CN117687032A - System and method for regulating and controlling micro rain radar - Google Patents
System and method for regulating and controlling micro rain radar Download PDFInfo
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Abstract
Embodiments of the present disclosure provide a system and method for controlling a light rain radar, the system comprising: the system comprises an environment monitoring component, a data acquisition module, a cleaning module, a solar module, a storage device, a regulation and control module and a communication module, wherein the environment monitoring component at least comprises a sensor and an optical camera; the data acquisition module is configured to acquire radar data; the cleaning module at least comprises a rotatable cleaning brush and a motor; the solar module is configured to convert light energy into electrical energy; the storage device is configured to store system data; the regulation and control module is configured to determine environmental monitoring parameters; based on the environment monitoring parameters, controlling the environment monitoring component to acquire environment data and image data; controlling the cleaning module to clean sundries of the antenna housing; controlling the rain radar to monitor based on radar monitoring parameters; the communication assembly is configured to communicatively connect the environmental monitoring component, the data acquisition module, the cleaning module, the solar assembly, the storage device, and the regulation module.
Description
Technical Field
The specification relates to the technical field of environmental monitoring, in particular to a system and a method for regulating and controlling a rain radar.
Background
The light rain radar is one of tools for effectively observing important rainfall parameters such as a low-layer atmospheric rain drop scale spectrum, a rain drop end speed, a rainfall intensity vertical structure and the like. The rain radar is easily interfered by environmental factors, such as areas with monitoring significance (e.g. mountain areas needing mountain flood warning, upstream hydropower stations needing water quantity evaluation, etc.), often have complex terrains, luxuriant vegetation and complicated and changeable environmental climates (e.g. fog interference, etc.), and all the factors can bring non-negligible interference to the monitoring result of the rain radar.
Therefore, the regulation and control system and the method for the micro-rain radar are required to be provided, the micro-rain radar can be assisted to monitor, the monitoring reliability of the micro-rain radar under complex conditions is further improved and ensured, and the monitoring result of the micro-rain radar has more practical significance.
Disclosure of Invention
One or more embodiments of the present specification provide a light rain radar regulation system comprising an environmental monitoring component, a data acquisition module, a sweeping module, a solar assembly, a storage device, a regulation module, and a communication assembly, wherein the environmental monitoring component comprises at least a sensor and an optical camera, the environmental monitoring component is configured to acquire environmental data and image data of a surrounding environment of the light rain radar; the data acquisition module is configured to acquire radar data; the cleaning module comprises at least a rotatable cleaning brush and a motor, wherein the rotatable cleaning brush is configured to clean sundries of a radome of the light rain radar; the solar module is configured to convert light energy into electrical energy; the storage device is configured to store system data including at least one of the environment data, the image data, and the radar data; the regulation module is configured to determine an environmental monitoring parameter; controlling the environment monitoring component to acquire the environment data and the image data based on the environment monitoring parameters; and controlling the cleaning module to clean the sundries of the radome; controlling the rain radar to monitor based on radar monitoring parameters; the communication assembly is configured to communicatively connect the environmental monitoring component, the data acquisition module, the cleaning module, the solar module, the storage device, and the regulation module.
One or more embodiments of the present specification provide a method of rain radar regulation, the method being performed based on a rain radar regulation system, comprising: acquiring radar data; determining environmental monitoring parameters; acquiring environmental data and image data based on the environmental monitoring parameters; cleaning sundries of an antenna housing of the micro-rain radar; and controlling the rain radar to monitor based on radar monitoring parameters.
One or more embodiments of the present specification provide a light rain radar regulation device, the device including at least one memory for storing computer instructions and at least one processor for executing the computer instructions or a portion of the instructions to implement the light rain radar regulation method.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform the method of rain-radar regulation.
The invention can realize the following beneficial effects: (1) The timeliness and rationality of acquiring the environmental data and the image data are improved through the environmental monitoring parameters, the interference of sundries on radar monitoring can be reduced or avoided through cleaning the sundries of the radome of the light rain radar, the light rain radar can be controlled to be combined with the actual situation to effectively monitor the radar according to the radar monitoring parameters, and the reliability of monitoring of the light rain radar under complex conditions is improved and ensured; (2) The cleaning resistance data when cleaning sundries is obtained through the resistance sensor, so that reasonable environment monitoring parameters are accurately determined, the waste of calculation resources and time caused by data redundancy caused by too frequent monitoring is avoided, the data loss caused by untimely monitoring can be avoided, and the change of the surrounding environment can be timely monitored under the condition of not wasting electric energy; (3) Through confirm cleaning cycle and radar monitoring parameter at mobile terminal, can practice thrift the computational resource and the computational power of light rain radar self, and utilize mobile terminal's powerful calculation power, can confirm more reasonable cleaning cycle and radar monitoring parameter through synthesizing multiple historical data fast, avoid cleaning cycle too short waste that leads to the electric energy, cleaning cycle overlength leads to debris to pile up excessive scheduling problem, guarantee to do not frequently carry out the radar monitoring when interference degree is high as far as possible, when avoiding the electric quantity extravagant, reduced the influence that the environment variation brought to the radar monitoring, do benefit to the going on of radar monitoring work, make the monitoring result of light rain radar have practical meaning more.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a system block diagram of a rain radar regulation system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart of a method of rain radar regulation according to some embodiments of the present disclosure;
FIG. 3 is an exemplary schematic diagram illustrating a determination of a cleaning cycle according to some embodiments of the present disclosure;
FIG. 4 is an exemplary schematic diagram illustrating determining radar monitoring parameters according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
FIG. 1 is a system block diagram of a rain radar regulation system according to some embodiments of the present description. As shown in fig. 1, the rain radar regulation system 100 may include an environmental monitoring component 110, a data acquisition module 120, a sweeping module 130, a solar module 140, a storage device 150, a regulation module 160, a communication module 170, and a mobile terminal 180.
The micro-rain radar is a low-power frequency modulation continuous wave Doppler radar, the central frequency of the transmitted electromagnetic wave is positioned at 24GHz, and by measuring parameters such as backward echo frequency offset, echo intensity and the like, the observation of important rainfall parameters such as a raindrop scale spectrum, a raindrop end speed, a rainfall intensity vertical structure and the like of the low-layer atmosphere (below 4.5 km) can be realized, and the rainfall condition is judged on the basis of the observation.
The environment monitoring section 110 refers to a device for monitoring data about the environment in which the light rain radar is located. In some embodiments, the environmental monitoring component 110 may include at least a sensor and an optical camera. In some embodiments, the environmental monitoring component 110 may be configured to acquire environmental data and image data of the surrounding environment of the light rain radar.
The surrounding environment is the environment in which the rain radar monitors. For example, mountainous areas open country, hydropower station upstream, etc. The environmental data refers to data related to environmental factors. Such as temperature data, humidity data, etc.
In some embodiments, the environmental data may be acquired by sensor monitoring in the environmental monitoring component. For example, a temperature sensor may acquire temperature data, a humidity sensor may acquire humidity data, and so on.
The video data is image data of the surrounding environment. Such as a debris image, a fog image, etc. The sundry image refers to an image of sundries of a radome of the light rain radar. The fog image is an image of fog in the surrounding environment of the rain radar.
In some embodiments, the image data may be captured by an optical camera in the environmental monitoring component. For example, a radome for capturing a light rain radar by an optical camera may acquire a debris image, and a surrounding environment by an optical camera may acquire a fog image. In some embodiments, the environmental monitoring component may be mounted alongside the data acquisition module.
The data acquisition module 120 refers to a module for acquiring data related to precipitation. In some embodiments, the data acquisition module 120 may be configured to acquire radar data.
The radar data refers to precipitation information obtained by analyzing the received signals by the rain radar. For example, radial velocity of the raindrops, intensity information of the precipitation, etc. In some embodiments, the data acquisition module may control the micro-rain radar to emit continuous waves (electromagnetic waves) into the air using frequency modulated continuous wave (Frequency Modulated Continuous Wave, FMCW) technology, where when the electromagnetic waves meet the rain drops, the rain drops may partially absorb and scatter the electromagnetic waves such that the wavelength between emission and reception varies. The data acquisition module can perform phase comparison on the received reflected signals and the transmitted signals to obtain frequency shift or frequency difference of the transmitted waves, and further can acquire radar data such as radial velocity of raindrops and intensity information of precipitation through analysis.
The cleaning module 130 is a module for cleaning foreign materials. In some embodiments, the sweeper module 130 may include at least a rotatable cleaning brush and a motor.
In some embodiments, the rotatable cleaning brush may be configured to sweep debris of the radome of the light rain radar. In some embodiments, in response to receiving the cleaning instruction issued by the regulation and control module, the motor can drive the rotatable cleaning brush to rotate so as to clean sundries of the radome of the light rain radar.
Radomes refer to planar antennas that consist of a parabolic reflector and a radiation source (e.g., feed, antenna element, etc.) at its focal point. Such as a cassegrain antenna, etc. In some embodiments, a radome may be used to transmit and receive electromagnetic waves. When in transmission, the feed source in the antenna housing can transmit electromagnetic wave signals, and the signals are reflected and focused by the parabolic reflector and radiated into the air (transmit signals). When receiving, the parabolic reflector can catch electromagnetic wave signals formed by the reflection of objects such as raindrops and the like, and focus the received signals (reflected signals) to the feed source in a reflection way.
In some embodiments, the debris of the radome may include dead branches, fallen leaves, snow, silt stains, and the like.
The solar module 140 refers to a photovoltaic device that can convert solar energy into electrical energy. In some embodiments, the solar module 140 may be configured to convert light energy into electrical energy. In some embodiments, the solar module may be comprised of a variety of devices such as solar panels, batteries, inverters, and the like. The solar panel can convert received solar light energy into current, the storage battery can store electric energy and display electric quantity, and the inverter can convert direct current generated by the solar panel into alternating current. In some embodiments, the solar assembly may enable solar-based powering of the rain radar.
The storage device 150 is a device having a storage function. In some embodiments, the storage device 150 may be configured to store system data. In some embodiments, the system data may include at least one of environmental data, image data, and radar data. In some embodiments, the system data may also include other data generated during operation of the rain radar regulation system. Such as environmental monitoring parameters, sweep resistance data, radar monitoring parameters, and the like.
The regulation module 160 refers to a module for controlling the light rain radar to perform monitoring. In some embodiments, regulation module 160 may be configured to determine environmental monitoring parameters; based on the environment monitoring parameters, controlling the environment monitoring component to acquire environment data and image data; controlling the cleaning module to clean sundries of the antenna housing; and controlling the rain radar to monitor based on the radar monitoring parameters.
In some embodiments, the sweeper module 130 may also include a resistive sensor. The resistance sensor may be used to obtain sweep resistance data.
In some embodiments, the regulation module 160 may be further configured to: acquiring cleaning resistance data based on the resistance sensor; based on the sweep resistance data, an environmental monitoring parameter is determined.
In some embodiments, the regulation module 160 may be further configured to: based on the sweep resistance data sequence and the environmental characteristics, an environmental monitoring parameter is determined.
In some embodiments, the regulation module 160 may be further configured to: the cleaning cycle is dynamically adjusted based on the cleaning resistance data.
In some embodiments, the regulation module 160 may be further configured to: the cleaning cycle is adjusted in advance based on the wind power data, the environmental characteristics, and the cleaning resistance data.
The communication component 170 refers to components for data transmission and information exchange. In some embodiments, the communication component 170 may be configured to communicatively connect the environmental monitoring component 110, the data acquisition module 120, the cleaning module 130, the solar module 140, the storage device 150, and the regulation module 160.
In some embodiments, the rain radar regulation system 100 may also include a mobile terminal 180.
