relationship analysis, representative environmental monitoring characteristics are extracted. These characteristics can be the correlation between data dimensions, the trend of change, etc. For example, if it is found that ambient temperature and humidity are positively correlated and both are increasing in the latest time interval, this correlation and trend can be used as an environmental monitoring characteristic. These environmental monitoring characteristics are combined to form the environmental condition features.

[0050] There are complex interrelationships among various factors in basic monitoring data and meteorological monitoring data. By analyzing the data mapping relationships, we can find the intrinsic connections and interaction mechanisms among these factors, thereby gaining a more comprehensive understanding of the impact of the environment on infrared transmitters. For example, understanding the correlation between ambient light intensity and meteorological conditions can predict the degree of interference of ambient light on infrared transmitters under different meteorological conditions. Environmental condition characteristics are a comprehensive description of the real-time working environment of infrared transmitters. They need to reflect the synergistic effects between various environmental factors. By extracting environmental monitoring characteristics, complex environmental data can be transformed into representative features. These features can more concisely and accurately describe the current working environment, providing an important basis for subsequent adjustment of the working parameters of infrared transmitters.

[0051] In one embodiment of the present invention, the step of adaptively adjusting the operating parameters of the infrared emitter according to the environmental conditions includes: S31: Adjust the parameters of the pre-built digital model of the working environment according to the various environmental monitoring characteristics included in the environmental conditions; S32: Analyze the impact of the working environment on the infrared transmitter based on the digital model of the working environment after parameter adjustment, and obtain the environmental impact mode of the environmental conditions on the infrared transmitter. S33: The environmental impact pattern is analyzed using a pre-deployed adaptive adjustment algorithm to generate an operational adaptation strategy for the infrared emitter; S34: Adjust the operating parameters of the infrared transmitter according to the operating adaptation strategy.

[0052] Carefully analyze the various environmental monitoring characteristics included in the environmental conditions, such as the correlation between ambient light intensity and temperature, and the temperature change trend under specific meteorological conditions. Clarify the correspondence between each parameter in the pre-constructed digital model of the working environment and the environmental monitoring characteristics. For example, one parameter in the model corresponds to the ambient temperature, and another parameter is related to the ambient light intensity. Adjust the corresponding parameters in the digital model of the working environment according to the specific values ​​and changes of the environmental monitoring characteristics. For example, if the environmental monitoring characteristics show that the current ambient temperature is rising, appropriately increase the value of the temperature-related parameter in the model.

[0053] The pre-built digital model of the working environment is based on general conditions or historical data and differs from the current actual environment. By adjusting the model parameters according to the characteristics of environmental monitoring, the model can more accurately reflect the actual working environment of the infrared transmitter, providing a reliable basis for subsequent analysis. Accurate model parameters can more precisely simulate the working state of the infrared transmitter in the actual environment, thereby improving the accuracy of the model's prediction of environmental impact. This helps to more accurately assess the degree of environmental impact on the infrared transmitter and provides a basis for formulating effective adaptation strategies.

[0054] Using a digital model of the working environment with adjusted parameters, the operating state of the infrared transmitter under the current environmental conditions is simulated. The model calculates various performance indicators of the infrared transmitter based on the input parameters, such as transmission power, signal strength, and transmission distance. The simulated operating state is compared with the operating state of the infrared transmitter under normal environmental conditions to identify changes in various performance indicators. For example, the difference in transmission power between the current environment and the normal environment is compared. Based on the comparison results, the environmental impact pattern of environmental conditions on the infrared transmitter is summarized. For example, it is found that an increase in ambient temperature will lead to a decrease in transmission power and a shortening of signal transmission distance, thus determining this environmental impact pattern.

