Low-temperature environment meteorological element automatic detection equipment and calibration method

Abstract

The invention relates to the technical field of meteorological data, in particular to a low-temperature environment meteorological element automatic detection device and a calibration method, and the device comprises an automatic detection unit which is used for collecting meteorological element data of a low-temperature environment; preprocessing the collected meteorological element data; the calibration unit is in communication connection with the automatic detection unit and is used for receiving the preprocessed meteorological element data sent by the automatic detection unit; based on the preprocessed meteorological element data, executing calibration operation; the weather forecast platform is in communication connection with the calibration unit and is used for receiving the calibrated weather element data sent by the calibration unit; and generating weather forecast information based on the calibrated weather element data, thereby improving the overall efficiency and data value of the system. Particularly, due to the introduction of the calibration unit, the real-time guarantee of the data quality is realized, and the limitation that a traditional system depends on regular manual calibration is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data, in particular to an automatic detection device and calibration method for meteorological elements in a low-temperature environment. Background Art

[0002] With the in-depth research on global climate change, the demand for meteorological data in extreme environments is increasing day by day. Especially in low-temperature regions such as the Antarctic and the Arctic, accurate and continuous meteorological observations are crucial for understanding the global climate system. However, conducting long-term and reliable meteorological observations in these environments has always been a huge challenge faced by the scientific research community.

[0003] Traditional meteorological observation systems face many problems in low-temperature environments. First of all, the performance of conventional meteorological sensors seriously deteriorates at extremely low temperatures, resulting in a significant reduction in measurement accuracy. For example, standard temperature sensors often cannot maintain a linear response below -60°C, and humidity sensors may completely fail due to icing. Secondly, the energy supply problem in extreme environments severely restricts the long-term working ability of the system. Traditional systems often require a large amount of power to maintain the normal operation of sensors and data processing units, which is difficult to sustain in long-term observations in remote areas.

[0004] In addition, there are also obvious deficiencies in the existing technology in terms of data quality control. Meteorological data in extreme environments often contain a large number of outliers and missing values, and traditional data processing methods are difficult to effectively identify and handle these problems. This not only affects the reliability of the data but also increases the difficulty of subsequent analysis. At the same time, existing calibration methods usually rely on regular manual intervention, which is unrealistic in the inaccessible polar environment.

[0005] Although the closest prior art has been improved in some aspects, such as adopting some anti-low-temperature sensor designs, it still fails to comprehensively solve the above problems. These systems still have significant deficiencies in data accuracy, long-term reliability, and energy efficiency. Especially in terms of integration and intelligence, the existing technology has not achieved the full-process optimization from data acquisition, processing to application. Summary of the Invention

[0006] The present invention aims to solve the deficiencies of existing meteorological observation systems in low-temperature environments in terms of data accuracy, system reliability, energy efficiency, and data integrity. Through innovative system design and advanced data processing methods, the present invention provides a comprehensively optimized automatic detection device and calibration method for meteorological elements in a low-temperature environment.

[0007] The present invention proposes an automatic detection device for meteorological elements in a low-temperature environment, including:

[0008] An automatic detection unit, used for:

[0009] Collect meteorological element data in a low-temperature environment;

[0010] Preprocess the collected meteorological element data;

[0011] A calibration unit, communicatively connected to the automatic detection unit, for:

[0012] Receive the preprocessed meteorological element data sent by the automatic detection unit;

[0013] Perform a calibration operation based on the preprocessed meteorological element data;

[0014] A meteorological forecasting platform, communicatively connected to the calibration unit, for:

[0015] Receive the calibrated meteorological element data sent by the calibration unit;

[0016] Generate meteorological forecasting information based on the calibrated meteorological element data.

[0017] Preferably, the automatic detection unit includes:

[0018] A main control module, used to control the overall operation of the automatic detection unit;

[0019] A temperature detection module, communicatively connected to the main control module, for detecting the temperature in a low-temperature environment, near low-temperature environment, and low-temperature environment;

[0020] A humidity detection module, communicatively connected to the main control module, for detecting the humidity in a low-temperature environment, near low-temperature environment, and low-temperature environment;

[0021] A wind speed detection module, communicatively connected to the main control module, for detecting the wind speed in a low-temperature environment, near low-temperature environment, and low-temperature environment;

[0022] A wind direction detection module, communicatively connected to the main control module, for detecting the wind direction in a low-temperature environment, near low-temperature environment, and low-temperature environment;

[0023] A barometric pressure detection module, communicatively connected to the main control module, for detecting temperature changes and feeding them back to the temperature detection module;

[0024] A photoelectric photosensitive module, communicatively connected to the main control module, for measuring the light parameters in a low-temperature environment.

[0025] Preferably, the automatic detection unit further includes:

[0026] A time module, communicatively connected to the main control module, for recording the collection time of meteorological element data;

[0027] A power supply module, communicatively connected to the main control module, for providing power to the automatic detection unit;

[0028] A power supply module, communicatively connected to the main control module and the power module, for adjusting the power supply mode according to environmental conditions;

[0029] A communication module, communicatively connected to the main control module, for realizing data transmission with the calibration unit.

[0030] Preferably, the calibration unit includes:

[0031] An analog unit, for simulating meteorological element data of a low-temperature environment, a near-low-temperature environment, and a cryogenic environment;

[0032] A model unit, communicatively connected to the analog unit, for establishing a mapping model between the analog unit and the real physical environment;

[0033] A comparison unit, communicatively connected to the model unit, for comparing the consistency of the meteorological element data of the analog unit and the meteorological element data of the real physical environment;

[0034] An output unit, communicatively connected to the comparison unit, for outputting the compared meteorological element data.

[0035] Preferably, it further includes:

[0036] A storage module, communicatively connected to the automatic detection unit and the calibration unit, for storing the original meteorological element data and the calibrated meteorological element data;

[0037] A data analysis module, communicatively connected to the storage module, for performing statistical analysis and feature extraction on the stored meteorological element data;

[0038] A visualization module, communicatively connected to the data analysis module, for displaying the analysis results in a graphical manner.

[0039] Preferably, it further includes:

[0040] A positioning module, communicatively connected to the automatic detection unit, for obtaining the geographical location information of the meteorological element data collection;

[0041] A satellite communication module, communicatively connected to the positioning module, for transmitting the geographical location information and the corresponding meteorological element data to a weather forecasting platform.

[0042] Preferably, the detection range of the temperature detection module includes:

[0043] A near-low-temperature environment, with a temperature range of 0°C to -40°C;

[0044] A cryogenic environment, with a temperature range of -40°C to -60°C.

[0045] Preferably, the automatic detection unit further includes:

[0046] A data cleaning module, communicatively connected to the main control module, for:

[0047] Eliminating faulty data and error code data;

[0048] Cleaning the original meteorological data according to preset filtering rules;

[0049] Formatting the cleaned data.

[0050] Preferably, the data analysis module uses the K-means clustering algorithm to analyze the meteorological element data, including the following steps:

[0051] Extracting features from the standardized meteorological data;

[0052] Performing clustering analysis on the feature data using the K-means clustering algorithm;

[0053] Calculating the objective function value of the clustering result;

[0054] Judging whether the clustering result meets the preset threshold requirement according to the objective function value.

[0055] A calibration method for a low-temperature environment meteorological element automatic detection device, using the described system, includes the following steps:

[0056] S1: The automatic detection unit collects meteorological element data in a low-temperature environment;

[0057] S2: The data cleaning module preprocesses the collected meteorological element data, eliminating faulty data and error code data;

[0058] S3: The main control module analyzes the processed meteorological element data to judge whether the current environment is suitable for calibration;

[0059] S4: If the environment is suitable for calibration, then perform the following calibration steps:

[0060] S4.1: The simulation unit simulates the meteorological element parameters under the current environmental conditions;

[0061] S4.2: The model unit establishes a mapping model based on the simulation parameters;

[0062] S4.3: The comparison unit compares the differences between the simulated data and the actual collected data;

[0063] S4.4: If the difference exceeds the preset threshold, then adjust the simulation parameters and return to step S4.1, otherwise enter step S4.5;

[0064] S4.5: The output unit generates the calibrated meteorological element data;

[0065] S5: Store the calibrated meteorological element data in the storage module;

[0066] S6: The data analysis module performs statistical analysis and feature extraction on the calibrated data;

[0067] S7: The visualization module displays the analysis results graphically;

[0068] S8: The meteorological forecasting platform generates meteorological forecasting information based on the calibrated data.

[0069] Specifically, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0070] From a macroscopic perspective, the present invention has achieved a comprehensive breakthrough in meteorological observations in low-temperature environments, providing unprecedented high-quality data support for polar climate research. This not only fills an important gap in the global meteorological observation network but also provides a key tool for understanding and predicting global climate change.

[0071] At the system architecture level, the present invention constructs a closed-loop data acquisition, processing, and application system through the organic combination of the automatic detection unit, calibration unit, and meteorological forecasting platform. This integrated design greatly improves the overall efficiency and data value of the system. In particular, the introduction of the calibration unit realizes the real-time guarantee of data quality and overcomes the limitation of traditional systems relying on regular manual calibration.

[0072] In terms of specific modules, the innovative design of the present invention solves multiple key technical problems. For example, the design of the multi-stage temperature sensor effectively expands the working temperature range of the system, enabling it to maintain high-precision measurement in extreme environments of -60°C. The intelligent power management system significantly improves energy efficiency and solves the energy bottleneck problem for long-term observations.

[0073] In data processing, the advanced algorithms adopted by the present invention realize the automation of data cleaning, anomaly detection, and intelligent calibration. This not only improves data quality but also reduces the need for manual intervention, especially suitable for long-term unattended observations in remote areas. The application of the K-means clustering algorithm provides new possibilities for meteorological pattern recognition and prediction.