The mobile terminal 180 refers to an electronic device with a data processing function used by a technician (such as a rain radar inspector). In some embodiments, the mobile terminal may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, and the like.
Because the data processing function of the rain radar is limited (for example, image data cannot be processed), a technician can only conduct regular inspection so as to extract data to the mobile terminal, and partial data analysis work is conducted on the mobile terminal. The period of the technician's inspection can be set autonomously by the human. For example once a week, etc.
In some embodiments, the mobile terminal 180 may be communicatively coupled to the regulatory module 160 for data interaction with the environmental monitoring component 110, the data acquisition module 120, the cleaning module 130, and the solar module 140 via the regulatory module 160. In some embodiments, the mobile terminal 180 may be configured to: determining environmental characteristics of the surrounding environment of the micro-rain radar based on the historical environmental data, the historical image data and the historical cleaning resistance data; based on the environmental characteristics, a cleaning cycle is determined.
In some embodiments, the mobile terminal 180 may be further configured to: acquiring future weather data of the surrounding environment; determining radar interference characteristics based on historical environment data, historical image data and historical radar data; based on the radar interference characteristics and future weather data, radar monitoring parameters are determined.
In some embodiments, the rain radar regulation system may further comprise a wind monitoring component.
In some embodiments, the wind monitoring component may be configured to monitor wind data of the surrounding environment. For example, the wind monitoring components may include anemometers, wind testers, and the like. In some embodiments, the wind monitoring component may be mounted near the rain radar and in communication with the regulatory module.
Further description of relevant parameters in the environmental monitoring component 110, the data acquisition module 120, the cleaning module 130, the solar module 140, the storage device 150, the regulation module 160, the communication module 170, the mobile terminal 180, the wind power monitoring component (e.g., cleaning resistance data, environmental characteristics, environmental monitoring parameters, wind power data, etc.) may be found in the relevant description of fig. 2-4.
It should be noted that the foregoing description of the light rain radar regulation system 100 and its modules is for convenience only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the environmental monitoring component 110, the data acquisition module 120, the cleaning module 130, the solar module 140, the storage device 150, the regulation module 160, the communication module 170, and the mobile terminal 180 disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. Such variations are within the scope of the present description.
FIG. 2 is an exemplary flow chart of a method of rain radar regulation according to some embodiments of the present description. In some embodiments, process 200 may be performed by rain radar regulation system 100. As shown in fig. 2, the process 200 includes the following steps.
Step 210, radar data is acquired.
In some embodiments, radar data may be acquired by the data acquisition module 120. For more on radar data see the relevant description of fig. 1.
At step 220, environmental monitoring parameters are determined.
The environmental monitoring parameter refers to an operational parameter of the environmental monitoring component 110 acquiring data. In some embodiments, the environmental monitoring parameter may include at least an environmental monitoring frequency. The environmental monitoring frequency refers to the frequency at which the environmental monitoring component acquires data. For example, once a half hour, once an hour, etc.
In some embodiments, the environmental monitoring parameters may be determined in a variety of ways. For example, during initial operation of the rain radar, the environmental monitoring parameters may be preset by a technician based on historical experience or set by system defaults.
In some embodiments, the regulation module may obtain the sweep resistance data based on the resistance sensor; based on the sweep resistance data, an environmental monitoring parameter is determined.
The cleaning resistance data refers to the resistance value of the antenna housing when cleaning sundries. For example, 5N, 10N, etc. In some embodiments, the sweep resistance data may be directly monitored by the resistance sensor and transmitted to the regulatory module via the communication assembly.
In some embodiments, the regulation module may determine the environmental monitoring parameter based on the sweep resistance data in a variety of ways. For example, the regulation module may determine the environmental monitoring parameter based on a trend of the sweep resistance data. When the cleaning resistance data show an increasing trend, the cleaning resistance data may be caused by more frequent environmental interference (such as thicker snow caused by snowing, more and more sediment stains caused by precipitation, etc.), and the environmental monitoring frequency may be reduced appropriately, so that a technician can determine which periods of time may have larger errors in radar data.
In some embodiments, the regulation module may determine the environmental monitoring parameter based on the sequence of cleaning resistance data and the environmental characteristics.
The cleaning resistance data sequence is a sequence composed of a plurality of cleaning resistance data within a preset period of time. The preset time period refers to a historical time period with a preset length from the current moment. The preset length may be a short duration set empirically by the skilled artisan. For example, the predetermined length may be 5 cleaning cycles (e.g., cleaning cycle is 1min, and the cleaning resistance data sequence may be a sequence of cleaning resistance data of the last 5 cleaning operations of the debris: [11, 13, 14, 10, 12 ]).
Environmental characteristics refer to the relevant characteristics of debris accumulation in the surrounding environment. For example, the accumulation speed of sundries in the radome of the rain radar. In some embodiments, the accumulation rate of debris may be represented by the amount of change in the weight of debris per unit time or the amount of change in the coverage area of debris. The change in the weight of the sundries can be represented by the change in the weighing data of a weight detector (such as a spring balance, etc.) mounted at the lower part of the radome. The change in the coverage area of the debris can be represented by the change in the area ratio of the debris area to the radome area in the debris image. For more about the surroundings, clutter images etc. see the relevant description of fig. 1.
In some embodiments, the regulation module may determine environmental characteristics of the ambient environment of the light rain radar based on historical environmental data, historical image data, historical sweep resistance data. For more on this part see the relevant description of fig. 3.
In some embodiments, the regulation module may determine the resistance variance based on the sequence of sweep resistance data; determining an environmental stability level based on the resistance variance; based on the degree of environmental stability and the environmental characteristics, an environmental monitoring parameter is determined.
The resistance variance refers to the variance of all the sweep resistance data in the sweep resistance data sequence. For example, the cleaning resistance data sequence is [11, 13, 14, 10, 12], then the resistance variance is 2.
The environmental stability refers to the stability of the surrounding environment, i.e. the stability that the surrounding environment cannot interfere with the rain radar. In some embodiments, the degree of environmental stability may be represented by a value (e.g., a value in the range of 0-10). The greater the number, the greater the degree of environmental stability.
In some embodiments, the regulation module may determine the degree of environmental stability in a variety of ways based on the resistance variance. For example, the regulation module may determine the degree of environmental stability based on a correlation that decreases the degree of environmental stability the greater the resistance variance.
By way of example, the degree of environmental stability may be determined by the following equation (1):
（1）
wherein,representing the degree of environmental stability, +.>Representing the resistance variance.
In some embodiments, the regulatory module may determine the environmental monitoring parameters in a variety of ways based on the degree of environmental stability and the environmental characteristics. For example, the regulatory module may determine the environmental monitoring parameters by looking up a table based on a preset relationship table including the degree of environmental stability, the environmental characteristics, and the corresponding environmental monitoring parameters. The lower the environmental stability, the faster the environmental characteristics (accumulation speed of impurities) and the larger the corresponding environmental monitoring parameters (environmental monitoring frequency). The preset relation table can be constructed by technicians according to the historical environment stability degree, the historical environment characteristics and the corresponding historical actual environment monitoring parameters in the historical data.
In some embodiments of the present disclosure, the accuracy of the environmental monitoring parameters may be improved according to the cleaning resistance data sequence and the environmental characteristics, which is beneficial to ensuring the timeliness and reliability of the acquisition of the data by the environmental monitoring component.
In some embodiments of the present disclosure, cleaning resistance data during cleaning sundries can be directly obtained through the resistance sensor, so that reasonable environmental monitoring parameters can be accurately determined, data redundancy caused by too frequent monitoring is avoided, waste of calculation resources and time is caused for later data processing, and data loss caused by untimely monitoring can also be avoided; and the change of the surrounding environment can be timely monitored under the condition of not wasting electric energy, so that the monitoring reliability of the micro-rain radar under the complex condition is further improved.
In step 230, environmental data and image data are acquired based on the environmental monitoring parameters.
In some embodiments, the regulation module may control the environmental monitoring component to acquire environmental data and image data based on the environmental monitoring parameters. For more on this part see the relevant description of fig. 1.
Step 240, cleaning sundries of the radome of the micro-rain radar.
In some embodiments, the regulation and control module can send instructions for cleaning sundries to the cleaning module, and the cleaning module can control the motor to drive the rotatable cleaning brush to start rotating so as to clean sundries of the radome of the rain-light radar. For more on this part see the relevant description of fig. 1.
And 250, controlling the rain radar to monitor based on the radar monitoring parameters.
The radar monitoring parameters refer to the working parameters of the rain radar for monitoring. In some embodiments, the radar monitoring parameters may include at least radar monitoring frequency. The radar monitoring frequency refers to the frequency at which the rain radar monitors. For example, once every five minutes, once every ten minutes, etc.
In some embodiments, the radar monitoring parameters may be determined in a variety of ways. For example, radar monitoring parameters may be preset by a technician based on historical experience or set by system defaults during initial operation of a rain-light radar.
In some embodiments, the mobile terminal may obtain future weather data for the surrounding environment; determining radar interference characteristics based on historical environment data, historical image data and historical radar data; based on the radar interference characteristics and future weather data, radar monitoring parameters are determined. For more on this part see the relevant description of fig. 4.
In some embodiments, the regulation module may control the rain radar to monitor based on the radar monitoring parameters. For example, the regulatory module may control the rain radar to monitor at a radar monitoring frequency.
In some embodiments of the present disclosure, by acquiring radar data, determining environmental monitoring parameters, and further based on the environmental monitoring parameters, timeliness and rationality of acquiring the environmental data and the image data may be improved; and cleaning sundries of the radome of the micro-rain radar to reduce or avoid interference of the sundries on radar monitoring; based on radar monitoring parameters, the light rain radar is controlled to monitor, and effective radar monitoring can be carried out in combination with actual conditions, so that the reliability of monitoring the light rain radar under complex conditions is further improved and ensured.
It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.
FIG. 3 is an exemplary schematic diagram illustrating a determination of a cleaning cycle according to some embodiments of the present description.
In some embodiments, as shown in fig. 3, the mobile terminal may determine environmental characteristics 340 of the ambient environment of the light rain radar based on historical environmental data 310, historical image data 320, historical cleaning resistance data 330; based on the environmental characteristics 340, a cleaning cycle 350 is determined.
The historical environmental data refers to all environmental data from the last inspection to the present inspection. The historical image data refers to all image data from the last inspection to the present inspection. The historical cleaning resistance data refers to all cleaning resistance data from the last inspection to the present inspection.
For more details on mobile terminals, environmental data, image data, cleaning resistance data, see the relevant description of fig. 1 and 2.
In some embodiments, the mobile terminal may determine the environmental characteristics in a variety of ways based on historical environmental data, historical image data, historical cleaning resistance data. For example, the mobile terminal may determine the environmental characteristics by an environmental characteristic determination model based on historical environmental data, historical image data, historical cleaning resistance data.
The environmental characteristic determination model refers to a model for determining environmental characteristics of the surrounding environment of the light rain radar. In some embodiments, the environmental feature determination model may be a machine learning model. For example, neural Networks (NN), convolutional Neural Networks (Convolutional Neural Networks, CNN), and the like, or any combination thereof.
In some embodiments, the input of the environmental characteristic determination model may include historical environmental data, historical image data, and historical cleaning resistance data, and the output may be an environmental characteristic.
In some embodiments, the mobile terminal may use the historical environmental data, the historical image data and the historical cleaning resistance data in the historical data as a first training sample, use the corresponding historical actual environmental feature as a first label corresponding to the first training sample, and train the environmental feature determination model by using the first training sample and the first label. The first label (historic actual environmental characteristics) may be the historic actual accumulation rate of debris. The first tag may be manually marked. For more on the speed of accumulation of debris, see the relevant description of fig. 2.
In some embodiments, the mobile terminal may construct a first loss function based on the environmental features and the first tag (the historical actual environmental features) output by the model, update the model parameters using the first loss function, and obtain the trained environmental feature determination model through the parameter update. Methods of updating parameters may include, but are not limited to, gradient descent or other iterative methods. The condition for completion of the update may be that the first loss function is less than a first threshold, the first loss function converges, the training period reaches a threshold, or the like, or any combination thereof.
The cleaning cycle is a cycle for cleaning sundries of the radome of the light rain radar. In some embodiments, the sweep period for the first sweep may be preset empirically by the technician or set by default in the system. For example, 1min, 2min, etc.
In some embodiments, the mobile terminal may determine the cleaning cycle in a variety of ways based on environmental characteristics. For example, the mobile terminal may determine the cleaning period by vector retrieval.
In some embodiments, the mobile terminal may construct a first vector database based on the historical data, the first vector database including a plurality of first reference vectors and corresponding historical cleaning cycles therein. Each first reference vector is constructed from historical environmental characteristics, historical precipitation data, historical temperature data, and historical season data. The historical cleaning period corresponding to each first reference vector can be one or more historical cleaning periods corresponding to the same first reference vector, and the historical cleaning period with the best actual monitoring effect (the most accurate radar data) of the rain radar.
In some embodiments, the mobile terminal may construct a first target vector based on the environmental characteristics, the future precipitation data, the future temperature data, and the seasonal data, retrieve a first reference vector having a minimum distance from the first target vector in the first vector database, and determine its corresponding historical cleaning period as the cleaning period. The distance may be any one of euclidean distance, cosine distance, mahalanobis distance, and the like.
Future precipitation data and future temperature data refer to precipitation data and temperature data, respectively, over a future time period (e.g., a future week, etc.). In some embodiments, future precipitation data and future temperature data may be obtained through a third party platform (e.g., a chinese weather network, weather forecast APP, etc.). Season data may include spring, summer, autumn, winter.
Because the cleaning resistance data of cleaning sundries is dynamically changed each time, the cleaning period needs to be dynamically adjusted. In some embodiments, the regulation module may dynamically adjust the sweep period based on the sweep resistance data.
For example, the regulation and control module may dynamically adjust the cleaning cycle according to a preset rule based on the cleaning resistance data. Wherein the preset rules may be set empirically by a technician. For example, when the cleaning resistance data shows an increasing trend (the cleaning resistance data of the current cleaning is larger than the cleaning resistance data of the previous cleaning), the cleaning cycle may be appropriately adjusted to be smaller; when the cleaning resistance data shows a tendency to decrease (the cleaning resistance data of the present cleaning is smaller than the cleaning resistance data of the previous cleaning), the cleaning cycle is appropriately set to be longer.
In some embodiments of the present disclosure, the cleaning cycle is dynamically adjusted according to the cleaning resistance data, and the cleaning cycle can be adjusted in time according to the environmental change, which is beneficial to more effective cleaning of the radome of the micro-rain radar.
Because wind power affects the accumulation speed of sundries of the radome of the rain radar, for example, when the wind power is relatively high, the accumulation speed of sundries of the radome can be high (for example, the fallen leaves are increased due to the high wind power); when the wind power is particularly high, the accumulation speed of sundries of the radome is slow (for example, fallen leaves are blown away due to the high wind power), so that the adjustment of the cleaning period is related to wind power data. When the cleaning resistance data is increased, the fact that the sundries of the radome are more at the moment is indicated, and when the cleaning period is adjusted in advance by combining the wind power data, the occurrence of the condition that the sundries are excessively accumulated can be reduced as much as possible.
In some embodiments, the regulation module may pre-adjust the sweep period based on wind data, environmental characteristics, sweep resistance data.
The wind power data refers to data related to wind power of the surrounding environment of the rain radar. For example, wind speed, wind power, etc. In some embodiments, wind data may be directly acquired by the wind monitoring component and transmitted to the regulatory module.
In some embodiments, the regulation module may pre-adjust the cleaning cycle in a variety of ways based on wind data, environmental characteristics, cleaning resistance data. For example, the conditioning module may determine the cleaning cycle through vector retrieval.
In some embodiments, the regulation module may construct a second vector database based on the historical data, the second vector database including a plurality of second reference vectors and corresponding historical cleaning cycles. Each second reference vector is constructed from historical wind data, historical environmental characteristics, historical sweep resistance data. The historical cleaning period corresponding to each second reference vector can be one or more historical cleaning periods corresponding to the same second reference vector, and the historical cleaning period with the best actual monitoring effect (the most accurate radar data) of the rain radar.
In some embodiments, the regulation and control module may construct a second target vector based on the wind power data, the environmental characteristics and the cleaning resistance data, search a second reference vector with the smallest distance from the second target vector in the second vector database, and determine a corresponding historical cleaning period as the cleaning period. The distance may be any one of euclidean distance, cosine distance, mahalanobis distance, and the like.
In some embodiments of the present disclosure, in combination with wind data, environmental considerations in dynamic adjustment of the cleaning cycle may be made more comprehensive, resulting in more accurate pre-adjusted cleaning cycles.
In some embodiments of the present disclosure, by determining the cleaning period at the mobile terminal, the computing resources and computing power of the light rain radar can be used for analyzing radar data, and the computing power of the mobile terminal is stronger, so that the determining speed of the cleaning period is faster and the effect is better; and according to the historical environment data, the historical image data and the historical cleaning resistance data, the environmental characteristics of the surrounding environment of the micro-rain radar can be determined by integrating the multi-aspect factor data, so that the determined cleaning period is more reasonable, and the problems caused by overlong or too short cleaning period (such as waste of electric energy caused by too short cleaning period and excessive accumulation of sundries caused by overlong cleaning period) are avoided.
FIG. 4 is an exemplary schematic diagram illustrating determining radar monitoring parameters according to some embodiments of the present description.
