Rolling stone multi-target radar monitoring and early warning method adaptive to complex terrain

Abstract

The invention discloses a rolling stone multi-target radar monitoring and early warning method adaptive to a complex terrain, and relates to the technical field of data processing, and the method comprises the following steps: obtaining an echo signal through a radar, obtaining a moving target according to the echo signal, obtaining basic data according to the moving target, obtaining a theoretical path according to the basic data, and carrying out the calculation of the theoretical path. And acquiring monitoring data according to weather forecast of a weather bureau, establishing a corresponding relation according to the monitoring data and the theoretical path and the duration, judging whether to give out early warning or not according to the corresponding relation, obtaining final evaluation, and updating the corresponding relation according to the final evaluation. According to the method, a fixed clutter filtering technology based on Doppler frequency difference analysis is adopted, zero-frequency channel signals are abandoned in combination with fast Fourier transform, a prediction-matching mechanism is combined, multiple rolling stones are stably tracked, the quality of the rolling stones is inversed through radar echoes, kinetic energy is calculated in combination with a velocity vector, and the accuracy of the rolling stones is improved. And quantitative evaluation of the collapsing force of the rolling stone is realized.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain. Background Technology

[0002] Rockfall hazards are a common and sudden geological disaster in mountainous areas, characterized by their sudden occurrence, complex trajectories, and high impact force, seriously threatening the safety of mountain roads, railways, villages, and various infrastructure. Recently, due to the dispersed nature of falling rocks and the poor accuracy and difficulty of manual monitoring, rocks from higher elevations fall to the ground or into low-lying areas under the influence of gravity. Rockfalls occurring within areas of human activity can cause disasters; therefore, predictive monitoring of rockfalls is necessary.

[0003] Currently, traditional methods for monitoring falling rocks include manual, experience-based ground patrols and observations, direct viewing of images through video monitoring technology, and seismic sensors and acoustic monitoring. These methods also rely on falling rock monitoring and early warning equipment, which is adapted to sensors from different manufacturers and can access various monitoring signals. These signals are used to issue early warnings of potential rockfall hazards, and once the warning information is received, staff begin preventative measures.

[0004] The relevant technologies are difficult to use widely, have many limitations, cannot adapt to different complex terrains, and cannot provide timely warnings and predictions. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are difficult to use widely, have many limitations, cannot adapt to different complex terrains, and cannot provide timely warnings and predictions. To solve the above technical problems, this invention provides the following technical solution: a multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain, comprising the following steps: Step S1, acquiring echo signals through radar, and acquiring moving targets based on the echo signals; Step S2, obtaining numbered targets based on the moving targets, obtaining basic data based on the numbered targets, and obtaining theoretical paths based on the basic data; Step S3, acquiring monitoring data based on weather forecasts from meteorological bureaus, determining whether to issue a blue warning based on the monitoring data, establishing a correspondence between the monitoring data and the theoretical paths according to the duration, and determining whether to issue a yellow warning; Step S4, determining whether to issue a red warning based on the yellow warning, and acquiring the rolling stone energy, classifying energy levels based on the rolling stone energy; Step S5, acquiring environmental information, obtaining a final assessment based on the environmental information and energy levels, and updating the correspondence based on the final assessment.

[0006] As a preferred embodiment of the rolling stone multi-target radar monitoring and early warning method adapted to complex terrain described in this invention, step S1 includes the following sub-steps: Step S11: The radar emits a first electromagnetic wave to obtain a first echo signal. Based on the first echo signal, the first wave frequency is obtained. The radar emits a first electromagnetic wave again to obtain a second echo signal. Based on the second echo signal, the second wave frequency is obtained. The first wave frequency and the second wave frequency are subtracted to obtain a difference value. When the difference value is 0, it is considered fixed clutter and is discarded. When the difference value is not 0, it is considered an echo signal. Step S12: The echo signal is subjected to a fast Fourier transform to obtain a Doppler frequency map. The Doppler frequency map includes a zero-frequency channel and a non-zero-frequency channel. Based on the Doppler frequency map, the echo signal in the zero-frequency channel is obtained and discarded. Based on the Doppler frequency map, the echo signal in the non-zero-frequency channel is obtained. The echo signal is converted from analog to digital to obtain a digital signal. The moving target is obtained based on the digital signal. The moving target is numbered to obtain a numbered target.

[0007] As a preferred embodiment of the multi-target radar monitoring and early warning method for adapting to complex terrain described in this invention, step S2 includes the following sub-steps: Step S21, numbering the moving targets to obtain numbered targets; obtaining basic data through radar based on the numbered targets, the basic data including range, azimuth, elevation, and radial velocity; establishing a spatial rectangular coordinate system based on the range, azimuth, and elevation to obtain real-time position data of the numbered targets; dividing the numbered targets according to time based on the real-time position information to obtain target observation points, and numbering the target observation points to obtain numbered observation points; Step S22, obtaining the next numbered observation point through radar based on the numbered observation point, obtaining real-time position data based on the numbered observation point, obtaining second real-time position data based on the next numbered observation point, obtaining a velocity vector based on the real-time position data and the second real-time position data, and obtaining predicted position data for the next moment based on the velocity vector and the real-time position data.

[0008] As a preferred embodiment of the multi-target radar monitoring and early warning method for adapting to complex terrain described in this invention, step S2 further includes: repeating step S22 to obtain the predicted location data and the number of repetitions until the rolling stone ends; matching the predicted location data with the location data and calculating the matching success rate, wherein the matching success rate is the ratio of the number of successful matches to the number of repetitions, and the number of successful matches is the number of times the current predicted location data matches the real-time location data obtained before the rolling stone ends; if the matching success rate is greater than or equal to A, then the predicted location data is connected to generate the theoretical path of the numbered target; if the matching success rate is less than A, then it is determined that the numbered target has disappeared and the predicted location information is deleted.

[0009] As a preferred embodiment of the multi-target radar monitoring and early warning method for rockfall adapted to complex terrain described in this invention, step S3 includes the following sub-steps: Step S31, acquiring monitoring data based on weather forecasts from the meteorological bureau, the monitoring data including rainfall, snowfall, temperature, duration, and wind speed; based on the monitoring data, when any one of the following conditions is met (condition 1: rainfall greater than B, condition 2: snowfall greater than C, condition 3: wind speed greater than D), a blue warning is issued to remind management personnel to pay attention to weather changes; Step S32, establishing a correspondence between the monitoring data and the theoretical path according to the duration, and based on the blue warning, acquiring monitoring data, when the duration is greater than E, and any one of the following conditions is met (conditions 1-3: rainfall greater than b, snowfall greater than c, wind speed greater than d), a yellow warning is issued to remind management personnel of an extremely high risk of rockfall.

[0010] As a preferred embodiment of the multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain described in this invention, step S4 includes the following sub-steps: Step S41, after a yellow warning is issued, according to the corresponding relationship, step S1 is repeated by the radar to acquire moving targets. If no moving targets are acquired, the radar continues to monitor in real time until the acquisition time ends. When the radar acquires a moving target, a red warning is issued to remind management personnel to confirm the rolling stone activity. Step S2 is repeated to record the basic data, real-time location data, and predicted location data, and to acquire the theoretical path.

[0011] As a preferred embodiment of the multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain described in this invention, step S4 includes the following sub-steps: Step S42, based on the moving target, the average RCS value of the moving target is acquired in real time by radar; the mass of the rolling stone is obtained based on a pre-stored RCS-mass database; the velocity vector is obtained based on the real-time position data and the predicted position data; the rolling stone energy is obtained based on the mass and velocity vector, wherein the rolling stone energy = 1 / 2 × mass × velocity vector.2 .

[0012] As a preferred embodiment of the multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain described in this invention, the rolling stone energy further includes: classifying energy levels according to the rolling stone energy; when the rolling stone energy is less than f, it is low energy, which is sufficient to shatter glass and cause dents to vehicles; when the rolling stone energy is greater than or equal to f and less than or equal to g, it is medium energy, which is sufficient to destroy vehicle hoods and cause surface damage to concrete structures; when the rolling stone energy is greater than g, it is high energy, which is sufficient to penetrate roofs and cause serious damage to reinforced concrete structures, threatening lives.

[0013] As a preferred embodiment of the rolling stone multi-target radar monitoring and early warning method adapted to complex terrain described in this invention, step S5 specifically includes the following: based on the predicted location data, the environment near the predicted location data is photographed by a UAV to obtain environmental information; based on the environmental information and energy level, a final assessment is obtained; based on the final assessment, it is determined whether early evacuation and prevention are necessary, wherein the environmental information is whether the surrounding area will cause damage; the final assessment is as follows: if the energy level is low and the environmental information indicates no damage, then monitoring is maintained; if the energy level is low and the environmental information indicates damage, then an evacuation alarm is issued and monitoring continues; if the energy level is medium and the environmental information indicates no damage, then monitoring is maintained; if the energy level is medium and the environmental information indicates damage, then an evacuation alarm is issued and monitoring continues; if the energy level is high and the environmental information indicates no damage, then early prevention is continued and monitoring continues; if the energy level is high and the environmental information indicates damage, then early prevention is continued, population is evacuated, and a report is submitted, and monitoring continues.

[0014] As a preferred embodiment of the multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain described in this invention, step S5 further includes: after a rolling stone occurs, acquiring the final occurrence location data, matching the occurrence location data with the predicted location information; if the matching is successful, the predicted location information is the actual location information; if the matching fails, acquiring the real-time path based on the basic data and the real-time location data, using the real-time path as the theoretical path, and updating the correspondence.

[0015] The beneficial effects of this invention are as follows: By employing a fixed clutter filtering technique based on Doppler frequency difference analysis and combining it with the discarding of zero-frequency channel signals using fast Fourier transform, the interference of strong background clutter such as rocks and vegetation in mountainous environments can be effectively suppressed, significantly reducing the false alarm rate, ensuring the accuracy of moving target detection, and providing a reliable data foundation for subsequent early warning. By assigning an independent number to each target and establishing a spatial rectangular coordinate system, combined with a prediction-matching mechanism based on the Kalman filter principle, multiple rolling stones can be stably tracked simultaneously, and a high-precision theoretical motion path can be generated. This overcomes the problems of trajectory confusion and loss of tracking that traditional methods easily encounter in multi-target scenarios, providing accurate kinematic basis for risk assessment. It deeply integrates meteorological forecast data with radar monitoring, establishing a progressive early warning mechanism of blue, yellow, and red, achieving full coverage from risk warning to extremely high risk and then to disaster confirmation. It moves the early warning point forward, transforming passive response into proactive prediction, greatly extending emergency response time. By using radar echoes to invert the mass of rolling stones and combining it with velocity vectors to calculate kinetic energy, it achieves a quantitative assessment of the destructive force of rolling stones, classifying them into low, medium, and high energy levels, and combining them with environmental information for final assessment. This makes early warning decisions no longer a simple matter of presence or absence, but a scientific and precise classification based on the potential degree of damage, guiding differentiated response measures. By matching and verifying the actual location of the rolling stones after they occur with the predicted path, and using real-time path data to update the theoretical model and correspondence, a closed-loop feedback system is formed. This system can continuously learn and adapt to the terrain and geological characteristics of specific areas, continuously optimize the prediction algorithm, and thus continuously improve the accuracy and reliability of early warnings over time. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of a multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain, provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example, refer to Figure 1This paper provides a multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain, including the following steps: Step S1, acquire echo signals through radar, and acquire moving targets based on the echo signals; Step S2, acquire numbered targets based on the moving targets, acquire basic data based on the numbered targets, and acquire theoretical paths based on the basic data; Step S3, acquire monitoring data based on weather forecasts from meteorological bureaus, determine whether to issue a blue warning based on the monitoring data, establish a correspondence between the monitoring data and theoretical paths according to duration, and determine whether to issue a yellow warning; Step S4, determine whether to issue a red warning based on a yellow warning, acquire rolling stone energy, and classify energy levels based on rolling stone energy; Step S5, acquire environmental information, obtain a final assessment based on the environmental information and energy levels, and update the correspondence based on the final assessment.

[0019] This invention employs a fixed clutter filtering technique based on Doppler frequency difference analysis, combined with Fast Fourier Transform to discard zero-frequency channel signals. This effectively suppresses interference from strong background clutter such as rocks and vegetation in mountainous environments, significantly reducing the false alarm rate and ensuring the accuracy of moving target detection. It provides a reliable data foundation for subsequent early warning. By assigning an independent number to each target and establishing a spatial rectangular coordinate system, combined with a prediction-matching mechanism based on Kalman filtering principles, it can simultaneously and stably track multiple rolling stones and generate high-precision theoretical motion paths. This overcomes the problems of trajectory confusion and loss of tracking that traditional methods easily encounter in multi-target scenarios, providing accurate kinematic basis for risk assessment. It deeply integrates meteorological forecast data with radar monitoring, establishing a progressive early warning mechanism of blue, yellow, and red, achieving full coverage from risk warning to extremely high risk and then to disaster confirmation. It moves the early warning point forward, transforming passive response into proactive prediction, greatly extending emergency response time. By using radar echoes to invert the mass of rolling stones and combining it with velocity vectors to calculate kinetic energy, it achieves a quantitative assessment of the destructive force of rolling stones, classifying them into low, medium, and high energy levels, and combining them with environmental information for final assessment. This makes early warning decisions no longer a simple matter of presence or absence, but a scientific and precise classification based on the potential degree of damage, guiding differentiated response measures. By matching and verifying the actual location of the rolling stones after they occur with the predicted path, and using real-time path data to update the theoretical model and correspondence, a closed-loop feedback system is formed. This system can continuously learn and adapt to the terrain and geological characteristics of specific areas, continuously optimize the prediction algorithm, and thus continuously improve the accuracy and reliability of early warnings over time.

[0020] Step S11: The radar emits a first electromagnetic wave and obtains a first echo signal. The first wave frequency is obtained from the first echo signal. The first electromagnetic wave is emitted again and a second echo signal is obtained. The second wave frequency is obtained from the second echo signal. The difference between the first and second wave frequencies is calculated. When the difference is 0, it is considered fixed clutter and is discarded. When the difference is not 0, it is considered an echo signal. Step S12: The echo signal undergoes a Fast Fourier Transform to obtain a Doppler frequency map. The Doppler frequency map includes a zero-frequency channel and non-zero-frequency channels. The echo signal in the zero-frequency channel is obtained from the Doppler frequency map and discarded. The echo signal in the non-zero-frequency channel is obtained from the Doppler frequency map. The echo signal is then converted from analog to digital to obtain a digital signal. Moving targets are identified from the digital signal. The moving targets are numbered to obtain numbered targets.

[0021] Step S21: Number the moving targets to obtain numbered targets; based on the numbered targets, obtain basic data via radar, including range, azimuth, elevation, and radial velocity; establish a spatial rectangular coordinate system based on the range, azimuth, and elevation to obtain real-time position data of the numbered targets; based on the real-time position information, divide the numbered targets according to time to obtain target observation points, and number these observation points to obtain numbered observation points; Step S22: based on the numbered observation points, obtain the next numbered observation point via radar; obtain real-time position data based on the numbered observation points; obtain second real-time position data based on the next numbered observation point; obtain the velocity vector based on the real-time position data and the second real-time position data; obtain the predicted position data for the next moment based on the velocity vector and the real-time position data.

[0022] Repeat step S22 to obtain the predicted location data and the number of repetitions until the rolling stone ends; match the predicted location data with the location data and calculate the matching success rate, which is the ratio of the number of successful matches to the number of repetitions. The number of successful matches is the number of times the current predicted location data matches the real-time location data before the rolling stone ends. If the matching success rate is greater than or equal to A, connect the predicted location data to generate the theoretical path for the numbered target; if the matching success rate is less than A, determine that the numbered target has disappeared and delete the predicted location information.

[0023] In practice, step S11, by comparing the frequency difference between two adjacent echoes, can almost perfectly filter out stationary clutter such as mountains and buildings, greatly reducing the number of false targets. Step S12, by transforming the signal to the frequency domain using FFT, can separate moving targets at different speeds. Discarding the zero-frequency channel further filters out low-speed clutter such as swaying leaves and slowly moving clouds, which may not be completely filtered out in step S11. This dual filtering mechanism ensures that the radar only focuses on truly meaningful moving targets, providing a very clean and reliable data source for subsequent tracking and calculation, fundamentally reducing the false alarm rate. A spatial rectangular coordinate system is established using range, azimuth, and elevation angles to obtain real-time three-dimensional position data of the target. This provides the target's precise coordinates in space, not just its radial distance relative to the radar, which is a prerequisite for accurate trajectory prediction and impact assessment. Based on the target's current and previous positions, its velocity vector is calculated in real time, and its position at the next moment is predicted accordingly. This prediction is updated and corrected using new observation data. By matching the predicted position with the measured position, the target can be stably tracked, even if the target briefly disappears from the radar's field of view or is obscured. The matching success rate is introduced as a key indicator to evaluate the effectiveness of the tracking algorithm. Targets with high success rates are considered valid targets and a theoretical path is generated; targets with low success rates are considered invalid targets, such as interference or targets that have disappeared, and are deleted. The system has self-evaluation capabilities, preventing the continuous waste of computational resources on incorrect targets and the generation of invalid alarms, thus ensuring the reliability of the output results.

[0024] Step S31: Obtain monitoring data based on the meteorological bureau's weather forecast. The monitoring data includes rainfall, snowfall, temperature, duration, and wind speed. Based on the monitoring data, if any one of the following conditions is met (condition 1: rainfall greater than B), condition 2: snowfall greater than C), or condition 3: wind speed greater than D), a blue warning is issued to remind management personnel to pay attention to weather changes. Step S32: Establish a correspondence between the monitoring data and the theoretical path based on duration. Based on the blue warning, obtain monitoring data. If the duration is greater than E, and any one of the following conditions is met (conditions 1, 2, 3, 4), a yellow warning is issued to remind management personnel of an extremely high risk of rockfall.

[0025] Step S41: After a yellow alert is issued, the radar repeats step S1 to acquire moving targets according to the corresponding relationship. If no moving targets are acquired, the radar continues to monitor in real time until the acquisition time ends. When the radar acquires a moving target, a red alert is issued to remind the management personnel to confirm the rockfall activity. Step S2 is repeated to record basic data, real-time location data, and predicted location data, and to obtain the theoretical path.

[0026] In practice, meteorological forecast data such as rainfall, snowfall, wind speed, and duration are used as leading indicators to classify disasters. Warnings can be issued hours or even days before a rockfall actually occurs, providing valuable lead time for disaster preparedness work such as personnel on duty and equipment checks, thus realizing a shift from post-disaster emergency response to pre-disaster prevention. Three progressive warning levels are designed: blue (weather alert), yellow (high risk), and red (confirmed occurrence), each corresponding to different response measures. A blue warning only requires management attention, a yellow warning requires the system to enter a high-alert state (continuous radar monitoring), and a red warning triggers a full-process emergency response. This tiered approach avoids overreaction and ensures efficient use of management resources. The issuance of a yellow warning is not merely the end point, but an instruction. It automatically triggers the radar system to repeat steps S1 and S2 according to a preset procedure, focusing on scanning and confirming high-risk areas. This shifts from weather-based forecasting to radar-based detection significantly improves the ability to detect initial moving targets in complex environments and avoids missed reports. From meteorological data access, threshold judgment, warning issuance to automatic radar scanning, the entire process is completed automatically without human intervention. It enables 24 / 7 uninterrupted monitoring and early warning, with a response speed far exceeding that of manual monitoring, ensuring immediate action at critical moments and maximizing the protection of people's lives and property.

[0027] Step S42: Based on the moving target, acquire the average RCS value of the moving target in real time via radar; obtain the mass of the rolling stone based on the pre-stored RCS-mass database; obtain the velocity vector based on real-time position data and predicted position data; obtain the rolling stone energy based on the mass and velocity vector, where rolling stone energy = 1 / 2 × mass × velocity vector. 2 .

[0028] The energy level of the rolling stones is classified into several grades. When the energy is less than f, it is considered low energy, capable of shattering glass and causing dents to vehicles. When the energy is greater than or equal to f and less than or equal to g, it is considered medium energy, capable of destroying vehicle hoods and causing surface damage to concrete structures. When the energy is greater than g, it is considered high energy, capable of penetrating roofs and causing severe damage to reinforced concrete structures, threatening lives. Based on predicted location data, drones are used to photograph the environment near the predicted location to obtain environmental information. A final assessment is obtained based on this environmental information and the energy level. This final assessment determines whether advance evacuation and preventative measures are necessary. The environmental information indicates whether nearby damage is likely. If the final assessment indicates low energy and the environmental information indicates no damage, monitoring will continue. If the energy level is low and... If environmental information indicates that the event will cause damage, an evacuation alert is issued, and monitoring continues. If the energy level is medium and environmental information indicates that the event will not cause damage, monitoring continues. If the energy level is medium and environmental information indicates that the event will cause damage, an evacuation alert is issued, and monitoring continues. If the energy level is high and environmental information indicates that the event will not cause damage, preventative measures are taken in advance, and monitoring continues. If the energy level is high and environmental information indicates that the event will cause damage, preventative measures are taken in advance, the population is evacuated, and the incident is reported, and monitoring continues. After a rockfall occurs, the final location data is obtained, and the location data is matched with the predicted location information. If the match is successful, the predicted location information is the actual location information. If the match fails, the real-time path is obtained based on the basic data and real-time location data, and the real-time path is used as the theoretical path to update the corresponding relationship.

[0029] In practice, by introducing radar RCS measurement to measure the reverse mass and combining it with velocity vector calculation to precisely calculate the kinetic energy, the destructive power of the rolling stones is quantified. This allows for the differentiation between rolling stones that can only "shatter glass" and those that can "penetrate roofs and threaten lives," providing a scientific basis for taking the most appropriate response measures for different levels of risk and greatly improving the accuracy and reliability of the early warning. Environmental information captured by drones, such as predicting whether there are houses, vehicles, or pedestrians near the impact point, is incorporated, enabling cross-judgment of risk energy levels and the environment. This enhances the system's situational awareness and the level of intelligent decision-making, ensuring that emergency resources are used where they are most needed. The final location of the rolling stones is matched and verified with the predicted location. If the prediction is successful, the accuracy of the current correspondence is verified; if the prediction fails, the correspondence is updated using real data. Over time and with the accumulation of events, the accuracy of the predicted trajectory will increase, thereby continuously enhancing the reliability and effectiveness of the entire early warning system. Based on the final assessment results, a tiered response measure, from maintaining monitoring to evacuation reporting, is adopted, ensuring that emergency management resources are allocated in the most reasonable and economical way, minimizing the social and economic costs of disasters. This invention employs a fixed clutter filtering technique based on Doppler frequency difference analysis, combined with Fast Fourier Transform to discard zero-frequency channel signals. This effectively suppresses interference from strong background clutter such as rocks and vegetation in mountainous environments, significantly reducing the false alarm rate and ensuring the accuracy of moving target detection. It provides a reliable data foundation for subsequent early warning. By assigning an independent number to each target and establishing a spatial rectangular coordinate system, combined with a prediction-matching mechanism based on Kalman filtering principles, it can simultaneously and stably track multiple rolling stones and generate high-precision theoretical motion paths. This overcomes the problems of trajectory confusion and loss of tracking that traditional methods easily encounter in multi-target scenarios, providing accurate kinematic basis for risk assessment. It deeply integrates meteorological forecast data with radar monitoring, establishing a progressive early warning mechanism of blue, yellow, and red, achieving full coverage from risk warning to extremely high risk and then to disaster confirmation. It moves the early warning point forward, transforming passive response into proactive prediction, greatly extending emergency response time. By using radar echoes to invert the mass of rolling stones and combining it with velocity vectors to calculate kinetic energy, it achieves a quantitative assessment of the destructive force of rolling stones, classifying them into low, medium, and high energy levels, and combining them with environmental information for final assessment. This makes early warning decisions no longer a simple matter of presence or absence, but a scientific and precise classification based on the potential degree of damage, guiding differentiated response measures. By matching and verifying the actual location of the rolling stones after they occur with the predicted path, and using real-time path data to update the theoretical model and correspondence, a closed-loop feedback system is formed. This system can continuously learn and adapt to the terrain and geological characteristics of specific areas, continuously optimize the prediction algorithm, and thus continuously improve the accuracy and reliability of early warnings over time.

