Information monitoring method and device and electronic equipment

Abstract

The invention provides an information monitoring method and apparatus, and an electronic device. The method comprises the steps of obtaining a radio frequency echo signal corresponding to a target radio frequency signal; generating a first power distribution diagram of the radio frequency echo signal in distance and speed dimensions according to the radio frequency echo signal; and according to the first power distribution diagram, determining the rainfall capacity and the wind power grade. According to the method, measurable weather information can be expanded, and the measurement precision is improved.

Description

Technical Field

[0001] This application relates to the field of wireless detection technology, and particularly to an information monitoring method, apparatus, and electronic device. Background Art

[0002] Rainfall monitoring refers to the real-time monitoring and recording of precipitation conditions to obtain information such as the intensity, duration, and spatial distribution of precipitation. Rainfall monitoring provides important information support for social production and life, and plays a positive role in disaster reduction and prevention, rational utilization of water resources, etc.

[0003] How to conduct rainfall monitoring has gradually become a popular research direction. Summary of the Invention

[0004] In view of this, this application provides an information monitoring method, apparatus, and electronic device.

[0005] Specifically, this application is implemented through the following technical solutions:

[0006] According to the first aspect of the embodiments of this application, an information monitoring method is provided, including:

[0007] Obtain a radio frequency echo signal corresponding to a target radio frequency signal;

[0008] Generate a first power distribution map of the radio frequency echo signal in the distance and velocity dimensions based on the radio frequency echo signal;

[0009] Determine the rainfall amount and wind force level based on the first power distribution map.

[0010] According to the second aspect of the embodiments of this application, an information monitoring apparatus is provided, including:

[0011] An obtaining unit, configured to obtain a radio frequency echo signal corresponding to a target radio frequency signal;

[0012] A generating unit, configured to generate a first power distribution map of the radio frequency echo signal in the distance and velocity dimensions based on the radio frequency echo signal;

[0013] A determining unit, configured to determine the rainfall amount and wind force level based on the first power distribution map.

[0014] According to the third aspect of the embodiments of this application, an electronic device is provided, including a processor and a memory, where the memory stores machine-executable instructions that can be executed by the processor, and the processor is configured to execute the machine-executable instructions to implement the method provided in the first aspect.

[0015] The information monitoring method according to the embodiment of the present application obtains a radio frequency echo signal corresponding to a target radio frequency signal, generates a first power distribution diagram of the radio frequency echo signal in the distance and velocity dimensions based on the radio frequency echo signal, and then determines the rainfall and wind force level based on the first power distribution diagram, expanding the measurable weather information and improving the measurement accuracy. Description of the Drawings

[0016] Figure 1 is a flowchart of an information monitoring method provided by an exemplary embodiment of the present application;

[0017] Figure 2 is a block diagram of an information monitoring system provided by an exemplary embodiment of the present application;

[0018] Figure 3 is a schematic diagram of an information monitoring implementation process provided by an exemplary embodiment of the present application;

[0019] Figure 4A 、 Figure 4B and Figure 4C are respectively power distribution diagrams under clear weather conditions, light rain weather conditions, and heavy rain weather conditions provided by an exemplary embodiment of the present application;

[0020] Figure 5A and Figure 5B are provided by an exemplary embodiment of the present application Figure 4B and Figure 4C are schematic diagrams of the labeling results of strong reflection regions in the power distribution diagrams shown;

[0021] Figure 6 is a schematic structural diagram of an information monitoring device provided by an exemplary embodiment of the present application;

[0022] Figure 7 is a schematic structural diagram of another information monitoring device provided by another exemplary embodiment of the present application;

[0023] Figure 8 is a schematic hardware structure diagram of an electronic device provided by an exemplary embodiment of the present application. Detailed Description of the Embodiment

[0024] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0026] To enable those skilled in the art to better understand the technical solutions provided by the embodiments of this application and to make the above-mentioned objects, features, and advantages of the embodiments of this application more obvious and understandable, the technical solutions in the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0027] Please refer to Figure 1 , which is a schematic flowchart of an information monitoring method provided by an embodiment of this application. As Figure 1 shown, as Figure 1 shown, the information monitoring method may include the following steps:

[0028] Step S100: Obtain a radio frequency echo signal corresponding to a target radio frequency signal.

[0029] Exemplarily, a target radio frequency signal refers to a radio frequency signal that can measure the probability distribution of an echo signal in the distance dimension and the velocity dimension.

[0030] For example, the target radio frequency signal may include, but is not limited to, Frequency Modulated Continuous Wave (FMCW for short) or Stepped Frequency (SF for short), etc.

[0031] It should be noted that in the embodiments of this application, in order to avoid the influence of obstacles or obstructions on information monitoring, when transmitting a target radio frequency signal, the target radio frequency signal may be transmitted to a clear area; where the clear area refers to an area without obstacles or obstructions.