In some embodiments, as shown in fig. 4, the mobile terminal may obtain future weather data 430 for the surrounding environment; determining radar interference features 420 based on the historical environmental data 310, the historical image data 320, the historical radar data 410; based on radar interference characteristics 420 and future weather data 430, radar monitoring parameters 440 are determined.
Future weather data refers to weather related data for a future time period. For example, future temperature data, future precipitation data, future wind data, and the like. The future time period may be preset empirically by the technician or by default by the system. For example, the future time period may be a time period of a future week after the current time.
In some embodiments, the mobile terminal may obtain future weather data for the surrounding environment in a variety of ways. For example, obtained through a third party platform (e.g., a chinese weather network, weather forecast APP, etc.).
The historical radar data refers to all radar data from the last inspection to the present inspection. For more on radar data see the relevant description of fig. 1.
Radar interference characteristics refer to the degree of interference with which a rain radar is monitored by various environmental factors. In some embodiments, radar interference signatures may be represented by a sequence of multiple interference levels. The degree of interference may be represented by a value (e.g., a value in the range of 0-10). For example, radar interference characteristics may be (temperature: 5, humidity: 3, wind: 1), indicating that the degree of interference of temperature on the light rain radar is 5, the degree of interference of humidity on the light rain radar is 3, and the degree of interference of wind on the light rain radar is 1, i.e., the degree of interference of temperature on the light rain radar is maximum.
In some embodiments, the mobile terminal may determine radar interference characteristics in a variety of ways based on historical environmental data, historical image data, historical radar data. For example, the mobile terminal may determine radar interference features through an interference feature determination layer of the radar feature determination model.
For more on historical environmental data and historical image data, see the associated description of FIG. 3.
In some embodiments, the mobile terminal may determine radar monitoring parameters in a variety of ways based on radar interference characteristics and future weather data. For example, the mobile terminal may determine radar monitoring parameters through a parameter determination layer of the radar feature determination model.
The radar feature determination model refers to a model for determining radar monitoring parameters. In some embodiments, the radar feature determination model may be a machine learning model. For example, neural Networks (NN), recurrent Neural Networks (Recurrent Neural Networks, RNN), and the like, or any combination thereof. For more on radar monitoring parameters see the relevant description of fig. 2.
In some embodiments, the radar feature determination model may include an interference feature determination layer and a parameter determination layer.
The interference feature determination layer refers to a layer structure for determining radar interference features. In some embodiments, the interference feature determination layer may be a machine learning model. For example, a recurrent neural network, etc. The input of the interference feature determination layer may include historical environmental data, historical image data, and historical radar data, and the output may be radar interference features.
The parameter determination layer refers to a layer structure for determining radar monitoring parameters. In some embodiments, the parameter determination layer may be a machine learning model. Such as neural networks, etc. The inputs to the parameter determination layer may include radar interference characteristics, future weather data, and environmental characteristics, and the output may be radar monitoring parameters.
In some embodiments, the radar feature determination model may be determined by a combined training of the interference feature determination layer and the parameter determination layer.
In some embodiments, the mobile terminal may use the sample environment data, the sample image data, the sample radar data, the sample weather data, and the sample environment feature as a second training sample for joint training, use the corresponding recommended radar monitoring parameter as a second tag corresponding to the second training sample, and train the radar feature determination model by using the second training sample and the second tag.
Wherein the second training sample may be obtained based on historical data. The mobile terminal can take the second training sample as an experimental environment, perform experiments by using different radar monitoring parameters, and select the radar monitoring parameter with the best monitoring effect (namely, the most accurate radar data) as the recommended radar monitoring parameter corresponding to the second training sample, namely, the second label corresponding to the second training sample.
In some embodiments, the mobile terminal may construct a second loss function based on the radar monitoring parameters and a second tag (recommended radar monitoring parameters) output by the model, update the model parameters using the second loss function, and obtain a trained radar feature determination model through parameter update. Methods of updating parameters may include, but are not limited to, gradient descent or other iterative methods. The condition for completion of the update may be that the second loss function is less than a second threshold, the second loss function converges, the training period reaches a threshold, or the like, or any combination thereof.
In some embodiments of the present disclosure, by determining the radar monitoring parameter at the mobile terminal, the computing resource and computing power of the light rain radar can be saved, and the computing power of the mobile terminal is stronger, so that the determining speed of the radar monitoring parameter is faster and the effect is better; based on historical environment data, historical image data and historical radar data, radar interference characteristics are determined, and then radar monitoring parameters are determined, so that radar monitoring can be prevented from being frequently carried out when the interference degree is high as much as possible, waste of electric energy and time is avoided, influence of environmental factors on radar monitoring can be effectively reduced, and accuracy of rain-light radar monitoring is improved.
In some embodiments, the determination of radar monitoring parameters may also be related to radar power and sweep resistance data. For more on the sweeping resistance data see the relevant description of fig. 2.
The radar electric quantity refers to electric quantity for the radar to monitor. In some embodiments, the radar power may be directly captured by a battery in the solar module. For more on solar modules see the relevant description of fig. 1.
In some embodiments, the mobile terminal may dynamically adjust radar monitoring parameters based on radar power and sweep resistance data. For example, the radar monitoring parameter (e.g., radar monitoring frequency) may be suitably reduced when the radar power is below a power threshold or the sweep resistance data is above a resistance threshold. Wherein the power threshold and the resistance threshold may be preset empirically by a technician or by default by the system. For example, the power threshold may be 5W and the resistance threshold may be 15N.
In some embodiments of the present disclosure, radar monitoring parameters are dynamically adjusted in combination with radar power and sweeping resistance data, so that the radar monitoring parameters more conform to the current state of a rain radar, and the influence of environmental changes on radar monitoring is reduced while power waste is avoided.
In some embodiments, the regulatory module may dynamically adjust the radar monitoring parameters based on the environmental data and the environmental characteristics. For more on the context data and context features see the relevant description of fig. 1 and 2.