[0055] The impact of environmental conditions on infrared transmitters is complex. By analyzing environmental impact patterns, we can gain a deeper understanding of how environmental factors affect the performance of infrared transmitters, clarify the specific mechanisms and methods of environmental impact, such as how rising ambient temperature leads to a decrease in transmission power. This helps in developing targeted countermeasures. Analyzing environmental impact patterns can quantify the impact of environmental factors on infrared transmitters and determine the magnitude of changes in various performance indicators under different environmental conditions. These quantitative results help to more accurately assess the degree of environmental impact on infrared transmitters and provide specific reference indicators for subsequent adjustments.

[0056] The obtained environmental impact patterns are input into a pre-deployed adaptive adjustment algorithm. This algorithm is pre-designed based on the working principle of the infrared transmitter, the laws of environmental change, and a large amount of experimental data. The adaptive adjustment algorithm analyzes the environmental impact patterns and generates corresponding adaptive strategies according to preset rules and objectives. For example, if the algorithm detects that the ambient temperature rises and causes a decrease in transmission power, it will generate a strategy to increase the transmission power to ensure normal signal transmission. The generated adaptive strategies are evaluated to check whether they can effectively cope with the current environmental changes and whether they will bring other negative impacts. If necessary, the strategies are optimized and adjusted to ensure their feasibility and effectiveness.

[0057] The adaptive adjustment algorithm can automatically generate working adaptation strategies based on environmental influence patterns, avoiding the lag and subjectivity of manual intervention. This automated approach can respond quickly and accurately to environmental changes, improving the system's response speed and adjustment efficiency. The working adaptation strategy is formulated for the current environmental conditions, aiming to ensure that the infrared transmitter maintains optimal working performance in different environments. The strategy generated by the adaptive adjustment algorithm can comprehensively consider various environmental factors and the working requirements of the infrared transmitter, thereby achieving optimized adjustment of working parameters and improving the stability and reliability of the infrared transmitter.

[0058] Based on the operational adaptation strategy, determine the operating parameters that need to be adjusted for the infrared transmitter, such as transmission power, transmission frequency, and signal modulation method. Through the infrared transmitter's control system, adjust the determined operating parameters according to the requirements of the operational adaptation strategy. For example, increase the transmission power to a specified value, or change the signal modulation method to enhance the signal's anti-interference capability. After adjusting the operating parameters, monitor and verify the operating status of the infrared transmitter to check whether the adjustment has achieved the expected effect, that is, whether it has effectively responded to environmental changes and improved the operating performance of the infrared transmitter. If the adjustment effect is not ideal, the operational adaptation strategy needs to be readjusted and the parameters adjusted again.

[0059] Adjusting the operating parameters of an infrared transmitter according to an adaptation strategy allows the device to adapt to constantly changing environmental conditions, ensuring its normal operation in various environments. For example, in environments with strong light, appropriately increasing the transmission power can ensure accurate signal reception; in environments with low temperatures, adjusting certain parameters can improve the device's cold resistance. Reasonable adjustment of operating parameters can improve the efficiency and quality of the infrared transmitter, reduce misoperation and malfunctions. By adjusting parameters in a timely manner according to environmental changes, the infrared transmitter can always be in optimal working condition, thereby improving the overall system's operating efficiency and reliability.

[0060] In one embodiment of the present invention, the method further includes continuously recording false detection records of the infrared transmitter, combining the false detection records according to the timestamps of each false detection record to obtain a false detection record sequence, continuously recording the working adaptation strategy of the infrared transmitter, combining the working adaptation strategies according to the time periods corresponding to each working adaptation strategy to obtain an adaptation strategy sequence, aligning the false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence in time to analyze the strategy adaptation effect of the infrared transmitter, and incorporating the strategy adaptation effect into the process of the adaptive adjustment algorithm analyzing the working adaptation strategy.

[0061] During the operation of the infrared transmitter, its working status is continuously monitored. When a false detection is detected (such as incorrect signal identification, missed target detection, etc.), the relevant information of the false detection is recorded, including the type of false detection, the specific time of occurrence (time stamp), etc. Based on the timestamp of each false detection record, all false detection records are arranged in chronological order to form a false detection record sequence. This sequence can clearly show the distribution of false detection events over time.

