CN115238206B - Detection point recommendation method and device based on group behavior analysis - Google Patents
Detection point recommendation method and device based on group behavior analysis Download PDFInfo
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Abstract
The invention relates to the technical field of group position analysis, and discloses a group behavior analysis-based detection point recommendation method and device, wherein the method comprises the following steps: predicting the number of people to be epidemic detection at an epidemic detection point based on crowd density information of the epidemic detection point; constructing an epidemic detection time prediction model, and predicting and obtaining the detection completion time of different epidemic detection points; and constructing an epidemic detection point recommendation model, obtaining the time from the current position of the user to be detected to finish epidemic detection of each epidemic detection point, and selecting the optimal epidemic detection point position which accords with the expected finish time of the user and takes the shortest time to recommend to the user to be detected. According to the method, the number of people to be epidemic detection at different epidemic detection points at different moments is rapidly predicted, more accurate epidemic detection completion time is calculated under the condition that waiting time and running time of a user are considered, and the epidemic detection point with highest selection efficiency is detected.
Description
Technical Field
The invention relates to the technical field of group detection analysis, in particular to a detection point recommendation method and device based on group behavior analysis.
Background
The normalized epidemic detection has achieved significant success since the outbreak of epidemic. People often select the most distant place when selecting epidemic detection points, so that a large number of people at partial epidemic detection points are gathered, and the people at partial detection points are rare, and meanwhile, the numbers of people at different epidemic detection points in different time periods are also greatly different, so that the epidemic detection efficiency is seriously affected. Aiming at the problem, the patent provides a method for recommending epidemic detection points, and the epidemic detection efficiency is improved.
Disclosure of Invention
In view of the above, the invention provides a group behavior analysis-based detection point recommendation method, which aims to (1) rapidly predict and obtain the number of people to be epidemic detection at different epidemic detection points at different moments; (2) The time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection is calculated based on the traffic flow congestion degree and the shortest driving road section, so that more accurate epidemic detection finishing time is calculated under the condition of considering the waiting time and the driving time of the user, and the method is favorable for selecting the epidemic detection point with highest efficiency to detect.
The invention provides a group behavior analysis-based detection point recommendation method, which comprises the following steps:
S1: collecting user position information, and predicting the number of people to be epidemic detection at an epidemic detection point based on crowd density information of the epidemic detection point;
s2: constructing an epidemic detection time prediction model, predicting and obtaining the time when detection of different epidemic detection points is completed, wherein the input of the model is the number of people to be epidemic detection at the epidemic detection points, and the input is the time when the detection of the epidemic detection points is completed for the number of people;
s3: establishing an epidemic detection point recommendation model, wherein the input of the model is the current position of a user to be detected, the real-time traffic flow and the travel mode, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection;
s4: and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
As a further improvement of the present invention:
optionally, in the step S1, user location information is collected, and crowd density information of epidemic detection points is calculated, including:
acquiring a user position based on a user mobile phone signal, establishing space position distribution based on epidemic detection points, determining that the detection radiation range of the epidemic detection points is 1km, and taking people in the range of 1km around the epidemic detection points as people to be epidemic detection of the epidemic detection points;
for any epidemic detection point I i Adjacent to the epidemic detection point is I j Will flowDetection point I for pedestrian disease i And epidemic detection point I j Overlapping people to be detected for epidemic as interactors r of two epidemic detection points ij Determining epidemic detection point I based on interactive person i And epidemic detection point I j Density weight w of (2) ij ：
Calculating arbitrary epidemic detection point I i At [ t ] 0 ，t n ]Crowd density information over time:
ρ i (t)＝(w ij (t，n i (t)，t∈[t 0 ，t n ]
wherein:
w ij (t) is epidemic detection point I i The density weight at the time t is given,
ρ i (t) is epidemic detection point I i Crowd density information at time t, n i (t) is epidemic detection point I i Number of people to be epidemic detected at time t.
Optionally, the predicting the number of people to be detected in the step S1 to obtain the epidemic detection point includes:
Constructing a regression model for predicting the number of people to be epidemic detection:
y i (t)＝a[n i (t] 2 +b·w ij (t)·n i (t)+c
wherein:
y i (t) is the predicted epidemic detection point I i The number of people to be epidemic detected at the next moment;
a, b, c are regression coefficients of the regression model;
will arbitrary epidemic detection point I i At [ t ] 0 ，t n ]Crowd density information substitution in time sequence rangeRegression model, regression coefficient a of the regression model is obtained by using least square method * ，b * ，c * And will [ t ] 0 ，t n ]Substituting the number of people to be epidemic detection in the time sequence range into a regression model, and outputting [ t ] by the model n+1 ，…，t n+s ]Epidemic detection point I in time sequence range i For the number of people to be detected in epidemic, wherein
Optionally, in the step S1, calculating parameters in the model for predicting the number of people to be detected for epidemic includes:
the calculation flow of the regression coefficient in the regression model is as follows:
s11: respectively constructing regression coefficients and crowd density information into matrix forms, wherein the matrix forms of the regression coefficients are A= [ a, b, c] A T represents a transpose;
the matrix form of crowd density information is:
s12: constructing a loss function of a regression model based on a least square method:
wherein:
tr (·) represents the trace of the calculation matrix;
s13: calculating partial derivatives of the regression coefficients based on the loss function:
Let the left formula be 0, then the solution of the regression coefficients is: a= (X i T X i ) -1 X i T X i 。
Optionally, constructing an epidemic detection time prediction model in the step S2 includes:
constructing an epidemic detection time prediction model, wherein the input of the model is the number of people to be epidemic detected of an epidemic detection point, and the output is the time for finishing the detection of the epidemic detection point;
the prediction flow of the epidemic detection time prediction model is as follows:
s21: setting the number of people detected by epidemic detection points per minute as m;
s22: calculating arbitrary epidemic detection point I i At future t n+s The number of epidemic detection personnel at the moment is n i (t n+s )；
S23: epidemic detection point I i Detection completion n i (t n+s ) The time t of the number of people to be detected for epidemic disease is n+s +n i (t n+s )/m。
Optionally, in the step S3, an epidemic detection point recommendation model is constructed, where input of the epidemic detection point recommendation model is a current position of a user to be detected, traffic flow is real-time, and a travel mode is output, and the output is a time when the user to be detected reaches each epidemic detection point from the current position to complete epidemic detection, and the method includes:
establishing an epidemic detection point recommendation model, wherein the input of the model is the current position of a user to be detected, the real-time traffic flow and the travel mode, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection;
The epidemic detection point recommendation model comprises an input layer, a calculation layer and an output layer, wherein the input layer receives the current position of a user to be detected, real-time traffic flow and a travel mode, calculates the congestion degree of the traffic flow, is used for determining the time when the user to be detected arrives at an epidemic detection point based on the congestion degree of the real-time traffic flow, and the output layer obtains the number of people to be epidemic detected when the user to be detected arrives at the epidemic detection point and the time after the epidemic detection is completed based on the prediction model, and takes the time as the time when the user to be detected arrives at each epidemic detection point from the current position to complete the epidemic detection;
the expected completion time calculation flow of the epidemic detection point recommendation model is as follows:
s31: the output layer receives the current position of the user to be detected, the real-time traffic flow and the travel mode, calculates the shortest travel section from the user to be detected to different epidemic detection points, and inputs the shortest travel section, the travel mode and the traffic flow congestion degree to the calculation layer, wherein the calculation formula of the traffic flow congestion degree is as follows:
wherein:
representing the traffic flow congestion degree of the driving road section;
q represents the number of traveling vehicles on the traveling road section, and V represents the average traffic speed on the traveling road section;
S32: the calculation layer calculates and obtains the running time of the user to be detected:
wherein:
u is the travel time of the user to be detected on the travel section;
l represents the length of a driving road section, v represents the speed of a travel mode selected by a user to be detected;
epsilon represents the waiting coefficient of the driving road section g L Setting epsilon to 1, wherein epsilon represents the number of traffic lights of a driving road section;
the calculating layer sends the calculated running time of the user to be detected on the running road section to the output layer;
s33: the output layer calculates that the time when the user to be detected arrives at the epidemic detection point is u+R, wherein R is the starting time of the user to be detected, the number of people to be epidemic detection at the epidemic detection point at the time u+R is n (u+R), the time when the user to be detected finishes epidemic detection is u+R+nu+R/m, and m represents the number of people detected by the epidemic detection point per minute.
Optionally, wherein the real-time traffic flow comprises:
the method comprises the steps of detecting the road section of a user reaching an epidemic detection point and the real-time traffic flow of the road section, wherein the traffic flow comprises the number of running vehicles and the average traffic flow speed.
Optionally, in the step S3, training and optimizing the recommended model of the travelling disease detection point to obtain a model after training and optimizing, including:
The training optimization part in the epidemic detection point recommendation model is a shortest driving road section calculation part in the model, and the training optimization flow of the shortest driving road section calculation model is as follows:
building a training objective function:
wherein:
gamma denotes a travel section, distance (gamma) denotes a length of the travel section,representing the traffic flow congestion degree of the driving road section;
generating a plurality of particles to form a particle swarm, placing all particles in the particle swarm at a departure position of a user to be detected, randomly selecting a path node by any particle k in the particle swarm as a position of the user to be detected at the next moment, and repeating the steps until the particles reach a target epidemic detection point to obtain a plurality of driving road sections, wherein the path node is a road corner and a road intersection;
substituting the driving road sections into the training objective function, and selecting the driving road section with the minimum training objective function as the shortest driving road section from the departure point to the epidemic detection point of the user to be detected.
Optionally, in the step S4, the time when the user to be detected reaches each epidemic detection point from the current position to complete the epidemic detection is ordered in ascending order, and the epidemic detection point is recommended to the user to be detected, including:
Inputting the current position of the user to be detected, real-time traffic flow and travel mode into a training optimized epidemic detection point recommendation model, and outputting the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection by the model;
and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time of the user, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, inputting the changed result into an epidemic detection point recommendation model to repeat the step after the user changes the expected time and the traveling mode, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
In order to solve the above problem, the present invention further provides a detection point recommending apparatus based on group behavior analysis, which is characterized in that the apparatus includes:
the epidemic detection personnel number prediction device is used for predicting the number of the people to be epidemic detection at the epidemic detection point based on the crowd density information of the epidemic detection point;
The epidemic detection time calculation module is used for constructing an epidemic detection time prediction model and predicting the detection completion time of different epidemic detection points;
the epidemic detection point recommendation module is used for constructing an epidemic detection point recommendation model, when a user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection is sequenced in ascending order, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, the condition-meeting epidemic detection point is indicated to be absent, the user is reminded of changing the condition, otherwise, the constraint-meeting epidemic detection point is indicated to be present, and the position of the epidemic detection point is recommended to the user.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the epidemic detection point recommendation method based on the group behavior analysis.
In order to solve the above-mentioned problems, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the above-mentioned group behavior analysis-based epidemic detection point recommendation method.
Compared with the prior art, the invention provides a detection point recommending method based on group behavior analysis, and the technology has the following advantages:
firstly, the scheme provides a method for predicting the number of people at epidemic detection points, which comprises the steps of acquiring user positions based on mobile phone signals of users, establishing spatial position distribution based on the epidemic detection points, determining that the detection radiation range of the epidemic detection points is 1km, and taking people in the range of 1km around the epidemic detection points as people to be epidemic detection points of the epidemic detection points; for any epidemic detection point I i Adjacent to the epidemic detection point is I j Detect point I of epidemic disease i And epidemic detection point I j Overlapping people to be detected for epidemic as interactors r of two epidemic detection points ij Determining epidemic detection point I based on interactive person i And epidemic detection point I j Density weight w of (2) ij ：
Calculating arbitrary epidemic detection point I i At [ t ] 0 ，t n ]Crowd density information over time:
ρ i (t)＝(w ij (t)，n i (t)),t∈[t 0 ,t n ]
wherein: w (w) ij (t) is epidemic detection point I i The density weight at the time t is given,ρ i (t) is epidemic detection point I i Crowd density information at time t, n i (t) is epidemic detection point I i Number of people to be epidemic detected at time t. Constructing a regression model for predicting the number of people to be epidemic detection:
y i (t)＝a[n i (t)] 2 +b·w ij (t)·n i (t)+c
Wherein: y is i (t) is the predicted epidemic detection point I i The number of people to be epidemic detected at the next moment; a, b, c are regression coefficients of the regression model; will arbitrary epidemic detection point I i At [ t ] 0 ,t n ]Substituting crowd density information in a time sequence range into a regression model, and obtaining a regression coefficient a of the regression model by using a least square method * ,b * ,c * And will [ t ] 0 ,t n ]Substituting the number of people to be epidemic detection in the time sequence range into a regression model, and outputting [ t ] by the model n+1 ,…,t n+s ]Epidemic detection point I in time sequence range i For the number of people to be detected in epidemic, whereinAccording to the epidemic detection personnel number prediction model, the scheme builds an epidemic detection time prediction model, wherein the input of the model is the number of the epidemic detection personnel to be detected at an epidemic detection point, and the model is output as epidemic diseaseDetecting the time for completing the number of people by the detection point; the prediction flow of the epidemic detection time prediction model is as follows: setting the number of people detected by epidemic detection points per minute as m; calculating arbitrary epidemic detection point I i At future t n+s The number of epidemic detection personnel at the moment is n i (t n+s ) The method comprises the steps of carrying out a first treatment on the surface of the Epidemic detection point I i Detection completion n i (t n+s ) The time t of the number of people to be detected for epidemic disease is n+s +n i (t n+s ) And/m. Compared with the traditional scheme, the method and the device have the advantages that the number of people waiting for epidemic detection at different epidemic detection points at different moments is rapidly predicted based on crowd density and regression models, the time waiting for epidemic detection of a user is predicted and quantified later, and the epidemic detection efficiency of the user is improved.
Meanwhile, the scheme provides an epidemic detection point recommendation method, and an epidemic detection point recommendation model is constructed, wherein the input of the model is the current position of a user to be detected, the traffic flow is real-time, and the travel mode is output as the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection; the epidemic detection point recommendation model comprises an input layer, a calculation layer and an output layer, wherein the input layer receives the current position of a user to be detected, real-time traffic flow and a travel mode, calculates the congestion degree of the traffic flow, is used for determining the time when the user to be detected arrives at an epidemic detection point based on the congestion degree of the real-time traffic flow, and the output layer obtains the number of people to be epidemic detected when the user to be detected arrives at the epidemic detection point and the time after the epidemic detection is completed based on the prediction model, and takes the time as the time when the user to be detected arrives at each epidemic detection point from the current position to complete the epidemic detection; the expected completion time calculation flow of the epidemic detection point recommendation model is as follows: the output layer receives the current position of the user to be detected, the real-time traffic flow and the travel mode, calculates the shortest travel section from the user to be detected to different epidemic detection points, and inputs the shortest travel section, the travel mode and the traffic flow congestion degree to the calculation layer, wherein the calculation formula of the traffic flow congestion degree is as follows:
Wherein:representing the traffic flow congestion degree of the driving road section; q represents the number of traveling vehicles on the traveling road section, and V represents the average traffic speed on the traveling road section;
the calculation layer calculates and obtains the running time of the user to be detected:
wherein: u is the travel time of the user to be detected on the travel section; l represents the length of a driving road section, v represents the speed of a travel mode selected by a user to be detected; epsilon represents the waiting coefficient of the driving road section g L Setting epsilon to 1, wherein epsilon represents the number of traffic lights of a driving road section; the calculating layer sends the calculated running time of the user to be detected on the running road section to the output layer; the output layer calculates that the time when the user to be detected arrives at the epidemic detection point is u+R, wherein R is the starting time of the user to be detected, the number of people to be epidemic detection at the epidemic detection point at the time u+R is n (u+R), the time when the user to be detected finishes epidemic detection is u+R+nu+R/m, and m represents the number of people detected by the epidemic detection point per minute. Inputting the current position of the user to be detected, real-time traffic flow and travel mode into a training optimized epidemic detection point recommendation model, and outputting the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection by the model;
And (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time of the user, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, inputting the changed result into an epidemic detection point recommendation model to repeat the step after the user changes the expected time and the traveling mode, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user. According to the method, the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection is calculated based on the traffic flow congestion degree and the shortest driving road section, so that more accurate epidemic detection finishing time is calculated under the condition of considering the waiting time and the driving time of the user, and the detection is facilitated to be performed by selecting the epidemic detection point with highest efficiency.
Drawings
Fig. 1 is a flow chart of a group behavior analysis-based detection point recommendation method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating one of the steps in the embodiment of FIG. 1;
FIG. 3 is a functional block diagram of a group behavior analysis-based detection point recommendation device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing a method for recommending a detection point of a current environmental change according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a detection point recommending method based on group behavior analysis. The execution subject of the group behavior analysis-based epidemic detection point recommendation method includes, but is not limited to, at least one of a server, a terminal and the like capable of being configured to execute the method provided by the embodiment of the application. In other words, the group behavior analysis-based epidemiological detection point recommendation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
s1: and acquiring user position information, and predicting the number of people to be epidemic detection at the epidemic detection point based on crowd density information of the epidemic detection point.
The step S1 is to collect user position information and calculate crowd density information of epidemic detection points, and comprises the following steps:
acquiring a user position based on a user mobile phone signal, establishing space position distribution based on epidemic detection points, determining that the detection radiation range of the epidemic detection points is 1km, and taking people in the range of 1km around the epidemic detection points as people to be epidemic detection of the epidemic detection points;
for any epidemic detection point I i Adjacent to the epidemic detection point is I j Detect point I of epidemic disease i And epidemic detection point I j Overlapping people to be detected for epidemic as interactors r of two epidemic detection points ij Determining epidemic detection point I based on interactive person i And epidemic detection point I j Density weight w of (2) ij ：
Calculating arbitrary epidemic detection point I i At [ t ] 0 ,t n ]Crowd density information over time:
ρ i (t)＝(w ij (t)，n i (t)，t∈[t 0 ，t n ]
wherein:
w ij (t) is epidemic detection point I i The density weight at the time t is given,
ρ i (t) is epidemic detection point I i Crowd density information at time t, n i (t) is epidemic detection point I i Number of people to be epidemic detected at time t.
And in the step S1, predicting the number of people to be epidemic detection at the epidemic detection point, including:
constructing a regression model for predicting the number of people to be epidemic detection:
y i (t)＝a[n i (t)] 2 +b·w ij (t)·n i (t)+c
wherein:
y i (t) is the predicted epidemic detection point I i The number of people to be epidemic detected at the next moment;
a, b, c are regression coefficients of the regression model;
will arbitrary epidemic detection point I i At [ t ] 0 ,t n ]Substituting crowd density information in a time sequence range into a regression model, and obtaining a regression coefficient a of the regression model by using a least square method * ,b * ,c * And will [ t ] 0 ,t n ]Substituting the number of people to be epidemic detection in the time sequence range into a regression model, and outputting [ t ] by the model n+1 ,…,t n+s ]Epidemic detection point I in time sequence range i For the number of people to be detected in epidemic, wherein
In the step S1, parameters in a prediction model of the number of people to be detected for epidemic disease are calculated, and the method comprises the following steps:
the calculation flow of the regression coefficient in the regression model is as follows:
s11: respectively constructing regression coefficients and crowd density information into matrix forms, wherein the matrix forms of the regression coefficients are A= [ a, b, c] T T represents a transpose;
the matrix form of crowd density information is:
s12: constructing a loss function of a regression model based on a least square method:
Wherein:
tr (·) represents the trace of the calculation matrix;
s13: calculating partial derivatives of the regression coefficients based on the loss function:
let the left formula be 0, then the solution of the regression coefficients is: a= (X i T X i ) -1 X i T Y i 。
S2: and constructing an epidemic detection time prediction model, and predicting and obtaining the time when detection of different epidemic detection points is completed, wherein the input of the model is the number of people to be epidemic detected of the epidemic detection points, and the input is the time when the detection of the epidemic detection points is completed.
And in the step S2, an epidemic detection time prediction model is constructed, which comprises the following steps:
constructing an epidemic detection time prediction model, wherein the input of the model is the number of people to be epidemic detected of an epidemic detection point, and the output is the time for finishing the detection of the epidemic detection point;
in detail, referring to fig. 2, the prediction flow of the epidemic detection time prediction model is as follows:
s21: setting the number m of people detected by epidemic detection points per minute;
s22: calculating the number B of epidemic detection personnel of any epidemic detection point at future time;
s23: the moment when the detection of the epidemic detection points is completed for B number of people to be detected is the current moment +B/m.
S3: and constructing an epidemic detection point recommendation model, wherein the input of the model is the current position of the user to be detected, the real-time traffic flow and the travel mode, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection.
In the step S3, an epidemic detection point recommendation model is constructed, wherein the input of the epidemic detection point recommendation model is the current position of a user to be detected, the real-time traffic flow and the travel mode are output as the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection, and the method comprises the following steps:
establishing an epidemic detection point recommendation model, wherein the input of the model is the current position of a user to be detected, the real-time traffic flow and the travel mode, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection;
the epidemic detection point recommendation model comprises an input layer, a calculation layer and an output layer, wherein the input layer receives the current position of a user to be detected, real-time traffic flow and a travel mode, calculates the congestion degree of the traffic flow, is used for determining the time when the user to be detected arrives at an epidemic detection point based on the congestion degree of the real-time traffic flow, and the output layer obtains the number of people to be epidemic detected when the user to be detected arrives at the epidemic detection point and the time after the epidemic detection is completed based on the prediction model, and takes the time as the time when the user to be detected arrives at each epidemic detection point from the current position to complete the epidemic detection;
The expected completion time calculation flow of the epidemic detection point recommendation model is as follows:
s31: the output layer receives the current position of the user to be detected, the real-time traffic flow and the travel mode, calculates the shortest travel section from the user to be detected to different epidemic detection points, and inputs the shortest travel section, the travel mode and the traffic flow congestion degree to the calculation layer, wherein the calculation formula of the traffic flow congestion degree is as follows:
wherein:
representing the traffic flow congestion degree of the driving road section;
q represents the number of traveling vehicles on the traveling road section, and V represents the average traffic speed on the traveling road section;
s32: the calculation layer calculates and obtains the running time of the user to be detected:
wherein:
u is the travel time of the user to be detected on the travel section;
l represents the length of a driving road section, v represents the speed of a travel mode selected by a user to be detected;
epsilon represents the waiting coefficient of the driving road section g L Setting epsilon to 1, wherein epsilon represents the number of traffic lights of a driving road section;
the calculating layer sends the calculated running time of the user to be detected on the running road section to the output layer;
s33: the output layer calculates that the time when the user to be detected arrives at the epidemic detection point is u+R, wherein R is the starting time of the user to be detected, the number of people to be epidemic detection at the epidemic detection point at the time u+R is n (u+R), the time when the user to be detected finishes epidemic detection is u+R+nu+R/m, and m represents the number of people detected by the epidemic detection point per minute.
Wherein the real-time traffic flow comprises:
the method comprises the steps of detecting the road section of a user reaching an epidemic detection point and the real-time traffic flow of the road section, wherein the traffic flow comprises the number of running vehicles and the average traffic flow speed.
In the step S3, training and optimizing the recommended model of the flow disease detection point to obtain a model after training and optimizing, wherein the method comprises the following steps:
the training optimization part in the epidemic detection point recommendation model is a shortest driving road section calculation part in the model, and the training optimization flow of the shortest driving road section calculation model is as follows:
building a training objective function:
wherein:
gamma denotes a travel section, distance (gamma) denotes a length of the travel section,representing the traffic flow congestion degree of the driving road section;
generating a plurality of particles to form a particle swarm, placing all particles in the particle swarm at a departure position of a user to be detected, randomly selecting a path node by any particle k in the particle swarm as a position of the user to be detected at the next moment, and repeating the steps until the particles reach a target epidemic detection point to obtain a plurality of driving road sections, wherein the path node is a road corner and a road intersection;
substituting the driving road sections into the training objective function, and selecting the driving road section with the minimum training objective function as the shortest driving road section from the departure point to the epidemic detection point of the user to be detected.
S4: and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
In the step S4, the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection is sequenced in ascending order, and the epidemic detection point is recommended to the user to be detected, including:
inputting the current position of the user to be detected, real-time traffic flow and travel mode into a training optimized epidemic detection point recommendation model, and outputting the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection by the model;
and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time of the user, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, inputting the changed result into an epidemic detection point recommendation model to repeat the step after the user changes the expected time and the traveling mode, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
Example 2:
fig. 3 is a functional block diagram of an epidemic detection point recommendation device based on group behavior analysis according to an embodiment of the present invention, which can implement the method for recommending an epidemic detection point in embodiment 1.
The epidemic detection point recommendation device 100 based on group behavior analysis can be installed in an electronic device. Depending on the functions implemented, the group behavior analysis-based epidemiological detection point recommendation device may include a number of epidemic detection personnel prediction device 101, an epidemic detection time calculation module 102, and an epidemic detection point recommendation module 103. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
Epidemic detection personnel number prediction means 101 for predicting the number of people to be epidemic detection personnel to obtain an epidemic detection point based on crowd density information of the epidemic detection point;
the epidemic detection time calculation module 102 is configured to construct an epidemic detection time prediction model, and predict and obtain the time when detection of different epidemic detection points is completed;
The epidemic detection point recommendation module 103 is configured to construct an epidemic detection point recommendation model, when a user to be detected arrives at each epidemic detection point from the current position to complete epidemic detection, sort the times when the user to be detected arrives at each epidemic detection point from the current position to complete epidemic detection in ascending order, if the time when the user arrives at the first epidemic detection point to complete epidemic detection is greater than the expected time, indicate that no epidemic detection point meeting the condition exists, and remind the user to change the condition, otherwise indicate that there is a epidemic detection point meeting the constraint, and recommend the position of the epidemic detection point to the user.
In detail, the modules in the group behavior analysis-based epidemic detection point recommendation apparatus 100 in the embodiment of the present invention use the same technical means as the group behavior analysis-based epidemic detection point recommendation method described in fig. 1, and can generate the same technical effects, which are not described herein.
Example 3:
fig. 4 is a schematic structural diagram of an electronic device for implementing a group behavior analysis-based epidemic detection point recommendation method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (programs 12, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 4 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
collecting user position information, and predicting the number of people to be epidemic detection at an epidemic detection point based on crowd density information of the epidemic detection point;
constructing an epidemic detection time prediction model, and predicting and obtaining the detection completion time of different epidemic detection points;
Constructing an epidemic detection point recommendation model;
and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 4, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. A group behavior analysis-based detection point recommendation method, the method comprising:
S1: collecting user position information, and predicting the number of people to be epidemic detection at an epidemic detection point based on crowd density information of the epidemic detection point;
s2: constructing an epidemic detection time prediction model, predicting and obtaining the time when detection of different epidemic detection points is completed, wherein the input of the model is the number of people to be epidemic detection at the epidemic detection points, and the input is the time when the detection of the epidemic detection points is completed for the number of people;
s3: establishing an epidemic detection point recommendation model, wherein the input of the model is the current position of a user to be detected, the real-time traffic flow and the travel mode are input, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection, and the method comprises the following steps:
establishing an epidemic detection point recommendation model, wherein the input of the model is the current position of a user to be detected, the real-time traffic flow and the travel mode, and the output is the time for the user to be detected to reach each epidemic detection point from the current position to finish epidemic detection;
the epidemic detection point recommendation model comprises an input layer, a calculation layer and an output layer, wherein the input layer receives the current position of a user to be detected, real-time traffic flow and a traveling mode, calculates the congestion degree of the traffic flow, the calculation layer is used for determining the time when the user to be detected arrives at an epidemic detection point based on the congestion degree of the real-time traffic flow, the output layer obtains the number of people to be detected when the user to be detected arrives at the epidemic detection point and the time after the epidemic detection of the number of people to be detected is completed based on the epidemic detection time prediction model, and the sum of the time after the epidemic detection of the number of people to be detected and the time when the user to be detected arrives at the epidemic detection point is used as the time when the user to be detected arrives at each epidemic detection point from the current position to complete the epidemic detection;
S4: the method comprises the steps that the time when a user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection is sequenced in an ascending order, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, the user is reminded of changing the condition without the epidemic detection point meeting the condition, otherwise, the user is reminded of changing the condition, and if the user does not know that the epidemic detection point meeting the constraint exists, the position of the epidemic detection point is recommended to the user;
the calculating of crowd density information of epidemic detection points in the step S1 comprises the following steps:
acquiring a user position based on a user mobile phone signal, establishing space position distribution based on epidemic detection points, determining that the detection radiation range of the epidemic detection points is 1km, and taking people in the range of 1km around the epidemic detection points as people to be epidemic detection of the epidemic detection points;
for any epidemic detection point I i Adjacent to the epidemic detection point is I j Detect point I of epidemic disease i And epidemic detection point I j Overlapping people to be detected for epidemic as interactors r of two epidemic detection points ij Determining epidemic detection point I based on interactive person i And epidemic detection point I j Density weight w of (2) ij ：
Calculating arbitrary epidemic detection point I i At [ t ] 0 ，t n ]Crowd density information over time:
ρ i (t)＝(w ij (t)，n i (t))，t∈[t 0 ，t n ]
wherein:
w ij (t) is epidemic detection point I i The density weight at the time t is given,
ρ i (t) is epidemic detection point I i Crowd density information at time t, n i (t) is epidemic detection point I i Number of people to be epidemic detected at time t.
2. The method for recommending detection points based on group behavior analysis according to claim 1, wherein the predicting the number of people to be detected for obtaining the epidemic detection point in step S1 comprises:
constructing a regression model for predicting the number of people to be epidemic detection:
y i (t)＝a[n i (t)] 2 +b·w ij (t)·n i (t)+c
wherein:
y i (t) is the predicted epidemic detection point I i The number of people to be epidemic detected at the next moment;
a, b, c are regression coefficients of the regression model;
will arbitrary epidemic detection point I i At [ t ] 0 ，t n ]Substituting crowd density information in a time sequence range into a regression model, and obtaining a regression coefficient a of the regression model by using a least square method * ，b * ，c * And will [ t ] 0 ，t n ]Substituting the number of people to be epidemic detection in the time sequence range into a regression model, and outputting [ t ] by the model n+1 ，...，t n+s ]Epidemic detection point I in time sequence range i For the number of people to be detected in epidemic, wherein
3. The method for recommending detection points based on group behavior analysis according to claim 2, wherein,
The calculation flow of the regression coefficient in the regression model is as follows:
s11: respectively constructing regression coefficients and crowd density information into matrix forms, wherein the matrix forms of the regression coefficients are A= [ a, b, c] T T represents a transpose;
the matrix form of crowd density information is:
s12: constructing a loss function of a regression model based on a least square method:
wherein:
tr (·) represents the trace of the calculation matrix;
s13: calculating partial derivatives of the regression coefficients based on the loss function:
let the left formula be 0, then the solution of the regression coefficients is: a= (X i T X i ) -1 X i T Y i 。
4. The method for recommending detection points based on group behavior analysis according to claim 1, wherein the constructing an epidemic detection time prediction model in step S2 comprises:
constructing an epidemic detection time prediction model, wherein the input of the model is the number of people to be epidemic detected of an epidemic detection point, and the output is the time for finishing the detection of the epidemic detection point;
the prediction flow of the epidemic detection time prediction model is as follows:
s21: setting the number of people detected by epidemic detection points per minute as m;
s22: calculating arbitrary epidemic detection point I i At future t n+s The number of epidemic detection personnel at the moment is n i (t n+s )；
S23: epidemic detection point I i Detection completion n i (t n+s ) The time t of the number of people to be detected for epidemic disease is n+s +n i (t n+s )/m。
5. The group behavior analysis based detection point recommendation method of claim 1, wherein the real-time traffic flow comprises:
the method comprises the steps of detecting the road section of a user reaching an epidemic detection point and the real-time traffic flow of the road section, wherein the traffic flow comprises the number of running vehicles and the average traffic flow speed.
6. The method for recommending detection points based on group behavior analysis according to claim 1, wherein the step S3 further comprises: training and optimizing the epidemic detection point recommendation model to obtain a model after training and optimizing, specifically:
the training optimization part in the epidemic detection point recommendation model is a shortest driving road section calculation part in the model, and the training optimization flow of the shortest driving road section calculation model is as follows:
building a training objective function:
wherein:
gamma denotes a travel section, distance (gamma) denotes a length of the travel section,representing the traffic flow congestion degree of the driving road section;
generating a plurality of particles to form a particle swarm, placing all particles in the particle swarm at a departure position of a user to be detected, randomly selecting a path node by any particle k in the particle swarm as a position of the user to be detected at the next moment, and repeating the steps until the particles reach a target epidemic detection point to obtain a plurality of driving road sections, wherein the path node is a road corner and a road intersection;
Substituting the driving road sections into the training objective function, and selecting the driving road section with the minimum training objective function as the shortest driving road section from the departure point to the epidemic detection point of the user to be detected.
7. The group behavior analysis-based detection point recommendation method according to claim 6, wherein in the step S4, the time when the user to be detected reaches each epidemic detection point from the current position to complete the epidemic detection is sorted in ascending order, and the epidemic detection point is recommended to the user to be detected, including:
inputting the current position of the user to be detected, real-time traffic flow and travel mode into a training optimized epidemic detection point recommendation model, and outputting the time when the user to be detected reaches each epidemic detection point from the current position to finish epidemic detection by the model;
and (3) carrying out ascending sorting on the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time of the user, indicating that no epidemic detection point meeting the condition exists, reminding the user to change the condition, inputting the changed result into an epidemic detection point recommendation model to repeat the step after the user changes the expected time and the traveling mode, otherwise, indicating that the epidemic detection point meeting the constraint exists, and recommending the position of the epidemic detection point to the user.
8. A group behavior analysis-based detection point recommendation device, the device comprising:
the epidemic detection personnel number prediction device is used for predicting the number of the people to be epidemic detection at the epidemic detection point based on the crowd density information of the epidemic detection point;
the epidemic detection time calculation module is used for constructing an epidemic detection time prediction model and predicting the detection completion time of different epidemic detection points;
the epidemic detection point recommendation module is used for constructing an epidemic detection point recommendation model, when a user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection, the time when the user to be detected arrives at each epidemic detection point from the current position to finish epidemic detection is sequenced in ascending order, if the time when the user arrives at the first epidemic detection point to finish epidemic detection is longer than the expected time, the condition is not met, the user is reminded to change the condition, otherwise, the condition is met, the position of the epidemic detection point is recommended to the user, and the group behavior analysis-based detection point recommendation method is realized according to any one of claims 1-7.
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