CN103237291A - Integrated positioning method for mobile terminal and active information service recommendation method - Google Patents

Abstract

The invention provides an integrated positioning method of a mobile terminal and an information service active recommendation method. The integrated positioning method includes: step 1, obtaining satellite positioning signals and WIFI positioning signals; step 2, comparing the signal strengths of satellite positioning signals and WIFI positioning signals, and selecting the one with high information strength as the mobile terminal positioning signal; The mobile terminal sends the positioning signal to the cloud computing server; step 4, the mobile terminal receives the location service information actively recommended by the cloud computing server through the positioning signal. The mobile terminal discriminates and processes according to the satellite positioning signal and the information strength of the AP point of WIFI, and selects the one with the largest RSS as the positioning signal, which improves the positioning accuracy, and obtains the cloud computing server from the service provider according to the user's customized needs. Actively recommend information to ensure that users can obtain highly reliable and high-precision recommendation services anytime, anywhere.

Description

Integrated positioning method of mobile terminal and active recommendation method of information service

technical field

The invention relates to the field of positioning, in particular to an integrated positioning method of a mobile terminal and an information service active recommendation method.

Background technique

Location-based services have gradually integrated into people's lives, providing a method for people to conveniently obtain location-based services. Usually, location information of terminals is obtained through positioning technology, and location-related and satisfying information is provided to mobile terminals through mobile networks/wireless networks. Information services required by end users, so as to provide customized end users with services anytime and anywhere. For example, after different passengers arrive at different scenic spots, they can provide passengers with information services related to the scenic spots through positioning technologies such as GPS (Global Positioning System), Beidou, mobile network or wireless network, Bluetooth, and RFID (Radio Frequency Identification Devices). However, GPS/Beidou positioning needs to be relatively accurate in a relatively open space, where high-rise buildings are not densely populated, and indoor positioning is difficult to meet the needs; but at this time, positioning can be achieved through mobile networks, but the current 2G/3G (3rd Generation Telecommunication), the network speed is difficult to meet the needs of customized users for bulk data transmission changes; at this time, consider wireless local area networks, especially WIFI (Wireless Fidelity) to achieve positioning, although WIFI has reflections when encountering obstacles , refraction, diffraction and other deficiencies, it has a very good effect on indoor positioning, and most of the current terminal smart phones have WIFI functions; while Bluetooth, RFID and other technologies require relatively short distances for positioning, positioning information is susceptible to interference and transmitted data issues such as limited quantities. The wireless calibration algorithms mainly include fingerprint algorithm, circular positioning algorithm, CELLID positioning algorithm and TOA (Time of Advent)/TDOA (Time Difference Of Arrival) positioning algorithm.

As far as location service itself is concerned, it is a service model that uses GIS (Geographic Information System) technology, mobile positioning technology and network communication technologies to provide spatial information services in a mobile environment; making data collection and processing a location service The important link and process of the application also provides an important guarantee for the service quality of the location service. Its positioning technology is based on the premise of location-based services, and is the basis for directly obtaining the geographical location of end users to provide information services for different users; at present, its positioning technologies usually include GPS/Beidou positioning, ground triangulation positioning/mobile network positioning and hybrid positioning. positioning method, etc. Different information services are provided by different providers, and the information services provided are often changed in real time; while users' needs are to obtain effective and accurate services anytime and anywhere to provide convenience, help, Simplify your own business arrangements.

In order to meet such user requirements and customization needs, the current calculation mode, service mode, and positioning mode all have certain limitations and deviations. , Accurate information service, if the passenger arrives at different scenic spots, then the information service of different scenic spots can be provided according to the geographical location of the passengers; and in this case, the passengers may be in the open, non-intensive , mobile, "indoor" and other spaces, and related information services are also distributed in different service providers in different spaces, and the data of these providers is also changing in real time. Therefore, the current positioning and location service methods are difficult to meet the customization needs of users.

Contents of the invention

Embodiments of the invention are described in detail below, but the invention can be practiced in many different ways as defined and covered by the claims.

The invention provides an integrated positioning method and an information service active recommendation method of a mobile terminal with high positioning accuracy and which can provide information services according to user customization.

For this reason, the first aspect of the present invention provides an integrated positioning method for a mobile terminal, which is characterized in that it includes: step 1, obtaining satellite positioning signals and WIFI positioning signals; step 2, comparing satellite positioning signals and WIFI positioning signals signal strength, select the one with higher signal strength as the mobile terminal positioning signal; step 3, send the mobile terminal positioning signal to the cloud computing server; step 4, the mobile terminal receives the active recommendation from the cloud computing server through the positioning signal location services information.

Further, the WIFI positioning signal is obtained by the following method: step A1, establish a fingerprint database according to the AP status in different areas; step A2, scan the AP signal, and when the strength of the AP signal meets the predetermined requirements, directly locate, otherwise perform the step A3; step A3, identify the AP signal obtained by scanning, and use a division algorithm to divide the AP signal; step A4, amplify the signal that is not enough for positioning in the divided AP signal; step A5, The amplified AP signal is processed to identify the source of the AP signal; if the AP signal can meet the positioning requirements, the AP signal is compared and matched with the fingerprint database through the preset positioning routing table to complete the positioning , and use the mapping function to realize fast mapping with the fingerprint database, otherwise switch to other partitions and execute step A4.

Further, the method for obtaining the WIFI positioning signal further includes: step A6, when the mobile terminal is in different divisions of the area, automatically switch according to different divisions, so as to continuously track all the WIFI signals in the area.

Further, the method for obtaining the WIFI positioning signal further includes: step A7, establishing a utility function satisfying WIFI positioning validity and user satisfaction, so as to effectively prevent uncertainty and stability of information strength in the area.

Further, in step A5, the matching is performed by the following method:

表示在时间t时蚂蚁在AP划分上实现状态转移的概率，并用禁忌表tabu_k表示蚂蚁k当前所寻优的AP信号，allowed_k={C-tabu_k}表示蚂蚁k下一次允许寻优的AP信号，C表示区域内所有AP信号；Y(t)为匹配映射率函数、F(t)为效用函数，则满足最优的匹配结果蚂蚁状态转换概率可按下式计算：1) with

Indicates the probability of an ant achieving state transition on AP division at time t, and uses the tabu table tabu _k to indicate the AP signal that ant k is currently seeking to optimize, and allowed _k = {C-tabu _k } indicates that ant k is allowed to optimize next time AP signal, C represents all AP signals in the area; Y(t) is the matching mapping rate function, F(t) is the utility function, then the ant state transition probability that satisfies the optimal matching result can be calculated as follows:

If j belongs to allowed _k , then $P_{i,j}^k\left(t\right)={\left[Y_{i,j}\left(t\right)\right]}^{\mathrm{\α}}\&Center\; Dot;{\left[f_{\mathrm{ik}}\left(t\right)\right]}^{\mathrm{\β}},$ otherwise $P_{i,j}^k\left(t\right)=0;$

Among them, α is the information amount heuristic factor, β is the expectation heuristic factor, and the value is (0,1);

2) Use the following formula to adjust the mapping rate function Y(t):

Y _i,j (t)=(1-ρ)·Y _i,j (t)+△Y _i,j (t)

${\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>$

In the formula, (1-ρ)∈(0,1] is the variation coefficient of the signal strength R from one division to another division on the mapping rate; △Y _i,j (t) means that in this cycle (i ,j) the change increment in the partition switching; initially, △Y _i,j (t)=0; Indicates the value of the signal strength R of the kth ant in the switching state (i, j) in a cycle, then:

${\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

In the formula, 0 represents the case except R.

3) When the predetermined positioning accuracy is reached, stop optimizing the matching.

Further, the location service recommendation information is the area where the mobile terminal is located and location services around the area.

As a second aspect of the present invention, there is provided a method for actively recommending location-oriented information services, which is characterized in that it includes: step 1, the cloud computing server acquires a mobile terminal positioning signal from a mobile terminal; step 2, the cloud computing server according to From the customization requirements of the mobile terminal and the positioning signal of the mobile terminal, obtain the recommended location service recommendation information from the service provider, and send the location service recommendation information to the mobile terminal; step 3, the cloud computing server receives The evaluation conclusion and evaluation reason of the location service recommendation information from the mobile terminal; step 4, if the recommended service does not meet the customization requirements, and the evaluation result is not satisfactory, the cloud computing server further receives the information from the mobile terminal According to the new customization requirements and the positioning signal of the mobile terminal, the recommended location service recommendation information is obtained from the service provider again, and the location service recommendation information is sent to the mobile terminal.

Further, in the steps 3 and 4, the cloud computing server also sends the unsatisfactory evaluation results and evaluation reasons to the customer service center.

Further, the location service recommendation information is the area where the mobile terminal is located and location services around the area.

When the user turns on the mobile terminal, the mobile terminal judges according to the satellite positioning signal (such as GPS and/or Beidou) and the signal strength (RSS: Received Signal Strength) of the AP (Access Point) point of WIFI, and selects the one with the largest RSS as the positioning Signal to obtain the location information of the terminal user to achieve terminal positioning, improve the positioning accuracy, in order to provide effective location services for the terminal user, and obtain the active recommendation information obtained by the decomputing server from the service provider according to the user's customized needs. In this way, users can obtain information services that meet their needs anytime and anywhere.

Description of drawings

Fig. 1 is a schematic diagram of unified location information acquisition and processing in a cloud computing environment in a preferred embodiment;

Fig. 2 is a schematic structural diagram of a WIFI positioning engine in a preferred embodiment; and

Fig. 3 is the active recommendation module structure of location-oriented information service in a preferred embodiment.

Detailed ways

Based on the analysis of the background technology, the present invention proposes a positioning method in a cloud computing environment to effectively solve the existing deficiencies. Cloud computing is a recent solution to the problems of distributed scalability, shared hardware and software resources, and information services. It is a distributed computing model based on the Internet, so that cloud computing can efficiently solve real-time location services, massive information and data processing. requirements.

Please refer to Figure 1 to Figure 3 for the description of the following parts.

As one aspect of the present invention, an integrated positioning method for a mobile terminal is provided. The method includes:

Step 1. Obtain satellite positioning signals and WIFI positioning signals; the integration of these two positioning signals is to ensure that end users can realize location services anytime and anywhere, that is, to enable terminals to obtain location services indoors and outdoors, especially for terminals only. Location-based services in and around anytime, anywhere. For example, after tourists arrive at different scenic spots, they generally only provide tourist information services for the scenic spot and its surrounding areas.

Step 2, compare the signal strength of the satellite positioning signal and the WIFI positioning signal, and select the one with the higher signal strength as the mobile terminal positioning signal, so as to ensure that the user can achieve more accurate positioning anytime and anywhere;

Step 3: Send the positioning signal of the mobile terminal to the cloud computing server; in this way, after the terminal user achieves accurate and fast positioning, they can realize the customized service of their respective terminals, and send the terminal information to the terminal according to the cloud computing server in the place and surrounding areas. Users can actively recommend service information to provide efficient location services for terminals with different customization needs.

Step 4, receiving location service recommendation information actively recommended by the cloud computing server according to the positioning signal of the mobile terminal.

When the user turns on the mobile terminal, the mobile terminal judges according to the satellite positioning signal (such as GPS and/or Beidou) and the signal strength (RSS: Received Signal Strength) of the AP (Access Point) point of WIFI, and selects the one with the largest RSS as the positioning Signal to obtain the location information of the terminal user to achieve terminal positioning, improve the positioning accuracy, in order to provide effective location services for the terminal user, and obtain the active recommendation information obtained by the decomputing server from the service provider according to the user's customized needs. In this way, users can obtain information services that meet their needs anytime and anywhere.

After passengers arrive at different scenic spots, locations, locations and environments, especially scenic spots, they can realize their needs through the present invention, and provide different passengers with information services that meet the location requirements, so that passengers can obtain satisfactory information in the scenic spots and surrounding areas. tourism consumption. Thereby, the accuracy of information service location is improved and the delay of transmitting information service is reduced. This is because when tourists arrive at different scenic spots, only the tourist information services (such as accommodation information, catering information, etc.) It simplifies the process of obtaining information services.

The fingerprint positioning method in the prior art realizes positioning by identifying the number of APs and signal strength, and then mapping and matching with the signal features stored in the established fingerprint database according to the signal features. Its fingerprint database effectively utilizes the multipath effect and combines it with location information, and uses the uniqueness of the multipath structure of the channel at the same location as the fingerprint in areas where the WIFI environment changes little. However, due to problems such as non-line-of-sight propagation, multipath propagation, environmental changes, and the need to establish multiple AP data reference points and the difficulty of updating the fingerprint database, it is difficult to achieve an ideal effect in terms of positioning accuracy and delay; but compared For other algorithms such as the circumference positioning algorithm and the CELLID positioning algorithm, the fingerprint positioning algorithm also has the advantages of high precision, short delay, and small error. In the specific application of the fingerprint positioning algorithm, two stages of training and positioning are usually used. In the training stage, the identification and judgment of the number of APs and information strength are mainly realized. In the positioning, such as the most NN (Nearest Neighbor), KNN (K-Nearest Neighbor ), KWNN (K-Weighted Nearest Neighbor) and NNA (Neural Network Algorithm) and other algorithms to achieve matching, and finally complete the positioning. However, there is no effective mapping relationship between the attenuation of the WIFI signal when it propagates, the attenuation of the signal strength and the distance between the transceiver devices, so it is difficult to obtain satisfactory positioning accuracy using the traditional positioning mode.

Therefore, the present invention proposes an ant colony-optimized WIFI positioning mechanism in a cloud environment to solve these deficiencies, and create conditions for improving positioning accuracy and facilitating rapid use by end users. Preferably, the WIFI positioning signal is obtained by the following method:

Step A1, set up fingerprint database according to the AP situation of different areas; This data can be constructed by Hadoop+Habse+Hive, to meet the requirement of cloud computing, and the signal set in the fingerprint database (usually including coordinates, name, address and RSS, etc.).

In order to solve the problems of AP signal instability, attenuation and asymmetrical mapping relationship between transceiver devices, etc., the present invention solves this problem through the following steps A2-A7, that is, provides a positioning that meets the requirements of WIFI fingerprint positioning engine.

Step A2, scan the AP signal, and when the strength of the AP signal meets the predetermined requirements, locate directly (that is, realize the calibration according to the strength of the signal, that is, when the signal is stable, the strength is strong, the attenuation is slow, and the distance between the receiving devices is moderate, directly realize positioning), otherwise execute step A3;

Step A3, identify the AP signal obtained by scanning, and use a division algorithm to divide the AP signal; for example, it can be divided by the division method of the following literature: Zhou Xiangbing, Yang Xingjiang, Ma Hongjiang. SaaS addressing interruption software based on division algorithm Generating Strategies. Computer Applications, 2012,32(2):561-565. Obviously, other partitioning algorithms in the art can also be used.

Preferably, the partition algorithm is described as: Let P={P ₁ ,P ₂ ,...,P _N } be a partition of the directed graph G=<V,E> (wherein, V is the set of vertices, and E is the set of edges) , P _i is a collection of some nodes in G, which is called a module, and X(P _i ) is the collection of all nodes belonging to P _i , and the minimum division is X<-1,-1,...,-1> Indicates that the maximum division is represented by X<-n,0,…,0> (where n represents a positive integer); P(v _i ) is the module where node v _i is located, and the division meets the following conditions:

1) $\underset{i=1}{\overset{N}{\&cap;}}x\left(P_i\right)=\mathrm{\&Phi;};$

2) $\underset{i=1}{\overset{N}{\&cup;}}x\left(P_i\right)=V;$

（其中，

表示为空集）3)

(in,

represented as an empty set)

4) $\&ForAll;e_k=(V_i,V_j)\&Element;\mathrm{E.},x\left(V_i\right)\&Right\; Arrow;x\left(V_j\right)$ (for $\&ForAll;e_k\&Element;\mathrm{E.},$ , then e _k =(v _i , v _j )∈E indicates that the node v _i and v _j belong to the edge set E), where e _k represents the edge item of the edge set E.

Then the AP signal in the area can be divided into three parts: <root(i,x), Jion_block(i,j,x), meet_hash(x ₁ ,x ₂ )>, where root(i,v) means Divide G into the root of the tree, Jion_block(i,j,v) represents the connection of two Pi modules, and meet_hash(v ₁ ,v ₂ ) represents the time to balance the connection of different blocks using the Hash table.

Step A4, amplifying the signals that are insufficient for positioning among the divided AP signals, so as to distinguish where these AP signals come from, and what causes these signals to be insufficient for positioning. A common weak signal table is usually set, which facilitates the adjustment of the cause of the weak signal.

Step A5, processing the amplified AP signal to identify the source of the AP signal; if the AP signal can meet the positioning requirements, then comparing the AP signal with the fingerprint database through a preset positioning routing table and Match to complete the positioning, and use the mapping function to realize fast mapping with the fingerprint database, otherwise switch to other divisions and execute step A4. For example, the mapping function efficiency (%) is defined as: Y: A→D, where Y represents the mapping function, A represents the signal in the signal routing table of the positioning engine, and D represents the fingerprint database.

Preferably, the method for obtaining the WIFI positioning signal further includes: step A6, when the mobile terminal is in different divisions of the area, automatically switch according to different divisions, so as to continuously track all the WIFI signals in the area. It can ensure that end users can continue to locate in the area, indoors and outdoors, and can continue to obtain recommended services.

Preferably, the method for obtaining the WIFI positioning signal further includes: step A7, establishing a utility function that satisfies WIFI positioning validity and user satisfaction, so as to effectively prevent uncertainty and stability of information strength in the area. In particular, the utility function is used to realize the relationship between positioning efficiency (%) E and user satisfaction S, and the utility function can be described as: F: E→S. Moreover, different divisions have different corresponding utility, and there are also deviations in the matching effect completed in the fingerprint database. Through this step and steps A2-A6, the uncertainty and stability of the RSS in the region can be effectively prevented.

Preferably, in step A5, the matching is performed by the following method, in particular, the optimization method that satisfies the signal matching, and uses the ant colony algorithm for optimization, then the optimization strategy of the ant colony algorithm is described as follows (the optimization stop condition is Successfully complete the user terminal positioning):

表示在时间t时蚂蚁k在AP划分上实现状态转移的概率，并用禁忌表tabu_k表示蚂蚁k当前所寻优的AP信号，allowed_k={C-tabu_k}表示蚂蚁k下一次允许寻优的AP信号，C表示区域内所有AP信号；Y(t)：A→D，其中Y(t)表示映射函数，A表示所述定位路由表中的信号，D表示指纹数据库；F(t)：E→S，其中F(t)是满足WIFI定位效度与用户满意度的效用函数；E是定位效率（%）、S是用户满意度，则满足最优的匹配结果蚂蚁状态转换概率可按下式计算：1) with

Indicates the probability that ant k achieves state transition in AP division at time t, and uses the tabu table tabu _k to indicate the AP signal that ant k is currently seeking to optimize, and allowed _k = {C-tabu _k } indicates that ant k is allowed to optimize next time AP signal, C represents all AP signals in the area; Y(t): A→D, wherein Y(t) represents the mapping function, A represents the signal in the positioning routing table, and D represents the fingerprint database; F(t) : E→S, where F(t) is a utility function that satisfies WIFI location validity and user satisfaction; E is location efficiency (%), S is user satisfaction, then the ant state transition probability that satisfies the optimal matching result can be Calculate according to the formula:

If j belongs to allowed _k , then $P_{i,j}^k\left(t\right)={\left[Y_{\mathrm{ij}}\left(t\right)\right]}^{\mathrm{\&alpha;}}\&Center\; Dot;{\left[f_{\mathrm{ik}}\left(t\right)\right]}^{\mathrm{\&beta;}},$ otherwise $P_{i,j}^k\left(t\right)=0;$

表示蚂蚁k在状态i与状态j之间的转换概率，F_ik(t)表示效用函数在时间t内的划分区域状态i与蚂蚁k所处的划分区域状态的效用值，Y_ij(t)表示映射函数在时间t内的划分区域状态i与j的映射值。Among them, α is the information amount heuristic factor, β is the expected heuristic factor, and the value is (0,1); where, i, j represent the value of the state transition of different divided areas, and both are positive integers greater than zero, such as

Indicates the transition probability of ant k between state i and state j, F _ik (t) represents the utility value of the divided area state i and the divided area state of ant k within the time t of the utility function, Y _ij (t) Indicates the mapping value of the partition state i and j of the mapping function at time t.

2) Use the following formula to adjust the mapping rate function Y(t):

Y _i,j (t)=(1-ρ)·Y _i,j (t)+△Y _i,j (t)

${\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>$

表示第k只蚂蚁在一次循环中在切换状态（i,j）上信号强度R的值，则：In the formula, (1-ρ)∈(0,1] is the variation coefficient of the signal strength R from one division to another division on the mapping rate, ρ∈[0,1), which represents the attenuation coefficient of R; △ Y _{i, j} (t) represents the change increment of (i, j) in the partition switching in this cycle; m represents the number of ants; initially, △Y _{i, j} (t)=0;

Indicates the value of the signal strength R of the kth ant in the switching state (i, j) in a cycle, then:

${\mathrm{<span\; class="notranslate">\; \&Delta;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; R</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

In the formula, 0 represents the case except R.

3) When the predetermined positioning accuracy is reached, stop optimizing the matching.

Preferably, the location service recommendation information is the scenic spot where the mobile terminal is located and location services around the scenic spot.

Please refer to Figure 3. In order to solve the problems of AP signal instability, attenuation and asymmetric mapping between transceiver devices, etc., a positioning engine that meets the requirements of WIFI fingerprint positioning is proposed to solve this problem. The engine is composed of a signal scanner , Signal Recognizer, Signal Amplifier, Signal Processor, Signal Controller and Signal Tracker, and the signal strength after passing through the positioning engine is represented by R.

Signal scanner: used to scan the AP signal and judge the signal strength; at the same time, realize the positioning according to the signal strength, that is, when the signal is stable, the strength is strong, the attenuation is slow, and the distance between the receiving devices is moderate, the positioning is directly realized. Otherwise, it is corrected through the WIFI positioning engine.

Signal recognizer: it performs signal recognition on the scanning results of the scanner, that is, recognizes and judges the AP signals in the area, and uses the division algorithm to divide the AP signals in the area.

Signal amplifier: It is to amplify the signals in the division and not enough for positioning, so as to distinguish where these AP signals come from, and what causes these signals to be insufficient for positioning. A common weak signal table is usually set, which facilitates the adjustment of the cause of the weak signal.

Signal processor: It is to process the result of signal amplification to find out where the AP signal comes from at this time. If the signal at this time can meet the positioning needs, it will directly compare and match with the fingerprint database. If the signal at this time If it is not enough to meet the positioning requirements, switch to other divisions.

Signal controller: It is to control, coordinate and balance the entire positioning engine, so that the engine can stably help WIFI to accurately locate and shorten the positioning delay.

Signal Tracker: Ensure that end users can continue to locate in the area, indoors and outdoors, and can continue to obtain recommended services. That is, when the user is in different divisions of the area, it can automatically switch according to different divisions, so as to continuously track all WIFI signals in the area.

As a second aspect of the present invention, a location-based service-oriented information service active recommendation method is provided, including:

Step 1, the cloud computing server obtains the mobile terminal positioning signal from the mobile terminal, especially, the mobile terminal positioning signal can be obtained through the above-mentioned integrated positioning method of the mobile terminal;

Step 2: The cloud computing server obtains recommended location service recommendation information from a service provider according to the customization requirements from the mobile terminal and the positioning signal of the mobile terminal, and sends the location service recommendation information to the mobile terminal. In particular, active service recommendation is realized according to the services provided by the service provider and the customization requirements of users, so as to maximize the revenue of service providers and operators.

Step 3, the cloud computing server receives the evaluation conclusion and evaluation reason of the location service recommendation information from the mobile terminal;

Step 4, if the evaluation result is unsatisfactory and the recommended service cannot meet the customization requirement, the cloud computing server further receives the new customization requirement from the mobile terminal, and according to the new customization requirement and the mobile terminal The positioning signal obtains recommended location service recommendation information from the service provider again, and sends the location service recommendation information to the mobile terminal.

In particular, through the positioning mechanism of GPS/Beidou and WIFI based on ant colony strategy, end users can provide barrier-free positioning anytime and anywhere, which effectively meets the basic requirements of location services. Therefore, the active recommendation method for location-oriented information services in the present invention adopts the active recommendation method, which can effectively provide on-demand positioning services for end users, thereby converting traditional passive recommendations to active recommendations for different users.

In addition, the present invention can perform information processing on location information and recommendation services based on location services, so that the entire location device can realize information exchange and data processing. At this time, it is necessary to realize efficient and scalable parallel real-time processing in the cloud computing environment to bring users an experience that meets the needs in real time. On the one hand, end users customize services according to their own needs, and send location information and location information to the cloud computing environment; it also has the function of receiving service information. On the other hand, the present invention can also enable the service provider to register and publish services at any time according to user customization requirements, so as to meet user customization requirements in time.

Preferably, in the step 4, the cloud computing server also sends the unsatisfactory evaluation results and evaluation reasons to the customer service center.

Preferably, the location service recommendation information is the area where the mobile terminal is located and location services around the area.

The present invention will be described below by taking passenger acquisition service as an example:

1. The positioning mechanism of the present invention is carried out in a cloud computing environment. Therefore, when the cloud computing environment is available and the communication mechanism and map technology are all satisfied, the efficiency of the present invention can be well reflected.

2. After the method of the present invention is developed with a program, it is respectively embedded in the application program and environment of each end/end according to the requirements of the service, client and cloud computing environment.

3. The present invention locates and implements active service recommendation by area, that is, when a passenger arrives at a different scenic spot, the positioning and service recommendation are only limited to the location service of the scenic spot and the surrounding areas of the scenic spot. In this way, passengers can easily obtain practical recommendation services in the scenic spot, and also reduce the search range and other advantages.

4. When passengers (end users) are about to arrive at a scenic spot. Can obtain following flow process according to the present invention:

1) When the passenger turns on the positioning terminal, the positioning is carried out according to the GPS/Beidou and WIFI signals in the area, and at the same time, the precise positioning of the scenic spot and its surroundings is realized according to the regional characteristics of GPS/Beidou and the AP signal source of WIFI.

2) Usually the critical value is the default value, that is, when the WIFI positioning based on the ant colony strategy is optimized to this critical value, the positioning can be successfully realized, but end users can change this critical value according to their own needs to make it more satisfying own positioning requirements in order to obtain more satisfactory location services and business services based on location services.

3) After the positioning is completed, passengers can set customized services according to their needs through the terminal equipment, and send the results to the cloud computing service center to provide users with fast and effective services through cloud computing.

4) After the cloud computing environment conducts rapid analysis and processing according to the customized needs of users, it actively provides users with information services of the scenic spot and its surrounding areas.

5) The user obtains the service and determines whether the provided service can meet his needs. If not, send the reason for dissatisfaction, and re-send the demand customization again. When the cloud computing service center receives the customized requirements and reasons, it will automatically make active recommendations again, and send the recommended service and the reasons for passenger dissatisfaction to the customer center of the cloud computing center.

6) Passengers obtain services that meet their needs and make comments.

In order to meet the requirement that the user terminal can be positioned at any time and anywhere, and can effectively obtain location-oriented services, the present invention makes innovations in the following aspects.

1. A unified location information acquisition and processing method that satisfies the integration of indoor and outdoor and multiple modes is proposed. This method can realize the acquisition and processing of positioning signals under GPS/Beidou and WIFI signals.

2. The positioning mechanism of the present invention is implemented in a cloud computing environment.

3. Established a positioning engine that meets indoor and outdoor positioning. The engine meets GPS/Beidou and also meets WIFI positioning requirements. Since GPS/Beidou positioning is directly provided by the provider, it is only necessary to add these positioning devices to the present invention; in this case, positioning automatically selects different positioning signals according to the position signal strength of the user terminal.

4. In order to make the WIFI signal in the area organized and structured, a division algorithm is used to divide it, which provides a switching method for the subsequent fingerprint matching optimization based on the ant colony strategy.

5. According to the WIFI signal strength (usually GPS/Beidou positioning signals are covered in the open environment), as well as the established mapping rate function and utility function; at the same time, set the WIFI positioning critical value (such as 0.9) to design a An ant colony algorithm that meets WIFI positioning optimization to achieve high-precision positioning.

6. Establish a positioning transmission component, a demand customization sending and receiving component, a service registration publishing component, a service recommendation component, and a positioning realization component to realize a location-oriented information service active recommendation module.

7. In order to process positioning information, location services and information services, an information service real-time collection and processing module is set up to realize data and information processing in the cloud computing environment.

Through the present invention, all-round positioning can be realized indoors and outdoors, anytime and anywhere, and the positioning accuracy can be effectively improved and time delay can be reduced, so as to bring perfect experience effects to terminal users. In particular, through the omnidirectional positioning mechanism of the present invention, and in the cloud computing environment, different terminal users can be provided with services customized on demand.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (9)

1. the integration localization method of a portable terminal is characterized in that, comprising:

Step 1 is obtained satellite positioning signal and WIFI framing signal etc.;

Step 2, the signal strength signal intensity of satellite positioning signal and WIFI framing signal relatively selects big one of signal strength signal intensity as the portable terminal framing signal;

Step 3 sends to cloud computing server with described portable terminal framing signal;

Step 4 receives the location-based service recommendation information of initiatively recommending according to described portable terminal framing signal from described cloud computing server.

2. integration localization method according to claim 1 is characterized in that, described WIFI framing signal obtains by following method:

Steps A 1 is set up fingerprint database according to the AP situation of zones of different;

Steps A 2, scan A P signal when the intensity of AP signal meets pre-provisioning request, is directly located, otherwise execution in step A3;

Steps A 3, the AP signal that identification scanning obtains, and adopt partitioning algorithm that described AP signal is divided;

Steps A 4 is amplified for the signal of location being not enough in the described AP signal after dividing;

Steps A 5 is handled the AP signal after amplifying, to identify the source of this AP signal; If this AP signal can satisfy the location needs, then this AP signal is compared by predefined location routing table and described fingerprint database and mates to finish the location, otherwise switch to other divide in execution in step A4 also.

3. integration localization method according to claim 2 is characterized in that, the method that obtains described WIFI framing signal also comprises:

Steps A 6, when described portable terminal was in the division section of zones of different, division was automaticallyed switch according to difference, in order to trace into all WIFI signals in the zone constantly.

4. integration localization method according to claim 2 is characterized in that, the method that obtains described WIFI framing signal also comprises:

Steps A 7 is set up the utility function satisfy WIFI location validity and user satisfaction, with the uncertainty that effectively prevents the information strength in the zone and stable.

5. integration localization method according to claim 2 is characterized in that, in steps A 5, described coupling is undertaken by following ant group policy optimization method:

1) uses

Ant k divides the probability of realizing state transitions at AP when being illustrated in time t, and shows tabu with taboo _kThe AP signal of the current institute of expression ant k optimizing, allowed _k={ C-tabu _kRepresenting that ant k allows the AP signal of optimizing next time, C represents all AP signals in the zone; Y (t): A → D, wherein Y (t) represents mapping function, and A represents the signal in the routing table of described location, and D represents fingerprint database; F (t): E → S, wherein F (t) is the utility function that satisfies WIFI location validity and user satisfaction; E is that location efficiency (%), S are user satisfaction, and then satisfying optimum matching result ant state transition probability can be calculated as follows:

If j belongs to allowed _k, then $P_{i,j}^k\left(t\right)={\left[Y_{\mathrm{ij}}\left(t\right)\right]}^{\mathrm{\&alpha;}}\&CenterDot;{\left[F_{\mathrm{ik}}\left(t\right)\right]}^{\mathrm{\&beta;}},$ Otherwise $P_{i,j}^k\left(t\right)=0;$

Wherein, α is that amount of information inspires the factor, and β is that expectation inspires the factor, and value is (0,1); I, j represent the value of the state transitions of different zonings, and are the positive integer greater than zero.

2) utilize following formula that mapping rate Function Y (t) is adjusted:

Y _i,j(t)＝(1-ρ)·Y _i,j(t)+ΔY _i,j(t)

$\mathrm{\&Delta;}{Y}_{i,j}\left(t\right)=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}\mathrm{\&Delta;}{Y}_{i,j}^k\left(t\right)$

In the formula, and (1-ρ) ∈ (0,1] be the variation coefficient from the signal strength signal intensity R that is divided into another division from the mapping rate, ρ ∈ [0,1), represent the attenuation coefficient of following R; Δ Y _{I, j}(t) represent that (i is j) in the increments of change of dividing on switching in this circulation; M represents the ant number; When initial, Δ Y _{I, j}(t)=0;

Represent k ant in circulation once switching state (i j) goes up the value of signal strength signal intensity R, then:

$\mathrm{\&Delta;}{Y}_{i,j}^k\left(t\right)=\left\{\begin{array}{c}R\\ 0\end{array}\right.$

In the formula, the situation of 0 expression except R.

3) after reaching predetermined positioning accuracy, stop to optimize coupling.

6. integration localization method according to claim 1 is characterized in that, described location-based service recommendation information is the zone at described portable terminal place and the location-based service of this area peripheral edge.

7. the information service active recommend method of a facing position service is characterized in that, comprising:

Step 1, cloud computing server are obtained the portable terminal framing signal from portable terminal;

Step 2, cloud computing server obtains the location-based service recommendation information of recommendation from serving the provider, and described location-based service recommendation information is sent to described portable terminal according to customized demand and described portable terminal framing signal from described portable terminal;

Step 3, cloud computing server receive from described portable terminal the evaluation conclusion of described location-based service recommendation information and evaluation reason;

Step 4, if described evaluation result can not satisfy customized demand for service dissatisfied and that recommend, then cloud computing server further receives the new customized demand from described portable terminal, and obtain the location-based service recommendation information of recommendation from serving the provider again according to described new customized demand and described portable terminal framing signal, and this location-based service recommendation information is sent to described portable terminal.

8. according to the described information service active of claim 7 recommend method, it is characterized in that in described step 3 or 4, cloud computing server also sends to client service center with described unsatisfied evaluation result and evaluation reason thereof.

9. according to the described information service active of claim 7 recommend method, it is characterized in that described location-based service recommendation information is the zone at described portable terminal place and the location-based service of this area peripheral edge.

Images