[0062] False positive logs provide a direct reflection of the infrared transmitter's operational quality. Continuously recording false positives allows for the timely detection of problems during operation, providing crucial information for analyzing the effectiveness of subsequent strategy adjustments. The sequence of false positive logs can demonstrate the trend of these events over time. Analyzing this trend helps determine whether false positives are accidental or exhibit a pattern, aiding in identifying the root cause of the problem.

[0063] Each time the operating parameters of the infrared transmitter are adjusted according to environmental conditions, the operating adaptation strategy adopted is recorded, including the specific adjustment parameters (such as the adjustment value of the transmission power, the change of the signal modulation mode, etc.) and the time period corresponding to the strategy. According to the time period corresponding to each operating adaptation strategy, all operating adaptation strategies are arranged and combined to form an adaptation strategy sequence. This sequence can reflect the changes in the operating adaptation strategies adopted in different time periods.

[0064] Job adaptation strategies are developed to cope with different environmental conditions. Recording these strategies and their corresponding time periods allows for a clear understanding of the adjustment measures taken under different environments. By comparing and analyzing with the false detection record sequence, it is possible to evaluate whether these strategies have effectively reduced the false detection rate and improved the performance of the infrared transmitter. The adaptation strategy sequence provides basic data for strategy optimization. By reviewing the strategies used in the past and their effects, lessons can be learned and a reference can be provided for developing more effective job adaptation strategies in the future.

[0065] Choose a unified time reference, such as the system startup time as the zero point, and align the false detection record sequence, adaptation strategy sequence, basic monitoring sequence, and meteorological monitoring sequence based on this time reference. On the basis of time alignment, match the data of the same time point or time period in each sequence. For example, find the work adaptation strategy, ambient light intensity, temperature, meteorological conditions, etc. corresponding to the occurrence of a certain false detection event.

[0066] There are complex relationships among false detection records, operational adaptation strategies, basic monitoring data, and meteorological monitoring data. Time alignment can link different data at the same point in time or within a time period, thereby more accurately analyzing the mutual influence between these data and identifying the intrinsic connection between environmental conditions, operational adaptation strategies, and false detection situations. Time-aligned multi-series data can provide a comprehensive perspective to understand the operating status of infrared emitters under different environmental conditions, the adjustment strategies adopted, and the changes in false detection situations. This helps to understand the system's operating mechanism more deeply and provides a more reliable basis for analyzing the effectiveness of strategy adaptation.

[0067] Features related to the effectiveness of strategy adaptation are extracted from the time-aligned sequence data, such as changes in the number of false positives and the correlation between the adjustment of operating parameters and false positives. These features are then quantified for subsequent analysis. Data analysis methods (such as correlation analysis and regression analysis) are used to analyze the correlation between the job adaptation strategy, false positives, and environmental conditions. For example, it can be determined whether a certain job adaptation strategy effectively reduces the number of false positives and how changes in environmental conditions affect the strategy's effectiveness. Based on the results of the correlation analysis, the effectiveness of the infrared emitter's strategy adaptation is evaluated. Evaluation indicators, such as the reduction in false positive rate and the degree of improvement in operating performance, can be set to measure the effectiveness of the strategy.

[0068] By analyzing the effectiveness of strategy adaptation, we can objectively evaluate the effectiveness and rationality of the work adaptation strategy, understand which strategies can achieve better results under which environmental conditions, and which strategies need to be improved. This provides direction for strategy optimization. Accurately evaluating the effectiveness of strategy adaptation helps to identify problems in the strategy in a timely manner and take corresponding improvement measures. This can improve the working reliability of infrared transmitters in different environments, reduce the occurrence of false detection events, and improve the performance of the entire system.

[0069] The analyzed strategy adaptation effect information is fed back into the adaptive adjustment algorithm. Based on this feedback, the algorithm adjusts and optimizes the subsequent analysis process of the adaptation strategy. For example, if a certain strategy is found to be ineffective under specific environmental conditions, the algorithm will avoid using that strategy or improve it when encountering similar environments in the future. Feeding the strategy adaptation effect back to the adaptive adjustment algorithm allows the algorithm to continuously learn and improve. The algorithm adjusts the analysis process of subsequent adaptation strategies based on the feedback information, thereby achieving continuous optimization of the strategy. Over time, the infrared emitter can better adapt to various complex environmental conditions, improving the stability and accuracy of its operation. By continuously optimizing the adaptive adjustment algorithm, the system can respond more flexibly to environmental changes. When facing new environmental conditions or complex situations, the algorithm can quickly generate more effective adaptation strategies based on past experience and feedback information, enhancing the overall adaptability of the system.

[0070] In one embodiment of the present invention, the step of time-aligning the false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence to analyze the strategy adaptation effect of the infrared emitter includes: The false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence, which are in a time-aligned state, are vectorized to obtain the strategy adaptation feature matrix; An adjacency matrix is ​​constructed based on the policy adaptation feature matrix, and cluster analysis is performed on the adjacency matrix. The cluster analysis results of the adjacency matrix are used to deploy an association resolution algorithm for the policy adaptation feature matrix. The correlation analysis algorithm is used to analyze the correlation of each feature vector in the strategy adaptation feature matrix to obtain the strategy adaptation effect of various work adaptation strategies relative to various environmental condition features.

[0071] The data in the false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence are encoded. For example, false detection records can be represented by different numbers to represent different false detection types; the adaptation strategy can quantify various adjustment parameters; the basic monitoring data (ambient light intensity, temperature, etc.) and the meteorological monitoring data (wind speed, humidity, etc.) are kept in their numerical form, and the data from the four sequences at each time point are combined into a vector in the order of time alignment.

[0072] The vectors generated at each time point are arranged in chronological order to form a matrix, namely the policy adaptation feature matrix. Each row of the matrix corresponds to a feature vector at a time point, and each column corresponds to a specific data dimension. The data types and formats in the false detection record sequence, the adaptation policy sequence, the basic monitoring sequence, and the meteorological monitoring sequence are different. Through vectorization, these different types of data can be uniformly converted into the form of numerical vectors, which facilitates subsequent mathematical operations and analysis. The policy adaptation feature matrix organizes the time-aligned multi-source data in a structured way, making the relationships between data clearer. The matrix form can facilitate various data analysis operations, such as similarity calculation and cluster analysis, which helps to discover potential patterns and regularities in the data.

[0073] Based on the strategy adaptation feature matrix, the similarity between each feature vector in the matrix is ​​calculated. Common similarity calculation methods include Euclidean distance and cosine similarity. Based on the calculated similarity, an adjacency matrix is ​​constructed, and cluster analysis is performed on the adjacency matrix $A$. Common clustering algorithms include hierarchical clustering and K-means clustering. The purpose of cluster analysis is to divide feature vectors into different categories, so that vectors within the same category have high similarity, and vectors between different categories have low similarity. Through cluster analysis, potential structures and patterns in the data can be discovered, such as grouping time points with similar environmental conditions and work adaptation strategies into one category.

[0074] Based on the clustering analysis results of the adjacency matrix, an association resolution algorithm is deployed to adapt the strategy to the feature matrix. The choice of association resolution algorithm depends on the clustering results and the analysis objectives. For example, if the clustering results show significant differences between different categories, a decision tree algorithm can be used to analyze the association between different categories; if the data has a strong linear relationship, a linear regression algorithm can be selected.

[0075] Adjacency matrices, by calculating the similarity between eigenvectors, can reveal the similarity relationships between data at different points in time. Similar data correspond to similar environmental conditions and work adaptation strategies. By grouping these similar data into one category through cluster analysis, potential structures and patterns in the data can be discovered, providing a foundation for subsequent association analysis. The results of cluster analysis can help select appropriate association analysis algorithms. Different clustering results reflect different characteristics and distributions of the data. Selecting appropriate algorithms based on these characteristics can improve the accuracy and efficiency of association analysis. For example, for data with obvious classification characteristics, decision tree algorithms are more suitable for association analysis.

[0076] The deployed correlation analysis algorithm is used to analyze the correlations of each feature vector in the strategy adaptation feature matrix. The algorithm analyzes the relationships between different data dimensions (such as false positives, job adaptation strategies, and environmental conditions) to identify which factors significantly impact the effectiveness of strategy adaptation. For example, it analyzes the correlation between transmit power adjustment and the number of false positives in job adaptation strategies, and how environmental factors such as temperature and humidity affect this correlation. Based on the correlation analysis results, the algorithm evaluates the effectiveness of various job adaptation strategies relative to various environmental conditions. This can be quantified by setting evaluation indicators such as the degree of reduction in false positive rate and the improvement in job performance. For example, it calculates the percentage decrease in false positive rate after adopting a specific job adaptation strategy under certain environmental conditions. The analyzed strategy adaptation effects are fed back into the adaptive adjustment algorithm to provide a reference for subsequent job adaptation strategy formulation. If a strategy is found to be ineffective under specific environmental conditions, it can be adjusted or improved; if a strategy is highly effective, it can be prioritized under similar environmental conditions.

[0077] By performing correlation analysis on feature vectors using correlation analysis algorithms, we can gain a deeper understanding of the intrinsic relationships between false detections, job adaptation strategies, and environmental conditions. This helps identify key factors affecting the effectiveness of strategy adaptation, providing a theoretical basis for optimizing job adaptation strategies. Feeding the effectiveness of strategy adaptation into the adaptive adjustment algorithm enables continuous optimization of the job adaptation strategy. With the continuous accumulation of data and feedback from analysis results, the algorithm can continuously adjust and improve subsequent job adaptation strategies, ensuring that the infrared emitter maintains optimal performance under different environmental conditions.

[0078] like Figure 2 As shown, the present invention provides an environment-aware infrared emission system for implementing the environment-aware infrared emission method described in any one of the first aspects, comprising: The environmental monitoring module is used to collect multi-dimensional environmental parameters from the infrared transmitter to obtain environmental monitoring data. An environmental analysis module is used to analyze the real-time working environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics. An adaptive adjustment module is used to adaptively adjust the operating parameters of the infrared emitter according to the characteristics of the environmental conditions.

[0079] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0080] The above description is merely a specific embodiment of the invention, but the scope of protection of the invention is not limited thereto. Any changes or substitutions conceived without creative effort should be included within the scope of protection of the invention.

Claims

1. An infrared emission method based on environmental perception, characterized in that, include: Multi-dimensional environmental parameters are collected from the infrared transmitter to obtain environmental monitoring data; Based on the environmental monitoring data, the infrared transmitter is analyzed in real time to obtain environmental condition characteristics. The operating parameters of the infrared emitter are adjusted adaptively according to the environmental conditions.

2. The infrared emission method based on environmental perception as described in claim 1, characterized in that, The steps for acquiring multi-dimensional environmental parameters from an infrared emitter to obtain environmental monitoring data include: The ambient light intensity and ambient temperature of the infrared transmitter are collected by a pre-set environmental sensing module to obtain basic monitoring data composed of ambient light intensity data and ambient temperature data. Meteorological observation data of the region where the infrared transmitter is located is obtained through a designated data platform, and specific meteorological analysis of the meteorological observation data of the infrared transmitter location is performed to obtain the meteorological monitoring data of the infrared transmitter. The basic monitoring data is combined with the meteorological monitoring data to obtain environmental monitoring data.

