一种基于大数据的野外救援分析系统

Open-air rescue analytic system based on big data

文档序号：9933 发布日期：2021-09-17 浏览：39次中文

1. The utility model provides a field rescue analytic system based on big data which characterized in that: the field rescue analysis system comprises a body and mind evaluation value acquisition module, a terrain evaluation value acquisition module, a search and rescue evaluation value acquisition module and a rescue condition pre-judgment analysis module, wherein the body and mind evaluation value acquisition module acquires a body and mind evaluation value according to the body and mind conditions of rescued people, the terrain evaluation value acquisition module acquires a terrain evaluation value according to the terrain conditions of a rescued area, the search and rescue evaluation value acquisition module acquires a search and rescue evaluation value according to the search and rescue history of the rescued people, and the rescue condition pre-judgment analysis module pre-judges and analyzes the field rescue condition according to the body and mind evaluation value, the terrain evaluation value and the search and rescue evaluation value;

the mind and body evaluation value acquisition module comprises a comprehensive psychological parameter acquisition module, a body parameter acquisition module, a pre-judgment parameter acquisition module and a mind and body evaluation value calculation module, the comprehensive psychological parameter acquisition module comprises a first psychological parameter acquisition module, a second psychological parameter acquisition module and a comprehensive psychological parameter calculation module, the first psychological parameter acquisition module comprises a relaxation heart rate statistic module, a clear driving heart rate statistic module, a foggy driving heart rate statistic module, a first driving heart rate reference quantity calculation module, a second driving heart rate reference quantity calculation module, a heart rate comparison parameter calculation module, a heart rate parameter calculation module and a first psychological parameter calculation module, the relaxation heart rate statistic module is used for counting 1 the average heart rate a of the rescued person in a relaxation state, the clear driving heart rate statistic module is used for counting 2 the average heart rate a of the rescued person in a vehicle driving in a clear day, the foggy day driving heart rate counting module is used for counting the heart rate a3 of rescued people when driving a vehicle in foggy days, the first driving heart rate reference quantity calculating module calculates a first driving heart rate reference quantity according to the average heart rate a1 and the average heart rate a2, the second driving heart rate reference quantity calculating module calculates a second driving heart rate reference quantity according to the average heart rate a2 and the average heart rate a3, the heart rate comparison parameter calculating module calculates heart rate comparison parameters according to the first driving heart rate reference quantity and the second driving heart rate reference quantity, the heart rate parameter calculating module outputs heart rate parameters according to the magnitude relation between the heart rate comparison parameters and the heart rate comparison threshold value, and the first heart parameter calculating module calculates first heart parameters according to the heart rate parameters and the heart rate threshold value; the second psychological parameter obtaining module comprises a compression-resistant score evaluation module and a second psychological parameter calculating module, the compression-resistant score evaluation module is used for counting the scores of the compression resistance of rescued persons and colleagues of rescued persons, and the second psychological parameter calculating module calculates a second psychological parameter according to the scores counted by the compression-resistant score evaluation module and the compression resistance threshold; the comprehensive psychological parameter calculating module calculates the comprehensive psychological parameters according to a first psychological parameter and a second psychological parameter, the physical parameter acquiring module outputs the physical parameters according to the physical examination report condition of the rescued person, the prejudgment parameter acquiring module comprises a first survival factor calculating module, a second survival factor calculating module, a third survival factor calculating module and a prejudgment parameter calculating module, the first survival factor calculating module is used for judging whether the rescued person participates in the outdoor survival teaching activity or not from the activity history of the rescued person and outputting a first survival factor according to the first survival factor, the second survival factor calculating module is used for judging whether the rescued person reads related books in the field or not from the reading history of the rescued person and outputting a second survival factor according to the second survival factor, the third survival factor calculating module outputs a third survival factor according to the age information of the rescued person, the pre-judging parameter calculating module calculates pre-judging parameters according to a first survival factor, a second survival factor and a third survival factor, and the mind and body evaluation value calculating module calculates the mind and body evaluation value according to the comprehensive psychological parameter, the physical parameter and the pre-judging parameters;

the analysis method of the field rescue analysis system comprises the following steps:

step S1: acquiring a body and mind evaluation value x0 according to the body and mind conditions of rescued people;

step S2: acquiring a terrain evaluation value y0 according to the terrain condition of the searched and rescued area;

step S3: acquiring a search and rescue evaluation value z0 according to the search and rescue history of the search and rescue personnel;

step S4: pre-judging and analyzing the field rescue situation according to the body and mind evaluation value, the terrain evaluation value and the search and rescue evaluation value;

the step S1 further includes:

step S11: acquiring the psychological condition of rescued people:

step S111: collecting the average heart rate a1 when the rescued person is in a relaxed state, collecting the average heart rate a2 when the rescued person drives the vehicle in a sunny day, and collecting the heart rate a3 when the rescued person drives the vehicle in a foggy day;

step S112: calculating a first driving heart rate reference amount h1= (a2-a1)/a1, calculating a second driving heart rate reference amount h2= (a3-a2)/a2, then calculating a heart rate comparison parameter g1= (h2-h1)/h1, when the heart rate comparison parameter is greater than or equal to a heart rate comparison threshold value, the heart rate parameter a0= a3, and when the heart rate comparison parameter is smaller than the heart rate comparison threshold value, the heart rate parameter a0= (a1+ a2+ a 3)/3;

step S112: calculating a first psychological parameter r1= (a0-ay)/ay, wherein ay is a heart rate threshold;

step S113: collecting the scoring evaluation of the pressure resistance of the rescued person by the rescued person and the colleagues of the rescued person, wherein 0 represents that the pressure resistance of the rescued person is very strong, 1 represents that the pressure resistance of the rescued person is very weak,

if the score of rescued people is k1 and the average score of rescued people is k2, the second psychological parameter r2= [ (k1+ k2)/2-k0]/k0, wherein k0 is a pressure resistance threshold value;

step S12: acquiring the physical condition of the rescued person:

collecting physical examination reports of rescued persons, judging whether acute diseases exist in the rescued persons, if the rescued persons have the acute diseases, the physical parameter c0=1, and if the rescued persons do not have the acute diseases, the physical parameter c0= 0;

step S13: the field survival situation of rescued people is judged in advance:

collecting the activity history of rescued persons, judging whether the rescued persons participate in the outdoor survival teaching activity, if the rescued persons participate in the outdoor survival teaching activity, the first survival factor d1=0, otherwise the first survival factor d1= 1;

acquiring the age information of rescued persons, wherein if the ages of the rescued persons are between 25 and 40 years, the third survival factor d3=0, otherwise, the third survival factor d3= 1;

calculating the prejudgment parameter d0=0.4 × d1+0.3 × d2+0.3 × d3 of the rescued person

Step S14: and calculating the psychosomatic evaluation value x0=0.39 × r0+0.25 × c0+0.36 × d0 of the rescued person.

2. A field rescue analysis method based on big data as claimed in claim 1, characterized in that: the step S2 further includes:

step S21: acquiring the terrain of a searched and rescued area, wherein the terrain evaluation value y1=0.2 when the terrain of the searched and rescued area is plain, and the terrain evaluation value y1=0.5 when the terrain of the searched and rescued area is hilly, basin, mountain and plateau;

step S22: acquiring the height and the corresponding coverage area of each vegetation in the search and rescue area, calculating the overall vegetation height of the search and rescue area, wherein the overall vegetation height is equal to the weighted sum of the heights of each vegetation, the weighted value of the height of each vegetation is the percentage of the coverage area of the vegetation in all vegetation,

calculating a vegetation evaluation value y2= (fh-f0)/f0, wherein fh is the overall vegetation height, and f0 is the overall vegetation height threshold;

step S23: and (3) calculating the terrain evaluation value y0=0.5 y1+0.5 y2 of the searched and rescued area.

3. A field rescue analysis method based on big data as claimed in claim 1, characterized in that: the step S3 further includes:

obtaining the area S1 of a current searched and rescued area, obtaining the number N1 of search and rescue personnel, calculating the unit search and rescue area t1= N1/S1, and then obtaining the search and rescue area index z1= (t1-tm)/tm, wherein tm is the unit search and rescue area threshold;

collecting the times b1 of search and rescue of the search and rescue personnel in the current searched and rescued area, when the times b1 is equal to 0, the search and rescue personnelThe individual search and rescue proficiency d =0.8 of the member, when the number of times b1 is equal to 1, the individual search and rescue proficiency d =0.5 of the search and rescue person, and when the number of times b1 is greater than or equal to 2, the individual search and rescue proficiency d =0.3 of the search and rescue person, then the overall search and rescue proficiencyWherein d is_iThe individual search and rescue proficiency degree of the ith search and rescue person is represented, the search and rescue proficiency index z2= (t2-ts)/ts, wherein ts is a proficiency threshold value;

and calculating a search and rescue evaluation value z0=0.5 xz 1+0.5 xz 2 of the search and rescue personnel.

4. A field rescue analysis method based on big data as claimed in claim 1, characterized in that: the step S4 includes:

calculating a comprehensive evaluation value u =0.4 × x0+0.3 × y0+0.4 × z0,

when the comprehensive evaluation value is less than or equal to 0.6, the probability that the search and rescue personnel safely and successfully rescue the rescued personnel is higher,

when the comprehensive evaluation value is more than 0.6, the probability that the search and rescue personnel safely and successfully rescue the rescued personnel is low, and the search and rescue personnel can be preferably dispatched.

Background

Outdoor activities refer to different types of physical experiences and exercises performed at places far from cities. The utility model can not only be close to the sunlight and the air, but also be used for physical exercise and mood relaxation. However, field activities are different from general indoor activities, uncertainty of field activities is larger, and safety factor is lower, so that personnel are often lost in field activities, and professional rescuers are needed to search and rescue at this time. But the prior art lacks a technology that facilitates field rescue analysis.

Disclosure of Invention

The invention aims to provide a field rescue analysis system and method based on big data, and aims to solve the problems in the prior art.

In order to achieve the purpose, the invention provides the following technical scheme:

a field rescue analysis system based on big data comprises a body and mind evaluation value acquisition module, a terrain evaluation value acquisition module, a search and rescue evaluation value acquisition module and a rescue condition pre-judgment analysis module, wherein the body and mind evaluation value acquisition module acquires a body and mind evaluation value according to the body and mind conditions of rescued people, the terrain evaluation value acquisition module acquires a terrain evaluation value according to the terrain conditions of a rescued area, the search and rescue evaluation value acquisition module acquires a search and rescue evaluation value according to the search and rescue history of the search and rescue people, and the rescue condition pre-judgment analysis module pre-judges and analyzes the field rescue condition according to the body and mind evaluation value, the terrain evaluation value and the search and rescue evaluation value.

