CN112650972A - Confidence-based track collision method, system, storage medium and processor - Google Patents

Confidence-based track collision method, system, storage medium and processor Download PDF

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CN112650972A
CN112650972A CN201910956526.XA CN201910956526A CN112650972A CN 112650972 A CN112650972 A CN 112650972A CN 201910956526 A CN201910956526 A CN 201910956526A CN 112650972 A CN112650972 A CN 112650972A
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刘若鹏
栾琳
季春霖
张莎莎
易友文
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Xi'an Guangqi Intelligent Technology Co ltd
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Xi'an Guangqi Future Technology Research Institute
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Abstract

The invention provides a track collision method, a track collision system, a storage medium and a processor based on confidence coefficient, wherein the method comprises the following steps: acquiring a result of collision with a preset track; acquiring user information of successful collision according to a collision result with a preset track; calculating the confidence of the user according to the user information of the successful collision; and outputting a final collision result by carrying out statistical analysis on the confidence coefficient of the user. Whether the initial track collision result is successfully collided or not is judged in advance, confidence coefficient of the successfully collided user information is calculated, and the confidence coefficient of the user information is subjected to statistical analysis to obtain a more reliable collision result; the problem that under the ordinary condition, all information is subjected to statistical analysis, lots of invalid information is doped, interference is caused to a matching result, and meanwhile, due to more data, the calculation efficiency is low is solved; therefore, the accuracy and the calculation efficiency of the collision result are improved.

Description

Confidence-based track collision method, system, storage medium and processor
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of trajectory collision, in particular to a trajectory collision method and system based on confidence coefficient, a storage medium and a processor.
[ background of the invention ]
The general method of track collision is to slice a section of appointed track according to time, find out other track information meeting a certain distance with the appointed track in each time slice in a space range, and record the track information attribute meeting the condition in the time slice. And (4) performing statistical analysis on track information attributes meeting the space distance in all the time slices, and outputting a result of collision with the specified track.
The collision method only takes space-time as a boundary, simple statistical analysis is carried out on all track information meeting the conditions, actual scenes are not combined, whether collision is successful or not is defined, and therefore output collision result information is not accurate enough, and the calculation efficiency is low.
[ summary of the invention ]
The invention aims to solve the technical problem of providing a track collision method, a track collision system, a storage medium and a processor based on confidence coefficient, which can pre-judge whether the initial track collision result is successful or not, calculate the confidence coefficient of the successful user information, and perform statistical analysis on the confidence coefficient of the user information to obtain a more reliable collision result; the problem that under the ordinary condition, all information is subjected to statistical analysis, lots of invalid information is doped, interference is caused to a matching result, and meanwhile, due to more data, the calculation efficiency is low is solved; therefore, the accuracy and the calculation efficiency of the collision result are improved.
In order to solve the above technical problem, an embodiment of the present invention provides a track collision method based on confidence, where the method includes: acquiring a result of collision with a preset track; acquiring user information of successful collision according to a collision result with a preset track; calculating the confidence of the user according to the user information of the successful collision; and outputting a final collision result by carrying out statistical analysis on the confidence coefficient of the user.
Preferably, the preset trajectory contains spatiotemporal information.
Preferably, calculating the confidence level of the user according to the user information that the collision is successful comprises:
derivation of collision distance
Figure BDA0002227486590000021
The confidence of the end-user is the sum of all confidences, i.e.
Figure BDA0002227486590000022
Wherein credit is a single collision confidence coefficient, d is a distance between two tracks of a single collision, C is an accumulated collision confidence coefficient, N is a collision frequency, i is an index of the collision frequency, and a value is from 1 to N.
Preferably, calculating the confidence level of the user according to the user information that the collision is successful comprises:
the calculation is performed according to a discrete function,
Figure BDA0002227486590000023
p1+p2+...+pn＝1，Didistance threshold, p, representing confidence correspondenceiAs confidence, the confidence of the user is
Figure BDA0002227486590000024
Figure BDA0002227486590000025
Representing a confidence of piWherein, the credit is the confidence coefficient of the single collision, d is the distance between two tracks of the single collision, n is the number of the confidence coefficients, and i is the index of the confidence coefficients, and the value is from 1 to n.
Preferably, calculating the confidence level of the user according to the user information that the collision is successful comprises:
calculated according to the same number of bits of the geohash,
Figure BDA0002227486590000026
p1+p2+...+pnthe confidence of the end user is 1
Figure BDA0002227486590000027
Wherein the content of the first and second substances,nidenotes the same number of geohash, piThe confidence level is indicated and the confidence level is indicated,
Figure BDA0002227486590000028
representing a confidence of piThe sameog represents the same bit number of the geohash, n is the number of confidence degrees, and i is the index of the confidence degrees, and the value is from 1 to n.
Preferably, before outputting the final collision result, the method further comprises: and sorting the confidence degrees of the users according to the confidence degree.
Preferably, the judging whether the collision is successful according to the initial track collision result comprises:
judging according to the collision times, and when the collision times are greater than or equal to a preset collision time threshold, considering that the collision is successful; or
And judging according to the collision success rate, wherein the collision success rate is (the number of collision times/total time slices) multiplied by 100%, and when the number of collision times is greater than the preset collision time percentage, the collision is considered to be successful.
Preferably, the judging whether the collision is successful according to the initial track collision result comprises: and when the judgment is unsuccessful, discarding the user information.
Preferably, the distance threshold is 30-100 meters.
Preferably, the preset collision frequency threshold value is 3-10 times.
In another aspect, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, where the program executes the above-mentioned confidence-based trajectory collision method.
In another aspect, an embodiment of the present invention provides a processor, configured to execute a program, where the program executes the above-mentioned confidence-based trajectory collision method.
In another aspect, an embodiment of the present invention provides a trajectory collision system based on confidence, where the system includes: a positioning device, the system executing the confidence-based trajectory collision method described above.
Preferably, the positioning device includes a WiFi probe device, a POE module, and a server, where the WiFi probe device is used to detect a device MAC; the POE module is used for transmitting data back to the server while supplying power to the WiFi probe equipment.
Preferably, the server includes a database server and a positioning server, the database server is configured to store the detected device MAC information, and the positioning server is configured to perform positioning calculation on data stored in the database server and store location information corresponding to the MAC information.
Compared with the prior art, the technical scheme has the following advantages: whether the initial track collision result is successfully collided or not is judged in advance, confidence coefficient of the successfully collided user information is calculated, and the confidence coefficient of the user information is subjected to statistical analysis to obtain a more reliable collision result; the problem that under the ordinary condition, all information is subjected to statistical analysis, lots of invalid information is doped, interference is caused to a matching result, and meanwhile, due to more data, the calculation efficiency is low is solved; therefore, the accuracy and the calculation efficiency of the collision result are improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flowchart of a confidence-based trajectory collision method of the present invention.
Fig. 2 is a key-value storage diagram used in fig. 1.
FIG. 3 is a schematic diagram of a confidence-based trajectory collision system according to the present invention.
Fig. 4 is a schematic diagram of a storage structure of the positioning server in fig. 3.
[ detailed description ] embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
FIG. 1 is a flowchart of a confidence-based trajectory collision method of the present invention. In specific implementation, the preset trajectory includes spatiotemporal information. The spatiotemporal information includes temporal and positional information, such as video trajectories or wifi trajectories. The preset track is an original track, which is not limited to a video or wifi track, but can be a track of other systems. In the following description, taking a video track and wifi track collision as an example, assuming that a video user is ID1, finding a wifi track user closest to the video user track through the track collision. The track collision method is as follows, and the flow chart is shown in the attached figure 1:
(1) inputting initial trajectory collision results
The collision results of all wifi users who collide with the video user ID1 satisfying the spatiotemporal condition are input, and the collision results contain the number of collisions N, and the distance d at each collision.
Collision result format such as MAC 1: { count: N, dist: [ d: [ [ d ]1,d2,..dN]And the meaning is that the wifi user MAC1 collides with the video user ID1 for N times, and the distance of each collision is d.
The method for obtaining the initial collision result generally includes the steps of slicing the track information of the specified user according to a certain time, calculating the track information to be matched in the time slice, which meets a certain distance with the specified track in a space range, and recording the track user information to be matched, which meets the conditions, in the time slice and the corresponding collision distance. And counting the track user information meeting the space distance in all the time slices, and outputting an initial track collision result with the specified user.
It should be noted that, in different scenarios, the user groups to be matched are different, for example, for trajectory collision of key people, routing devices in a collision scenario and a white list of surrounding resident users may be filtered out.
(2) Recording the successful user information of collision in the initial track collision result
The method for judging the success of collision comprises a plurality of methods, and the method comprises the following steps:
a. and judging according to the collision times, and when the collision times is greater than or equal to a preset threshold of the collision times, determining that the collision is successful. The threshold of the number of collisions is related to the length of the selected track time period and the slicing time, for example, if a track of 10min is selected and sliced in 10s, the threshold of the number of collisions may be set to 3-10 times. b. And judging according to the collision success rate, wherein the collision success rate is equal to the collision times/total time slice number multiplied by 100%, when the collision times is greater than a preset percentage, the collision is considered to be successful, and the threshold range of the collision success rate is 20% -50%. This method requires recording the total number of time slices for a given user in (1).
If the user collision is successful, performing the step (3); otherwise, the user collision is considered to be failed, and the user information is discarded.
(3) Computing confidence of a user
Confidence is inversely proportional to distance, i.e., the farther the collision distance, the lower the confidence. The confidence calculation method includes many methods, as follows:
a. derivation of collision distance
Figure BDA0002227486590000051
The confidence of the end-user is the sum of all confidences, i.e.
Figure BDA0002227486590000052
Wherein credit is a single collision confidence coefficient, d is a distance between two tracks of a single collision, C is an accumulated collision confidence coefficient, N is a collision frequency, i is an index of the collision frequency, and a value is from 1 to N.
b. The design is carried out according to a discrete function,
Figure BDA0002227486590000053
p1+p2+...+pn＝1，Didistance threshold corresponding to confidence level, and actual scene and system positioningThe accuracy is relevant. If the selected area is small, the distance threshold value can be controlled to be 30-100 meters, and if the area is large, the distance threshold value of hundreds of meters is also possible. p is a radical ofiAs confidence, the confidence of the user is
Figure BDA0002227486590000061
Figure BDA0002227486590000062
Representing a confidence of piWherein, the credit is the confidence coefficient of the single collision, d is the distance between two tracks of the single collision, n is the number of the confidence coefficients, and i is the index of the confidence coefficients, and the value is from 1 to n.
c. The method is designed according to the same number of bits of the geohash, and is similar to the discrete function in b, and samegeo represents the same number of bits of the geohash as follows.
Figure BDA0002227486590000063
p1+p2+...+pnThe confidence of the end user is 1
Figure BDA0002227486590000064
Wherein n isiDenotes the same number of geohash, piThe confidence level is indicated and the confidence level is indicated,
Figure BDA0002227486590000065
representing a confidence of piThe sameog represents the same bit number of the geohash, n is the number of confidence degrees, and i is the index of the confidence degrees, and the value is from 1 to n.
The method is suitable for big data scenes, has high requirement on timeliness, can be stored in combination with a Hash idea and a key-value mode of hbase/redis, is convenient for quick search, and the same geohash digit of the user to be matched and the designated user in each time slice needs to be recorded in the step (1). Taking wifi system as an example, a schematic diagram of the hash storage result is shown in fig. 2. Fig. 2 is a key-value storage diagram used in fig. 1.
The geohash code defaults to 12 bits, the corresponding precision of different bits is different, and the precision range corresponding to the first 9 bits is as follows.
Figure BDA0002227486590000066
Figure BDA0002227486590000071
(4) Counting confidence of users
And calculating the confidence degrees of all the users with successful collision, and sequencing according to the confidence degrees.
(5) Outputting final collision result
And outputting a final collision result according to the sequencing of the confidence degrees, namely the user information of other tracks which are most similar to the track of the specified user.
Example two
Assuming that a wifi user matching the video user ID1 is found, the result of the initial collision has 4 MACs satisfying the collision condition, and the collision result is as follows:
MAC1：{count:7,dist:[80,90,50,60,70,55,60]}；
MAC2：{count:5,dist:[60,40,30,10,20]}；
MAC3：{count:2,dist:[100,30]}；
MAC4：{count:6,dist:[60,50,50,30,40,20]}。
the general collision method is that the highest number of collisions is the closest to the video user trajectory, and according to the method, the highest number of collisions of the MAC1 is the closest to the video user trajectory, that is, the electronic device held by the video user ID1 is considered to be the most likely MAC 1.
The collision method adopting the embodiment of the invention comprises the following steps:
(1) inputting initial trajectory collision results
Initial collision results are as described above for 4 MACs.
(2) Recording the successful user information of collision in the initial track collision result
And judging according to the collision times, and when the collision times is greater than or equal to a preset threshold value, determining that the collision is successful. Here, taking the threshold 5 as an example, it can be seen that the MAC3 does not satisfy the condition, and the MAC3 information is discarded when the collision is considered to have failed. The only collision success information is MAC1, MAC2, MAC 4.
(3) Computing confidence of a user
Designing the confidence level according to the discrete function, and assuming that the discrete function of the confidence level is as follows:
Figure BDA0002227486590000081
according to the formula
Figure BDA0002227486590000082
The confidence for each user is calculated as follows:
CMAC1＝2×0.1+5×0.2＝1.2；
CMAC2＝2×0.2+3×0.7＝2.5；
CMAC4＝4×0.2+2×0.7＝2.2。
(4) counting confidence of users
The calculated confidence of MAC1 is 1.2, the confidence of MAC2 is 2.5, and the confidence of MAC4 is 2.2.
(5) Outputting final collision result
Rank confidence, get the highest confidence of MAC 2.
As compared with the general collision method, the MAC1 has a large number of collisions, but the distance from the designated user ID1 is always a little bit longer in terms of collision distance; the collision frequency of the MAC2 is not as much as that of the MAC1, but the collision distance is always close; in combination with the confidence determination method, the confidence of MAC2 exceeds MAC 1.
In an actual application scene, the phenomenon is not rare, so that after judgment of track collision and calculation of confidence are added, an obtained collision result is more reliable, otherwise, misleading is caused to further analysis.
EXAMPLE III
FIG. 3 is a schematic diagram of a confidence-based trajectory collision system according to the present invention. As shown in FIG. 3, a confidence-based trajectory collision system includes: the system executes the track collision method based on the confidence coefficient. The Wi-Fi probe can provide basic identity identification data, and can associate the collected MAC address data with data of telecommunication enterprises and public security organs, so that a multi-dimensional public security monitoring system can be established. The MAC address serves as a unique identification code of the smart phone and can serve as identification of identity information. The Wi-Fi probe has wide coverage by combining video perception deployment position construction, can collect MAC addresses within a range, is not limited by data, and can collect massive MAC addresses. The Wi-Fi probe can realize real-time transmission of data, and monitoring data can be transmitted back in real time; identity matching: the MAC address is used as a unique identification code of the mobile phone, and identity matching can be realized by combining other data.
The Wi-Fi positioning system applies Wi-Fi positioning technology to a scene of real-time tracking and identification of personnel, and discovers and tracks suspicious personnel on site in time through the real-time positioning technology. The Wi-Fi positioning system comprises Wi-Fi probe equipment, a POE module, a database server and a positioning server, and a system structure diagram is shown in figure 3.
Wherein the Wi-Fi probe equipment comprises the following purposes:
(1) the built-in misleading module transmits the SSID with high connection frequency, misleading the equipment connection and increasing the MAC capturing probability.
(2) And scanning all channels, and capturing the MAC packet of the equipment without missing the packet.
(3) And information such as marked MAC signal strength, connection time difference and the like is encrypted and transmitted back to the position calculation server for accurate position calculation.
And the POE module is used for transmitting the data back to the database server while supplying power to the Wi-Fi probe equipment.
And the database server is used as a database for storing the MAC address, quickly compares the MAC captured by the WiFi probe equipment, transmits the successfully compared data to the positioning server, and updates and stores the information such as the connection duration, the connection time, the position and the like of the equipment marked with the MAC.
And the positioning server runs a positioning algorithm, matches the signals received in real time through calculation with the data of the fingerprint database, calculates the coordinates to be positioned according to the fingerprint coordinates, and a schematic diagram for storing the positioning result is shown in the attached figure 4. Fig. 4 is a schematic diagram of a storage structure of the positioning server in fig. 3.
The storage format of the positioning server is that each row of data represents the number ID of the equipment to be positioned, the MAC address of the equipment to be positioned, the name of the equipment to be positioned, the X coordinate of the equipment to be positioned, the Y coordinate of the equipment to be positioned and the report time.
From the above description, it can be seen that the confidence-based trajectory collision method, system, storage medium and processor according to the present invention have the following beneficial effects: whether the initial track collision result is successfully collided or not is judged in advance, confidence coefficient of the successfully collided user information is calculated, and the confidence coefficient of the user information is subjected to statistical analysis to obtain a more reliable collision result; the problem that under the ordinary condition, all information is subjected to statistical analysis, lots of invalid information is doped, interference is caused to a matching result, and meanwhile, due to more data, the calculation efficiency is low is solved; therefore, the accuracy and the calculation efficiency of the collision result are improved. The invention is not limited to the track collision among different electronic devices, and can also be used for the analysis of the same line among all the electronic devices, such as Bluetooth and Bluetooth, Bluetooth and wifi, wifi and video, video and Bluetooth and the like; the application scenes comprise indoor and outdoor various wireless scenes, and the application field of the method can be expanded to speech recognition, face recognition, big data analysis and the like.
The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (15)

1. A track collision method based on confidence coefficient is characterized in that:
acquiring a result of collision with a preset track;
acquiring user information of successful collision according to a collision result with a preset track;
calculating the confidence of the user according to the user information of the successful collision;
and outputting a final collision result by carrying out statistical analysis on the confidence coefficient of the user.
2. The confidence-based trajectory collision method of claim 1, wherein the preset trajectory contains spatiotemporal information.
3. The confidence-based trajectory collision method of claim 1, wherein calculating the confidence of the user based on the user information that the collision was successful comprises:
derivation of collision distance
Figure FDA0002227486580000011
The confidence of the end-user is the sum of all confidences, i.e.
Figure FDA0002227486580000012
Wherein credit is a single collision confidence coefficient, d is a distance between two tracks of a single collision, C is an accumulated collision confidence coefficient, N is a collision frequency, i is an index of the collision frequency, and a value is from 1 to N.
4. The confidence-based trajectory collision method of claim 1, wherein calculating the confidence of the user based on the user information that the collision was successful comprises:
the calculation is performed according to a discrete function,
Figure FDA0002227486580000013
Didistance threshold, p, representing confidence correspondenceiAs confidence, the confidence of the user is
Figure FDA0002227486580000014
Figure FDA0002227486580000015
Representing a confidence of piWherein, the credit is the confidence coefficient of the single collision, d is the distance between two tracks of the single collision, n is the number of the confidence coefficients, and i is the index of the confidence coefficients, and the value is from 1 to n.
5. The confidence-based trajectory collision method of claim 1, wherein calculating the confidence of the user based on the user information that the collision was successful comprises:
calculated according to the same number of bits of the geohash,
Figure FDA0002227486580000021
confidence of the end user is
Figure FDA0002227486580000022
Wherein n isiRepresenting the same number of collisions, piThe confidence level is indicated and the confidence level is indicated,
Figure FDA0002227486580000023
representing a confidence of piThe sameog represents the same bit number of the geohash, n is the number of confidence degrees, and i is the index of the confidence degrees, and the value is from 1 to n.
6. The confidence-based trajectory collision method of claim 1, further comprising, prior to outputting a final collision result: and sorting the confidence degrees of the users according to the confidence degree.
7. The confidence-based trajectory collision method of claim 2, wherein determining whether the collision was successful based on the initial trajectory collision result comprises:
judging according to the collision times, and when the collision times are greater than or equal to a preset collision time threshold, considering that the collision is successful; alternatively, the first and second electrodes may be,
and judging according to the collision success rate, wherein the collision success rate is (the number of collision times/total time slices) multiplied by 100%, and when the number of collision times is greater than the preset collision time percentage, the collision is considered to be successful.
8. The confidence-based trajectory collision method of claim 2, wherein determining whether the collision was successful based on the initial trajectory collision result comprises: and when the judgment is unsuccessful, discarding the user information.
9. The confidence-based trajectory collision method according to claim 4, wherein the distance threshold is 30-100 meters.
10. The confidence-based trajectory collision method according to claim 7, wherein the preset collision number threshold is 3-10 times.
11. A storage medium comprising a stored program, wherein the program when executed performs the confidence-based trajectory collision method of any one of claims 1 to 10.
12. A processor, configured to run a program, wherein the program when executed performs the confidence-based trajectory collision method of any one of claims 1 to 10.
13. A confidence-based trajectory collision system, comprising: a localization device, the system performing the confidence-based trajectory collision method of any one of claims 1-10.
14. The confidence-based trajectory collision system of claim 13, wherein the positioning device comprises a WiFi probe device, a POE module, a server, the WiFi probe device is used to detect a device MAC; the POE module is used for transmitting data back to the server while supplying power to the WiFi probe equipment.
15. The confidence-based trajectory collision system of claim 13, wherein the server comprises a database server and a location server, the database server is configured to store the detected device MAC information, and the location server is configured to perform location calculation on data stored in the database server and store location information corresponding to the MAC information.
CN201910956526.XA 2019-10-10 2019-10-10 Confidence-based track collision method, system, storage medium and processor Pending CN112650972A (en)

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