CN103913165A - Indoor emergency response and context awareness navigation system and method - Google Patents

Abstract

The invention relates to an indoor emergency response situation awareness navigation system and a navigation method. The navigation system at least includes a positioning sensor network, a server and an early warning sensor. The positioning sensor network is composed of a positioning base station wired or wirelessly connected to the server, a mobile terminal and/or a positioning tag wirelessly connected to the positioning base station, and a positioning The base station is composed of an LED screen connected by wire; the early warning sensor is wirelessly connected with the server. The navigation method combines various contexts of the indoor environment to calculate the navigation path, and adaptively presents the navigation path to the user in various ways according to the user's situation, and provides real-time, accurate, and personalized navigation services. The invention can be used for personnel positioning, emergency evacuation, post-disaster rescue, safety management, etc. in large and complex indoor venues such as hospitals, shopping malls, office buildings, and stations, and can improve the scientificity, timeliness, and effectiveness of emergency response and disposal in public places.

Description

An indoor emergency response situation awareness navigation system and navigation method

technical field

The invention relates to an indoor positioning and navigation system, which belongs to the field of indoor positioning and navigation and location services, and in particular to an indoor emergency response situation awareness navigation system and navigation method.

Background technique

With the rapid development of technologies such as geographic information system (GIS), location-based services (LBS) and the Internet of Things, spatial information technology and applications have gradually expanded to indoor spaces. It is estimated that 80% of the information that people obtain is related to location, and people spend as much as 87% of their time in indoor spaces every day on average. People's demand for indoor location information is becoming more and more urgent. In particular, hospitals, shopping malls, office buildings, stations and other large indoor places have complex environments and highly concentrated personnel. In an emergency, how to obtain the real-time location of personnel for evacuation navigation is the key to rapid evacuation and rescue.

The indoor navigation system in the prior art usually includes a server, a positioning sensor, a positioning sensor base station, and a navigation receiving terminal. The navigation basis is mainly static map information, and the navigation route is provided by setting the starting point and the ending point and using the path algorithm. There are also more advanced indoor navigation systems, which consider the dynamic personnel position and density when calculating the navigation route, so that the navigation route is more accurate.

However, the indoor navigation system in the prior art usually does not have a fire alarm sensor or a smoke sensor, so it cannot provide a safe and effective escape route when a fire alarm or a toxic gas diffusion event occurs. At the same time, there are many influencing factors in the actual indoor environment, such as the moving speed of different types of people, road types, road safety, fire protection facilities, lighting conditions, etc., which play a key role in realizing efficient real-time evacuation navigation and auxiliary decision-making services. However, there is currently no indoor navigation system that takes these influencing factors as input data. In addition, different types of users such as ordinary people and disabled people have quite different navigation needs, and the existing indoor navigation systems usually only provide two or three maps or voice navigation methods, and cannot provide navigation paths and guide information suitable for different groups of people. It cannot meet the user's personalized navigation service needs.

Contents of the invention

In order to solve the deficiencies in the prior art, the present invention provides an indoor emergency response situation awareness navigation system, which is equipped with a positioning device and an early warning sensor, which can collect personnel positions in real time and sense fire or poisonous gas disasters; the present invention also provides a system based on The navigation method of the system considers various static and dynamic influencing factors of the indoor environment on navigation, and can provide real-time, accurate and efficient navigation routes. At the same time, the present invention can set user preferences for different types of users, and provide navigation routes through indoor 2D and 3D maps, voice, text messages or LED screens, making the navigation methods more personalized and humanized.

The technical scheme that the present invention adopts for solving its technical problem is:

The invention provides an indoor emergency response situation awareness navigation system, the system at least includes a positioning sensor network, a server and an early warning sensor; the positioning sensor network is composed of a positioning base station wired or wirelessly connected Composed of mobile terminals and/or positioning tags, and LED screens wired to the positioning base station, the positioning base station receives the positioning parameters sent by each mobile terminal and each positioning tag and sends them to the server, and sends the positioning and navigation results calculated by the server to Mobile terminal and/or LED screen; the positioning tag sends positioning parameters to the positioning base station; the mobile terminal sends positioning parameters to the positioning base station and receives the navigation result sent by the positioning base station; the LED screen receives and displays the navigation result sent by the positioning base station; the server Receive the data sent by the positioning base station and the early warning sensor, store and manage the data, and use the optimal path planning algorithm to calculate the navigation path, and send the navigation path data to the positioning base station; the early warning sensor is wirelessly connected to the server for sensing disasters and Send an early warning signal to the server in the event of a disaster. The positioning sensor base station can be a Wi-Fi base station; the mobile terminal can be mobile devices such as mobile phones or tablet computers with positioning and wireless transmission functions; the positioning tag can be a Wi-Fi positioning tag; the The server is a computer with wired and wireless transmission functions.

The early warning sensor is a fire alarm sensor and/or a smoke sensor, which is used to sense fire and/or toxic gas and send an early warning signal to the server.

The system also includes a monitoring terminal connected to the server by wire.

The present invention also provides a navigation method, which specifically includes the following steps:

(1) Build an indoor emergency response situational awareness navigation system: deploy positioning sensor networks, servers, early warning sensors, and LED screens in indoor scenes. The early warning sensors are fire sensors and/or smoke sensors, and store the type and distribution location information of each early warning sensor. In the server; mobile terminals and positioning tags are carried by personnel, and each person carries a mobile terminal or a positioning tag; the LED screen is placed in a conspicuous place on the wall;

(2) Define the type of personnel for each mobile terminal and positioning tag, and store the data of the type of personnel in the server; the types of personnel include ordinary people and disabled people;

(3) Edit the navigation request data through the mobile terminal and send it to the server. The navigation request data includes navigation preference, start location and destination location. The navigation preference is used as the basis for the mobile terminal to receive navigation information. The navigation method includes map Navigation, voice navigation, SMS navigation and LED screen navigation;

(4) Establish an indoor emergency response navigation position model and store it in the server; the indoor emergency response navigation position model includes a spatial context and a non-spatial context, where the spatial context includes a two-dimensional vector map of an indoor scene, a grid map, a two-dimensional Navigation network model and 3D space model, 3D navigation network model; non-spatial context includes static context and dynamic context, where static context includes personnel type, road type, road distance, road safety, fire protection facilities and lighting conditions in the emergency response environment , the dynamic context includes disaster type, disaster location and range, real-time location of personnel, personnel density, and average evacuation speed of personnel; wherein, the static context data in the spatial context data and the non-spatial context are constructed using GIS software according to the actual indoor environment The dynamic context data in the non-spatial context is obtained through the following steps:

(4a) Disaster type, disaster location and scope:

When a fire or toxic gas diffusion disaster occurs, the early warning sensor sends an early warning signal to the server, and the server determines that the disaster type is a fire or toxic gas diffusion disaster according to the type of early warning sensor that sends the signal, and adopts a disaster spread model based on cellular automata to convert the early warning The distribution position of the sensor is used as an input to dynamically calculate the disaster process and spread situation to obtain the scope of the disaster;

(4b) Personnel real-time location:

The mobile terminal and the positioning tag send the positioning parameters to the positioning base station in real time, and the positioning base station then sends the positioning parameters to the server. real-time location;

(4c) Personnel density:

Using the calculation model of human flow density proposed by Predtechenski and Milinskii, it is calculated by the following formula:

D=Nf/WL……………………………..(1)

Among them, D is the flow density of people, N is the number of people, which is obtained from the real-time position statistics of each person; f is the personal horizontal projected area, which is the experience value directly preset in the server; W is the width of the flow of people, that is, the leftmost person and The real-time maximum distance of the rightmost person is calculated according to the real-time position of each person; L indicates the length of the flow of people, that is, the real-time maximum distance between the frontmost person and the last person of the flow of people is calculated according to the real-time position of each person;

(4d) Average evacuation speed of personnel:

Judging the type of personnel: If the type of personnel is disabled, set the average evacuation speed V ₀ of personnel to 1.20-1.40m/s; if the type of personnel is ordinary people, calculate the evacuation speed according to the following scenarios:

When ordinary people pass the road without disasters, the average evacuation speed V of people is calculated by the following formula:

V=112D ⁴ -38D ³ +434D ² -217D+57...(2)

Among them, V is the average evacuation speed of people in m/min, and D is the flow density of people, which is calculated according to formula (1);

The average evacuation speed _V1 of people when ordinary people pass through the door without disaster is calculated by the following formula:

V ₁ =V[1.17+0.13sin(6.03D-0.12)]……………(3)

Among them, D is the flow density of people, which is calculated according to the formula (1);

The average evacuation speed V ₂ of ordinary people in a disaster situation is calculated by the following formula:

V ₂ =V·μ ₁ …………………………………..(4)

Among them, μ ₁ is an empirical coefficient, which is calculated according to the following two scenarios: when passing through a horizontal passage or through an open door, μ ₁ is calculated by the following formula:

μ ₁ =1.49-0.36D…………………………..(5)

Among them, D is the flow density of people, which is calculated according to the formula (1);

When going downstairs, μ ₁ is set to the empirical value 1.21;

(5) Construct navigation network data and store it in the server. The navigation network data is a weighted directed graph G, G=<V′, E, W>, where V′ is a set of vertices in the graph, and E is an edge W is the set of weights of each edge; the set of vertices V′ and the set of edges E are defined according to the two-dimensional vector map of the indoor scene in the space context, the roads of the indoor scene are represented by the edges, and the intersection points of the roads are represented by the vertices ; For each edge of G, there is a real number called the weight of the edge. The weight of each edge is greater than or equal to zero. The larger the weight, the more serious the congestion. When the weight is positive and infinite, it means the direction from one node to another node. The road does not exist or the target node is unreachable; the weight set W of each edge is determined by the following steps:

(5a) Using the expert analysis method, two or more experts define the weights for each decision attribute, the decision attributes are expressed in non-spatial context, and the weights of each decision attribute are scaled by exponential scaling method;

(5b) For each decision attribute, average the expert decision weights given by each expert, and take the scale closest to the average value as the comprehensive weight of the decision attribute; if the upper and lower adjacent scales are the same as If the differences of the average values obtained are the same, the scale with the smaller variance is taken as the comprehensive weight of the decision attribute;

(5c) For each edge in the weighted directed graph G, set the importance percentage of each decision attribute to this edge, and multiply the comprehensive weight of each decision attribute with its importance percentage to this edge, The result of the addition of each product is the weight of the edge, and the weight of each edge is calculated separately to finally obtain the set W;

(6) The server matches the starting point and end point of the navigation in step (3) with the points in the weighted directed graph G, using the weighted directed graph G, the starting point S and the ending point T as input, using the Dijkastra algorithm or the A* algorithm Output the optimal path data between the starting point S and the ending point T;

(7) The server determines the navigation method according to the user preference received in step (3), and sends the optimal route data to the positioning base station in the form of map, voice or SMS, and then the positioning base station sends it to the mobile terminal and/or LED screen;

(8) The mobile terminal and/or LED screen displays the received optimal path data and guides the target for navigation;

(9) Repeat steps (4) to (8) until the location information sent by the mobile terminal is the end location set in step (3), and the navigation ends.

The beneficial effect that the present invention produces based on its technical scheme is:

(1) The present invention uses early warning sensors to collect disaster data in real time, and uses the disaster data as input to make navigation routes safer and more reliable;

(2) The present invention uses various contexts of indoor scenes collected in real time and/or stored in the server as parameters to participate in the calculation of the optimal navigation route, making the navigation process both real-time, accurate and efficient;

(3) The present invention calculates the population density according to the characteristics of different types of users, making navigation more accurate and reliable;

(4) According to different user preferences, the present invention can send navigation information to mobile terminals and/or LED screens in the form of maps, text messages, and voices to guide targets and make navigation and guidance methods more personalized and humanized.

Description of drawings

Figure 1 is a block diagram of the navigation system.

Fig. 2 is a flowchart of the navigation method.

Detailed ways

The present invention will be further described below in conjunction with drawings and embodiments.

The invention provides an indoor emergency response situation awareness navigation system, the system at least includes a positioning sensor network, a server and an early warning sensor; the positioning sensor network is composed of a positioning base station wired or wirelessly connected Composed of mobile terminals and/or positioning tags, and LED screens wired to the positioning base station, the positioning base station receives the positioning parameters sent by each mobile terminal and each positioning tag and sends them to the server, and sends the positioning and navigation results calculated by the server to Mobile terminal and/or LED screen; the positioning tag sends positioning parameters to the positioning base station; the mobile terminal sends positioning parameters to the positioning base station and receives the navigation result sent by the positioning base station; the LED screen receives and displays the navigation result sent by the positioning base station; the server Receive the data sent by the positioning base station and the early warning sensor, store and manage the data, and use the optimal path planning algorithm to calculate the navigation path, and send the navigation path data to the positioning base station; the early warning sensor is wirelessly connected to the server for sensing disasters and Send an early warning signal to the server in the event of a disaster. The positioning sensor base station can be a Wi-Fi base station; the mobile terminal can be a mobile device such as a mobile phone or a tablet computer with positioning and wireless transmission functions; the positioning tag can be a Wi-Fi positioning tag; The server is a computer with wired and wireless transmission functions.

The early warning sensor is a fire sensor and/or a smoke sensor, which is used to sense fire and/or toxic gas and send a signal to the server.

The system also includes a monitoring terminal connected to the server by wire.

The present invention also provides a navigation method, which specifically includes the following steps:

(1) Build an indoor emergency response situational awareness navigation system: deploy positioning sensor networks, servers, early warning sensors, and LED screens in indoor scenes. The early warning sensors are fire sensors and/or smoke sensors, and store the type and distribution location information of each early warning sensor. In the server; mobile terminals and positioning tags are carried by personnel, and each person carries a mobile terminal or a positioning tag; the LED screen is placed in a conspicuous place on the wall;

(2) Define the type of personnel for each mobile terminal and positioning tag, and store the data of the type of personnel in the server; the types of personnel include ordinary people and disabled people;

(3) Edit the navigation request data through the mobile terminal and send it to the server. The navigation request data includes navigation preference, start location and destination location. The navigation preference is used as the basis for the mobile terminal to receive navigation information. The navigation method includes map Navigation, voice navigation, SMS navigation and LED screen navigation;

(4) Establish an indoor emergency response navigation position model and store it in the server; the indoor emergency response navigation position model includes a spatial context and a non-spatial context, where the spatial context includes a two-dimensional vector map of an indoor scene, a grid map, a two-dimensional Navigation network model and 3D space model, 3D navigation network model; non-spatial context includes static context and dynamic context, where static context includes personnel type, road type, road distance, road safety, fire protection facilities and lighting conditions in the emergency response environment , the dynamic context includes disaster type, disaster location and range, real-time location of personnel, personnel density, and average evacuation speed of personnel; wherein, the static context data in the spatial context data and the non-spatial context are constructed using GIS software according to the actual indoor environment The dynamic context data in the non-spatial context is obtained through the following steps:

(4a) Disaster type, disaster location and scope:

When a fire or toxic gas diffusion disaster occurs, the early warning sensor sends a signal to the server, and the server determines that the disaster type is a fire or toxic gas diffusion disaster according to the type of early warning sensor that sends the signal, and adopts a disaster spread model based on cellular automata, with cellular Based on the automaton, combined with the distribution position of the early warning sensors, the disaster process and spread situation are dynamically calculated to obtain the scope of the disaster;

(4b) Personnel real-time location:

The mobile terminal and the positioning tag send the local coordinate data to the positioning sensor base station in real time, and the positioning sensor base station then sends the local coordinate data to the server, and the server receives the local coordinate data to obtain the real-time position of the person;

(4c) Personnel density:

Using the calculation model of human flow density proposed by Predtechenski and Milinskii, it is calculated by the following formula:

D=Nf/WL……………………………..(1)

Among them, D is the flow density of people, N is the number of people, which is obtained from the real-time position statistics of each person; f is the personal horizontal projected area, which is the experience value directly preset in the server; W is the width of the flow of people, that is, the leftmost person and The real-time maximum distance of the rightmost person is calculated according to the real-time position of each person; L indicates the length of the flow of people, that is, the real-time maximum distance between the frontmost person and the last person of the flow of people is calculated according to the real-time position of each person;

(4d) Average evacuation speed of personnel:

Judging the type of personnel: If the type of personnel is disabled, set the average evacuation speed V ₀ of personnel to 1.20-1.40m/s; if the type of personnel is ordinary people, calculate the evacuation speed according to the following scenarios:

When ordinary people pass the road without disasters, the average evacuation speed V of people is calculated by the following formula:

V=112D ⁴ -38D ³ +434D ² -217D+57...(2)

Among them, V is the average evacuation speed of people in m/min, and D is the flow density of people, which is calculated according to formula (1);

The average evacuation speed _V1 of people when ordinary people pass through the door without disaster is calculated by the following formula:

V ₁ =V[1.17+0.13sin(6.03D-0.12)]……………(3)

Among them, D is the flow density of people, which is calculated according to the formula (1);

The average evacuation speed V ₂ of people under the disaster situation is calculated by the following formula:

V ₂ =V·μ ₁ …………………………………..(4)

Among them, μ ₁ is an empirical coefficient, which is calculated according to the following two scenarios: when passing through a horizontal passage or through an open door, μ ₁ is calculated by the following formula:

μ ₁ =1.49-0.36D…………………………..(5)

Among them, D is the flow density of people, which is calculated according to the formula (1);

When going downstairs, μ ₁ is set to the empirical value 1.21;

(5) Construct navigation network data and store it in the server. The navigation network data is a weighted directed graph G, G=<V′, E, W>, where V′ is a set of vertices in the graph, and E is an edge W is the set of weights of each edge; the set of vertices V′ and the set of edges E are defined according to the two-dimensional vector map of the indoor scene in the space context, the roads of the indoor scene are represented by the edges, and the intersection points of the roads are represented by the vertices ; For each edge of G, there is a real number called the weight of the edge. The weight of each edge is greater than or equal to zero. The larger the weight, the more serious the congestion. When the weight is positive and infinite, it means the direction from one node to another node. The road does not exist or the target node is unreachable; the weight set W of each edge is determined by the following steps:

(5a) Using the expert analysis method, let more than two experts define weights for each decision-making attribute, the decision-making attributes are expressed in non-spatial context, and the weights of each decision-making attribute are scaled by exponential scaling method;

(5b) For each decision attribute, average the expert decision weights given by each expert, and take the scale closest to the average value as the comprehensive weight of the decision attribute; if the upper and lower adjacent scales are the same as If the differences of the average values obtained are the same, the scale with the smaller variance is taken as the comprehensive weight of the decision attribute;

(5c) For each edge in the weighted directed graph G, set the importance percentage of each decision attribute to this edge, and multiply the comprehensive weight of each decision attribute with its importance percentage to this edge, The result of the addition of each product is the weight of the edge, and the weight of each edge is calculated separately to finally obtain the set W;

(6) The server matches the starting point and end point of the navigation in step (3) with the points in the weighted directed graph G, using the weighted directed graph G, the starting point S and the ending point T as input, using the Dijkastra algorithm or the A* algorithm Output the optimal path data between the starting point S and the ending point T;

(7) The server determines the navigation method according to the user preference received in step (3), and sends the optimal route data to the positioning base station in the form of map, voice or SMS, and then the positioning base station sends it to the mobile terminal and/or LED screen;

(8) The mobile terminal and/or LED screen displays the received optimal path data and guides the target for navigation;

(9) Repeat steps (4) to (8) until the location information sent by the mobile terminal is the end location set in step (3), and the navigation ends.

Claims (4)

1. an indoor emergency response context aware navigational system, is characterized in that: this system at least comprises alignment sensor network, server and early warning sensor; Described alignment sensor network is by the locating base station of the wired or wireless connection of same server, with the mobile terminal of locating base station wireless connections and/or positioning label, form with the LED screen of locating base station wired connection, wherein locating base station receives the positional parameter of each mobile terminal and the transmission of each positioning label and sends it to server, sends location navigation result to the mobile terminal and/or LED being calculated by server and shields; Positioning label sends positional parameter to locating base station; Mobile terminal sends positional parameter and receives the navigation results that locating base station sends to locating base station; LED screen receives and shows the navigation results of locating base station transmission; Described server receives data, the storage administration data of locating base station and early warning sensor transmission, and utilizes optimum path planning algorithms to calculate guidance path, and guidance path data are sent to locating base station; Described early warning sensor is with server wireless connections, for responding to disaster and send early warning signal to server in the time there is disaster.

2. indoor emergency response context aware navigational system according to claim 1, is characterized in that: described early warning sensor is fire perceiving device and/or smoke transducer, for responding to fire and/or toxic gas and sending early warning signal to server.

3. indoor emergency response context aware navigational system according to claim 1, is characterized in that: the monitor terminal that also comprises same server wired connection.

4. the air navigation aid based on system described in claim 1, is characterized in that specifically comprising the following steps:

(1) build indoor emergency response context aware navigational system: dispose alignment sensor network, server, early warning sensor and LED screen at indoor scene, early warning sensor is fire perceiving device and/or smoke transducer, and the type of each early warning sensor and distributing position information are stored in server; Mobile terminal and positioning label are carried by personnel, and everyone carries a mobile terminal or a positioning label; LED screen is placed at the obvious place of metope;

(2) for each mobile terminal and positioning label define respectively personnel's type, and personnel's categorical data is stored in server; Described personnel's type comprises ordinary people and the disabled;

(3) by mobile terminal editing navigation request msg and be sent to server, described navigation requests data comprise navigation preference, start position and final position, described navigation preference receives the foundation of navigation information mode as mobile terminal, navigate mode comprises digital map navigation, Voice Navigation, note navigation and the navigation of LED screen;

(4) set up indoor emergency response navigation position model and be stored in server; Described indoor emergency response navigation position model comprises spatial context and non-space context, and wherein spatial context comprises indoor scene two-dimension vector map, grating map, two dimensional navigation network model and three-dimensional space model, three-dimensional navigation network model; Non-space context comprises static context and dynamic context, wherein static context comprises personnel's type, road type, road distance, road safety, fire fighting device and the lighting condition in emergency response environment, and dynamic context comprises Disasters Type, disaster position and involves scope, personnel's real time position, density of personnel, the average evacuation speed of personnel; Wherein, the static context data in described spatial context data and non-space context utilize GIS software modeling to obtain according to actual indoor environment, and the dynamic context data in non-space context is obtained by following steps:

(4a) Disasters Type, disaster position and involve scope:

When breaking out of fire or toxic gas diffusion disaster, early warning sensor sends early warning signal to server, server determines that according to the early warning sensor type of transmitted signal Disasters Type is fire or toxic gas diffusion disaster, and the disaster Spread Model of employing based on cellular automaton, using the distributing position of early warning sensor as input, dynamic calculation disaster process and spread situation, involves scope to obtain disaster;

(4b) personnel's real time position:

Mobile terminal and positioning label are sent to locating base station by positional parameter in real time, locating base station is sent to server by positional parameter again, server receives positional parameter and sets it as input, by the Wi-Fi/DR location blending algorithm computing staff real time position based on particle filter;

(4c) density of personnel:

The stream of people's density calculation model that utilizes Predtechenski and Milinskii to propose calculates by following formula:

D=Nf/WL………………………..（1）

Wherein, D is people's current density, and N represents number, is drawn by everyone's real time position statistics; F represents individual horizontally-projected area, for being directly preset in the empirical value in server; W represents stream of people's width, and the stream of people's Zuoren person and the real-time ultimate range of the rightest personnel, calculate according to everyone's real time position; L represents stream of people's length, and the stream of people's the most front personnel and the last real-time ultimate range of personnel, calculate according to everyone's real time position;

(4d) the average evacuation speed of personnel:

Judge personnel's type: if personnel's type is the disabled, the average evacuation speed V of the personnel that arrange ₀for 1.20-1.40m/s; If personnel's type is ordinary people, calculate evacuation speed according to following sight:

When ordinary people without under disaster scenarios it when the road, the average evacuation speed V of personnel calculates by following formula:

V=112D ⁴-38D ³+434D ²-217D+57………（2）

Wherein, V is the average evacuation speeds of personnel, and unit is m/min, and D is people's current density, calculates according to formula (1);

When ordinary people is without the average evacuation speed V of personnel by when door under disaster scenarios it ₁calculate by following formula:

V ₁=V[1.17+0.13sin(6.03D-0.12)]……….......（3）

Wherein, D is people's current density, calculates according to formula (1);

When ordinary people is at the average evacuation speed V of the personnel under disaster scenarios it ₂calculate by following formula:

V ₂=V·μ ₁……………………………..………..（4）

Wherein, μ ₁be experience factor, calculate according to following two kinds of scenes: during by horizontal channel or by open doors, μ ₁calculate by following formula:

μ ₁=1.49-0.36D………………………...……..（5）

Wherein, D is people's current density, calculates according to formula (1);

Downstairs time, μ ₁be set to empirical value 1.21;

(5) build navigation network data and be stored in server, described navigation network data are Weighted Directed Graph G, G=<V ', E, W>, wherein V ' is the set on summit in figure, E is the set on limit, and W is the set of the power on each limit; The set V ' on summit and the set E on limit, according to indoor scene two-dimension vector map definition in spatial context, represent the road of indoor scene, with the joint of the each road of vertex representation with limit; For each limit of G, exist a real number to be called the power on limit, the power on each limit is more than or equal to zero, and the larger expression of weights is crowded more serious, and weights represent while being just infinite that the road from a node to another node direction does not exist or destination node is unreachable; The set W of the power on each limit is determined by following steps:

(5a) utilize analysis expert method, define respectively weights by more than two expert for each decision attribute, described decision attribute is expressed with non-space context, and the weights of each decision attribute adopt exponential scale method scale;

(5b) for each decision attribute, the expert decision-making weights that each expert is provided are averaged, the comprehensive weights that to get with the immediate scale of mean value be this decision attribute; If upper and lower two adjacent scales are identical with the difference of the mean value of trying to achieve, the comprehensive weights that to get scale that variance is less be this decision attribute;

(5c) for every limit in Weighted Directed Graph G, the importance number percent of each decision attribute to this edge is set, respectively the comprehensive weights of each decision attribute and its importance number percent to this edge are multiplied each other, the i.e. weights on this limit of result that each product is added, the weights that calculate respectively every limit finally obtain gathering W;

(6) server mates the navigation start position of step (3) and final position with the point of Weighted Directed Graph G, using Weighted Directed Graph G, starting point S and terminal T as input, utilize the optimal path data between Dijkastra algorithm or A* algorithm output starting point S and terminal T;

(7) user preference that server receives according to step (3) is determined navigate mode, with the form of map, voice or note, optimal path data is sent to locating base station, then is sent to mobile terminal and/or LED shields by locating base station;

(8) mobile terminal and/or LED screen display are shown the optimal path data that receive and are guided target to navigate;

(9) repeating step (4) is to step (8), until the positional information that mobile terminal sends is the final position that step (3) arranges, navigation finishes.