CN118097922A - Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis - Google Patents
Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis Download PDFInfo
- Publication number
- CN118097922A CN118097922A CN202410508455.8A CN202410508455A CN118097922A CN 118097922 A CN118097922 A CN 118097922A CN 202410508455 A CN202410508455 A CN 202410508455A CN 118097922 A CN118097922 A CN 118097922A
- Authority
- CN
- China
- Prior art keywords
- fire
- value
- agriculture
- forestry
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 136
- 238000012544 monitoring process Methods 0.000 claims abstract description 117
- 238000011156 evaluation Methods 0.000 claims abstract description 98
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000007726 management method Methods 0.000 claims description 114
- 238000001514 detection method Methods 0.000 claims description 40
- 238000004364 calculation method Methods 0.000 claims description 26
- 238000007689 inspection Methods 0.000 claims description 26
- 238000005259 measurement Methods 0.000 claims description 24
- 238000011835 investigation Methods 0.000 claims description 12
- 238000005286 illumination Methods 0.000 claims description 9
- 239000000779 smoke Substances 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 4
- 238000007405 data analysis Methods 0.000 claims 3
- 238000013480 data collection Methods 0.000 claims 3
- 230000011218 segmentation Effects 0.000 abstract description 5
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 241001122767 Theaceae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002420 orchard Substances 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Alarm Systems (AREA)
Abstract
The invention belongs to the technical field of agriculture and forestry safety supervision, and particularly relates to an agriculture and forestry area fire disaster intelligent early warning system based on data acquisition and analysis, which comprises an intelligent early warning platform, a segmentation evaluation module, a neighbor list evaluation module, a fire disaster real-time monitoring module and an agriculture and forestry management end; according to the invention, the fire risk degree of the agriculture and forestry area in the evaluation stage in the historical process is judged by analyzing by the sectional evaluation module, the weather performance condition of the agriculture and forestry area in the previous tracing period is analyzed by the adjacent table evaluation module when the low risk signal is generated, and the early warning is sent when the high risk signal or the agriculture and forestry strong supervision signal is generated, so that the safety of the agriculture and forestry area is guaranteed, and the fire real-time monitoring module is used for carrying out real-time monitoring analysis on all fire monitoring points of the agriculture and forestry area, reasonably judging the fire emergency degree when the fire early warning signal is generated, so that the effective monitoring and accurate feedback early warning of the agriculture and forestry area are realized, and the safety of the agriculture and forestry area is further ensured.
Description
Technical Field
The invention relates to the technical field of agriculture and forestry safety supervision, in particular to an agriculture and forestry area fire disaster intelligent early warning system based on data acquisition and analysis.
Background
An agricultural and forestry area generally refers to a specific piece of land mainly used for development of agriculture and forestry, including various types of land such as farmlands, orchards, tea gardens, forests, etc., by performing production activities such as planting, cultivation, picking, deforestation, etc., in the agricultural and forestry area to obtain resources such as agricultural products and wood, etc., and in the agricultural and forestry area, sustainable development of agriculture and forestry is ensured by managing and protecting natural resources;
The fire disaster is a common disaster in the agriculture and forestry area, and can bring great damage to the ecological environment, so that the historical fire disaster performance condition and the current weather condition are difficult to combine to accurately judge the fire disaster occurrence risk of the agriculture and forestry area, the comprehensive monitoring of the agriculture and forestry area is difficult to realize, the accurate fire early warning and the fire emergency assessment are timely carried out, the safety of the agriculture and forestry area is not guaranteed, and the intelligent degree is low at present;
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an intelligent early warning system for fire disaster in an agriculture and forestry area based on data acquisition and analysis, which solves the problems that in the prior art, historical fire disaster performance conditions and current weather conditions are difficult to combine to accurately judge the risk of fire disaster occurrence in the agriculture and forestry area, comprehensive monitoring of the agriculture and forestry area is difficult to realize, accurate early warning of fire disaster and emergency assessment of fire disaster are difficult to timely, safety of the agriculture and forestry area is not easy to guarantee, the intelligent degree is low, and management and control difficulty of the agriculture and forestry area is high.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent early warning system for fire disaster in an agriculture and forestry area based on data acquisition and analysis comprises an intelligent early warning platform, a sectional evaluation module, a neighbor table evaluation module, a fire disaster real-time monitoring module and an agriculture and forestry management terminal; the intelligent early warning platform acquires an agriculture and forestry area to be monitored and defines the agriculture and forestry area to be monitored as a management and control area, the sectional evaluation module divides each year into twenty-four stages, each stage corresponds to half a month respectively, the stage where the current date is located is defined as an evaluation stage, the fire risk degree of the management and control area in the evaluation stage in the history process is judged through analysis, an evaluation high risk signal or an evaluation low risk signal is generated, the evaluation high risk signal is sent to an agriculture and forestry management end through the intelligent early warning platform, and the evaluation low risk signal is sent to the adjacent table evaluation module through the intelligent early warning platform;
When the neighbor list evaluation module receives the low risk signal, the neighbor list evaluation module takes the current moment as a time end point to trace forward and sets a forward tracing period with the duration of P1, analyzes the weather performance condition of a management and control area in the forward tracing period, generates an agriculture and forestry strong supervision signal or an agriculture and forestry weak supervision signal, and sends the agriculture and forestry strong supervision signal or the agriculture and forestry weak supervision signal to an agriculture and forestry management end through an intelligent early warning platform; the fire real-time monitoring module sets a plurality of fire monitoring points in the management and control area, monitors all the fire monitoring points in real time, judges fire probability of the corresponding fire monitoring points through analysis, marks the corresponding fire monitoring points as risk points or safety points, generates fire early warning signals if the risk points exist in the management and control area, and sends the fire early warning signals and the corresponding risk points to the agriculture and forestry management end through the intelligent early warning platform.