[0030] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0031] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain, characterized in that, The process includes the following steps: Step S1, acquiring echo signals via radar, and acquiring moving targets based on the echo signals; Step S2, obtaining numbered targets based on the moving targets, obtaining basic data based on the numbered targets, and obtaining theoretical paths based on the basic data; Step S3, acquiring monitoring data based on weather forecasts from meteorological bureaus, determining whether to issue a blue warning based on the monitoring data, establishing a correspondence between the monitoring data and the theoretical paths according to the duration, and determining whether to issue a yellow warning. Step S4: Based on the yellow warning, determine whether to issue a red warning, and obtain the rolling stone energy, and classify the energy level according to the rolling stone energy; Step S5: Obtain environmental information, obtain a final assessment based on the environmental information and energy level, and update the correspondence based on the final assessment.

2. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S11, the radar emits a first electromagnetic wave and obtains a first echo signal. Based on the first echo signal, the first wave frequency is obtained. The radar emits a first electromagnetic wave again and obtains a second echo signal. Based on the second echo signal, the second wave frequency is obtained. The first wave frequency and the second wave frequency are subtracted to obtain a difference value. When the difference value is 0, it is considered fixed clutter and is directly discarded. When the difference is not 0, it is an echo signal; in step S12, the echo signal is subjected to a fast Fourier transform to obtain a Doppler frequency map, which includes a zero-frequency channel and non-zero-frequency channels. According to the Doppler frequency map, the echo signal in the zero-frequency channel is obtained and discarded; according to the Doppler frequency map, the echo signal in the non-zero-frequency channel is obtained, and the echo signal is subjected to analog-to-digital conversion to obtain a digital signal. The moving target is obtained according to the digital signal; the moving target is numbered to obtain a numbered target.

3. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 2, characterized in that, Step S2 includes the following sub-steps: Step S21, number the moving target to obtain numbered targets; based on the numbered targets, obtain basic data through radar, the basic data including range, azimuth, elevation, and radial velocity; establish a spatial rectangular coordinate system based on the range, azimuth, and elevation to obtain real-time position data of the numbered targets; based on the real-time position information, divide the numbered targets according to time to obtain target observation points, and number the target observation points to obtain numbered observation points; Step S22, based on the numbered observation points, obtain the next numbered observation point through radar, obtain real-time position data based on the numbered observation point, obtain second real-time position data based on the next numbered observation point, obtain a velocity vector based on the real-time position data and the second real-time position data, and obtain predicted position data for the next moment based on the velocity vector and the real-time position data.

4. The multi-target radar monitoring and early warning method for rolling stones adapted to complex terrain as described in claim 3, characterized in that, Step S2 further includes: repeating step S22 to obtain the predicted location data and the number of repetitions until the rolling stone ends; matching the predicted location data with the location data and calculating the matching success rate, wherein the matching success rate is the ratio of the number of successful matches to the number of repetitions, and the number of successful matches is the number of times the current predicted location data matches the real-time location data obtained before the rolling stone ends; if the matching success rate is greater than or equal to A, then the predicted location data is connected to generate the theoretical path of the numbered target; if the matching success rate is less than A, then it is determined that the numbered target has disappeared and the predicted location information is deleted.

5. The method for monitoring and warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 4, characterized in that, Step S3 includes the following sub-steps: Step S31, obtain monitoring data based on the meteorological bureau's weather forecast. The monitoring data includes rainfall, snowfall, temperature, duration, and wind speed. Based on the monitoring data, if any one of the following conditions is met (condition 1: rainfall greater than B, condition 2: snowfall greater than C, condition 3: wind speed greater than D), a blue warning is issued to remind management personnel to pay attention to weather changes. Step S32, establish a correspondence between the monitoring data and the theoretical path according to the duration. Based on the blue warning, obtain monitoring data. If any one of the following conditions is met (condition 1: duration greater than E, rainfall greater than b, snowfall greater than c, wind speed greater than d), a yellow warning is issued to remind management personnel of an extremely high risk of rockfall.

6. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 5, characterized in that, Step S4 includes the following sub-steps: Step S41, when a yellow warning is issued, according to the correspondence, step S1 is repeated by radar to acquire moving targets. If no moving targets are acquired, the radar continues to monitor in real time until the acquisition time ends. When the radar acquires a moving target, a red warning is issued to remind management personnel to confirm the rockfall activity. Step S2 is repeated to record the basic data, real-time location data, and predicted location data, and to acquire the theoretical path.

7. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 6, characterized in that, The step S4 includes the following sub-steps: Step S42, based on the moving target, the average RCS value of the moving target is obtained in real time by radar, and the mass of the rolling stone is obtained according to the pre-stored RCS-mass database. The velocity vector is obtained based on the real-time location data and the predicted location data; the rolling stone energy is obtained based on the mass and velocity vector, where the rolling stone energy = 1 / 2 × mass × velocity vector. 2 .

8. The method for monitoring and warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 7, characterized in that, The rolling stone energy also includes: classifying energy levels based on the rolling stone energy. When the rolling stone energy is less than f, it is low energy, which is enough to shatter glass and cause dents to vehicles; when the rolling stone energy is greater than or equal to f and less than or equal to g, it is medium energy, which is enough to destroy a vehicle's hood and cause surface damage to the concrete structure; when the rolling stone energy is greater than g, it is high energy, which is enough to penetrate a building's roof, cause serious damage to reinforced concrete structures, and threaten lives.

9. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 8, characterized in that, The specific steps of step S5 include the following: based on the predicted location data, taking pictures of the environment near the predicted location data by drone to obtain environmental information; obtaining a final assessment based on the environmental information and energy level; and determining whether early evacuation and prevention are necessary based on the final assessment, wherein the environmental information is whether the surrounding area will cause damage. The final assessment is as follows: if the energy level is low and the environmental information indicates no harm will occur, monitoring will continue; if the energy level is low and the environmental information indicates harm will occur, an evacuation alert will be issued, and monitoring will continue; if the energy level is medium and the environmental information indicates no harm will occur, monitoring will continue; if the energy level is medium and the environmental information indicates harm will occur, an evacuation alert will be issued, and monitoring will continue; if the energy level is high and the environmental information indicates no harm will occur, preventative measures will be taken in advance, and monitoring will continue; if the energy level is high and the environmental information indicates harm will occur, preventative measures will be taken in advance, the population will be evacuated, and the situation will be reported, while monitoring will continue.

10. The method for monitoring and early warning of rolling stones using multi-target radar adapted to complex terrain as described in claim 9, characterized in that, Step S5 further includes: after the stone falls, obtaining the final occurrence location data, matching the occurrence location data with the predicted location information, if the match is successful, the predicted location information is the actual location information; if the match fails, obtaining the real-time path based on the basic data and the real-time location data, using the real-time path as the theoretical path, and updating the correspondence.

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