CN108632929B - Big data aggregation method for quick service - Google Patents

Big data aggregation method for quick service Download PDF

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CN108632929B
CN108632929B CN201810339125.5A CN201810339125A CN108632929B CN 108632929 B CN108632929 B CN 108632929B CN 201810339125 A CN201810339125 A CN 201810339125A CN 108632929 B CN108632929 B CN 108632929B
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SHANGHAI SHIZHUANG INFORMATION TECHNOLOGY Co.,Ltd.
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0066—Transmission or use of information for re-establishing the radio link of control information between different types of networks in order to establish a new radio link in the target network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/18—Selecting a network or a communication service

Abstract

The invention belongs to the technical field of express service big data, and discloses a big data aggregation method facing rapid service, which comprises the following steps: the large site module is connected with the information compensation module, the site information combination module and the error information processing module, and the small site module is connected with the information compensation module, the site information combination module and the error information processing module; the large station module, the small station module and the road condition information acquisition module are connected with the information aggregation module; the information gathering module is connected with the city information processing module and the information integration module. According to the invention, through the arrangement of the large station module, the small station module and the station information combination module, the integration and collection of information in different areas are realized, and the method is not limited by areas; the lost information is found through the setting of the information compensation module, and the compensation and correction of the error information are realized through the error information processing module, so that the express information is accurate; the information aggregation module is arranged to realize information integration.

Description

Big data aggregation method for quick service
Technical Field
The invention belongs to the technical field of express service big data, and particularly relates to a big data aggregation method for quick service.
Background
At present, the express service industry is popular in the society today, and convenience is brought to mailing and shopping of people, so that the life of people is more beautiful. However, the big data aggregation of the existing express service has the defects that the collected information is inaccurate, express delivery is delayed, express service in remote areas cannot be popularized in time, inconvenience is brought to some people, meanwhile, road condition information is also the reason for delaying express delivery when the express is delivered, and the existing method cannot reflect the road condition information in time and cannot be well suitable for express delivery transportation.
In summary, the problems of the prior art are as follows: the big data aggregation of the existing express service has the defects that the collected information is inaccurate, and the wrong information cannot be corrected in time. The express delivery service in remote areas cannot be popularized in time, inconvenience is brought to some people, road condition information is also a reason for delaying express delivery when the express delivery is carried out, the road condition information cannot be reflected in time by the existing method, and the express delivery service cannot be well suitable for express delivery transportation.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a big data aggregation method facing to quick service.
The invention is realized by that, a big data aggregation system facing rapid service is provided with,
a large site module;
the large site module is connected with the information compensation module, the site information combination module and the error information processing module, and the small site module is connected with the information compensation module, the site information combination module and the error information processing module;
the method for selecting/switching the wireless access technology of the large site module comprises the following steps: a user terminal UE collects candidate access point AP or evolved node (H) eNB load information through a local access network discovery and selection function entity ANDSF; the UE checks the received signal strength RSS values, reference signal received quality, traffic sensitivity to delay of the candidate AP or (H) eNB; evaluating the suitability of the available radio access network (RAT) according to the information; a specific trigger event triggers the fuzzy logic controller to select the most suitable radio access network (RAT) for each session; establishing a new session to execute admission control or switching;
the specific trigger event comprises: receiving an access network discovery and selection function ANDSF new message, creating a new session, authentication control point technology hotspot2.0 new message;
the selecting the most suitable RAT for each session specifically includes: setting a user configuration file, and respectively corresponding specific trigger events to different RAT triggering branches, wherein the dual selection triggering branches of the cellular network and the wireless broadband network WiFi network correspond to trigger events of receiving ANDSF new messages, detecting received signal strength RSS changes, and creating new sessions; the 1 auto-authenticate connect WiFi network trigger branch corresponds to the trigger event "hotspot 2.0 new message received";
the UE evaluating the suitability of the available RATs further comprises: the fuzzification unit converts the input value into a grade, and the input measurement value of the parameter is converted into a fuzzy logic set; inputting the fuzzy logic set into a fuzzy inference engine, and obtaining a fuzzy decision set according to rules to obtain a fuzzy set; inputting the fuzzy set into a defuzzification unit to be converted into RAT fitness output;
when the triggering event is 'receiving an ANDSF new message', triggering the dual selection triggering branch I of the cellular network and the WiFi network, wherein the triggering process includes: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT in the candidate RAT list;
when the triggering event is 'create new session', triggering the cellular network and the WiFi dual-selection triggering branch III, the triggering process includes the following steps: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT;
the large station module, the small station module and the road condition information acquisition module are connected with the information aggregation module;
the information gathering module is connected with the city information processing module and the information integration mailing module.
The nonlinear Gaussian system model of the information complementing module is as follows:
xk+1＝fk(xk)+ωk+1，k，
Zk＝hk(xk)+υk，
where k is a discrete time series, xk∈Rn×1Is the state vector of the system, zk∈Rm×1Is a measurement vector, f (-) and h (-) are known nonlinear state transfer functions and measurement functions and are in xkProcess noise sequence omega of continuous micro processk+1，kAnd measuring the noise sequence upsilonkAre Gaussian white noise sequences with the mean value E (omega)k+1，k)＝qk，E(υk)＝rkVariance Qk+1，kAnd RkThe following conditions are satisfied:
Figure BDA0001630146850000041
wherein ω isk+1，kIs process noise, upsilonkFor measuring noise, Qk+1，kIs the process noise variance, RkIn order to measure the variance of the noise,
Figure BDA0001630146850000042
δkjis the Kronecker function;
initial state x0And omegak+1，k、υkIrrelevant, and satisfy:
Figure BDA0001630146850000043
for a nonlinear Gaussian system model, a process noise sequence omega is setk+1，kAnd measuring the noise sequence upsilonkAnd obtaining a noise-related extended Kalman filter by using a Gaussian white noise sequence with a mean value of zero as follows:
Figure BDA0001630146850000044
Figure BDA0001630146850000045
Figure BDA0001630146850000046
Figure BDA0001630146850000047
wherein
Figure BDA0001630146850000048
Figure BDA0001630146850000049
Figure BDA00016301468500000410
Figure BDA00016301468500000411
Figure BDA00016301468500000412
Obtaining a noise-related extended information update matrix
Figure BDA00016301468500000413
And information update vector
Figure BDA00016301468500000414
The following were used:
Figure BDA0001630146850000051
Figure BDA0001630146850000052
wherein
Figure BDA0001630146850000053
In order to contribute to the information matrix,
Figure BDA0001630146850000054
contributes to the information vector; wherein the content of the first and second substances,
Figure BDA0001630146850000055
Figure BDA0001630146850000056
Figure BDA0001630146850000057
Figure BDA0001630146850000058
，
Figure BDA0001630146850000059
in order to be an innovation vector, the information vector,
Figure BDA00016301468500000510
furthermore, an information input module is arranged in the large site module.
Furthermore, an information input module is arranged in the small station module.
Further, the information supplementing module is connected with the information gathering module.
The invention has the advantages and positive effects that: the invention realizes the integrated collection of information in different areas by the arrangement of the large station module and the small station module without the limitation of areas; the information compensation module is arranged to realize the compensation and correction of error information, so that the express information is accurate and correct; the information aggregation module is arranged to realize information integration.
The express delivery system is clear in design thought, suitable for popularization and production and capable of meeting express delivery requirements.
Drawings
Fig. 1 is a schematic structural diagram of a big data aggregation system for quick service provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of an internal structure of an information aggregation module of a big data aggregation system facing a fast service according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of a road condition information collection module of a big data aggregation system for quick service according to an embodiment of the present invention;
in the figure: 1. a large site module; 2. a small site module; 3. a road condition information acquisition module; 4. an information input module; 5. an information supplementing module; 6. an information aggregation module; 7. a city information processing module; 8. an information integration mailing module; 9. processing error information; 10. combining site information; 11, information is summarized; 12. a service number; 13. individual information; 14. address information; 15. service content; 16. additional information; 17. the type of service.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the big data aggregation system for fast services provided in the embodiment of the present invention includes a big site module 1, a small site module 2, a traffic information collection module 3, an information input module 4, an information review module 5, an information aggregation module 6, a city information processing module 7, an information integration mailing module 8, an error information processing module 9, a site information combination 10, an information summary 11, a service number 12, individual information 13, address information 14, service content 15, additional information 16, and a service type 17.
The large site module 1 is connected with the information compensation module 5, the site information combination module 10 and the error information processing module 9, and the small site module 2 is connected with the information compensation module 5, the site information combination module 10 and the error information processing module 9;
the large station module 1, the small station module 2 and the road condition information acquisition module 3 are connected with an information aggregation module 6;
the method for selecting/switching the wireless access technology of the large site module comprises the following steps: a user terminal UE collects candidate access point AP or evolved node (H) eNB load information through a local access network discovery and selection function entity ANDSF; the UE checks the received signal strength RSS values, reference signal received quality, traffic sensitivity to delay of the candidate AP or (H) eNB; evaluating the suitability of the available radio access network (RAT) according to the information; a specific trigger event triggers the fuzzy logic controller to select the most suitable radio access network (RAT) for each session; establishing a new session to execute admission control or switching;
the specific trigger event comprises: receiving an access network discovery and selection function ANDSF new message, creating a new session, authentication control point technology hotspot2.0 new message;
the selecting the most suitable RAT for each session specifically includes: setting a user configuration file, and respectively corresponding specific trigger events to different RAT triggering branches, wherein the dual selection triggering branches of the cellular network and the wireless broadband network WiFi network correspond to trigger events of receiving ANDSF new messages, detecting received signal strength RSS changes, and creating new sessions; the 1 auto-authenticate connect WiFi network trigger branch corresponds to the trigger event "hotspot 2.0 new message received";
the UE evaluating the suitability of the available RATs further comprises: the fuzzification unit converts the input value into a grade, and the input measurement value of the parameter is converted into a fuzzy logic set; inputting the fuzzy logic set into a fuzzy inference engine, and obtaining a fuzzy decision set according to rules to obtain a fuzzy set; inputting the fuzzy set into a defuzzification unit to be converted into RAT fitness output;
when the triggering event is 'receiving an ANDSF new message', triggering the dual selection triggering branch I of the cellular network and the WiFi network, wherein the triggering process includes: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT in the candidate RAT list;
when the triggering event is 'create new session', triggering the cellular network and the WiFi dual-selection triggering branch III, the triggering process includes the following steps: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT;
the information gathering module 6 is connected with a city information processing module 7 and an information integration mailing module 8.
The nonlinear gaussian system model of the information complementing module 5:
xk+1＝fk(xk)+ωk+1，k，
Zk＝hk(xk)+υk，
where k is a discrete time series, xk∈Rn×1Is the state vector of the system, zk∈Rm×1Is a measurement vector, f (-) and h (-) are known nonlinear state transfer functions and measurement functions and are in xkProcess noise sequence omega of continuous micro processk+1，kAnd measuring the noise sequence upsilonkAre Gaussian white noise sequences with the mean value E (omega)k+1，k)＝qk，R(υk)＝rkVariance Qk+1，kAnd RkThe following conditions are satisfied:
Figure BDA0001630146850000091
wherein ω isk+1，kIs process noise, upsilonkFor measuring noise, Qk+1，kIs the process noise variance, RkIn order to measure the variance of the noise,
Figure BDA0001630146850000092
δkjis the Kronecker function;
initial state x0And omegak+1，k、υkIrrelevant, and satisfy:
Figure BDA0001630146850000093
for a nonlinear Gaussian system model, a process noise sequence omega is setk+1，kAnd measuring the noise sequence upsilonkAnd obtaining a noise-related extended Kalman filter by using a Gaussian white noise sequence with a mean value of zero as follows:
Figure BDA0001630146850000094
Figure BDA0001630146850000095
Figure BDA0001630146850000096
Figure BDA0001630146850000097
wherein
Figure BDA0001630146850000098
Figure BDA0001630146850000099
Figure BDA00016301468500000910
Figure BDA00016301468500000911
Figure BDA00016301468500000912
Obtaining a noise-related extended information update matrix
Figure BDA0001630146850000101
And information update vector
Figure BDA0001630146850000102
The following were used:
Figure BDA0001630146850000103
Figure BDA0001630146850000104
wherein
Figure BDA0001630146850000105
In order to contribute to the information matrix,
Figure BDA0001630146850000106
contributes to the information vector; wherein the content of the first and second substances,
Figure BDA0001630146850000107
Figure BDA0001630146850000108
Figure BDA0001630146850000109
Figure BDA00016301468500001010
，
Figure BDA00016301468500001011
in order to be an innovation vector, the information vector,
Figure BDA00016301468500001012
further, an information input module 4 is arranged inside the large site module 1.
Further, an information input module 4 is arranged inside the small site module 2.
Further, the information complementing module 5 is connected with the information gathering module 6.
The working principle of the invention is as follows: the information input module 4 in the large site module 1 and the small site module 2 is used for inputting express information and transmitting the information into the information gathering module 6, and the small site module 2 solves the problem that express services in remote areas cannot be synchronized in time; the road condition information acquisition module 3 transmits the road condition information in the express transportation process into the information aggregation module 6, the site information combination 10 and the information supplement module 5 transmits the missing information into the information aggregation module 6, and the error information processing 9 transmits the error information to the information aggregation module 6 for processing; after receiving the information, the information aggregation module 6 aggregates the express information and the road condition information, and the information aggregation module 6 also classifies and processes the information in detail; and then the information gathering module 6 selects the city-sharing information processing module 7 or the information integration mailing module 8 according to the information, the city-sharing information processing module 7 can process the city-sharing express information, and the information integration mailing module 8 can process the non-city-sharing express information.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (5)

1. A big data aggregation system facing rapid services is characterized in that the big data aggregation system facing rapid services is provided with:
a large site module;
the large site module is connected with an information repairing module, a site information combining module and an error information processing module; the small station module is connected with the information compensation module, the station information combination module and the error information processing module;
the method for selecting/switching the wireless access technology of the large site module comprises the following steps: user Equipment (UE) collects candidate Access Point (AP) or evolved node load information through a local access network discovery and selection functional entity (ANDSF); the UE checks the received signal strength RSS value, the reference signal receiving quality and the sensitivity of the service to delay of the candidate AP or the evolution node; evaluating the suitability of the available radio access network (RAT) according to the information; a specific trigger event triggers the fuzzy logic controller to select the most suitable radio access network (RAT) for each session; establishing a new session to execute admission control or switching;
the specific trigger event comprises: receiving an Access Network Discovery and Selection Function (ANDSF) new message, a new session creation and an authentication control point technology (Hotspot2.0) new message;
the selecting the most suitable RAT for each session specifically includes: setting a user configuration file, and respectively corresponding specific trigger events to different RAT triggering branches, wherein the dual selection triggering branches of the cellular network and the wireless broadband network WiFi network correspond to trigger events of receiving ANDSF new messages, detecting received signal strength RSS changes and creating new sessions; the 1 auto-authenticate connect WiFi network trigger branch corresponds to the trigger event "hotspot 2.0 new message received";
the UE evaluating the suitability of the available RATs further comprises: the fuzzification unit converts the input value into a grade, and the input measurement value of the parameter is converted into a fuzzy logic set; inputting the fuzzy logic set into a fuzzy inference engine, and obtaining a fuzzy decision set according to rules to obtain a fuzzy set; inputting the fuzzy set into a defuzzification unit to be converted into RAT fitness output;
when the triggering event is 'receiving an ANDSF new message', triggering the dual selection triggering branch I of the cellular network and the WiFi network, wherein the triggering process includes: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT in the candidate RAT list;
when the triggering event is 'create new session', triggering the cellular network and the WiFi dual-selection triggering branch III, the triggering process includes the following steps: the UE detects the load indication and the RSS and updates the moving behavior information; calculating a list of candidate RATs for each session; judging whether a first candidate RAT in the candidate RAT list is a current RAT or not, if so, keeping the current RAT, if not, judging whether the switching is successful or not, if the switching is successful, ending the access control, and if the switching is unsuccessful, selecting a second candidate RAT;
the large station module, the small station module and the road condition information acquisition module are connected with the information aggregation module;
the information gathering module is connected with the city information processing module and the information integration mailing module;
the nonlinear Gaussian system model of the information complementing module is as follows:
xk+1＝fk(xk)+ωk+1，k，
Zk＝hk(xk)+υk，
where k is a discrete time series, xk∈Rn×1Is the state vector of the system, zk∈Rm×1Is a measurement vector, f (-) and h (-) are known nonlinear state transfer functions and measurement functions and are in xkProcess noise sequence omega of continuous micro processk+1，kAnd measuring the noise sequence upsilonkAre Gaussian white noise sequences with the mean value E (omega)k+1，k)＝qk，E(υk)＝rkVariance Qk+1，kAnd PkThe following conditions are satisfied:
Figure FDA0002672836410000021
wherein ω isk+1，kIs process noise, vk is measurement noise, Qk+1，kIs the process noise variance, RkIn order to measure the variance of the noise,
Figure FDA0002672836410000022
δkjis the Kronecker function;
initial state x0And omegak+1，k、υkIrrelevant, and satisfy:
Figure FDA0002672836410000023
for a nonlinear Gaussian system model, a Gaussian white noise sequence with mean values of a process noise sequence omega k +1, k and a measurement noise sequence upsilok being zero is set, and an extended Kalman filter related to noise is obtained as follows:
Figure FDA0002672836410000024
wherein
Figure FDA0002672836410000025
Obtaining a noise-related extended information update matrix
Figure FDA0002672836410000027
And information update vector
Figure FDA0002672836410000028
The following were used:
Figure FDA0002672836410000026
Figure FDA0002672836410000031
wherein
Figure FDA0002672836410000038
In order to contribute to the information matrix,
Figure FDA0002672836410000039
contributes to the information vector; wherein the content of the first and second substances,
Figure FDA0002672836410000032
Figure FDA0002672836410000033
Figure FDA0002672836410000034
Figure FDA0002672836410000035
Figure FDA0002672836410000036
in order to be the vector of information,
Figure FDA0002672836410000037
2. the big data aggregation system facing rapid services as claimed in claim 1, wherein the big site module is internally provided with an information input module.
3. The big data aggregation system facing rapid services as claimed in claim 1, wherein the small site module is internally provided with an information input module.
4. The big data aggregation system facing rapid services according to claim 1, wherein the information complementing module is connected with the site information combining module, the error information processing module, and the information aggregating module.
5. The big data aggregation method facing the rapid service of the big data aggregation system facing the rapid service as claimed in claim 1, wherein the big data aggregation method facing the rapid service inputs express delivery information through the information input module in the big site module and the small site module, and transmits the information to the information aggregation module, and the small site module solves the problem that the express delivery service in the remote area cannot be synchronized in time; the road condition information acquisition module transmits road condition information in the express transportation process into the information aggregation module, the site information combination and information supplement module transmits missing information into the information aggregation module, and the error information processing module transmits error information to the information aggregation module for processing; after receiving the information, the information aggregation module aggregates the express information and the road condition information, and the information aggregation module also classifies and processes the information in detail; and then the information gathering module selects the same-city information processing module or the information integration mailing module according to the information, the same-city information processing module can process the same-city express information, and the information integration mailing module can process the non-same-city express information.
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