Further, a specific analysis process for judging the fire risk degree in the evaluation stage in the history process by analysis is as follows:
The current year is used as the ending year to trace forward for n years and marked as the previous tracing year, and n is a positive integer greater than or equal to 5; the method comprises the steps of collecting the number of times of fire disaster occurring in a corresponding early year control area in an evaluation stage, marking the number of times as agriculture and forestry fire disaster frequency, collecting the influence area, the caused loss and the duration of each occurrence of the fire disaster, marking the influence area, the caused loss and the duration of each occurrence of the fire disaster as fire shadow data, fire damage data and fire duration data respectively, and carrying out numerical calculation on the fire shadow data, the fire damage data and the fire duration data to obtain a fire disaster meter value;
The fire hazard measurement method comprises the steps of comparing a fire hazard measurement value with a preset fire hazard measurement threshold value, marking the corresponding fire hazard measurement value as a fire hazard analysis value if the fire hazard measurement value exceeds the preset fire hazard measurement threshold value, and calculating the ratio of the number of the fire hazard analysis values to the agriculture and forestry fire hazard frequency to obtain a fire hazard high risk value; summing all fire measurement table values corresponding to the previous year control area in the evaluation stage, calculating and taking an average value to obtain a fire table analysis value, and carrying out numerical calculation on the fire table analysis value, the fire high risk value and the agriculture and forestry fire frequency to obtain a fire famine year analysis value; and carrying out summation calculation on fire annual analysis values of all previous years at an analysis terminal, taking an average value to obtain a fire evaluation value, carrying out numerical comparison on the fire evaluation value and a preset fire evaluation threshold value, and generating an evaluation high risk signal if the fire evaluation value exceeds the preset fire evaluation threshold value.
Further, if the fire evaluation value does not exceed the preset fire evaluation threshold, comparing the fire year analysis value of the corresponding previous tracing year with the preset fire famine year analysis threshold, and if the fire famine year analysis value exceeds the preset fire famine year analysis threshold, marking the corresponding previous tracing year as the risk table year;
Calculating the ratio of the number of the dangerous table years to the number of the previous tracing years to obtain a dangerous table number measured value, marking the dangerous table year closest to the current year as a dangerous adjacent year, and marking the interval time between the dangerous adjacent year and the current year as a dangerous adjacent detection value; performing numerical calculation on the risk neighbor detection value, the risk table number measurement value and the fire investigation value to obtain a fire investigation value, performing numerical comparison on the fire investigation value and a preset fire investigation threshold, and generating an investigation high risk signal if the fire investigation value exceeds the preset fire investigation threshold; and if the fire hazard analysis value does not exceed the preset fire hazard analysis threshold, generating an assessment low risk signal.
Further, a specific analysis process for analyzing the weather performance condition of the management and control area in the previous tracing period is as follows:
Collecting total rainfall of a control area of a front tracing period, collecting interval duration of a current moment from a last rainfall moment and rainfall of a last rainfall moment, marking the interval duration and the rainfall as rainfall adjacent interval duration and rainfall adjacent measured value, carrying out numerical calculation on the total rainfall, the rainfall adjacent interval duration and the rainfall adjacent measured value to obtain a front tracing rainfall detection value, carrying out numerical comparison on the front tracing rainfall detection value and a preset front tracing rainfall detection threshold, and generating an agriculture and forestry weak supervision signal if the front tracing rainfall detection value exceeds the preset front tracing rainfall detection threshold;
If the front tracing rainfall detection value does not exceed the preset front tracing rainfall detection threshold, judging that the control area is in a light overtime state when the illumination intensity exceeds the preset illumination intensity threshold, judging that the control area is in a wet state when the atmospheric humidity does not exceed the preset atmospheric humidity threshold, summing the total duration of the control area in the light overtime state and the total duration of the control area in the wet state in the front tracing period to obtain a light wet total duration, and marking the overlapped duration of the control area in the light overtime state and the wet state in the front tracing period as a light wet stacking duration;
Calculating the ratio of the average value of the illumination intensity and the average value of the atmospheric humidity in the front tracing period of the management and control area to obtain a light-humidity performance value, calculating the values of the front tracing rainfall detection value, the light-humidity performance value, the light-humidity total value and the light-humidity superposition value to obtain an agriculture and forestry monitoring value, comparing the agriculture and forestry monitoring value with a preset agriculture and forestry monitoring threshold value, and generating an agriculture and forestry strong monitoring signal if the agriculture and forestry monitoring value exceeds the preset agriculture and forestry monitoring threshold value; and if the agriculture and forestry monitoring value does not exceed the preset agriculture and forestry monitoring threshold value, generating an agriculture and forestry weak supervision signal.
Further, a specific analysis process for judging fire probability of fire monitoring points and marking the fire monitoring points as risk points or safety points by analysis is as follows:
Acquiring a smoke concentration value of a corresponding fire monitoring point, marking an excess value of the real-time temperature of the corresponding fire monitoring point compared with the atmospheric temperature as a monitoring Wen Chaozhi, and carrying out numerical calculation on the smoke concentration value and the monitoring Wen Chaozhi to obtain a fire analysis value; a preset fire analysis threshold value matched with the fire analysis threshold value is distributed to the management and control area, the fire analysis value is compared with the preset fire analysis threshold value in a numerical mode, and if the fire analysis value exceeds the preset fire analysis threshold value, the corresponding fire monitoring point is marked as a risk point; if the fire behavior analysis value does not exceed the preset fire behavior analysis threshold value, marking the corresponding fire behavior monitoring point as a safety point.
Further, after the corresponding fire monitoring points are marked as risk points or safety points, summing and calculating fire analysis values of all the risk points, taking an average value to obtain a fire comprehensive analysis value, collecting the number of the fire risk points and the number of the safety points in a management and control area, and calculating the ratio of the number of the risk points to the number of the safety points to obtain a fire risk occupation value;
the method comprises the steps of obtaining positions of all risk points, marking distances between any two groups of risk points as risk distance measurement values, summing all risk distance measurement values, and obtaining an average value to obtain a risk distance table value; and carrying out numerical calculation on the fire risk occupation value, the fire comprehensive analysis value and the risk distance table value to obtain a fire management easy value, carrying out numerical comparison on the fire management easy value and a preset fire management easy threshold value, generating a fire high emergency signal if the fire management easy value exceeds the preset fire management easy threshold value, and sending the fire high emergency signal to an agriculture and forestry management end through an intelligent early warning platform.
Further, the specific process of allocating the preset fire behavior analysis threshold value adapted to the management and control area is as follows:
If a high risk evaluation signal or an agriculture and forestry strong supervision signal of the management and control area is generated, a preset fire analysis threshold YP1 is distributed to the management and control area; if the agriculture and forestry weak supervision signal of the control area is generated, a preset fire analysis threshold YP2 is distributed to the control area, and YP2 is more than YP1 is more than 0.
Further, the intelligent early warning platform is in communication connection with the agriculture and forestry inspection management and control module, the agriculture and forestry inspection management and control module is used for setting a detection period, collecting the times of generating fire early warning signals and the times of generating fire high-emergency signals in a management area in the detection period, marking the times as a fire frequency analysis value and a high-emergency frequency analysis value respectively, and carrying out numerical calculation on the fire frequency analysis value and the high-emergency frequency analysis value to obtain a global inspection analysis value; comparing the global patrol value with a preset global patrol threshold value, if the global patrol value exceeds the preset global patrol threshold value, generating a global strict patrol signal of a management and control area, and transmitting the global strict patrol signal to an agriculture and forestry management end through an intelligent early warning platform;
If the global routing value does not exceed the preset global routing threshold, marking the corresponding fire monitoring point as the dangerous point times in the detection period and positioning the corresponding fire monitoring point as a dangerous frequency marking value, marking the interval duration between the moment when the corresponding fire monitoring point is marked as the dangerous point last time and the current moment as a dangerous time-interval value, and carrying out numerical calculation on the dangerous frequency marking value and the dangerous time-interval value to obtain a monitoring ignition risk value; comparing the value of the monitored ignition risk with a preset monitored ignition risk threshold value, and generating a point position strict inspection signal corresponding to a fire monitoring point if the value of the monitored ignition risk exceeds the preset monitored ignition risk threshold value; and the point position severe inspection signals and the corresponding fire monitoring points are sent to the agriculture and forestry management end through the intelligent early warning platform.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, the fire risk degree of the agriculture and forestry area in the evaluation stage in the history process is judged by analyzing by the segmentation evaluation module, the weather performance condition of the agriculture and forestry area in the front tracing period is analyzed by the neighbor list evaluation module when the evaluation low risk signal is generated, and early warning is sent when the evaluation high risk signal or agriculture and forestry strong supervision signal is generated, so that a background manager is reminded to strengthen the fire monitoring management and control of the agriculture and forestry area, the background manager can conveniently and reasonably and quickly formulate a matched management scheme, and the safety of the agriculture and forestry area is ensured;
2. According to the invention, all fire monitoring points in the agriculture and forestry area are monitored and analyzed in real time through the fire real-time monitoring module, the fire emergency degree is reasonably judged in the generated fire early warning signal, the effective monitoring and accurate feedback early warning of the agriculture and forestry area are realized, the safety of the agriculture and forestry area is further ensured, the management and control planning analysis is carried out through the agriculture and forestry inspection management and control module to judge whether the global severe inspection signal is generated, if the global severe inspection signal is not generated, the point severe inspection signal of the corresponding fire monitoring point is judged whether to be generated, the inspection is more targeted, the safety of the agriculture and forestry area is obviously improved, and the management and control difficulty of the agriculture and forestry area is reduced.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the intelligent early warning system for the fire disaster in the agriculture and forestry area based on the data acquisition and analysis comprises an intelligent early warning platform, a segmentation evaluation module, an adjacent meter evaluation module, a fire disaster real-time monitoring module and an agriculture and forestry management end, wherein the intelligent early warning platform is in communication connection with the segmentation evaluation module, the adjacent meter evaluation module, the fire disaster real-time monitoring module and the agriculture and forestry management end;
The intelligent early warning platform acquires an agriculture and forestry area to be monitored and defines the agriculture and forestry area to be monitored as a management and control area, the sectional inspection and evaluation module divides each year into twenty-four stages, each stage corresponds to half month respectively, the stage where the current date is located is defined as an evaluation stage, the fire risk degree of the management and control area in the evaluation stage in the history process is judged through analysis, an evaluation high risk signal or an evaluation low risk signal is generated, the evaluation high risk signal is sent to an agriculture and forestry management end through the intelligent early warning platform, corresponding early warning is sent when the agriculture and forestry management end receives the evaluation high risk signal, so that a background manager is reminded to strengthen fire monitoring and control of the agriculture and forestry area, the fire risk degree of the history year agriculture and forestry area in the evaluation stage can be accurately judged, a management scheme which is reasonably and rapidly matched with the background manager is facilitated, and safety of the agriculture and forestry area is guaranteed; the specific analysis process for judging the fire risk degree of the evaluation stage in the history process by analysis is as follows:
The current year is used as the ending year to trace forward for n years and marked as the previous tracing year, and n is a positive integer greater than or equal to 5; the method comprises the steps of collecting the number of times of fire disaster occurring in a corresponding early year control area in an evaluation stage, marking the number of times as agriculture and forestry fire disaster frequency, collecting the influence area of each occurrence of the fire disaster, the loss caused by the fire disaster (mainly referring to economic loss amount caused by the fire disaster) and the duration, and marking the influence area, the loss caused by the fire disaster and the duration as fire disaster shadow data, fire disaster damage data and fire disaster duration data respectively; the larger the values of the fire shadow data, the fire damage data and the fire duration data are, the more serious the corresponding fire is;
Performing numerical calculation on the fire shadow data TK, the fire damage data TP and the fire duration data TS according to a formula TY= (eq1+eq2) TP+eq3)/3 to obtain a fire measurement table value TY; wherein, eq1, eq2 and eq3 are preset proportionality coefficients, and the values of eq1, eq2 and eq3 are all larger than zero; and, the larger the value of the fire meter value TY, the more serious the corresponding fire is;
the fire disaster measuring table value TY is compared with a preset fire disaster measuring table threshold value, if the fire disaster measuring table value TY exceeds the preset fire disaster measuring table threshold value, the corresponding fire disaster measuring table value is marked as a fire disaster analysis value, the ratio of the number of fire disaster analysis values to the agriculture and forestry fire disaster frequency is calculated to obtain a fire disaster high risk value, and all the fire disaster measuring table values corresponding to the previous year management and control area in the analysis stage are summed up and calculated and averaged to obtain the fire disaster analysis value;
By the formula Performing numerical calculation on the fire disaster table analysis value TZ, the fire disaster high risk value TG and the agriculture and forestry fire disaster frequency TQ to obtain a fire famine year analysis value YN; wherein, es1, es2 and es3 are preset proportionality coefficients, and es2 is more than es3 is more than es1 is more than 0; and the larger the numerical value of the fire annual analysis value YN is, the more serious the fire performance of the corresponding early year control area in the evaluation stage is;
Summing fire annual analysis values of all previous years at an analysis end, and taking an average value to obtain a fire evaluation value, wherein the larger the number of the fire evaluation value is, the larger the fire risk of the management and control area at the analysis stage is; and comparing the fire evaluation value with a preset fire evaluation threshold value, and if the fire evaluation value exceeds the preset fire evaluation threshold value, indicating that the greater the fire risk of the control area in the evaluation stage, generating an evaluation high risk signal.
Further, if the fire evaluation value does not exceed the preset fire evaluation threshold, comparing the fire annual analysis value of the corresponding previous tracing year with the preset fire famine year analysis threshold, and if the fire famine year analysis value exceeds the preset fire famine year analysis threshold, marking the corresponding previous tracing year as the risk table year; calculating the ratio of the number of the dangerous table years to the number of the previous tracing years to obtain a dangerous table number measured value, marking the dangerous table year closest to the current year as a dangerous adjacent year, and marking the interval time between the dangerous adjacent year and the current year as a dangerous adjacent detection value;
Calculating the risk neighbor detection value MY, the risk table number measurement value MP and the fire hazard evaluation value MX according to a formula MK=c1/(MY+0.532) +c2+c3, wherein c1, c2 and c3 are preset proportionality coefficients, and the values of c1, c2 and c3 are positive numbers; moreover, the larger the value of the fire tracing value MK is, the greater the historical fire risk degree of the control area in the evaluation period is; the fire tracing value MK is compared with a preset fire tracing threshold value, and if the fire tracing value MK exceeds the preset fire tracing threshold value, the condition that the historical fire risk degree of the control area in the evaluation period is large is indicated, a tracing high risk signal is generated; if the fire hazard analysis value MK does not exceed the preset fire hazard analysis threshold, the historical fire hazard risk degree of the control area in the analysis period is smaller, and a hazard assessment low risk signal is generated.
The sectional evaluation module sends the low risk evaluation signal to the adjacent table evaluation module through the intelligent early warning platform, and when the adjacent table evaluation module receives the low risk evaluation signal, the adjacent table evaluation module takes the current moment as a time end point to trace forward and sets a forward tracing period with the duration of P1, and the preferential forward tracing period is 150 hours; analyzing the weather performance of the management and control area in a previous tracing period, generating an agriculture and forestry strong supervision signal or an agriculture and forestry weak supervision signal, transmitting the agriculture and forestry strong supervision signal or the agriculture and forestry weak supervision signal to an agriculture and forestry management end through an intelligent early warning platform, and sending out corresponding early warning when the agriculture and forestry strong supervision signal is received by the agriculture and forestry management end so as to remind a background manager to strengthen fire monitoring and management and control of the agriculture and forestry area, so that the current fire risk degree of the agriculture and forestry area can be accurately judged, and a matched management scheme is formulated reasonably and quickly by the background manager conveniently, and the safety of the agriculture and forestry area is further ensured; the specific analysis process for analyzing the weather performance of the management and control area in the previous tracing period is as follows:
Collecting total rainfall of a control area of a previous tracing period, collecting interval duration of a current moment from a last rainfall moment and rainfall of a last rainfall moment, marking the interval duration and the rainfall as rainfall neighbor interval duration and rainfall neighbor measurement values, and carrying out numerical calculation on the total rainfall WP, the rainfall neighbor interval duration WS and the rainfall neighbor measurement value WL through a formula WY=kp1+kp2/(WS+kp3) +kp3+kp3 to obtain a previous tracing rainfall detection value WY, wherein kp1, kp2 and kp3 are preset proportionality coefficients, and the values of kp1, kp2 and kp3 are positive numbers; moreover, the larger the value of the forward-tracing rainfall detection value WY is, the less fire disaster is easy to occur in the agriculture and forestry area due to rainfall factors; comparing the front rainfall tracing detection value WY with a preset front rainfall tracing detection threshold value, and generating an agriculture and forestry weak supervision signal if the front rainfall tracing detection value WY exceeds the preset front rainfall tracing detection threshold value, which indicates that the agriculture and forestry area is not easy to fire currently;
If the front tracing rainfall detection value WY does not exceed a preset front tracing rainfall detection threshold value, judging that the management and control area is in a light overload state when the illumination intensity exceeds a preset illumination intensity threshold value (namely, the agriculture and forestry area at the corresponding moment is hot), judging that the management and control area is in a wet state when the atmospheric humidity does not exceed a preset atmospheric humidity threshold value (namely, the agriculture and forestry area at the corresponding moment is dry), summing the total duration of the management and control area in the light overload state and the total duration of the wet state in the front tracing period to obtain a light-humidity total value, marking the overlapped duration of the light overload state and the wet state in the front tracing period as a light-humidity superposition value, and calculating the ratio of the average value of the illumination intensity and the average value of the atmospheric humidity in the front tracing period of the management and control area to obtain a light-humidity appearance value;
Carrying out numerical calculation on a front tracing rainfall detection value WY, a light humidity representation value RS, a light humidity total value RK and a light humidity superposition value RF through a formula RX= (f2×RS+f3×RK+f4×RF)/(f1×WY+1) to obtain an agriculture and forestry monitoring value RX, wherein f1, f2, f3 and f4 are preset proportionality coefficients, and the values of f1, f2, f3 and f4 are all larger than zero; moreover, the larger the value of the agriculture and forestry monitoring value RX is, the more easily the agriculture and forestry area is in comprehensive terms; comparing the agriculture and forestry monitoring value RX with a preset agriculture and forestry monitoring threshold value, and generating an agriculture and forestry strong monitoring signal if the agriculture and forestry monitoring value RX exceeds the preset agriculture and forestry monitoring threshold value, which indicates that the agriculture and forestry area is easy to fire currently; if the agriculture and forestry monitoring value RX does not exceed the preset agriculture and forestry monitoring threshold value, indicating that the agriculture and forestry area is not easy to fire, generating agriculture and forestry weak supervision signals.
The fire real-time monitoring module sets a plurality of fire monitoring points in the management and control area, monitors all the fire monitoring points in real time, judges the fire probability of the corresponding fire monitoring points through analysis, marks the corresponding fire monitoring points as risk points or safety points, generates fire early warning signals if the risk points exist in the management and control area, sends the fire early warning signals and the corresponding risk points to an agriculture and forestry management end through the intelligent early warning platform, sends corresponding early warning when the agriculture and forestry management end receives the fire early warning signals, effectively monitors the agriculture and forestry area and accurately feeds back the fire conditions to remind a background manager to arrange personnel to process before, and the safety of the agriculture and forestry area is guaranteed; the specific analysis process for judging the fire probability of the fire monitoring points and marking the fire monitoring points as risk points or safety points by analysis is as follows:
The smoke concentration value of the corresponding fire monitoring point is collected, the excess value of the real-time temperature of the corresponding fire monitoring point compared with the atmospheric temperature is marked as a monitoring Wen Chaozhi, and the smoke concentration value HQ and the monitoring Wen Chaozhi HW are subjected to numerical calculation through the formula HY=b1 HQ+b2 HW to obtain a fire analysis value HY; wherein b1 and b2 are preset weight coefficients, and b1 is more than b2 is more than 0; and, the larger the value of the fire analysis value HY is, the larger the probability of fire occurrence of the corresponding fire monitoring point is;
the preset fire analysis threshold value matched with the management and control area is distributed to the management and control area, and the specific steps are as follows: if a high risk evaluation signal or an agriculture and forestry strong supervision signal of the management and control area is generated, a preset fire analysis threshold YP1 is distributed to the management and control area; if an agriculture and forestry weak supervision signal of the management and control area is generated, a preset fire analysis threshold YP2 is distributed to the management and control area, YP2 is larger than YP1 and larger than 0, and the corresponding preset fire analysis threshold is reasonably distributed to improve the accuracy of a corresponding analysis result;
Comparing the fire behavior analysis value HY with a preset fire behavior analysis threshold value, and marking the corresponding fire behavior monitoring point as a risk point if the fire behavior analysis value HY exceeds the preset fire behavior analysis threshold value, which indicates that the probability of fire behavior of the corresponding fire behavior monitoring point is high; if the fire analysis value HY does not exceed the preset fire analysis threshold, the probability of fire occurrence of the corresponding fire monitoring point is smaller, and the corresponding fire monitoring point is marked as a safety point.
Further, after marking the corresponding fire monitoring points as risk points or safety points, summing and calculating fire analysis values of all the risk points and taking an average value to obtain a fire comprehensive analysis value, collecting the number of the risk points and the number of the safety points in the management and control area, and calculating the ratio of the number of the risk points to the number of the safety points to obtain a fire occupation value; the method comprises the steps of obtaining positions of all risk points, marking distances between any two groups of risk points as risk distance measurement values, summing all risk distance measurement values, and obtaining an average value to obtain a risk distance table value; the larger the value of the risk distance table value is, the less concentrated the distribution of all risk points is, and the greater the current fire processing difficulty is;
By the formula Carrying out numerical calculation on the fire risk occupation value GF, the fire analysis value GZ and the risk distance table value GK to obtain a fire management easy value GY, wherein eg1, eg2 and eg3 are preset proportion coefficients, and the values of eg1, eg2 and eg3 are positive numbers; moreover, the larger the value of the fire management easy value GY is, the greater the current fire processing difficulty of the agriculture and forestry area is, and the more urgent the fire condition is; and comparing the fire management easy value GY with a preset fire management easy threshold value, if the fire management easy value GY exceeds the preset fire management easy threshold value, generating a fire high emergency signal, and sending the fire high emergency signal to an agriculture and forestry management end through an intelligent early warning platform, and sending out corresponding early warning when the agriculture and forestry management end receives the fire high emergency signal so as to remind a background manager to arrange personnel in time to process and increase personnel investment, thereby ensuring fire treatment timeliness.
Embodiment two: as shown in fig. 2, the difference between the embodiment and embodiment 1 is that the intelligent early warning platform is in communication connection with the agriculture and forestry inspection management and control module, and the agriculture and forestry inspection management and control module is used for setting a detection period, preferably thirty-five days; collecting the times of generating fire early warning signals and the times of generating fire high emergency signals in a control area in a detection period, marking the times as a fire frequency analysis value and a high emergency frequency analysis value respectively, and carrying out numerical calculation on the fire frequency analysis value QP and the high emergency frequency analysis value QK through a formula QY= (a1×QP+a2×QK)/2 to obtain a global patrol analysis value QY; wherein a1 and a2 are preset proportionality coefficients, and a2 is more than a1 and more than 0;
It should be noted that, the larger the value of the global patrol value QY, the more the global patrol of the management and control area needs to be enhanced, so as to reduce the fire risk of the corresponding agriculture and forestry area; comparing the global patrol value QY with a preset global patrol threshold value, if the global patrol value QY exceeds the preset global patrol threshold value, indicating that the global patrol of the management and control area needs to be enhanced in time, generating a global strict patrol signal of the management and control area, and transmitting the global strict patrol signal to an agriculture and forestry management end through an intelligent early warning platform; the agriculture and forestry management side receives the global inspection early warning signal and sends out corresponding early warning to remind a background manager to timely strengthen global inspection of the management and control area so as to effectively reduce fire occurrence risks of corresponding agriculture and forestry areas;
if the global routing value QY does not exceed the preset global routing threshold, marking the corresponding fire monitoring point as a dangerous point number of times in a detection period and positioning the corresponding fire monitoring point as a dangerous frequency value, marking the interval duration between the moment of marking the corresponding fire monitoring point as the dangerous point last time and the current moment as a dangerous interval value, and carrying out numerical calculation on the dangerous frequency value QF and the dangerous interval value QT through a formula QW=ew1+ew2 QT/ew1 to obtain a monitoring ignition risk value QW; wherein, ew1 and ew2 are preset proportionality coefficients, and the ratio of the ew1 to the ew2 is more than 0; moreover, the larger the value of the fire risk value QW of the monitoring point is, the larger the fire occurrence risk of the corresponding fire monitoring point in the detection period is, and the more important inspection management and control is needed;
Comparing the value QW of the monitoring ignition risk with a preset monitoring ignition risk threshold value, and if the value QW of the monitoring ignition risk exceeds the preset monitoring ignition risk threshold value, indicating that the risk of fire occurrence of the corresponding fire monitoring point in the detection period is large, generating a point position strict inspection signal of the corresponding fire monitoring point; and the intelligent early warning platform sends the point position severe inspection signals and the corresponding fire monitoring points to the agriculture and forestry management end, and the agriculture and forestry management end sends corresponding early warning when receiving the point position severe inspection signals so as to remind a background manager to timely strengthen inspection management and control of the corresponding fire monitoring points, so that inspection is more targeted, the fire occurrence probability of the corresponding fire monitoring points is reduced, and the safety of agriculture and forestry areas is further ensured.
The working principle of the invention is as follows: when the system is used, the analysis is carried out through the segmentation evaluation module to judge the fire risk degree of the agriculture and forestry area in the evaluation stage in the history process, a high risk evaluation signal or a low risk evaluation signal is generated, a front tracing period is set through the neighbor table evaluation module when the low risk evaluation signal is generated, the weather performance condition of the agriculture and forestry area in the front tracing period is analyzed, an agriculture and forestry strong supervision signal or an agriculture and forestry weak supervision signal is generated, and early warning is sent out when the high risk evaluation signal or the agriculture and forestry strong supervision signal is generated to remind a background manager to strengthen the fire monitoring management and control of the agriculture and forestry area, so that the background manager can conveniently and reasonably and rapidly formulate a matched management scheme, and the safety of the agriculture and forestry area is guaranteed; and a plurality of fire monitoring points are set in the management and control area through the fire real-time monitoring module, real-time monitoring analysis is carried out on all the fire monitoring points so as to determine risk points and safety points, if the risk points exist in the management and control area, a fire early warning signal is generated, the fire urgency degree is judged, the effective monitoring on the agriculture and forestry area is realized, the fire condition is accurately fed back, and the safety of the agriculture and forestry area is further ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The intelligent early warning system for the fire disaster in the agriculture and forestry area based on the data acquisition and analysis is characterized by comprising an intelligent early warning platform, a sectional evaluation module, an adjacent meter evaluation module, a fire disaster real-time monitoring module and an agriculture and forestry management end; the intelligent early warning platform acquires an agriculture and forestry area to be monitored and defines the agriculture and forestry area to be monitored as a management and control area, the sectional evaluation module divides each year into twenty-four stages, each stage corresponds to half a month respectively, the stage where the current date is located is defined as an evaluation stage, the fire risk degree of the management and control area in the evaluation stage in the history process is judged through analysis, an evaluation high risk signal or an evaluation low risk signal is generated, the evaluation high risk signal is sent to an agriculture and forestry management end through the intelligent early warning platform, and the evaluation low risk signal is sent to the adjacent table evaluation module through the intelligent early warning platform;
When the neighbor list evaluation module receives the low risk signal, the neighbor list evaluation module takes the current moment as a time end point to trace forward and sets a forward tracing period with the duration of P1, analyzes the weather performance condition of a management and control area in the forward tracing period, generates an agriculture and forestry strong supervision signal or an agriculture and forestry weak supervision signal, and sends the agriculture and forestry strong supervision signal or the agriculture and forestry weak supervision signal to an agriculture and forestry management end through an intelligent early warning platform; the fire real-time monitoring module sets a plurality of fire monitoring points in the management and control area, monitors all the fire monitoring points in real time, judges fire probability of the corresponding fire monitoring points through analysis, marks the corresponding fire monitoring points as risk points or safety points, generates fire early warning signals if the risk points exist in the management and control area, and sends the fire early warning signals and the corresponding risk points to the agriculture and forestry management end through the intelligent early warning platform.
2. The intelligent early warning system for fire disaster in agriculture and forestry area based on data collection and analysis according to claim 1, wherein the specific analysis process for judging fire disaster risk degree in the evaluation stage in the history process is as follows:
The current year is used as the ending year to trace forward for n years and marked as the previous tracing year, and n is a positive integer greater than or equal to 5; the method comprises the steps of collecting the number of times of fire disaster occurring in a corresponding early year control area in an evaluation stage, marking the number of times as agriculture and forestry fire disaster frequency, collecting the influence area, the caused loss and the duration of each occurrence of the fire disaster, marking the influence area, the caused loss and the duration of each occurrence of the fire disaster as fire shadow data, fire damage data and fire duration data respectively, and carrying out numerical calculation on the fire shadow data, the fire damage data and the fire duration data to obtain a fire disaster meter value;
If the fire hazard measurement value exceeds the preset fire hazard measurement threshold, marking the corresponding fire hazard measurement value as a fire hazard measurement value, and calculating the ratio of the number of the fire hazard measurement values to the agriculture and forestry fire hazard frequency to obtain a fire hazard high risk value; summing all fire measurement table values corresponding to the previous year control area in the evaluation stage, calculating and taking an average value to obtain a fire table analysis value, and carrying out numerical calculation on the fire table analysis value, the fire high risk value and the agriculture and forestry fire frequency to obtain a fire famine year analysis value; and carrying out summation calculation on fire annual analysis values of all previous tracing years at an analysis terminal, and taking an average value to obtain a fire evaluation value, and if the fire evaluation value exceeds a preset fire evaluation threshold value, generating an evaluation high risk signal.
3. The intelligent early warning system for fire disaster in agriculture and forestry area based on data collection and analysis according to claim 2, wherein if the fire disaster follow-up evaluation value does not exceed the preset fire disaster follow-up evaluation threshold, the fire disaster annual analysis value of the corresponding previous tracing year is compared with the preset fire famine year analysis threshold, and if the fire famine year analysis value exceeds the preset fire famine year analysis threshold, the corresponding previous tracing year is marked as the risk table year;
Calculating the ratio of the number of the dangerous table years to the number of the previous tracing years to obtain a dangerous table number measured value, marking the dangerous table year closest to the current year as a dangerous adjacent year, and marking the interval time between the dangerous adjacent year and the current year as a dangerous adjacent detection value; performing numerical calculation on the risk neighbor detection value, the risk table number measurement value and the fire investigation value to obtain a fire investigation value, and generating investigation high risk signals if the fire investigation value exceeds a preset fire investigation threshold; and if the fire hazard analysis value does not exceed the preset fire hazard analysis threshold, generating an assessment low risk signal.
4. The intelligent early warning system for the fire disaster in the agriculture and forestry area based on the data acquisition and analysis according to claim 1 is characterized in that the specific analysis process for analyzing the weather performance condition of the management and control area in the previous tracing period is as follows:
Collecting total rainfall of a control area of a front tracing period, collecting interval duration of a current moment from a last rainfall moment and rainfall of a last rainfall moment, marking the interval duration and the rainfall as rainfall adjacent interval duration and rainfall adjacent measured value, carrying out numerical calculation on the total rainfall, the rainfall adjacent interval duration and the rainfall adjacent measured value to obtain a front tracing rainfall detection value, and generating an agriculture and forestry weak supervision signal if the front tracing rainfall detection value exceeds a preset front tracing rainfall detection threshold;
If the front tracing rainfall detection value does not exceed the preset front tracing rainfall detection threshold, judging that the control area is in a light overtime state when the illumination intensity exceeds the preset illumination intensity threshold, judging that the control area is in a wet state when the atmospheric humidity does not exceed the preset atmospheric humidity threshold, summing the total duration of the control area in the light overtime state and the total duration of the control area in the wet state in the front tracing period to obtain a light wet total duration, and marking the overlapped duration of the control area in the light overtime state and the wet state in the front tracing period as a light wet stacking duration;
Calculating the ratio of the average value of the illumination intensity and the average value of the atmospheric humidity in the front tracing period of the management and control area to obtain a light-humidity performance value, calculating the front tracing rainfall detection value, the light-humidity performance value, the light-humidity total value and the light-humidity superposition value to obtain an agriculture and forestry monitoring value, and generating an agriculture and forestry strong supervision signal if the agriculture and forestry monitoring value exceeds a preset agriculture and forestry monitoring threshold value; and if the agriculture and forestry monitoring value does not exceed the preset agriculture and forestry monitoring threshold value, generating an agriculture and forestry weak supervision signal.
5. The intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis according to claim 1, wherein the specific analysis process of judging fire disaster probability of fire monitoring points and marking the fire monitoring points as risk points or safety points by analysis is as follows:
Acquiring a smoke concentration value of a corresponding fire monitoring point, marking an excess value of the real-time temperature of the corresponding fire monitoring point compared with the atmospheric temperature as a monitoring Wen Chaozhi, and carrying out numerical calculation on the smoke concentration value and the monitoring Wen Chaozhi to obtain a fire analysis value; a preset fire analysis threshold value matched with the fire analysis threshold value is distributed to the management and control area, and if the fire analysis value exceeds the preset fire analysis threshold value, the corresponding fire monitoring point is marked as a risk point; if the fire behavior analysis value does not exceed the preset fire behavior analysis threshold value, marking the corresponding fire behavior monitoring point as a safety point.
6. The intelligent early warning system for the fire disaster in the agriculture and forestry area based on the data acquisition and analysis is characterized in that after corresponding fire monitoring points are marked as risk points or safety points, the fire analysis values of all the risk points are summed, calculated and averaged to obtain a fire analysis value, the number of risk points and the number of safety points in a management and control area are acquired, and the ratio of the number of the risk points to the number of the safety points is calculated to obtain a fire risk occupation value;
The method comprises the steps of obtaining positions of all risk points, marking distances between any two groups of risk points as risk distance measurement values, summing all risk distance measurement values, and obtaining an average value to obtain a risk distance table value; and carrying out numerical calculation on the fire hazard occupation value, the fire comprehensive analysis value and the risk distance table value to obtain a fire management easy value, if the fire management easy value exceeds a preset fire management easy threshold value, generating a fire high emergency signal, and sending the fire high emergency signal to an agriculture and forestry management end through an intelligent early warning platform.
7. The intelligent early warning system for fire disaster in agriculture and forestry area based on data collection and analysis according to claim 5, wherein the specific process of distributing the preset fire analysis threshold adapted to the management and control area is as follows:
If a high risk evaluation signal or an agriculture and forestry strong supervision signal of the management and control area is generated, a preset fire analysis threshold YP1 is distributed to the management and control area; if the agriculture and forestry weak supervision signal of the control area is generated, a preset fire analysis threshold YP2 is distributed to the control area, and YP2 is more than YP1 is more than 0.
8. The intelligent early warning system for the fire disaster in the agriculture and forestry area based on the data acquisition and analysis according to claim 1 is characterized in that an intelligent early warning platform is in communication connection with an agriculture and forestry inspection management and control module, the agriculture and forestry inspection management and control module is used for setting a detection period, acquiring the times of generating fire early warning signals and the times of generating fire high-emergency signals in the management and control area in the detection period, marking the times as a fire frequency analysis value and a high-emergency frequency analysis value respectively, and carrying out numerical calculation on the fire frequency analysis value and the high-emergency frequency analysis value to obtain a global inspection analysis value; if the global patrol value exceeds a preset global patrol threshold, generating a global strict patrol signal of the management and control area, and transmitting the global strict patrol signal to an agriculture and forestry management end through an intelligent early warning platform;
if the global routing value does not exceed the preset global routing threshold, marking the corresponding fire monitoring point as the dangerous point times in the detection period and positioning the corresponding fire monitoring point as a dangerous frequency marking value, marking the interval duration between the moment when the corresponding fire monitoring point is marked as the dangerous point last time and the current moment as a dangerous time-interval value, and carrying out numerical calculation on the dangerous frequency marking value and the dangerous time-interval value to obtain a monitoring ignition risk value; if the ignition risk value exceeds a preset ignition risk threshold value, generating a point position strict inspection signal corresponding to the ignition risk point; and the point position severe inspection signals and the corresponding fire monitoring points are sent to the agriculture and forestry management end through the intelligent early warning platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410508455.8A CN118097922A (en) | 2024-04-26 | 2024-04-26 | Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410508455.8A CN118097922A (en) | 2024-04-26 | 2024-04-26 | Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118097922A true CN118097922A (en) | 2024-05-28 |
Family
ID=91157808
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410508455.8A Pending CN118097922A (en) | 2024-04-26 | 2024-04-26 | Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118097922A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160313120A1 (en) * | 2013-12-16 | 2016-10-27 | Obshestvo S Ogranichennoj Otvetstvennostyu "Disikon" | Method for determination of optimal forest video monitoring system configuration |
| CN108846987A (en) * | 2018-06-28 | 2018-11-20 | 武汉智网兴电科技开发有限公司 | A kind of transmission line forest fire system based on Beidou satellite transmission system |
| KR20220051521A (en) * | 2020-10-19 | 2022-04-26 | 주식회사 케이티 | Method, server and computer program for detecting fire based on virtual sensor |
| CN116246407A (en) * | 2023-05-04 | 2023-06-09 | 浙江农林大学 | Agriculture and forestry area fire early warning supervisory systems based on artificial intelligence |
| CN116645776A (en) * | 2023-05-30 | 2023-08-25 | 电子科技大学 | Forest surface fire and crown fire combined monitoring method and system based on multidimensional data |
| CN117557409A (en) * | 2024-01-11 | 2024-02-13 | 中国建筑科学研究院有限公司 | Intelligent building fire risk visual evaluation management system based on Internet of things |
-
2024
- 2024-04-26 CN CN202410508455.8A patent/CN118097922A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160313120A1 (en) * | 2013-12-16 | 2016-10-27 | Obshestvo S Ogranichennoj Otvetstvennostyu "Disikon" | Method for determination of optimal forest video monitoring system configuration |
| CN108846987A (en) * | 2018-06-28 | 2018-11-20 | 武汉智网兴电科技开发有限公司 | A kind of transmission line forest fire system based on Beidou satellite transmission system |
| KR20220051521A (en) * | 2020-10-19 | 2022-04-26 | 주식회사 케이티 | Method, server and computer program for detecting fire based on virtual sensor |
| CN116246407A (en) * | 2023-05-04 | 2023-06-09 | 浙江农林大学 | Agriculture and forestry area fire early warning supervisory systems based on artificial intelligence |
| CN116645776A (en) * | 2023-05-30 | 2023-08-25 | 电子科技大学 | Forest surface fire and crown fire combined monitoring method and system based on multidimensional data |
| CN117557409A (en) * | 2024-01-11 | 2024-02-13 | 中国建筑科学研究院有限公司 | Intelligent building fire risk visual evaluation management system based on Internet of things |
Non-Patent Citations (3)
| Title |
|---|
| 吴赵盼: "基于多目标决策与数据挖掘技术在农林火灾中的应用研究", 中国优秀硕士学位论文全文数据库 农业科技辑, no. 2, 15 February 2020 (2020-02-15) * |
| 王越超: "无线传感器网络用于火灾监测的阈值算法", 计算机光盘软件与应用, no. 11, 15 June 2013 (2013-06-15) * |
| 鲁钰雯等: "基于多源数据的城市火灾风险评估及应用——以厦门市为例", 灾害学, vol. 34, no. 1, 20 January 2019 (2019-01-20) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tian et al. | Wildfires and the Canadian Forest Fire Weather Index system for the Daxing'anling region of China | |
| CN101699315A (en) | Monitoring device and method for crop growth uniformity | |
| CN102779391A (en) | Drought early-warning method and drought early-warning system | |
| Li et al. | Effects of climate variability and change on Chinese agriculture: a review | |
| CN107239651A (en) | A kind of method that power network birds droppings class failure risk grade is assessed | |
| Ma et al. | Observed trends in the timing of wet and dry season in China and the associated changes in frequency and duration of daily precipitation. | |
| CN111898838B (en) | Tree height prediction method, and power transmission line tree obstacle early warning method and system | |
| CN115938083B (en) | Agricultural monitoring and early warning method and system based on mobile terminal | |
| Shuai et al. | ENSO, climate variability and crop yields in China | |
| CN111624682A (en) | Quantitative precipitation estimation method based on multi-source data fusion | |
| CN116245414A (en) | Forestry pest quarantine information management system | |
| CN114065111A (en) | Carbon emission data verification method for single building | |
| CN111811578A (en) | Forest growth simulation prediction system based on forest model | |
| CN115237982A (en) | Agricultural big data management system and method based on cloud computing | |
| Greenberg et al. | A rapid hard‐mast index from acorn presence‐absence tallies | |
| CN117172959B (en) | Forestry ecological environment monitoring system and method | |
| CN118097922A (en) | Intelligent early warning system for fire disaster in agriculture and forestry area based on data acquisition and analysis | |
| CN117172952A (en) | Agricultural disaster monitoring system based on Internet of things and remote sensing technology | |
| CN105894706A (en) | Forest fire prediction method and system | |
| CN206133265U (en) | Long -range monitoring server of greenhouse insect pest | |
| CN116990491A (en) | Automatic soil information monitoring system based on Internet of things | |
| CN116307729A (en) | Dry-hot air disaster assessment method based on multi-source satellite remote sensing data | |
| CN111583050A (en) | Crop pest and disease early warning method and system fusing multi-scale remote sensing images | |
| CN110956322B (en) | Summer maize flowering phase high-temperature disaster risk prediction method under climate warming trend | |
| CN111563824B (en) | Artificial forest land research system and method based on Chinese pine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |