CN106709663B - Method for determining stability of human body in flood based on urban image data - Google Patents

Method for determining stability of human body in flood based on urban image data Download PDF

Info

Publication number
CN106709663B
CN106709663B CN201710002224.XA CN201710002224A CN106709663B CN 106709663 B CN106709663 B CN 106709663B CN 201710002224 A CN201710002224 A CN 201710002224A CN 106709663 B CN106709663 B CN 106709663B
Authority
CN
China
Prior art keywords
human body
image data
actual
flow velocity
length
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.)
Active
Application number
CN201710002224.XA
Other languages
Chinese (zh)
Other versions
CN106709663A (en
Inventor
夏军强
果鹏
陈倩
周美蓉
李洁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Wuhan University WHU filed Critical Wuhan University WHU
Priority to CN201710002224.XA priority Critical patent/CN106709663B/en
Publication of CN106709663A publication Critical patent/CN106709663A/en
Application granted granted Critical
Publication of CN106709663B publication Critical patent/CN106709663B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • 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
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
    • 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

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Multimedia (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Measuring Volume Flow (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses a method for determining the stability of a human body in flood based on urban image data, which comprises the following steps: firstly, according to whether a video or a picture has a reference object with a known length, a method for determining the scale relation between the human body and the actual human body in the data is provided, and then the water depth of the position where the human body is located is determined; for video data with floats, determining the flow velocity of the water surface according to the moving distance of the floats in a period of time, and calculating the average flow velocity of water flow in front of a human body by considering the relation between the flow velocity of the water surface and the average flow velocity of a vertical line; for the picture or the video data without the floating object, the water blocking height of the position of the human body can be measured, and then the flow velocity of the position of the human body is solved by adopting a Bernoulli equation; and finally, the reliability of the human body instability curve in the flood and the existing human body instability data inspection method is utilized. The method provided by the invention is beneficial to establishing the instability standard of the human body in the flood, and meanwhile, a reference basis is provided for urban flood risk analysis and management.

Description

Method for determining stability of human body in flood based on urban image data
Technical Field
The invention relates to the technical field of hydraulic engineering, in particular to a method for determining human body stability in flood based on urban image data.
Background
China is one of the countries deeply threatened by flood disasters, and casualties caused by over-standard flood caused by heavy rain are very serious. According to incomplete statistics, the number of direct deaths caused by flood disasters in each year is about 5200 in 1950-2000 years. Since 2000, the number of dead people directly caused by flood disaster is reduced to about 1600 people every year. However, with the rapid growth of the current population, the continuous expansion of the range of human activities, and the climate change with global warming as the main characteristic, the probability of flood disasters is increased, and the life safety of people still faces very serious challenges. Therefore, the human body is easy to be unstable under the action of the flood, and the pedestrians are very likely to die directly after being washed away by the flood. Therefore, the instability standard of the human body in the flood is established, and the method has important significance for flood risk assessment.
With the development of science and technology, photography and photography become an indispensable part of urban traffic and security work of each unit, and are also important means for recording flood processes. The instability process of the human body in the image data reflects the actual flood situation, and is more beneficial to the development of related researches. Therefore, how to determine the stability of the human body in the flood according to the urban image data is beneficial to establishing the instability standard of the human body in the flood, can be popularized and expanded to the acquisition of instability conditions of flooded objects such as vehicles and houses, and provides a reference basis for urban flood risk analysis and management.
Disclosure of Invention
The invention aims to provide a method for determining the stability of a human body in flood based on urban image data, which provides scientific basis for estimating the human body danger degree in the flood, guiding the escape of disaster-stricken people and analyzing urban flood risks.
The principle of the invention is as follows: the object length in the image data related to the urban flood and the actual object length have a certain proportional relation, and a scale of the distance on the graph and the actual distance is determined according to the proportional relation, so that the water depth of the position where the human body is located is calculated; and determining the flow velocity of the position of the human body according to the floating distance of the object in the video within a period of time or the water depth congestion and Bernoulli equation of the position of the human body in the picture. The invention determines the scale relation based on the existing image data, calculates the water depth and the flow velocity of the human body instability, and verifies the accuracy of the human body instability data in the data by adopting the curve of the human body instability in the flood. A method for determining the stability of human body in flood based on urban image data is provided.
The invention provides a method for determining the stability of a human body in flood based on urban image data, which comprises the following steps:
step (1), a method for providing a scale relation between a human body and an actual human body in image data is provided, and then the water depth of the position of the human body is determined, and the specific steps are as follows:
step (1.1), if a reference object with known actual length exists near the human body in the image data, determining a proportional relation according to the actual length of the reference object and the length on the image;
step (1.2), if the image data does not have a reference object with known actual length, determining the arm length of the human body by taking the average height of the area where the image data is located as a reference basis and utilizing the relationship between the height and the arm length of the human body, and then determining the proportional relationship between the actual length of the object in the data and the length on the graph;
step (1.3), calculating the corresponding inflow water depth according to the proportional relation determined in step (1.1) or step (1.2);
step (2), dividing the image data into video data with floats, picture data and video data without floats, then calculating the water flow velocity in the video data according to the moving distance of the floats in the video data, and calculating the water flow velocity in the picture or the video data without floats according to the Bernoulli equation, wherein the specific steps are as follows:
step (2.1), for video data with floats, determining the flow velocity of the water surface according to the moving distance of the floats in a period of time, and calculating the average flow velocity of the water flow in front of the human body by considering the relation between the flow velocity of the water surface and the average flow velocity of the vertical line;
