CN113902168A - Flood emergency evacuation method based on real-time crowd data fusion - Google Patents
Flood emergency evacuation method based on real-time crowd data fusion Download PDFInfo
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Abstract
The invention discloses a flood emergency evacuation method based on real-time crowd data fusion. The method comprises the following steps: constructing a two-dimensional hydrodynamic model, and determining the flood risk area and the safety area range; step two: developing a position service interface, and accessing the Internet to acquire real-time crowd monitoring data in an emergency state; step three: developing mobile application of flood emergency evacuation, and collecting user crowd report data; step four: the LBS real-time crowd monitoring data and the crowd data collected by mobile application are subjected to superposition analysis by adopting a multi-source data fusion technology; step five: constructing a flood emergency evacuation model based on the real-time crowd fusion data, and dynamically planning crowd evacuation routes; step six: and pushing the flood early warning message and the evacuation route information to the crowd in the risk area. The method has the advantages of improving the accuracy and timeliness of crowd identification and improving the efficiency and effect of emergency risk avoiding and evacuation of the crowd in the flood risk area.
Description
Technical Field
The invention relates to the field of flood crowd evacuation, in particular to a flood emergency evacuation method based on real-time crowd data fusion.
Background
The out-of-standard flood events in China occur frequently, and the disaster risks are centralized. Influenced by climate change and human activities, the frequency and scale of over-standard flood are obviously increased in recent years, and in addition, the population and wealth are gathered in the urbanization process, so that the destructive power of flood disasters is greatly increased.
Emergency evacuation is an important non-engineering measure for dealing with over-standard flood, and comprises emergency evacuation planning, emergency evacuation plan, flood early warning, evacuation and evacuation, rescue and risk avoidance and the like. The flood emergency evacuation mode adopted in China at present generally carries out early warning in modes of gong sounding, tweeter broadcasting and the like, and evacuation guidance is carried out according to an emergency plan formulated in advance. The method has low notification efficiency, slow emergency response and evacuation path, and is difficult to meet the new requirements of the real-time emergency evacuation of people in the varying environment due to the over-standard flood.
Therefore, how to utilize the increasingly developed information technology to assist in supporting emergency evacuation of flood risk people is worthy of further research.
Disclosure of Invention
The invention aims to provide a flood emergency evacuation method based on real-time crowd data fusion, which improves the accuracy and timeliness of crowd identification, realizes intelligent optimization or generation of an emergency risk avoiding crowd transfer scheme, can optimize a transfer path in real time for areas with a plan, can generate a transfer path in real time for areas without a plan, greatly improves the precision of emergency evacuation path planning and crowd transfer notification, and improves the efficiency and effect of emergency risk avoiding evacuation of crowds in a flood risk area; the method overcomes the defects that the existing risk evacuation mode is low in notification efficiency, slow in emergency response and difficult to meet the new requirements of the real-time crowd emergency evacuation of the over-standard flood in the changing environment.
In order to achieve the purpose, the technical scheme of the invention is as follows: a flood emergency evacuation method based on real-time crowd data fusion is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
the method comprises the following steps: constructing a two-dimensional hydrodynamic model, and determining the flood risk area and the safety area range;
step two: a Location Based Service (LBS) interface (prior art) is developed, and the Internet is accessed to acquire real-time crowd monitoring data in an emergency state;
step three: developing mobile application of flood emergency evacuation, and collecting user crowd report data;
step four: the multisource data fusion technology in the invention specifically comprises the following steps of S42, S43, a ratio analysis method of S44, a geographic weighted regression model and a geostatistical analysis model, and carries out superposition analysis on LBS real-time crowd monitoring data and crowd data acquired by mobile application;
step five: constructing a flood emergency evacuation model based on the real-time crowd fusion data, and dynamically planning crowd evacuation routes;
step six: and pushing the flood early warning message and the evacuation route information to the crowd in the risk area.
In the above technical solution, in the first step, determining the flood risk area and the safety area range specifically includes the following steps:
s11: collecting topographic data of a target area, historical water level and flow data of a river channel key section and historical flood submerging data;
s12: mesh generation is carried out on a target area by using a mesh generator, and a topological relation between each computational mesh unit and a control element thereof is constructed (the prior art);
s13: setting initial conditions, boundary conditions (including an upper boundary condition and a lower boundary condition), a calculation step length and a calculation step number of model calculation, and constructing a two-dimensional hydrodynamic model of a target area (the two-dimensional hydrodynamic model is used in the field of crowd evacuation and used for determining the range of a flood risk area and a safety area);
s14: calibrating and verifying parameters of the two-dimensional hydrodynamic model based on historical water level and flow data of a river channel key section and historical flood inundation data;
s15: inputting real-time monitoring data or forecast data of the upper boundary, and simulating the evolution process of the flood (including the arrival time of the flood, the submerging range, the submerging depth, the flow velocity and the like) so as to determine the flood risk area and the safety area range.
In the above technical solution, in the second step, the obtaining of the real-time crowd monitoring data specifically includes the following steps:
s21: developing an LBS (location based service) interface, and accessing real-time crowd thermal data of the internet (Tencent, Baidu and the like) LBS;
s22: the real-time crowd portrait data accessed to the internet LBS comprises a regional position portrait, an age portrait, a gender portrait and the like.
In the above technical solution, in step three, the collecting of the user population report data specifically includes the following steps:
s31: in consideration of the fact that coverage of personnel data obtained by S2 is incomplete and repeated screening difficulty of data from different sources is high, mobile applications for flood emergency evacuation are developed, and the mobile applications comprise functional modules such as a personal center, plan information, early warning information and route planning (including real-time positioning); a gridding management mode is adopted in the application area, and personnel are informed of code scanning registration so as to improve the accuracy of personnel input and ensure the safety of transfer personnel;
s32: when a user logs in a mobile application for the first time, the user needs to authorize and provide a geographical position, and complete personal information and information of other family members; personal information is required to fill in the name, sex, birth date, physical condition, household address, current address, mobile phone number, whether the mobile phone is networked or not and the like of the user; the information of other family members needs to fill in the names, sexes, relationships with the family members, birth dates, physical conditions, household addresses, current addresses, mobile phone numbers, whether mobile phones are networked or not and the like of other family members of the user, so as to count the data of people (generally young and old) which are difficult to be monitored by the internet LBS;
s33: the mobile application background performs duplicate removal and verification on the filled crowd data, and counts crowd portrait data by taking a family as a unit, wherein the crowd portrait data comprises family membership, name, gender, birth date, physical condition, household address, mobile phone number, whether a mobile phone is networked or not and the like;
s34: after the user uses the mobile application to position, the background automatically records the positioning position and updates and stores the position information.
In the above technical solution, in step four, the overlay analysis is performed on the LBS real-time crowd monitoring data and the crowd data collected by the mobile application, and the method specifically includes the following steps:
s41: respectively counting real-time crowd portrait data of an internet LBS and crowd portrait data of an emergency evacuation mobile application by taking a parcel as a unit, wherein the counting content comprises an age portrait, a gender portrait and a mobile equipment networking portrait;
s42: correcting the age portrait data of the internet LBS subarea by using a ratio analysis method by taking the age portrait of the mobile application subarea and the mobile equipment networking portrait data as references; further correcting gender portrait data of internet LBS based on gender portrait data of the mobile application fragmentation zone to obtain crowd portrait fusion data of the fragmentation zone;
s43: the method comprises the steps of constructing a geographical weighted regression model of the crowd portrait (the invention uses a geographical weighted regression method in the field of crowd prediction) based on crowd portrait fusion data and LBS crowd portrait data of a subarea, wherein the specific model is constructed, namely the geographical weighted regression model of the crowd portrait and is used for predicting the crowd portrait data, and the crowd portrait data of a data-free subarea (the emergency evacuation mobile application in the subarea has no sufficient users or more obvious errors in filling information) is predicted;
s44: and establishing a geostatistical relationship (namely constructing a geostatistical analysis model) between the thermal data of the LBS and the crowd portrait data of the subareas, and mapping the crowd portrait fusion data of each subarea to a space grid by utilizing the geostatistical relationship to obtain real-time crowd thermal fusion data.
In the above technical solution, in step S42, the method for fusing internet LBS real-time monitoring and emergency evacuation mobile application-collected crowd image data specifically includes:
correcting real-time crowd age portrait data of the internet LBS by using an age portrait of a mobile application and a mobile equipment networking portrait as references and adopting a ratio analysis method;
and further correcting the sex portrait data of the internet LBS based on the sex portrait of the mobile application to obtain the fusion data of the real-time crowd portrait.
In the above technical solution, in the step five, the dynamic planning of the crowd evacuation route specifically includes the following steps:
s51: comprehensively considering constraints such as capacity of a placement place (namely a safety area), accessibility of the placement place, road grade, road congestion degree and the like, and constructing a flood emergency evacuation model by taking the minimum total time consumption for crowd transfer in a risk area as a target;
s52: constructing topological relations and data sets of all risk areas, safety areas and transferable roads;
s53: counting the real-time crowd number and portrait distribution of each risk area and each safety area according to the fused real-time crowd thermal data and portrait data;
s54: calculating the residual capacity of each safety zone; determining the per-capita area of the safety zone according to the allocation condition of the living goods and materials, wherein the ratio of the area of the safety zone to the per-capita area is the total capacity of the safety zone; the number of the crowd with the total capacity of the safety area subtracted by the current time is the remaining capacity of the safety area;
s55: determining road grades (road surface width, design vehicle speed and the like) based on urban road traffic planning and designing specifications, urban road designing specifications and road actual investigation conditions;
s56: accessing road congestion degree data of the internet (hundredths, tench and the like) through an API (application programming interface) interface, and setting the congestion degree data at the moment of starting transferring as an initial condition of the congestion degree;
s57: and (4) taking the data calculated in the steps S52-S56 as input, driving the flood emergency evacuation model established in the step S51, and dynamically planning the evacuation route for avoiding risks of people.
In the above technical solution, in S51, the flood emergency evacuation model is composed of an objective function and a constraint condition:
(a) an objective function: the total time consumption for crowd evacuation in all risk areas is minimum:
in formula (1): t is total evacuation time, min; li,jThe j evacuation route is the ith risk area; t (l)i,j) Time consumption corresponding to evacuation route, min; k is the number of people in the ith risk area who avoid the risk through the evacuation route j; m is the number of risk areas; n is the number of evacuation routes of the risk area;
(b) constraint conditions (constraint conditions include risk zone complete evacuation constraint, safety zone capacity constraint, road grade constraint, road congestion degree constraint):
i) and (3) completely evacuating the dangerous area: flood evacuation transfer aims to guarantee the life and property safety of people in the risk area, and the complete evacuation constraint of the risk area is met when people are evacuated, namely all people in the risk area are evacuated to the safety area;
ii) safety zone capacity constraints: the safety zone area and the living goods and materials are equipped limitedly, people can not be evacuated from the risk zone without limitation, and the capacity constraint of the safety zone is met when the people are transferred and placed, namely:
ha≤Ha max(2)
in formula (2): h isaThe number of persons who are settled in the a-th safety zone, Ha maxMaximum installation capacity for the a-th security zone;
iii) road class constraints: roads can be divided into different grades, including national roads, provincial roads, county roads, rural roads and the like, and the road surface width of each grade of road is different from the designed vehicle speed; the method comprises the following steps that road grade constraint is met during transfer route planning, namely, the limitation of road surface width and design vehicle speed is considered;
iv) road congestion degree constraint: in the transfer process, the crowdedness of each transfer road changes and affects the transfer speed; calculating the weight change of the transfer road by adopting a road weight function representation method, wherein the road weight t (o, p) on the road section [ o, p ] is as follows:
t(o,p)＝tt(o,p)+td(o,p) (3)
in formula (3): t is tt(o.p) represents a road section [ o, p ]]Time of travel of td(o.p) indicating the average delay time for transitioning from intersection o to intersection p;
road section travel time tt(o.p) calculating and obtaining by referring to a road section characteristic function BPR model proposed by the Federal road administration in America:
tt(o,p)＝[1+α(Q0/Q)β]t0(o,p) (4)
in formula (4): t is t0(o, p) is a road section [ o, p ]]Free-running time at zero flow, Q0Is a section of road [ o, p ]]Q is the actual traffic capacity of the road section, and alpha and beta are retardation coefficients;
average delay time t of road sectiond(o, p) is calculated using the following formula:
td(o,p)＝D(o,p)/vd (5)
in formula (5): d (o, p) is the distance between o and p at the intersection, vdThe vehicle running speed;
according to the method, the flood emergency evacuation model is formed through combination, the crowd evacuation route is dynamically planned, the crowd evacuation route planning accuracy is improved, and the efficiency and the effect of the crowd emergency risk avoiding and evacuation in the flood risk area are improved.
In the above technical solution, in the sixth step, the flood early warning message and the evacuation route information are pushed to the crowd in the risk area, and the method specifically includes the following steps:
s61: editing flood early warning information and evacuation route information in the mobile application in the step three;
s62: and pushing the edited flood early warning message and evacuation route information to the crowd in the risk area through the mobile application.
The invention has the following beneficial effects: by fusing the real-time crowd data and the mobile application crowd data of the Internet LBS, the bottlenecks that the information of old and young people is difficult to monitor by the Internet LBS and the real-time property of the mobile application acquisition data is insufficient are broken through, the accuracy of risk avoiding crowd identification and crowd evacuation path planning is greatly improved, and the efficiency and the effect of emergency risk avoiding and evacuation of the crowd in the flood risk area are improved.
The current internet LBS real-time crowd monitoring data is mainly used for advertising, old and young crowd data are obtained without fusion, and comprehensive crowd data can be collected only in a special field (such as emergency evacuation) in mobile application, so that crowd data fusion is supported. The technology for fusing internet LBS (location based service) crowd data and crowd data applied in a mobile manner is still blank. From the technical point of view, the invention adopts the ratio analysis method, the geographical weighted regression model and the ground statistic model to perform fusion processing on the data, furthest exerts the advantages of LBS (location based service) crowd data and mobile application crowd data, provides comprehensive and accurate basis for dynamically planning crowd evacuation routes, is convenient for accurately pushing flood early warning information and evacuation route information to crowds in a risk area, greatly improves the accuracy and efficiency of flood emergency evacuation, and furthest ensures the life safety of people.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 illustrates a risk zone and a safety zone range for a two-dimensional hydrodynamic model simulation in accordance with an embodiment of the present invention;
FIG. 3 is a result of real-time crowd data fusion in an embodiment of the invention;
fig. 4 is an evacuation route map planned in real time by the flood emergency evacuation model in the embodiment of the present invention;
fig. 5 is a diagram illustrating real-time dynamic planning of crowd evacuation paths based on fused real-time crowd data according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings, which are not intended to limit the present invention, but are merely exemplary. While the advantages of the invention will be clear and readily understood by the description.
The invention provides a flood emergency evacuation method based on real-time crowd data fusion, which is characterized in that the characteristic attributes and regional distribution of crowds in a disaster area are dynamically drawn by fusing internet LBS real-time crowd data and mobile application crowd data, so that the accuracy and timeliness of crowd identification are improved; an emergency evacuation model is constructed based on real-time crowd data and flood situations, intelligent optimization or generation of an emergency risk avoiding crowd transfer scheme is achieved, transfer paths can be optimized in real time for areas with plans, transfer paths can be generated in real time for areas without plans, the accuracy of emergency evacuation path planning and crowd transfer notification is greatly improved, and the efficiency and the effect of emergency risk avoiding evacuation of crowds in flood risk areas are improved.
Examples
The embodiment of the invention which is tried to be applied to flood emergency evacuation in a certain area is used for explaining the invention in detail, and the invention also has a guiding function on the application of the invention to flood emergency evacuation in other areas.
With reference to the accompanying drawings: as shown in fig. 1, in this embodiment, the flood emergency evacuation method based on real-time crowd data fusion includes the following steps:
(1) and constructing a two-dimensional hydrodynamic model (such as an SWMM model and a MIKE model) based on the topographic data, the river section data, the historical water level flow data of the key section of the river and the historical flood submerging data, predicting the characteristics of a target area, such as the flood evolution process, the flood arrival time, the submerging depth, the submerging range, the flow velocity and the like, and determining a flood risk area and a safety area, which are shown in figure 2.
(2) A Location Based Service (LBS) interface is developed, and real-time crowd monitoring data of the LBS (such as Tencent and Baidu) which is accessed to the Internet is developed, wherein the real-time crowd monitoring data comprises crowd thermal data and crowd portrait data (such as area portrait, age portrait, gender portrait and the like). LBS utilizes the mobile internet service platform to obtain the current position information of the positioning equipment, and carries out data updating and interaction on the basic information of the equipment user through big data technology, thereby realizing the real-time monitoring of the crowd data of the equipment user. Due to the reliance on internet location positioning for mobile devices, LBS is difficult to effectively monitor the elderly, young population that lack mobile devices.
(3) Developing a mobile application for flood emergency evacuation, wherein when a user logs in the mobile application for the first time, personal information and family member information including name, gender, birth date, physical condition, household address, current residence address, mobile phone number, whether a mobile phone is networked or not are filled, and the like, as shown in fig. 3; and the mobile application background performs duplicate removal and verification on the filled data, and counts the people portrait data by taking a family as a unit. And positioning the position of the user each time the user uses the mobile application, and automatically updating and recording the position information. And when the positioning position deviates far from the filled current address, updating the current address as the user positioning position. The crowd without mobile phones and mobile networks is generally old and young, the crowd rarely has large-scale position flow, and the current address of the crowd is generally kept unchanged; and in special cases, the current address information of other members of the family is updated. The crowd data real-time performance of the flood emergency evacuation mobile application is poor, but the information of old and young crowds can be captured to a certain extent.
(4) Respectively counting real-time crowd image data monitored by an internet LBS and crowd image data acquired by emergency evacuation mobile application by taking a parcel as a unit, and fusing the internet LBS and the crowd data acquired by the mobile application by adopting a ratio analysis method to obtain real-time crowd image fusion data of the parcel; constructing a geographical weighted regression model of crowd portrait fusion data and LBS crowd portrait data of the partitioned areas, and predicting crowd portrait data of the data-free areas; and constructing a geostatistical analysis model of the thermal data of the LBS and the crowd portrait data of the subareas, and mapping the crowd portrait fusion data to each space grid to obtain the thermal fusion data of the real-time crowd, which is shown in figure 4.
(5) And constructing a flood emergency evacuation model considering road grade, road congestion degree and safety area capacity, and dynamically planning the crowd evacuation path in real time based on the fused real-time crowd data of the risk area, as shown in fig. 5. Before constructing the flood emergency evacuation model, the topological relations and data sets of all the risk areas, the safety areas and the transferable roads are constructed. In addition, the road grade needs to be determined, the initial condition of the road congestion degree is set, the crowd capacity of each safety area is estimated according to the living goods and materials allocation condition and the safety area, and the real-time crowd data volume of each risk area and each safety area is counted by the crowd thermal distribution fusion data to be used as necessary input data of the flood emergency evacuation model.
(6) By utilizing a real-time communication technology and a flood emergency evacuation mobile application, the edited flood early warning message and evacuation route information are pushed to the crowd in the risk area in time, and the crowd is assisted to be quickly and effectively transferred to the safety area. When the crowd transferred to the safety area is about to deviate or deviates from the safety area again, the flood emergency evacuation mobile application sends out early warning information of the deviation from the safety area by utilizing the electronic fence technology.
Fig. 2-5 show the implementation of the steps of the embodiment of the present invention, and it can be seen from fig. 2-5 that the method of the present invention is feasible.
And (4) conclusion: by adopting the method disclosed by the embodiment of the invention, the bottlenecks that the internet LBS is difficult to monitor the information of old and young people and the real-time property of the mobile application acquisition data is insufficient are broken through by fusing the internet LBS real-time crowd data and the mobile application crowd data, the accuracy of risk avoiding crowd identification and crowd evacuation path planning is greatly improved, and the efficiency and the effect of emergency risk avoiding and evacuation of the crowd in the flood risk area are improved.
Other parts not described belong to the prior art.
Claims (9)
1. A flood emergency evacuation method based on real-time crowd data fusion is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
the method comprises the following steps: constructing a two-dimensional hydrodynamic model, and determining the flood risk area and the safety area range;
step two: developing a position service interface, and accessing the Internet to acquire real-time crowd monitoring data in an emergency state;
step three: developing mobile application of flood emergency evacuation, and collecting user crowd report data;
step four: the LBS real-time crowd monitoring data and the crowd data collected by mobile application are subjected to superposition analysis by adopting a multi-source data fusion technology;
step five: constructing a flood emergency evacuation model based on the real-time crowd fusion data, and dynamically planning crowd evacuation routes;
step six: and pushing the flood early warning message and the evacuation route information to the crowd in the risk area.
2. The flood emergency evacuation method based on real-time crowd data fusion according to claim 1, wherein: in the first step, determining the range of the flood risk area and the safety area, specifically comprising the following steps:
s11: collecting topographic data of a target area, historical water level and flow data of a river channel key section and historical flood submerging data;
s12: mesh generation is carried out on the target area by using a mesh generator, and the topological relation between each computational mesh unit and the control element thereof is constructed;
s13: setting initial conditions, boundary conditions, calculation step length and calculation step number of model calculation, and constructing a two-dimensional hydrodynamics model of the target area;
s14: calibrating and verifying parameters of the two-dimensional hydrodynamic model based on historical water level and flow data of a river channel key section and historical flood inundation data;
s15: and inputting real-time monitoring data or forecast data of the upper boundary, and simulating the evolution process of the flood so as to determine the flood risk area and the safety area range.
3. The flood emergency evacuation method based on real-time crowd data fusion according to claim 2, wherein: in the second step, the real-time crowd monitoring data is obtained, and the method specifically comprises the following steps:
s21: developing an LBS interface, and accessing real-time crowd thermal data of Internet LBS;
s22: and accessing real-time crowd image data of the Internet LBS.
4. The flood emergency evacuation method based on real-time crowd data fusion according to claim 3, wherein: in the third step, collecting the user population report data, specifically comprising the following steps:
s31: developing mobile application for flood emergency evacuation; a gridding management mode is adopted in the application area, and personnel are informed of code scanning registration;
s32: when a user logs in a mobile application for the first time, the user authorizes to provide a geographical position, and fills and perfects personal information and information of other family members so as to count crowd data which is difficult to monitor by internet LBS;
s33: the mobile application background performs duplicate removal and verification on the filled crowd data, and counts crowd portrait data by taking a family as a unit;
s34: after the user uses the mobile application to position, the background automatically records the positioning position and updates and stores the position information.
5. The flood emergency evacuation method based on real-time crowd data fusion according to claim 4, wherein: the mobile application of flood emergency evacuation comprises a personal center, plan information, early warning information and a functional module of route planning with real-time positioning.
6. The flood emergency evacuation method based on real-time crowd data fusion according to claim 5, wherein: in the fourth step, the LBS real-time crowd monitoring data and the crowd data collected by the mobile application are subjected to superposition analysis, and the method specifically comprises the following steps:
s41: respectively counting real-time crowd portrait data of an internet LBS and crowd portrait data of an emergency evacuation mobile application by taking a parcel as a unit, wherein the counting content comprises an age portrait, a gender portrait and a mobile equipment networking portrait of the parcel;
s42: correcting the age portrait data of the internet LBS subarea by using a ratio analysis method by taking the age portrait of the mobile application subarea and the mobile equipment networking portrait data as references; further correcting gender portrait data of internet LBS based on gender portrait data of the mobile application fragmentation zone to obtain crowd portrait fusion data of the fragmentation zone;
s43: based on the crowd portrait fusion data and LBS crowd portrait data of the partitioned areas, a geographical weighted regression model of the crowd portrait is constructed, and the crowd portrait data of the data-free partitioned areas are predicted;
s44: and establishing a local statistical relationship between the thermal data of the LBS and the crowd portrait data of the subareas, and mapping the crowd portrait fusion data of each subarea to a space grid by using the local statistical relationship to obtain real-time crowd thermal fusion data.
7. The flood emergency evacuation method based on real-time crowd data fusion according to claim 6, wherein: in the fifth step, dynamically planning the crowd evacuation route, specifically comprising the following steps:
s51: comprehensively considering constraints of capacity, accessibility, road grade and road congestion degree of a placement place, and constructing a flood emergency evacuation model by taking minimum total time consumption for crowd transfer in a risk area as a target;
s52: constructing topological relations and data sets of all risk areas, safety areas and transferable roads;
s53: counting the real-time crowd number and portrait distribution of each risk area and each safety area according to the fused real-time crowd thermal data and portrait data;
s54: calculating the residual capacity of each safety zone; determining the per-capita area of the safety zone according to the allocation condition of the living goods and materials, wherein the ratio of the area of the safety zone to the per-capita area is the total capacity of the safety zone; the number of the crowd with the total capacity of the safety area subtracted by the current time is the remaining capacity of the safety area;
s55: determining a road grade;
s56: accessing road congestion degree data of the internet through an API (application programming interface) interface, and setting the congestion degree data at the moment of starting transferring as initial conditions of the congestion degree;
s57: and (4) taking the data calculated in the steps S52-S56 as input, driving the flood emergency evacuation model established in the step S51, and dynamically planning the evacuation route for avoiding risks of people.
8. The flood emergency evacuation method based on real-time crowd data fusion according to claim 7, wherein: in S51, the flood emergency evacuation model is composed of an objective function and a constraint condition:
(a) an objective function: the total time consumption for crowd evacuation in all risk areas is minimum:
in formula (1): t is total evacuation time, min; li,jThe j evacuation route is the ith risk area; t (l)i,j) Time consumption corresponding to evacuation route, min; k is the number of people in the ith risk area who avoid the risk through the evacuation route j; m is the number of risk areas; n is the number of evacuation routes of the risk area;
(b) constraint conditions are as follows:
i) and (3) completely evacuating the dangerous area: flood evacuation transfer aims to guarantee the life and property safety of people in the risk area, and the complete evacuation constraint of the risk area is met when people are evacuated, namely all people in the risk area are evacuated to the safety area;
ii) safety zone capacity constraints: the safety zone area and the living goods and materials are equipped limitedly, people can not be evacuated from the risk zone without limitation, and the capacity constraint of the safety zone is met when the people are transferred and placed, namely:
ha≤Ha max (2)
in formula (2): h isaThe number of persons who are settled in the a-th safety zone, Ha maxMaximum installation capacity for the a-th security zone;
iii) road class constraints: the roads are divided into different grades; the method comprises the following steps of (1) meeting road grade constraint during route transfer planning, namely considering road surface width and design vehicle speed limit;
iv) road congestion degree constraint: in the transfer process, the crowdedness of each transfer road changes and affects the transfer speed; calculating the weight change of the transfer road by adopting a road weight function representation method, wherein the road weight t (o, p) on the road section [ o, p ] is as follows:
t(o,p)＝tt(o,p)+td(o,p) (3)
in formula (3): t is tt(o.p) represents a road section [ o, p ]]Time of travel of td(o.p) indicating the average delay time for transitioning from intersection o to intersection p;
road section travel time tt(o.p) calculating and obtaining by referring to a road section characteristic function BPR model proposed by the Federal road administration in America:
tt(o,p)＝[1+α(Q0/Q)β]t0(o,p) (4)
in formula (4): t is t0(o, p) is a road section [ o, p ]]Free-running time at zero flow, Q0Is a section of road [ o, p ]]Q is the actual traffic capacity of the road section, and alpha and beta are retardation coefficients;
average delay time t of road sectiond(o, p) is calculated using the following formula:
td(o,p)＝D(o,p)/vd (5)
in formula (5): d (o, p) is the distance between o and p at the intersection, vdIs the vehicle running speed.
9. The flood emergency evacuation method based on real-time crowd data fusion according to claim 8, wherein: in the sixth step, the flood early warning message and the evacuation route information are pushed to the crowd in the risk area, and the method specifically comprises the following steps:
s61: editing flood early warning information and evacuation route information in the mobile application in the step three;
s62: and pushing the edited flood early warning message and evacuation route information to the crowd in the risk area through the mobile application.
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