CN112185580A - Travel risk information processing method based on cloud - Google Patents

Travel risk information processing method based on cloud Download PDF

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CN112185580A
CN112185580A CN202010840952.XA CN202010840952A CN112185580A CN 112185580 A CN112185580 A CN 112185580A CN 202010840952 A CN202010840952 A CN 202010840952A CN 112185580 A CN112185580 A CN 112185580A
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刘世杰
童小华
冯永玖
谢欢
陈鹏
魏超
金雁敏
许雄
柳思聪
王超
郭艺友
肖长江
晏雄锋
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Abstract

The invention relates to a cloud-based travel risk information processing method which is characterized by comprising the following steps: acquiring the planned trip information of a user, and extracting all stop points and transfer points in the planned trip information; the method comprises the steps of obtaining the residence time and epidemic situation information of each stop point and each transfer point, and calculating the risk of each stop point and each transfer point based on the obtained information; and sending the calculated risk of each stop point and transfer point to the user terminal. Compared with the prior art, the method has the advantages that the risks of all the stop points and the transfer points in the planned journey of the user are calculated, guidance is provided for the user to go out, the position of the user is positioned in real time, the risk of the current stop point or transfer point is calculated and updated, safety guarantee is provided for the user, the actual journey path of the user is evaluated after the user reaches the destination, epidemic infection risks of the user are effectively evaluated, and the method has an important guidance effect on avoiding the epidemic risks and inhibiting the epidemic expansion.

Description

Travel risk information processing method based on cloud
Technical Field
The invention relates to a travel risk information processing method, in particular to a travel risk information processing method based on a cloud.
Background
In recent years, serious infectious diseases such as SARS, influenza a H1N1, ebola and COVID-2019 are continuously occurring in the world, and the problem of the serious infectious diseases is becoming a serious risk that the human society must prevent and deal with. During the period of stable and normalized epidemic situation prevention and control, the work of re-work and re-study is gradually carried out, the society recovers normal operation, people go out and travel and the like recover to normal, and public transportation is the guarantee of re-work and re-study and is also an important transportation tool for people to go out.
The ideal traffic mode during epidemic situations is self-driving return, which can avoid the concentration of people and reduce the infection risk. However, people usually choose public transportation modes such as airplanes and high-speed rails. However, the passenger is in a closed environment when riding public transportation, and the transportation junction is dense and has high mobility, so that the infection risk is increased. Therefore, travel information monitoring and travel risk information evaluation are necessary during travel of people.
The conventional travel risk assessment mainly assesses the risk of a travel destination or a departure place, and lacks the risk assessment of a stop point and a transfer point of travel personnel on the way.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a travel risk information processing method based on a cloud end.
The purpose of the invention can be realized by the following technical scheme:
a cloud-based travel risk information processing method comprises the following steps:
s1: acquiring the planned trip information of a user, and extracting all stop points and transfer points in the planned trip information;
s2: the method comprises the steps of obtaining the residence time and epidemic situation information of each stop point and each transfer point, and calculating the risk of each stop point and each transfer point based on the obtained information;
s3: and sending the calculated risk of each stop point and transfer point to the user terminal.
Further, the epidemic situation information comprises the epidemic situation in the city epidemic situation grade, the stop point or the transfer point buffer area range.
Further, the step S1 specifically includes the following steps:
s11: creating a form according to a pre-configured template and sending the form to a user terminal, wherein the form is used for collecting the planned travel information of a user;
s12: and receiving form data returned by the user terminal, and extracting all the stop points and the transfer points.
Further, the form data includes a departure place, a destination, a vehicle, a departure time and an arrival time of the vehicle, a transfer point, and a transfer point stay time.
Further, the step S2 includes the following steps:
s21: respectively and sequentially inputting the residence time and epidemic situation information of each residence point and transfer point into a first model;
s22: the first model outputs the risk for each stop point and transfer point in turn.
Further, the first model is a convolutional neural network model.
Further, a cloud-based travel risk information processing method further includes:
s4: and acquiring the position information of the user in real time, recording the stay time of the user at the stay point or the transfer point when the user is positioned at any one of the stay point and the transfer point, acquiring the stay time of the stay point or the transfer point and the current epidemic situation information, calculating the risk of the stay point or the transfer point, and updating.
Further, in step S4, the location information of the user is accurately located and collected by the base station location and GPS technology.
Further, a cloud-based travel risk information processing method further includes:
s5: the method comprises the steps of collecting position information of a user in real time, generating an actual travel path of the user when the user is located at a destination, and outputting risks of the user at each stop point and transfer point by combining planned travel information of the user.
Further, in step S5, the location information of the user is accurately located and collected by the base station location and GPS technology.
Compared with the prior art, the invention has the following beneficial effects:
(1) calculating the risks of all the stop points and the transfer points in the planned journey of the user, providing guidance for the user to go out, positioning the position of the user in real time, calculating and updating the risks of the current stop points or transfer points, providing safety guarantee for the user, evaluating the actual journey path of the user after the user arrives at the destination, effectively evaluating the epidemic infection risk of the user, and having important guidance functions for avoiding the epidemic risk and inhibiting the epidemic expansion.
(2) The planned travel information of the trip personnel is collected on line at the user terminal through the form, so that the user travel information can be collected conveniently and efficiently.
(3) The risk assessment is carried out through the convolutional neural network, data characteristics can be automatically learned from input data so as to replace manual decision making, and the convolutional neural network has strong expression capability and learning capability.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram illustrating the reporting of planned trip information in an embodiment;
FIG. 3 is a diagram of a planned travel route in an embodiment;
FIG. 4 is a schematic illustration of an embodiment of A-GPS location;
FIG. 5 is a schematic diagram of the overall technical route in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1:
in the embodiment, the return journey of the rework and production staff is taken as an example, journey information is collected at a user terminal through a questionnaire star and an App small program, risk assessment is carried out through a convolutional neural network, and a user is positioned in real time through an A-GPS technology.
A cloud-based travel risk information processing method, a flowchart of which is shown in fig. 1, includes:
s1: and acquiring the planned trip information of the user, and extracting all the stop points and transfer points in the planned trip information.
S11: creating a form according to a pre-configured template and sending the form to a user terminal, wherein the form is used for collecting planned trip information of a user, specifically, reporting the planned trip information by using questionnaire stars, APP applets and the like by a trip person one week or two weeks before the trip, and reporting the planned trip based on the questionnaire stars is shown in fig. 2.
S12: receiving form data returned by a user terminal, and extracting all stop points and transfer points, wherein the form data mainly comprises travel time, an intercity traffic mode, an urban traffic mode and a transfer place, and the details are as follows:
1) time: the method mainly comprises planning travel time, starting time and arrival time of an intercity traffic mode to be taken, and if transfer is needed in the middle, the stopping time of a transfer point and the starting time and the arrival time of a transfer car number need to be filled.
2) The traffic mode is as follows: the inter-city traffic modes mainly include airplanes, high-speed rails, motor cars, ordinary trains, buses, ferries and the like, and the carriage numbers and the seat numbers of the vehicles need to be reported. The urban transportation modes comprise subways, public buses, taxis and the like.