3. The infrared emission method based on environmental perception as described in claim 2, characterized in that, The steps for performing specific meteorological analysis on the meteorological observation data to obtain the meteorological monitoring data of the infrared transmitter at its location include: Obtain the specific location data of the infrared transmitter's setting location and surrounding scene features; The meteorological observation data is decomposed into units based on the specific positioning data to obtain the specific meteorological performance data of the unit area where the infrared transmitter is located. Based on the characteristics of the surrounding scene, the specific meteorological performance data is analyzed to assess the impact of the scene on the weather, and the specific meteorological performance data is adjusted to obtain the meteorological monitoring data of the infrared transmitter.

4. The infrared emission method based on environmental perception as described in claim 2, characterized in that, The steps of analyzing the real-time operating environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics include: The basic monitoring data and meteorological monitoring data contained in the environmental monitoring data of each time period are arranged according to time order to obtain the basic monitoring sequence and meteorological monitoring sequence in time alignment. Data fluctuation patterns are analyzed for the basic monitoring sequence and the meteorological monitoring sequence respectively, so as to divide the latest time interval on the basic monitoring sequence and the meteorological monitoring sequence according to the analysis results; The data mapping relationship between the basic monitoring sequence and the meteorological monitoring sequence in the latest time interval is analyzed to obtain several environmental monitoring characteristics in the latest time interval, which are used together as environmental condition features.

5. The infrared emission method based on environmental perception as described in claim 4, characterized in that, The steps for adaptively adjusting the operating parameters of the infrared emitter based on the environmental conditions include: Based on the various environmental monitoring characteristics included in the environmental conditions, the parameters of the pre-constructed digital model of the working environment are adjusted. The working environment digital model after parameter adjustment is used to analyze the impact on the operation of the infrared transmitter, and the environmental impact pattern of the environmental conditions on the infrared transmitter is obtained. The environmental impact pattern is analyzed by a pre-deployed adaptive adjustment algorithm to generate an operational adaptation strategy for the infrared emitter. The infrared transmitter's operating parameters are adaptively adjusted according to the aforementioned operating adaptation strategy.

6. The infrared emission method based on environmental perception as described in claim 5, characterized in that, It also includes continuously recording the false detection records of the infrared transmitter, combining the false detection records according to the timestamp of each false detection record to obtain a false detection record sequence, continuously recording the working adaptation strategy of the infrared transmitter, combining the working adaptation strategies according to the time period corresponding to each working adaptation strategy to obtain an adaptation strategy sequence, aligning the false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence in time to analyze the strategy adaptation effect of the infrared transmitter, and incorporating the strategy adaptation effect into the process of the adaptive adjustment algorithm analyzing the working adaptation strategy.

7. The infrared emission method based on environmental perception as described in claim 6, characterized in that, The steps of time-aligning the false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence to analyze the strategy adaptation effect of the infrared emitter include: The false detection record sequence, the adaptation strategy sequence, the basic monitoring sequence, and the meteorological monitoring sequence, which are in a time-aligned state, are vectorized to obtain the strategy adaptation feature matrix; An adjacency matrix is ​​constructed based on the policy adaptation feature matrix, and cluster analysis is performed on the adjacency matrix. The cluster analysis results of the adjacency matrix are used to deploy an association resolution algorithm for the policy adaptation feature matrix. The correlation analysis algorithm is used to analyze the correlation of each feature vector in the strategy adaptation feature matrix to obtain the strategy adaptation effect of various work adaptation strategies relative to various environmental condition features.

8. An infrared emission system based on environmental perception, characterized in that, An infrared emission method based on environmental perception as described in any one of claims 1-7, comprising: The environmental monitoring module is used to collect multi-dimensional environmental parameters from the infrared transmitter to obtain environmental monitoring data. An environmental analysis module is used to analyze the real-time working environment of the infrared transmitter based on the environmental monitoring data to obtain environmental condition characteristics. An adaptive adjustment module is used to adaptively adjust the operating parameters of the infrared emitter according to the characteristics of the environmental conditions.

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