A smart forest fire monitoring and early warning system and method based on remote sensing

Abstract

The present invention proposes a remote sensing-based intelligent forest fire monitoring and early warning system and method, wherein a forestry patrol map is established through the early warning system, and a plurality of patrol points are set in the forestry patrol map; and the forestry environment data of each patrol point is monitored; at each patrol point, a forestry safety trend graph is established through the real-time monitored forestry environment data and time; a threshold point is set on the ordinate of the forestry safety trend graph, a threshold line is obtained according to the threshold point, and the number of abnormal points of the plurality of curves is calculated according to the positional relationship between the monitoring points set on the plurality of curves of the forestry safety trend graph and the threshold line; a threshold value for the number of abnormal points is set, and the number of abnormal points of the plurality of curves is compared with the threshold value for the abnormal points, and corresponding early warning measures are taken according to the comparison result. The present invention can realize real-time monitoring of the forestry environment of the patrol points, and conduct comprehensive analysis and early warning of the forestry safety status through the forestry safety trend graph.

Description

Technical Field

[0001] The present invention proposes a remote sensing-based intelligent forest fire monitoring and early warning system and method, which relate to the technical field of intelligent forest fire monitoring and early warning, and specifically to the technical field of remote sensing-based intelligent forest fire monitoring and early warning. Background Art

[0002] As the importance of forestry management and the attention to forest safety continue to increase, it is crucial to establish an effective early warning system to monitor and manage changes in the forest environment. In traditional patrol work, patrol officers often need to spend a lot of time and energy to monitor and evaluate the safety status of the forest environment, and the data monitoring is not accurate, targeted, and timely enough, and it is impossible to conduct intelligent safety analysis of real-time data, which leads to untimely warnings and inaccurate warning locations. Summary of the invention

[0003] The present invention provides a remote sensing-based intelligent forest fire monitoring and early warning system and method, which is used to solve the problem that patrol personnel need to spend a lot of time and energy to monitor and evaluate the safety status of the forestry environment, and the data monitoring is not accurate, targeted and timely enough, and it is impossible to perform intelligent safety analysis on real-time data, which leads to the problem that the early warning is not timely enough and the early warning location is not accurate enough:

[0004] The present invention proposes a remote sensing-based intelligent forest fire monitoring and early warning system and method, the system comprising:

[0005] An environmental monitoring module is used to establish a forestry patrol map through an early warning system, set multiple patrol points in the forestry patrol map, and monitor the forestry environmental data of each patrol point;

[0006] The forestry safety trend chart establishment module is used to establish a forestry safety trend chart at each patrol point through real-time monitored forestry environment data and time;

[0007] An abnormality judgment module is used to set a threshold point on the ordinate of the forestry safety trend graph, obtain a threshold line according to the threshold point, and calculate the number of abnormal points of multiple curves according to the positional relationship between the monitoring points set on multiple curves of the forestry safety trend graph and the threshold line;

[0008] The early warning module is used to set a threshold value for the number of abnormal points, compare the number of abnormal points of the multiple curves with the threshold value for the abnormal points, and take corresponding early warning measures according to the comparison result.

[0009] Furthermore, the environment monitoring module includes:

[0010] A point marking module is used to collect forestry geomorphic information through satellite remote sensing technology combined with monitoring devices to establish a forestry patrol map, set up multiple patrol points in the forest according to the forestry patrol map, and mark the patrol points in the map;

[0011] A data acquisition module is used to monitor the environmental information of each patrol point in real time through a sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence;

[0012] The map updating module is used to regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

[0013] Furthermore, the forestry safety trend map establishment module includes:

[0014] A data extraction module, used to extract forestry environmental data at each patrol point in the forestry patrol map through the early warning system;

[0015] The trend chart establishment module is used to establish multiple coordinate systems at each patrol point, using the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate. According to the real-time monitored forestry environment data, change trend curves are drawn in the multiple coordinate systems respectively, and the horizontal and vertical coordinates of the multiple coordinate systems are overlapped to establish a forestry safety trend chart.

[0016] Furthermore, the abnormality judgment module includes:

[0017] A threshold setting module is used to set a threshold point on the vertical axis, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line;

[0018] The calculation formula of the value of the threshold point on the ordinate is:

[0019] Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z max is the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time;

[0020] The abnormal point calculation module is used to set multiple monitoring points for each curve according to each time node, calculate the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculate the number of monitoring points below the threshold point of the humidity curve, and obtain the number of abnormal points of multiple curves.

[0021] Furthermore, the early warning module includes:

[0022] An abnormality threshold setting module, used to set an abnormality point quantity threshold, the abnormality point quantity threshold is 2 / 3 of the number from the initial time node to the current time node;

[0023] The first-level warning module is used to issue a first-level alarm when the number of abnormal points on two or fewer curves is greater than 1 / 2 of the threshold of the number of abnormal points. The LED lights connected to the patrol points corresponding to the curves on the map are lit as yellow lights. This is a mild warning, and the warning system increases the frequency of environmental data collection to twice the original frequency.