3) Transfer points: if the transfer is needed on the way of going out, the location and the residence time of the transfer point need to be reported.
The user terminal generates a planned travel route according to the planned travel information, so that a user can conveniently look up the travel route, and the method specifically comprises the following steps: and based on the Gauss map, obtaining a planned travel path according to the departure point, the transfer point and the vehicle by adopting a shortest path algorithm. The shortest path method selects Dijkstra algorithm.
The Dijkstra algorithm principle is to give a weighted undirected graph G ═ (V, E), where V represents the set of vertices in the graph and E represents the set of edges in the graph containing weights. Each edge (i, j) is marked with a non-negative real number Cj as its weight; and designating a vertex v as a starting point in the undirected graph, solving the minimum path length from the starting point v to other vertices, then gradually updating the vertices and paths corresponding to the vertices, and repeating the operation to know that the final shortest path is found by traversing all the vertices. In order to solve the shortest path, the Dijkstra algorithm proposes a greedy algorithm for gradually generating the shortest path according to the increasing order of the path length. The weights are defined as follows:
Figure BDA0002641395550000041
in the formula, LC [ i ] [ j ] represents the length of side [ i ] [ j ]
The operation steps for solving the shortest path by using the Dijkstra algorithm are as follows:
1) initially, V only comprises a starting point s; e contains vertices other than the start point s, and the distance of a vertex in E is "distance from start point s to the vertex", e.g., the distance of vertex v in E is the length of (s, v), then s and v are not adjacent, then the distance of v is ∞;
2) selecting 'vertex k with the shortest distance' from the E, and adding the vertex k into the V; at the same time, vertex k is removed from vertex set E.
3) And updating the distance from each vertex in the E to the starting point s. The reason for updating the distance of the vertex in E is that k is determined to be the vertex for obtaining the shortest path in the previous step, so that the distance of other vertices can be updated by using k; for example, the distance of (s, v) may be greater than the distance of (s, k) + (k, v).
4) And (3) repeating the steps (2) and (3) until all the vertexes are traversed.
S2: the method comprises the steps of obtaining the residence time and epidemic situation information of each stop point and each transfer point, and calculating the risk of each stop point and each transfer point based on the obtained information;
s21: the residence time and epidemic situation information of each residence point and transfer point are respectively and sequentially input into a first model, and the method specifically comprises the following steps:
the input to the first model includes: the epidemic situation grade of the city where the parking point or the transfer point is located, the epidemic situation condition in a buffer area range set for the parking point or the transfer point, the residence time, the selected traffic mode and the time required by the whole journey, and the information is distributed and mapped to a specific interval through a normalization method and converted into a matrix and vector form.
S22: the first model outputs risks of each stop point and each transfer point in sequence, the first model is a convolutional neural network, and the extraction of the characteristics of input data is mainly performed in a convolutional layer. The convolution layer internally comprises a plurality of convolution kernels, elements forming the convolution kernels comprise weight coefficients and deviation values, and each neuron is connected with a local region of the characteristic surface of the previous layer through the convolution kernels. The convolution layer utilizes local connection and weight sharing, reduces the number of free parameters of the network, and reduces the complexity of the network parameters. The convolutional layer calculation formula is as follows:
X(I+1)＝F(WI+1ΔXI+bI) (2)
in the formula, wherein X(I)And X(I-1)Represents the convolution input and output of the I +1 th layer, WI+1Representing the convolution kernel and b the offset.
The convolutional layer outputs are fused at the fully-connected layer, and the Softmax function is selected as the output classifier. The Softmax function estimates the probability that an input x belongs to a particular class j ∈ k as:
Figure BDA0002641395550000051
the choice of a commonly used modified linear element as the excitation function prevents gradient vanishing and overfitting problems. The modified linear element excitation function is defined as:
∫cov(x)＝max(0，x) (4)
s3: sending the calculated risk of each stop point and transfer point to the user terminal, as shown in fig. 3;
s4: collecting position information of a user in real time, recording the staying time of the user at the staying point or the transfer point when the user is positioned at any one of the staying point and the transfer point, acquiring the staying time of the staying point or the transfer point and current epidemic situation information, calculating the risk of the staying point or the transfer point and updating the risk;
s5: the method comprises the steps of collecting position information of a user in real time, generating an actual travel path of the user when the user is located at a destination, and outputting risks of the user at each stop point and transfer point by combining planned travel information of the user.
The step S4 and the step S5 realize the real-time location positioning of the user by the base station positioning and the GPS technology, i.e., the a-GPS technology, and realize the positioning by the designed location acquisition applet. When the program is used for positioning the actual travel path, the auxiliary GPS (A-GPS) technology is mainly adopted to track the position of the mobile phone, so that the return track of a return person is acquired. The A-GPS technology is a technology for improving the positioning precision by comprehensively utilizing the base station positioning and GPS positioning technologies.
The traditional base station positioning utilizes the measured distance of the base station relative to the mobile phone to determine the position of the mobile phone, but the accuracy of the traditional base station positioning depends on the distribution of the base station and the size of the coverage area, the positioning accuracy is generally within 50 meters, and sometimes the error exceeds one kilometer. In GPS positioning, the distance between a satellite with a known position and a user receiver is measured, and then the data received by a plurality of satellites is combined to determine the specific position of the user receiver. Ideally, the target can be located by using the position and distance data of three pairs of GPS, and the position and distance data of the fourth satellite is generally added to correct the error, so as to improve the position accuracy of the target, which is about 15 meters. However, the positioning accuracy of the GPS satellite is affected by the ionosphere and multipath effects in the atmosphere, resulting in a reduction in the positioning accuracy.
The A-GPS technology combines base station positioning and traditional GPS positioning, utilizes a base station to transmit auxiliary satellite information, and reduces the delay time of a GPS chip in mobile equipment for acquiring satellite signals. And the signal of the base station can be used to make up for the loss of GPS signals in the covered room, and the dependence of a GPS chip on satellites is reduced. The A-GPS technology has wider positioning range and higher speed, and the positioning error is within 5 meters. The a-GPS positioning schematic may be represented as shown in fig. 4.
The overall technical route of the present embodiment is shown in fig. 5.

Claims (10)

1. A travel risk information processing method based on a cloud end is characterized by comprising the following steps:
s1: acquiring the planned trip information of a user, and extracting all stop points and transfer points in the planned trip information;
s2: the method comprises the steps of obtaining the residence time and epidemic situation information of each stop point and each transfer point, and calculating the risk of each stop point and each transfer point based on the obtained information;
s3: and sending the calculated risk of each stop point and transfer point to the user terminal.
2. The cloud-based travel risk information processing method according to claim 1, wherein the epidemic situation information includes an epidemic situation in an urban epidemic situation level, a parking point or a transfer point buffer area.
3. The cloud-based travel risk information processing method according to claim 1, wherein the step S1 specifically includes the following steps:
s11: creating a form according to a pre-configured template and sending the form to a user terminal, wherein the form is used for collecting the planned travel information of a user;
s12: and receiving form data returned by the user terminal, and extracting all the stop points and the transfer points.
4. The cloud-based travel risk information processing method according to claim 3, wherein the form data includes a departure place, a destination, a vehicle, departure time and arrival time of the vehicle, a transfer point and a transfer point stay time.
5. The cloud-based travel risk information processing method according to claim 1, wherein the step S2 includes the following steps:
s21: respectively and sequentially inputting the residence time and epidemic situation information of each residence point and transfer point into a first model;
s22: the first model outputs the risk for each stop point and transfer point in turn.
6. The cloud-based travel risk information processing method according to claim 5, wherein the first model is a convolutional neural network model.
7. The cloud-based travel risk information processing method according to any one of claims 1-6, wherein the method further comprises:
s4: and acquiring the position information of the user in real time, recording the stay time of the user at the stay point or the transfer point when the user is positioned at any one of the stay point and the transfer point, acquiring the stay time of the stay point or the transfer point and the current epidemic situation information, calculating the risk of the stay point or the transfer point, and updating.
8. The cloud-based travel risk information processing method according to claim 8, wherein in step S4, the position information of the user is accurately located and collected by a base station location and a GPS technology.
9. The cloud-based travel risk information processing method according to any one of claims 1-6, wherein the method further comprises:
s5: the method comprises the steps of collecting position information of a user in real time, generating an actual travel path of the user when the user is located at a destination, and outputting risks of the user at each stop point and transfer point by combining planned travel information of the user.
10. The cloud-based travel risk information processing method according to claim 9, wherein in step S5, the position information of the user is accurately located and collected by a base station location and a GPS technology.
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