An intelligent perception alarm system and method based on big data

Abstract

The present invention relates to the field of big data applications, and in particular to an intelligent perception alarm system based on big data, comprising a landslide early warning model; a data acquisition module: the data acquisition module is used to collect geological environment information and video information of a slope area; the geological environment information is transmitted to the landslide early warning model to generate first model data, and if the first model data exceeds a threshold, landslide early warning data is generated; an alarm module: the alarm module is set in a user's home, and when the landslide early warning data is generated, it starts broadcasting a pre-stored first landslide broadcast; and an intelligent perception alarm method based on big data, comprising the following steps: S1: collecting environmental parameters and video image information of the slope area; S2: constructing a landslide early warning model, inputting environmental parameter information of the slope area, simulating the impact of future weather change parameters on the slope area, and then generating landslide early warning data; thereby solving the problem of being unable to issue early warnings in a timely manner when a landslide occurs.

Description

Technical Field

[0001] The present invention relates to the field of big data applications, and in particular to an intelligent perception alarm system and method based on big data. Background Art

[0002] Debris flows are special torrents of water that occur in mountainous areas or other areas with deep ravines and rugged terrain, often caused by heavy rain, heavy snow, or other natural disasters, carrying large amounts of mud, sand, and rocks. Debris flows are characterized by suddenness, rapid velocity, high flow volume, large material capacity, and strong destructive power. Debris flows often destroy transportation infrastructure such as roads and railways, and even villages and towns, causing massive damage. Debris flows typically last only a few hours, sometimes just a few minutes. They are a common natural disaster found in areas with unique topography and landforms worldwide. In places such as winding mountain roads or houses under gullies, mudslides are very likely to form in the event of heavy rain. It is difficult for people to judge the arrival of mudslides without careful observation. However, due to blocked vision and jungle obstruction, it is impossible to get a glimpse of the full picture, which makes observation difficult. In addition, due to the sudden nature of mudslides, the process of causing harm is extremely short, but the impact of the harm lasts for a very long time. When a mudslide occurs, there is only a few minutes of evacuation time. If the affected areas are not evacuated in time before the arrival of the mudslide, they are likely to be buried by the mudslide, which greatly endangers people's lives and safety.

[0003] The invention patent with application number 202011193485.2 provides a landslide risk monitoring and early warning method and system, which relates to the field of geological disaster monitoring and early warning technology. By building a simulated landslide scene, obtaining simulated sensor data to train a first classifier, and obtaining an initial micro-motion unit event detection model, then obtaining real sensor data of a real landslide scene, performing enhanced training on the initial micro-motion unit event detection model, and obtaining a micro-motion unit event detection model. The second classifier is then trained with the acquired fault event data to obtain a fault model. Finally, the micro-motion unit event detection model and the fault model are combined to construct and train a landslide early warning model to perform landslide risk warning. The present invention can automatically analyze a large amount of sensor data and make more effective use of the data, thereby improving the credibility and effectiveness of landslide monitoring and early warning.

[0004] The above technology simulates and measures the mountain conditions through data sent back by sensors, outputs the simulation results of landslides through artificial intelligence models, and issues early warning signals based on the results. This technology collects a large amount of data on the actual situation of the mountain and performs real-time simulation. The data calculation volume is large, the calculation cycle is long, and it has a certain degree of repeatability. Combined with the influence of weather, the simulated weather conditions are different from the actual conditions. When landslides and mudslides actually occur, the data is not calculated in time. Now there is a need for a technology that can issue early warnings before and during landslides to remind evacuations. Summary of the Invention

[0005] The present invention provides an intelligent perception alarm system and method based on big data, which can solve the problem of failure to issue early warnings in time when sudden landslides occur.

