CN113643066A - Passenger flow inference model training method and passenger flow inference method and device - Google Patents
Passenger flow inference model training method and passenger flow inference method and device Download PDFInfo
- Publication number
- CN113643066A CN113643066A CN202110938943.9A CN202110938943A CN113643066A CN 113643066 A CN113643066 A CN 113643066A CN 202110938943 A CN202110938943 A CN 202110938943A CN 113643066 A CN113643066 A CN 113643066A
- Authority
- CN
- China
- Prior art keywords
- passenger flow
- grid
- place
- determining
- region
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 89
- 238000012549 training Methods 0.000 title claims abstract description 62
- 230000015654 memory Effects 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 230000004044 response Effects 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000013135 deep learning Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 13
- 230000008569 process Effects 0.000 description 12
- 238000004590 computer program Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 238000012886 linear function Methods 0.000 description 5
- 239000003795 chemical substances by application Substances 0.000 description 4
- 238000010438 heat treatment Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- 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
- G06N3/045—Combinations of networks
-
- 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
- G06N3/048—Activation functions
-
- 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
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/041—Abduction
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Development Economics (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Accounting & Taxation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
Abstract
The disclosure provides a training method of a passenger flow inference model and a method and a device for inferring passenger flow, and relates to the technical field of artificial intelligence including deep learning. The specific implementation mode comprises the following steps: determining the boundary of a place in the region, and determining the minimum circumscribed rectangle of the boundary; gridding the circumscribed rectangle, and determining a grid in the boundary as a place grid in each obtained grid; determining the characteristics of the place based on the heat value of each place grid; acquiring passenger flow real data corresponding to the characteristics of places in the region; and training a passenger flow prediction model to be trained by taking the characteristics of the places in the region as input and the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow prediction model. The passenger flow inference model of the scenic spot can be better trained through the heat power value so as to obtain a more accurate passenger flow inference model, and therefore accurate passenger flow inference can be realized through the trained passenger flow inference model.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to the field of artificial intelligence technologies including deep learning, and in particular, to a method for training a passenger flow inference model, and a method and an apparatus for inferring passenger flow.
Background
Along with the increasing convenience of traffic, the travel frequency of people is remarkably improved. Such as traveling in scenic spots, shopping in commercial areas, etc.
In a region, there may also be various locations, such as sights in a scenic spot, or shops in a commercial area. There are often laws regarding the amount of traffic in these areas. For example, the volume of traffic during holidays can be significantly higher than usual. In the related art, based on these rules, inference can be made about the passenger flow volume.
Disclosure of Invention
Provided are a training method and device for a passenger flow estimation model, an electronic device and a storage medium, and a processing method and device for estimating passenger flow, an electronic device and a storage medium.
According to a first aspect, there is provided a method for training a passenger flow inference model, comprising: determining the boundary of a place in the region, and determining the minimum circumscribed rectangle of the boundary; gridding the circumscribed rectangle, and determining a grid in the boundary as a place grid in each obtained grid; determining the characteristics of the place based on the heat value of each place grid; acquiring passenger flow real data corresponding to the characteristics of places in the region; and training a passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
According to a second aspect, there is provided a method of inferring passenger flow comprising: the method adopts the passenger flow inference model obtained in the first aspect.
According to a third aspect, there is provided a training device for a passenger flow inference model, comprising: a rectangle determination unit configured to determine a boundary of a place in an area and determine a minimum bounding rectangle of the boundary; a grid unit configured to grid the circumscribed rectangle and determine a grid within the boundary among the obtained grids as a place grid; a feature determination unit configured to determine a feature of the place based on the thermal values of the respective place grids; an acquisition unit configured to acquire passenger flow volume real data corresponding to characteristics of a place in a region; and the training unit is configured to train the passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
According to a fourth aspect, there is provided an apparatus for inferring passenger flow comprising: the device adopts the passenger flow inference model obtained in any one of the third aspects.
According to a fifth aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of the embodiments of the method.
According to a sixth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of the above method embodiments.
According to a seventh aspect, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of any of the above-described method embodiments.
According to the scheme disclosed by the invention, the passenger flow inference model of the scenic spot can be better trained through the thermal value so as to obtain a more accurate passenger flow inference model, and therefore, accurate passenger flow inference can be realized through the trained passenger flow inference model.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which some embodiments of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a training method for a passenger flow inference model according to the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of a method of training a passenger flow inference model according to the present disclosure;
FIG. 4A is a flow diagram of one embodiment of a method of inferring passenger flow according to the present disclosure;
FIG. 4B is a schematic illustration of a regional grid in a method of inferring passenger traffic according to the present disclosure;
FIG. 4C is a schematic flow chart of a training process for a passenger flow inference model according to the present disclosure;
FIG. 5 is a schematic diagram of an embodiment of a training apparatus for a passenger flow inference model according to the present disclosure;
FIG. 6 is a block diagram of an electronic device for implementing a method of training a passenger flow inference model of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which an embodiment of a method of training a passenger flow inference model or a device of training a passenger flow inference model of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as video applications, live applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 101, 102, and 103.
Here, the terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for the terminal devices 101, 102, 103. The background server may analyze and perform other processing on the received data such as the boundary of the place, and feed back the processing result (e.g., the trained passenger flow inference model) to the terminal device.
It should be noted that the methods for training the passenger flow inference model and inferring the passenger flow provided by the embodiments of the present disclosure may be executed by the server 105 or the terminal devices 101, 102, and 103, and accordingly, the means for training the passenger flow inference model and inferring the passenger flow may be disposed in the server 105 or the terminal devices 101, 102, and 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method of training a passenger flow inference model in accordance with the present disclosure is illustrated. The passenger flow inference model training method comprises the following steps:
In this embodiment, an execution subject (for example, the server or the terminal device shown in fig. 1) on which the training method of the passenger flow inference model operates may determine a boundary, that is, an edge, of a place in the region, and determine a circumscribed rectangle of the boundary. The boundary may be a polygon. The above-mentioned circumscribed rectangle may be a minimum circumscribed rectangle. In some implementations, a region may include only the above-described locations, as well as other locations.
Specifically, the boundary may be represented by a series of latitude and longitude points, for example, when the latitude and longitude points include the ith position, i.e., the 1 st and 2 … n positions on the boundary, the latitude and longitude points in the boundary may be represented as [ (ng)1,lat1),(lng2,lat2),…,(lngn,latn)]。
In this embodiment, the execution body may grid the circumscribed rectangle, and determine a grid within the boundary among the grids obtained by the grid. The execution subject may use the determined grid as a place grid. In practice, the grid may be represented by latitude and longitude points, and in particular, the latitude and longitude points may be diagonal intersections of the grid.
Specifically, the execution main body may enumerate grids within the circumscribed rectangle, and determine a positional relationship between each enumerated grid and the boundary to determine whether the enumerated grids are within the boundary, so as to find the grids within the boundary as the location grids.
And step 203, determining the characteristics of the place based on the heat value of each place grid.
In this embodiment, the execution subject may determine the characteristics of the place based on the thermal value of the place grid. In practice, the execution body may determine the characteristics of the sites in various ways based on the thermal values of the respective site grids. For example, the execution subject may directly use the sum of the thermal values of the respective site grids as the characteristic of the site. Alternatively, the execution subject may input the thermal values of the respective site grids into a preset model, and obtain the characteristics of the sites output from the preset model. The preset model can calculate the characteristics of the place through the heat value of each place grid in the place. The features are model inputs and may be site specific.
The thermal values of the place grid may have a time attribute that represents the value of the thermal values within the place grid at a time.
And step 204, acquiring real passenger flow volume data corresponding to the characteristics of the places in the region.
In this embodiment, the execution subject may acquire real data of the passenger flow volume corresponding to the feature of the place in the region. The passenger flow volume real data corresponding to the features may refer to the passenger flow volume real data at the location and time of the features.
The real passenger flow data can be obtained in various ways, such as ticket checking data, namely secondary ticket checking data required for entering the scenic spot.
And step 205, training the passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
In this embodiment, the execution subject may train the passenger flow volume inference model to be trained by using the characteristics of the places in the region as input and using the passenger flow volume real data corresponding to the characteristics as input as target output, so as to obtain the trained passenger flow volume inference model.
The passenger flow inference model is a deep neural network. For example, the method may be a feedforward neural network, a convolutional neural network, a residual neural network, or the like.
The traffic inference model can be used to infer traffic by characteristics of a location, where the location can be a sight, a business center, and the like. During training, the features of a single place in the region and corresponding real passenger flow data are generally adopted for training. In some cases, the features of at least two attractions and corresponding traffic volume truth data may also be used for training. In particular, the resulting traffic inference model may be used to infer traffic for a location, a region, a local region in a location, and/or at least two locations in a region.
The method provided by the embodiment of the disclosure can better train the passenger flow rate inference model of the scenic spot through the thermal value to obtain a more accurate passenger flow rate inference model, so that accurate passenger flow rate inference can be realized through the trained passenger flow rate inference model.
In some optional implementations of this embodiment, step 203 may include: determining characteristics of the sites based on the thermal values of the respective site grids, including: determining the sum of the thermal values of each site grid of the site in response to the relationship between the thermal values and the passenger flow volume for the sites in the area being a linear relationship; and determining the characteristics of the place according to the sum of the thermal values.
In these alternative implementations, the executive agent may assume that the relationship between the thermal value and the passenger flow volume at the site is a linear relationship. In this case, the execution body may determine a sum of thermal values of respective site grids of the site. And, the execution subject may determine the feature of the site according to the sum of the thermal values.
In practice, the execution agent may determine the characteristic of the site from the sum of the thermal values in various ways. For example, the execution subject may directly determine the sum of the thermal values as the feature of the location. Alternatively, the execution agent may input the sum of the thermal values into a specified model, and obtain the feature of the site output from the specified model. The specified model can be used to infer the characteristics of the site by summing thermal values.
Specifically, the linear mapping relationship (linear function relationship) between the heat value heat and the passenger flow cnt can be expressed as f: f (heat)k)→cntkWhere k represents the kth location in the area, i.e., location k, heatkIndicating the heat value, cnt, of the location kkIndicating the passenger flow at location k. For a place grid i and a place grid j in a place, there is f (heat)i+heatj)＝f(heati)+f(heatj)。
The boundary of location k can be represented as boundarykThe sum of the thermal values of the site can be expressed as
The sum of the thermal values can be used as the characteristic feat of the location kk。
These alternative implementations may determine the characteristics of a location more accurately based on the sum of thermal values, with a linear relationship between the thermal value and passenger flow at the location.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method of training a passenger traffic inference model according to the present embodiment. In the application scenario of fig. 3, the executive body 301 determines the boundaries 302 of the locations in the region and determines the bounding rectangles 303 of the boundaries. The execution body 301 gridds the circumscribed rectangle, and determines a mesh within the boundary among the resultant meshes as a place mesh 304. The execution body 301 determines the characteristics 306 of the sites based on the thermal values 305 of the respective site grids 304. The execution subject 301 acquires the passenger-flow volume real data 307 corresponding to the feature 306 of the place in the region. The execution subject 301 trains the passenger flow inference model to be trained by taking the feature 306 of the place in the region as input and taking the passenger flow real data 307 corresponding to the feature 306 as target output, so as to obtain a trained passenger flow inference model 308.
With further reference to FIG. 4A, a flow 400 of one embodiment of a method of training a passenger flow inference model is illustrated. The process 400 includes the following steps:
And 403, in response to the nonlinear relation between the heat value and the passenger flow of the place in the region, adopting the sum of nonlinear functions of the heat value of each place grid of the place to express the passenger flow of the place.
In this embodiment, the executive (e.g., the server or terminal device shown in fig. 1) on which the method of training the passenger flow inference model operates may assume a non-linear relationship between the thermal value and the passenger flow at the site. In this case, the executing agent may determine a non-linear functional relationship between the thermal value of the location and the passenger flow volume of the location. For a place grid i and a place grid j in a place, there is f (heat)i+heatj)≠f(heati)+f(heatj)。
In practice, the passenger flow can be represented by the sum of non-linear functions of the thermal force values of the individual site grids of the sites.
The non-linear function of the place grid i may be g (heat)i) The sum of the non-linear functions of the individual location grids in the location may then be
Therefore, the passenger volume cnt of the place kkCan be expressed in terms of the sum of the non-linear functions.
And step 404, performing Taylor expansion processing on the sum of the nonlinear functions to obtain a polynomial.
In this embodiment, the execution body may perform taylor expansion processing on the sum of the nonlinear functions to obtain a polynomial.
In practice, for
And performing k-order Taylor expansion to obtain a polynomial:
in practice, k in the above k order may be 2, that is, greater than or equal to 2. In order to improve the accuracy of the polynomial and thus more accurately determine the location feature, k may be 3, i.e., greater than or equal to 3.
And step 405, determining the characteristics of the place according to the non-coefficient content of each item in the polynomial, wherein the non-coefficient content is expressed by adopting the heat value of the place grid.
In this embodiment, the execution subject may determine the feature of the location in various ways according to the non-coefficient content of the polynomial in the polynomial. For example, the execution subject may directly determine the non-coefficient content as the feature of the location. Alternatively, the executing entity may perform a preset process on the non-coefficient content, for example, input a pre-trained model, and obtain the feature of the location output from the model. The model may determine the characteristics of the location by non-coefficient content. The non-coefficient content in the polynomial is expressed in terms of the thermal value.
In practice, the polynomial equation
The coefficients in (1) are: g (0), g' (0), …, gk(0). The execution body may be a pair of o (x) in a polynomialk) Neglecting, as such, the non-coefficient content of each term in the above polynomial is:
And 407, training a passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
The implementation manners of step 401, step 402, step 406, and step 407 are the same as or similar to the implementation manners of step 201, step 202, step 204, and step 205, respectively, and are not described herein again.
In the embodiment, under the condition that the thermal value and the passenger flow volume of the place are in a nonlinear relation, the characteristic of the place can be more accurately determined based on the thermal value by processing the nonlinear function.
In some optional implementations of any embodiment of the disclosure, the method may further include: acquiring the grid size of a region, wherein the grid size is determined based on longitude and latitude step lengths set for a grid; according to the grid size, carrying out grid division on the region to obtain region grids; and the determining the grids in the circumscribed rectangle and the grids within the boundary in each grid as the location grids may include: and determining the area grid in the range of the circumscribed rectangle as a candidate area grid, and determining the candidate area grid in the boundary as a place grid.
In these alternative implementations, the execution subject may obtain a grid size of the region. The execution main body or other electronic devices may first obtain a longitude and latitude step length, where the longitude and latitude step length refers to a longitude difference value and a latitude difference value, and may correspond to a segment of geographic length. For example, the longitude and latitude step length refers to a longitude difference and a latitude difference both of which are 0.0001 degree, the corresponding geographic length is about 10 meters, and the length can be used as the length and the width of the grid size. That is, the execution subject or other electronic device may directly convert the longitude and latitude step size into the grid size.
The execution body may perform meshing on the region in various manners, such as directly obtaining a circumscribed rectangle of the region boundary from the device or other electronic devices, or determining the circumscribed rectangle of the region boundary at the device. Then, the execution main body may perform meshing on the circumscribed rectangle of the region boundary according to the mesh size, so as to perform meshing on the region, and obtain a region mesh. Alternatively, the execution body may input the polygon and mesh size of the region into a preset gridding model to obtain the region mesh output from the model.
The execution body may first determine a region grid within a circumscribed rectangular range of the location as the candidate region grid. Thereafter, the execution subject may determine candidate area meshes within the boundary of the location, and take the candidate area meshes as the location mesh.
As shown in fig. 4B, a region grid obtained by meshing a region is shown.
As shown in fig. 4C, a schematic diagram of a training flow of the passenger traffic inference model is shown. Specifically, the execution body may first determine the boundaries of the region. And then, gridding the region through the boundary, thereby obtaining region grids, namely region longitude and latitude data. The gridding may be to determine an external rectangle of the boundary of the region, and then grid the external rectangle.
Then, the execution body can acquire the positioning data within the region. The format of the positioning data is a quadruple (id, time, lng, lat), the id representing the device generating the current positioning data and the time representing the timestamp generating the current positioning data. And carrying out grid positioning thermodynamic calculation through the positioning data and the area grid. The grid positioning thermal computation may position each grid for which thermal computation is performed. The calculated regional grid positioning heat has a time attribute.
The execution main body can aggregate the heating power of each grid in the place through the regional grid positioning heating power and the place boundary to obtain the place positioning heating power. The site-specific heating power is characterized by having a time attribute. And obtaining the place ticket checking data corresponding to the characteristics, namely the passenger flow real data, training a deep learning model, namely a passenger flow inference model to be trained, and obtaining the passenger flow inference model of the trained place.
These implementations may determine a grid of locations from a grid of regions, thereby improving the accuracy of determining a grid of locations and enhancing the consistency of determining grids of locations in a region.
In some optional implementations of any embodiment of the disclosure, the passenger flow inference model is a multi-layer feed-forward neural network.
In these alternative implementations, the passenger flow inference model may be a Multi-Layer feedforward neural network, that is, a Multi-Layer Perceptron (MLP).
In practice, the execution body or other electronic device may acquire a training data set. The data set D comprises a plurality of pairs xkAnd yk. Wherein xk＝featk,yk＝cntk. Wherein the process of each process layer can be represented as σ (Wfeat)k+ b). Wherein, W and b are parameters needing to be modified in the training process, and sigma is an activation function. For example, if the passenger flow inference model exists in three layers, two of which are processing layers, that is, each of the two processing layers processes the output of the previous layer to change the output, the final output of the model is yk＝σ(W2σ(W1featk+b1)+b2) Wherein W is1And b1As a parameter of the first process layer, W2And b2Is a parameter of the second process layer.
It should be noted that, the number of processing layers of the multi-layer perceptron is greater than or equal to 3, and the specific number of processing layers may be determined by cross validation.
These implementations can utilize feed-forward neural network training to derive accurate passenger flow inference models.
In some optional implementations of any embodiment of the present disclosure, the region is a scenic spot, and the locations in the region are scenic spots.
In these alternative implementations, the executive can infer traffic to the sights in the scenic spot, thereby increasing the universality of the solution in the present disclosure. In addition, the area of the region may be limited, and the area of the region does not exceed the target geographic area, i.e., the area of the region is less than or equal to the target geographic area.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a training apparatus for a passenger flow volume inference model, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and besides the features described below, the embodiment of the apparatus may further include the same or corresponding features or effects as the embodiment of the method shown in fig. 2. The device can be applied to various electronic equipment.
As shown in fig. 5, the training apparatus 500 of the passenger flow volume estimation model of the present embodiment includes: a rectangle determination unit 501, a grid unit 502, a feature determination unit 503, an acquisition unit 504, and a training unit 505. Wherein the rectangle determining unit 501 is configured to determine a boundary of a place in the region, and determine a circumscribed rectangle of the boundary; a grid unit 502 configured to grid the circumscribed rectangle and determine a grid within the boundary among the obtained grids as a place grid; a feature determination unit 503 configured to determine features of the locations based on the thermal values of the respective location grids; an acquisition unit 504 configured to acquire passenger flow volume real data corresponding to a feature of a place in a region; and the training unit 505 is configured to train the passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output, so as to obtain the trained passenger flow inference model.
In this embodiment, specific processes of the rectangle determining unit 501, the grid unit 502, the feature determining unit 503, the obtaining unit 504, and the training unit 505 of the training apparatus 500 for a passenger flow volume inference model and technical effects thereof may refer to the related descriptions of step 201, step 202, step 203, step 204, and step 205 in the corresponding embodiment of fig. 2, and are not described herein again.
In some optional implementations of this embodiment, the feature determination unit is further configured to perform determining the features of the locations based on the thermal values of the respective location grids as follows: determining the sum of the thermal values of each site grid of the site in response to the relationship between the thermal values and the passenger flow volume for the sites in the area being a linear relationship; and determining the characteristics of the place according to the sum of the thermal values.
In some optional implementations of this embodiment, the feature determination unit is further configured to perform determining the features of the locations based on the thermal values of the respective location grids as follows: responding to the nonlinear relation between the heat value and the passenger flow of the place in the region, and expressing the passenger flow of the place by adopting the sum of nonlinear functions of the heat value of each place grid of the place; carrying out Taylor expansion processing on the sum of the nonlinear functions to obtain a polynomial; and determining the characteristics of the place according to the non-coefficient content of each term in the polynomial, wherein the non-coefficient content is expressed by the heat value of the place grid.
In some optional implementations of this embodiment, the apparatus further includes: an acquisition unit configured to acquire a grid size of an area, wherein the grid size is determined based on a longitude and latitude step length set for a grid; the dividing unit is configured to divide the grids of the regions according to the grid sizes to obtain region grids; and a grid unit further configured to perform gridding of the circumscribed rectangle and determine a grid within the boundary as a place grid among the resultant grids as follows: and determining the area grid in the range of the circumscribed rectangle as a candidate area grid, and determining the candidate area grid in the boundary as a place grid.
In some optional implementations of the present embodiment, the passenger flow inference model is a multi-layer feed-forward neural network.
The present disclosure also illustrates the flow of one embodiment of a method of inferring passenger flow. The process comprises the following steps: the passenger flow inference model obtained by any one of the above embodiments is adopted in the method.
The method provided by the embodiment can accurately infer the scenic spot passenger flow volume through the thermal value.
In some optional implementations of this embodiment, the method may further include: and inputting the characteristics of the place to be inferred into the passenger flow volume inference model to obtain the passenger flow volume output from the passenger flow volume inference model, wherein the place to be inferred is the same as or different from a target place corresponding to the characteristics adopted by training, and the distance between the target place and the place to be inferred does not exceed a preset distance threshold.
In these alternative implementations, the location corresponding to the feature used for training may be used as the target location. The place to be inferred, which is inferred by adopting the passenger flow inference model, and the target place can be the same place or different places. The execution subject can realize more accurate passenger flow volume inference under the condition that the distance between the target place and the place to be predicted by adopting the passenger flow volume inference model is short. For example, the target location and the location to be inferred are sights in the same scenic spot.
Further, as an implementation of the method shown in the above embodiment, the present disclosure provides an embodiment of an apparatus for inferring a passenger flow volume, where the embodiment of the apparatus corresponds to the embodiment of the method for inferring a passenger flow volume, and the embodiment of the apparatus may further include the same or corresponding features or effects as the embodiment of the method, except for the features described below. The device can be applied to various electronic equipment.
The device for deducing the model of the passenger flow comprises the following components: and obtaining the passenger flow inference model by adopting any one of the passenger flow inference model training methods.
In some optional implementations of this embodiment, the method may further include: and inputting the characteristics of the place to be inferred into the passenger flow volume inference model to obtain the passenger flow volume output from the passenger flow volume inference model, wherein the place to be inferred is the same as or different from a target place corresponding to the characteristics adopted by training, and the distance between the target place and the place to be inferred does not exceed a preset distance threshold.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 6, a block diagram of an electronic device of a method of training a passenger flow inference model and an electronic device of a method of inferring passenger flow according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium provided by the present disclosure. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method of training a passenger flow inference model and a method of inferring passenger flow provided by the present disclosure. A non-transitory computer-readable storage medium of the present disclosure stores computer instructions for causing a computer to perform a method of training a passenger flow inference model and a method of inferring passenger flow provided by the present disclosure.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods of training a passenger flow inference model and inferring passenger flow in the embodiments of the present disclosure (e.g., the rectangle determination unit 501, the grid unit 502, the feature determination unit 503, the acquisition unit 504, and the training unit 505 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing, namely, a training method of the passenger flow inference model and a method of inferring passenger flow in the above method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 602.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of an electronic device that trains a passenger flow volume inference model and infers passenger flow volume, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 602 optionally includes memory located remotely from processor 601, and these remote memories may be connected over a network to an electronic device that trains the passenger flow inference model and infers passenger flow. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method of training a passenger flow inference model and the electronic device of the method of inferring passenger flow may further comprise: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device for training the passenger flow inference model and inferring passenger flow, such as input devices like a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, etc. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a rectangle determination unit, a grid unit, a feature determination unit, an acquisition unit, and a training unit. Where the names of the cells do not in some cases constitute a definition of the cell itself, for example, a rectangle determination cell may also be described as a "cell circumscribing a rectangle that determines the boundaries of locations in a region, and determines the boundaries".
As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: determining the boundary of a place in the region and determining a circumscribed rectangle of the boundary; gridding the circumscribed rectangle, and determining a grid in the boundary as a place grid in each obtained grid; determining the characteristics of the place based on the heat value of each place grid; acquiring passenger flow real data corresponding to the characteristics of places in the region; and training a passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: and obtaining the passenger flow inference model by adopting any one of the passenger flow inference model training methods.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (15)
1. A method of training a passenger flow inference model, the method comprising:
determining a boundary of a place in a region, and determining a circumscribed rectangle of the boundary;
gridding the circumscribed rectangle, and determining a grid in the boundary as a place grid in each obtained grid;
determining characteristics of the sites based on the thermal values of each of the site grids;
acquiring passenger flow real data corresponding to the characteristics of the places in the region;
and training a passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
2. The method of claim 1, wherein said determining a characteristic of said site based on thermal values of each of said site grids comprises:
in response to the relationship between the thermal value and the passenger flow volume of the sites in the region being a linear relationship, determining the sum of the thermal values of the individual site grids of the site;
and determining the characteristics of the place according to the sum of the thermal values.
3. The method of claim 1, wherein said determining a characteristic of said site based on thermal values of each of said site grids comprises:
responding to the nonlinear relation between the heat value and the passenger flow of the place in the region, and expressing the passenger flow of the place by adopting the sum of nonlinear functions of the heat value of each place grid of the place;
carrying out Taylor expansion processing on the sum of the nonlinear functions to obtain a polynomial;
and determining the characteristics of the place according to the non-coefficient content of each term in the polynomial, wherein the non-coefficient content is expressed by adopting the heat value of a place grid.
4. The method of claim 1, wherein the method further comprises:
acquiring the grid size of the region, wherein the grid size is determined based on longitude and latitude step lengths set for a grid;
according to the grid size, carrying out grid division on the region to obtain a region grid; and
the gridding the circumscribed rectangle and determining the grid in the boundary as the place grid in each obtained grid includes:
and determining the area grid in the circumscribed rectangular range as a candidate area grid, and determining the candidate area grid in the boundary as a place grid.
5. The method according to one of claims 1 to 4, wherein the passenger flow inference model is a multi-layer feed-forward neural network.
6. A method of inferring passenger flow, wherein the method employs the passenger flow inference model derived from any of claims 1-5.
7. The method of claim 6, wherein the method comprises:
inputting the characteristics of the place to be inferred into the passenger flow volume inference model to obtain the passenger flow volume output from the passenger flow volume inference model, wherein the place to be inferred is the same as or different from a target place corresponding to the characteristics adopted by training, and the distance between the target place and the place to be inferred does not exceed a preset distance threshold.
8. A training apparatus for a passenger flow inference model, the apparatus comprising:
a rectangle determination unit configured to determine a boundary of a place in an area and determine a circumscribed rectangle of the boundary;
a grid unit configured to grid the circumscribed rectangle and determine a grid within the boundary among the obtained grids as a place grid;
a feature determination unit configured to determine a feature of the place based on a thermal value of each of the place grids;
an acquisition unit configured to acquire passenger flow volume real data corresponding to a feature of a place in the region;
and the training unit is configured to train the passenger flow inference model to be trained by taking the characteristics of the places in the region as input and taking the passenger flow real data corresponding to the characteristics as target output to obtain the trained passenger flow inference model.
9. The apparatus of claim 8, wherein the feature determination unit is further configured to perform the determining the features of the sites based on the thermal values of the respective site grids as follows:
in response to the relationship between the thermal value and the passenger flow volume of the sites in the region being a linear relationship, determining the sum of the thermal values of the individual site grids of the site;
and determining the characteristics of the place according to the sum of the thermal values.
10. The apparatus of claim 8, wherein the feature determination unit is further configured to perform the determining the features of the sites based on the thermal values of the respective site grids as follows:
responding to the nonlinear relation between the heat value and the passenger flow of the place in the region, and expressing the passenger flow of the place by adopting the sum of nonlinear functions of the heat value of each place grid of the place;
carrying out Taylor expansion processing on the sum of the nonlinear functions to obtain a polynomial;
and determining the characteristics of the place according to the non-coefficient content of each term in the polynomial, wherein the non-coefficient content is expressed by adopting the heat value of a place grid.
11. The apparatus of claim 8, wherein the apparatus further comprises:
an acquisition unit configured to acquire a grid size of the region, wherein the grid size is determined based on a longitude and latitude step length set for a grid;
the dividing unit is configured to perform grid division on the region according to the grid size to obtain a region grid; and
the grid unit is further configured to perform the gridding of the circumscribed rectangle and determine a grid within the boundary among the obtained grids as a location grid as follows:
and determining the area grid in the circumscribed rectangular range as a candidate area grid, and determining the candidate area grid in the boundary as a place grid.
12. An apparatus for inferring passenger flow, wherein said apparatus employs a passenger flow inference model derived from any of claims 8-11.
13. The apparatus of claim 12, wherein the apparatus comprises:
inputting the characteristics of the place to be inferred into the passenger flow volume inference model to obtain the passenger flow volume output from the passenger flow volume inference model, wherein the place to be inferred is the same as or different from a target place corresponding to the characteristics adopted by training, and the distance between the target place and the place to be inferred does not exceed a preset distance threshold.
14. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110938943.9A CN113643066A (en) | 2021-08-16 | 2021-08-16 | Passenger flow inference model training method and passenger flow inference method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110938943.9A CN113643066A (en) | 2021-08-16 | 2021-08-16 | Passenger flow inference model training method and passenger flow inference method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113643066A true CN113643066A (en) | 2021-11-12 |
Family
ID=78422167
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110938943.9A Pending CN113643066A (en) | 2021-08-16 | 2021-08-16 | Passenger flow inference model training method and passenger flow inference method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113643066A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118171035A (en) * | 2024-05-13 | 2024-06-11 | 中移(苏州)软件技术有限公司 | Method and device for early warning of people stream heating power, electronic equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110245579A (en) * | 2019-05-24 | 2019-09-17 | 北京百度网讯科技有限公司 | Density of stream of people prediction technique and device, computer equipment and readable medium |
| US20200143566A1 (en) * | 2018-11-06 | 2020-05-07 | International Business Machines Corporation | Passenger travel route inferencing in a subway system |
| TWM599956U (en) * | 2020-05-28 | 2020-08-11 | 上傑國際有限公司 | Intelligent real-name body temperature measurement and passenger flow analysis system |
| WO2020199468A1 (en) * | 2019-04-04 | 2020-10-08 | 平安科技（深圳）有限公司 | Image classification method and device, and computer readable storage medium |
| CN112269930A (en) * | 2020-10-26 | 2021-01-26 | 北京百度网讯科技有限公司 | Method and device for establishing regional heat prediction model and regional heat prediction |
| CN113222239A (en) * | 2021-05-08 | 2021-08-06 | 浙江科技学院 | Short-time traffic flow prediction method based on CNN-LSTM-At neural network |
-
2021
- 2021-08-16 CN CN202110938943.9A patent/CN113643066A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200143566A1 (en) * | 2018-11-06 | 2020-05-07 | International Business Machines Corporation | Passenger travel route inferencing in a subway system |
| WO2020199468A1 (en) * | 2019-04-04 | 2020-10-08 | 平安科技（深圳）有限公司 | Image classification method and device, and computer readable storage medium |
| CN110245579A (en) * | 2019-05-24 | 2019-09-17 | 北京百度网讯科技有限公司 | Density of stream of people prediction technique and device, computer equipment and readable medium |
| TWM599956U (en) * | 2020-05-28 | 2020-08-11 | 上傑國際有限公司 | Intelligent real-name body temperature measurement and passenger flow analysis system |
| CN112269930A (en) * | 2020-10-26 | 2021-01-26 | 北京百度网讯科技有限公司 | Method and device for establishing regional heat prediction model and regional heat prediction |
| CN113222239A (en) * | 2021-05-08 | 2021-08-06 | 浙江科技学院 | Short-time traffic flow prediction method based on CNN-LSTM-At neural network |
Non-Patent Citations (1)
| Title |
|---|
| 杨福民 等: "高等数学（下）", vol. 2, 30 November 2020, 北京邮电大学出版社, pages: 46 - 47 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118171035A (en) * | 2024-05-13 | 2024-06-11 | 中移(苏州)软件技术有限公司 | Method and device for early warning of people stream heating power, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112037760B (en) | Training method and device of voice spectrum generation model and electronic equipment | |
| CN109740499A (en) | Methods of video segmentation, video actions recognition methods, device, equipment and medium | |
| JP2021531541A (en) | Systems and methods for geolocation prediction | |
| US10891442B2 (en) | Message tone evaluation between entities in an organization | |
| CN111612249A (en) | Method, device, equipment and storage medium for predicting human flow | |
| CN115085196B (en) | Power load predicted value determination method, device, equipment and computer readable medium | |
| US11238369B2 (en) | Interactive visualization evaluation for classification models | |
| US11593384B2 (en) | Parking lot free parking space predicting method, apparatus, electronic device and storage medium | |
| CN113240778A (en) | Virtual image generation method and device, electronic equipment and storage medium | |
| CN113657467B (en) | Model pre-training method and device, electronic equipment and storage medium | |
| CN112529180A (en) | Method and apparatus for model distillation | |
| US20210239480A1 (en) | Method and apparatus for building route time consumption estimation model, and method and apparatus for estimating route time consumption | |
| EP4123595A2 (en) | Method and apparatus of rectifying text image, training method and apparatus, electronic device, and medium | |
| CN112269930A (en) | Method and device for establishing regional heat prediction model and regional heat prediction | |
| CN113643066A (en) | Passenger flow inference model training method and passenger flow inference method and device | |
| JP2022091919A (en) | Backbone network generation method and device, electronic apparatus, storage medium and computer program | |
| CN113158030B (en) | Recommendation method and device for remote interest points, electronic equipment and storage medium | |
| CN112966193B (en) | Travel intention deducing method, model training method, related device and electronic equipment | |
| CN114266937A (en) | Model training method, image processing method, device, equipment and storage medium | |
| CN111709778B (en) | Travel flow prediction method and device, electronic equipment and storage medium | |
| CN110751853B (en) | Parking space data validity identification method and device | |
| CN114723045B (en) | Model training method, device, system, apparatus, medium, and program product | |
| CN115423919A (en) | Image rendering method, device, equipment and storage medium | |
| CN114913533A (en) | Method and device for changing character weight | |
| CN111324747A (en) | Method and device for generating triples and electronic equipment |
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 |