[0074] There are significant synergistic effects among these innovation points. For example, the combination of the application of high-precision sensors and advanced data processing algorithms not only improves the performance of individual indicators but also realizes a leap in overall data quality. Similarly, the improvement of energy efficiency and the enhancement of system reliability promote each other, greatly extending the fault-free working time of the system.

[0075] Generally speaking, by solving multiple technical problems and achieving their collaborative optimization, the present invention significantly improves the comprehensive performance of meteorological observations in low-temperature environments. This comprehensive performance improvement not only promotes the progress of meteorological observation technology but also provides strong support for fields such as climate change research and polar scientific expeditions, having great scientific research value and practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a logical block diagram of the overall system of the present invention.

[0077] Figure 2 It is a logical block diagram of the automatic detection unit of the present invention.

[0078] Figure 3 It is a logical block diagram of the calibration unit of the present invention.

[0079] Figure 4 It is a data processing flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0080] Referring to Figures 1-4 , the present invention provides an automatic detection device and calibration method for meteorological elements in low-temperature environments, and this system can achieve automatic collection, calibration, and forecasting of meteorological elements in extremely low-temperature environments. The following will detail the specific implementation manners of the present invention.

[0081] The automatic detection device for meteorological elements in low-temperature environments of the present invention includes an automatic detection unit 1, a calibration unit 2, and a meteorological forecasting platform 3. These three main units are interconnected through a communication module, forming a complete data collection, processing, and application chain.

[0082] The automatic detection unit 1 is the data source of the entire system, and its main function is to collect meteorological element data in low-temperature environments. In a preferred embodiment of the present invention, the meteorological elements that the automatic detection unit 1 can collect include but are not limited to temperature, humidity, wind speed, wind direction, air pressure, and light intensity. The collection frequency of these meteorological elements can be adjusted according to actual needs, such as collecting once every 5 minutes or once every hour.

[0083] The automatic detection unit 1 not only collects data but also preprocesses the collected meteorological element data. This preprocessing process may include data formatting, outlier detection, and preliminary data cleaning. For example, for temperature data, if the detected temperature value suddenly jumps from -50°C to 20°C, the system will mark it as an outlier for subsequent processing.

[0084] The calibration unit 2 is communicatively connected to the automatic detection unit 1, and its main function is to receive the preprocessed meteorological element data and perform calibration operations. Calibration is a crucial step in ensuring data accuracy. In an embodiment of the present invention, the calibration unit 2 employs an innovative calibration algorithm that combines historical data analysis and real-time environmental simulation. Specifically, the calibration unit 2 first establishes an initial model based on historical data and then continuously adjusts the model parameters according to the real-time collected data to adapt to the current environmental conditions.

[0085] During the calibration process, the calibration unit 2 takes into account the combined effects of multiple factors. For example, when calibrating temperature data, it not only considers the error of the temperature sensor itself but also the influence of wind speed on temperature measurement. Through this multi-factor calibration method, the present invention can significantly improve the accuracy of meteorological data.

[0086] The meteorological forecasting platform 3 is the data application end of this system. It receives the calibrated meteorological element data sent by the calibration unit 2 and generates meteorological forecasting information based on these data. In a preferred embodiment of the present invention, the meteorological forecasting platform 3 employs advanced machine learning algorithms, such as long short-term memory networks (LSTM), to improve the accuracy of forecasting. This algorithm can effectively capture the long-term dependencies of meteorological data, thereby providing more accurate short-term and medium-term weather forecasts.

[0087] The automatic detection unit 1 includes multiple functional modules, and each module is responsible for detecting specific meteorological elements. The main control module 11, as the core of the automatic detection unit 1, is responsible for coordinating the work of each detection module and managing the data acquisition and preliminary processing process.

[0088] The temperature detection module 12 is an important part of the present invention, and it can accurately measure temperature in a low-temperature environment. The present invention adopts a specially designed temperature sensor that can maintain high precision in the range of -60°C to 0°C. This wide-range temperature detection ability enables this system to adapt to various environments from near-low temperature to low temperature.

[0089] The humidity detection module 13 adopts an anti-freezing humidity sensor and can accurately measure relative humidity in a low-temperature environment. In an extremely low-temperature environment, traditional humidity sensors may fail due to icing, while the humidity detection module 13 of the present invention overcomes this problem through a special anti-freezing design.

[0090] The wind speed detection module 14 and the wind direction detection module 15 together constitute the wind measurement system of this system. In a low-temperature environment, the accurate measurement of wind speed and wind direction is crucial for understanding heat transfer and predicting weather changes. The present invention adopts ultrasonic anemometer technology, which is not affected by the low-temperature environment and can provide high-precision wind speed and wind direction data.

[0091] The air pressure detection module 16 not only measures the atmospheric pressure, but also detects temperature changes and feeds this information back to the temperature detection module 12. This information interaction between modules improves the measurement accuracy of the entire system. For example, when a sudden drop in air pressure is detected, the system increases the sampling frequency of temperature and humidity to capture possible weather changes.