[0032] Step S110: Generate a first power distribution map of the radio frequency echo signal in the distance and velocity dimensions based on the radio frequency echo signal.

[0033] In the embodiments of this application, for the radio frequency echo signal received after transmitting the target radio frequency signal, a power distribution map of the radio frequency echo signal in the distance and velocity dimensions (which can be the first power distribution map) may be generated based on the received radio frequency echo signal.

[0034] For example, the received radio frequency echo signal may be processed to extract the distribution information of the echo signal power with respect to the distance and velocity dimensions.

[0035] Exemplarily, the processing methods for processing the radio frequency echo signal may include, but are not limited to, Fourier transform, wavelet transform, matched filtering, etc.

[0036] Exemplarily, according to the specific type of the target radio frequency signal, a corresponding processing method can be selected.

[0037] Step S120: Determine the rainfall amount and the wind force level according to the first power distribution map.

[0038] In the embodiments of the present application, when performing information monitoring, it is no longer limited to monitoring rainfall, but also wind force can be monitored.

[0039] Correspondingly, in the case of obtaining the first power distribution map in the above manner, the rainfall amount and the wind force level can also be determined according to the first power distribution map.

[0040] It should be noted that in the embodiments of the present application, the rainfall result determined in the above manner includes no rain; similarly, the wind force level result determined in the above manner can also include no wind.

[0041] In addition, in the embodiments of the present application, if not specially stated, the rainfall amount can also include the snowfall amount in the case of snow weather.

[0042] It can be seen that in Figure 1 the shown method flow, by transmitting a target radio frequency signal, receiving a radio frequency echo signal, generating a first power distribution map of the radio frequency echo signal in the distance and velocity dimensions according to the radio frequency echo signal, and then determining the rainfall amount and the wind force level according to the first power distribution map, the measurable weather information is expanded and the measurement accuracy is improved.

[0043] In some embodiments, the above determining the rainfall amount according to the first power distribution map may include:

[0044] Determine the rainfall amount according to the first power distribution map by using a pre-trained first neural network model.

[0045] Exemplarily, in order to improve the accuracy of rainfall determination, the determination of rainfall can be realized by a deep learning method.

[0046] In order to realize the determination of rainfall amount, a neural network model for determining rainfall amount (which can be called the first neural network model) can be pre-trained. The first neural network model can take the power distribution map of the radio frequency echo signal in the distance and velocity dimensions as input and output the rainfall amount corresponding to the power distribution map.

[0047] Exemplarily, during the training process of the first neural network model, power distribution diagrams of the received RF echo signals in the range and velocity dimensions after transmitting the target RF signal under different rainfall amounts (including clear weather) can be collected, and actual rainfall amount labels can be set for the collected power distribution diagrams to form a training sample set, and the first neural network model can be trained based on this training sample set until the network converges, and / or the number of training epochs reaches a preset maximum value.

[0048] Correspondingly, in the case where the first power distribution diagram is obtained in the manner described in the above embodiments, the rainfall amount can be determined based on the first power distribution diagram by using the pre-trained first neural network model.

[0049] In some embodiments, the above-mentioned determining the wind force level based on the first power distribution diagram may include:

[0050] Determining the wind force level based on the first power distribution diagram by using the pre-trained second neural network model.

[0051] Exemplarily, in order to improve the accuracy of determining the wind force level, the determination of the wind force level can be achieved through a deep learning method.

[0052] In order to achieve the determination of the wind force level, a neural network model for determining the wind force level (which can be referred to as the second neural network model) can be pre-trained. The second neural network model can take the power distribution diagram of the RF echo signal in the range and velocity dimensions as the input and output the wind force level corresponding to this power distribution diagram.

[0053] Exemplarily, during the training process of the second neural network model, power distribution diagrams of the received RF echo signals in the range and velocity dimensions after transmitting the target RF signal under different wind force levels can be collected, and actual wind force level labels can be set for the collected power distribution diagrams to form a training sample set, and the second neural network model can be trained based on this training sample set until the network converges, and / or the number of training epochs reaches a preset maximum value.

[0054] Correspondingly, in the case where the first power distribution diagram is obtained in the manner described in the above embodiments, the wind force level can be determined based on the first power distribution diagram by using the pre-trained second neural network model.

[0055] In some embodiments, the above-mentioned determining the rainfall amount and the wind force level based on the first power distribution diagram may include:

[0056] Determining the strong reflection area in the first power distribution diagram;

[0057] Determining the rainfall amount and the wind force level based on the strong reflection area.

[0058] Exemplarily, in order to reduce the complexity of rainfall and wind level determination, the determination of rainfall and wind level can be implemented based on an empirical model.

[0059] Considering that when the rainfall and / or wind level are different, the distribution of positions with higher power in the power distribution map usually also varies. Therefore, the rainfall and wind level can be determined based on the distribution of positions with higher power in the power distribution map.