[0006] In order to solve the above technical problems, this application provides the following technical solutions:

[0007] An intelligent perception and alarm system based on big data, including a landslide early warning model, also includes:

[0008] Data acquisition module: The data acquisition module is used to collect geological environment information and video information of the slope area; the geological environment information is transmitted to the landslide early warning model to generate first model data. If the first model data exceeds a threshold, landslide early warning data is generated;

[0009] Alarm module: The alarm module is set in the user's home and starts broadcasting the pre-stored first landslide broadcast when landslide warning data is generated;

[0010] Data processing module: The slope warning model is built into the data processing module. The data processing module is used to obtain video information of the slope area, use the inter-frame analysis method to identify and analyze the images in the video information, compare two adjacent frames of video information to obtain their difference pixels, extract the contours of the difference pixels, and use their initial positions as the starting point to calculate their movement trajectory. If the movement trajectory is a parabola and the direction of movement is downward, a falling object instruction is generated. If the number of falling object instructions exceeding a first threshold is generated within a certain period of time, a second landslide broadcast is generated. If the first landslide broadcast has been broadcast for the area before, an evacuation alarm is immediately generated; if the first landslide advertisement has not been broadcast before, the starting positions of each falling object are connected. If the falling points exceed the first threshold and are arranged vertically along the mountain, an evacuation alarm is generated; if the falling points are distributed horizontally, the image of the falling object is analyzed, and the falling area is determined by the image proportion relationship. If the falling area exceeds the second threshold, an evacuation alarm is generated.

[0011] The basic principle and beneficial effects of this program are as follows: through the establishment of a landslide early warning model, possible landslides in the future can be simulated in advance. If a landslide is generated through the simulated data, when the real sensor data, that is, the data in the data acquisition module, matches the landslide data, a landslide early warning data will be generated. If the subsequent weather forecast shows that the area may reach this data, the first landslide broadcast will be broadcast. It is actually a predicted value. A landslide, that is, a mud-rock flow, is expected to occur. This is the first warning, which is an advance warning. After receiving this information, people can choose to evacuate the dangerous area and have enough time to prepare. If the local residents do not evacuate as scheduled, this move will also remind them subconsciously. When the initial signs of a landslide occur later, they will recall the previous prompts. Under the double prompts, the prompt strength and credibility are increased, and they can evacuate quickly according to the instructions to ensure life safety. In case of actual landslides, the actual situation in the slope area is monitored through video images, and the video image information is analyzed in real time. If an object falls in the video, an evacuation alarm will be generated based on the trajectory of the falling object and the amount of the falling object. If the threshold is exceeded, the evacuation alarm and the first landslide broadcast are both transmitted through the alarm module. The alarm module is set in the residents' homes to facilitate timely and accurate broadcasting.

[0012] This plan broadcasts early warnings before the landslide begins, issues real-time evacuation signals when signs of a landslide begin, and uses landslide early warning models to make advance calculations. When the data simulates a landslide, the first early warning signal is sent to residents, giving them enough time to transfer assets, strengthen defenses, and protect property and life safety. When a landslide occurs, an evacuation signal is sent to residents as soon as possible to ensure life safety. This effectively ensures the safety of people's lives. In the event of a low degree of cooperation during the early warning, residents will have a deeper understanding of evacuation when the evacuation signal is issued, with the first reminder, and will have a higher degree of trust and cooperation, and will respond to the evacuation in a timely manner to ensure their own safety.

[0013] Compared to traditional early warning methods, this solution promptly locks in and issues a warning, sounding an alarm when a landslide occurs. When conventional sensors fail, video information serves as a secondary safeguard, independent of traditional early warning calculations. The dual-threaded approach ensures the accuracy of early warnings, and the timely return of video information allows for a clear observation of the current situation. Relevant emergency management personnel can prepare rescue plans based on on-site conditions. Detailed analysis of the video information allows for the simultaneous issuance of evacuation messages to residents' alarm modules when a landslide risk is identified, sounding the alarm and informing them that a landslide is imminent and requiring immediate evacuation to ensure their safety. This solution ensures the immediate issuance of early warning signals and evacuation reminders when a landslide strikes, thus resolving the issue of inability to issue timely early warnings when landslides occur.

[0014] Furthermore, it also includes a display platform. The data processing module is also used to three-dimensionally model the slope area based on the video information, and display the model on the display platform, and annotate the data collected by the data acquisition module one by one on it; the video information is updated in real time and displayed synchronously on the display platform; the warning structure of the landslide warning model is also displayed synchronously on the display platform, and emergency personnel edit the text according to the information on the display platform and report it to relevant channels for dissemination.

[0015] Beneficial effects: A single graph (display platform) can be used to collect and display all data information, making it easier for managers to monitor the current status of slope areas.

[0016] Furthermore, the data acquisition module at least includes one or more of a displacement sensor, a vibrating string sensor, a tilt sensor, and an acoustic wave sensor, and a camera.

[0017] Furthermore, the data acquisition module, alarm module, data processing module, and display platform interact with each other and select any one or a combination of 2G, 3G, 4G, 5G, Beidou, and wired to transmit information.

[0018] Beneficial effect: According to the convenience of information transmission between the sensor and the data processing module and display platform, an applicable and suitable transmission method is selected to achieve efficient and fast signal and data transmission.

[0019] Furthermore, the camera is powered by a solar panel and is set at a place with a wide field of view on a slope in a sloping area. It also includes a rain gauge powered by a solar panel, and the rain gauge is used to detect real-time rainfall. The rain gauge and the data processing module exchange information.

[0020] Beneficial effects: Using solar panels for power supply reduces the problem of power supply. Since most of the areas where cameras are laid are steep slopes, it is more complicated to set up power facilities, and the cost of laying special wires is too high. By using solar power supply, the cost is reduced. The problem that the laying of cameras is restricted by the region will be curbed, and cameras can be laid in more places.

[0021] Furthermore, the data acquisition module is also used to obtain meteorological data, which includes weather forecasts. When the weather forecast predicts rain within three days, the data processing module begins to extract geological environment information for calculation by the landslide early warning model.

[0022] Beneficial effects: Saving computing power. When there are signs of rain (weather forecast reports), the calculation of the landslide model is started. There is sufficient computing time and sufficient time for plan formulation. It also reduces the energy loss caused by continuous calculations and saves manpower allocation.

[0023] Furthermore, the data processing module counts the weather conditions of the previous month, obtains temperature data, and lists and analyzes the temperature data, pre-dividing the high-temperature interval. If the temperature exceeds the third threshold time and is within the high-temperature interval, a rock and soil loosening signal is generated. When simulating the landslide warning model, the simulated rainfall value when a landslide occurs is reduced by one level to match the actual rainfall.

[0024] Beneficial effects: Continuous high temperatures will make the soil loose, and the risk of landslides will be higher under the erosion of heavy rain. Therefore, the degree of danger caused by rainfall after continuous high temperature weather is upgraded and calculated. That is, when the rainfall reaches a certain value in the simulation, there will be a landslide risk. Then, in reality, when the value measured by the sensor is lower than the value in the simulation (one level lower), it is considered to have reached the state in the simulation, and a response is made on this basis, and a corresponding early warning signal is issued according to the calculated results.

[0025] An intelligent perception alarm method based on big data includes the following steps:

[0026] S1: Collect environmental parameters and video information of slope areas;

[0027] S2: Construct a landslide early warning model, input environmental parameter information of the slope area, simulate the impact of future weather change parameters on the slope area, and then generate landslide early warning data;

[0028] S3: Generate corresponding warning signals according to landslide warning data;

[0029] S4: Analyze the video information and generate corresponding warning information based on the falling object situation detected by the video monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a structural diagram of an intelligent perception alarm system based on big data;

[0031] Figure 2 This is a structural diagram of an intelligent perception alarm method based on big data. DETAILED DESCRIPTION

[0032] The following is further described in detail through specific implementation methods:

[0033] Example 1 is as shown in the attached Figure 1 As shown,

[0034] A big data-based intelligent perception and alarm system, including a landslide warning model, is used for monitoring and warning landslide risks. By building a simulated landslide scene, obtaining simulated sensor data to train a first classifier, an initial micro-motion unit event detection model is obtained. Then, real sensor data from a real landslide scene is obtained, and enhancement training is performed on the initial micro-motion unit event detection model and a second classifier is trained to obtain a micro-motion unit event detection model and a fault model, respectively. Finally, an initial landslide warning model is established by combining the micro-motion unit event detection model and the fault model. This initial landslide warning model is then trained using a convolutional neural network model to obtain a landslide warning model. The system also includes:

[0035] Data Acquisition Module: This module is used to collect geological and video information from sloped areas. This geological information is transmitted to the landslide early warning model to generate first model data. If the first model data exceeds a threshold, landslide early warning data is generated. The data acquisition module includes at least one or more of a displacement sensor, a vibrating wire sensor, a tilt sensor, and an acoustic wave sensor, as well as a camera. Specific sensors include: soil pressure gauges, piezometers, crack meters, tensile displacement meters, mud level meters, laser rangefinders, GNSS, fixed inclinometers, accelerometers, rain gauges, inclinometers, infrasound sensors, and geoacoustic sensors. The measured data is used by the landslide early warning model.

[0036] Alarm module: The alarm module is set in the user's home. When landslide warning data is generated, it starts broadcasting the pre-stored first landslide broadcast; the alarm module includes an alarm broadcast and a home alarm. When the warning data is received, it will issue the first landslide broadcast, indicating that a landslide has occurred in the mountain in the model through simulation data combined with future weather forecast data, that is, an early warning information is issued in the real world, that is, the first landslide broadcast notification, which is transmitted through home alarms and broadcasts to let local residents at home and outside know about the occurrence of this incident.

[0037] Data Processing Module: This module is a CPU processor. It can be a CPU processor and related components that meet its performance requirements, or a cloud server, relying on cloud computing power to process the relevant data. The landslide warning model is built into the data processing module. The landslide warning model relies on the data processing module for simulation calculations, leveraging the computing power of the data processing module to complete the model's construction, optimization, and subsequent application. The data processing module is used to acquire video information from a sloping area, identify and analyze the images in the video information using an inter-frame analysis method, compare two adjacent frames of video information to obtain their difference pixels, extract the contours of the difference pixels, and use their initial position as the starting point to calculate their trajectory. If the trajectory is parabolic and the movement direction is downward, a falling object command is generated. If more than three falling object commands are generated within three minutes, a second landslide announcement is generated. If the first landslide announcement has been previously broadcast for the area, an evacuation alarm is immediately generated. If the first landslide announcement has not been previously broadcast, the starting positions of each falling object are connected. If the falling points exceed a first threshold and are arranged longitudinally along the mountain, an evacuation alarm is generated. If the falling points are distributed horizontally, the falling object images are analyzed and the falling area is determined based on the image proportion relationship. If the falling area exceeds a second threshold, an evacuation alarm is generated. Based on the monitoring image as a plane, an evacuation alarm is issued when the falling area occupies 20% of the monitoring image.

[0038] It also includes a display platform. The data processing module is also used to create a three-dimensional model of the slope area based on the video information, and display the model on the display platform, and annotate the data collected by the data acquisition module one by one on it; the video information is updated in real time and displayed synchronously on the display platform; the warning structure of the landslide warning model is also displayed synchronously on the display platform. Emergency personnel edit the text based on the information on the display platform and report it to relevant channels for dissemination. The display platform can be an LED screen. It can also be a display web page. The purpose is to display all data and test results accurately and clearly on the page for people who need them to view and process them in a timely manner. Based on the data obtained, relevant management personnel also process the data in a timely manner and form a report. After review, it is sent to relevant channels, such as official documents, public accounts, news releases, radio stations, etc., so that more people can learn about the occurrence of this incident through more channels.

[0039] The data acquisition module, alarm module, data processing module, and display platform interact with each other and transmit information using any one or a combination of 2G, 3G, 4G, 5G, Beidou, and wired networks. The appropriate information transmission method between modules is determined based on terrain, transmission cost, and transmission efficiency.

[0040] The camera is powered by a solar panel and is set at a place with a wide field of view on a slope in a sloping area. It also includes a rain gauge powered by a solar panel, which is used to detect real-time rainfall. The rain gauge interacts with the data processing module.

[0041] The data acquisition module also acquires meteorological data, including weather forecasts. If the forecast predicts rain within three days, the data processing module begins extracting geological environmental information for the landslide early warning model to calculate. This reduces manpower and computing power costs, allowing calculations to be performed only when signs emerge, resulting in highly efficient and targeted calculations.

[0042] The data processing module compiles weather data from the previous month, obtains temperature data, and tabulates and analyzes this data, pre-dividing it into high-temperature intervals. If the temperature exceeds the third threshold and falls within the high-temperature interval, a rock and soil loosening signal is generated. When simulating the landslide warning model, the simulated rainfall value at the time of the landslide is downgraded to match the actual rainfall. High temperatures are defined as 30°C, and temperatures above 30°C are considered high-temperature intervals. If high temperatures exceed 10 consecutive days, a rock and soil loosening signal is generated, with rainfall values ​​being graded in 2mm increments.

[0043] Also included is an intelligent perception alarm method based on big data applied to the above system, comprising the following steps:

[0044] S1: Collect environmental parameters and video information of slope areas;

[0045] S2: Construct a landslide early warning model, input environmental parameter information of the slope area, simulate the impact of future weather change parameters on the slope area, and then generate landslide early warning data;

[0046] S3: Generate corresponding warning signals according to landslide warning data;

[0047] S4: Analyze the video information and generate corresponding warning information based on the falling object situation detected by the video monitoring.

[0048] Example 2

[0049] During the detection process of the vibration signal, there may be some external factors that cause interference. In order to reduce interference and to monitor geological disasters while monitoring meteorological disasters, the difference between Example 2 and Example 1 is that: it also includes an impeller, which is connected to the first generator motor. When the wind drives the impeller to rotate, the first generator motor generates wind electromotive force data under the rotation of the impeller and transmits it to the data processing module; it also includes a waterwheel and a second generator motor. The second generator motor is coaxially fixed with the waterwheel. When the waterwheel rotates, it drives the second generator motor to rotate, generates rain electromotive force data, and transmits it to the data processing module. This can also be replaced by the rain gauge in Example 1. The waterwheel is arranged in a through pipe, which is arranged vertically. A funnel is fixed at the upper end of the pipe for collecting rainwater. It also includes a vibration detection module for detecting the vibration conditions of the planting area in real time and transmitting the measurement data to the data processing module. The vibration detection module selects the ground sound sensor or vibration sensor mentioned in Example 1, such as the inductive vibration sensor, which is a vibration sensor designed based on the principle of electromagnetic induction (the inductive vibration sensor is provided with a magnet and a magnetic conductor. When measuring the vibration of an object, it can convert the mechanical vibration parameters into electrical parameter signals. The inductive vibration sensor can be used for measuring parameters such as vibration velocity and acceleration, or the eddy current vibration sensor is a vibration sensor with eddy current effect as its working principle. It is a non-contact sensor) or the eddy current vibration sensor measures the vibration data of the planting area by the change in the distance between the end of the sensor and the object being measured) The eddy current vibration sensor is mainly used for measuring vibration displacement). The data processing module uses the transmitted wind electromotive force, rain electromotive force and vibration data. When the vibration data exceeds the preset geological disaster data (parameters of geological disaster conditions in various places are queried from local geological and environmental protection departments), if the wind electromotive force reaches the preset value (the standard value range is set according to the actual wind speed), it indicates that the vibration is caused by the wind instrument. If there is no wind electromotive force, but a vehicle or animal passes by in the picture, the vibration data after the vehicle or animal leaves the area is compared. If the vibration decreases or is within the normal range, no warning signal is generated. If the vibration data remains unchanged or increases after leaving, an abnormal warning signal is generated. This can effectively eliminate the impact of animals and vehicles on the detection equipment. When heavy rain hits, the vibration detection module may generate abnormal vibrations, or it may actually measure abnormal vibrations, such as mud and rock flows, landslides, etc. By performing real-time detection on the rain electromotive force, wind electromotive force, and vibration data, and drawing charts for comparison, when the three sets of data have the same frequency, that is, they increase and decrease at the same time, it can be determined that the abnormal vibration is caused by natural conditions; if they are not of the same frequency, such as taking the three sets of data measured in the same time period as the standard value, at a certain subsequent time point the wind electromotive force and rain electromotive force reach the standard value, but the vibration data is significantly higher than the value under the standard condition, then an early warning signal is generated, indicating that a mud and rock flow may occur.

[0050] The above are only embodiments of the present invention. The invention is not limited to the fields involved in this implementation case. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An intelligent perception alarm system based on big data, including a landslide early warning model, characterized in that: Also includes: Data acquisition module: The data acquisition module is used to collect geological environment information and video information of slope areas; The geological environment information is transmitted to a landslide early warning model to generate first model data, and if the first model data exceeds a threshold, landslide early warning data is generated; Alarm module: The alarm module is set in the user's home and starts broadcasting the pre-stored first landslide broadcast when landslide warning data is generated; Data processing module: The landslide warning model is built into the data processing module, which is used to obtain video information of the slope area, use the inter-frame analysis method to identify and analyze the images in the video information, compare two adjacent frames of video information to obtain their difference pixels, extract the contours of the difference pixels, and use their initial positions as the starting point to calculate their movement trajectory. If the movement trajectory is a parabola and the direction of movement is downward, a falling object instruction is generated. If the number of falling object instructions exceeding a first threshold is generated within a certain period of time, a second landslide broadcast is generated. If the first landslide broadcast has been broadcast to the area before, an evacuation alarm is generated immediately; if the first landslide broadcast has not been broadcast before, the starting positions of each falling object are connected. If the falling points exceed the first threshold and are arranged vertically along the mountain, an evacuation alarm is generated; if the falling points are distributed horizontally, the image of the falling object is analyzed, and the falling area is determined by the image proportion relationship. If the falling area exceeds the second threshold, an evacuation alarm is generated; The system further comprises an impeller and a first generator motor, wherein the impeller is connected to the first generator motor. When the wind blows the impeller to rotate, the first generator motor generates wind electromotive force data under the rotation of the impeller and transmits the data to the data processing module. The waterwheel and the second generator motor are coaxially fixed to the waterwheel. When the waterwheel rotates, the second generator motor is driven to rotate to generate rain electromotive force data, which is then transmitted to the data processing module. The waterwheel is arranged in a through pipe, which is arranged vertically, and a funnel is fixed at the upper end of the pipe for collecting rainwater; It also includes a vibration detection module for detecting vibration conditions in the planting area in real time and transmitting the measurement data to the data processing module; The data processing module is based on the transmitted wind electromotive force, rain electromotive force and vibration data. When the vibration data exceeds the preset geological disaster data, if the wind electromotive force reaches the preset value at this time, it indicates that the vibration is caused by the wind instrument. If there is no wind electromotive force, but a vehicle or animal passes by in the picture, the vibration data after the vehicle or animal leaves the area is compared. If the vibration decreases or is within the normal range, no warning signal is generated. If the vibration data remains unchanged or increases after leaving, an abnormal warning signal is generated.

2. The intelligent perception alarm system based on big data according to claim 1, characterized in that: It also includes a display platform. The data processing module is also used to three-dimensionally model the slope area based on the video information, and display the model on the display platform, and annotate the data collected by the data acquisition module one by one on the model; the video information is updated in real time and displayed synchronously on the display platform; the warning structure of the landslide warning model is also displayed synchronously on the display platform, and emergency personnel edit the text according to the information on the display platform and report it to relevant channels for dissemination.

3. The intelligent perception alarm system based on big data according to claim 1 is characterized in that: The data acquisition module at least includes one or more of a displacement sensor, a vibrating string sensor, a tilt sensor, and an acoustic wave sensor, and a camera.

4. The intelligent perception alarm system based on big data according to claim 2, characterized in that: The data acquisition module, alarm module, data processing module, and display platform interact with each other and select any one or a combination of 2G, 3G, 4G, 5G, Beidou, and wired to transmit information.

5. The intelligent perception alarm system based on big data according to claim 3 is characterized in that: The camera is powered by a solar panel and is set at a place with a wide field of view on a slope in a sloping area. It also includes a rain gauge powered by a solar panel, which is used to detect real-time rainfall. The rain gauge interacts with the data processing module.

6. The intelligent perception alarm system based on big data according to claim 1, characterized in that: The data acquisition module is also used to obtain meteorological data, which includes weather forecasts. When the weather forecast predicts rain within three days, the data processing module begins to extract geological environment information for calculation by the landslide early warning model.

7. The intelligent perception alarm system based on big data according to claim 6, characterized in that: The data processing module collects statistics on the weather conditions of the previous month, obtains temperature data, and lists and analyzes the temperature data, pre-dividing it into high-temperature intervals. If the temperature exceeds the third threshold time and is within the high-temperature interval, a rock and soil loosening signal is generated. When simulating the landslide warning model, the simulated rainfall value when a landslide occurs is reduced by one level to match the actual rainfall.

8. An intelligent perception alarm method based on big data, characterized in that: A system as claimed in any one of claims 1 to 7 is employed.

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