[0092] The photoelectric light-sensing module 17 is used to measure the light parameters in a low-temperature environment. In low-temperature environments such as the polar regions, the lighting conditions have an important impact on meteorological changes. By measuring the light intensity and duration, this system can more accurately predict temperature changes and possible weather phenomena.

[0093] The automatic detection unit 1 also includes several auxiliary modules, which jointly ensure the stable operation of the system in a low-temperature environment and the accurate acquisition of data.

[0094] The time module 18 is responsible for recording the acquisition time of meteorological element data. In the present invention, the time module 18 adopts high-precision atomic clock technology and can maintain accurate timing even in extreme temperatures. Accurate timestamps are crucial for subsequent data analysis and weather forecasting.

[0095] The power supply module 19 provides power for the automatic detection unit 1. Considering the impact of low-temperature environments on battery performance, the present invention adopts a specially designed lithium battery combined thermal management system. This design can maintain stable power supply in an environment of -60°C, greatly extending the working time of the system.

[0096] The power supply module 20 is communicatively connected to the power supply module 19 and the main control module 11, and it can intelligently adjust the power supply mode according to environmental conditions. For example, when the detected environmental temperature drops below -50°C, the power supply module 20 automatically activates the battery heating system to ensure the normal operation of the battery.

[0097] The communication module 21 is responsible for realizing data transmission between the automatic detection unit 1 and the calibration unit 2. Considering that low-temperature environments may affect conventional communication devices, the present invention adopts anti-low-temperature optical fiber communication technology. This technology can not only maintain stable data transmission in extreme temperatures, but also achieve high-speed and large-capacity data transmission, meeting the system's requirements for real-time performance and data volume.

[0098] These designs and technological innovations of the present invention enable the automatic meteorological element detection device in a low-temperature environment to work stably for a long time in extreme environments, provide high-precision meteorological data, and provide reliable data support for meteorological research and forecasting. As described in claim 4, the calibration unit 2 of the present invention includes an analog unit 21, a model unit 22, a comparison unit 23, and an output unit 24. These units jointly constitute an advanced calibration system, which can ensure the accuracy and reliability of meteorological data collected in a low-temperature environment.

[0099] The main function of the simulation unit 21 is to simulate the meteorological element data of low-temperature environments, near-low-temperature environments, and low-temperature environments. In a preferred embodiment of the present invention, the simulation unit 21 employs a high-precision numerical simulation technology based on a physical model. This technology takes into account the interactions of various factors, such as temperature, humidity, wind speed, air pressure, etc., and can thus generate simulation data that highly coincides with the actual environment.

[0100] For example, when simulating a low-temperature environment of -50°C, the simulation unit 21 not only considers the temperature itself but also simulates the effects of such extreme temperatures on other meteorological elements, such as changes in air density and reduction in water vapor content. This comprehensive simulation method makes the calibration process more accurate and reliable.

[0101] The model unit 22 is communicatively connected to the simulation unit 21, and its main task is to establish a mapping model between the simulation unit and the real physical environment. In an embodiment of the present invention, the model unit 22 uses a deep learning algorithm to construct this mapping relationship. Specifically, the model unit 22 uses an improved convolutional neural network (CNN) structure, which can effectively capture the complex non-linear relationships between meteorological elements.

[0102] The working process of the model unit 22 can be expressed as the following mathematical model:

[0103] M = f(S, R, θ),

[0104] where M represents the mapping model, f represents the deep learning function, S represents the simulation data, R represents the real environment data, and θ represents the model parameters. The model unit 22 continuously adjusts θ to minimize the difference between the simulation data and the real data.

[0105] The comparison unit 23 is communicatively connected to the model unit 22, and its main function is to compare the consistency of the meteorological element data of the simulation unit and the meteorological element data of the real physical environment. In a preferred embodiment of the present invention, the comparison unit 23 employs an innovative multi-dimensional comparison algorithm. This algorithm not only compares the absolute values of individual meteorological elements but also considers the correlations and time series characteristics between the elements.

[0106] The comparison process can be expressed by the following formula:

[0107]

[0108] where D represents the overall difference degree, n represents the number of meteorological elements, w i represents the weight of the i-th element, and d(S i , R i ) represents the difference function between the simulated value and the real value of the i-th element. In practical applications, the weight wi It can be dynamically adjusted according to the importance of different meteorological elements.

[0109] The output unit 24 is communicatively connected to the comparison unit 23 and is responsible for outputting the meteorological element data after comparison. In an embodiment of the present invention, the output unit 24 not only outputs the calibrated data but also generates a detailed calibration report. The report includes the comparison before and after calibration of each meteorological element, the credibility assessment of the calibration, and warnings of possible anomalies.

[0110] The system of the present invention further includes a storage module 4, a data analysis module 5, and a visualization module 6. These modules together constitute a powerful data management and analysis platform, greatly improving the practicality and scientific research value of the system.

[0111] The storage module 4 is communicatively connected to the automatic detection unit 1 and the calibration unit 2 and is used to store the original meteorological element data and the calibrated meteorological element data. In a preferred embodiment of the present invention, the storage module 4 adopts a distributed storage technology and can efficiently process a large amount of time series data. At the same time, the storage module 4 also implements a real-time data backup and fault tolerance mechanism to ensure the security of data in extreme environments.

[0112] The data analysis module 5 is communicatively connected to the storage module 4, and its main function is to perform statistical analysis and feature extraction on the stored meteorological element data. In an embodiment of the present invention, the data analysis module 5 integrates a variety of advanced statistical analysis methods and machine learning algorithms. For example, it can use time series decomposition technology to analyze the long-term trend, seasonal variation, and random fluctuation of meteorological elements; use principal component analysis (PCA) technology to reduce the data dimension and extract key features; use clustering analysis to identify typical weather patterns, etc.

[0113] The visualization module 6 is communicatively connected to the data analysis module 5 and is responsible for presenting the analysis results in a graphical manner. The visualization module 6 of the present invention adopts advanced data visualization technology and can generate a rich variety of chart types, such as time series charts, heat maps, scatter plots, 3D topographic maps, etc. These intuitive visualization results not only facilitate meteorological researchers to interpret data but also help to disseminate meteorological information to the public.

[0114] The system of the present invention further includes a positioning module 7 and a satellite communication module 8. The introduction of these two modules greatly enhances the geographical information processing ability and long-distance communication ability of the system.

[0115] The positioning module 7 is communicatively connected to the automatic detection unit 1 and is used to obtain the geographical location information of meteorological element data collection. In a preferred embodiment of the present invention, the positioning module 7 adopts multi-mode positioning technology, combining GPS, Beidou and ground-based augmentation systems, and can provide high-precision positioning information even in special geographical locations such as polar regions. This precise geographical information is of great significance for understanding local meteorological phenomena and conducting microclimate research.

[0116] The satellite communication module 8 is communicatively connected to the positioning module 7, and its main function is to transmit the geographical location information and the corresponding meteorological element data to the meteorological forecasting platform 3. Considering that the low-temperature environment may be in remote areas and conventional communication methods may not be available, the present invention adopts reliable satellite communication technology. This technology can achieve real-time data transmission globally, ensuring the timeliness and continuity of meteorological data.

[0117] The detection range of the temperature detection module 12 of the present invention includes a near-low temperature environment (0 °C to -40 °C) and a low temperature environment (-40 °C to -60 °C). This wide range of temperature detection ability is a major feature of the present invention, enabling the system to adapt to various extreme environments from near-low temperature to low temperature.

[0118] In a preferred embodiment of the present invention, the temperature detection module 12 adopts an innovative multi-stage temperature sensor design. For the near-low temperature range of 0 °C to -40 °C, the system uses a high-precision platinum resistance temperature sensor, which has excellent linearity and stability in this temperature range. For the low temperature range of -40 °C to -60 °C, the system switches to a specially designed thermocouple sensor, which has been specially processed to maintain accuracy at extremely low temperatures.

[0119] This multi-stage design not only ensures high-precision measurement throughout the temperature range but also improves the reliability of the system. For example, when the temperature approaches -40 °C, the system will simultaneously enable both sensors, and verify the accuracy of the measurement results through data comparison, thus achieving smooth transition and self-calibration of the measurement.

[0120] From the above detailed description, it can be seen that the automatic detection device and calibration method of meteorological elements in low-temperature environments of the present invention have innovations and breakthroughs in multiple aspects such as system structure, data collection, data processing, and calibration technology. These innovations enable the present system to provide high-precision and high-reliability meteorological data in extreme environments, providing a powerful tool for meteorological research, environmental monitoring, and climate change research.

[0121] The automatic detection unit 1 of the present invention further includes a data cleaning module 9. The data cleaning module 9 is communicatively connected to the main control module 11 and plays a crucial role in the processing of meteorological element data.

[0122] The main functions of the data cleaning module 9 include eliminating faulty data and error code data, cleaning the original meteorological data according to preset filtering rules, and formatting the cleaned data. In a preferred embodiment of the present invention, the data cleaning module 9 adopts a multi-stage cleaning strategy to ensure the quality and reliability of the data.

[0123] First, the data cleaning module 9 will conduct a preliminary screening of the received original data to eliminate obvious faulty data. For example, when the value output by the temperature sensor exceeds its range (such as reporting a temperature of -150°C), the system will mark it as faulty data and eliminate it. This step effectively prevents incorrect data caused by hardware failures or sensor abnormalities from entering the subsequent processing flow.

[0124] Secondly, the data cleaning module 9 will conduct in-depth cleaning of the data according to preset filtering rules. These rules are formulated based on meteorological principles and statistical analysis methods and can identify and process various complex data anomalies. For example, the system will check the physical relationships between elements such as temperature, humidity, and wind speed. If it finds data combinations that do not conform to natural laws (such as high humidity at extremely low temperatures), it will mark or correct the relevant data.

[0125] In another embodiment of the present invention, the data cleaning module 9 also adopts an anomaly detection algorithm based on machine learning. This algorithm can identify subtle data anomalies, such as slow sensor drift or intermittent measurement errors, by learning the patterns of historical data. This intelligent cleaning method greatly improves the data quality and lays a solid foundation for subsequent analysis and prediction.

[0126] Finally, the data cleaning module 9 will format the cleaned data. This step ensures the consistency and availability of the data, facilitating the processing and analysis of subsequent modules. The formatting process includes operations such as unifying the timestamp format, adjusting the data precision, and standardizing the units.

[0127] The data analysis module 5 of the present invention uses the K-means clustering algorithm to analyze meteorological element data. This advanced data analysis method enables the system to extract valuable information and patterns from a large amount of meteorological data.

[0128] In a preferred embodiment of the present invention, the specific steps of K-means clustering analysis are as follows:

[0129] First, feature extraction is performed on the standardized meteorological data. This step aims to extract the most representative and discriminative features from the original data. For example, features such as the average value, maximum value, minimum value of the daily temperature, and the temperature change rate can be calculated. The mathematical expression for feature extraction can be expressed as:

[0130] Fi = φ(D i )

[0131] where F i represents the feature vector of the i-th data point, D i represents the original data, and φ represents the feature extraction function.

[0132] Next, the K-means clustering algorithm is used to perform clustering analysis on the feature data. The core idea of the K-means algorithm is to divide n data points into k clusters, such that each data point belongs to the cluster center closest to it. The objective function of the algorithm can be expressed as:

[0133]

[0134] where k is the number of clusters, n is the number of data points, is the i-th data point belonging to the j-th cluster, and c j is the center of the j-th cluster. During the clustering process, the system calculates the objective function value of the clustering result. This value reflects the tightness of the clustering, and the smaller it is, the better the clustering effect. The calculation formula is as follows:

[0135]

[0136] where O is the objective function value, x i is the i-th data point, and c a (i) is the center of the cluster to which x i belongs.

[0137] Finally, the system determines whether the clustering result meets the preset threshold requirement based on the objective function value. If it does not meet the requirement, the parameters (such as the number of clusters k) are adjusted and the clustering is performed again until a satisfactory result is obtained. In the embodiments of the present invention, the preset threshold is usually set between 0.1 and 0.3, and this range can achieve a good balance between the clustering effect and the calculation efficiency.

[0138] The present invention also provides a calibration method for the meteorological element automatic detection device in a low-temperature environment. This method ensures the accuracy and reliability of the meteorological data collected in extreme environments through a series of carefully designed steps.

[0139] First, the automatic detection unit 1 collects the meteorological element data in the low-temperature environment (step S1). In this step, each sensor module (such as the temperature detection module 12, the humidity detection module 13, etc.) continuously collects data at a preset time interval. Preferably, in an extreme environment, the system appropriately increases the sampling frequency to capture possible rapid changes.

[0140] Next, the data cleaning module 9 preprocesses the collected meteorological element data, removing faulty data and error code data (step S2). This step uses the multi-stage cleaning strategy described in detail above to ensure the quality of the data entering the subsequent processing flow.

[0141] Then, the main control module 11 analyzes the processed meteorological element data to determine whether the current environment is suitable for calibration (step S3). This determination is based on multiple factors, such as environmental stability, data continuity, etc. For example, if it is detected that the environmental temperature is changing rapidly, the system may postpone the calibration process and wait for the environment to stabilize.

[0142] If the environment is suitable for calibration, the system will perform a series of calibration steps (step S4). First, the simulation unit 21 simulates the meteorological element parameters under the current environmental conditions (step S4.1). This simulation process takes into account the interactions of multiple factors, such as the influence of temperature on humidity measurement, the influence of wind speed on temperature perception, etc.

[0143] Next, the model unit 22 establishes a mapping model based on the simulation parameters (step S4.2). This mapping model describes the relationship between the simulated data and the actual environment and is the basis for subsequent calibration.

[0144] Then, the comparison unit 23 compares the differences between the simulated data and the actual collected data (step S4.3). If the difference exceeds the preset threshold, the system will adjust the simulation parameters and return to step S4.1 to restart the calibration process. This iterative process ensures the accuracy of calibration. In an embodiment of the present invention, the preset threshold is set to 1%, that is, when the relative error between the simulated data and the actual data is less than 1%, the calibration result is considered acceptable.

[0145] Finally, the output unit 24 generates the calibrated meteorological element data (step S4.5). These calibrated data not only include the corrected measurement values but also the uncertainty estimates of each measurement value, providing important quality information for subsequent data analysis and applications.

[0146] The calibrated meteorological element data will be stored in the storage module 4 (step S5) for subsequent analysis and query. The data analysis module 5 will perform statistical analysis and feature extraction on these calibrated data (step S6) to discover valuable meteorological patterns and trends.

[0147] The analysis results will ultimately be presented graphically through the visualization module 6 (step S7). These intuitive visualization results help meteorological researchers better understand and interpret the data.

[0148] Finally, the meteorological forecasting platform 3 generates meteorological forecasting information based on the calibrated data (step S8). This step transforms the results of all the previous work into actual meteorological forecasts, providing valuable information for scientific research and public services.

[0149] From the above detailed description, it can be seen that the automatic detection device and calibration method for meteorological elements in a low-temperature environment of the present invention adopt advanced technologies and innovative methods in aspects such as data processing, analysis, and calibration. These innovations enable the system to provide high-quality and highly reliable meteorological data in extreme environments, providing strong support for meteorological research and forecasting.

[0150] To verify the superiority of the automatic detection device and calibration method for meteorological elements in a low-temperature environment of the present invention, a series of simulation experiments were conducted. Genhe City in Inner Mongolia was selected as the simulation environment, where the winter temperature often drops below -60°C, which is very suitable for testing the performance of the system in a low-temperature environment.

[0151] Example 1 adopted the complete system configuration of the present invention, including all the aforementioned innovative modules and algorithms. Comparative Example 1 adopted the configuration of a traditional meteorological station, using standard meteorological sensors and simple data processing methods. Comparative Example 2 adopted a partially improved system, including sensors resistant to low temperatures, but without using the advanced data processing and calibration methods of the present invention.

[0152] The simulation experiment lasted for a complete winter (about 3 months), simulating various extreme weather conditions, including blizzards, sharp temperature changes, etc. The main test indicators included data accuracy, system reliability, energy efficiency, and data integrity. The specific detection standards and methods are as follows:

[0153] 1. Data accuracy: Evaluated by comparing with a high-precision reference instrument. The mean absolute error (MAE) and root mean square error (RMSE) were used as evaluation indicators.

[0154] 2. System reliability: Recorded the number of system failures and the continuous working time, and calculated the mean time between failures (MTBF).

[0155] 3. Energy efficiency: Measured the average power consumption of the system, and calculated the ratio of the data acquisition amount to the energy consumption per unit time.

[0156] 4. Data integrity: Statistically analyzed the data missing rate and the proportion of outliers.

[0157] The test results are shown in the following table:

[0158]

[0159]

[0160] As can be seen from the test results, Example 1 of the present invention significantly outperforms Comparative Example 1 and Comparative Example 2 in all indicators. The specific analysis is as follows:

[0161] 1. Data accuracy: The average absolute error of the temperature in Example 1 is only 0.2 °C, far lower than 2.5 °C in Comparative Example 1 and 0.8 °C in Comparative Example 2. This proves the effectiveness of the multi-stage temperature sensor design and the advanced calibration algorithm of the present invention. Especially in extremely low-temperature environments, the error of traditional sensors (Comparative Example 1) increases significantly, while the system of the present invention can still maintain high precision.

[0162] 2. System reliability: The mean time between failures of Example 1 reaches 180 days, covering the entire experimental period, while Comparative Example 1 and Comparative Example 2 are only 15 days and 60 days respectively. This shows that the system of the present invention has excellent stability and durability in extreme environments, which benefits from its intelligent power management and adaptive working mode.

[0163] 3. Energy efficiency: The average power consumption of Example 1 is only 5W, far lower than 20W in Comparative Example 1 and 12W in Comparative Example 2. This high energy efficiency not only extends the working time of the system but also reduces the maintenance requirements, especially suitable for long-term work in remote areas.

[0164] 4. Data integrity: The data missing rate and the proportion of outliers in Example 1 are only 0.1% and 0.5% respectively, far lower than the other two comparative examples. This proves the effectiveness of the data cleaning module and the outlier detection algorithm of the present invention, which can ensure the continuity and reliability of data in extreme environments.

[0165] These test results fully prove the superiority of the present invention in low-temperature environments. It is particularly worth noting that the present invention not only performs well in individual indicators, but more importantly, achieves the comprehensive optimization of multiple indicators. For example, while improving data accuracy, it also reduces energy consumption and improves system reliability. This comprehensive performance improvement makes the present invention particularly suitable for long-term meteorological observations in extreme environments such as polar regions and high altitudes.

[0166] The best embodiment is to deploy the system of the present invention in Genhe City, Inner Mongolia, in cooperation with the satellite communication system, realizing the uninterrupted collection and transmission of low-temperature meteorological data throughout the year. In the actual application for one year, the system not only provided unprecedented high-quality meteorological data but also captured several rare extreme weather events, providing valuable first-hand information for climate change research.

[0167] These test results and actual application cases indicate that the automatic meteorological element detection device and calibration method of the present invention have great scientific research and practical value in the field of meteorological observation in extreme environments. It not only promotes the progress of meteorological observation technology but also provides an important tool for understanding global climate change, and is expected to play a key role in future polar research, climate model improvement, and other fields.

[0168] It should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Automatic detection equipment for low temperature environment meteorological elements, characterized in that: include: Automatic detection unit for: Collect meteorological element data of low temperature environment; Preprocess the collected meteorological element data; A calibration unit, in communication with the automatic detection unit, is used to: Receiving the pre-processed meteorological element data sent by the automatic detection unit; Based on the preprocessed meteorological element data, performing a calibration operation; A weather forecast platform, in communication with the calibration unit, is used to: Receiving the calibrated meteorological element data sent by the calibration unit; Based on the calibrated meteorological element data, meteorological forecast information is generated.

2. The automatic detection device for low temperature environment meteorological elements according to claim 1 is characterized in that: The automatic detection unit comprises: A main control module, used to control the overall operation of the automatic detection unit; A temperature detection module, which is in communication with the main control module and is used to detect the temperature of a low-temperature environment, a near-low-temperature environment, and a low-temperature environment; A humidity detection module, which is in communication with the main control module and is used to detect the humidity in a low-temperature environment, a near-low-temperature environment, and a low-temperature environment; A wind speed detection module, which is in communication with the main control module and is used to detect the wind speed in a low temperature environment, a near low temperature environment, and a low temperature environment; A wind direction detection module, which is in communication with the main control module and is used to detect the wind direction in a low temperature environment, a near low temperature environment, and a low temperature environment; An air pressure detection module, which is in communication with the main control module and is used to detect air temperature changes and feed back to the temperature detection module; The photoelectric sensing module is connected to the main control module for measuring the lighting parameters in the low temperature environment.

3. The automatic detection device for low temperature environment meteorological elements according to claim 2 is characterized in that: The automatic detection unit also includes: A time module, which is in communication with the main control module and is used to record the collection time of meteorological element data; A power supply module, which is in communication with the main control module and is used to provide power to the automatic detection unit; A power supply module, which is in communication with the main control module and the power supply module, and is used to adjust the power supply mode according to environmental conditions; The communication module is connected to the main control module for realizing data transmission with the calibration unit.

4. The automatic detection device for low temperature environment meteorological elements according to claim 1, characterized in that: The calibration unit comprises: A simulation unit, used to simulate meteorological element data of low temperature environment, near low temperature environment and low temperature environment; A model unit, which is in communication with the simulation unit and is used to establish a mapping model between the simulation unit and the real physical environment; A comparison unit, which is in communication with the model unit and is used to compare the consistency of the meteorological element data of the simulation unit with the meteorological element data of the real physical environment; The output unit is connected to the comparison unit for outputting the compared meteorological element data.

5. The automatic detection device for low temperature environment meteorological elements according to claim 1, characterized in that: Also includes: A storage module, which is in communication with the automatic detection unit and the calibration unit, and is used to store the original meteorological element data and the calibrated meteorological element data; A data analysis module, which is in communication with the storage module and is used for performing statistical analysis and feature extraction on the stored meteorological element data; The visualization module is connected to the data analysis module for displaying the analysis results in a graphical manner.

6. The automatic detection device for low temperature environment meteorological elements according to claim 1, characterized in that: Also includes: A positioning module, which is in communication with the automatic detection unit and is used to obtain geographical location information for collecting meteorological element data; The satellite communication module is connected to the positioning module for transmitting the geographical location information and the corresponding meteorological element data to the meteorological forecast platform.

7. The automatic detection device for low temperature environment meteorological elements according to claim 2 is characterized in that: The detection range of the temperature detection module includes: Near low temperature environment, the temperature range is 0℃ to -40℃; Low temperature environment, the temperature range is -40℃ to -60℃.

8. The automatic detection device for low temperature environment meteorological elements according to claim 1, characterized in that: The automatic detection unit also includes: The data cleaning module is connected to the main control module for: Eliminate faulty data and erroneous data; Clean the raw meteorological data according to the preset filtering rules; Format the cleaned data.

9. The automatic detection device for low temperature environment meteorological elements according to claim 5, characterized in that: The data analysis module uses the K-means clustering algorithm to analyze the meteorological element data, including the following steps: Extract features from standardized meteorological data; Use K-means clustering algorithm to perform cluster analysis on feature data; Calculate the objective function value of the clustering results; The clustering result is judged according to the objective function value whether it meets the preset threshold requirement.

10. A calibration method for automatic detection equipment of meteorological elements in low temperature environment, using the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: The automatic detection unit collects meteorological element data of low temperature environment; S2: The data cleaning module pre-processes the collected meteorological element data and removes fault data and error data; S3: The main control module analyzes the processed meteorological element data and determines whether the current environment is suitable for calibration; S4: If the environment is suitable for calibration, perform the following calibration steps: S4.1: The simulation unit simulates the meteorological element parameters under the current environmental conditions; S4.2: The model unit establishes a mapping model based on the simulation parameters; S4.3: The comparison unit compares the differences between the simulated data and the actual collected data; S4.4: If the difference exceeds the preset threshold, adjust the simulation parameters and return to step S4.1, otherwise go to step S4.5; S4.5: The output unit generates calibrated meteorological element data; S5: storing the calibrated meteorological element data into a storage module; S6: The data analysis module performs statistical analysis and feature extraction on the calibrated data; S7: The visualization module displays the analysis results in a graphical form; S8: The weather forecast platform generates weather forecast information based on the calibrated data.

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