In some embodiments, the regulation module may determine the degree of environmental interference based on the environmental data and the environmental characteristics; based on the degree of environmental interference, radar monitoring parameters are dynamically adjusted.
The environmental interference degree refers to the interference degree of monitoring the rain radar by all environmental factors of the surrounding environment. In some embodiments, the degree of environmental interference may be represented by a value (e.g., a value in the range of 0-10).
In some embodiments, the regulatory module may determine the degree of environmental interference in a variety of ways based on the environmental data and the environmental characteristics.
For example, the regulation module may cluster experimental environment data and experimental environment features in experimental data to determine a plurality of cluster centers. A cluster center may include a center environment data, a center environment feature, and a center environment interference level. The central environmental interference level may be an average value of all experimental environmental interference levels in the cluster corresponding to the cluster center. The experimental environment interference degree can be the difference value between experimental radar data and real rainfall information. The experimental radar data is precipitation information obtained by monitoring with a light rain radar under the environment corresponding to the experimental environment data and the experimental environment characteristics. The real precipitation information is obtained by directly monitoring the obtained precipitation information by using related equipment (such as a raindrop spectrometer and the like) under the environment corresponding to the experimental environment data and the experimental environment characteristics. For more information about precipitation see the relevant description of fig. 1.
The regulation and control module can take a vector formed by the central environment data and the central environment characteristics corresponding to each cluster center as each third reference vector; and constructing a third target vector based on the environmental data and the environmental characteristics, calculating the similarity with each third reference vector, and selecting the central environmental interference degree corresponding to the third reference vector with the highest similarity as the environmental interference degree. Among them, clustering methods include, but are not limited to, K-Means Clustering (K-Means Clustering), mean-Shift Clustering (Mean-Shift Clustering), and the like. The calculation method of the similarity may include, but is not limited to, euclidean distance algorithm, cosine similarity, and the like. The euclidean distance is the smallest and the cosine similarity is the largest, i.e. the similarity is the highest.
In some embodiments, the regulatory module may dynamically adjust the radar monitoring parameters in a variety of ways based on the level of environmental interference. For example, the radar monitoring parameter (e.g., radar monitoring frequency) is appropriately turned down when the environmental interference level is greater than the first interference threshold, and the radar monitoring parameter (e.g., radar monitoring frequency) is appropriately turned up when the environmental interference level is less than the second interference threshold.
The first interference threshold and the second interference threshold refer to the maximum value and the minimum value of the corresponding environmental interference degree when the radar monitoring parameter needs to be adjusted. For example, the first interference threshold may be 7 and the second interference threshold may be 3.
In some embodiments, the first interference threshold and the second interference threshold may be related to a rain drop particle size range that is measurable by a rain-light radar.
The raindrop particle size range refers to the range of raindrop sizes. In some embodiments, the range of raindrop particle sizes that can be measured by different models (or types) of micro-rain radar varies. For example, the rain drop size range of the METEK MRR-2 of the micro-rain radar is 0.109-6 mm, and the rain drop size range of the LY-M1 micro-rain radar is 0.1-6 mm.
In some embodiments, the first interference threshold and the second interference threshold may be set by a technician or by the system based on a correlation with the rain drop size range. Wherein, the correlation may include: the wider the range of the particle size of the raindrops (the stronger the anti-jamming capability of the rain-light radar), the larger the first jamming threshold value and the smaller the second jamming threshold value.
In some embodiments of the present disclosure, the first interference threshold and the second interference threshold are flexibly determined according to the size range of the raindrops, so that unreasonable adjustment of radar monitoring parameters caused by using the same first and second interference thresholds by different types of rainy radars is avoided, and the rainy radar regulation and control method can be flexibly applied to multiple types (or types) of rainy radars.
In some embodiments of the present disclosure, based on the environmental data and the environmental characteristics, the degree of environmental interference may be accurate, and then the radar monitoring parameter may be dynamically adjusted according to the degree of environmental interference, so that the radar monitoring parameter may better conform to the current environment, and the radar monitoring work may be facilitated.
One or more embodiments of the present disclosure provide a rain radar regulation device. The device comprises at least one memory and at least one processor, wherein the at least one memory is used for storing computer instructions, and the at least one processor is used for executing the computer instructions or part of the instructions so as to realize the micro rain radar regulation method.
One or more embodiments of the present description provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a method of rain-radar regulation.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. A micro-rain radar regulation system, the system comprising: the system comprises an environment monitoring component, a data acquisition module, a cleaning module, a solar module, a storage device, a regulation and control module and a communication module, wherein,
The environment monitoring component at least comprises a sensor and an optical camera, and is configured to acquire environment data and image data of the surrounding environment of the light rain radar;
the data acquisition module is configured to acquire radar data;
the cleaning module comprises at least a rotatable cleaning brush and a motor, wherein the rotatable cleaning brush is configured to clean sundries of a radome of the light rain radar;
the solar module is configured to convert light energy into electrical energy;
the storage device is configured to store system data including at least one of the environment data, the image data, and the radar data;
the regulation module is configured to determine an environmental monitoring parameter; controlling the environment monitoring component to acquire the environment data and the image data based on the environment monitoring parameters; and controlling the cleaning module to clean the sundries of the radome; controlling the rain radar to monitor based on radar monitoring parameters;
the communication assembly is configured to communicatively connect the environmental monitoring component, the data acquisition module, the cleaning module, the solar module, the storage device, and the regulation module.
2. The system of claim 1, wherein the cleaning module further comprises a resistance sensor, the regulation module further configured to:
acquiring cleaning resistance data based on the resistance sensor;
the environmental monitoring parameter is determined based on the sweep resistance data.
3. The system of claim 1, further comprising a mobile terminal in communication with the regulation module, through which data interaction with the environmental monitoring component, the data acquisition module, the cleaning module, and the solar module occurs, the mobile terminal configured to:
determining environmental characteristics of the surrounding environment of the rain radar based on historical environmental data, historical image data, historical cleaning resistance data;
based on the environmental characteristics, a cleaning cycle is determined.
4. The system of claim 3, wherein the mobile terminal is further configured to:
acquiring future weather data of the surrounding environment;
determining radar interference characteristics based on the historical environmental data, the historical image data and the historical radar data;
The radar monitoring parameters are determined based on the radar interference characteristics and the future weather data.
5. A method of rain radar regulation, performed based on the rain radar regulation system of claim 1, the method comprising:
acquiring radar data;
determining environmental monitoring parameters;
acquiring environmental data and image data based on the environmental monitoring parameters; and
cleaning sundries of an antenna housing of the micro-rain radar;
and controlling the rain radar to monitor based on radar monitoring parameters.
6. The method of claim 5, wherein the cleaning module further comprises a resistance sensor, the determining environmental monitoring parameters further comprising:
acquiring cleaning resistance data based on the resistance sensor;
the environmental monitoring parameter is determined based on the sweep resistance data.
7. The method of claim 5, wherein the method further comprises:
determining environmental characteristics of the surrounding environment of the rain radar based on historical environmental data, historical image data and historical cleaning resistance data;
based on the environmental characteristics, a cleaning cycle is determined.
8. The method of claim 7, wherein the method further comprises:
Acquiring future weather data of the surrounding environment;
determining radar interference characteristics based on the historical environmental data, the historical image data and the historical radar data;
the radar monitoring parameters are determined based on the radar interference characteristics and the future weather data.
9. A light rain radar regulation device, characterized in that it comprises at least one memory for storing computer instructions and at least one processor executing said computer instructions or part of said instructions to implement the light rain radar regulation method of any of claims 5-8.
10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer performs the method of controlling a light rain radar according to any one of claims 5 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118225982A (en) * | 2024-05-15 | 2024-06-21 | 上海联赣光电科技有限公司 | Greenhouse gas monitoring device management and control system, method and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8203480B1 (en) * | 2003-07-31 | 2012-06-19 | Rockwell Collins, Inc. | Predictive and adaptive weather radar detection system and method |
| JP2017151004A (en) * | 2016-02-26 | 2017-08-31 | 古野電気株式会社 | Sensor picture processor and sensor picture processing method |
| US20180175495A1 (en) * | 2016-12-17 | 2018-06-21 | Andrew Bennett | Methods and systems for wet radome attenuation mitigation in phased-array antennae applications and networked use of such applications |
| CN112540357A (en) * | 2020-12-02 | 2021-03-23 | 中国电子科技集团公司第三十八研究所 | Radar intelligent processing system based on environment perception |
| CN112754363A (en) * | 2021-02-07 | 2021-05-07 | 美智纵横科技有限责任公司 | Cleaning control method, cleaning control device, cleaning apparatus, and storage medium |
| CN115113207A (en) * | 2022-06-21 | 2022-09-27 | 南京理工大学 | Atmospheric environment parameter active and passive microwave remote sensing device |
| CN115782969A (en) * | 2023-02-13 | 2023-03-14 | 济南铁路信息技术有限公司 | Data acquisition system applied to environmental monitoring of railway system |
| EP4155770A1 (en) * | 2021-09-24 | 2023-03-29 | HENSOLDT Sensors GmbH | Apparatus and method for improving and scheduling of radar systems |
| DE102022115241A1 (en) * | 2022-06-20 | 2023-12-21 | Bayerische Motoren Werke Aktiengesellschaft | Method for detecting precipitation deposits on a radome and for controlling and/or regulating at least one heating element of the radome, computing device, computer-readable (storage) medium and temperature-controlled radar sensor system |
| CN117310611A (en) * | 2023-09-05 | 2023-12-29 | 中山大学 | Radar parameter self-adaptive adjustment method and system |
-
2024
- 2024-02-04 CN CN202410153578.4A patent/CN117687032B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8203480B1 (en) * | 2003-07-31 | 2012-06-19 | Rockwell Collins, Inc. | Predictive and adaptive weather radar detection system and method |
| JP2017151004A (en) * | 2016-02-26 | 2017-08-31 | 古野電気株式会社 | Sensor picture processor and sensor picture processing method |
| US20180175495A1 (en) * | 2016-12-17 | 2018-06-21 | Andrew Bennett | Methods and systems for wet radome attenuation mitigation in phased-array antennae applications and networked use of such applications |
| CN112540357A (en) * | 2020-12-02 | 2021-03-23 | 中国电子科技集团公司第三十八研究所 | Radar intelligent processing system based on environment perception |
| CN112754363A (en) * | 2021-02-07 | 2021-05-07 | 美智纵横科技有限责任公司 | Cleaning control method, cleaning control device, cleaning apparatus, and storage medium |
| EP4155770A1 (en) * | 2021-09-24 | 2023-03-29 | HENSOLDT Sensors GmbH | Apparatus and method for improving and scheduling of radar systems |
| DE102022115241A1 (en) * | 2022-06-20 | 2023-12-21 | Bayerische Motoren Werke Aktiengesellschaft | Method for detecting precipitation deposits on a radome and for controlling and/or regulating at least one heating element of the radome, computing device, computer-readable (storage) medium and temperature-controlled radar sensor system |
| CN115113207A (en) * | 2022-06-21 | 2022-09-27 | 南京理工大学 | Atmospheric environment parameter active and passive microwave remote sensing device |
| CN115782969A (en) * | 2023-02-13 | 2023-03-14 | 济南铁路信息技术有限公司 | Data acquisition system applied to environmental monitoring of railway system |
| CN117310611A (en) * | 2023-09-05 | 2023-12-29 | 中山大学 | Radar parameter self-adaptive adjustment method and system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118225982A (en) * | 2024-05-15 | 2024-06-21 | 上海联赣光电科技有限公司 | Greenhouse gas monitoring device management and control system, method and storage medium |
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