[0024] The secondary warning module is used to issue a secondary alarm when the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold. The early warning system will light up the LED lights connected to the patrol points corresponding to the curves in the map as red lights. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

[0025] Further, the method comprises:

[0026] S1. Establish a forestry patrol map through the early warning system, set multiple patrol points in the forestry patrol map; and monitor the forestry environment data of each patrol point;

[0027] S2. At each patrol point, a forestry safety trend chart is established through real-time monitoring of forestry environmental data and time;

[0028] S3, setting a threshold point on the ordinate of the forestry safety trend graph, obtaining a threshold line according to the threshold point, and calculating the number of abnormal points of the multiple curves according to the positional relationship between the monitoring points set on the multiple curves of the forestry safety trend graph and the threshold line;

[0029] S4. Setting an abnormal point quantity threshold, comparing the abnormal point quantity of the plurality of curves with the abnormal point threshold, and taking corresponding early warning measures according to the comparison result.

[0030] Furthermore, the S1 includes:

[0031] S101, using satellite remote sensing technology combined with monitoring devices to collect forestry geomorphic information to establish a forestry patrol map, setting up multiple patrol points in the forest according to the forestry patrol map, and marking the patrol points in the map;

[0032] S102, monitoring the environmental information of each patrol point in real time through the sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence;

[0033] S103. Regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

[0034] Further, the S2 includes:

[0035] S201, the early warning system extracts the forestry environment data at each patrol point in the forestry patrol map respectively;

[0036] S202. At each patrol point, multiple coordinate systems are established with the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate. Change trend curves are drawn in the multiple coordinate systems according to the real-time monitored forestry environment data. The horizontal coordinates and vertical coordinates of the multiple coordinate systems are overlapped to establish a forestry safety trend chart.

[0037] Further, the S3 includes:

[0038] S301, set a threshold point on the ordinate, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line; the calculation formula of the value of the threshold point on the ordinate is:

[0039] Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z max is the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time;

[0040] S302, setting multiple monitoring points for each curve according to each time node, respectively calculating the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculating the number of monitoring points below the threshold point of the humidity curve, and obtaining the number of abnormal points of multiple curves.

[0041] Further, the S4 includes:

[0042] S401, setting a threshold value for the number of abnormal points, where the threshold value for the number of abnormal points is 2 / 3 of the number from the initial time node to the current time node;

[0043] S402: When the number of abnormal points of two or fewer curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a first-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map yellow; this is a mild warning, and the early warning system increases the frequency of environmental data collection to twice the original frequency;

[0044] S403. When the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a second-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map red. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

[0045] Beneficial effects of the present invention:

[0046] The present invention proposes a remote sensing-based intelligent forest fire monitoring and early warning system and method, which establishes a map including forest areas, patrol paths and patrol points for monitoring and managing forestry patrol work. Sensor equipment is installed at each patrol point to monitor forestry environmental data in real time, such as temperature, humidity, wind speed, smoke concentration, etc. A forestry safety trend chart is established by associating the real-time monitored forestry environmental data with time. This trend chart shows the changes in forestry environmental data and can help analyze and predict the forestry safety status. A threshold point is set on the ordinate of the forestry safety trend chart, and a threshold line is obtained based on the point. This threshold marks the critical point between safety and risk and can be used to judge abnormal situations. According to the positional relationship between the monitoring point and the threshold line, the number of abnormal points of multiple curves is calculated. A threshold value for the number of abnormal points is set, and the number of abnormal points of multiple curves is compared with the threshold value. If the number of abnormal points exceeds the threshold, early warning measures are triggered. According to the comparison results, corresponding early warning measures are taken, such as sending an alarm to notify patrol personnel, triggering an automated fire prevention system, and turning on sound and light early warning functions. The implementation of this early warning system can achieve real-time monitoring of the forestry environment at patrol points, and conduct comprehensive analysis and early warning of forestry safety conditions through forestry safety trend charts. This will help to identify abnormal situations in a timely manner and take appropriate measures to protect the safety and sustainable development of forest areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of a remote sensing-based intelligent forest fire monitoring and early warning method;

[0048] Figure 2 This is a schematic diagram of the forestry safety trend chart. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] In one embodiment of the present invention, a remote sensing-based intelligent forest fire monitoring and early warning system is proposed, the system comprising:

[0051] An environmental monitoring module is used to establish a forestry patrol map through an early warning system, set multiple patrol points in the forestry patrol map, and monitor the forestry environmental data of each patrol point;

[0052] The forestry safety trend chart establishment module is used to establish a forestry safety trend chart at each patrol point through real-time monitored forestry environment data and time;

[0053] An abnormality judgment module is used to set a threshold point on the ordinate of the forestry safety trend graph, obtain a threshold line according to the threshold point, and calculate the number of abnormal points of multiple curves according to the positional relationship between the monitoring points set on multiple curves of the forestry safety trend graph and the threshold line;

[0054] The early warning module is used to set a threshold value for the number of abnormal points, compare the number of abnormal points of the multiple curves with the threshold value for the abnormal points, and take corresponding early warning measures according to the comparison result.

[0055] The working principle of the above technical scheme is: an embodiment of the present invention, a remote sensing-based intelligent forest fire monitoring and early warning system and method, the system includes: an environmental monitoring module is used to establish a forestry patrol map through the early warning system, and set multiple patrol points in the forestry patrol map; and monitor the forestry environmental data of each patrol point; a forestry safety trend chart establishment module is used to establish a forestry safety trend chart at each patrol point through real-time monitored forestry environmental data and time; an abnormality judgment module is used to set a threshold point on the vertical coordinate of the forestry safety trend chart, obtain a threshold line according to the threshold point, and calculate the number of abnormal points of multiple curves according to the positional relationship between the monitoring points set on the multiple curves of the forestry safety trend chart and the threshold line; the early warning module is used to set an abnormal point number threshold, compare the number of abnormal points of the multiple curves with the abnormal point threshold, and take corresponding early warning measures according to the comparison result.

[0056] The technical effect of the above technical solution is as follows: the environmental monitoring module establishes a map including forest areas, patrol paths and patrol points for monitoring and managing forestry patrol work. Sensor equipment is installed at each patrol point to monitor forestry environmental data in real time, such as temperature, humidity, wind speed, smoke concentration, etc. The forestry safety trend chart establishment module establishes a forestry safety trend chart by associating the real-time monitored forestry environmental data with time. This trend chart shows the changes in forestry environmental data and can help analyze and predict forestry safety status. The abnormal judgment module sets a threshold point on the ordinate of the forestry safety trend chart and obtains a threshold line based on the point. This threshold marks the critical point between safety and risk and can be used to judge abnormal situations. According to the positional relationship between the monitoring point and the threshold line, the number of abnormal points of multiple curves is calculated. The early warning module sets a threshold value for the number of abnormal points and compares the number of abnormal points of multiple curves with the threshold. If the number of abnormal points exceeds the threshold, the early warning measures are triggered. According to the comparison results, corresponding early warning measures are taken, such as sending an alarm to notify patrol personnel, triggering an automated fire prevention system, and turning on the sound and light early warning function. The implementation of this early warning system can achieve real-time monitoring of the forestry environment at patrol points, and conduct comprehensive analysis and early warning of forestry safety conditions through forestry safety trend charts. This will help to identify abnormal situations in a timely manner and take appropriate measures to protect the safety and sustainable development of forest areas.

[0057] In one embodiment of the present invention, the environment monitoring module includes:

[0058] A point marking module is used to collect forestry geomorphic information through satellite remote sensing technology combined with monitoring devices to establish a forestry patrol map, set up multiple patrol points in the forest according to the forestry patrol map, and mark the patrol points in the map;

[0059] A data acquisition module is used to monitor the environmental information of each patrol point in real time through a sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence;

[0060] The map updating module is used to regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

[0061] The working principle of the above technical solution is: a point marking module is used to collect forestry geomorphic information through satellite remote sensing technology combined with a monitoring device to establish a forestry patrol map, set up multiple patrol points in the forest according to the forestry patrol map, and mark the patrol points in the map; a data acquisition module is used to monitor the environmental information of each patrol point in real time through a sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence; the sensor group includes a camera, a temperature and humidity sensor, a wind speed and direction sensor, and a smoke sensor; a map update module is used to regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

[0062] The technical effect of the above technical solution is: the point marking module uses satellite remote sensing technology to obtain the topographic information of the forest area, and establishes a forestry patrol map based on the information. This map accurately describes the topographic and geomorphic features of the forest. Set patrol points and mark: set multiple patrol points in the forestry patrol map, and mark these points. This makes it convenient for patrol personnel to understand and record the location and characteristics of each patrol point. Through the configured sensor group, the environmental information of each patrol point is monitored in real time. The sensor group of the data acquisition module includes a camera, a temperature and humidity sensor, a wind speed and direction sensor, and a smoke sensor. The sensor group collects and transmits forestry environmental data. These data include real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence. Patrol personnel can understand the forestry environment in real time. The map update module regularly re-collects forestry geomorphic information to ensure the accuracy and timeliness of the forestry patrol map. At the same time, the patrol map is updated or rebuilt to reflect the latest terrain and geomorphic conditions. This system realizes the effective monitoring and management of forestry patrol points and environment. Patrol personnel can quickly understand forest conditions and promptly detect abnormal conditions such as high temperature, low humidity or fire according to the annotations on forestry patrol maps and real-time forestry environmental data. At the same time, regular re-collection of landform information and map updates ensure the accuracy of maps and better support patrol work.

[0063] In one embodiment of the present invention, the forestry safety trend map establishing module includes:

[0064] A data extraction module, used to extract forestry environmental data at each patrol point in the forestry patrol map through the early warning system;

[0065] The trend chart establishment module is used to establish multiple coordinate systems at each patrol point, using the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate. According to the real-time monitored forestry environment data, change trend curves are drawn in the multiple coordinate systems respectively, and the horizontal and vertical coordinates of the multiple coordinate systems are overlapped to establish a forestry safety trend chart.

[0066] The working principle of the above technical solution is as follows: the data extraction module is used to extract the forestry environment data at each patrol point in the forestry patrol map through the early warning system; the trend chart establishment module is used to establish multiple coordinate systems at each patrol point, using the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate, and draw change trend curves in the multiple coordinate systems according to the real-time monitored forestry environment data, overlap the horizontal coordinates and vertical coordinates of the multiple coordinate systems, and establish a forestry safety trend chart, and the vertical coordinate at this time is called the comprehensive vertical coordinate, such as Figure 2 As shown. The forestry safety trend graph is set on the patrol point through a graphic design tool, and is triggered and viewed through a specific button. The forestry safety trend graph includes multiple change trend curves. After overlapping the horizontal and vertical coordinates of multiple coordinate systems, the values ​​of the vertical coordinates are reordered. The sorting values ​​can correspond to the time values, and can also be set by the user according to actual conditions. Each patrol point includes a forestry safety trend graph.

[0067] The technical effect of the above technical solution is as follows: the data extraction module of the early warning system extracts the forestry environment data at each patrol point from the forestry patrol map, including information such as temperature, humidity, wind speed and smoke concentration. The trend chart establishment module establishes multiple coordinate systems for each patrol point, taking time as the horizontal coordinate and the temperature, humidity, wind speed and smoke concentration of the forestry environment data as the vertical coordinate. According to the forestry environment data monitored in real time, the early warning system draws the corresponding change trend curve in each coordinate system to reflect the change of forestry environment data over time. The horizontal coordinates and vertical coordinates of multiple coordinate systems are overlapped to form a comprehensive forestry safety trend chart. This figure shows the comprehensive forestry environment change trend of each patrol point, which is used to analyze the forestry safety status of the patrol point. The forestry safety trend chart is set on the corresponding patrol point using a graphic design tool. The corresponding trend chart is triggered to display or view by a specific key or operation. The forestry safety trend chart includes multiple curves to represent the multi-faceted forestry environment change trends of the patrol point. The vertical coordinate values ​​of multiple coordinate systems are reordered to correspond to the time values. Users can also set the numerical sorting method according to actual conditions. The early warning system realizes the visual display and trend analysis of the forestry environmental data of patrol points. Users can use the forestry safety trend chart to comprehensively monitor and analyze the changes in the forestry environment of multiple patrol points, so as to identify abnormal situations in time and take corresponding early warning measures.

[0068] In one embodiment of the present invention, the abnormality judgment module includes:

[0069] A threshold setting module is used to set a threshold point on the vertical axis, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line;

[0070] The calculation formula of the value of the threshold point on the ordinate is:

[0071] Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z max is the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time;

[0072] The abnormal point calculation module is used to set multiple monitoring points for each curve according to each time node, calculate the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculate the number of monitoring points below the threshold point of the humidity curve, and obtain the number of abnormal points of multiple curves.

[0073] The working principle of the above technical solution is as follows: Figure 2 As shown, the threshold setting module is used to set a threshold point on the ordinate, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line; the value of the threshold point on the ordinate is calculated by the total number of curves, the maximum value of the ordinate corresponding to any curve, the minimum value of the ordinate corresponding to any curve, the value corresponding to the ordinate of any curve at the initial time, and the value corresponding to the ordinate of any curve at the current time; the initial time is the first time node of the horizontal axis, and the unit time and time node are set by the user according to the actual environment. The abnormal point calculation module is used to set multiple monitoring points for each curve according to each time node, respectively calculate the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculate the number of monitoring points below the threshold point of the humidity curve, and obtain the number of abnormal points of multiple curves. The wind direction of each time node of the patrol point is monitored, and the monitoring point of each time node of the wind speed curve is marked, and different wind directions are marked with different symbols; the wind direction corresponding to the marking symbol with the largest number of the same symbol markings on the wind speed curve is set as the wind direction of the current patrol point. The different symbols include triangle symbols, square symbols, circle symbols and polygonal symbols. The monitoring points are the intersection points of each curve and the ordinate at each time node, and the abnormal points are the monitoring points where the temperature curve, wind speed curve and smoke concentration curve are above the threshold line, and the monitoring points where the humidity curve is below the threshold point, that is, the values ​​that are not within the normal range.

[0074] The technical effect of the above technical solution is: the threshold setting module sets a threshold point on the ordinate as the starting point, and draws a horizontal line to the right as the threshold line. The data status of multiple curves is visualized by setting the threshold point and the threshold line, which is convenient for the system to judge the abnormal points, and multiple monitoring points are set for each curve according to each time node. The abnormal point calculation module of the early warning system can analyze and calculate the number of abnormal points of multiple curves according to the changing trend of the threshold line and the curve. This can help the system quickly identify whether the curve exceeds or falls below the preset threshold, so as to issue early warnings and take corresponding measures in time. This method provides an accurate calculation of the number of abnormal points to better judge the safety status of the forestry environment. The calculation of abnormal points combines multiple environmental factors and refers to the threshold point, making the judgment method of forestry safety more accurate and standardized. Make the reference of early warning more intelligent and automated.

[0075] In one embodiment of the present invention, the early warning module includes:

[0076] An abnormality threshold setting module, used to set an abnormality point quantity threshold, the abnormality point quantity threshold is 2 / 3 of the number from the initial time node to the current time node;

[0077] The first-level warning module is used to issue a first-level alarm when the number of abnormal points on two or fewer curves is greater than 1 / 2 of the threshold of the number of abnormal points. The LED lights connected to the patrol points corresponding to the curves on the map are lit as yellow lights. This is a mild warning, and the warning system increases the frequency of environmental data collection to twice the original frequency.

[0078] The secondary warning module is used to issue a secondary alarm when the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold. The early warning system will light up the LED lights connected to the patrol points corresponding to the curves in the map as red lights. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

[0079] The working principle of the above technical solution is as follows: the abnormal threshold setting module is used to set the abnormal point number threshold, and the abnormal point number threshold is 2 / 3 of the number from the initial time node to the current time node; the first-level warning module is used when the number of abnormal points of 2 or less curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a first-level alarm, and the LED lights connected to the patrol points corresponding to the curves in the map are lit as yellow lights; this is a mild warning, and the early warning system increases the frequency of environmental data collection to twice the original frequency; the second-level warning module is used when the number of abnormal points of at least 3 curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a second-level alarm, and the LED lights connected to the patrol points corresponding to the curves in the map are lit as red lights; this is a severe warning, the early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifies the supervisor to conduct on-site inspections, enters the inspection results into the database, generates early warning orders, and records and regularly monitors the warning-related information.

[0080] The technical effect of the above technical solution is: the threshold value of the number of abnormal points in the abnormal threshold setting module is 2 / 3 of the number from the initial time node to the current time node. This threshold is used to determine whether the number of abnormal points exceeds the preset threshold. When the number of abnormal points in 2 or less curves is greater than 1 / 2 of the threshold value of the number of abnormal points, the warning system issues a first-level alarm. At this time, the LED lights connected to the patrol points corresponding to these curves in the map are lit as yellow lights. This is a mild warning. When the number of abnormal points in at least 3 curves of the second-level warning module is greater than 1 / 2 of the threshold value of the number of abnormal points, the warning system issues a second-level alarm. At this time, the LED lights connected to the patrol points corresponding to these curves in the map are lit as red lights. This is a severe warning. Gradual warning is conducive to graded monitoring of forestry and taking graded measures, ensuring the timeliness of warning while making rational use of resources. For the first-level warning, the warning system increases the frequency of collecting environmental data to twice the original frequency. This can improve the real-time and accuracy of the data so as to monitor environmental changes more quickly. For the second-level warning, the warning system turns on the sound and light alarm for warning. The early warning system will also send early warning information to mobile terminals that are connected to the early warning system, notifying supervisors to conduct on-site inspections. The test results will be entered into a database to generate early warning forms, record and regularly monitor early warning related information. The early warning system can provide first-level and second-level warnings based on the number of abnormal points and preset thresholds, and provide alarms and notifications of different severity. This can help supervisors understand environmental abnormalities in a timely manner and take emergency measures to protect the safety of forest areas. The early warning system also provides data support and record management by increasing the frequency of data collection and recording test results to achieve more effective early warning and control.

[0081] In one embodiment of the present invention, the method comprises:

[0082] S1. Establish a forestry patrol map through the early warning system, set multiple patrol points in the forestry patrol map; and monitor the forestry environment data of each patrol point;

[0083] S2. At each patrol point, a forestry safety trend chart is established through real-time monitoring of forestry environmental data and time;

[0084] S3, setting a threshold point on the ordinate of the forestry safety trend graph, obtaining a threshold line according to the threshold point, and calculating the number of abnormal points of the multiple curves according to the positional relationship between the monitoring points set on the multiple curves of the forestry safety trend graph and the threshold line;

[0085] S4. Setting an abnormal point quantity threshold, comparing the abnormal point quantity of the plurality of curves with the abnormal point threshold, and taking corresponding early warning measures according to the comparison result.

[0086] The working principle of the above technical solution is: establish a forestry patrol map through an early warning system, set multiple patrol points in the forestry patrol map; and monitor the forestry environmental data of each patrol point; at each patrol point, establish a forestry safety trend chart through real-time monitored forestry environmental data and time; set a threshold point on the vertical coordinate of the forestry safety trend chart, obtain a threshold line according to the threshold point, and calculate the number of abnormal points of multiple curves based on the positional relationship between the monitoring points set on the multiple curves of the forestry safety trend chart and the threshold line; set an abnormal point number threshold, compare the number of abnormal points of the multiple curves with the abnormal point threshold, and take corresponding early warning measures according to the comparison result.

[0087] The technical effect of the above technical solution is: establish a map including forest areas, patrol paths and patrol points for monitoring and managing forestry patrol work. Install sensor equipment at each patrol point to monitor forestry environmental data in real time, such as temperature, humidity, wind speed, smoke concentration, etc. Establish a forestry safety trend chart by associating the real-time monitored forestry environmental data with time. The trend chart shows the changes in forestry environmental data and can help analyze and predict the forestry safety status. Set a threshold point on the ordinate of the forestry safety trend chart, and obtain a threshold line based on the point. This threshold marks the critical point between safety and risk and can be used to judge abnormal situations. Calculate the number of abnormal points of multiple curves based on the positional relationship between the monitoring point and the threshold line. Set a threshold for the number of abnormal points, and compare the number of abnormal points of multiple curves with the threshold. If the number of abnormal points exceeds the threshold, trigger early warning measures. Take corresponding early warning measures based on the comparison results, such as sending an alarm to notify patrol personnel, triggering an automated fire prevention system, and turning on sound and light early warning functions. The implementation of this early warning system can achieve real-time monitoring of the forestry environment at patrol points, and conduct comprehensive analysis and early warning of forestry safety conditions through forestry safety trend charts. This will help to identify abnormal situations in a timely manner and take appropriate measures to protect the safety and sustainable development of forest areas.

[0088] In one embodiment of the present invention, the S1 includes:

[0089] S101, using satellite remote sensing technology combined with monitoring devices to collect forestry geomorphic information to establish a forestry patrol map, setting up multiple patrol points in the forest according to the forestry patrol map, and marking the patrol points in the map;

[0090] S102, monitoring the environmental information of each patrol point in real time through the sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence;

[0091] S103. Regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

[0092] The working principle of the above technical solution is: to collect forestry geomorphic information through satellite remote sensing technology combined with monitoring equipment to establish a forestry patrol map, set up multiple patrol points in the forest according to the forestry patrol map, and mark the patrol points in the map; to monitor the environmental information of each patrol point in real time through a sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence; the sensor group includes a camera, a temperature and humidity sensor, a wind speed and direction sensor, and a smoke sensor; the forestry geomorphic information is re-collected regularly, and the forestry patrol map is updated or rebuilt.

[0093] The technical effect of the above technical solution is: using satellite remote sensing technology to obtain the geomorphic information of the forest area, and establishing a forestry patrol map based on the information. This map accurately describes the topography and geomorphic features of the forest. Setting patrol points and marking: setting multiple patrol points in the forestry patrol map, and marking these points. This makes it convenient for patrol personnel to understand and record the location and characteristics of each patrol point. Through the configured sensor group, the environmental information of each patrol point is monitored in real time. The sensor group includes a camera, a temperature and humidity sensor, a wind speed and direction sensor, and a smoke sensor. The sensor group collects and transmits forestry environmental data. These data include real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence. Patrol personnel can understand the forestry environmental conditions in real time. Regularly re-collect forestry geomorphic information to ensure the accuracy and timeliness of the forestry patrol map. At the same time, the patrol map is updated or rebuilt to reflect the latest terrain and geomorphic conditions. This system realizes the effective monitoring and management of forestry patrol points and environment. Patrol personnel can quickly understand forest conditions and promptly detect abnormal conditions such as high temperature, low humidity or fire according to the annotations on forestry patrol maps and real-time forestry environmental data. At the same time, regular re-collection of landform information and map updates ensure the accuracy of maps and better support patrol work.

[0094] In one embodiment of the present invention, S2 includes:

[0095] S201, the early warning system extracts the forestry environment data at each patrol point in the forestry patrol map respectively;

[0096] S202. At each patrol point, multiple coordinate systems are established with the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate. Change trend curves are drawn in the multiple coordinate systems according to the real-time monitored forestry environment data. The horizontal coordinates and vertical coordinates of the multiple coordinate systems are overlapped to establish a forestry safety trend chart.

[0097] The working principle of the above technical solution is as follows: the early warning system extracts the forestry environment data at each patrol point in the forestry patrol map; at each patrol point, multiple coordinate systems are established with the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the vertical coordinate and time as the horizontal coordinate, and change trend curves are drawn in the multiple coordinate systems according to the real-time monitored forestry environment data, and the horizontal and vertical coordinates of the multiple coordinate systems are overlapped to establish a forestry safety trend chart. The forestry safety trend chart is set on the patrol point through a graphic design tool, and is triggered and viewed through a specific button. The forestry safety trend chart includes multiple change trend curves. After the horizontal and vertical coordinates of the multiple coordinate systems are overlapped, the values ​​of the vertical coordinates are reordered. The sorting values ​​can correspond to the time values, or can be set by the user according to actual conditions.

[0098] The technical effect of the above technical solution is: the early warning system extracts forestry environmental data at each patrol point from the forestry patrol map, including information such as temperature, humidity, wind speed and smoke concentration. Multiple coordinate systems are established for each patrol point, with time as the horizontal coordinate and the temperature, humidity, wind speed and smoke concentration of the forestry environmental data as the vertical coordinate. According to the forestry environmental data monitored in real time, the early warning system draws a corresponding change trend curve in each coordinate system to reflect the change of forestry environmental data over time. The horizontal coordinates and vertical coordinates of multiple coordinate systems are overlapped to form a comprehensive forestry safety trend map. The map shows the comprehensive forestry environmental change trend of each patrol point, which is used to analyze the forestry safety status of the patrol point. The forestry safety trend map is set on the corresponding patrol point using a graphic design tool. The corresponding trend map is triggered to be displayed or viewed through a specific key or operation. The forestry safety trend map includes multiple curves to represent the multi-faceted forestry environmental change trends of the patrol point. The vertical coordinate values ​​of multiple coordinate systems are reordered to correspond to the time values. The user can also set the numerical sorting method according to the actual situation. The early warning system realizes the visualization and trend analysis of forestry environmental data at patrol points. Users can use the forestry safety trend chart to comprehensively monitor and analyze forestry environmental changes at multiple patrol points, so as to identify abnormal situations in a timely manner and take corresponding early warning measures.

[0099] In one embodiment of the present invention, S3 includes:

[0100] S301, set a threshold point on the ordinate, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line; the calculation formula of the value of the threshold point on the ordinate is:

[0101] Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z maxis the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time;

[0102] S302, setting multiple monitoring points for each curve according to each time node, respectively calculating the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculating the number of monitoring points below the threshold point of the humidity curve, and obtaining the number of abnormal points of multiple curves.

[0103] The working principle of the above technical solution is as follows: a threshold point is set on the ordinate, and a horizontal line is drawn to the right with the threshold point as the starting point as the threshold line; the value of the threshold point on the ordinate is calculated by the total number of curves, the maximum value of the ordinate corresponding to any curve, the minimum value of the ordinate corresponding to any curve, the value corresponding to the ordinate of any curve at the initial time, and the value corresponding to the ordinate of any curve at the current time; the initial time is the first time node of the abscissa, and the time node is set by the user according to the actual environment. For each curve, multiple monitoring points are set according to each time node, and the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve is calculated respectively, and the number of monitoring points below the threshold point of the humidity curve is calculated to obtain the number of abnormal points of multiple curves. The wind direction of each time node of the patrol point is monitored, and the monitoring point of each time node of the wind speed curve is marked, and different wind directions are marked with different symbols; the wind direction corresponding to the marking symbol with the largest number of the same symbol markings on the wind speed curve is set as the wind direction of the current patrol point. The different symbols include triangle symbols, square symbols, circle symbols and polygonal symbols. The monitoring point is the intersection point formed by each curve and the vertical coordinate at each time node. The abnormal point is the monitoring point where the temperature curve, wind speed curve and smoke concentration curve are above the threshold line, and the monitoring point where the humidity curve is below the threshold point, that is, the value is not in the normal range. The threshold point is the threshold used to judge the abnormal point.

[0104] The technical effect of the above technical solution is: a threshold point is set on the ordinate as the starting point, and a horizontal line is drawn to the right as the threshold line. The data status of multiple curves is visualized by setting the threshold point and the threshold line, which is convenient for the system to judge the abnormal points, and multiple monitoring points are set for each curve according to each time node. The early warning system can analyze and calculate the number of abnormal points of multiple curves according to the changing trend of the threshold line and the curve. This can help the system quickly identify whether the curve exceeds or falls below the preset threshold, so as to issue early warnings and take corresponding measures in time. This method provides an accurate calculation of the number of abnormal points to better judge the safety status of the forestry environment. The calculation of abnormal points combines multiple environmental factors and refers to the threshold point, making the judgment method of forestry safety more accurate and standardized. Make the reference of early warning more intelligent and automated.

[0105] In one embodiment of the present invention, the S4 includes:

[0106] S401, setting a threshold value for the number of abnormal points, where the threshold value for the number of abnormal points is 2 / 3 of the number from the initial time node to the current time node;

[0107] S402: When the number of abnormal points of two or fewer curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a first-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map yellow; this is a mild warning, and the early warning system increases the frequency of environmental data collection to twice the original frequency;

[0108] S403. When the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a second-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map red. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

[0109] The working principle of the above technical solution is: setting a threshold for the number of abnormal points, which is 2 / 3 of the number from the initial time node to the current time node; when the number of abnormal points of 2 or fewer curves is greater than 1 / 2 of the threshold for the number of abnormal points, the early warning system issues a first-level alarm, and turns on the LED lights connected to the patrol points corresponding to the curves in the map as yellow lights; this is a mild early warning, and the early warning system increases the frequency of environmental data collection to twice the original frequency; when the number of abnormal points of at least 3 curves is greater than 1 / 2 of the threshold for the number of abnormal points, the early warning system issues a second-level alarm, and turns on the LED lights connected to the patrol points corresponding to the curves in the map as red lights; this is a severe early warning, and the early warning system turns on the sound and light alarm for early warning, and sends the early warning information to the mobile terminal connected to the early warning system, notifies the supervisor to conduct on-site inspections, enters the inspection results into the database, generates early warning forms, and records and regularly monitors the early warning related information.

[0110] The technical effect of the above technical solution is: the threshold value of the number of abnormal points is 2 / 3 of the number from the initial time node to the current time node. This threshold is used to determine whether the number of abnormal points exceeds the preset threshold. If the number of abnormal points of 2 or less curves is greater than 1 / 2 of the threshold value of the number of abnormal points, the early warning system issues a first-level alarm. At this time, the LED lights connected to the patrol points corresponding to these curves in the map are lit as yellow lights. This is a mild warning. If the number of abnormal points of at least 3 curves is greater than 1 / 2 of the threshold value of the number of abnormal points, the early warning system issues a second-level alarm. At this time, the LED lights connected to the patrol points corresponding to these curves in the map are lit as red lights. This is a severe warning. Gradual warning is conducive to graded monitoring and graded measures for forestry, and ensures the timeliness of early warning while making rational use of resources. For the first-level warning, the early warning system increases the frequency of environmental data collection to twice the original frequency. This can improve the real-time and accuracy of the data so as to monitor environmental changes more quickly. For the second-level warning, the early warning system turns on the sound and light alarm for early warning. The early warning system also sends the early warning information to the mobile terminal connected to the early warning system to notify the supervisor to conduct on-site inspections. The test results will be entered into the database, and an early warning sheet will be generated to record and regularly monitor the early warning related information. The early warning system can provide primary and secondary warnings based on the number of abnormal points and preset thresholds, and provide alarms and notifications of different severity. This can help supervisors to understand environmental abnormalities in a timely manner and take emergency measures to protect the safety of forest areas. The early warning system also provides data support and record management by increasing the frequency of data collection and recording test results to achieve more effective early warning and control.

[0111] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A remote sensing-based intelligent forest fire monitoring and early warning system, characterized in that: The system comprises: An environmental monitoring module is used to establish a forestry patrol map through an early warning system, set multiple patrol points in the forestry patrol map, and monitor the forestry environmental data of each patrol point; The forestry safety trend chart establishment module is used to establish a forestry safety trend chart at each patrol point through real-time monitored forestry environment data and time; Wherein, the forestry safety trend map establishment module includes: A data extraction module, used to extract forestry environmental data at each patrol point in the forestry patrol map through the early warning system; A trend chart establishment module is used to establish multiple coordinate systems at each patrol point, using the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the ordinate and time as the abscissa, and draw change trend curves in the multiple coordinate systems according to the real-time monitored forestry environment data, overlap the abscissas and ordinates of the multiple coordinate systems, and establish a forestry safety trend chart; An abnormality judgment module is used to set a threshold point on the ordinate of the forestry safety trend graph, obtain a threshold line according to the threshold point, and calculate the number of abnormal points of multiple curves according to the positional relationship between the monitoring points set on multiple curves of the forestry safety trend graph and the threshold line; The early warning module is used to set a threshold value for the number of abnormal points, compare the number of abnormal points of the multiple curves with the threshold value for the number of abnormal points, and take corresponding early warning measures according to the comparison result.

2. According to claim 1, a remote sensing-based intelligent forest fire monitoring and early warning system is characterized in that: The environmental monitoring module comprises: A point marking module is used to collect forestry geomorphic information through satellite remote sensing technology combined with monitoring devices to establish a forestry patrol map, set up multiple patrol points in the forest according to the forestry patrol map, and mark the patrol points in the map; A data acquisition module is used to monitor the environmental information of each patrol point in real time through a sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence; The map updating module is used to regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

3. According to claim 1, a remote sensing-based intelligent forest fire monitoring and early warning system is characterized in that: The abnormality judgment module comprises: A threshold setting module is used to set a threshold point on the vertical axis, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line; The calculation formula of the value of the threshold point on the ordinate is: Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z max is the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time; The abnormal point calculation module is used to set multiple monitoring points for each curve according to each time node, calculate the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculate the number of monitoring points below the threshold point of the humidity curve, and obtain the number of abnormal points of multiple curves.

4. According to claim 1, a remote sensing-based intelligent forest fire monitoring and early warning system is characterized in that: The early warning module comprises: An abnormality threshold setting module, used to set an abnormality point quantity threshold, the abnormality point quantity threshold is 2 / 3 of the number from the initial time node to the current time node; The first-level warning module is used to issue a first-level alarm when the number of abnormal points on two or fewer curves is greater than 1 / 2 of the threshold of the number of abnormal points. The LED lights connected to the patrol points corresponding to the curves on the map are lit as yellow lights. This is a mild warning, and the warning system increases the frequency of environmental data collection to twice the original frequency. The secondary warning module is used to issue a secondary alarm when the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold. The early warning system will light up the LED lights connected to the patrol points corresponding to the curves in the map as red lights. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

5. A remote sensing-based intelligent forest fire monitoring and early warning method, characterized in that: The method comprises: S1. Establish a forestry patrol map through the early warning system, set multiple patrol points in the forestry patrol map; and monitor the forestry environment data of each patrol point; S2. At each patrol point, a forestry safety trend chart is established through real-time monitoring of forestry environmental data and time; Wherein, the S2 includes: S201, the early warning system extracts the forestry environment data at each patrol point in the forestry patrol map respectively; S202, at each patrol point, using the temperature, humidity, wind speed and smoke concentration of the forestry environment data of the patrol point as the ordinate and time as the abscissa, establishing multiple coordinate systems, drawing change trend curves in the multiple coordinate systems according to the real-time monitored forestry environment data, overlapping the abscissas and ordinates of the multiple coordinate systems, and establishing a forestry safety trend graph; S3, setting a threshold point on the ordinate of the forestry safety trend graph, obtaining a threshold line according to the threshold point, and calculating the number of abnormal points of the multiple curves according to the positional relationship between the monitoring points set on the multiple curves of the forestry safety trend graph and the threshold line; S4. Setting an abnormal point quantity threshold, comparing the abnormal point quantity of the plurality of curves with the abnormal point quantity threshold, and taking corresponding early warning measures according to the comparison result.

6. The remote sensing-based intelligent forest fire monitoring and early warning method according to claim 5 is characterized in that: The S1 includes: S101, using satellite remote sensing technology combined with monitoring devices to collect forestry geomorphic information to establish a forestry patrol map, setting up multiple patrol points in the forest according to the forestry patrol map, and marking the patrol points in the map; S102, monitoring the environmental information of each patrol point in real time through the sensor group to obtain forestry environmental data; the forestry environmental data includes real-time forestry temperature and humidity, real-time wind direction and speed, and fire occurrence; S103. Regularly re-collect forestry geomorphic information and update or rebuild the forestry patrol map.

7. The remote sensing-based intelligent forest fire monitoring and early warning method according to claim 5 is characterized in that: The S3 includes: S301, set a threshold point on the ordinate, and draw a horizontal line to the right with the threshold point as the starting point as the threshold line; the calculation formula of the value of the threshold point on the ordinate is: Among them, Y is the value of the threshold point on the ordinate, j is the total number of curves, and Z max is the maximum value of the ordinate corresponding to any curve, Z min is the minimum value of the ordinate corresponding to any one of the curves, D is the value corresponding to the ordinate of any one of the curves at the initial time, and U is the value corresponding to the ordinate of any one of the curves at the current time; S302, setting multiple monitoring points for each curve according to each time node, respectively calculating the number of monitoring points above the threshold line of the temperature curve, wind speed curve and smoke concentration curve, calculating the number of monitoring points below the threshold point of the humidity curve, and obtaining the number of abnormal points of multiple curves.

8. The remote sensing-based intelligent forest fire monitoring and early warning method according to claim 5 is characterized in that: The S4 includes: S401, setting a threshold value for the number of abnormal points, where the threshold value for the number of abnormal points is 2 / 3 of the number from the initial time node to the current time node; S402: When the number of abnormal points of two or fewer curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a first-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map yellow; this is a mild warning, and the early warning system increases the frequency of environmental data collection to twice the original frequency; S403. When the number of abnormal points of at least three curves is greater than 1 / 2 of the abnormal point number threshold, the early warning system issues a second-level alarm and turns the LED lights connected to the patrol points corresponding to the curves on the map red. This is a severe warning. The early warning system turns on the sound and light alarm for warning, and sends the warning information to the mobile terminal connected to the early warning system, notifying the supervisor to conduct on-site inspections, entering the inspection results into the database, generating an early warning form, and recording and regularly monitoring the warning-related information.

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