step (2.2), for the picture or the video data without the floating object, the height of the congested water at the position of the human body can be measured, and then the Bernoulli equation is adopted to solve the average flow velocity of the water flow in front of the human body;
and (3) verifying the reliability of the method by using the established instability curve of the human body in the flood, and specifically comprising the following steps:
step (3.1), establishing a critical flow velocity curve of the human body instability through theoretical analysis, and adopting water tank data calibration formula relevant parameters of the model human body instability;
step (3.2), drawing a curve of the instability of the real human body under different water depths by using the formula calibrated in the step (3.1);
and (3.3) comparing the test data of the instability of the human body in the flood and the test data of the instability of the real person of other people obtained in the steps (1) and (2) with the curve drawn in the step (3.2), and checking the reliability of the method.
In the step (1.1), the scale S of the image data is:
Figure GDA0002719926300000021
wherein L ispIs the actual length, L, of a reference object of known length in the image datasThe on-image length of a reference object of known length in the image data; the selected reference object of known length should be close to the human body in the flood.
In the step (1.2), the scale S of the image data is:
S＝LaP/LaS
wherein L isaPIs the actual arm length, L, of the human body in the image dataaP＝HPavg×34.4％，HPavgIs the actual average height, L, of the human body in the region of the image dataaSThe length of the arm on the figure of the human body in the image data.
In the step (1.3), if the image data has a reference object with a known actual length, the actual depth H in the image data is:
H＝Hp-HbPOUT
wherein HPThe actual height H of the human body in the image dataP＝LaP÷34.4％，LapThe actual arm length of the human body in the image data; hbPOUTThe actual height of the human body exposed to the water surface H in the image databPOUT＝HbSOUTXS, S is the scale of the image data, determined by the method described in step (1.1), HbSOUTThe length of the human body on the image exposed to the water surface in the image data.
In the step (1.3), if the image data does not have a reference object with a known actual length, the actual depth H in the image data is:
H＝HPavg-HbPOUT
wherein HPavgIs the image data stationActual average height of the person in the area; hbPOUTThe actual height of the human body exposed out of the water surface in the image data; hbPOUT＝HbSOUTXS, S is the scale of the image data, determined by the method described in step (1.2), HbSOUTThe length of the human body on the image exposed to the water surface in the image data.
In the step (2.1), the average flow velocity U of the vertical line near the human body in the video data is usedVLThe approximate average flow velocity U of the water flow in front of the human body is represented, and the calculation formula of the average flow velocity U of the water flow in front of the human body in the video data is as follows:
Figure GDA0002719926300000031
wherein, UmaxThe actual flow rate of the water surface in the video data,
Figure GDA0002719926300000032
Δ T is a time period, and Δ L is a movement distance on the graph in the time period of Δ T; s is the scale of the image data.
In the step (2.2), the backwater height delta H of the position of the human body is measured firstly, and then the Bernoulli equation is adopted to solve the average flow velocity of the water flow in front of the human body:
Figure GDA0002719926300000033
wherein Δ H is the height of the stagnant water, g is the acceleration of gravity, and S is the scale of the image data.
The method has the beneficial effects that:
(1) 2 methods for determining the scale relationship between the actual object and the image data are provided. If there is an object with definite length near the human body in the data, the ratio of the actual length to the length on the graph is used as a scale; if no object with determined length is near the human body in the data, taking the average height of the area where the data are located as a reference value, and determining the ratio of the actual arm length to the arm length on the graph as a scale according to the relationship between the arm length of the human body and the height;
(2) the water depth and the flow velocity are determined according to the scale relation, and the calculation methods of the flow velocity are divided into 2 types. For video data, the flow velocity of water flow on the water surface can be determined according to the moving distance of a floater in a period of time, and the average flow velocity of the current water flow is calculated by considering the relation between the water flow velocity and the average flow velocity; for picture data, the height of the stagnant water at the position of the human body can be measured, and then the Bernoulli equation is adopted to solve the flow velocity at the position of the human body.
(3) And (4) checking the reliability of the method by using the established instability curve of the human body in the flood. The method is favorable for establishing the instability standard of the human body in the flood, and provides reference basis for flood risk analysis and management of cities, beach areas, stagnant flood areas and the like.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the length measurement of various parts of a human body in flood;
FIG. 3 is a schematic diagram of two cross-sections in Bernoulli's equation;
figure 4 compares the human instability curve with the human instability data obtained by the present invention.
Detailed Description
The invention will be further explained with reference to the drawings. As shown in fig. 1-4, a method for determining human body stability in flood based on urban image data includes the following steps:
step (1), a method for providing a scale relation between a human body and an actual human body in image data is provided, and then the water depth of the position of the human body is determined, and the specific steps are as follows:
step (1.1), if a reference object with a known length is near the human body in the image data, determining a proportional relation according to the actual length of the reference object and the length on the image;
considering the problems of shooting angle, distance and the like, the reference object with the actual length is known to be close to the human body in the flood. Assuming that the actual length of the reference object with known actual length in the image data is LpLength of L on the drawingsThen, the scale S of the image data is:
Figure GDA0002719926300000041
step (1.2), if the image data does not have a reference object with known length, as shown in fig. 2, determining the arm length of the human body by taking the average height of the area where the image data is located as a reference and utilizing the relationship between the height and the arm length of the human body, so as to calculate the scale of the actual length of the object in the image data and the length on the image;
gordon et al (1989) showed that the arm length of a human is approximately 34.3% of the height, and the actual average height of the human in the area of FIG. 2 is taken to be HPavgThen the actual arm length L of the human bodyaPComprises the following steps:
LaP＝HPavg×34.4％ (2)
while the bending of the arm in FIG. 2 can be divided into La1And La2Two parts, thereby calculating the arm length L on the figure of the human bodyaSComprises the following steps:
LaS＝La1+La2 (3)
then scale S is:
S＝LaP/LaS (4)
and (1.3) determining a scale relation S between the distance and the actual distance on the graph according to the step (1.1) or the step (1.2), and calculating the corresponding inflow water depth.
If the image data has a reference object with a known actual length, the on-image arm length L of the human body in FIG. 2 is measured and calculatedaSAnd then calculating the actual arm length of the human body according to the scale as follows:
LaP＝LaS×S (5)
then the actual height H of the personPComprises the following steps:
HP＝LaP÷34.4％ (6)
in FIG. 2, the part of the human body exposed to the water surface has a curve which can be divided into Lb1、Lb2And Lb3Part 3, the length of the human body exposed out of the water surface on the graph is HbSOUTComprises the following steps:
HbSOUT＝Lb1+Lb2+Lb3 (7)
calculating the actual height H of the exposed human bodybPOUTComprises the following steps:
HbPOUT＝HbSOUT×S (8)
the actual water depth H is the difference between the actual height of the human body and the actual height of the human body exposed to the water:
H＝Hp-HbPOUT (9)
if the image data does not have an object with a known actual length, the actual length of the human body exposed to the water surface is calculated according to the length of the human body exposed to the water surface on the graph and a scale, and only the calculated water depth is changed into the following formula:
H＝HPavg-HbPOUT (10)
step (2), dividing the image data into video data with floats, picture data and video data without floats, then calculating the water flow velocity in the video data according to the moving distance of the floats in the video data, and calculating the water flow velocity in the picture or the video data without floats according to the Bernoulli equation, wherein the specific steps are as follows:
step (2.1), for video data with floats, determining the flow velocity of the water surface according to the moving distance of the floats in a period of time, and calculating the average flow velocity of the water flow in front of the human body by considering the relation between the flow velocity of the water surface and the average flow velocity of the vertical line;
if there is some floating object in the video data, the image moves by the distance of DeltaL in the time interval of DeltaT, then the actual flow speed U of the water surfacemaxComprises the following steps:
Figure GDA0002719926300000061
current studies have shown that the maximum flow rate at the water surface is 16% greater than the average flow rate at the vertical, so the vertical lineAverage flow rate UVLComprises the following steps:
Figure GDA0002719926300000062
the above equation calculates the average flow velocity of the vertical line near the human body in the video data, so that the flow velocity can approximately represent the average flow velocity of the water flow in front of the human body, i.e. the average flow velocity in front of the human body is:
U＝UVL (13)
and (2.2) for the picture or the video data without the floating objects, as shown in fig. 3, firstly measuring the backwater height delta H of the position of the human body, and then solving the average flow velocity of the water flow in front of the human body by adopting a Bernoulli equation.
The position of the human body in water is shown in FIG. 3, and the Bernoulli equation for sections 1-1 'and 2-2' is:
Figure GDA0002719926300000063
in the formula, the physical meanings of the left and right 3 terms of the equation are a position waterhead, a pressure waterhead and a flow velocity waterhead respectively. ρ is the water flow density and g is the local gravitational acceleration. The section 1-1' has: z1Is the elevation, P, of a point in the section 1-1' relative to a reference plane1Is the pressure at that point, U1Is the flow rate at that point. Corresponding to a 2-2' cross section having a value of Z2Is the elevation, P, of a point in the section 2-2' relative to a reference plane2Is the pressure at that point, U2Is the flow rate at that point.
The invention approximately considers that the water flow is constant and gradual flow, the average flow speed in front of the human body is equal to the average flow speed of the section 1-1 ', and the flow speed U of the section 2-2' where the human body is positioned is taken as2Since 0m/s, the piezometric head (sum of the position head and the pressure head) of the section 2-2' is increased, and the stagnant water of Δ H is generated. The water depth of the two sections 1-1 'and 2-2' in FIG. 3 is H1、H2(ΔH＝H2-H1). The water head of the piezometer pipe is constant when the gradual change flow exists in the water passing section, and the flow velocity of the water flow is verticalThe uniform distribution is the average flow velocity. In FIG. 3, the ground is taken as a reference surface, and then the piezometric tube water heads of the sections 1-1 'and 2-2' are respectively:
section 1-1':
Figure GDA0002719926300000064
section 2-2':
Figure GDA0002719926300000071
the substitution uses bernoulli's equation (equation 14) to solve for the average flow velocity in front of the human body in real flood, i.e.:
Figure GDA0002719926300000072
in the formula, g is the local gravitational acceleration. Transformed by the equation:
Figure GDA0002719926300000073
and (3) verifying the reliability of the method by using the established instability curve of the human body in the flood, and specifically comprising the following steps:
step (3.1), establishing a critical flow velocity formula of the human body instability in the flood through theoretical analysis, adopting water tank data of the model human body instability to rate relevant parameters of the formula, and drawing a critical curve of the human body instability in the flood by using the formula after the calibration;
xia et al (2014) establish height HPBody weight MPThe formula for calculating the critical flow rate of the human body falling instability is as follows:
Figure GDA0002719926300000074
in the formula: ρ is the water flow density, a1，b1，a2，b2Is a parameter related to the physical characteristics of a person, and a can be obtained by calculation1＝0.633、b1＝0.367，a2＝1.015×10-3m3/kg、b2＝-4.937×10-3m3. Alpha and beta need to be calibrated according to the result of a water tank test for instability of the human body model, and alpha is 3.472, and beta is 0.188 through calculation.
Step (3.2), drawing a curve of the instability of the real human body under different water depths by using the formula calibrated in the step (3.1);
and (3.3) comparing the test data (the water flow speed and the water depth when the human body is unstable) of the human body instability in the flood obtained in the step (1) and the step (2) and the test data (the water flow speed and the water depth when the human body is unstable) of the human body instability of other people with the curve drawn in the step (3.2), and checking the reliability of the method.
Fig. 4 compares the instability curve of human body, the water flow condition data of the instability of human body under the action of flood in the image data, and the experimental data of Abt (1989), Karvonen (2000), Field, Chanson (2014) for true instability, and it can be seen that the instability standard curve proposed by Xia et al (2013) is lower because the formula adopts the test result of model human body water tank for calibration, and the adjustment effect of human body is not considered. The data of the instability of the human body in the image data is closer to the data of other real person water tank tests, and the method can be considered to be reliable.

Claims (5)

1. A method for determining and inspecting water flow conditions when a human body in a flood is unstable based on urban image data is characterized by comprising the following steps:
step (1), a method for providing a scale relation between a human body and an actual human body in image data is provided, and then the water depth of the position of the human body is determined, and the specific steps are as follows:
step (1.1), if a reference object with known actual length exists near the human body in the image data, determining a proportional relation according to the actual length of the reference object and the length on the image;
step (1.2), if the image data does not have a reference object with known actual length, determining the arm length of the human body by taking the average height of the area where the image data is located as a reference basis and utilizing the relationship between the height and the arm length of the human body, and then determining the proportional relationship between the actual length of the object in the data and the length on the graph;
step (1.3), calculating the corresponding inflow water depth according to the proportional relation determined in step (1.1) or step (1.2);
step (2), dividing the image data into video data with floats, picture data and video data without floats, then calculating the water flow velocity in the video data according to the moving distance of the floats in the video data, and calculating the water flow velocity in the picture or the video data without floats according to the Bernoulli equation, wherein the specific steps are as follows:
step (2.1), for video data with floats, determining the flow velocity of the water surface according to the moving distance of the floats in a period of time, and calculating the average flow velocity of the water flow in front of the human body by considering the relation between the flow velocity of the water surface and the average flow velocity of the vertical line;
step (2.2), for the picture or the video data without the floating object, measuring the height of the backwater at the position of the human body, and then solving the average flow velocity of the water flow in front of the human body by adopting a Bernoulli equation;
and (3) checking the accuracy of the unstable water flow conditions of the human body in the flood, namely the depth and the flow velocity of incoming flow, obtained in the steps (1) and (2) by using the established unstable curve of the human body in the flood, and specifically comprising the following steps:
step (3.1), establishing a critical flow velocity formula of the human body instability in the flood through theoretical analysis, and adopting water tank data of the model human body instability to rate relevant parameters of the formula, wherein the rated critical flow velocity formula of the human body instability in the flood is as follows:
Figure FDA0002719926290000011
in the formula: h is the actual depth of water in the image data, HPThe actual height of the human body in the image data, MPThe actual weight of the human body in the image data, and rho is the water flow density; a is1，b1，a2，b2Is a parameter related to a physical characteristic of the person, a1＝0.633、b1＝0.367，a2＝1.015×10-3m3/kg、b2＝-4.937×10-3m3；α＝3.472，β＝0.188；
Step (3.2), drawing a curve of the instability of the real human body under different water depths by using the formula calibrated in the step (3.1);
step (3.3), comparing the flow conditions of human body instability in the flood obtained in the step (1) and the step (2), namely the incoming flow depth and the incoming flow velocity test data with the curve drawn in the step (3.2), and checking the accuracy of the human body instability flow conditions in the flood obtained in the step (1) and the step (2), namely the incoming flow depth and the incoming flow velocity;
in the step (2.1), the average flow velocity U of the vertical line near the human body in the video data is usedVLThe approximate average flow velocity U of the water flow in front of the human body is represented, and the calculation formula of the average flow velocity U of the water flow in front of the human body in the video data is as follows:
Figure FDA0002719926290000021
wherein, UmaxThe actual flow rate of the water surface in the video data,
Figure FDA0002719926290000022
Δ T is a time period, and Δ L is a movement distance on the graph in the time period of Δ T; s is the scale of the image data;
in the step (2.2), the backwater height delta H of the position of the human body is measured firstly, and then the Bernoulli equation is adopted to solve the average flow velocity of the water flow in front of the human body:
Figure FDA0002719926290000023
wherein Δ H is the height of the stagnant water, g is the acceleration of gravity, and S is the scale of the image data.
2. The method of claim 1, wherein the method comprises the steps of:
in the step (1.1), the scale S of the image data is:
Figure FDA0002719926290000024
wherein L ispIs the actual length, L, of a reference object of known length in the image datasThe on-image length of a reference object of known length in the image data; the selected reference object of known length should be close to the human body in the flood.
3. The method of claim 1, wherein the method comprises the steps of:
in the step (1.2), the scale S of the image data is:
S＝LaP/LaS
wherein L isaPIs the actual arm length, L, of the human body in the image dataaP＝HPavg×34.4％，HPavgIs the actual average height, L, of the human body in the region of the image dataaSThe length of the arm on the figure of the human body in the image data.
4. The method of claim 2, wherein the method comprises the steps of:
in the step (1.3), if the image data has a reference object with a known actual length, the actual depth H in the image data is:
H＝Hp-HbPOUT
wherein HPThe actual height H of the human body in the image dataP＝LaP÷34.4％，LapThe actual arm length of the human body in the image data; hbPOUTThe actual height of the human body exposed to the water surface H in the image databPOUT＝HbSOUTXS, S is the scale of image data, the scale adopts the method described in step (1.1)Determination of HbSOUTThe length of the human body on the image exposed to the water surface in the image data.
5. The method of claim 3, wherein the method comprises the steps of:
in the step (1.3), if the image data does not have a reference object with a known actual length, the actual depth H in the image data is:
H＝HPavg-HbPOUT
wherein HPavgThe actual average height of the human body in the area where the image data is located; hbPOUTThe actual height of the human body exposed out of the water surface in the image data; hbPOUT＝HbSOUTXS, S is the scale of the image data, determined by the method described in step (1.2), HbSOUTThe length of the human body on the image exposed to the water surface in the image data.
CN201710002224.XA 2017-01-03 2017-01-03 Method for determining stability of human body in flood based on urban image data Active CN106709663B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710002224.XA CN106709663B (en) 2017-01-03 2017-01-03 Method for determining stability of human body in flood based on urban image data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710002224.XA CN106709663B (en) 2017-01-03 2017-01-03 Method for determining stability of human body in flood based on urban image data

Publications (2)

Publication Number Publication Date
CN106709663A CN106709663A (en) 2017-05-24
CN106709663B true CN106709663B (en) 2020-12-01

Family

ID=58906591

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710002224.XA Active CN106709663B (en) 2017-01-03 2017-01-03 Method for determining stability of human body in flood based on urban image data

Country Status (1)

Country Link
CN (1) CN106709663B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019082376A1 (en) 2017-10-27 2019-05-02 株式会社アシックス Motion state evaluation system, motion state evaluation device, motion state evaluation server, motion state evaluation method, and motion state evaluation program
CN113314013B (en) * 2021-05-17 2022-03-04 武汉大学 Testing device and testing method for escaping from underground space by flood along variable-gradient stairs

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008110909A2 (en) * 2007-03-15 2008-09-18 Universita' Degli Studi Di Palermo Measurement method for measuring the peak flow discharges and the channel bed roughness in a natural or artificial water course and related system
CN105808859A (en) * 2016-03-11 2016-07-27 武汉大学 Method for judging stability degree of vehicle under action of flood

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8311786B2 (en) * 2008-10-02 2012-11-13 Geza Kisch Accurate methods for modeling the spatial distribution for irrigation systems for landscapes

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008110909A2 (en) * 2007-03-15 2008-09-18 Universita' Degli Studi Di Palermo Measurement method for measuring the peak flow discharges and the channel bed roughness in a natural or artificial water course and related system
CN105808859A (en) * 2016-03-11 2016-07-27 武汉大学 Method for judging stability degree of vehicle under action of flood

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
人在洪水中临界失稳判定公式的对比及分析;曹留伟;《人民长江》;20131231;第44卷(第24期);全文 *
现有洪水中人体失稳标准的评价及改进;夏军强 等;《同济大学学报（自然科学版）》;20160229;第44卷(第2期);全文 *

Also Published As

Publication number Publication date
CN106709663A (en) 2017-05-24

Similar Documents

Publication Publication Date Title
Le Coz et al. Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers
JP7120581B2 (en) CCTV image-based real-time automatic flowmeter-side system and method
CN108010278B (en) Mud-stone flow disaster dangerous situation dynamic early-warning method, fining classified Monitoring method for early warning
Mason et al. Pulsed wall jet simulation of a stationary thunderstorm downburst, Part A: Physical structure and flow field characterization
CN109635435A (en) A kind of natural river course stage discharge relation based on bayesian theory determines method
CN106709663B (en) Method for determining stability of human body in flood based on urban image data
JP2021531450A (en) River flow velocity measuring device and method using optical flow video processing
CN104652347B (en) Mountain area non-static water body water level affects population assessed in relation method with flooding
CN106595777A (en) Calculation method for detecting flow of section of river in non-contact manner
CN105989697A (en) Multi-source sensor-based landslide monitoring and early-warning device
CN110849335A (en) Remote sensing rapid determination method for reservoir capacity of dammed lake water of waterless underground form data
CN108133578A (en) Mountain flood dangerous situation dynamic early-warning method, the classified Monitoring that becomes more meticulous method for early warning
CN110991822A (en) Three-dimensional hydrodynamic numerical simulation method based on oblique image modeling
CN109186706A (en) A method of for the early warning of Urban Storm Flood flooding area
CN107356398A (en) A kind of wind tunnel methods and device
CN111984914B (en) Method for measuring and calculating height of dam body of collapse type dam and application
Arattano et al. Video-derived velocity disribution along a debris flow surge
CN110532648A (en) A kind of barrier lake risk assessment platform
CN108876124A (en) Evacuation bottleneck crowd's congestion risk analysis method based on data-driven
Orendorff et al. Using PTV through an embankment breach channel
CN106768193A (en) A kind of water-level measuring post
CN115290148A (en) Flow measuring method and system under complex water flow condition
CN115326026A (en) Method and device for acquiring hydraulic characteristics based on non-contact measurement-hydrodynamic fusion assimilation
CN108593023A (en) Utilize the method for radar meter automatic measurement open channel flow rate
CN105808859A (en) Method for judging stability degree of vehicle under action of flood

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant