CN118450333B - People stream track measuring and calculating method and system based on millimeter wave base station - Google Patents
People stream track measuring and calculating method and system based on millimeter wave base station Download PDFInfo
- Publication number
- CN118450333B CN118450333B CN202410905265.XA CN202410905265A CN118450333B CN 118450333 B CN118450333 B CN 118450333B CN 202410905265 A CN202410905265 A CN 202410905265A CN 118450333 B CN118450333 B CN 118450333B
- Authority
- CN
- China
- Prior art keywords
- base station
- millimeter wave
- wave base
- target
- mobile terminal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 239000011159 matrix material Substances 0.000 claims abstract description 29
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 28
- 238000004891 communication Methods 0.000 claims abstract description 19
- 239000013598 vector Substances 0.000 claims description 17
- 238000001228 spectrum Methods 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 10
- 238000000691 measurement method Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 description 13
- 238000004590 computer program Methods 0.000 description 10
- 230000003287 optical effect Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000000593 degrading effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 208000035473 Communicable disease Diseases 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000006806 disease prevention Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention relates to a traffic track measuring and calculating method and a traffic track measuring and calculating system based on a millimeter wave base station, which belong to the technical field of communication, and the method comprises the steps of obtaining a target signal received by the millimeter wave base station; the method comprises the steps of constructing a signal covariance matrix based on target signals, inputting the signal covariance matrix into a pre-trained target convolutional neural network model, and outputting a target angle, wherein the target angle represents the angle of a receiving antenna of a millimeter wave base station and the target signal in the vertical direction, determining the horizontal distance between the millimeter wave base station and a mobile terminal based on the target signals and the target angle, and calculating a people flow track corresponding to the mobile terminal based on the horizontal distance and an orientation angle, wherein the orientation angle is the direction angle of the receiving antenna corresponding to the mobile terminal with the millimeter wave base station as the center. The method has higher measuring and calculating precision, is less affected by the environment, can not capture the personal image, and has wide applicability.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly provides a traffic track measuring and calculating method and system based on a millimeter wave base station.
Background
The detection of the flowing signs of the crowd is of great significance in public security, city planning and commercial retail directions.
In public safety, monitoring the movement track of the crowd can help to prevent and manage emergencies such as terrorist attacks, fire and trampling events, infectious disease prevention and treatment and the like, and timely take countermeasures to ensure the safety of the crowd. In city planning and management, the crowd track data can provide scientific basis for traffic flow management, public facility layout, business area planning and the like, and improve city operation efficiency and resident life quality. In the business and retail fields, analyzing the crowd trajectories can help merchants to learn consumer behavior and preferences, optimize store site selection and marketing strategies, and thereby promote sales performance and customer satisfaction.
In the traditional field of people stream monitoring, common methods include optical video analysis and infrared sensors. Although these two technologies are widely favored because of their maturity and popularity, they still have non-negligible limitations in practical applications. The optical video analysis technology, which is one of the main means of monitoring, often has performance affected by illumination conditions. In the night or under the environment of insufficient light, the monitoring effect is greatly reduced, and the accuracy of data is difficult to ensure. In addition, once there is a line of sight obstruction, such as a tree, building, or other obstruction, the video analytics system cannot capture a complete picture, thereby reducing the reliability of the monitoring. While infrared sensors can overcome limitations of lighting conditions to some extent, their performance is susceptible to environmental temperature fluctuations. For example, in an environment where temperature is rapidly changed, the infrared sensor may be subjected to false alarm or false omission, thereby degrading the accuracy of monitoring.
Furthermore, both of the above techniques typically involve the capture of personal images, which in some cases may raise public concerns about privacy protection. Along with the importance of the society on personal privacy protection, how to balance people flow track monitoring requirements and privacy protection becomes a problem to be solved urgently.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a people flow track measuring and calculating method and system based on a millimeter wave base station.
In a first aspect, an embodiment of the present application provides a traffic track measurement method based on a millimeter wave base station, which is applied to a millimeter wave base station, where the millimeter wave base station has a plurality of antennas arranged in an array, and the plurality of antennas are uniformly distributed around a main body of the millimeter wave base station, and the method includes:
acquiring a target signal received by a millimeter wave base station, wherein the target signal is a return signal of a mobile terminal in a communication range of the millimeter wave base station;
Constructing a signal covariance matrix based on the target signal;
Inputting the signal covariance matrix into a target convolutional neural network model which is trained in advance, and outputting a target angle, wherein the target angle represents the angle of the target signal and a receiving antenna of the millimeter wave base station in the vertical direction;
determining a horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle;
and calculating the people flow track corresponding to the mobile terminal based on the horizontal distance and the orientation angle, wherein the orientation angle is the direction angle of the receiving antenna corresponding to the mobile terminal with the millimeter wave base station as the center.
Optionally, training the target convolutional neural network model by:
Acquiring a sample signal, wherein the sample signal is a return signal of a sample mobile terminal in a communication range of the millimeter wave base station;
Constructing a sample signal covariance matrix based on the sample signals;
Optimizing a weight vector based on a Lagrangian multiplier to obtain an optimal weight vector, wherein the optimization strategy is to minimize the output power of the sample signal and keep the gain of the required signal direction unchanged;
constructing a power spectrum based on the optimal weight vector;
And taking the sample signal covariance matrix as input of an initial convolutional neural network model, taking the power spectrum as a label, and training the initial convolutional neural network model to obtain the target convolutional neural network model.
Optionally, the target convolutional neural network model includes two convolutional layers with a ReLU function, one BN layer, and one fully connected layer.
Optionally, the determining the horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle includes:
Determining an arrival time of the target signal based on the target signal;
determining a linear distance between the millimeter wave base station and the mobile terminal based on the arrival time of the target signal;
The horizontal distance is determined based on the target angle and the straight line distance.
Optionally, determining the orientation angle comprises:
Sequentially numbering the plurality of antennas and determining a starting antenna, wherein the number of the starting antenna is 0, and the starting antenna is any antenna of the plurality of antennas;
determining an access antenna of the target signal;
the orientation angle is determined based on a difference between the number of the access antenna and the starting antenna, and a total number of the plurality of antennas.
Optionally, the method further comprises:
Acquiring a plurality of position nodes of the mobile terminal in a preset time period, wherein each position node comprises a horizontal distance and an orientation angle between the mobile terminal and the millimeter wave base station under the current time node;
clustering a plurality of position nodes of the mobile terminal in the preset time period to obtain a plurality of clusters;
Constructing a circle with a set radius by taking the geometric center point of each cluster as the circle center;
And measuring and calculating the people flow track by taking the circle as an individual.
Optionally, the setting radius is determined by the steps comprising:
Obtaining the distance from the geometric center point to each position node in the cluster;
and determining the maximum value of the distance between the geometric center point in the cluster and the nodes at the rest positions as the set radius.
Optionally, the clustering the plurality of location nodes of the mobile terminal in the preset time period to obtain a plurality of clusters includes:
and clustering a plurality of position nodes of the mobile terminal in the preset time period by adopting a self-adaptive elliptic distance density method to obtain a plurality of clusters.
Optionally, the method further comprises:
and accessing the people stream track to a third party map by taking the millimeter wave base station as a reference point, and visually displaying the people stream track.
In a second aspect, the present application provides a traffic track measurement system based on a millimeter wave base station, applied to a millimeter wave base station, the millimeter wave base station having a plurality of antennas arranged in an array, the plurality of antennas being uniformly distributed around a main body of the millimeter wave base station, the system comprising:
the acquisition module is used for acquiring a target signal received by a millimeter wave base station, wherein the target signal is a return signal of a mobile terminal in a communication range of the millimeter wave base station;
the construction module is used for constructing a signal covariance matrix based on the target signal;
The prediction module is used for inputting the signal covariance matrix into a target convolutional neural network model which is trained in advance and outputting a target angle, wherein the target angle represents the angle of the target signal and a receiving antenna of the millimeter wave base station in the vertical direction;
a determining module, configured to determine a horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle;
And the measuring and calculating module is used for measuring and calculating the people flow track corresponding to the mobile terminal based on the horizontal distance and the orientation angle, wherein the orientation angle is a direction angle with the millimeter wave base station as the center and the receiving antenna corresponding to the mobile terminal is positioned.
The beneficial effects of the invention include:
firstly, the people flow track measuring and calculating method provided by the application utilizes millimeter wave base station resources, and compared with the optical video analysis and infrared sensors in the prior art, the method is less affected by the environment, namely the accuracy and the reliability of people flow track measurement and calculation are better. Meanwhile, the personal image is not captured, so that the personal privacy safety can be protected;
In the second embodiment of the application, when the traffic track is calculated, a signal covariance matrix is firstly constructed based on the target signal, then the angle between the target signal and the received antenna in the vertical direction is obtained by utilizing the target convolutional neural network model which is trained in advance, and then the horizontal distance between the millimeter wave base station and the mobile terminal can be effectively calculated by utilizing the angle, and then the orientation angle of the mobile terminal is determined by taking the millimeter wave base station as the center. The method is simple and efficient, and has high measuring and calculating precision.
In summary, the people flow track measuring and calculating method based on the millimeter wave base station provided by the application provides a brand new people flow track measuring and calculating method, which has higher measuring and calculating precision, is less affected by environment, can not capture personal images, and has wide applicability.
Drawings
Fig. 1 is a step flowchart of a traffic track measurement method based on a millimeter wave base station according to an embodiment of the present invention.
Fig. 2 is a flowchart of steps of another method for measuring and calculating a traffic track based on a millimeter wave base station according to an embodiment of the present invention.
Fig. 3 is a block diagram of a traffic track measurement system based on a millimeter wave base station according to an embodiment of the present invention.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Currently, in the traditional field of people stream monitoring, common methods include optical video analysis and infrared sensors. The optical video analysis technology, which is one of the main means of monitoring, often has performance affected by illumination conditions. In the night or under the environment of insufficient light, the monitoring effect is greatly reduced, and the accuracy of data is difficult to ensure. In addition, once there is a line of sight obstruction, such as a tree, building, or other obstruction, the video analytics system cannot capture a complete picture, thereby reducing the reliability of the monitoring. While infrared sensors can overcome limitations of lighting conditions to some extent, their performance is susceptible to environmental temperature fluctuations. For example, in an environment where temperature is rapidly changed, the infrared sensor may be subjected to false alarm or false omission, thereby degrading the accuracy of monitoring. Furthermore, both of the above techniques typically involve the capture of personal images, which in some cases may raise public concerns about privacy protection. Along with the importance of the society on personal privacy protection, how to balance people flow track monitoring requirements and privacy protection becomes a problem to be solved urgently.
In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.
Referring to fig. 1, an embodiment of the present application provides a traffic track measurement method based on a millimeter wave base station, which is applied to the millimeter wave base station, wherein the millimeter wave base station has a plurality of antennas arranged in an array, and the plurality of antennas are uniformly distributed around a main body of the millimeter wave base station, and the method includes steps 101 to 105.
Step 101, obtaining a target signal received by a millimeter wave base station.
The target signal is a return signal of the mobile terminal in the communication range of the millimeter wave base station.
The communication system based on the traffic track measuring and calculating method of the millimeter wave base station is described below, and comprises a service end and a client end, wherein the service end refers to the millimeter wave base station used for communication, and the client end refers to a mobile terminal used for user communication. It should be noted that, after receiving the signal from the server, the ue returns the signal to the server.
Step 102, constructing a signal covariance matrix based on the target signal.
Here, the target signal may be denoised before constructing the signal covariance matrix based on the target signal. Conventional noise handling may be used herein.
And 103, inputting the signal covariance matrix into a target convolutional neural network model which is trained in advance, and outputting a target angle.
The target angle represents the angle between the target signal and the receiving antenna of the millimeter wave base station in the vertical direction.
That is, in the embodiment of the present application, the target convolutional neural network model is trained in advance, and the network model can output the angle (target angle) of the target signal and the receiving antenna of the millimeter wave base station in the vertical direction through the input signal covariance matrix corresponding to the target signal.
It should be noted that, the millimeter wave base station has a plurality of antennas arranged in an array, and the plurality of antennas are uniformly distributed around the main body of the millimeter wave base station, for example, the plurality of antennas may be distributed around the main body of the millimeter wave base station like a circumference, that is, the plurality of antennas enclose a circle for receiving signals of different angles. For example, antenna a receives the target signal. When the antenna a receives the target signal, the target angle characterizes the angle of the target signal and the antenna a in the vertical direction.
And 104, determining the horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle.
Because the height of the antenna of the millimeter wave base station is relatively common in the configuration of about 35-55 meters, in order to obtain an accurate people flow track, the horizontal distance between the millimeter wave base station and the mobile terminal is determined by combining the target angle output by the network model.
And 105, calculating the people flow track corresponding to the mobile terminal based on the horizontal distance and the orientation angle.
The orientation angle is the direction angle of the receiving antenna corresponding to the mobile terminal by taking the millimeter wave base station as the center.
And finally, calculating the people flow track corresponding to the mobile terminal through the horizontal distance between the mobile terminal and the millimeter wave base station and the orientation angle of the mobile terminal.
In summary, the traffic track measuring and calculating method based on the millimeter wave base station provided by the embodiment of the application has the following beneficial effects that:
Firstly, the people flow track measuring and calculating method provided by the embodiment of the application utilizes millimeter wave base station resources, and compared with the optical video analysis and infrared sensors in the prior art, the method is less affected by the environment, namely the accuracy and the reliability of people flow track measuring and calculating are better. Meanwhile, the personal image is not captured, so that the personal privacy safety can be protected;
In the second embodiment of the application, when the traffic track is calculated, a signal covariance matrix is firstly constructed based on the target signal, then the angle between the target signal and the received antenna in the vertical direction is obtained by utilizing the target convolutional neural network model which is trained in advance, and then the horizontal distance between the millimeter wave base station and the mobile terminal can be effectively calculated by utilizing the angle, and then the orientation angle of the mobile terminal is determined by taking the millimeter wave base station as the center. The method is simple and efficient, and has high measuring and calculating precision.
In summary, the people flow track measuring and calculating method based on the millimeter wave base station provided by the embodiment of the application provides a brand new people flow track measuring and calculating method, which has higher measuring and calculating precision, is less affected by environment, can not capture personal images, and has wide applicability.
For the above steps, the formula parameters involved are as follows:
first, the expression of the target signal received by the millimeter wave base station is: Wherein, the method comprises the steps of, Representing the target signal received by the millimeter wave base station,A signal representing the kth antenna at the t-th moment,Representing gaussian additive white noiseIs thatCorresponding to the steering vector.
Wherein, ;The angle between the target signal and the vertical direction (which is the parameter to be solved) when the target signal reaches the kth antenna of the millimeter wave base station is represented, d represents the linear distance (which can be obtained by time calculation) from the mobile terminal to the millimeter wave base station, lambda represents the wavelength of the target signal, and j represents the imaginary unit.
The signal covariance matrix is calculated as follows:
;
Wherein the method comprises the steps of Representing the computational mathematical expectation that,The conjugate transpose is calculated, and M represents the total number of antennas.
Optionally, referring to FIG. 2, the target convolutional neural network model is trained by steps including steps 201-205.
Step 201, acquiring a sample signal.
Wherein, the sample signal is the feedback signal of the sample mobile terminal in the communication range of the millimeter wave base station. For the description of the sample signal, reference may be made to the description of the target signal in the foregoing embodiment, which is not repeated here.
Step 202, constructing a sample signal covariance matrix based on the sample signal.
Similarly, the construction of the signal covariance matrix may refer to the description of the signal covariance matrix in the foregoing embodiment, and will not be repeated herein.
And 203, optimizing the weight vector based on the Lagrangian multiplier to obtain an optimal weight vector.
The optimal weight vector is associated with the angle of the sample signal and the corresponding receiving antenna in the vertical direction.
The calculation formula of the weight vector is as follows:
;
wherein, Is the inverse of the signal covariance matrix; Is that Corresponding steering vectors. H represents the calculated conjugate transpose.
By the above-described optimization strategy, it is possible to calculate a steering vector corresponding to the angle of the sample signal and the corresponding receiving antenna in the vertical direction.
And 204, constructing a power spectrum based on the optimal weight vector.
The calculation formula of the power spectrum is as follows:
;
wherein, Is a transpose of w.
And 205, taking the sample signal covariance matrix as input of the initial convolutional neural network model, taking the power spectrum as a label, and training the initial convolutional neural network model to obtain a target convolutional neural network model.
That is, the model is made to learn to output power spectrum based on the covariance matrix of the sample signal, and the power spectrum can be obtainedI.e. the target angle.
In summary, the embodiment of the application optimizes the weight vector based on the Lagrangian multiplier to obtain the optimal weight vector, then utilizes the optimal weight vector to construct the power spectrum, and further trains the initial convolutional neural network model by combining the covariance moment of the sample signal and the power spectrum, so that the initial convolutional neural network model can learn and calculate the target angle, thereby facilitating the follow-up realization of accurate measurement and calculation of the people flow track.
Optionally, the target convolutional neural network model includes two convolutional layers with a ReLU function, one BN layer, and one fully connected layer.
Optionally, the step of determining the horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle comprises determining the arrival time of the target signal based on the target signal, determining the linear distance between the millimeter wave base station and the mobile terminal based on the arrival time of the target signal, and determining the horizontal distance based on the target angle and the linear distance.
Specifically, the calculation formula of the horizontal distance is:
;
Wherein l represents a horizontal distance, d represents a straight line distance, Representing the target angle. Through the process, the horizontal distance between the millimeter wave base station and the mobile terminal is accurately calculated.
Optionally, the orientation angle is determined by sequentially numbering a plurality of antennas and determining a starting antenna, wherein the number of the starting antenna is 0, the starting antenna is any antenna of the plurality of antennas, determining an access antenna of the target signal, and determining the orientation angle based on the difference between the number of the access antenna and the starting antenna and the total number of the plurality of antennas.
The antennas are distributed on the main body of the millimeter wave base station like a circle, namely, the antennas enclose a circle and are used for receiving signals at different angles. Here, any one of the antennas is used as a starting antenna and is numbered 0. Angle at which other antennas are locatedIs equal to. Where K represents the kth antenna and M represents the total number of antennas.
Finally, one location node of the mobile terminal may be obtained as。
Optionally, the method further comprises the steps of obtaining a plurality of position nodes of the mobile terminal in a preset time period, wherein each position node comprises the horizontal distance and the orientation angle of the mobile terminal under the current time node and the millimeter wave base station, clustering the plurality of position nodes of the mobile terminal in the preset time period to obtain a plurality of clusters, constructing a circle with a set radius by taking the geometric center point of each cluster as the circle center, and measuring and calculating the traffic track by taking the circle as an individual.
The preset time period may be set according to actual requirements, such as 10 minutes, 5 minutes, 1 minute, and the like.
Considering that the user can have the error influence on the people flow track measurement caused by the fact that the user is not moving but the mobile terminal moves up and down and left and right in the process of holding the mobile terminal. Therefore, in the embodiment of the application, clustering is performed through the position nodes, and the circles corresponding to the clusters are used for representing the mobile individuals to calculate the people flow track, so that the influence of errors caused by the movement of the hand-held communication tool (namely the mobile terminal) can be eliminated.
Specifically, the position of the mobile terminal at each moment is taken as a particle, the movement of the mobile terminal represents the movement of a person, and the particle with a fixed time period is takenCorresponding to n position nodes, pairClustering is carried out to obtain A clusters. Subsequently, the geometric center is calculated for each cluster:
;
Wherein the method comprises the steps of Representing clustersThe number of midpoints. Then with the geometric center of each clusterAnd constructing a circle with a set radius for the center, and then measuring and calculating the people flow track by taking the circle as an individual.
Optionally, the set radius is determined by acquiring the distance from the geometric center point to each position node in the cluster, and determining the maximum value of the distance between the geometric center point and the rest position nodes in the cluster as the set radius r.
That is to say,。
By determining the maximum value of the distances between the geometric center point and the rest position nodes in the cluster as a set radius, the points of the cluster can be ensured to fall in the range of a circle.
Optionally, clustering a plurality of position nodes of the mobile terminal in a preset time period to obtain a plurality of clusters, wherein the clustering of the plurality of position nodes of the mobile terminal in the preset time period by adopting the self-adaptive elliptical distance density method is adopted to obtain a plurality of clusters.
It should be noted that the adaptive elliptical distance density method is an algorithm for cluster analysis, and it replaces the conventional euclidean distance by the adaptive elliptical distance to better describe the structure of the data. In the embodiment of the application, the clustering is performed by adopting a self-adaptive elliptical distance density method so as to obtain the cluster group aiming at the position node very accurately.
Optionally, the method further comprises the step of accessing the people stream track into a third party map by taking the millimeter wave base station as a reference point, and performing visual display on the people stream track.
It should be noted that, because the position of the millimeter wave base station is known and accurate, the real measurement and the visual display of the people stream track can be completed by accessing the people stream track into the third party map data by taking the millimeter wave base station as a reference point.
In summary, the invention provides a traffic track measuring and calculating method based on a millimeter wave base station, which is characterized in that the linear distance between a communication tool used by a person and the base station is measured and calculated through the conventional millimeter wave base station, and the angle of a signal reaching in the vertical direction is calculated (fitted) through a convolutional neural network, so that the coordinate taking the millimeter wave base station as an origin is constructed. And secondly, the influence of movement of communication tools moving by an unaided person on track measurement is eliminated by means of clustering and obtaining a geometric center, so that the measurement accuracy is improved. Compared with the prior art, the method does not need extra equipment, can utilize the prior base station, and has better precision.
Referring to fig. 3, based on the same inventive concept, the present application provides a traffic track measurement and calculation system 30 based on a millimeter wave base station, which is applied to a millimeter wave base station, wherein the millimeter wave base station has a plurality of antennas arranged in an array, and the plurality of antennas are uniformly distributed around a main body of the millimeter wave base station, and the system comprises:
The acquisition module 301 is configured to acquire a target signal received by a millimeter wave base station, where the target signal is a backhaul signal of a mobile terminal within a communication range of the millimeter wave base station;
a construction module 302, configured to construct a signal covariance matrix based on the target signal;
the prediction module 303 is configured to input the signal covariance matrix into a target convolutional neural network model that is trained in advance, and output a target angle, where the target angle represents an angle between the target signal and a receiving antenna of the millimeter wave base station in a vertical direction;
A determining module 304, configured to determine a horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle;
And the calculating module 305 is configured to calculate a traffic track corresponding to the mobile terminal based on the horizontal distance and an orientation angle, where the orientation angle is a direction angle with the millimeter wave base station as a center, where the receiving antenna corresponding to the mobile terminal is located.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present application provides a module frame of an electronic device 400 applying the above method. The electronic device 400 comprises at least one processor 401 (only one shown in fig. 4), a memory 402, a computer program 403 stored in the memory 402 and executable on the at least one processor 401, the processor 401 executing the steps of the method in any of the embodiments described above.
The electronic device 400 may be any hardware device with data processing capabilities in a millimeter wave base station.
It will be appreciated by those skilled in the art that fig. 4 is merely an example of an electronic device 400 and is not limiting of the electronic device 400 and may include more or fewer components than shown, or may combine certain components, or different components.
The Processor 401 may be a central processing unit (Central Processing Unit, CPU), but the Processor 401 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 402 may be an internal storage unit of the electronic device 400, such as a hard disk or a memory of the electronic device 400, in some embodiments. The memory 402 may also be an external storage device of the electronic device 400 in other embodiments, such as a plug-in hard disk provided on the electronic device 400, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. Further, the memory 402 may also include both internal storage units and external storage devices of the electronic device 400.
It should be noted that, because the system, the device and the like are based on the same concept as the method embodiment of the present application, the modules designed by the system, the steps executed by the device and the technical effects brought by the steps may be referred to the method embodiment section, and will not be described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.
Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium can include at least any entity or device capable of carrying computer program code to a camera device/electronic apparatus, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference may be made to related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing embodiments are merely illustrative of the technical solutions of the present application, and not restrictive, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that modifications may still be made to the technical solutions described in the foregoing embodiments or equivalent substitutions of some technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The people stream track measuring and calculating method based on the millimeter wave base station is characterized by being applied to the millimeter wave base station, wherein the millimeter wave base station is provided with a plurality of antennas which are arranged in an array, and the plurality of antennas are uniformly distributed around the main body of the millimeter wave base station, and the method comprises the following steps:
acquiring a target signal received by a millimeter wave base station, wherein the target signal is a return signal of a mobile terminal in a communication range of the millimeter wave base station;
Constructing a signal covariance matrix based on the target signal;
Inputting the signal covariance matrix into a target convolutional neural network model which is trained in advance, and outputting a target angle, wherein the target angle represents the angle of the target signal and a receiving antenna of the millimeter wave base station in the vertical direction;
determining a horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle;
and calculating the people flow track corresponding to the mobile terminal based on the horizontal distance and the orientation angle, wherein the orientation angle is the direction angle of the receiving antenna corresponding to the mobile terminal with the millimeter wave base station as the center.
2. The millimeter wave base station-based people flow path measurement method of claim 1, wherein training the target convolutional neural network model by:
Acquiring a sample signal, wherein the sample signal is a return signal of a sample mobile terminal in a communication range of the millimeter wave base station;
Constructing a sample signal covariance matrix based on the sample signals;
Optimizing a weight vector based on a Lagrangian multiplier to obtain an optimal weight vector, wherein the optimization strategy is to minimize the output power of the sample signal and keep the gain of the required signal direction unchanged;
constructing a power spectrum based on the optimal weight vector;
And taking the sample signal covariance matrix as input of an initial convolutional neural network model, taking the power spectrum as a label, and training the initial convolutional neural network model to obtain the target convolutional neural network model.
3. The millimeter wave base station-based people flow path measurement method of claim 2, wherein the target convolutional neural network model comprises two convolutional layers with a ReLU function, a BN layer, and a fully connected layer.
4. The traffic trajectory measurement method based on the millimeter wave base station according to claim 1, wherein the determining the horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle comprises:
Determining an arrival time of the target signal based on the target signal;
determining a linear distance between the millimeter wave base station and the mobile terminal based on the arrival time of the target signal;
The horizontal distance is determined based on the target angle and the straight line distance.
5. The traffic trajectory measurement method based on the millimeter wave base station according to claim 1, wherein determining the orientation angle by:
Sequentially numbering the plurality of antennas and determining a starting antenna, wherein the number of the starting antenna is 0, and the starting antenna is any antenna of the plurality of antennas;
determining an access antenna of the target signal;
The orientation angle is determined based on a difference between the number of the access antenna and the number of the origin antenna, and a total number of the plurality of antennas.
6. The millimeter wave base station-based traffic trajectory measurement method according to claim 1, further comprising:
Acquiring a plurality of position nodes of the mobile terminal in a preset time period, wherein each position node comprises a horizontal distance and an orientation angle between the mobile terminal and the millimeter wave base station under the current time node;
clustering a plurality of position nodes of the mobile terminal in the preset time period to obtain a plurality of clusters;
Constructing a circle with a set radius by taking the geometric center point of each cluster as the circle center;
And measuring and calculating the people flow track by taking the circle as an individual.
7. The traffic trajectory measurement method based on the millimeter wave base station according to claim 6, wherein the set radius is determined by:
Obtaining the distance from the geometric center point to each position node in the cluster;
and determining the maximum value of the distance between the geometric center point in the cluster and the nodes at the rest positions as the set radius.
8. The method for measuring and calculating traffic trajectories based on millimeter wave base stations according to claim 6, wherein clustering the plurality of location nodes of the mobile terminal in the preset time period to obtain a plurality of clusters comprises:
and clustering a plurality of position nodes of the mobile terminal in the preset time period by adopting a self-adaptive elliptic distance density method to obtain a plurality of clusters.
9. The millimeter wave base station-based traffic trajectory measurement method according to claim 1, further comprising:
and accessing the people stream track to a third party map by taking the millimeter wave base station as a reference point, and visually displaying the people stream track.
10. A traffic track measurement and calculation system based on a millimeter wave base station, characterized in that the system is applied to a millimeter wave base station, the millimeter wave base station is provided with a plurality of antennas which are arranged in an array, the plurality of antennas are uniformly distributed around a main body of the millimeter wave base station, and the system comprises:
the acquisition module is used for acquiring a target signal received by a millimeter wave base station, wherein the target signal is a return signal of a mobile terminal in a communication range of the millimeter wave base station;
the construction module is used for constructing a signal covariance matrix based on the target signal;
The prediction module is used for inputting the signal covariance matrix into a target convolutional neural network model which is trained in advance and outputting a target angle, wherein the target angle represents the angle of the target signal and a receiving antenna of the millimeter wave base station in the vertical direction;
a determining module, configured to determine a horizontal distance between the millimeter wave base station and the mobile terminal based on the target signal and the target angle;
And the measuring and calculating module is used for measuring and calculating the people flow track corresponding to the mobile terminal based on the horizontal distance and the orientation angle, wherein the orientation angle is a direction angle with the millimeter wave base station as the center and the receiving antenna corresponding to the mobile terminal is positioned.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410905265.XA CN118450333B (en) | 2024-07-08 | 2024-07-08 | People stream track measuring and calculating method and system based on millimeter wave base station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410905265.XA CN118450333B (en) | 2024-07-08 | 2024-07-08 | People stream track measuring and calculating method and system based on millimeter wave base station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118450333A CN118450333A (en) | 2024-08-06 |
| CN118450333B true CN118450333B (en) | 2024-12-06 |
Family
ID=92341060
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410905265.XA Active CN118450333B (en) | 2024-07-08 | 2024-07-08 | People stream track measuring and calculating method and system based on millimeter wave base station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118450333B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217245A (en) * | 2014-08-27 | 2014-12-17 | 高阳 | People stream trajectory tracking and area dwell time statistics method and system based on heterogeneous network |
| CN108271115A (en) * | 2016-12-30 | 2018-07-10 | 中国移动通信集团内蒙古有限公司 | A kind of method of estimation and device of regional population's density |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102268675B1 (en) * | 2019-01-21 | 2021-06-22 | 주식회사 케이티 | Method for measuring location and apparatus therefor |
| CN110940973B (en) * | 2019-12-10 | 2022-11-04 | 成都纳雷科技有限公司 | Angle measurement method and device for radar target detection |
| CN113965874B (en) * | 2020-07-03 | 2023-04-07 | 大唐移动通信设备有限公司 | Wave beam forming signal sending method and base station equipment |
| CN113784357B (en) * | 2021-09-15 | 2024-11-19 | 江苏大学 | A method for dynamic 5G network configuration on highways in intelligent networking scenarios |
| GB202218836D0 (en) * | 2022-12-14 | 2023-01-25 | Samsung Electronics Co Ltd | Inactive and dual connectivity support for artificial intelligence/machine learning |
| CN117434532A (en) * | 2023-10-24 | 2024-01-23 | 四川启睿克科技有限公司 | Vital sign monitoring method based on millimeter wave radar and deep learning |
-
2024
- 2024-07-08 CN CN202410905265.XA patent/CN118450333B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217245A (en) * | 2014-08-27 | 2014-12-17 | 高阳 | People stream trajectory tracking and area dwell time statistics method and system based on heterogeneous network |
| CN108271115A (en) * | 2016-12-30 | 2018-07-10 | 中国移动通信集团内蒙古有限公司 | A kind of method of estimation and device of regional population's density |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118450333A (en) | 2024-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11164329B2 (en) | Multi-channel spatial positioning system | |
| Singh et al. | Crowd forecasting based on wifi sensors and lstm neural networks | |
| US10740964B2 (en) | Three-dimensional environment modeling based on a multi-camera convolver system | |
| US11615620B2 (en) | Systems and methods of enforcing distancing rules | |
| JP2004531842A (en) | Method for surveillance and monitoring systems | |
| Santana et al. | A privacy-aware crowd management system for smart cities and smart buildings | |
| JP2004534315A (en) | Method and system for monitoring moving objects | |
| JP2004537790A (en) | Moving object evaluation system and method | |
| CN111954886B (en) | System and method for object tracking | |
| Yang et al. | CEDAR: A cost-effective crowdsensing system for detecting and localizing drones | |
| CN111209892A (en) | Crowd density and quantity estimation method based on convolutional neural network | |
| CN106559749A (en) | A kind of multiple target passive type localization method based on radio frequency tomography | |
| CN110659391A (en) | Video detection method and device | |
| US10176379B1 (en) | Integrating computer vision and wireless data to provide identification | |
| Morita et al. | Beacon‐Based Time‐Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes | |
| CN109508583A (en) | A kind of acquisition methods and device of distribution trend | |
| CN112950668A (en) | Intelligent monitoring method and system based on mold position measurement | |
| EP4033399B1 (en) | Computer device and method for estimating the density of a crowd | |
| CN113505643B (en) | Method and related device for detecting violation target | |
| CN110751081A (en) | Construction safety monitoring method and device based on machine vision | |
| CN111899279A (en) | Method and device for detecting movement speed of target object | |
| EP3910539A1 (en) | Systems and methods of identifying persons-of-interest | |
| Hu et al. | A spatiotemporal intelligent framework and experimental platform for urban digital twins | |
| CN118450333B (en) | People stream track measuring and calculating method and system based on millimeter wave base station | |
| CN114169423B (en) | A method and system for global positioning of pedestrian flow integrating wireless signals and video |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |