CN112581761B

CN112581761B - Collaborative analysis method, device, equipment and medium for 5G mobile Internet of things node

Info

Publication number: CN112581761B
Application number: CN202011437144.5A
Authority: CN
Inventors: 周迪; 曹广; 贺正方; 徐爱华; 贺建飚; 张健; 李玺; 王建新; 章坚武; 郭春生
Original assignee: Zhejiang Uniview Technologies Co Ltd
Current assignee: Zhejiang Uniview Technologies Co Ltd
Priority date: 2020-12-07
Filing date: 2020-12-07
Publication date: 2022-04-19
Anticipated expiration: 2040-12-07
Also published as: CN112581761A

Abstract

The embodiment of the invention discloses a collaborative analysis method, a device, equipment and a medium for a 5G mobile Internet of things node. The method comprises the following steps: acquiring first data around the first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle, and establishing a second vehicle space-time trajectory based on the first data and the second data; determining a similarity between the first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory; if the similarity is larger than or equal to the first similarity threshold, the second vehicle is preliminarily determined to follow the first vehicle, a verification request is sent to the cloud server, and a verification result of whether the second vehicle follows the first vehicle or not is received by the cloud server according to the verification request. According to the embodiment of the invention, the vehicle-mounted terminal and the cloud server are cooperatively processed, so that the cloud server does not need to analyze and process all data, the computing pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

Description

Collaborative analysis method, device, equipment and medium for 5G mobile Internet of things node

Technical Field

The embodiment of the invention relates to the technical field of Internet of things, in particular to a collaborative analysis method, a collaborative analysis device, collaborative analysis equipment and collaborative analysis media for 5G mobile Internet of things nodes.

Background

With the improvement of living standard, the use of vehicles becomes one of indispensable tools for people to go out daily. However, while the vehicle brings convenience to people's life, there is also an event that the vehicle follows, so that personal privacy and security are threatened.

In order to prevent vehicles from following, data uploaded by each vehicle is analyzed and processed through a cloud server at present to determine vehicles which are suspected to follow other vehicles, and vehicle information with following suspicions is carried in early warning information and sent to other vehicles, so that drivers of other vehicles can give an alarm or take corresponding precautionary measures and the like according to the early warning information. In this way, the cloud server is required to analyze and process data uploaded by all vehicles, which easily causes too much computing pressure of the cloud server, resulting in reduced performance of the cloud server.

Disclosure of Invention

The embodiment of the invention provides a collaborative analysis method, a collaborative analysis device and a collaborative analysis medium for nodes of a 5G mobile Internet of things, and the collaborative processing is carried out on the nodes of the 5G mobile Internet of things and a cloud server, so that the cloud server does not need to analyze and process all data, the computing pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

In a first aspect, an embodiment of the present invention provides a collaborative analysis method for a 5G mobile internet of things node, which is applied to a vehicle-mounted terminal, and the method includes:

acquiring first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle, and establishing a second vehicle space-time trajectory based on the first data and the second data, wherein the number of the roadside terminal and the second vehicle is at least one;

determining a similarity between a first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory;

if the similarity is larger than or equal to a first similarity threshold value, preliminarily determining that the second vehicle follows the first vehicle, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory.

In a second aspect, an embodiment of the present invention provides a collaborative analysis method for a 5G mobile internet of things node, which is applied to a cloud server, and the method includes:

receiving a verification request sent by each first vehicle, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and a first vehicle space-time trajectory of each first vehicle;

establishing a second vehicle verification space-time trajectory based on all second data of the second vehicle corresponding to the obtained second vehicle identification in the following time period, and determining the similarity between the first vehicle space-time trajectory and the second vehicle verification space-time trajectory;

and determining a verification result according to the relation between the similarity and a second similarity threshold, and sending the verification result to the first vehicle.

In a third aspect, an embodiment of the present invention further provides a collaborative analysis apparatus for a 5G mobile internet of things node, configured in a vehicle-mounted terminal, including:

the data acquisition module is used for acquiring first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle, and establishing a second vehicle space-time track based on the first data and the second data, wherein the number of the roadside terminal and the second vehicle is at least one;

a first determination module to determine a similarity between a first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory;

the first verification module is used for preliminarily determining that the second vehicle follows the first vehicle if the similarity is larger than or equal to a first similarity threshold, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory.

In a fourth aspect, an embodiment of the present invention further provides a collaborative analysis apparatus for a 5G mobile internet of things node, configured in a cloud server, including:

the request receiving module is used for receiving a verification request sent by each first vehicle, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and a first vehicle space-time trajectory of each first vehicle;

the second determination module is used for establishing a second vehicle verification space-time trajectory based on all second data of the second vehicle in the following time period corresponding to the obtained second vehicle identification, and determining the similarity between the first vehicle space-time trajectory and the second vehicle verification space-time trajectory;

and the result sending module is used for determining a verification result according to the relation between the similarity and the second similarity threshold and sending the verification result to the first vehicle.

In a fifth aspect, an embodiment of the present invention further provides an electronic device, including:

one or more processors;

a storage device for storing one or more programs,

when the one or more programs are executed by the one or more processors, the one or more processors implement the collaborative analysis method of the 5G mobile internet of things node according to any one of the embodiments of the present invention.

In a sixth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the collaborative analysis method for a 5G mobile internet of things node according to any one of the embodiments of the present invention.

The technical scheme disclosed by the embodiment of the invention has the following beneficial effects:

the method comprises the steps of establishing a second vehicle space-time track based on first data and second data by obtaining the first data around a first vehicle and the second data sent by a roadside terminal and/or a second vehicle around the first vehicle, determining the similarity between the first vehicle space-time track and the second vehicle space-time track, preliminarily determining that the second vehicle follows the first vehicle when the similarity is larger than or equal to a first similarity threshold, sending a verification request to a cloud server, and receiving a verification result returned by the cloud server. According to the embodiment of the invention, the vehicle-mounted terminal determines the similarity between the space-time trajectory of the first vehicle and the space-time trajectory of the second vehicle in the same line based on the acquired data, determines whether the suspicion that the second vehicle follows the first vehicle exists, and sends the verification request to the cloud server when the suspicion exists so as to verify through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

Drawings

Fig. 1 is a schematic flowchart of a first collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

fig. 2A is a schematic flowchart of a second collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

FIG. 2B is a schematic illustration of a method of determining similar space-time points in a first vehicle space-time trajectory and a second vehicle space-time trajectory provided by an embodiment of the present invention;

fig. 3A is a schematic flowchart of a third collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

FIG. 3B is a schematic diagram of establishing a first vehicle spatiotemporal trajectory and a second vehicle spatiotemporal trajectory provided by embodiments of the present invention;

fig. 4 is a schematic flowchart of a fourth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

fig. 5A is a schematic flowchart of a fifth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

FIG. 5B is a schematic illustration of establishing a first vehicle spatiotemporal trajectory and a second vehicle verification spatiotemporal trajectory provided by embodiments of the present invention;

FIG. 5C is a schematic illustration of establishing a first vehicle spatiotemporal trajectory and a plurality of second vehicle verification spatiotemporal trajectories provided by an embodiment of the present invention;

fig. 6 is a schematic flowchart of a sixth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention;

fig. 7 is a schematic structural diagram of a cooperative analysis apparatus of a 5G mobile internet of things node according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of a cooperative analysis apparatus of a 5G mobile internet of things node according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad invention. It should be further noted that, for convenience of description, only some structures, not all structures, relating to the embodiments of the present invention are shown in the drawings.

The following describes in detail a collaborative analysis method, apparatus, device, and medium for a 5G mobile internet of things node according to an embodiment of the present invention with reference to the accompanying drawings. First, a collaborative analysis method of a 5G mobile internet node according to an embodiment of the present invention is described by taking a vehicle-mounted terminal as an execution subject. The vehicle-mounted terminal is applied to a vehicle, and the application of the vehicle-mounted terminal to the first vehicle is taken as an example in the present embodiment for description.

Fig. 1 is a schematic flowchart of a first collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention. The embodiment of the invention can be suitable for the identification scene that the vehicle is followed, and the method can be executed by a collaborative analysis device of a 5G mobile Internet of things node configured on a vehicle-mounted terminal, and the device can be realized by software and/or hardware and can be integrated in electronic equipment. The vehicle-mounted terminal may be a camera with a camera and a fifth-Generation mobile communication technology (5th-Generation, abbreviated as 5G), or another terminal, such as a vehicle-mounted T-BOX. The method comprises the following steps:

s101, first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle are obtained, and a second vehicle space-time trajectory is established based on the first data and the second data, wherein the number of the roadside terminal and the second vehicle is at least one.

In this embodiment, the roadside terminal may be a device having a camera, a sensor with different functions, and a fifth-generation mobile communication technology. For example, a camera or the like integrated with various functional sensors and a fifth generation mobile communication technology may be used, which is not particularly limited herein. And the mounting position of the roadside terminal can be optionally mounted at a roadside portal frame or a street lamp pole or the like without influencing the normal running of the vehicle.

The second vehicle is a vehicle in the same traveling direction as the first vehicle. The second vehicle may be a vehicle having a camera and a 5G camera or another terminal, for example, an on-vehicle T-BOX.

Wherein the first data and the second data are multidimensional data. The optional first data may include: a location of the first vehicle, a first peripheral vehicle image, a first peripheral vehicle identification, and/or a data acquisition time; the second data may include: the position of the roadside terminal, the position of the vehicle around the roadside terminal, the image and/or the vehicle identification, and the position of the second vehicle, the second surrounding vehicle image, the second surrounding vehicle identification and/or the data acquisition time. The position in this embodiment may be longitude and latitude information obtained through a Global Positioning System (GPS), that is, the GPS information obtained by the vehicle and the roadside terminal includes the longitude and latitude of the current position.

It should be noted that the first surrounding vehicle identifier, the vehicle identifier of the vehicle around the roadside terminal, and the second surrounding vehicle identifier may be obtained by respectively performing image recognition on the acquired first surrounding vehicle image by the vehicle-mounted terminal on the first vehicle; and carrying out image recognition on a second surrounding vehicle image collected by a vehicle-mounted terminal on a second vehicle to obtain a second surrounding vehicle identifier.

The first surrounding vehicle identifier, the vehicle identifier of the vehicle around the roadside terminal, and the second surrounding vehicle identifier may be any identification information capable of uniquely identifying the vehicle, such as information such as a license plate number.

Specifically, in the running process of the first vehicle, the vehicle-mounted terminal can acquire first data around the first vehicle where the vehicle-mounted terminal is located and GPS data acquired by a GPS module on the first vehicle in real time, and store the acquired first data and the acquired GPS data into a storage unit of the vehicle-mounted terminal, or store the acquired first data and the acquired GPS data into other equipment connected with the vehicle-mounted terminal. Such as an optional Network Video Recorder (NVR) or the like, to subsequently determine whether a second vehicle is following the first vehicle.

Because the quantity of the first data acquired by the vehicle-mounted terminal is limited, the accuracy of the identification result of the following vehicle identification based on the first data is low, and the risk of misjudgment exists. Therefore, in this embodiment, while acquiring the first data around the first vehicle in real time based on the internet of things coordination function, the vehicle-mounted terminal may further establish a communication connection with the roadside terminal around the first vehicle and/or the second vehicle through the internet of things coordination function, and send a data acquisition request to the surrounding roadside terminal establishing the communication connection and/or the second vehicle in the same row based on millimeter-scale transmission capacity, so as to acquire multi-dimensional data, that is, the second data, acquired by the roadside terminal and/or the second vehicle in the same row, so that the vehicle-mounted terminal can acquire more dimensional data smoothly. Therefore, conditions such as image data and the like are provided for acquiring more comprehensive data around the vehicle, and further the vehicle-mounted terminal can acquire more dimensional data only by continuously upgrading the road side terminal, so that the vehicle-mounted terminal is prevented from being upgraded, different sensors are prevented from being additionally arranged for the vehicle, and the cost of vehicle equipment is effectively reduced.

The data acquisition request sent to the roadside terminal and/or the second vehicle in the same line can carry different information according to the receiving object. For example, if the receiving object is a roadside terminal, when the data acquisition request is sent to the roadside terminal, the data acquisition request may carry a data acquisition range in addition to the data acquisition instruction. The data acquisition range can be set according to actual needs. For example, a range with the preset distance as a radius may be optionally used as the data acquisition range with the vehicle-mounted terminal as a center. The preset distance may be set according to actual needs, for example, 100 meters (m), 300m, or 500 m. If the receiving object is a second vehicle in the same row, when a data acquisition request is sent to the second vehicle in the same row, only a data acquisition instruction can be carried in the data acquisition request. Therefore, the multidimensional data in different ranges can be acquired according to the attributes of surrounding equipment.

It should be noted that in this embodiment, the second vehicle in the same row in the same traveling direction as the first vehicle may be a vehicle belonging to the same group as the first vehicle, or may be a vehicle not belonging to the same group as the first vehicle, and is not limited herein. When the second vehicle and the first vehicle belong to the same group, an autonomous domain can be established through the vehicle-mounted terminal in the driving process of the first vehicle, so that other vehicles can be added into the autonomous domain through authentication control, and therefore all vehicles of the same group can share information in the same autonomous domain, and the reliability of obtaining the multidimensional data is guaranteed.

In the concrete implementation, the vehicle-mounted terminal on each vehicle in the same group can periodically refresh and send the vehicle identification and the key information of the vehicle to the group server, and can also periodically obtain the vehicle identification and the key information of other vehicles belonging to the group from the group server so as to realize that the vehicle identification of any vehicle is continuously broadcasted to the outside in the running process of the vehicle, when the other vehicles determine that the received vehicle identification and the vehicle identification belong to the same group and are in the same autonomous domain range, the vehicle-mounted terminal establishes communication connection with the vehicle-mounted terminal of the vehicle, determines the key information based on the vehicle identification, encrypts the multidimensional data collected by the vehicle, and sends the encrypted multidimensional data to the vehicle-mounted terminal of the vehicle, thereby realizing the purpose of sharing the multidimensional data among the vehicles in the same group and ensuring the safety of the multidimensional data, therefore, the information quantity of the data acquired by the vehicle-mounted terminal is further improved.

In an embodiment of the present invention, after the vehicle-mounted terminal sends the data acquisition request to the roadside terminal, if it is determined that the sending duration of the data acquisition request has reached the preset waiting duration, it indicates that the vehicle-mounted terminal and the roadside terminal may not normally communicate for some reasons, for example, the identity authorization authentication fails, and thus the second data sent by the roadside terminal cannot be acquired. For this purpose, by inquiring whether the second vehicle in the same row establishes a communication connection with the roadside terminal, if any second vehicle feeds back that it establishes a communication connection with the roadside terminal, the second vehicle is used as a relay device for establishing communication with the roadside terminal, and a relay request is sent to the second vehicle, so that the second vehicle feeds back second data sent by the roadside terminal based on the relay request. Therefore, the vehicle-mounted terminal on the first vehicle is cooperatively connected with other vehicles to acquire the second data acquired by the road side terminal, and a foundation is laid for increasing the data quantity acquired by the vehicle-mounted terminal. The preset waiting time can be set according to actual needs, for example, 1 minute.

In the same time period under general conditions, the vehicle identifier and the corresponding vehicle type pair should be consistent in the first data acquired by the vehicle-mounted terminal and the second data acquired from the roadside terminal and/or the second vehicle end in the same row. However, due to some reasons, such as too dark light, at least one terminal of the second data acquired from the roadside terminal and/or the second vehicle in the same row may not be consistent with the pairing data acquired by other terminals, and at this time, the abnormal data needs to be processed to ensure the reliability of the acquired second data. Wherein, the terminal refers to a road side terminal and/or a second vehicle.

Specifically, the abnormal data can be directly ignored; or eliminating abnormal data according to a few majority-obeying strategies; alternatively, the image with the best quality may be selected based on the second data transmitted from each terminal, and the image with the best quality may be used as the most reliable data. That is, after acquiring the first data and the second data, the present embodiment further includes: and performing error correction processing on the first data and the second data, and storing the error-corrected data. Thus, when the second vehicle following the first vehicle is determined based on the error-corrected data, the recognition accuracy and reliability can be further improved.

It is understood that, in this embodiment, the second data sent by the roadside terminal and/or the second vehicle may be the second data sent by the roadside terminal; alternatively, the second data sent by the second vehicle may be acquired; alternatively, the second data transmitted by the roadside terminal and the second vehicle may be acquired.

Further, after the first data, the second data and the first vehicle GPS data are acquired, the vehicle-mounted terminal can establish a second vehicle space-time track of the second vehicle in the same line based on the first data and the second data, and establish a first vehicle space-time track of the first vehicle based on the first vehicle GPS data. Establishing the second vehicle spatiotemporal trajectories of the co-traveling second vehicles in this embodiment means establishing the second vehicle spatiotemporal trajectories of each second vehicle.

The method for establishing the space-time trajectory of the second vehicle in the same line based on the first data and the second data specifically comprises the following steps: acquiring a vehicle identifier of a second vehicle and a plurality of first positions of the second vehicle at a plurality of time points in time from the acquired first data and second data, and establishing a second vehicle space-time track based on the vehicle identifier of the second vehicle and the first positions corresponding to the plurality of time points; similarly, a plurality of second positions of the first vehicle within the data acquisition time can be acquired from the acquired GPS data, and the first vehicle space-time track is established based on the vehicle identification of the first vehicle and the second positions corresponding to the plurality of time points.

For example, the vehicle identifier of the second vehicle is acquired as identifier a from the first data and the second data, and the plurality of time points include: t1, t2, t3, t4, t5 and t6, and the first positions corresponding to the plurality of time points include: l1, L2, L3, L4, L5 and L6, the space-time trajectory of the second vehicle a can be established by taking the 6 time points as independent variables (abscissa) and taking the 6 first positions corresponding to the 6 time points as dependent variables (ordinate).

S102, determining the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory.

Optionally, the similarity between the first vehicle spatiotemporal trajectory and each of the second vehicle spatiotemporal trajectories may be determined by a network model or other similarity algorithms. The network model may be, but is not limited to, a machine learning model, a deep learning model, a neural network model, and the like.

S103, if the similarity is larger than or equal to a first similarity threshold value, preliminarily determining that the second vehicle follows the first vehicle, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request.

Wherein the verification request includes a preliminary determination result, a following time period, a second vehicle identification, a first vehicle identification, and the first vehicle spatiotemporal trajectory.

In this embodiment, the first similarity threshold may be set according to actual needs, and is not limited herein. For example, the first similarity threshold is set to 60%, 75%, 85%, or the like.

Specifically, after the similarity between the first vehicle space-time trajectory and each second vehicle space-time trajectory is determined, each similarity may be compared with a first similarity threshold, so as to obtain a comparison result. If the comparison result shows that any similarity is greater than or equal to the first similarity threshold, the second vehicle corresponding to the second vehicle space-time trajectory and the first vehicle corresponding to the first vehicle space-time trajectory have multiple coincident space-time points in the driving process, so that the possibility that the second vehicle follows the first vehicle can be preliminarily determined. And if all the similarity degrees are smaller than the first similarity threshold value as a result of the comparison, determining that the first vehicle is not followed by the second vehicle, and continuously acquiring first data around the first vehicle and second data sent by the roadside terminal around the first vehicle and/or the second vehicle by the vehicle-mounted terminal at the moment so as to provide conditions for identifying that the second vehicle follows the first vehicle at the first time.

The multi-dimensional data acquired by the vehicle-mounted terminal is less than that acquired by the cloud server, so that the vehicle-mounted terminal preliminarily determines that the first vehicle is followed by at least one second vehicle, and the identification result is possibly biased due to the limitation of the acquired data amount. For example, although there are a plurality of coincident space-time points in the second vehicle space-time trajectory of the second vehicle X and the first vehicle space-time trajectory of the first vehicle W, it may be that only the plurality of space-time points coincide, and the other plurality of space-time points do not coincide, which causes the in-vehicle terminal to determine that the recognition result of the second vehicle X following the first vehicle W is not a true result.

Based on this, the vehicle-mounted terminal can also send a verification request to the cloud server after determining that the first vehicle is followed by at least one second vehicle, so that the cloud server verifies information such as a primary identification result carried in the verification request sent by the vehicle-mounted terminal based on the more comprehensive multidimensional data stored by the cloud server, so as to verify whether the primary identification result is correct, and the verification result is returned to the vehicle-mounted terminal, so that the vehicle-mounted terminal executes different operations according to the received verification result. Therefore, the initial identification result is obtained through the vehicle-mounted terminal based on local big data analysis, and then the initial identification result of the vehicle-mounted terminal is verified through the cloud server, so that the pressure of the cloud server for real-time mass data analysis is effectively avoided, the calculation pressure of the cloud server can be greatly reduced, the cooperative processing of local data and global data is realized, the data analysis accuracy is improved, and the calculation pressure of the cloud server can be reduced.

According to the technical scheme provided by the embodiment of the invention, the first data around the first vehicle and the second data sent by the roadside terminal around the first vehicle and/or the second vehicle are obtained to establish the space-time track of the second vehicle based on the first data and the second data, the similarity between the space-time track of the first vehicle and the space-time track of the second vehicle is determined, when the similarity is greater than or equal to the first similarity threshold, the second vehicle is preliminarily determined to follow the first vehicle, the verification request is sent to the cloud server, and the verification result returned by the cloud server is received. According to the embodiment of the invention, the vehicle-mounted terminal determines the similarity between the space-time trajectory of the first vehicle and the space-time trajectory of the second vehicle in the same line based on the acquired data, determines whether the suspicion that the second vehicle follows the first vehicle exists, and sends the verification request to the cloud server when the suspicion exists so as to verify through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

As can be seen from the above description, in the embodiment of the present invention, based on the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory, whether the second vehicle follows the first vehicle is preliminarily determined, and when the second vehicle follows the first vehicle, the preliminarily determined result is verified by the cloud server, so that a verification result is obtained. On the basis of the above embodiment, the similarity between the first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory determined in the embodiment of the present invention is further optimized. As shown in fig. 2A, the method specifically includes:

s201, first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle are obtained, and a second vehicle space-time trajectory is established based on the first data and the second data, wherein the number of the roadside terminal and the second vehicle is at least one.

S202, determining time difference values and distance difference values between each first space-time point in the first vehicle space-time trajectory and each second space-time point in the second vehicle space-time trajectory.

S203, if the time difference value between any first space-time point and any second space-time point is smaller than a time threshold value, and the distance difference value is smaller than a distance threshold value, determining that the first space-time point is similar to the second space-time point.

The time threshold and the distance threshold are empirical values, and may be specifically set according to actual application requirements, which is not specifically limited herein.

Continuing with the example in the previous embodiment, as shown in fig. 2B, the first vehicle a spatiotemporal trajectory includes 6 spatiotemporal points, a (t1, L1), B (t2, L2), c (t3, L3), d (t4, L4), e (t5, L5), and f (t6, L6); the second vehicle B spatiotemporal trajectory comprises 6 spatiotemporal points, a '(t 1', L1 '), B' (t2 ', L2'), c '(t 3', L3 '), d' (t4 ', L4'), e '(t 5', L5 ') and f' (t6 ', L6'), at which time and distance differences between the 6 spatiotemporal points in the first vehicle A spatiotemporal trajectory and the 6 spatiotemporal points in the second vehicle B spatiotemporal trajectory can be calculated by the vehicle terminals, respectively.

If the time difference values between a first time-space point a in the first vehicle A space-time track and a second time-space point a 'in the second vehicle A space-time track, a first time-space point c in the first vehicle A space-time track and a second time-space point c' in the second vehicle B space-time track, and a first time-space point e in the first vehicle A space-time track and a second time-space point e 'in the second vehicle B space-time track are both smaller than a time threshold value, and the distance difference values are both smaller than a distance threshold value, determining that the first time-space point a and the second time-space point a', the first time-space point c and the second time-space point c ', and the first time-space point e and the second time-space point e' are similar points.

S204, counting the number of second space-time points similar to the first space-time points, and determining the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory based on the number of the second space-time points and the total number of the first space-time points.

Continuing with the above example, based on the second spatiotemporal points similar to the first spatiotemporal points in the first vehicle a spatiotemporal trajectory, the number of the second spatiotemporal points similar to the first spatiotemporal points may be determined to be 3, and the total data amount of the first spatiotemporal points in the first vehicle a spatiotemporal trajectory is 6, then based on the above-mentioned number of the second spatiotemporal points 3 and the total number of the first spatiotemporal points 6, the similarity of the first vehicle a spatiotemporal trajectory and the second vehicle B spatiotemporal trajectory may be determined to be 50%. Specifically, it can be determined by the following formula (1):

wherein X is the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory.

S205, if the similarity is larger than or equal to a first similarity threshold value, preliminarily determining that the second vehicle follows the first vehicle, sending a verification request to a cloud server, and receiving a verification result of the cloud server determining whether the second vehicle follows the first vehicle according to the verification request.

According to the technical scheme provided by the embodiment of the invention, the similarity between the space-time trajectory of the first vehicle and the space-time trajectory of the second vehicle in the same line is established based on the acquired data through the vehicle-mounted terminal, whether the suspicion that the second vehicle follows the first vehicle exists is determined, and when the suspicion exists, a verification request is sent to the cloud server to be verified through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

Fig. 3A is a schematic flowchart of a third collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention. On the basis of the above embodiment, when the second vehicle number is plural, it is determined whether the first vehicle is followed by the second vehicle to be further optimized. As shown in fig. 3A, the method is specifically as follows:

s301, first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle are obtained, and each second vehicle space-time trajectory is established based on the first data and the second data, wherein the roadside terminal is at least one, and the number of the second vehicles is multiple.

S302, respectively determining the similarity between the first vehicle space-time trajectory and each second vehicle space-time trajectory.

S303, if the similarity degrees are larger than or equal to the first similarity threshold value, preliminarily determining that a plurality of second vehicles track the first vehicle in a group mode, sending verification requests to a cloud server, and receiving verification results of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification requests.

Wherein the verification request includes a preliminary determination result, a following time period, each second vehicle identification, a first vehicle identification, and the first vehicle spatiotemporal trajectory.

The implementation principle of establishing the first vehicle space-time trajectory and each second vehicle space-time trajectory and calculating the similarity between the first vehicle space-time trajectory and each second vehicle space-time trajectory is similar to the process of establishing the first vehicle space-time trajectory and the second vehicle space-time trajectory and calculating the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory in the foregoing embodiment, which is specifically referred to the foregoing embodiment and is not described in detail herein.

For example, as shown in fig. 3B, the in-vehicle terminal acquires vehicle identifiers C and D of a plurality of second vehicles from the acquired first data and second data, respectively, and the plurality of time points of the second vehicle C include: t11, t12, t13, t14, t15, t16, t17, t18, t19, and t20, the plurality of time points of the second vehicle D including: t11 ', t 12', t13 ', t 14', t15 ', t 16', t17 ', t 18', t19 'and t 20', wherein the first positions of the second vehicle C corresponding to the plurality of time points comprise: l11, L12, L13, L14, L15, L16, L17, L18, L19, and L20, and the first location of the second vehicle D corresponding to the plurality of time points includes: l11 ', L12', L13 ', L14', L15 ', L16', L17 ', L18', L19 'and L20'. The vehicle-mounted terminal may respectively establish the space-time trajectory of the second vehicle C and the space-time trajectory of the second vehicle D by using the 10 time points of the second vehicles C and D as independent variables (abscissa) and using the 10 second positions corresponding to the 10 time points as dependent variables (ordinate).

Similarly, the vehicle-mounted terminal may further obtain the vehicle identifier of the first vehicle as the identifier W from the stored GPS data and the located first vehicle identifier data, and the plurality of time points include: t1, t2, t3, t4, t5, t6, t7, t8, t9 and t10, and the second positions corresponding to the plurality of time points comprise: l1, L2, L3, L4, L5, L6, L7, L8, L9 and L10, the space-time trajectory of the first vehicle W can be established by taking the 10 time points as independent variables (abscissa) and taking the 10 first positions corresponding to the 10 time points as dependent variables (ordinate).

Furthermore, based on a network model or other similarity algorithms, the similarity between the first vehicle W space-time trajectory and the second vehicle C space-time trajectory and the second vehicle D space-time trajectory is calculated to be 50%. And when 50% is greater than the first similarity threshold, determining a second vehicle C space-time trajectory and a second vehicle D space-time trajectory, and respectively having a plurality of coincident space-time points with the first vehicle W space-time trajectory, so that the possibility that the second vehicles C and D have a group to follow the first vehicle W can be determined.

Because the multidimensional data acquired by the vehicle-mounted terminal is less than that acquired by the cloud server, the vehicle-mounted terminal preliminarily determines that the first vehicle is followed by a plurality of second vehicles in a group, and the acquired data amount is limited, so that the recognition result has deviation. For example, although there are a plurality of coincident spatiotemporal points of the second vehicle spatiotemporal trajectories of the second vehicles X and Y with the first vehicle spatiotemporal trajectory of the first vehicle W, it may be only that the plurality of spatiotemporal points coincide, and the other plurality of spatiotemporal points do not coincide, which causes the in-vehicle terminal to determine that the recognition result of the second vehicles X and Y gang following the first vehicle W is not a true result.

Based on this, after the vehicle-mounted terminal determines that the first vehicle is followed by the second vehicles, the vehicle-mounted terminal can also send a verification request to the cloud server, so that the cloud server verifies information such as the primary identification result carried in the verification request sent by the vehicle-mounted terminal based on the more comprehensive multidimensional data stored by the cloud server, so as to verify whether the primary identification result is correct, and the verification result is returned to the vehicle-mounted terminal, so that the vehicle-mounted terminal executes different operations according to the received verification result. Therefore, the initial identification result is obtained through the vehicle-mounted terminal based on local big data analysis, and then the initial identification result of the vehicle-mounted terminal is verified through the cloud server, so that the pressure of the cloud server for real-time mass data analysis is effectively avoided, the calculation pressure of the cloud server can be greatly reduced, the cooperative processing of local data and global data is realized, the data analysis accuracy is improved, and the calculation pressure of the cloud server can be reduced.

Fig. 4 is a schematic flowchart of a fourth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention. On the basis of the above embodiments, the present embodiment can be further optimized. Specifically, after determining that the first vehicle is followed by one or more second vehicles and sending a verification request to the cloud server, different operations are executed according to a verification result fed back by the cloud server. As shown in fig. 4, the method specifically includes:

s401, first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle are obtained, and a second vehicle space-time trajectory is established based on the first data and the second data, wherein the number of the roadside terminal and the second vehicle is at least one.

S402, determining the similarity between the first vehicle space-time trajectory and the second vehicle space-time trajectory.

And S403, if the similarity is greater than or equal to a first similarity threshold, preliminarily determining that the second vehicle follows the first vehicle, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory.

S404, if the verification result is that the second vehicle follows the first vehicle, early warning information is sent.

Specifically, after the verification result returned by the cloud server is obtained, the verification result can be analyzed, and whether the at least one second vehicle follows the first vehicle or not is determined. If it is determined that at least one second vehicle follows the first vehicle, early warning information can be sent to a driver of the first vehicle, so that the driver can take countermeasures based on the early warning information to ensure personal safety, property safety and the like.

When the early warning information is sent to the driver, the vehicle-mounted terminal can also superpose the image of each second vehicle on the image acquired by the first vehicle in an on-screen display (OSD) mode, so that the driver can determine which vehicles currently follow the vehicle by checking the image displayed by the vehicle-mounted terminal, and a foundation is laid for subsequent alarming and taking acceleration or deceleration operation.

S405, if the second vehicle does not follow the first vehicle according to the verification result, new first data around the first vehicle and new second data sent by the road side terminal and/or the second vehicle are obtained again, and a new second vehicle space-time trajectory is established based on the new first data and the new second data; the number of the roadside terminals and the second vehicles is at least one, and the second vehicles comprise historical second vehicles with following suspicions.

Specifically, when the verification result is analyzed, it is determined whether at least one second vehicle follows the first vehicle. If all the second vehicles are determined not to follow the first vehicle, the vehicle-mounted terminal can continuously focus on the motion track of the at least one second vehicle to determine whether each second vehicle is deliberately disguised to be different from the space-time track of the first vehicle through a complex track so as to avoid being identified to follow the first vehicle.

During specific implementation, the vehicle-mounted terminal can obtain new first data around the first vehicle in real time and send a data obtaining request to the road-side terminal around the first vehicle and/or the second vehicle in the same row to obtain new second data sent by the road-side terminal around the first vehicle and/or the second vehicle in the same row, so that whether the second vehicle frequently coincides with a plurality of space-time points of the space-time trajectory of the first vehicle or not in the history of following suspicion is determined based on the new first data and the new second data.

S406, determining the similarity between the new first vehicle space-time trajectory and the new second vehicle space-time trajectory.

S407, when the similarity between the new second vehicle space-time trajectory of the historical second vehicle and the new first vehicle space-time trajectory is greater than or equal to a first similarity threshold value, re-determining that the historical second vehicle follows the first vehicle, sending a new verification request to a cloud server, and receiving a new verification result that the cloud server determines whether the historical second vehicle follows the first vehicle according to the new verification request, wherein the new verification request comprises a new preliminary determination result, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory.

It should be noted that, the implementation principle of determining the similarity between the new first vehicle space-time trajectory and the new second vehicle space-time trajectory in S406 is similar to the foregoing embodiment, and reference is specifically made to the foregoing embodiment, which is not described in detail herein.

Specifically, when the similarity between the new second vehicle space-time trajectory of the historical second vehicle and the new first vehicle space-time trajectory is determined to be greater than or equal to the first similarity threshold, the suspicion that the historical second vehicle still follows the first vehicle is determined again, at the moment, the vehicle-mounted terminal can send a new verification request to the cloud server, so that the cloud server verifies information such as a new primary identification result carried in the new verification request sent by the vehicle-mounted terminal based on the comprehensive multidimensional data stored by the cloud server, so as to verify whether the new primary identification result is correct, and the new verification result is returned to the vehicle-mounted terminal, so that the vehicle-mounted terminal performs alarming or monitoring operation according to the new verification result.

According to the technical scheme provided by the embodiment of the invention, the first data around the first vehicle and the second data sent by the roadside terminal around the first vehicle and/or the second vehicle are obtained to establish the space-time track of the second vehicle based on the first data and the second data, the similarity between the space-time track of the first vehicle and the space-time track of the second vehicle is determined, when the similarity is greater than or equal to the first similarity threshold, the second vehicle is preliminarily determined to follow the first vehicle, the verification request is sent to the cloud server, and the verification result returned by the cloud server is received. According to the embodiment of the invention, the vehicle-mounted terminal determines the similarity between the space-time trajectory of the first vehicle and the space-time trajectory of the second vehicle in the same line based on the acquired data, determines whether the suspicion that the second vehicle follows the first vehicle exists, and sends the verification request to the cloud server when the suspicion exists so as to verify through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved. In addition, when at least one second vehicle is determined to follow the first vehicle, the driver of the first vehicle is sent with early warning information, so that the driver can take countermeasures based on the early warning information to ensure personal safety, property safety and the like. And when all the second vehicles are determined not to follow the first vehicle, the vehicle-mounted terminal continuously pays attention to the motion track of at least one second vehicle to determine whether each second vehicle is deliberately disguised to be different from the space-time track of the first vehicle through a complex track so as to avoid being identified to follow the first vehicle, so that the accuracy and the reliability of the following identification result are further improved, and the user satisfaction is greatly improved.

The following describes a collaborative analysis method for a 5G mobile internet node, which is provided by an embodiment of the present invention, with a cloud server as an execution subject. Fig. 5A is a schematic flowchart of a fifth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention. The embodiment of the invention is applicable to the identification scene that the vehicle is followed, the method can be executed by a collaborative analysis device of the 5G mobile Internet of things node configured in the cloud server, and the device can be realized by software and/or hardware and can be integrated in the electronic equipment. The method comprises the following steps:

s501, receiving a verification request sent by each first vehicle, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory of each first vehicle.

S502, establishing a second vehicle verification space-time trajectory based on all second data of the second vehicle corresponding to the obtained second vehicle identification in the following time period, and determining the similarity between the first vehicle space-time trajectory and the second vehicle verification space-time trajectory.

In S502, a first vehicle spatiotemporal trajectory and a second vehicle verification spatiotemporal trajectory are established, and a similarity between the first vehicle spatiotemporal trajectory and the second vehicle verification spatiotemporal trajectory is calculated, which is similar to the implementation principle of establishing the similarity between the spatiotemporal trajectory and the calculation spatiotemporal trajectory in the foregoing embodiments, and reference is specifically made to the foregoing embodiments, and redundant description thereof is omitted here.

S503, determining a verification result according to the relation between the similarity and the second similarity threshold, and sending the verification result to the first vehicle.

In this embodiment, the second similarity threshold may be set according to actual needs, and is not limited herein. For example, the second similarity threshold is set to 85%, 90%, or 95%, etc.

The verification request sent by each first vehicle refers to a verification request sent by a vehicle-mounted terminal located on each first vehicle.

For example, assuming that the in-vehicle terminal on the first vehicle a is recognizing the second vehicle B and follows the first vehicle a within a period of time t1 '-t 6', the preliminary recognition result that the second vehicle B follows the first vehicle W, the following period of time t1 '-t 6', the second vehicle identification B, and the first vehicle identification a and the first vehicle a space-time trajectory may be transmitted to the cloud server in the verification request, with being carried therein.

And then, after receiving the verification request, the cloud server analyzes the verification request to obtain a preliminary identification result, a following time period, a second vehicle identification B, a first vehicle identification A and a first vehicle A space-time track carried by the verification request. Then, the cloud server establishes a verification space-time trajectory of the second vehicle B by combining the more comprehensive multidimensional data acquired by the cloud server, as shown in fig. 5B.

When the space-time trajectory verified by the second vehicle B is a vehicle-B space-time trajectory 1, namely the similarity between the vehicle-A space-time trajectory and the vehicle-B verified space-time trajectory 1 is greater than a second similarity threshold value, the second vehicle B is determined to follow the first vehicle W; when the second vehicle B verifies that the space-time trajectory is the vehicle B space-time trajectory 2, namely the similarity between the vehicle A space-time trajectory and the vehicle B space-time trajectory 2 is smaller than a second similarity threshold value, it is indicated that the second vehicle B only coincides with some space-time points on the first vehicle A space-time trajectory at some space-time points, but the first vehicle A space-time trajectory and the second vehicle B verify that the space-time trajectory is different as a whole, and then it is determined that the second vehicle B does not follow the first vehicle W. Furthermore, the cloud server can feed back the verification result to the first vehicle based on the first vehicle identifier A.

For another example, assuming that the in-vehicle terminal on the first vehicle a is following the first vehicle a during a period of time t1-t10, t11-t20, or t11 '-t 20' while recognizing the second vehicles C and D, the first recognition result of the second vehicles C and D following the first vehicle a, the following period of time t1-t10, t11-t20, or t11 '-t 20', the second vehicle identifications C and D, the first vehicle identification a, and the first vehicle a space-time trajectory may be carried in the verification request, and the verification request is transmitted to the cloud server.

Furthermore, after receiving the verification request, the cloud server analyzes the verification request to acquire the first verification request carrying the preliminary identification result, the following time period, the second vehicle identifications C and D, the first vehicle identification A and the first vehicle A space-time trajectory. Then, the cloud server establishes a second vehicle C verification space-time trajectory and a second vehicle D verification space-time trajectory by combining more comprehensive multidimensional data, as shown in fig. 5C.

When the second vehicle C verifies that the space-time trajectory is the vehicle C space-time trajectory 1 and the second vehicle D verifies that the space-time trajectory is the vehicle D space-time trajectory 1, namely the similarity between the vehicle A space-time trajectory and the vehicle C verification space-time trajectory 1 and the similarity between the vehicle D space-time trajectory 1 and the vehicle C verification space-time trajectory 1 are both greater than a second similarity threshold value, the second vehicle C and the second vehicle D are determined to follow the first vehicle A in a group; when the second vehicle C verifies that the space-time trajectory is the vehicle-time trajectory 2 when the second vehicle D verifies that the space-time trajectory is the vehicle-C, the similarity of the vehicle-time trajectory A and the similarity of the vehicle-C verification space-time trajectory 1 and the similarity of the vehicle-D verification space-time trajectory D are both smaller than a second similarity threshold value, the second vehicle C and the second vehicle D are only coincided with some space-time points on the first vehicle-A space-time trajectory at some space-time points, but the first vehicle-A space-time trajectory, the second vehicle-C verification space-time trajectory and the second vehicle-D verification space-time trajectory are not the same as a whole, and then the second vehicle C and the second vehicle D are determined not to be coincided with the first vehicle W. Furthermore, the cloud server can feed back the verification result to the first vehicle based on the first vehicle identifier A.

And then, the first vehicle executes early warning or continuously monitors whether the operation that the second vehicle follows the first vehicle exists according to the verification result fed back by the cloud server. That is, the present embodiment determines the verification result according to the relationship between the similarity and the second similarity threshold, including: if the similarity is greater than or equal to the second similarity threshold, determining that the second vehicle follows the first vehicle as a verification result; if the similarity is smaller than the second similarity threshold, determining that the second vehicle does not follow the first vehicle as a verification result.

According to the technical scheme provided by the embodiment of the invention, all second data of a second vehicle corresponding to a second vehicle identifier in a following time period are obtained based on parameters carried by a verification request by receiving the verification request sent by each first vehicle, a second vehicle verification space-time track is established, the similarity between the first vehicle space-time track and the second vehicle verification space-time track is determined, then a verification result is determined according to the relation between the similarity and a second similarity threshold, and the verification result is sent to the first vehicle. According to the embodiment of the invention, the vehicle-mounted terminal determines the similarity between the space-time trajectory of the first vehicle and the space-time trajectory of the second vehicle in the same line based on the acquired data, determines whether the suspicion that the second vehicle follows the first vehicle exists, and sends the verification request to the cloud server when the suspicion exists so as to verify through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved.

Fig. 6 is a schematic flowchart of a sixth collaborative analysis method for a 5G mobile internet of things node according to an embodiment of the present invention. On the basis of the above embodiments, the present embodiment can be further optimized. Specifically, after the verification result is sent to the first vehicle, the method further includes receiving a new verification request sent by any first vehicle, and verifying the new verification request. As shown in fig. 6, the method is as follows:

s601, receiving a new verification request sent by any first vehicle, wherein the new verification request comprises a new preliminary determination result of the first vehicle, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory.

In this embodiment, after the cloud server sends the verification result back to the first vehicle, the first vehicle may continue to pay attention to the historical second vehicle driving track following the first vehicle when it is determined that the verification result fed back by the cloud server is inconsistent with the preliminary determination result of the first vehicle, so that the situation follows the first vehicle is suspected exists is intentionally disguised through the complex track is recognized is avoided. During specific implementation, the vehicle-mounted terminal on the first vehicle can obtain new first data around the first vehicle in real time and send a data obtaining request to the road-side terminal around the first vehicle and/or the second vehicle in the same row to obtain new second data sent by the road-side terminal and/or the second vehicle in the same row, so that whether the second vehicle with the history of following suspicion is still frequently coincided with a plurality of time-space points of the space-time trajectory of the first vehicle or not is determined based on the new first data and the new second data.

When the similarity between the new second vehicle space-time trajectory of the historical second vehicle and the new first vehicle space-time trajectory is larger than or equal to the first similarity threshold value, the suspicion that the historical second vehicle still follows the first vehicle is re-determined, at the moment, the vehicle-mounted terminal can send a new verification request to the cloud server, so that the cloud server verifies information such as a new primary recognition result carried in the new verification request sent by the vehicle-mounted terminal based on the comprehensive multidimensional data stored by the cloud server, so as to verify whether the new primary recognition result is correct, and the new verification result is returned to the vehicle-mounted terminal, so that the vehicle-mounted terminal executes alarming or monitoring operation according to the new verification result.

Specifically, when a new verification request sent by any first vehicle is received, the cloud server can analyze the new verification request to obtain a new preliminary determination result, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory, so that a foundation is laid for subsequently verifying the new verification request sent by the first vehicle.

S602, establishing a new second vehicle verification space-time trajectory based on all new second data of the obtained historical second vehicle corresponding to the historical second vehicle identifier in the new following time period, and determining the similarity between the new first vehicle space-time trajectory and the new second vehicle verification space-time trajectory.

S603, determining a new verification result according to the relation between the similarity and the second similarity threshold, and sending the new verification result to the first vehicle.

For example, assuming that the on-board terminal of the first vehicle a is still following the first vehicle W during the period from t21 '-t 30' when recognizing the second vehicle B, the new preliminary recognition result of the second vehicle B following the first vehicle a, the new following period from t21 '-t 30', the second vehicle identification B, and the first vehicle identification a and the new first vehicle a space-time trajectory may be carried in the new verification request, and the new verification request may be sent to the cloud server.

And then, after receiving the new verification request, the cloud server analyzes the new verification request to acquire the related information carried by the new verification request. And then, the cloud server establishes a verification space-time trajectory of the new second vehicle B by combining more comprehensive multidimensional data. And then, by taking the space-time trajectory of the new first vehicle A as a reference trajectory, projecting the verification space-time trajectory of the new second vehicle B onto the space-time trajectory of the new first vehicle A, and calculating adjacent coincident space-time points in the space-time trajectories of the first vehicle A and the second vehicle B. Further, the distance value and the time between the adjacent coincident space-time points are calculated, and the equivalent vehicle speed of the second vehicle B is obtained by dividing the distance value by the time. Similarly, the cloud server may calculate the travel speed of the first vehicle a based on the distance and time between adjacent space-time points on the space-time trajectory of the first vehicle a.

And the cloud server compares the equivalent vehicle speed of the second vehicle B with the running speed of the first vehicle A to obtain a comparison result. And if the comparison result shows that the equivalent vehicle speed of the second vehicle B is equal to the running speed of the first vehicle A, sending a speed increasing or reducing instruction to the first vehicle according to the first vehicle identification A, and receiving the adjusted speed sent by the first vehicle A, so that the cloud server establishes the verification space-time trajectory of the second vehicle B again, and calculates the equivalent vehicle speed of the second vehicle B. And if the equivalent vehicle speed of the second vehicle B is determined to synchronously change along with the change of the vehicle speed of the first vehicle A, and the established new verification space-time trajectory is still the coincident space-time point of the first vehicle A and the second vehicle B, determining that the first vehicle A is followed by the second vehicle B, otherwise, determining that the first vehicle A is not followed by the second vehicle B. Furthermore, the cloud server can feed back the verification result to the first vehicle based on the first vehicle identifier A, so that the vehicle-mounted terminal on the first vehicle A can take different processing measures according to the verification result sent by the cloud server. For example, if the second vehicle follows the first vehicle as a result of the verification, warning information or the like is transmitted.

According to the technical scheme provided by the embodiment of the invention, the vehicle-mounted terminal is used for determining the similarity between the space-time track of the first vehicle and the space-time track of the second vehicle in the same line based on the acquired data, determining whether the suspicion that the second vehicle follows the first vehicle exists, and sending the verification request to the cloud server when the suspicion exists so as to perform verification through the cloud server, so that the cooperative processing of the vehicle-mounted terminal and the cloud server is realized, the cloud server does not need to analyze and process all data, the calculation pressure of the cloud server is reduced, and the use performance of the cloud server is improved. In addition, when all the second vehicles are determined not to follow the first vehicle, the vehicle-mounted terminal continuously pays attention to the motion track of at least one second vehicle to determine whether each second vehicle is deliberately disguised to be different from the space-time track of the first vehicle through a complex track so as to avoid being identified to follow the first vehicle, the accuracy and the reliability of the following identification result are further improved, and the user satisfaction is greatly improved.

Fig. 7 is a schematic structural diagram of a cooperative analysis apparatus of a 5G mobile internet of things node according to an embodiment of the present invention. The collaborative analysis device of the 5G mobile Internet of things node is configured in the vehicle-mounted terminal. As shown in fig. 7, a collaborative analysis apparatus 700 for a node of a mobile internet of things according to embodiment 5G of the present invention includes: a data acquisition module 710, a first determination module 720, and a first verification module 730.

The data acquisition module 710 is configured to acquire first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle, and establish a second vehicle space-time trajectory based on the first data and the second data, where the number of the roadside terminals and the second vehicles is at least one;

a first determination module 720 for determining a similarity between a first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory;

the first verification module 730 is configured to preliminarily determine that the second vehicle follows the first vehicle, send a verification request to a cloud server, and receive a verification result that the cloud server determines whether the second vehicle follows the first vehicle according to the verification request if the similarity is greater than or equal to a first similarity threshold, where the verification request includes a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier, and the first vehicle space-time trajectory.

As an optional implementation manner of the embodiment of the present invention, the data obtaining module 710 is specifically configured to:

acquiring a vehicle identifier of the second vehicle and first positions of the second vehicle corresponding to a plurality of time points from the first data and the second data;

establishing the second vehicle spatiotemporal trajectory based on the vehicle identification of the second vehicle and the first location corresponding to the plurality of time points.

As an optional implementation manner of the embodiment of the present invention, the first determining module 720 is specifically configured to:

determining time difference values and distance difference values between each first space-time point in the first vehicle space-time trajectory and each second space-time point in the second vehicle space-time trajectory;

if the time difference value between any first space-time point and any second space-time point is smaller than a time threshold value, and the distance difference value is smaller than a distance threshold value, determining that the first space-time point is similar to the second space-time point;

counting a second number of spatiotemporal points similar to the first spatiotemporal points, and determining a similarity between a first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory based on the second number of spatiotemporal points and the total number of the first spatiotemporal points.

the method comprises the steps of acquiring first data around a first vehicle in real time based on the cooperative function of the internet of things, establishing communication connection with a road side terminal and/or a second vehicle around the first vehicle based on the cooperative function of the internet of things, and sending a data acquisition request to the road side terminal and/or the second vehicle which establish the communication connection so as to acquire second data sent by the road side terminal and/or the second vehicle.

As an optional implementation manner of the embodiment of the present invention, the method further includes: an early warning information sending module;

the early warning information sending module is used for sending early warning information if the verification result indicates that the second vehicle follows the first vehicle;

the data acquisition module is specifically used for re-acquiring new first data around the first vehicle and new second data sent by the roadside terminal and/or the second vehicle if the verification result indicates that the second vehicle does not follow the first vehicle, and establishing a new second vehicle space-time trajectory based on the new first data and the new second data; the number of the roadside terminals and the number of the second vehicles are at least one, and the second vehicles comprise history second vehicles with following suspicions;

a first determination module, specifically configured to determine a similarity between a new first vehicle spatiotemporal trajectory and the new second vehicle spatiotemporal trajectory;

the first verification module is specifically configured to, when the similarity between a new second vehicle space-time trajectory of the historical second vehicle and the new first vehicle space-time trajectory is greater than or equal to a first similarity threshold, re-determine that the historical second vehicle follows the first vehicle, send a new verification request to a cloud server, and receive a new verification result that the cloud server determines whether the historical second vehicle follows the first vehicle according to the new verification request, where the new verification request includes a new preliminary determination result, a new following time period, a historical second vehicle identifier, a first vehicle identifier, and a new first vehicle space-time trajectory.

It should be noted that the foregoing explanation on the embodiment of the collaborative analysis method for the 5G mobile internet-of-things node is also applicable to the collaborative analysis apparatus for the 5G mobile internet-of-things node in the embodiment, and the implementation principle thereof is similar, and is not described herein again.

Fig. 8 is a schematic structural diagram of a cooperative analysis apparatus of a 5G mobile internet of things node according to an embodiment of the present invention. The collaborative analysis device of the 5G mobile Internet of things node is configured in the cloud server. As shown in fig. 8, a collaborative analysis apparatus 800 for a mobile internet of things node according to embodiment 5G of the present invention includes: a request receiving module 810, a second determining module 820, and a result transmitting module 830.

The request receiving module 810 is configured to receive a verification request sent by each first vehicle, where the verification request includes a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier, and the first vehicle spatiotemporal trajectory of each first vehicle;

a second determining module 820, configured to establish a second vehicle verification space-time trajectory based on all second data of the second vehicle in the following time period corresponding to the obtained second vehicle identifier, and determine a similarity between the first vehicle space-time trajectory and the second vehicle verification space-time trajectory;

and a result sending module 830, configured to determine a verification result according to a relationship between the similarity and the second similarity threshold, and send the verification result to the first vehicle.

As an optional implementation manner of the embodiment of the present invention, the result sending module 830 is specifically configured to:

if the similarity is greater than or equal to the second similarity threshold, determining that the second vehicle follows the first vehicle as a verification result;

if the similarity is smaller than the second similarity threshold, determining that the second vehicle does not follow the first vehicle as a verification result.

As an alternative implementation of the embodiments of the present invention,

a request receiving module 810, further configured to receive a new verification request sent by any of the first vehicles, where the new verification request includes a new preliminary determination result of the first vehicle, a new following time period, a historical second vehicle identifier, a first vehicle identifier, and a new first vehicle spatiotemporal trajectory;

the second determining module 820 is further configured to establish a new second vehicle verification spatiotemporal trajectory based on all new second data of the obtained historical second vehicle corresponding to the historical second vehicle identifier in the new following time period, and determine a similarity between the new first vehicle spatiotemporal trajectory and the new second vehicle verification spatiotemporal trajectory;

the result sending module 830 is further configured to determine a new verification result according to a relationship between the similarity and the second similarity threshold, and send the new verification result to the first vehicle.

Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. FIG. 9 illustrates a block diagram of an exemplary electronic device 900 suitable for use in implementing embodiments of the present invention. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.

As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: one or more processors or processing units 910, a system memory 920, and a bus 930 that couples the various system components (including the system memory 920 and the processing unit 910).

Bus 930 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 900 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 900 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 920 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)921 and/or cache memory 922. The electronic device 900 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 923 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 9 and commonly referred to as "hard drives"). Although not shown in FIG. 9, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 930 by one or more data media interfaces. System memory 920 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 924 having a set (at least one) of program modules 925 may be stored, for example, in system memory 920, such program modules 925 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 925 generally perform the functions and/or methods of the described embodiments of the invention.

The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, display 941, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 960 communicates with the other modules of the electronic device 900 via the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 910 executes various functional applications and data processing by running the program stored in the system memory 920, for example, implementing a collaborative analysis method for a 5G mobile internet node provided by an embodiment of the present invention, which is applied to a vehicle-mounted terminal, and the method includes:

Or, the collaborative analysis method for the 5G mobile internet of things node provided by the embodiment of the present invention is applied to a cloud server, and the method includes:

It should be noted that the foregoing explanation on the embodiment of the collaborative analysis method for the 5G mobile internet of things node is also applicable to the electronic device of the embodiment, and the implementation principle and the implementation effect are similar, and are not described herein again.

In order to achieve the above object, the present invention also provides a computer-readable storage medium.

The computer-readable storage medium provided by the embodiment of the present invention stores thereon a computer program, and when the computer program is executed by a processor, the method for collaborative analysis of a 5G mobile internet of things node according to the embodiment of the present invention is implemented, where the method is applied to a vehicle-mounted terminal, and the method includes:

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A collaborative analysis method of a 5G mobile Internet of things node is applied to a vehicle-mounted terminal, and the method comprises the following steps:

if the similarity is larger than or equal to a first similarity threshold, preliminarily determining that the second vehicle follows the first vehicle, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory;

the acquiring first data around a first vehicle and second data sent by a roadside terminal and/or a second vehicle around the first vehicle comprises: the data acquisition request sent to the roadside terminal and/or the second vehicle carries different information according to a receiving object;

after the receiving the verification result that the cloud server determines whether the second vehicle follows the first vehicle according to the verification request, the method further includes:

if the verification result is that the second vehicle follows the first vehicle, sending early warning information;

if the verification result is that the second vehicle does not follow the first vehicle, new first data around the first vehicle and new second data sent by the road side terminal and/or the second vehicle are obtained again, and a new second vehicle space-time track is established based on the new first data and the new second data; the number of the roadside terminals and the number of the second vehicles are at least one, and the second vehicles comprise history second vehicles with following suspicions;

determining a similarity between a new first vehicle spatiotemporal trajectory and the new second vehicle spatiotemporal trajectory;

when the similarity between the new second vehicle space-time trajectory of the historical second vehicle and the new first vehicle space-time trajectory is larger than or equal to a first similarity threshold value, re-determining that the historical second vehicle follows the first vehicle, sending a new verification request to a cloud server, and receiving a new verification result of whether the historical second vehicle follows the first vehicle or not according to the new verification request by the cloud server, wherein the new verification request comprises a new preliminary determination result, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory.

2. The method of claim 1, wherein establishing a second vehicle spatiotemporal trajectory based on the first data and the second data comprises:

3. The method of claim 1, wherein the determining a similarity between the first vehicle spatiotemporal trajectory and the second vehicle spatiotemporal trajectory comprises:

4. The method according to claim 1, wherein the obtaining of the first data around the first vehicle and the second data transmitted by the roadside terminal and/or the second vehicle around the first vehicle comprises:

5. A collaborative analysis method of a 5G mobile Internet of things node is applied to a cloud server, and the method comprises the following steps:

determining a verification result according to the relation between the similarity and a second similarity threshold, and sending the verification result to the first vehicle;

receiving a new verification request sent by any first vehicle, wherein the new verification request comprises a new preliminary determination result of the first vehicle, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory;

establishing a new second vehicle verification space-time trajectory based on all new second data of the obtained historical second vehicle corresponding to the historical second vehicle identification in the new following time period, and determining the similarity between the new first vehicle space-time trajectory and the new second vehicle verification space-time trajectory;

and determining a new verification result according to the relation between the similarity and the second similarity threshold, and sending the new verification result to the first vehicle.

6. The method of claim 5, wherein determining the verification result according to the relationship between the similarity and the second similarity threshold comprises:

7. The utility model provides a collaborative analysis device of 5G mobile internet of things node which characterized in that disposes in vehicle mounted terminal, includes:

the first verification module is used for preliminarily determining that the second vehicle follows the first vehicle if the similarity is greater than or equal to a first similarity threshold, sending a verification request to a cloud server, and receiving a verification result of the cloud server for determining whether the second vehicle follows the first vehicle according to the verification request, wherein the verification request comprises a preliminary determination result, a following time period, a second vehicle identifier, a first vehicle identifier and the first vehicle space-time trajectory;

the data acquisition module is further configured to send a data acquisition request to the roadside terminal and/or the second vehicle, where the data acquisition request carries different information according to a receiving object;

8. The utility model provides a collaborative analysis device of 5G mobile internet of things node, its characterized in that disposes in the high in the clouds server, includes:

the result sending module is used for determining a verification result according to the relation between the similarity and a second similarity threshold value and sending the verification result to the first vehicle;

the request receiving module is further used for receiving a new verification request sent by any first vehicle, wherein the new verification request comprises a new preliminary determination result of the first vehicle, a new following time period, a historical second vehicle identifier, a first vehicle identifier and a new first vehicle space-time trajectory;

the second determination module is further used for establishing a new second vehicle verification space-time trajectory based on all new second data of the obtained historical second vehicle corresponding to the historical second vehicle identification in the new following time period, and determining the similarity between the new first vehicle space-time trajectory and the new second vehicle verification space-time trajectory;

and the result sending module is also used for determining a new verification result according to the relation between the similarity and the second similarity threshold and sending the new verification result to the first vehicle.

9. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the collaborative analysis method of a 5G mobile internet of things node of any of claims 1-6.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the collaborative analysis method of a 5G mobile internet of things node according to any one of claims 1 to 6.