Preferably, the psychosomatic evaluation value acquisition module comprises a comprehensive psychological parameter acquisition module, a physical parameter acquisition module, a prejudgment parameter acquisition module and a psychosomatic evaluation value calculation module, the comprehensive psychological parameter acquisition module comprises a first psychological parameter acquisition module, a second psychological parameter acquisition module and a comprehensive psychological parameter calculation module, the first psychological parameter acquisition module comprises a relaxation heart rate statistic module, a clear driving heart rate statistic module, a foggy driving heart rate statistic module, a first driving heart rate reference quantity calculation module, a second driving heart rate reference quantity calculation module, a heart rate comparison parameter calculation module, a heart rate parameter calculation module and a first psychological parameter calculation module, the relaxation heart rate statistic module is used for counting the average heart rate a1 when the rescued person is in a relaxation state, the clear driving heart rate statistic module is used for counting the average heart rate a2 when the rescued person drives a vehicle in a clear day, the foggy day driving heart rate counting module is used for counting the heart rate a3 of rescued people when driving a vehicle in foggy days, the first driving heart rate reference quantity calculating module calculates a first driving heart rate reference quantity according to the average heart rate a1 and the average heart rate a2, the second driving heart rate reference quantity calculating module calculates a second driving heart rate reference quantity according to the average heart rate a2 and the average heart rate a3, the heart rate comparison parameter calculating module calculates heart rate comparison parameters according to the first driving heart rate reference quantity and the second driving heart rate reference quantity, the heart rate parameter calculating module outputs heart rate parameters according to the magnitude relation between the heart rate comparison parameters and the heart rate comparison threshold value, and the first heart parameter calculating module calculates first heart parameters according to the heart rate parameters and the heart rate threshold value; the second psychological parameter obtaining module comprises a compression-resistant score evaluation module and a second psychological parameter calculating module, the compression-resistant score evaluation module is used for counting the scores of the compression resistance of rescued persons and colleagues of rescued persons, and the second psychological parameter calculating module calculates a second psychological parameter according to the scores counted by the compression-resistant score evaluation module and the compression resistance threshold; the comprehensive psychological parameter calculating module calculates the comprehensive psychological parameters according to a first psychological parameter and a second psychological parameter, the physical parameter acquiring module outputs the physical parameters according to the physical examination report condition of the rescued person, the prejudgment parameter acquiring module comprises a first survival factor calculating module, a second survival factor calculating module, a third survival factor calculating module and a prejudgment parameter calculating module, the first survival factor calculating module is used for judging whether the rescued person participates in the outdoor survival teaching activity or not from the activity history of the rescued person and outputting a first survival factor according to the first survival factor, the second survival factor calculating module is used for judging whether the rescued person reads related books in the field or not from the reading history of the rescued person and outputting a second survival factor according to the second survival factor, the third survival factor calculating module outputs a third survival factor according to the age information of the rescued person, the pre-judging parameter calculating module calculates pre-judging parameters according to the first survival factor, the second survival factor and the third survival factor, and the mind and body evaluation value calculating module calculates the mind and body evaluation value according to the comprehensive psychological parameter, the physical parameter and the pre-judging parameters.

The method comprises the following steps that preferably, the terrain evaluation value acquisition module comprises a terrain evaluation value acquisition module, a vegetation evaluation value acquisition module and a terrain evaluation value calculation module, the terrain evaluation value acquisition module outputs a terrain evaluation value according to the terrain of a searched and rescued region, the vegetation evaluation value acquisition module comprises a vegetation height statistics module, a coverage area statistics module, a whole vegetation height calculation module and a vegetation evaluation value calculation module, the vegetation height statistics module is used for counting the height of each vegetation, the coverage area statistics module is used for counting the coverage area corresponding to the height of each vegetation, the whole vegetation height calculation module is used for calculating the whole vegetation height according to the vegetation height and the coverage area, and the vegetation evaluation value calculation module is used for calculating a vegetation evaluation value according to the whole vegetation height and the whole vegetation height threshold; the relief evaluation value calculation module calculates a relief evaluation value according to a terrain evaluation value and a vegetation evaluation value, the search and rescue evaluation value acquisition module comprises a search and rescue area index acquisition module, a search and rescue proficiency index acquisition module and a search and rescue evaluation value calculation module, the search and rescue area index acquisition module comprises a unit search and rescue area calculation module and a search and rescue area index calculation module, the unit search and rescue area calculation module counts the area of a searched and rescued area and the number of search and rescue personnel to calculate the unit search and rescue area, the search and rescue area index calculation module calculates a search and rescue area index according to the unit search and rescue area and a unit search and rescue area threshold, the search and rescue proficiency index acquisition module comprises a search and rescue proficiency degree calculation module and a search and rescue proficiency degree calculation module, and the personal search and rescue proficiency degree calculation module obtains the personal search and rescue proficiency degree according to the number of search and rescue carried out by the rescue personnel in the current searched and rescued area, the overall search and rescue proficiency calculating module calculates overall search and rescue proficiency according to the individual search and rescue proficiency, the search and rescue proficiency index calculating module calculates a search and rescue proficiency index according to the overall search and rescue proficiency and a proficiency threshold value, and the search and rescue evaluation value calculating module calculates a search and rescue evaluation value according to the search and rescue area index and the search and rescue proficiency index; the rescue condition pre-judging and analyzing module comprises a comprehensive evaluation value calculating module and a comprehensive evaluation value comparing module, wherein the comprehensive evaluation value calculating module calculates a comprehensive evaluation value according to a body and mind evaluation value, a terrain evaluation value and a search and rescue evaluation value, and the comprehensive evaluation value comparing module pre-judges and analyzes the field rescue condition according to the size of the comprehensive evaluation value.

A field rescue analysis method based on big data, comprising the following steps:

step S1: acquiring a body and mind evaluation value x0 according to the body and mind conditions of rescued people;

step S2: acquiring a terrain evaluation value y0 according to the terrain condition of the searched and rescued area;

step S3: acquiring a search and rescue evaluation value z0 according to the search and rescue history of the search and rescue personnel;

step S4: and pre-judging and analyzing the field rescue situation according to the body and mind evaluation value, the terrain evaluation value and the search and rescue evaluation value.

Preferably, the step S1 further includes:

step S11: acquiring the psychological condition of rescued people:

step S112: calculating a first psychological parameter r1= (a0-ay)/ay, wherein ay is a heart rate threshold; the anti-compression condition of the rescued person is judged according to the average heart rate when the rescued person is relaxed, the average heart rate when the rescued person drives the vehicle in sunny days and the average heart rate when the rescued person drives the vehicle in foggy days, and when the heart rate fluctuation of the rescued person is small, the rescued person has stronger anti-compression capability and can be more calm when the rescued person is scattered in the field;

if the score of rescued people is k1 and the average score of rescued people is k2, the second psychological parameter r2= [ (k1+ k2)/2-k0]/k0, wherein k0 is a pressure resistance threshold value;

step S114: calculating a comprehensive psychological parameter r0= (1-p) × r1+ p × r2, wherein a compression resistance evaluation comparison parameter L1= | k2-k1|/k1 is calculated, and when the compression resistance evaluation comparison parameter is less than or equal to a compression resistance evaluation comparison threshold value, p = 0.5; when the compression resistance evaluation comparison parameter is greater than the compression resistance evaluation comparison threshold value, p = 0.3; the accuracy of the comprehensive psychological parameters is improved through the compression resistance evaluation comparison parameters, and when the compression resistance evaluation comparison parameters are larger than the compression resistance evaluation comparison threshold, the judgment of the compression resistance of rescued people by colleagues of rescued people and rescued people is more diverged, so that the accuracy of the comprehensive psychological parameters is improved by reducing the weight of the second psychological parameters;

step S12: acquiring the physical condition of the rescued person:

step S13: the field survival situation of rescued people is judged in advance:

collecting the reading history of rescued persons, judging whether rescued persons read related books for outdoor survival, if rescued persons read related books for outdoor survival, then the second survival factor d2=0, otherwise the second survival factor d2= 1; rescued people participate in outdoor survival teaching activities or read related books which live in the field, and the rescued people can return the mood by contacting the books in the field, keep cool and improve the success rate of rescue;

acquiring the age information of rescued persons, wherein if the ages of the rescued persons are between 25 and 40 years, the third survival factor d3=0, otherwise, the third survival factor d3= 1;

calculating the prejudgment parameter d0=0.4 × d1+0.3 × d2+0.3 × d3 of the rescued person

Step S14: and calculating the psychosomatic evaluation value x0=0.39 × r0+0.25 × c0+0.36 × d0 of the rescued person.

Preferably, the step S2 further includes:

calculating a vegetation evaluation value y2= (fh-f0)/f0, wherein fh is the overall vegetation height, and f0 is the overall vegetation height threshold; the higher the vegetation height is, the wider the covered area is, the more difficult the rescued people can be searched and rescued, and the higher the search and rescue difficulty is;

step S23: and (3) calculating the terrain evaluation value y0=0.5 y1+0.5 y2 of the searched and rescued area.

Preferably, the step S3 further includes:

collecting the number b1 of times that a search and rescue person carries out search and rescue in the current searched and rescued area, when the number b1 is equal to 0, the individual search and rescue proficiency d =0.8 of the search and rescue person, when the number b1 is equal to 1, the individual search and rescue proficiency d =0.5 of the search and rescue person, when the number b1 is more than or equal to 2, the individual search and rescue proficiency d =0.3 of the search and rescue person, and then the overall search and rescue proficiencyWherein d is_iThe individual search and rescue proficiency degree of the ith search and rescue person is represented, the search and rescue proficiency index z2= (t2-ts)/ts, wherein ts is a proficiency threshold value; the higher the familiarity of the search and rescue personnel to the searched and rescued area is, the search and rescue efficiency is improved;

and calculating a search and rescue evaluation value z0=0.5 xz 1+0.5 xz 2 of the search and rescue personnel.

Preferably, the step S4 includes:

calculating a comprehensive evaluation value u =0.4 × x0+0.3 × y0+0.4 × z0,

when the comprehensive evaluation value is less than or equal to 0.6, the probability that the search and rescue personnel safely and successfully rescue the rescued personnel is higher,

Compared with the prior art, the invention has the beneficial effects that: the invention analyzes the probability of the rescue personnel for successfully rescuing the rescued personnel from the rescued personnel, the rescued area and the search and rescue personnel, and proposes the rescue suggestion under the condition of lower probability of successful rescue, thereby further improving the probability of successfully rescuing the personnel.

Drawings

FIG. 1 is a block diagram of a big data based field rescue analysis system according to the present invention;

fig. 2 is a schematic flow chart of a field rescue analysis method based on big data according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1-2, in an embodiment of the present invention, a field rescue analysis system based on big data includes a body and mind evaluation value acquisition module, a terrain evaluation value acquisition module, a search and rescue evaluation value acquisition module, and a rescue condition pre-judgment analysis module, where the body and mind evaluation value acquisition module acquires a body and mind evaluation value according to a body and mind condition of a person to be rescued, the terrain evaluation value acquisition module acquires a terrain evaluation value according to a terrain condition of a region to be rescued, the search and rescue evaluation value acquisition module acquires a search and rescue evaluation value according to a search and rescue history of the person to be rescued, and the rescue condition pre-judgment analysis module pre-judges and analyzes the field rescue condition according to the body and mind evaluation value, the terrain evaluation value, and the search and rescue evaluation value.

The mind and body evaluation value acquisition module comprises a comprehensive psychological parameter acquisition module, a body parameter acquisition module, a pre-judgment parameter acquisition module and a mind and body evaluation value calculation module, the comprehensive psychological parameter acquisition module comprises a first psychological parameter acquisition module, a second psychological parameter acquisition module and a comprehensive psychological parameter calculation module, the first psychological parameter acquisition module comprises a relaxation heart rate statistic module, a clear driving heart rate statistic module, a foggy driving heart rate statistic module, a first driving heart rate reference quantity calculation module, a second driving heart rate reference quantity calculation module, a heart rate comparison parameter calculation module, a heart rate parameter calculation module and a first psychological parameter calculation module, the relaxation heart rate statistic module is used for counting 1 the average heart rate a of the rescued person in a relaxation state, the clear driving heart rate statistic module is used for counting 2 the average heart rate a of the rescued person in a vehicle driving in a clear day, the foggy day driving heart rate counting module is used for counting the heart rate a3 of rescued people when driving a vehicle in foggy days, the first driving heart rate reference quantity calculating module calculates a first driving heart rate reference quantity according to the average heart rate a1 and the average heart rate a2, the second driving heart rate reference quantity calculating module calculates a second driving heart rate reference quantity according to the average heart rate a2 and the average heart rate a3, the heart rate comparison parameter calculating module calculates heart rate comparison parameters according to the first driving heart rate reference quantity and the second driving heart rate reference quantity, the heart rate parameter calculating module outputs heart rate parameters according to the magnitude relation between the heart rate comparison parameters and the heart rate comparison threshold value, and the first heart parameter calculating module calculates first heart parameters according to the heart rate parameters and the heart rate threshold value; the second psychological parameter obtaining module comprises a compression-resistant score evaluation module and a second psychological parameter calculating module, the compression-resistant score evaluation module is used for counting the scores of the compression resistance of rescued persons and colleagues of rescued persons, and the second psychological parameter calculating module calculates a second psychological parameter according to the scores counted by the compression-resistant score evaluation module and the compression resistance threshold; the comprehensive psychological parameter calculating module calculates the comprehensive psychological parameters according to a first psychological parameter and a second psychological parameter, the physical parameter acquiring module outputs the physical parameters according to the physical examination report condition of the rescued person, the prejudgment parameter acquiring module comprises a first survival factor calculating module, a second survival factor calculating module, a third survival factor calculating module and a prejudgment parameter calculating module, the first survival factor calculating module is used for judging whether the rescued person participates in the outdoor survival teaching activity or not from the activity history of the rescued person and outputting a first survival factor according to the first survival factor, the second survival factor calculating module is used for judging whether the rescued person reads related books in the field or not from the reading history of the rescued person and outputting a second survival factor according to the second survival factor, the third survival factor calculating module outputs a third survival factor according to the age information of the rescued person, the pre-judging parameter calculating module calculates pre-judging parameters according to the first survival factor, the second survival factor and the third survival factor, and the mind and body evaluation value calculating module calculates the mind and body evaluation value according to the comprehensive psychological parameter, the physical parameter and the pre-judging parameters.

The terrain evaluation value acquisition module comprises a terrain evaluation value acquisition module, a vegetation evaluation value acquisition module and a terrain evaluation value calculation module, wherein the terrain evaluation value acquisition module outputs a terrain evaluation value according to the terrain of a searched and rescued region, the vegetation evaluation value acquisition module comprises a vegetation height statistical module, a coverage area statistical module, a whole vegetation height calculation module and a vegetation evaluation value calculation module, the vegetation height statistical module is used for counting the height of each vegetation, the coverage area statistical module is used for counting the coverage area corresponding to the height of each vegetation, the whole vegetation height calculation module is used for calculating the whole vegetation height according to the vegetation height and the coverage area, and the vegetation evaluation value calculation module is used for calculating a vegetation evaluation value according to the whole vegetation height and the whole vegetation height threshold; the relief evaluation value calculation module calculates a relief evaluation value according to a terrain evaluation value and a vegetation evaluation value, the search and rescue evaluation value acquisition module comprises a search and rescue area index acquisition module, a search and rescue proficiency index acquisition module and a search and rescue evaluation value calculation module, the search and rescue area index acquisition module comprises a unit search and rescue area calculation module and a search and rescue area index calculation module, the unit search and rescue area calculation module counts the area of a searched and rescued area and the number of search and rescue personnel to calculate the unit search and rescue area, the search and rescue area index calculation module calculates a search and rescue area index according to the unit search and rescue area and a unit search and rescue area threshold, the search and rescue proficiency index acquisition module comprises a search and rescue proficiency degree calculation module and a search and rescue proficiency degree calculation module, and the personal search and rescue proficiency degree calculation module obtains the personal search and rescue proficiency degree according to the number of search and rescue carried out by the rescue personnel in the current searched and rescued area, the overall search and rescue proficiency calculating module calculates overall search and rescue proficiency according to the individual search and rescue proficiency, the search and rescue proficiency index calculating module calculates a search and rescue proficiency index according to the overall search and rescue proficiency and a proficiency threshold value, and the search and rescue evaluation value calculating module calculates a search and rescue evaluation value according to the search and rescue area index and the search and rescue proficiency index; the rescue condition pre-judging and analyzing module comprises a comprehensive evaluation value calculating module and a comprehensive evaluation value comparing module, wherein the comprehensive evaluation value calculating module calculates a comprehensive evaluation value according to a body and mind evaluation value, a terrain evaluation value and a search and rescue evaluation value, and the comprehensive evaluation value comparing module pre-judges and analyzes the field rescue condition according to the size of the comprehensive evaluation value.