CN118671714B - Radar data acquisition method and system combined with scene error analysis and electronic equipment - Google Patents
Radar data acquisition method and system combined with scene error analysis and electronic equipment Download PDFInfo
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Abstract
The invention relates to a radar data acquisition method, a radar data acquisition system and radar data acquisition electronic equipment combined with scene error analysis, and relates to the technical field of radar data acquisition, wherein the radar data acquisition method comprises the steps of constructing a terrain three-dimensional model, acquiring on-site fog, humidity and temperature data, and carrying out fusion accuracy analysis and fusion treatment to obtain fog distribution, humidity distribution and temperature distribution; analyzing distribution rationality, detecting reproducibility and carrying out distribution compensation to obtain compensated fog distribution, compensated humidity distribution and compensated temperature distribution, collecting radar data, carrying out error analysis and correction according to a terrain three-dimensional model to obtain first correction data, carrying out error analysis and correction according to the compensated fog distribution, the compensated humidity distribution and the compensated temperature distribution to obtain second correction data, and fusing the two correction results to obtain corrected radar data. The method solves the technical problem that the accuracy of the acquired data of the radar is poor in severe weather due to the lack of relevant means to effectively fit disturbance caused by multiparty factors in the prior art.
Description
Technical Field
The invention relates to the field of radar data acquisition, in particular to a radar data acquisition method, a system and electronic equipment combined with scene error analysis.
Background
Radar is an electronic device that detects a target using electromagnetic waves, and acquires information of the target, such as distance, speed, direction, etc., by transmitting electromagnetic waves and receiving echoes. The radar can detect long-distance targets in the daytime and at night, is not blocked by fog, cloud and rain, has the characteristics of all weather and all the time, and has certain penetrating capacity. The method is widely applied to weather forecast, aviation field, aerospace field, navigation field, military application, security field, traffic management and scientific research.
Weather conditions are one of the main factors affecting radar measurement errors. In bad weather such as fog, rain, frost and the like, radar signals can be absorbed, scattered or reflected by particles such as raindrops and the like, so that the accuracy of radar measurement is reduced. In addition, variations in wind speed and direction can also affect radar measurement errors.
In the prior art, a noise reduction filtering algorithm, a Doppler filtering algorithm and other technologies are generally adopted to process radar signals under the conditions of rain and snow, and a signal reconstruction method, a target recognition method and other methods are adopted to improve the detection capability of the radar under the large fog weather.
The lack of related means in the prior art effectively fits disturbance caused by multiparty factors, so that the technical problem of poor accuracy of the acquired data of the radar in severe weather is caused.
Disclosure of Invention
Aiming at the technical problem of poor accuracy of radar data acquisition under severe weather caused by disturbance caused by lack of relevant means to effectively fit multiparty factors in the weather condition influence problem in the prior art, the invention provides a radar data acquisition method, a radar data acquisition system and electronic equipment combined with scene error analysis.
According to the method, a radar data acquisition method combining scene error analysis is provided, the radar data acquisition method comprises the steps of acquiring topographic feature data in a measurement scene, constructing a topographic three-dimensional model, acquiring fog data, humidity data and temperature data in the measurement scene, carrying out fusion accuracy analysis and fusion processing on the fog data, the humidity data and the temperature data according to historical monitoring data of the measurement scene, obtaining fog distribution, humidity distribution and temperature distribution, analyzing distribution rationality of the fog distribution, the humidity distribution and the temperature distribution according to the topographic three-dimensional model, analyzing detection reproducibility of the fog distribution, the humidity distribution and the temperature distribution according to the historical monitoring data of the measurement scene, obtaining fog accuracy coefficients, temperature accuracy coefficients and humidity accuracy coefficients, compensating the fog distribution, humidity distribution and the temperature distribution, obtaining compensation fog distribution, compensating humidity distribution and temperature distribution, acquiring radar data acquired by radar measurement in the measurement scene, carrying out radar data error analysis correction according to the topographic three-dimensional model, obtaining first correction radar data according to the topographic three-dimensional model, analyzing the fog distribution, humidity distribution and temperature distribution, obtaining correction radar data based on the humidity accuracy coefficients, and the radar data is corrected according to the integrated correction accuracy coefficients.
In a second aspect, the invention provides a radar data acquisition system combined with scene error analysis, which comprises a terrain data acquisition module, a data analysis module and a data analysis module, wherein the terrain data acquisition module is used for acquiring terrain characteristic data in a measurement scene and constructing a terrain three-dimensional model; the system comprises a data fusion module, a distribution compensation module, a first correction module, a second correction module, a radar data correction module and a second correction module, wherein the data fusion module is used for acquiring fog data, humidity data and temperature data in a measurement scene, carrying out fusion accuracy analysis and fusion processing on the fog data, the humidity data and the temperature data according to historical monitoring data of the measurement scene to acquire fog distribution, humidity distribution and temperature distribution, the distribution compensation module is used for analyzing distribution rationality of the fog distribution, the humidity distribution and the temperature distribution according to the terrain three-dimensional model, analyzing detection reproducibility of the fog distribution, the humidity distribution and the temperature distribution according to the historical monitoring data of the measurement scene to acquire fog accuracy coefficient, temperature accuracy coefficient and humidity accuracy coefficient, compensating the fog distribution, compensating the humidity distribution and the temperature distribution, acquiring radar data acquired by radar measurement in the measurement scene, carrying out radar data error analysis correction according to the terrain three-dimensional model, acquiring radar data, the second correction module is used for acquiring radar data according to the radar accuracy coefficient, the temperature coefficient and the humidity distribution, the humidity distribution and the temperature distribution is corrected according to the integrated correction data, the integrated correction is carried out correction according to the radar accuracy coefficient and the integrated correction data, and the temperature distribution is corrected according to the integrated correction data, corrected radar data is obtained.
In a third aspect, the present invention provides an electronic device, including a memory for storing a computer software program, and a processor for reading and executing the computer software program, thereby implementing the radar data acquisition method in combination with scene error analysis according to any one of the first aspect
The radar data acquisition method has the beneficial effects that the secondary correction of radar acquisition data is realized by comprehensively considering influence factors of four dimensions of topography, fog, humidity and temperature and respectively carrying out multidimensional analysis on errors generated by the four dimensions, and the technical effect of improving the accuracy of the radar acquisition data in severe weather is improved through accuracy analysis and fusion processing.
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FIG. 1 is a schematic flow chart of a radar data acquisition method combining scene error analysis provided by the invention;
fig. 2 is a schematic structural diagram of a radar data acquisition system combined with scene error analysis according to the present invention;
fig. 3 is a schematic block diagram of an electronic device provided by the present invention.
In the drawings, the list of components represented by the various numbers is as follows:
the system comprises a terrain data acquisition module 1000, a data fusion module 2000, a distribution compensation module 3000, a first correction module 4000, a second correction module 5000, an electronic device 500, a memory 510, a processor 520 and a computer program 511.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "for example" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a radar data acquisition method in combination with scene error analysis, including:
s10, collecting topographic feature data in a measurement scene and constructing a topographic three-dimensional model;
Specifically, the topographic feature data within the measurement scene is acquired by various means such as satellite imagery, aerial photogrammetry, liDAR (laser radar) scanning, and the like. The terrain three-dimensional model is completed by means of special Geographic Information System (GIS) software, three-dimensional modeling software and other tools through the terrain characteristic data in the measurement scene, specifically, the collected terrain characteristic data are subjected to operations such as cleaning, correction, format conversion and the like, a specific algorithm and software are used for constructing a network structure of the terrain according to the data, and the collected data are accurately endowed to the network nodes. Further, the model is subjected to image mapping, simplification, smoothing and the like, and is visually presented by a tool such as a rendering engine. The terrain three-dimensional model can display terrain elevation data (representing the relief form of the ground), landform characteristic data (such as the position and form of mountains, valleys, cliffs and the like), surface coverage data (such as vegetation types, building distribution and the like) water system data (such as the position and form of water systems of rivers, lakes and the like), road and traffic facility data and the like. By collecting the topographic data and constructing the three-dimensional model of the measurement scene, data support can be provided for correcting the data errors caused by the topography.
S20, acquiring fog data, humidity data and temperature data in the measurement scene, and carrying out fusion accuracy analysis and fusion treatment on the fog data, the humidity data and the temperature data according to historical monitoring data of the measurement scene to obtain fog distribution, humidity distribution and temperature distribution;
further, the collecting mist data, humidity data and temperature data in the measurement scene, and performing fusion accuracy analysis and fusion processing on the mist data, the humidity data and the temperature data according to the historical monitoring data of the measurement scene, where step S20 includes:
s21, collecting fog data, humidity data and temperature data of a plurality of mark points in the measurement scene;
S22, constructing a fog fusion branch, a humidity fusion branch and a temperature fusion branch according to historical monitoring data of the measurement scene, and carrying out fusion accuracy analysis to obtain a fog fusion accuracy coefficient set, a humidity fusion accuracy coefficient set and a temperature fusion accuracy coefficient set, wherein the fog fusion branch comprises a first fog distribution construction path, a first humidity distribution construction path and a first temperature distribution construction path, and the fog fusion accuracy coefficient set comprises a first fog distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient;
S23, respectively inputting the fog data, the humidity data and the temperature data into the fog fusion branch, the humidity fusion branch and the temperature fusion branch, and processing to obtain a fog distribution set, a humidity distribution set and a temperature distribution set;
S24, adopting the mist fusion accuracy coefficient set, the humidity fusion accuracy coefficient set and the temperature fusion accuracy coefficient set to carry out fusion weighting treatment on the mist distribution set, the humidity distribution set and the temperature distribution set, so as to obtain mist distribution, humidity distribution and temperature distribution.
Specifically, the size of the mist in the air is mainly related to the humidity and temperature of the air. The greater the air humidity, the more advantageous the formation of mist. When the air temperature is higher, the amount of water vapor which can be contained in the air is also increased, so that the formation of fog is reduced, and when the air temperature is lower, the amount of water vapor which can be contained in the air is reduced, and the redundant water vapor is condensed to form the fog. The measurement scene is a real scene of radar acquisition data, a plurality of mark points are preset in the measurement scene, data acquisition is carried out on the mark points through a plurality of sensors, and meanwhile, meteorological data of a measurement scene area can be taken as auxiliary data to complement and perfect the fog data, the humidity data and the temperature data.
In order to describe the mist distribution, the humidity distribution and the temperature distribution of the measurement scene, the mist fusion branch, the humidity fusion branch and the temperature fusion branch need to be constructed. Because the concentration of the mist is related to humidity and temperature, the mist fusion branch, the humidity fusion branch and the temperature fusion branch are respectively formed by three paths, and specifically comprise a first mist distribution construction path, a first humidity distribution construction path and a first temperature distribution construction path, the humidity fusion branch comprises a second mist distribution construction path, a second humidity distribution construction path and a second temperature distribution construction path, and the temperature fusion branch comprises a third mist distribution construction path, a third humidity distribution construction path and a third temperature distribution construction path.
And calling the historical monitoring data of the measurement scene, and taking the historical monitoring data as a branch training data set. The historical monitoring data comprise historical fog data, historical humidity data, historical temperature data and corresponding fog distribution data, humidity distribution data and temperature distribution data. And inputting the historical fog data, the fog distribution data, the humidity distribution data and the temperature distribution data in the historical monitoring data into the fog fusion branch, wherein all training data train a first fog distribution construction path, a first humidity distribution construction path and a first temperature distribution construction path in sequence. And when the training times meet the preset training times, completing the mist fusion branch training. The humidity fusion branch is trained by taking historical humidity data, mist distribution data, humidity distribution data and temperature distribution data as training data, and the temperature fusion branch is trained by taking the historical temperature data, the mist distribution data, the humidity distribution data and the temperature distribution data as training data, and the training process is consistent with the mist fusion branch training process.
And further carrying out fusion accuracy analysis. Because the fog fusion branch, the humidity fusion branch and the temperature fusion branch have different processing accuracy on the fog data, the humidity data and the temperature data, the fog fusion branch, the humidity fusion branch and the temperature fusion branch need to be accurately analyzed. The accuracy analysis can be judged according to the difference between the predicted distribution data and the historical distribution data output in the process of merging the branch training, if the temperature distribution difference is 20%, the temperature distribution accuracy coefficient is 80%, and the sum of the two is 100%.
The mist fusion accuracy coefficient set comprises a first mist distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient. The humidity fusion accuracy coefficient set comprises a second mist distribution accuracy coefficient, a second humidity distribution accuracy coefficient and a second temperature distribution accuracy coefficient. The temperature fusion accuracy coefficient set comprises a third mist distribution accuracy coefficient, a third humidity distribution accuracy coefficient and a third temperature distribution accuracy coefficient. And after the mist fusion branch, the humidity fusion branch and the temperature fusion branch are trained and are subjected to fusion accuracy analysis, the mist data, the humidity data and the temperature data are respectively input into the mist fusion branch, the humidity fusion branch and the temperature fusion branch, and after the branches process the data, a mist distribution set, a humidity distribution set and a temperature distribution set are output.
And carrying out fusion weighting treatment on the mist distribution set, the humidity distribution set and the temperature distribution set, taking the first mist distribution accuracy coefficient, the second mist distribution accuracy coefficient and the third mist distribution accuracy coefficient as weight values of fusion weighting of the mist distribution set, and multiplying the data of each position point of the mist distribution set by the weights to obtain mist distribution. And similarly, taking the first humidity distribution accuracy coefficient, the second humidity distribution accuracy coefficient and the third humidity distribution accuracy coefficient as a weight value for fusion weighting of the mist distribution set, and multiplying the data of each position point by the weight of the mist distribution set to obtain humidity distribution, wherein the fusion weighting process of the temperature distribution is consistent with that of the mist and the humidity. By fusing branch construction and branch distribution accuracy test, the accuracy of mist, humidity and temperature distribution prediction can be improved.
S30, analyzing distribution rationality of the mist distribution, the humidity distribution and the temperature distribution according to the terrain three-dimensional model, analyzing detection reproducibility of the mist distribution, the humidity distribution and the temperature distribution according to historical monitoring data of the measurement scene, obtaining a mist accuracy coefficient, a temperature accuracy coefficient and a humidity accuracy coefficient, and compensating the mist distribution, the humidity distribution and the temperature distribution to obtain compensated mist distribution, compensated humidity distribution and compensated temperature distribution;
Further, according to the terrain three-dimensional model, analyzing distribution rationality of the mist distribution, the humidity distribution and the temperature distribution, and according to historical monitoring data of the measurement scene, analyzing detection reproducibility of the mist distribution, the humidity distribution and the temperature distribution, obtaining a mist accuracy coefficient, a temperature accuracy coefficient and a humidity accuracy coefficient, and compensating the mist distribution, the humidity distribution and the temperature distribution, the step S30 includes:
S31, collecting a sample terrain three-dimensional model set, a sample fog distribution set, a sample humidity distribution set and a sample temperature distribution set according to environmental monitoring data in a plurality of measurement scenes, and evaluating and obtaining a sample fog rationality set, a sample humidity rationality set and a sample temperature rationality set;
S32, respectively combining the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set by using the sample terrain three-dimensional model set as input, respectively using the sample mist rationality set, the sample humidity rationality set and the sample temperature rationality set as output, and training a mist rationality analysis branch, a humidity rationality analysis branch and a temperature rationality analysis branch;
S33, adopting the mist rationality analysis branch, the humidity rationality analysis branch and the temperature rationality analysis branch, and analyzing and obtaining mist rationality coefficients, humidity rationality coefficients and temperature rationality coefficients of the mist distribution, the humidity distribution and the temperature distribution according to the terrain three-dimensional model;
s34, analyzing the detection reproducibility of the fog distribution, the humidity distribution and the temperature distribution according to the historical monitoring data of the measurement scene to obtain a fog reproducibility coefficient, a humidity reproducibility coefficient and a temperature reproducibility coefficient;
S35, respectively correcting the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient according to the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient to obtain a mist accuracy coefficient, a temperature accuracy coefficient and a humidity accuracy coefficient;
And S36, compensating the mist distribution, the humidity distribution and the temperature distribution according to the mist accuracy coefficient, the temperature accuracy coefficient and the humidity accuracy coefficient to obtain compensated mist distribution, compensated humidity distribution and compensated temperature distribution.
Further, the analyzing the detecting reproducibility of the mist distribution, the humidity distribution and the temperature distribution according to the historical monitoring data of the measurement scene, and the step S34 includes:
s341, searching in the historical monitoring data of the measurement scene by adopting the mist distribution, the humidity distribution and the temperature distribution to obtain mist distribution reproducibility parameters, humidity distribution reproducibility parameters and temperature distribution reproducibility parameters;
S342, calculating to obtain a mist reproducibility coefficient, a humidity reproducibility coefficient and a temperature reproducibility coefficient according to the mist distribution reproducibility parameter, the humidity distribution reproducibility parameter, the temperature distribution reproducibility parameter, the preset mist reproducibility parameter, the preset humidity reproducibility parameter and the preset temperature reproducibility parameter.
The method comprises the steps that a plurality of measuring scenes are different scenes for data acquisition by using a radar, and the same topographic data extraction is carried out in the environmental monitoring data of the measuring scenes according to the sample topographic three-dimensional model. And if the sample terrain three-dimensional model is displayed as a valley, extracting the valley scene from a plurality of terrain scenes such as a valley scene, a plain scene, a cliff scene and the like, and carrying out subsequent data analysis.
And extracting a sample mist distribution set, a sample humidity distribution set and a sample temperature distribution set from the environment monitoring data by taking the sample terrain three-dimensional model set as constraint, carrying out rationality assessment on the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set according to sample terrains, limiting constraint that mist distribution, humidity distribution and temperature distribution of different terrains have highest values and lowest values can be judged according to constraint combination experience of terrain attributes, and analyzing rationality of the distribution set to obtain the sample mist rationality set, the sample humidity rationality set and the sample temperature rationality set.
The fog rationality analysis branch, the humidity rationality analysis branch and the temperature rationality analysis branch are branches which are obtained through a large amount of data training and are used for carrying out fog distribution, humidity distribution and temperature distribution rationality judgment. And taking the sample terrain three-dimensional model set and the sample fog distribution set as input data, and taking the sample fog rationality set as output data to carry out fog rationality analysis branch training. And taking the sample terrain three-dimensional model and the sample humidity distribution set as input data, taking the sample humidity rationality set as output data, and carrying out humidity rationality analysis branch training. And taking the sample terrain three-dimensional model and the sample temperature distribution set as input data, taking the sample temperature rationality set as output data, and carrying out temperature rationality analysis branch training. After the branch training is completed, inputting the terrain three-dimensional model and the mist distribution into the mist rationality analysis branch, inputting the terrain three-dimensional model and the humidity distribution into the humidity rationality analysis branch, and inputting the terrain three-dimensional model and the temperature distribution into the temperature rationality analysis branch to obtain rationality coefficients output by three branches, namely the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient.
The detection reproducibility represents the probability of the repeated occurrence of the item of detection data. The detection reproducibility is realized by searching in historical data, mist distribution, humidity distribution and temperature distribution are used as searching conditions respectively, whether the current scene appears in past detection data or not is determined, the searching method is selected according to the characteristics and the requirements of the data, such as sequential searching or binary searching, the recording of successful matching data is carried out through the matching judgment of the data, and after all the data are traversed, the ratio of the successful matching data to all the data is used as the mist distribution reproducibility parameter, the humidity distribution reproducibility parameter and the temperature distribution reproducibility parameter.
Taking the mist reproduction parameter as an example to explain the preset reproduction parameter, the mist preset reproduction parameter is the average probability of repeatedly appearing a mist distribution in all mist monitoring data in the historical data in the scene, for example, 10%. Assuming that the probability of the current mist distribution search result mist distribution reproducibility parameter is 8%, the mist reproducibility coefficient is 80%. The larger the reproducibility parameter, the more accurate the current mist distribution. Therefore, the accuracy of fog distribution, humidity distribution and temperature distribution can be judged through the fog reproducibility coefficient, the humidity reproducibility coefficient and the temperature reproducibility coefficient. The mist accuracy coefficient is obtained by correcting the mist rationality coefficient according to the mist repeatability coefficient, and can be obtained by multiplying the mist rationality coefficient by the mist repeatability coefficient by way of example. The humidity accuracy coefficient is obtained by correcting the humidity rationality coefficient according to the humidity reproducibility coefficient and can be obtained by multiplying the humidity rationality coefficient. The temperature accuracy coefficient obtaining method is consistent with the former two. The obtained mist accuracy coefficient, temperature accuracy coefficient and humidity accuracy coefficient represent the deviation condition of the predicted distribution result and the real result, and the mist distribution, the humidity distribution and the temperature distribution are compensated according to the deviation condition, if the predicted result is lower than the real result, the distribution condition after compensation is higher than the distribution condition before compensation by a certain value. After corresponding compensation is carried out through the accuracy coefficient, the compensated mist distribution, the compensated humidity distribution and the compensated temperature distribution are obtained, further correction and adjustment of distribution data are realized, and the mist distribution, the humidity distribution and the temperature distribution data are more scientific and accurate.
S40, acquiring radar data acquired by radar measurement in the measurement scene, and performing radar data error analysis and correction according to the terrain three-dimensional model to obtain first corrected radar data;
Further, the acquiring radar data acquired by radar measurement in the measurement scene, and performing error analysis and correction on the radar data according to the terrain three-dimensional model to obtain first corrected radar data, and step S40 includes:
s41, acquiring radar data acquired by radar measurement in the measurement scene;
s42, acquiring a sample radar data set, a sample terrain three-dimensional model set and sample first correction radar data based on error influence data of radar data on different terrains, and training to obtain a terrain error radar data corrector;
S43, adopting the terrain error radar data corrector to perform error analysis correction on the radar data to obtain first corrected radar data.
Specifically, different terrains have certain influence on radar data accuracy, and mainly comprise that the terrains are blocked, complex terrains such as mountain areas possibly block radar beams, partial area data are lost or inaccurate, atmospheric refraction is caused, atmospheric conditions of different terrains are different, refraction of radar waves can be influenced, and accordingly detected target positions are deviated, altitude differences of different terrains can influence radar detection ranges, echo intensities and the like, and beam propagation path differences can change actual propagation paths of radar beams in complex terrain environments. The radar data errors caused by actual topography need to be subjected to corresponding error analysis correction, such as data missing or inaccuracy, corresponding filling and correction, and false echo screening and correction.
The radar data errors are corrected by a terrain error radar data corrector, and as the terrain in the measurement scene has a certain complexity, a plurality of terrains can be contained, and a three-dimensional model corresponding to the contained terrains is formed into the sample terrain three-dimensional model set. According to the measurement data of the radar, error influence data of different terrains, such as radar beams may be blocked by complex terrains such as mountain areas, so that partial area data are missing or inaccurate, corresponding filling and correction are needed, and the part of error filled and corrected belongs to the error influence data. The terrain error radar data corrector is obtained by training based on a neural network model structure, a sample radar data set, a sample terrain three-dimensional model set and sample first correction radar data are acquired in error influence data, and the terrain error radar data corrector is trained. And correcting radar data acquired by radar measurement in the measurement scene by using a terrain error radar data corrector to obtain the first corrected radar data. Various influences brought by the topography factors are fully considered, and targeted adjustment and correction are carried out, so that the accuracy and reliability of data are improved.
S50, according to the fog accuracy coefficient, the temperature accuracy coefficient and the humidity accuracy coefficient, based on integrated learning, according to the compensated fog distribution, the compensated humidity distribution and the compensated temperature distribution, radar data error analysis and correction are carried out to obtain second correction radar data, and fusion is carried out by combining the first correction radar data to obtain correction radar data.
Further, based on the integrated learning, according to the mist accuracy coefficient, the temperature accuracy coefficient and the humidity accuracy coefficient, the radar data error analysis and correction are performed according to the compensated mist distribution, the compensated humidity distribution and the compensated temperature distribution, and step S50 includes:
s51, collecting a sample fog distribution set, a sample radar data set and a sample fog correction radar data set according to error influence data of radar data in different fog distributions;
s52, adopting the sample fog distribution set, the sample radar data set and the sample fog correction radar data set, and constructing a plurality of fog radar data correction branches based on integrated learning to obtain a fog radar data corrector;
s53, determining a fog radar data correction proportion according to the fog accuracy coefficient, randomly selecting a corresponding number of fog radar data correction branches in the fog radar data corrector, performing radar data error analysis correction on the compensated fog distribution and radar data to obtain a plurality of fog correction radar data, and calculating to obtain the fog correction radar data;
S54, according to the temperature accuracy coefficient and the humidity accuracy coefficient, based on integrated learning, carrying out radar data error analysis and correction according to the compensated humidity distribution and the compensated temperature distribution to obtain humidity correction radar data and temperature correction radar data;
s55, fusing the fog correction radar data, the humidity correction radar data and the temperature correction radar data to obtain second correction radar data;
and S56, carrying out fusion calculation on the first correction radar data and the second correction radar data to obtain correction radar data.
Further, the sample fog distribution set, the sample radar data set and the sample fog correction radar data set are adopted, based on the ensemble learning, a plurality of fog radar data correction branches are constructed, and step S52 includes:
s521, randomly selecting sample data with preset proportion in the sample fog distribution set, the sample radar data set and the sample fog correction radar data set, and constructing and training to obtain a first fog radar data correction branch based on integrated learning;
S522, continuing to randomly select sample data with preset proportion in the sample fog distribution set, the sample radar data set and the sample fog correction radar data set, and constructing and training to obtain a second fog radar data correction branch based on integrated learning;
and S523, continuing to construct and obtain other multiple fog radar data correction branches based on the ensemble learning.
In particular, different fog distributions can have a variety of effects on radar data, such as reduced radar signal strength, increased clutter in radar images, reduced resolution, signal distortion and errors, and the like. Sample data information of the same type of radar under different fog conditions is collected, wherein the sample data information comprises a sample fog distribution set, a sample radar data set and a sample fog correction radar data set. The sample fog distribution set, the sample radar data set and the sample fog correction radar data set are in one-to-one correspondence.
The fog radar data corrector comprises a plurality of fog radar data correction branches and is used for correcting radar data. The plurality of fog radar data correction branches can process different fog distributions, and have different fog distribution pertinence.
And training the first fog radar data correction branch, the second fog radar data correction branch and the Nth fog radar data correction branch sequentially by taking the sample fog distribution set, the sample radar data set and the sample fog correction radar data set as training data. For example, the classification of the sample data (such as radiation fog, advection fog, evaporation fog, uphill fog and frontal fog) can be performed according to different fog states, the data of the sample data is divided, for example, the sample data with a preset proportion is randomly selected, and different radar data correction branches are trained according to different types of divided sample data.
The integrated learning can be applied to data correction and error elimination, and the accuracy and reliability of the data can be improved by combining the prediction results of a plurality of models. Each branch is obtained through ensemble learning, each branch possibly comprises a plurality of data correction models, and a fog radar correction branch is formed through ensemble learning. And fusing the plurality of correction branches, namely forming the fog radar data corrector by the plurality of correction branches. The training data of different fog radar data correction branches are sampled and weight adjusted, the first fog radar data correction branch and the second fog radar data correction branch are trained respectively until the Nth fog radar data correction branch, and the prediction results of the first fog radar data correction branch and the second fog radar data correction branch are processed, such as simple average, majority voting and the like, so that a final integrated result is obtained, and a plurality of fog radar data correction branches are obtained to form the fog radar data corrector.
The fog accuracy coefficient is obtained by correcting the fog rationality coefficient according to the fog reproducibility coefficient, and the larger the fog accuracy coefficient is, the higher the accuracy of the corresponding compensation fog distribution is, and the greater the reliability of radar data error analysis and correction is. And determining a fog radar data correction proportion according to the fog accuracy coefficient, for example, if the fog accuracy coefficient is 80%, combining the number of fog radar data correction branches in the fog radar data corrector, calculating to obtain the number of fog radar data correction branches for carrying out radar data error analysis correction, for example, if the number of fog radar data correction branches in the fog radar data corrector is 20, and adopting the number of fog radar data correction branches for carrying out radar data correction to be 16.
And multiplying the fog accuracy coefficient by the number of fog radar data correction branches in the fog radar data corrector to obtain the number of the corrected fog radar data correction branches. The more the fog accuracy coefficient is, the more the fog radar data correction branches are corrected, the more accurate the result is, if the fog accuracy coefficient is smaller, the fewer the fog radar data correction branches are adopted to carry out radar data error analysis correction, so that the processing calculation power of data with lower accuracy is saved, and other types of data support to carry out radar data error analysis correction, so that the accuracy can be ensured.
The fog radar data corrector comprises a plurality of fog radar data correction branches, error analysis correction of the fog radar data is carried out according to the number of the fog radar data correction branches determined by the correction proportion, specifically, the radar data is corrected according to the compensated fog distribution and the input of the fog radar data correction branches, and a plurality of fog correction radar data can be output and obtained. The compensated mist distribution is more accurate mist distribution data. And according to the plurality of fog correction radar data, the fog correction radar data are obtained through modes such as simple average, majority voting and the like, the accuracy of the fog correction radar data correction is improved based on integrated learning, and the data processing calculation force is saved.
The humidity correction radar data and the temperature correction radar data are obtained in the same mode as the fog correction radar data, and are obtained through radar data error analysis and correction. The second correction radar data is obtained by fusing the fog correction radar data, the humidity correction radar data and the temperature correction radar data, and can be obtained through artificial weight, and weighting calculation. Further, the first correction radar data and the second correction radar data are fused, weights are given by people, and then the correction radar data are obtained through a weighting calculation mode. The secondary correction of the radar acquisition data is realized, and the technical effect of improving the accuracy of the radar acquisition data in severe weather is achieved.
Further, according to the historical monitoring data of the measurement scene, a mist fusion branch, a humidity fusion branch and a temperature fusion branch are constructed, fusion accuracy analysis is performed, and a mist fusion accuracy coefficient set, a humidity fusion accuracy coefficient set and a temperature fusion accuracy coefficient set are obtained, wherein step S22 includes:
s221, collecting a sample fog data set, a sample humidity data set and a sample temperature data set, and constructing a sample fog distribution set, a sample humidity distribution set and a sample temperature distribution set;
S222, training the first mist distribution construction path, the first humidity distribution construction path and the first temperature distribution construction path by taking the sample mist data set as input and respectively taking the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set as output to obtain mist fusion branches;
s223, continuing to construct training to obtain a humidity fusion branch and a temperature fusion branch;
s224, testing the mist fusion branch to obtain a first mist distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient as a mist fusion accuracy coefficient set;
s225, testing the humidity fusion branch and the temperature fusion branch to obtain the humidity fusion accuracy coefficient set and the temperature fusion accuracy coefficient set.
In particular, the sample mist data set, sample humidity data set, and sample temperature data set are extracted from historical monitoring data of the measurement scenario. In order to ensure the validity of the data, the data within a certain time threshold from the training time can be selected from the historical monitoring data. Under the same monitoring time, a plurality of fog data, humidity data and temperature data exist, and a sample fog distribution set, a sample humidity distribution set and a sample temperature distribution set are constructed according to the data and the topographic information of the measurement scene. For example, the temperature data distribution construction is performed according to different temperature data of different coordinate positions in the measurement scene.
The mist fusion branch comprises a first mist distribution construction path, a first humidity distribution construction path and a first temperature distribution construction path which are parallel models, and the first mist distribution construction path, the first humidity distribution construction path and the first temperature distribution construction path are constructed on the basis of a feedforward neural network model in an exemplary manner. And dividing the sample mist data set, the sample humidity data set, the sample temperature data set, the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set into 8:2, namely training data: verification data=8:2. The model is trained using the prepared training data, with parameters being continually adjusted to optimize model performance. And evaluating and verifying the model through the verification set, and further improving the optimization adjustment of the model according to the verification result of the verification set. The humidity fusion branch and the temperature fusion branch are similar to the mist fusion branch and are formed by three parallel models, wherein the humidity fusion branch comprises a second mist distribution construction path, a second humidity distribution construction path and a second temperature distribution construction path, the temperature fusion branch comprises a third mist distribution construction path, a third humidity distribution construction path and a third temperature distribution construction path, and the training process is similar to the mist fusion branch and is not repeated herein.
And testing the fog fusion branch, the humidity fusion branch and the temperature fusion branch, wherein the fog fusion accuracy coefficient set comprises a first fog distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient. The humidity fusion accuracy coefficient set comprises a second mist distribution accuracy coefficient, a second humidity distribution accuracy coefficient and a second temperature distribution accuracy coefficient. The temperature fusion accuracy coefficient set comprises a third mist distribution accuracy coefficient, a third humidity distribution accuracy coefficient and a third temperature distribution accuracy coefficient. The judgment can be carried out according to the difference between the predicted distribution data and the historical distribution data output in the process of merging the branch training, if the temperature distribution difference is 20%, the temperature distribution accuracy coefficient is 80%, and the sum of the two is 100%. For example, the historical data in the historical sample mist distribution set is respectively compared with the mist distribution output results of the first mist distribution construction path, the second mist distribution construction path and the third mist distribution construction path to obtain the distribution difference results of different construction paths, and according to the correlation between the accuracy and the difference, the accuracy coefficient of each path is obtained, wherein the accuracy coefficient is the average value of the accuracy coefficient test results of each path and comprises a first mist distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient, and the three accuracy coefficients form a mist fusion accuracy coefficient set. And the humidity fusion branch and the temperature fusion branch are tested in the same way as the testing method of the mist fusion branch, so that the humidity fusion accuracy coefficient set and the temperature fusion accuracy coefficient set are obtained. The data support can be provided for the data weighting of the subsequent use of the distributed data through fusion accuracy analysis.
Example 2
As shown in fig. 2, based on the same inventive concept as the radar data acquisition method combined with scene error analysis provided in embodiment 1, an embodiment of the present invention provides a radar data acquisition system combined with scene error analysis, including:
The topographic data acquisition module 1000 is used for acquiring topographic feature data in the measurement scene and constructing a topographic three-dimensional model;
the data fusion module 2000 is configured to collect mist data, humidity data, and temperature data in the measurement scene, and perform fusion accuracy analysis and fusion processing on the mist data, the humidity data, and the temperature data according to historical monitoring data of the measurement scene, so as to obtain mist distribution, humidity distribution, and temperature distribution;
The distribution compensation module 3000 is configured to analyze distribution rationality of the mist distribution, the humidity distribution, and the temperature distribution according to the terrain three-dimensional model, and analyze detection reproducibility of the mist distribution, the humidity distribution, and the temperature distribution according to historical monitoring data of the measurement scene, obtain a mist accuracy coefficient, a temperature accuracy coefficient, and a humidity accuracy coefficient, and compensate the mist distribution, the humidity distribution, and the temperature distribution, to obtain a compensated mist distribution, a compensated humidity distribution, and a compensated temperature distribution;
the first correction module 4000 is configured to collect radar data collected by radar measurement in the measurement scene, and perform error analysis and correction on the radar data according to the terrain three-dimensional model to obtain first corrected radar data;
And the second correction module 5000 is configured to perform radar data error analysis correction according to the mist accuracy coefficient, the temperature accuracy coefficient and the humidity accuracy coefficient, and perform radar data error analysis correction according to the compensated mist distribution, the compensated humidity distribution and the compensated temperature distribution based on ensemble learning to obtain second corrected radar data, and combine the first corrected radar data to obtain corrected radar data.
Further, the system further comprises:
the marking point data acquisition module is used for acquiring fog data, humidity data and temperature data of a plurality of marking points in the measurement scene;
The fusion analysis module is used for constructing a fog fusion branch, a humidity fusion branch and a temperature fusion branch according to the historical monitoring data of the measurement scene, and carrying out fusion accuracy analysis to obtain a fog fusion accuracy coefficient set, a humidity fusion accuracy coefficient set and a temperature fusion accuracy coefficient set, wherein the fog fusion branch comprises a first fog distribution construction path, a first humidity distribution construction path and a first temperature distribution construction path, and the fog fusion accuracy coefficient set comprises a first fog distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient;
The branch processing module is used for inputting the fog data, the humidity data and the temperature data into the fog fusion branch, the humidity fusion branch and the temperature fusion branch respectively, and processing the fog fusion branch, the humidity fusion branch and the temperature fusion branch to obtain a fog distribution set, a humidity distribution set and a temperature distribution set;
and the fusion weighting module is used for carrying out fusion weighting treatment on the mist distribution set, the humidity distribution set and the temperature distribution set by adopting the mist fusion accuracy coefficient set, the humidity fusion accuracy coefficient set and the temperature fusion accuracy coefficient set to obtain mist distribution, humidity distribution and temperature distribution.
Further, the system further comprises:
The sample data acquisition module is used for acquiring a sample fog data set, a sample humidity data set and a sample temperature data set and constructing a sample fog distribution set, a sample humidity distribution set and a sample temperature distribution set;
the fusion branch training module is used for training the first mist distribution construction path, the first humidity distribution construction path and the first temperature distribution construction path by taking the sample mist data set as input and respectively taking the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set as output to obtain a mist fusion branch;
the branch construction module is used for continuing to construct training to obtain a humidity fusion branch and a temperature fusion branch;
The mist branch test module is used for testing the mist fusion branches to obtain a first mist distribution accuracy coefficient, a first humidity distribution accuracy coefficient and a first temperature distribution accuracy coefficient, and the first mist distribution accuracy coefficient, the first humidity distribution accuracy coefficient and the first temperature distribution accuracy coefficient are used as a mist fusion accuracy coefficient set;
and the temperature and humidity branch test module is used for testing the humidity fusion branch and the temperature fusion branch to obtain the humidity fusion accuracy coefficient set and the temperature fusion accuracy coefficient set.
Further, the system further comprises:
The rationality evaluation module is used for acquiring a sample terrain three-dimensional model set, a sample mist distribution set, a sample humidity distribution set and a sample temperature distribution set according to environmental monitoring data in a plurality of measurement scenes, and evaluating and acquiring the sample mist rationality set, the sample humidity rationality set and the sample temperature rationality set;
The rationality analysis branch construction module is used for respectively combining the sample mist distribution set, the sample humidity distribution set and the sample temperature distribution set as inputs by adopting the sample topography three-dimensional model set, respectively adopting the sample mist rationality set, the sample humidity rationality set and the sample temperature rationality set as outputs, and training a mist rationality analysis branch, a humidity rationality analysis branch and a temperature rationality analysis branch;
The rationality coefficient analysis module is used for analyzing and obtaining the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient of the mist distribution, the humidity distribution and the temperature distribution according to the terrain three-dimensional model by adopting the mist rationality analysis branch, the humidity rationality analysis branch and the temperature rationality analysis branch;
the repeatability analysis module is used for analyzing the fog distribution, the humidity distribution and the detection repeatability of the temperature distribution according to the historical monitoring data of the measurement scene to obtain a fog reproducibility coefficient, a humidity reproducibility coefficient and a temperature reproducibility coefficient;
The coefficient correction module is used for correcting the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient according to the mist rationality coefficient, the humidity rationality coefficient and the temperature rationality coefficient to obtain a mist accuracy coefficient, a temperature accuracy coefficient and a humidity accuracy coefficient;
And the accurate distribution compensation module is used for compensating the mist distribution, the humidity distribution and the temperature distribution according to the mist accuracy coefficient, the temperature accuracy coefficient and the humidity accuracy coefficient to obtain compensated mist distribution, compensated humidity distribution and compensated temperature distribution.
Further, the system further comprises:
the data retrieval module is used for retrieving in the historical monitoring data of the measurement scene by adopting the mist distribution, the humidity distribution and the temperature distribution to obtain mist distribution reproducibility parameters, humidity distribution reproducibility parameters and temperature distribution reproducibility parameters;
The recurrence coefficient analysis module is used for calculating and obtaining a fog recurrence coefficient, a humidity recurrence coefficient and a temperature recurrence coefficient according to the fog distribution recurrence parameter, the humidity distribution recurrence parameter, the temperature distribution recurrence parameter, the preset fog recurrence parameter, the preset humidity recurrence parameter and the preset temperature recurrence parameter.
Further, the system further comprises:
The first radar data acquisition module is used for acquiring radar data acquired by radar measurement in the measurement scene;
the terrain data correction module is used for acquiring a sample radar data set, a sample terrain three-dimensional model set and sample first correction radar data based on error influence data of radar data on different terrains, and training to obtain a terrain error radar data corrector;
The first error analysis and correction module is used for carrying out error analysis and correction on the radar data by adopting the terrain error radar data corrector to obtain first corrected radar data.
Further, the system further comprises:
The second radar data acquisition module is used for acquiring a sample fog distribution set, a sample radar data set and a sample fog correction radar data set according to error influence data of radar data in different fog distributions;
The fog data correction module is used for constructing a plurality of fog radar data correction branches based on integrated learning by adopting the sample fog distribution set, the sample radar data set and the sample fog correction radar data set to obtain a fog radar data corrector;
The fog correction data calculation module is used for determining the fog radar data correction proportion according to the fog accuracy coefficient, randomly selecting a corresponding number of fog radar data correction branches in the fog radar data corrector, carrying out radar data error analysis correction on the compensated fog distribution and radar data to obtain a plurality of fog correction radar data, and calculating to obtain the fog correction radar data;
The temperature and humidity correction module is used for carrying out radar data error analysis and correction according to the temperature accuracy coefficient and the humidity accuracy coefficient, the compensated humidity distribution and the compensated temperature distribution based on integrated learning, and obtaining humidity correction radar data and temperature correction radar data;
the corrected data fusion module is used for fusing the fog corrected radar data, the humidity corrected radar data and the temperature corrected radar data to obtain second corrected radar data;
And the data fusion analysis module is used for carrying out fusion calculation on the first correction radar data and the second correction radar data to obtain the correction radar data.
Further, the system further comprises:
the first branch construction module is used for randomly selecting sample data with preset proportion in the sample fog distribution set, the sample radar data set and the sample fog correction radar data set, and constructing and training to obtain a first fog radar data correction branch based on integrated learning;
The second branch construction module is used for continuing to randomly select sample data with preset proportion in the sample fog distribution set, the sample radar data set and the sample fog correction radar data set, and constructing and training to obtain a second fog radar data correction branch based on integrated learning;
and the third branch construction module is used for continuously constructing and obtaining a plurality of other fog radar data correction branches based on the ensemble learning.
Referring to fig. 3, fig. 3 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 3, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and capable of running on the processor 520, wherein the processor 520 when executing the computer program 511 implements the steps of collecting topography characteristic data in a measurement scene to construct a topography three-dimensional model, collecting mist data, humidity data and temperature data in the measurement scene, performing fusion accuracy analysis and fusion processing on the mist data, the humidity data and the temperature data according to historical monitoring data of the measurement scene to obtain mist distribution, humidity distribution and temperature distribution, analyzing distribution rationality of the mist distribution, the humidity distribution and the temperature distribution according to the topography three-dimensional model, analyzing detection reproducibility of the mist distribution, the humidity distribution and the temperature distribution according to the historical monitoring data of the measurement scene to obtain mist accuracy coefficient, temperature accuracy coefficient, compensating the humidity distribution and the temperature distribution according to obtain compensated radar distribution, compensating humidity distribution and temperature distribution according to the radar temperature coefficient, performing fusion accuracy analysis and the radar temperature distribution, and the radar temperature distribution is corrected according to the first radar data, the radar temperature distribution and the radar temperature distribution is corrected according to the measurement accuracy coefficient, and the radar temperature distribution is corrected according to the radar temperature coefficient, and the radar temperature distribution is corrected, corrected radar data is obtained.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and the equivalent techniques thereof, the present invention is intended to include such modifications and variations as well.
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