[0060] Exemplarily, when the first power distribution map is determined in the above manner, the strong reflection area in the first power distribution map can be determined.

[0061] Exemplarily, the strong reflection area can be an area with a higher power of the echo signal.

[0062] When the strong reflection area in the first power distribution map is determined, the rainfall and wind level can be determined based on the strong reflection area in the first power distribution map.

[0063] In one example, the above determination of the strong reflection area in the first power distribution map may include:

[0064] Based on a first preset energy threshold, the area in the first power distribution map where the power is greater than or equal to the first preset energy threshold is determined as the strong reflection area.

[0065] Exemplarily, the area in the first power distribution map where the power exceeds the preset energy threshold (i.e., the power threshold, which can be called the first preset energy threshold, and the specific value can be set according to the actual scenario) can be determined as the strong reflection area.

[0066] In another example, the above determination of the strong reflection area in the first power distribution map may include:

[0067] For any position in the first power distribution map, when the difference between the first power value corresponding to this position in the first power distribution map and the second power value corresponding to this position in the second power distribution map is greater than or equal to a second preset energy threshold, it is determined that this position in the first power distribution map belongs to the strong reflection area; where the second power distribution map is the power distribution map of the radio frequency echo signal in the range and velocity dimensions after transmitting a target radio frequency signal under clear weather conditions.

[0068] Exemplarily, considering that there are obvious differences in the power distribution diagrams of the radio frequency echo signals in the range and velocity dimensions after transmitting a target radio frequency signal under rainy weather conditions and sunny weather conditions, and this difference is mainly caused by the influence of rainfall and wind, therefore, the areas with obvious differences in the power distribution diagrams under rainy weather conditions and sunny weather conditions can be determined as strong reflection areas.

[0069] Correspondingly, under sunny weather conditions, a target radio frequency signal can be transmitted, and a power distribution diagram of the radio frequency echo signal generated based on the radio frequency echo signal in the range and velocity dimensions (which can be called the second power distribution diagram) can be generated.

[0070] It should be noted that for the same observation area, it is not necessary to determine the second power distribution diagram in the above manner every time rainfall monitoring is carried out. Instead, the second power distribution diagram can be generated in advance once, or the second power distribution diagram can be generated periodically.

[0071] Exemplarily, for any position in the first power distribution diagram, when the difference between the power value corresponding to this position in the first power distribution diagram (which can be called the first power value) and the power value corresponding to this position in the second power distribution diagram (which can be called the second power value) is greater than or equal to the second preset energy threshold, it is determined that this position in the first power distribution diagram belongs to the strong reflection area.

[0072] In one example, the determination of the rainfall amount and the wind force level based on the strong reflection area may include:

[0073] Extract information from the strong reflection area to obtain target information; wherein, the target information includes one or more of the area of the strong reflection area, the center position offset of the strong reflection area, and the average echo power of the strong reflection area, and the center position offset of the strong reflection area is the difference between the velocity corresponding to the center position of the strong reflection area and 0;

[0074] Determine the rainfall amount and the wind force level based on the target information.

[0075] Exemplarily, considering that when the rainfall amount and / or the wind force level are different, the area of the strong reflection area, the velocity corresponding to the center position of the strong reflection area, and the average echo power of the strong reflection area, etc. may all have differences.

[0076] Therefore, the rainfall amount and the wind force level can be determined based on one or more of the area of the strong reflection area, the center position offset of the strong reflection area, and the average echo power of the strong reflection area (referred to as target information in this article).

[0077] As an example, the target information includes the area of the strong reflection region, the center position offset of the strong reflection region, and the average echo power of the strong reflection region;

[0078] Based on the above target information, determining the rainfall amount may include:

[0079] Based on the area of the strong reflection region, the center position offset of the strong reflection region, and the average echo power of the strong reflection region, use the first weighting algorithm to determine the rainfall amount.

[0080] Exemplarily, considering that rainfall affects the area of the strong reflection region, the center position offset of the strong reflection region, and the average echo power of the strong reflection region, therefore, the rainfall amount can be determined based on the area of the strong reflection region, the center position offset of the strong reflection region, and the average echo power of the strong reflection region.

[0081] Exemplarily, the rainfall amount can be determined based on the area of the strong reflection region, the center position offset of the strong reflection region, and the average echo power of the strong reflection region by using a preset weighting algorithm (which can be called the first weighting algorithm), and its specific implementation will be described below in combination with specific examples.

[0082] As an example, the target information includes the area of the strong reflection region and the center position offset of the strong reflection region;

[0083] Based on the above target information, determining the wind force level may include:

[0084] Based on the area of the strong reflection region and the center position offset of the strong reflection region, use the second weighting algorithm to determine the wind force level.

[0085] Exemplarily, considering that during rainfall, the wind force level mainly affects the area of the strong reflection region and the center position offset of the strong reflection region, therefore, the wind force level can be determined based on the area of the strong reflection region and the center position offset of the strong reflection region.

[0086] Exemplarily, the wind force level can be determined based on the area of the strong reflection region and the center position offset of the strong reflection region by using a preset weighting algorithm (which can be called the second weighting algorithm), and its specific implementation will be described below in combination with specific examples.

[0087] In some embodiments, after determining the rainfall amount and the wind force level based on the above first power distribution map, it may further include:

[0088] Determine the risk assessment value based on the rainfall amount and the wind force level;

[0089] Perform event response processing based on the risk assessment value.

[0090] Exemplarily, considering that the impacts caused by different rainfall amounts and wind force levels are not the same, different countermeasures can be taken for different rainfall amounts and wind force levels.

[0091] Exemplarily, when the rainfall amount and wind force level are determined in the above manner, a risk assessment value can be determined based on the rainfall amount and wind force level, and event response handling can be performed based on this risk assessment value.

[0092] In one example, the determination of the risk assessment value based on the rainfall amount and wind force level may include:

[0093] When the rainfall amount is greater than the preset rainfall threshold or the wind force level is greater than the preset wind force threshold, determine the risk assessment value as the target risk assessment value;

[0094] Otherwise, determine the risk assessment value based on the rainfall amount and wind force level using the third weighted algorithm.

[0095] Exemplarily, considering that in the case of excessive rainfall amount or excessive wind force level, relatively serious consequences may occur and need to be focused on. Therefore, in the process of determining the risk assessment value, for the case of excessive rainfall amount, such as the rainfall amount being greater than the preset rainfall threshold (which can be set according to actual needs), or excessive wind force level, such as the wind force level being greater than the preset wind force threshold (which can be set according to actual needs), the risk assessment value can be determined as the target risk assessment value.

[0096] When the rainfall amount is less than or equal to the preset rainfall threshold and the wind force level is greater than the preset wind force threshold, the risk assessment value can be determined based on the rainfall amount and wind force level using a preset weighted algorithm (which can be called the third weighted algorithm), and its specific implementation will be described in combination with examples below.

[0097] In one example, the event response handling based on the risk assessment value may include:

[0098] When the risk assessment value is greater than the first risk assessment threshold and less than or equal to the second risk assessment threshold, report the real-time on-site weather information through the network;

[0099] When the risk assessment value is greater than the second risk assessment threshold, link the specified sensing devices to collect data in the observation area and report it, and / or link the alarm devices for alarm handling.

[0100] Exemplarily, to improve the flexibility of event response handling, multiple different risk assessment thresholds can be set in advance, and the risk assessment threshold can at least include the first risk assessment threshold and the second risk assessment threshold.

[0101] Exemplarily, the first risk assessment threshold is less than the second risk assessment threshold.

[0102] When the risk assessment value is determined in the above manner, the risk assessment value can be compared with the first risk assessment threshold and the second risk assessment threshold respectively.

[0103] For the case where the risk assessment value is greater than the first risk assessment threshold and less than or equal to the second risk assessment threshold, it can be considered that the current risk is relatively low. In this case, the real-time on-site weather information can be reported through the network.

[0104] For the case where the risk assessment value is greater than the second risk assessment value, it can be considered that the current risk is relatively high. In this case, the designated sensing devices can be linked to collect data in the observation area and report it.

[0105] For example, the cameras deployed in the observation area can be linked to collect the image data of the observation area and report the collected image data, such as reporting it to the disaster prevention and control center.

[0106] Exemplarily, for the case where the risk assessment value is greater than the second risk assessment value, the alarm device can also be linked for alarm processing.

[0107] For example, the voice alarm device is linked to send out alarm information.

[0108] To enable those skilled in the art to better understand the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application will be described below in combination with specific application scenarios.

[0109] Please refer to Figure 2 , a block diagram of an information monitoring system provided in an embodiment of the present application, as Figure 2 shown. The information monitoring system may include: an information transceiver module, a signal processing module, and an event response module; where:

[0110] The signal transceiver module may include an antenna unit and a radio frequency transceiver unit to implement the functions of transmitting and receiving radar radio frequency signals.

[0111] Exemplarily, the signal types of the radio frequency signals include, but are not limited to, frequency-modulated continuous wave (FMCW), stepped frequency (SF), etc., which are radio frequency signals that can measure the power distribution of the reflected signal (i.e., the echo signal) in the distance and speed dimensions.

[0112] Exemplarily, the signal transceiver module can be implemented by using a radio frequency integrated circuit or discrete components, and the embodiments of the present application do not make any limitations in this regard.

[0113] The signal processing module is used to complete the processing of the radio frequency signal and the processing of the linked control input signal.

[0114] Exemplarily, the processing process of the signal processing module for the radio frequency signal may include:

[0115] 1) Generating a power distribution map of the echo signal in two dimensions of distance and speed by processing the radio frequency echo signal;

[0116] 2) Judging the rainfall (snowfall) by one or more of the information such as the shape, position, and intensity of the strong reflection area in the power distribution map;

[0117] 3) Judging the wind force level by the shape and position of the strong reflection area in the power distribution map;

[0118] 4) Calculating a risk assessment value based on the rainfall (snowfall) and wind force level;

[0119] 5) Sending the information of rainfall (snowfall), wind force level, and risk assessment value to the event response module.

[0120] The event response module may include an information reporting unit, a linkage device unit, and an alarm unit, and is used to respond to events according to the information of rainfall (snowfall), wind force level, and risk assessment value output by the signal processing module.

[0121] Exemplarily, the event response measures may include but are not limited to:

[0122] 1) Linking specified sensing devices according to the risk assessment value, such as deploying cameras in the observation area to collect image information of the observation area;

[0123] 2) Reporting real-time on-site weather information through the network according to the risk assessment value;

[0124] 3) Transmitting information of other sensing devices through the network according to the risk assessment value;

[0125] 4) Sending an alarm message by voice or other means according to the risk assessment value.

[0126] Based on Figure 2 the information monitoring system shown, the information monitoring implementation process may be as Figure 3 shown, and it may include:

[0127] 1. The signal transceiver module transmits a radio frequency signal (i.e., the above-mentioned target radio frequency signal).

[0128] Exemplarily, the signal type of the radio frequency signal may include but is not limited to radio frequency signals such as frequency modulated continuous wave (FMCW) and stepped frequency (SF) that can measure the power distribution of the reflected signal in the distance and speed dimensions.

[0129] 2. The signal transceiver module receives the radio frequency echo signal and transmits the echo signal to the signal processing module for processing.

[0130] For example, the signal transceiver module can transmit a frequency-modulated continuous wave (FMCW) radio frequency signal to the clear area and receive the radio frequency echo signal in the clear area. Among them:

[0131] a) Under clear weather conditions, due to the absence of reflectors, the reflection of the radio frequency echo signal in the short-distance area is very weak;

[0132] b) Under rainy or snowy weather conditions, rain and snow will form reflections, and the reflection of the radio frequency echo signal in the short-distance area is enhanced.

[0133] 3. The signal processing module processes the radio frequency echo signal, extracts the distribution information of the echo power with respect to the distance and velocity dimensions, and obtains the corresponding power distribution map P(r, v) (i.e., the above-mentioned first power distribution map).

[0134] Exemplarily, according to the type of the radio frequency signal, corresponding processing methods can be adopted, which may include but are not limited to Fourier transform, wavelet transform, matched filtering, etc.

[0135] For example, the radio frequency echo signal can be processed by 2D FFT (Fast Fourier Transform), and the distribution information of the echo power with respect to the distance and velocity dimensions can be extracted to obtain the power distribution map P(r, v).

[0136] Exemplarily, the power distribution maps under clear weather conditions, light rain weather conditions, and heavy rain weather conditions can be respectively as Figure 4A , Figure 4B , and Figure 4C shown.

[0137] Among them, the abscissa of the power distribution map is the velocity (unit: m / s), the ordinate is the distance (unit: m), and the lower the gray level at each position (a distance and a velocity, corresponding to a position) in the distribution map, the higher the power; the higher the gray level, the lower the power.

[0138] 4. Based on the power distribution map P(r, v), determine the rainfall (snowfall) amount and the wind force level.

[0139] Exemplarily, determining the rainfall (snowfall) amount and the wind force level based on the power distribution map can be achieved through deep learning or based on empirical models, etc.

[0140] 4.1. Realize the determination of the rainfall (snowfall) amount and the wind force level based on deep learning.

[0141] Exemplarily, the obtained power distribution map P(r, v) can be input into a pre-trained neural network model (i.e., the above-mentioned first neural network model) to obtain the rainfall (snowfall) amount lr.

[0142] Exemplarily, the obtained power distribution map P(r, v) can be input into a pre-trained neural network model (i.e., the second neural network model mentioned above) to obtain the wind force level l_w.

[0143] 4.2. Implementing the determination of rainfall (snowfall) and wind force level based on an empirical model.

[0144] Exemplarily, in the case of implementing the determination of rainfall (snowfall) and wind force level based on an empirical model, the strong reflection region of the power distribution map obtained in step 3 can be extracted first.

[0145] Exemplarily, the determination of the strong reflection region is achieved by means such as a preset energy threshold and modeling the power distribution in clear weather.

[0146] In one example, through a preset energy threshold P THD1 (r, v): When P(r, v) ≥ P THD1 (r, v), it is judged as a strong reflection region.

[0147] Exemplarily, P THD1 (r, v) can be set according to prior data.

[0148] In another example, through clear weather modeling, the power distribution map P ref (r, v) (i.e., the second power distribution map mentioned above) under clear weather conditions is collected. When P(r, v) ≥ P ref (r, v) + P THD2 (r, v), it is judged as a strong reflection region.

[0149] Exemplarily, P THD2 (r, v) can be set according to prior data.

[0150] In one example, taking the power distribution maps shown in Figure 4B and Figure 4C as an example, assuming P THD1 (r, v) ≡ 85 and P THD2 (r, v) ≡ 15, the labeling results of the strong reflection regions can be respectively as shown in Figure 5A and Figure 5B shown.

[0151] 4.2.1. Implementing the determination of rainfall (snowfall) based on an empirical model.

[0152] Exemplarily, the rainfall (snowfall) l_r can be judged according to the shape, position, and intensity information of the strong reflection region.

[0153] Exemplarily, the shape and size (i.e., area) P sizeIs proportional to the rainfall (snowfall);

[0154] When there is an offset in the center position of the strong reflection area, it indicates that there is a wind influence. The offset amount P of the center position of the strong reflection area offset Is inversely proportional to the rainfall (snowfall);

[0155] The signal intensity P of the strong reflection area amp Is proportional to the rainfall (snowfall).

[0156] Exemplarily, P size Can be the area of the circumscribed quadrilateral of the strong reflection area.

[0157] P offset Is the distance between the coordinates of the center point of the strong reflection area and the origin (such as Figures 4A to 4C The abscissa origin in, corresponding to a speed of 0).

[0158] Among them, when the velocity component corresponds to the abscissa, the distance between the coordinates of this center point and the origin is the horizontal distance; when the velocity component corresponds to the ordinate, the distance between the coordinates of this center point and the origin is the vertical distance.

[0159] Exemplarily, a negative speed can indicate that the wind direction is towards the RF signal transmitting device (such as a radar); a positive speed can indicate that the wind direction is away from the RF signal transmitting device (such as a radar).

[0160] P amp Is the average echo power of the strong reflection area.

[0161] Exemplarily, the rainfall (snowfall) can be obtained by comprehensively constructing a weighted evaluation function with information such as the shape and size of the strong reflection area, the degree of deviation of the center position, and the average signal intensity.

[0162] For example, the determination of rainfall (snowfall) is achieved through the following formula:

[0163] l_r = c1×P size +c2×P offset +c3×P amp

[0164] Among them, c1, c2, and c3 are weighting coefficients, c1>0, c2<0, c3>0.

[0165] For example, when the calculated l_r is 10mm, it can be considered that it is light rain at present; when the calculated l_r is 50mm, it can be considered that it is heavy rain at present.

[0166] 4.2.2. Determine the wind force level based on the empirical model method.

[0167] Exemplarily, the wind force level may be determined by the shape and position of the strong reflection area of ​​the power distribution diagram.

[0168] For example, the shape and size (i.e., area) of the strong reflection area P size Directly proportional to the wind force level.

[0169] Exemplarily, the center position offset P of the strong reflection area offset Directly proportional to the wind force level.

[0170] The wind force level can be obtained by constructing a weighted evaluation function by comprehensively considering information such as the shape and size of the strong reflection area and the degree of deviation of the center position.

[0171] For example, the wind force level is determined by the following formula:

[0172] l_w=c4×P size +c5×P offset

[0173] Among them, c4 and c5 are weighting coefficients, c4>0, c5>0.

[0174] For example, for light wind conditions, the calculated l_w is 2 m / s, and for slight wind conditions, the calculated l_w is 4.5 m / s.

[0175] It should be noted that the above-mentioned correspondence between rainfall and rainfall level, and wind speed and wind force level is merely an example and is not a limitation on the scope of protection of the present application. In actual scenarios, other level division methods can be achieved by adjusting the weighting coefficient.

[0176] 5. Determine the risk assessment value based on rainfall (snowfall) and wind level.

[0177] In one example, the risk assessment value may be determined by the following formula:

[0178]

[0179] Among them, c6 and c7 are weighting coefficients, c6>0, c7>0.

[0180] For example, the corresponding risk in the case of light rain may be 2, and the corresponding risk in the case of heavy rain may be 6.

[0181] 6. Send the risk assessment value to the incident response module for incident response processing.

[0182] Exemplary incident response strategies may include:

[0183] 6.1. When the risk assessment value is greater than alarm1 (i.e., the above-mentioned first risk assessment threshold) and less than or equal to alarm2 (i.e., the above-mentioned second risk assessment threshold), report the real-time on-site weather information through the network.

[0184] 6.2. When the risk assessment value is greater than alarm2, according to the rainfall (snowfall) and wind force level, link other sensing devices such as cameras to collect image information of the observation area, report it to the disaster prevention and control center through the network, and send alarm information by means of linked voice alarms, etc.

[0185] For example, assuming alarm1 = 1.5 and alarm2 = 5, in the case where the risk corresponding to light rain is 2 and the risk corresponding to heavy rain is 6, for light rain weather, the real-time on-site weather information can be reported through the network; for heavy rain weather, other sensing devices such as cameras can be linked to collect image information of the observation area, report it to the disaster prevention and control center through the network, and send alarm information by means of linked voice alarms, etc.

[0186] The method provided in this application has been described above. Next, the device provided in this application will be described:

[0187] Please refer to Figure 6 , which is a schematic structural diagram of an information monitoring device provided in an embodiment of this application. As Figure 6 shown, the information monitoring device may include:

[0188] An acquisition unit 610, configured to acquire a radio frequency echo signal corresponding to a target radio frequency signal;

[0189] A generation unit 620, configured to generate a first power distribution map of the radio frequency echo signal in the distance and velocity dimensions based on the radio frequency echo signal;

[0190] A determination unit 630, configured to determine the rainfall and wind force level based on the first power distribution map.

[0191] In some embodiments, the determination unit 630 determines the rainfall based on the first power distribution map, including:

[0192] Determining the rainfall based on the first power distribution map by using a pre-trained first neural network model.

[0193] In some embodiments, the determination unit 630 determines the wind force level based on the first power distribution map, including:

[0194] Determining the wind force level based on the first power distribution map by using a pre-trained second neural network model.

[0195] In some embodiments, the determining unit 630 determines the rainfall amount and the wind force level according to the first power distribution map, including:

[0196] Determine the strong reflection area in the first power distribution map;

[0197] Determine the rainfall amount and the wind force level according to the strong reflection area.

[0198] In some embodiments, the determining unit 630 determines the strong reflection area in the first power distribution map, including:

[0199] According to a first preset energy threshold, determine, as the strong reflection area, the area in the first power distribution map where the power is greater than or equal to the first preset energy threshold;

[0200] Or,

[0201] For any position in the first power distribution map, when the difference between the first power value corresponding to this position in the first power distribution map and the second power value corresponding to this position in the second power distribution map is greater than or equal to a second preset energy threshold, determine that this position in the first power distribution map belongs to the strong reflection area; wherein, the second power distribution map is a power distribution map of the radio frequency echo signal in the range and velocity dimensions generated according to the radio frequency echo signal after transmitting a target radio frequency signal under clear weather conditions.

[0202] In some embodiments, the determining unit 630 determines the rainfall amount and the wind force level according to the strong reflection area, including:

[0203] Extract information from the strong reflection area to obtain target information; wherein, the target information includes one or more of the area of the strong reflection area, the center position offset of the strong reflection area, and the average echo power of the strong reflection area, and the center position offset of the strong reflection area is the difference between the velocity corresponding to the center position of the strong reflection area and 0;

[0204] Determine the rainfall amount and the wind force level according to the target information.

[0205] In some embodiments, the target information includes the area of the strong reflection area, the center position offset of the strong reflection area, and the average echo power of the strong reflection area;

[0206] The determining unit 630 determines the rainfall amount according to the target information, including:

[0207] Determine the rainfall amount by using a first weighting algorithm according to the area of the strong reflection area, the center position offset of the strong reflection area, and the average echo power of the strong reflection area.

[0208] In some embodiments, the target information includes the area of the strong reflection region and the offset of the center position of the strong reflection region;

[0209] The determining unit 630 determines the wind level according to the target information, including:

[0210] According to the area of the strong reflection region and the offset of the center position of the strong reflection region, the wind level is determined by using a second weighting algorithm.

[0211] In some embodiments, as Figure 7 shown, the device further includes:

[0212] A processing unit 640, configured to determine a risk assessment value according to the rainfall amount and the wind level; and perform event response processing according to the risk assessment value.

[0213] In some embodiments, the processing unit 640 determines the risk assessment value according to the rainfall amount and the wind level, including:

[0214] When the rainfall amount is greater than a preset rainfall threshold, or the wind level is greater than a preset wind level threshold, it is determined that the risk assessment value is a target risk assessment value;

[0215] Otherwise, according to the rainfall amount and the wind level, a third weighting algorithm is used to determine the risk assessment value.

[0216] In some embodiments, the processing unit 640 performs event response processing according to the risk assessment value, including:

[0217] When the risk assessment value is greater than a first risk assessment threshold and less than or equal to a second risk assessment threshold, the real-time on-site weather information is reported through the network;

[0218] When the risk assessment value is greater than the second risk assessment threshold, the designated sensing device is linked to collect data of the observation area and report it, and / or the alarm device is linked to perform alarm processing.

[0219] An embodiment of the present application provides an electronic device, including a processor and a memory. Among them, the memory stores machine-executable instructions that can be executed by the processor, and the processor is configured to execute the machine-executable instructions to implement the information monitoring method described above.

[0220] Please refer to Figure 8, which is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application. The electronic device may include a processor 801 and a memory 802 storing machine-executable instructions. The processor 801 and the memory 802 may communicate via a system bus 803. And by reading and executing the machine-executable instructions corresponding to the information monitoring logic in the memory 802, the processor 801 may execute the information monitoring method described above.

[0221] The memory 802 mentioned herein may be any electronic, magnetic, optical or other physical storage device that can contain or store information, such as executable instructions, data, etc. For example, the machine-readable storage medium may be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or a combination thereof.

[0222] In some embodiments, a machine-readable storage medium is also provided, such as Figure 8 the memory 802 in, which stores machine-executable instructions. When the machine-executable instructions are executed by a processor, the information monitoring method described above is implemented. For example, the storage medium may be ROM, RAM, CD-ROM, magnetic tapes, floppy disks, and optical data storage devices, etc.

[0223] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0224] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An information monitoring method, characterized in that, Including: Obtaining a radio frequency echo signal corresponding to a target radio frequency signal; Generating a first power distribution map of the radio frequency echo signal in the range and velocity dimensions based on the radio frequency echo signal; Determining the rainfall amount and wind force level based on the first power distribution map.

2. The method according to claim 1, wherein The determining the rainfall amount based on the first power distribution map includes: Determining the rainfall amount by using a pre-trained first neural network model based on the first power distribution map; And / or The determining the wind force level based on the first power distribution map includes: Determining the wind force level by using a pre-trained second neural network model based on the first power distribution map.

3. The method according to claim 1, wherein The determining the rainfall amount and wind force level based on the first power distribution map includes: Determining a strong reflection area in the first power distribution map; Determining the rainfall amount and wind force level based on the strong reflection area.

4. The method according to claim 3, wherein The determining the strong reflection area in the first power distribution map includes: Based on a first preset energy threshold, determining, as the strong reflection area, an area in the first power distribution map where the power is greater than or equal to the first preset energy threshold; Or For any position in the first power distribution map, when the difference between the first power value corresponding to the position in the first power distribution map and the second power value corresponding to the position in the second power distribution map is greater than or equal to a second preset energy threshold, determining that the position in the first power distribution map belongs to the strong reflection area; wherein, the second power distribution map is a power distribution map of the radio frequency echo signal in the range and velocity dimensions generated based on the radio frequency echo signal after emitting the target radio frequency signal under clear weather conditions.

5. The method according to claim 3, characterized in that, The determining the rainfall amount and wind force level based on the strong reflection area includes: Extracting information from the strong reflection area to obtain target information; wherein, the target information includes one or more of the area of the strong reflection area, the central position offset of the strong reflection area, and the average echo power of the strong reflection area, and the central position offset of the strong reflection area is the difference between the velocity corresponding to the central position of the strong reflection area and 0; Determining the rainfall amount and wind force level based on the target information.

6. The method according to claim 5, wherein The target information includes the area of the strong reflection area, the central position offset of the strong reflection area, and the average echo power of the strong reflection area; The determining the rainfall amount based on the target information includes: Determining the rainfall amount by using a first weighting algorithm based on the area of the strong reflection area, the central position offset of the strong reflection area, and the average echo power of the strong reflection area.

7. The method according to claim 5, wherein The target information includes the area of the strong reflection area and the central position offset of the strong reflection area; The determining the wind force level based on the target information includes: Determining the wind force level by using a second weighting algorithm based on the area of the strong reflection area and the central position offset of the strong reflection area.

8. The method according to claim 1, wherein After determining the rainfall amount and wind force level based on the first power distribution map, it further includes: Determining a risk assessment value based on the rainfall amount and wind force level; Performing event response processing based on the risk assessment value.

9. The method according to claim 8, characterized in that, Determining the risk assessment value according to the rainfall and wind force level includes: When the rainfall is greater than a preset rainfall threshold, or the wind level is greater than a preset wind level threshold, determining the risk assessment value as a target risk assessment value; Otherwise, a risk assessment value is determined according to the rainfall and wind force level using a third weighted algorithm.

10. The method according to claim 8, characterized in that, The event response process according to the risk assessment value includes: When the risk assessment value is greater than a first risk assessment threshold and less than or equal to a second risk assessment threshold, reporting real-time on-site weather information through a network; When the risk assessment value is greater than the second risk assessment threshold, a designated sensor device is linked to collect data of the observation area and report it, and / or an alarm device is linked to perform alarm processing.

11. An information monitoring device, characterized in that, include: An acquisition unit, used to acquire a radio frequency echo signal corresponding to a target radio frequency signal; A generating unit, configured to generate a first power distribution diagram of the radio frequency echo signal in distance and speed dimensions according to the radio frequency echo signal; A determination unit is used to determine the rainfall and wind force level according to the first power distribution diagram.

12. An electronic device, characterized in that, The method comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor is used to execute the machine executable instructions to implement the method according to any one of claims 1 to 10.

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