A Simulation Method and System for Urban Population Distribution

Abstract

An embodiment of this specification provides a method and system for simulating the distribution of urban populations. The method is executed by a processor and includes: determining a first area and a second area from the area to be simulated; obtaining the identified population distribution and first flow data of the first area; determining a population simulation result based on the identified population distribution of the first area; determining a planning recommendation based on the population simulation result, and pushing the population simulation result and the planning recommendation to a user terminal.

Description

Technical Field

[0001] This specification relates to the technical field of population simulation, and particularly to a method and system for simulating urban population distribution. Background Art

[0002] With the accelerating process of urbanization, the reasonable management and utilization of urban space have become increasingly important. As an important tool for urban planning, public safety, and emergency response, population simulation technology is being widely used by major urban management departments. The results of population simulation relying on static data and preset model parameters are relatively rigid and often do not conform to the actual situation.

[0003] To solve the above problems, CN107292064B discloses a method and system for simulating population evacuation based on a multi-swarm algorithm. Different types of swarm algorithms can simulate the dynamic behaviors of various types of people at different times and in different environments. At the same time, the practice of regular iteration and re-initialization can also capture the changes in population distribution. This method can simulate the population based on the dynamically changing time to a certain extent, but still cannot reasonably analyze the reliability of population simulation.

[0004] Therefore, providing a method and system for simulating urban population distribution helps to analyze the reliability of the simulation while analyzing the changes in population distribution at different times, thereby improving the rationality of urban planning. Summary of the Invention

[0005] One embodiment of this specification provides a method for simulating urban population distribution, which is executed by a processor. The method includes: determining a first area and a second area from the area to be simulated. The first area is a sub-area in the area to be simulated where the data completeness meets the preset data conditions, and the second area is a sub-area in the area to be simulated where the data completeness does not meet the preset data conditions; obtaining the identified population distribution and the first flow data of the first area. The identified population distribution includes the identified number of people in the first area within at least one first time period; determining a population simulation result based on the identified population distribution of the first area. The population simulation result is a visual result of the simulated population distribution in the area to be simulated; determining a planning suggestion based on the population simulation result, and pushing the population simulation result and the planning suggestion to a user terminal.

[0006] One embodiment of this specification provides a system for simulating urban population distribution. The system includes: a first determination module configured to determine a first area and a second area from the area to be simulated. The first area is a sub-area in the area to be simulated where the data completeness meets a preset data condition, and the second area is a sub-area in the area to be simulated where the data completeness does not meet the preset data condition; an acquisition module configured to acquire the identified population distribution and first flow data of the first area. The identified population distribution includes the number of identified people in the first area within at least one first time period; a second determination module configured to determine a population simulation result based on the identified population distribution of the first area. The population simulation result is a visualization result of the simulated population distribution in the area to be simulated; a third determination module configured to determine a planning suggestion based on the population simulation result and push the population simulation result and the planning suggestion to a user terminal.

[0007] One embodiment of this specification provides an apparatus for simulating urban population distribution. The apparatus includes at least one storage medium and at least one processor. The at least one storage medium is used to store computer instructions; the at least one processor is used to execute the computer instructions to implement a method for simulating urban population distribution.

[0008] One embodiment of this specification provides a computer-readable storage medium. The storage medium stores computer instructions, and when the computer instructions are executed by a computer, a method for simulating urban population distribution is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0010] Figure 1 is a system module diagram of a system for simulating urban population distribution according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flowchart of a method for simulating urban population distribution according to some embodiments of this specification;

[0012] Figure 3 is an exemplary schematic diagram of generating a population simulation result according to some embodiments of this specification;

[0013] Figure 4 is an exemplary schematic diagram of a simulation model according to some embodiments of this specification;

[0014] Figure 5It is an exemplary schematic diagram for determining a target simulation result shown in some embodiments of this specification. Detailed implementation manners

[0015] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0017] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the previous or subsequent operations are not necessarily executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0019] Crowd simulation technology, as a key tool to support urban planning, public safety strategy formulation and emergency response plan design, needs to reflect the real dynamic crowd distribution. CN107292064B can simulate the dynamic behaviors of various types of people at different times and in different environments through different types of swarm algorithms, but it cannot analyze the reliability of crowd simulation, and the simulation of crowd evacuation is also different from the simulation of daily urban crowd distribution.

[0020] In view of this, in some embodiments of this specification, by obtaining the identified crowd distribution and the first flow data of the first area of the area to be simulated, and then determining the crowd simulation result, the changing trends of the crowd scale over time and space can be simulated; based on the crowd simulation result, planning suggestions are determined, which can provide more comprehensive and accurate data support for urban planners and help improve the rationality of urban planning.

[0021] Figure 1 It is a system module diagram of an urban population distribution simulation system shown in some embodiments of this specification.

[0022] As Figure 1 shown, the urban population distribution simulation system 100 may include a first determination module 110, an acquisition module 120, a second determination module 130, and a third determination module 140.

[0023] The first determination module 110 may be configured to determine a first area and a second area from the area to be simulated.

[0024] The acquisition module 120 may be configured to acquire the identified population distribution and the first flow data of the first area.

[0025] The second determination module 130 may be configured to determine the population simulation result based on the identified population distribution of the first area.

[0026] In some embodiments, the second determination module 130 may be further configured to: construct a first simulation map based on the identified population distribution and the first flow data; determine the simulated population distribution of the first area, the simulated population distribution of the second area, and the second flow data through a simulation model based on the first simulation map; determine the simulated population distribution of the area to be simulated based on the simulated population distribution of the first area, the simulated population distribution of the second area, and the second flow data; generate a population simulation result based on the simulated population distribution.

[0027] In some embodiments, the second determination module 130 may be further configured to: construct a first simulation map based on the identified population distribution and the confidence population distribution of the first area.

[0028] The third determination module 140 may be configured to determine a planning recommendation based on the population simulation result, and push the population simulation result and the planning recommendation to the user terminal.

[0029] In some embodiments, the urban population distribution simulation system 100 may include a storage device, a processor, etc. The processor may obtain the data and / or information pre-stored related to the urban population distribution simulation system 100 from the storage device. In some embodiments, the first determination module 110, the acquisition module 120, the second determination module 130, and the third determination module 140 of the urban population distribution simulation system 100 may be integrated onto the processor. In some embodiments, the urban population distribution simulation system 100 may include a network. The processor may obtain the data and / or information related to the urban population distribution simulation system 100 through the network.

[0030] In some embodiments, the urban population distribution simulation system 100 may further include a user terminal. The user terminal may refer to one or more terminal devices or software used by a user. The user may refer to a manager or operator of the urban population distribution simulation system 100, etc. For example, the user terminal may include a mobile phone, a tablet computer, an interactive screen, etc.

[0031] It should be noted that the above description of the urban population distribution simulation system and its modules is only for convenience of description and does not limit this specification to the scope of the exemplified embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of the various modules, or form a subsystem and connect it with other modules.

[0032] Figure 2 is an exemplary flowchart of the urban population distribution simulation method according to some embodiments of this specification.

[0033] In some embodiments, the processor may determine a first area and a second area from the area to be simulated; obtain the identified population distribution and the first flow data of the first area; determine the population simulation result based on the identified population distribution of the first area; determine the planning suggestion based on the population simulation result, and push the population simulation result and the planning suggestion to the user terminal.

[0034] In some embodiments, process 200 may be executed by the processor of the urban population distribution simulation system. As Figure 2 shown, process 200 includes the following steps:

[0035] Step 210, determine a first area and a second area from the area to be simulated.

[0036] The area to be simulated refers to the area where population distribution simulation needs to be carried out. In some embodiments, the processor may determine the area to be simulated based on user input.

[0037] A sub-area refers to one or more areas divided from the area to be simulated. In some embodiments, the processor may divide the area to be simulated based on the functional structure to determine the sub-areas. The functional structure refers to the function of the area to be simulated in urban planning and construction. For example, the functional structure may include roads, lawns, flower beds, etc. The processor may divide the area to be simulated based on the functional structure, and determine an area with the same functional structure and being connected as a sub-area. Exemplarily, the processor may determine a connected road in the area to be simulated as a sub-area.

[0038] Data completeness is an indicator used to measure whether a sub-region has corresponding data. Among them, the corresponding data of the sub-region refers to the sensing data captured by the sensing devices deployed in the sub-region. Exemplary sensing devices may include cameras, surveillance cameras, etc.

[0039] In some embodiments, the data completeness can be a numerical value of 0 or 1. Among them, if the data completeness of a sub-region is 1, it means that the sub-region contains the corresponding data; if the data completeness of a sub-region is 0, it means that the sub-region does not contain the corresponding data, that is, no sensing device is deployed in the sub-region, or the sensing device deployed in the sub-region is damaged and no sensing data is captured. The processor can determine the data completeness of the sub-region without corresponding data as 0; and determine the data completeness of the sub-region with corresponding data as 1.

[0040] The preset data condition refers to the judgment condition preset in advance for determining the first region and the second region. The preset data condition can be preset by the system or manually. An exemplary preset data condition can be: the data completeness is 1.

[0041] In some embodiments, the processor can determine the first region and the second region in various ways based on the data completeness of the sub-region. For example, the processor can determine the region where the data completeness meets the preset data condition as the first region, and determine the region where the data completeness does not meet the preset data condition as the second region.

[0042] Step 220, obtain the identified population distribution and the first flow data of the first region.

[0043] The identified population distribution refers to the number of identified people in the first region. In some embodiments, the identified population distribution may include the number of identified people in the first region during at least one first time period.

[0044] The first time period refers to the time period when the quality of the sensing data captured by the sensing device meets the preset quality condition. The preset quality condition can be preset by the system or manually. For example, the preset quality condition can be that the resolution of the sensing data captured by the sensing device is not lower than 1080P, etc.

[0045] The first time period can be preset by the user. For example, when the sensing device deployed in the sub-region is a camera without night vision function, the resolution of the sensing data captured by the sensing device during the day is not lower than 1080P, while the resolution of the sensing data captured by the sensing device at night is lower than 1080P. The user can determine the corresponding time period during the day as at least one first time period. The length of the first time period can also be preset based on the user. For example, if the length of the first time period is one hour and the corresponding time period during the day includes 6:00 to 18:00, then 6:00 to 7:00 can be a first time period.

[0046] The first flow data refers to the personnel flow data related to the first area. In some embodiments, the first flow data may include the personnel flow direction and the personnel flow volume between the first areas and / or between the first area and the second area within at least one first time period. For example, the first flow data may be that 12 people flow from the first area A to the second area B between 8 o'clock and 9 o'clock.

[0047] In some embodiments, the processor can obtain and identify the population distribution and the first flow data in various ways. For example, the processor can obtain the identified population distribution of the first area based on the sensing data captured by the sensing devices deployed in the first area, and then through an image recognition algorithm. For another example, the processor can, based on the sensing data captured by the sensing devices deployed in the first area, combined with the orientations of the first area and the second area, obtain the personnel flow volume and the personnel flow direction between the first areas and / or between the first area and the second area within the first time period through an image recognition algorithm, and then determine the first flow data.

[0048] Step 230: Determine the population simulation result based on the identified population distribution of the first area.

[0049] The population simulation result refers to the visualization result of the simulated population distribution in the area to be simulated.

[0050] The simulated population distribution refers to the population distribution data predicted through simulation. In some embodiments, the simulated population distribution may include the simulated population distribution of the first area, the simulated population distribution of the second area, and the second flow data. Among them, the simulated population distribution of the first area may include the number of people in the first area predicted after the first time period, and the simulated population distribution of the second area may include the number of people in the second area predicted after the first time period.

[0051] The second flow data refers to the personnel flow data related to the first area and the second area. In some embodiments, the second flow data may include the personnel flow direction and the personnel flow volume between the first areas, or between the first area and the second area, or between the second areas within at least one first time period. It should be noted that in the first flow data, the personnel flow direction and the personnel flow volume between the first areas, or between the first area and the second area within at least one first time period are actually measured through sensing data, etc., while the second flow data is determined based on prediction and calculation.

[0052] In some embodiments, the processor can determine the simulated population distribution in various ways based on the identified population distribution of the first area.

[0053] In some embodiments, the processor may set a corresponding basic number of people for the second area, determine this basic number of people as the population distribution in the second area before the first time period; and based on the first flow data, the identified population distribution, and the basic number of people corresponding to the second area, deduce the simulated population distribution.

[0054] The basic number of people refers to the predicted population distribution in the second area before the first time period. In some embodiments, the processor may determine the basic number of people corresponding to the second area through a first preset rule based on the data recognition difficulty of the first area.

[0055] The data recognition difficulty refers to the difficulty of recognizing the data corresponding to the sub-area. The higher the resolution of the sensing data captured by the sensing devices deployed in the sub-area and the shorter the time for the processor to recognize the sensing data captured by the sensing devices deployed in the sub-area, the smaller the data recognition difficulty corresponding to this sub-area.

[0056] The first preset rule refers to the rule preset for determining the basic number of people corresponding to the second area. An exemplary first preset rule may be: the greater the data recognition difficulty of the first area adjacent to the second area, the smaller the corresponding basic number of people in the second area; conversely, the smaller the data recognition difficulty of the first area adjacent to the second area, the closer the basic number of people in the second area is to the mean value of the identified population distributions of all first areas within the area to be simulated.

[0057] The processor may deduce the simulated population distribution of the first area and the simulated population distribution of the second area based on the first flow data, the identified population distribution, and the basic number of people corresponding to the second area; and combine the simulated population distribution of the first area and the simulated population distribution of the second area to deduce the second flow data.

[0058] For example, if the first time period is from 8:00 to 9:00, the identified population distribution in the first area A at 8:00 is 20 people, the first flow data is that 12 people flow from the first area A to the second area B within the first time period, and the basic number of people corresponding to the second area B is 10, then the simulated population distribution in the first area A is 8 people, and the simulated population distribution in the second area B is 22 people.

[0059] For another example, if the first time period is from 8:00 to 9:00, the identified population distribution in the first area A at 8:00 is 12 people, the basic number of people corresponding to the second area B is 10, the simulated population distribution in the first area A is 10 people, the simulated population distribution in the second area B is 12 people, the simulated population distributions of other first areas are the same as the identified population distributions, and the simulated population distributions of other second areas are the same as the basic number of people, then the second flow data is: 2 people flow from the first area A to the second area B from 8:00 to 9:00.

[0060] In some embodiments, the processor may determine the simulated population distribution based on the simulation model. For more content, please refer to Figure 3, Figure 4 Related description.

[0061] In some embodiments, the processor can visualize the simulated crowd distribution through simulation and visualization techniques such as a crowd simulation model to determine the crowd simulation result.

[0062] Step 240: Based on the crowd simulation result, determine a planning suggestion, and push the crowd simulation result and the planning suggestion to the user terminal.

[0063] The planning suggestion refers to relevant suggestions for urban planning. For example, the planning suggestion can include areas where public service projects need to be added in the city.

[0064] In some embodiments, the processor can determine a planning suggestion based on the crowd simulation result. For example, the processor can, based on the crowd simulation result, determine the areas with a simulated crowd distribution greater than the number threshold or the areas with the inflow or outflow of personnel in the first flow data greater than the flow threshold as the areas where public service projects need to be added in the planning suggestion. The number threshold and the flow threshold can be preset by the system or manually.

[0065] In some embodiments, the processor can push the crowd simulation result and the planning suggestion to the user terminal based on the network.

[0066] In some embodiments of this specification, by identifying the crowd distribution to determine the crowd simulation result and then determining the planning suggestion, it is possible to predict the complete crowd distribution using incomplete data and determine the crowd simulation result, reducing the data acquisition cost in urban planning and construction; the crowd simulation result can evaluate the changes in the number of people in different areas and the crowd aggregation and transfer situations, serving as reference data for road, traffic, and municipal facility planning, being able to quantify the crowd situation in the area, facilitating urban planning and design, and improving the rationality of urban planning.

[0067] In some embodiments, the processor can perform at least one round of simulation model prediction before determining the planning suggestion to obtain at least one candidate simulation result; based on the at least one candidate simulation result, determine the target simulation result.

[0068] The candidate simulation result refers to the crowd simulation result that is a candidate for the target simulation result. In some embodiments, the processor can determine the output of the simulation model as the candidate simulation result based on the simulation model. For the relevant description of the simulation model, refer to Figure 3 and Figure 4 Related description.

[0069] Simulation model prediction refers to predicting the simulation results of the population for different first time periods based on a simulation model. The processor can input the first simulation atlas constructed based on the identified population distribution and the first flow data for different first time periods into the simulation model to obtain different candidate simulation results.

[0070] In some embodiments, the number of rounds of performing simulation model prediction can be determined based on the environmental data for a preset time period.

[0071] The preset time period refers to a pre-set time period. The preset time period can be preset by the system or manually. For example, the preset time period can be the past 12 hours, the past day, etc.

[0072] The environmental data refers to the data related to the environment of the area to be simulated. For example, temperature, humidity, etc. The processor can obtain the environmental data based on temperature sensors, humidity sensors, etc. deployed in the area to be simulated, or can access a meteorological website through the network to obtain the environmental data for the preset time period.

[0073] In some embodiments, the processor can determine the number of rounds of performing simulation model prediction based on the environmental data for a preset time period in various ways.

[0074] In some embodiments, the processor can determine the environmental change for the preset time period based on the environmental data for the preset time period, and determine the number of rounds of performing simulation model prediction based on the environmental change. The environmental change for the preset time period refers to the magnitude of the change in the environmental data within the preset time period. The processor can determine the ratio of the standard deviation of the environmental data at multiple time points within the preset time period to the mean value of the environmental data as the environmental change for the preset time period.

[0075] Exemplarily, the smaller the environmental change for the preset time period, the fewer the number of rounds of performing simulation model prediction; conversely, the larger the environmental change for the preset time period, the more the number of rounds of performing simulation model prediction.

[0076] The larger the environmental change for the preset time period indicates that the environment of the current area to be simulated is more complex, and the gap between the candidate simulation results corresponding to different first time periods is larger. In some embodiments of this specification, determining the number of rounds of performing simulation model prediction based on the environmental data for the preset time period can appropriately increase the number of prediction rounds when the environmental change is large to obtain more candidate simulation results, which helps to determine a more accurate target simulation result subsequently.

[0077] The target simulation result refers to the population simulation result that ultimately serves as the basis for determining the planning recommendation.

[0078] In some embodiments, the processor may calculate the similarity of candidate simulation results corresponding to two adjacent rounds of simulation model predictions by methods such as Euclidean distance and cosine similarity, and use one of the two candidate simulation results with the highest similarity as the target simulation result.

[0079] In some embodiments, the processor may also determine the target simulation result based on the data determination model. For more details, please refer to Figure 5 the relevant description.

[0080] In some embodiments of this specification, before determining the planning suggestion, at least one round of simulation model prediction is performed, and based on at least one candidate simulation result, the target simulation result is determined. By repeating the prediction multiple times, the most practical population simulation result can be selected from multiple sets of data as the basis for determining the planning suggestion, which helps to improve the accuracy of the population simulation result and the rationality of urban planning.

[0081] Figure 3 is an exemplary diagram showing the generation of the population simulation result according to some embodiments of this specification.

[0082] In some embodiments, the processor may construct a first simulation map 320 based on the identified population distribution 311 and the first flow data 312; based on the first simulation map, determine the simulated population distribution 341 in the first area, the simulated population distribution 342 in the second area, and the second flow data 343 through the simulation model 330; based on the simulated population distribution in the first area, the simulated population distribution in the second area, and the second flow data, determine the simulated population distribution 350 in the area to be simulated; and generate the population simulation result 360 based on the simulated population distribution.

[0083] For the relevant descriptions of the identified population distribution, the first flow data, the simulated population distribution in the first area, the simulated population distribution in the second area, the second flow data, the simulated population distribution, and the population simulation result, please refer to Figure 2 and its relevant descriptions.

[0084] The first simulation map is a map used to represent the relationships between various information in the first area and the second area. In some embodiments, the processor may construct the first simulation map based on the identified population distribution and the first flow data. The first simulation map may include multiple nodes and multiple edges. The nodes of the first simulation map may include a first node and a second node.

[0085] The first node refers to the node corresponding to the first area. In some embodiments, the node attributes of the first node may include the identified population distribution of the first area corresponding to the first node.

[0086] The second node refers to the node corresponding to the second region. In some embodiments, the node attribute of the second node can be set to a null value.

[0087] In some embodiments, the node attributes of the first node and the second node may further include the region types of the corresponding first region or second region.

[0088] The region type refers to the type of the environment where the sub-region of the region to be simulated is located. For example, the region type may include parks, schools, business districts, squares, sidewalks, footpaths, etc. The processor can obtain the region types corresponding to different first regions and second regions based on maps, user inputs, or storage devices.

[0089] The population flow in different region types is different. In some embodiments of this specification, using the region type as the node attribute of the first node and the second node helps to improve the accuracy of simulating the population distribution.

[0090] The edges of the first simulation graph are used to connect adjacent nodes, and the edges can reflect the adjacency relationship between a pair of adjacent nodes corresponding to the edges. There are edge attributes for the edges between the first nodes corresponding to the first flow data, or between the first nodes corresponding to the first flow data and the second node. The edge attributes are the personnel flow direction and the personnel flow volume between the sub-regions corresponding to the adjacent nodes in different first time periods. For the remaining edges, that is, the edges between the nodes corresponding to the regions where there is no personnel flow in all first time periods, the edge attributes are null.

[0091] In some embodiments, the edge attribute of each edge may further include a transition probability interval.

[0092] The transition probability interval of an edge refers to the probability interval for personnel to flow between the sub-regions corresponding to the two nodes connected by the edge.

[0093] In some embodiments, the midpoint of the transition probability interval of each edge is determined based on the average situation of the historical second flow data corresponding to this edge in the region to be simulated.

[0094] The historical second flow data refers to the personnel flow volume and the personnel flow direction between the sub-regions corresponding to the two nodes connected by the edge in the historical first time period. The processor can determine the average situation of the historical second flow data corresponding to this edge by taking the mean value of the personnel flow volumes between the sub-regions corresponding to the two nodes connected by the edge in multiple historical first time periods.

[0095] In some embodiments, the processor can determine the average situation of the historical second flow data corresponding to this edge as the midpoint of the transition probability interval of this edge.

[0096] In some embodiments, the interval width of the transition probability interval for each edge can be determined based on the regional difference degree corresponding to that edge. In some embodiments, the greater the regional difference degree, the wider the corresponding interval width.

[0097] The regional difference degree corresponding to an edge refers to the degree of difference between the sub-regions corresponding to the two nodes connected by the edge. In some embodiments, the processor can determine the regional difference degree based on the regional types corresponding to the sub-regions of the two nodes connected by the edge through a second preset rule. For the relevant description of regional types, reference can be made to Figure 3 the relevant description above.

[0098] The second preset rule refers to a rule preset for determining the regional difference degree, and the second preset rule can be determined by the user based on the actual situation. An exemplary second preset rule can be: the same regional type, the smaller the regional difference degree; different regional types, the greater the regional difference degree.

[0099] The processor can determine the transition probability interval based on the interval midpoint and the interval width.

[0100] In some embodiments of this specification, taking the transition probability interval as an edge attribute helps to improve the accuracy of simulating the population distribution.

[0101] In some embodiments, the processor can also construct the first simulation map based on the identified population distribution and the confidence population distribution of the first region.

[0102] The confidence population distribution refers to the number interval of the preset first region in multiple different second time periods. In some embodiments, the confidence population distribution includes the confidence number interval of the first region in at least one second time period.

[0103] The second time period refers to the time period other than the first time period. For example, if 8:00 - 9:00 is the first time period and 10:00 - 11:00 is the first time period, then 9:00 - 10:00 is the second time period.

[0104] The confidence number interval refers to the number interval that may be included in the first region during the second time period as preset. In some embodiments, the confidence number interval can be preset by the system or manually. For example, the processor can set different number ranges for different regional types, and based on the regional type of the first region and the corresponding number range, determine the confidence number interval. The processor can determine the maximum and minimum values of the identified number distribution of the first regions of the same regional type as the upper and lower intervals of the aforementioned number range, and randomly determine the confidence number interval corresponding to the first region of this regional type within this number range.

[0105] In some embodiments, the confidence population distribution may be related to the region type of the first region. For example, the processor may determine the highest value of the identified population distribution among all the first regions with the same region type as the first region as the confidence population distribution of the first region.

[0106] In some embodiments, the confidence population distribution may also be related to the spatial confidence of the first region.

[0107] The spatial confidence refers to the degree of credibility of the data of the first region in space.

[0108] In some embodiments, the spatial confidence may be determined based on the data volume of the corresponding first region and the data source credibility. Exemplarily, the larger the data volume of the first region and the higher the data source credibility, the higher the corresponding spatial confidence.

[0109] The data volume refers to the number of data containing population information in the first region. The processor may obtain the number of data containing population information corresponding to the first region based on the storage device to determine the data volume of the first region.

[0110] The data source credibility refers to the degree of credibility of the data source for obtaining the data containing population information of the first region. The data sources of the first region may include various types. For example, the data sources of the first region may include sensing devices, human monitoring, model prediction, etc. The processor may pre-test each data source to judge the deviation and mean value among multiple experimental data obtained by the same data source in multiple tests under the same experimental scenario; the smaller the deviation, the smaller the difference between the mean value and the actual data value in this experimental scenario, and the higher the data source credibility corresponding to this data source. The actual data value, as one of the experimental parameters, may be determined in advance by the user before the experiment.

[0111] In some embodiments, the higher the spatial confidence of the first region, the smaller the interval width of the confidence population distribution of the first region.

[0112] In some embodiments, the confidence population distribution may also be related to the spatial confidence and time confidence of the first region.

[0113] The time confidence refers to the degree of confidence of the data of the first region in time. In some embodiments, the processor may determine the time confidence by taking the ratio of the simulated population distribution of the first region to the identified population distribution of the first region at the same time point.

[0114] Exemplarily, the time period from 8:00 to 9:00 is the first time period. The identified population distributions of the first area A, the first area B, and the first area C at 8:00 are 10 people, 20 people, and 30 people respectively. During this first time period, 7 people flow out from the first area C to the first area B, and 3 people flow out from the first area A to the first area C; the simulated population distribution of the first area C is 26 people, but the identified population distribution of the first area C at 9:00 is 33 people. Then the time confidence level of the first area C is 26 / 33.

[0115] In some embodiments, the higher the time confidence level of the first area, the higher the space confidence level, and the smaller the interval width of the corresponding confidence population distribution.

[0116] Since the identified population distribution of the first area and the second flow data may come from different methods, there may be conflicts between the identified population distribution and the second flow data. Therefore, the time confidence level can reflect the degree of fit between the identified population distribution and the second flow data. In some embodiments of this specification, relating the confidence population distribution to the time confidence level helps to improve the accuracy of the simulated population distribution in subsequent predictions.

[0117] In some embodiments of this specification, relating the space confidence level to the confidence population distribution can determine a more realistic confidence population distribution by combining the confidence level of the data in the first area in space, which helps to make a more accurate prediction of the simulated population distribution subsequently.

[0118] In some embodiments, the node attribute of the first node in the first simulation graph may further include the confidence population distribution of the first area corresponding to the first node.

[0119] In some embodiments of this specification, based on the identified population distribution and the confidence population distribution of the first area, a first simulation graph is constructed, which can make a more accurate prediction of the simulated population distribution by combining the confidence population distribution.

[0120] A simulation model refers to a model used to determine the simulated population distribution of the first area, the simulated population distribution of the second area, and the second flow data. In some embodiments, the simulation model can be a machine learning model, such as a graph neural network model (GNN), etc.

[0121] In some embodiments, the input of the simulation model can include the first simulation graph, and the output can include the simulated population distribution of the first area, the simulated population distribution of the second area, and the second flow data. Among them, the simulated population distribution of the first area can be output through the first node, the simulated population distribution of the second area can be output through the second node, and the second flow data can be output through the edge.

[0122] In some embodiments, the processor may train and obtain a simulation model based on a large number of first training samples with a first label. For example, multiple first training samples with a first label may be input into an initial simulation model, a loss function may be constructed based on the first label and the prediction results of the initial simulation model, the initial simulation model may be iteratively updated based on the loss function, and when the loss function of the initial simulation model meets a preset condition, the simulation model training is completed, where the preset condition may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0123] In some embodiments, the first training samples may include sample simulation graphs. The processor may obtain complete population distribution data for each sub-region of the area to be simulated based on historical data, and construct a sample simulation graph based on the foregoing population distribution data. The complete population distribution data includes the actual population distribution of each sub-region in the area to be simulated, as well as the amount of personnel flow and the direction of personnel flow between sub-regions.

[0124] The first label may include the simulated population distribution of a first region corresponding to the first training sample, the simulated population distribution of a second region, and second flow data. In some embodiments, the processor may randomly select some nodes from the sample simulation graph as second nodes, set the node attributes of the second nodes to be empty, and at the same time determine the node attributes of the second nodes before being set empty as the simulated population distribution of the second region in the first label; the processor may determine the remaining unselected points from the sample simulation graph as first nodes, and use the node attributes of the first nodes as the simulated population distribution of the first region in the first label; the processor may randomly select some edges from the sample simulation graph, set the edge attributes of the selected edges to be empty, and at the same time use the edge attributes of the selected edges before being set empty as the second flow data in the first label. It should be noted that the edges randomly selected by the processor may be the edges formed by the first nodes and the second nodes, or the edges formed by the second nodes and the second nodes, but not the edges formed by the first nodes and the first nodes.

[0125] In some embodiments, the training labels in the training dataset of the simulation model may be determined based on the video data of the area to be simulated.

[0126] Video data refers to data related to the video of the area to be simulated. In some embodiments, the processor may determine the video data in various ways. For example, the processor may obtain video data based on a sensing device or manual shooting.

[0127] In some embodiments, the processor may, based on methods such as manual analysis and machine learning statistics, obtain the population distribution data in the video data, and then Figure 3 determine the first label in the training dataset of the simulation model by the foregoing method.

[0128] In some embodiments of the present specification, determining the first label from video data can improve the accuracy of determining the first label, which helps to accurately train the simulation model.

[0129] In some embodiments, the processor may summarize the results output by the simulation model to obtain the simulated population distribution.

[0130] The generation of the crowd simulation results is similar to the foregoing, and reference may be made to Figure 2 the relevant description above.

[0131] In some embodiments of the present specification, inputting the first simulation atlas into the simulation model can reflect the topological structure between sub-regions of the area to be simulated, and the machine learning model can automatically, quickly, and accurately output the simulated population distribution.

[0132] Figure 4 is an exemplary schematic diagram of the simulation model shown in some embodiments of the present specification.

[0133] In some embodiments, the simulation model may include an inference layer 331 and a correction layer 332.

[0134] In some embodiments, the inference layer may determine the predicted population distribution of the second region based on the first simulation atlas. The inference layer may be a machine learning model, such as a graph neural network model, etc.

[0135] The predicted population distribution refers to the number of people in the predicted second region within the first time period.

[0136] In some embodiments, the input of the inference layer may include the first simulation atlas 320, and the output may include the predicted population distribution 421 of the second region. For more descriptions about the first simulation atlas, reference may be made to Figure 3 the relevant description above.

[0137] In some embodiments, the input of the inference layer may further include environmental data. For more content about environmental data, reference may be made to Figure 2 the relevant description above.

[0138] Environmental data has a certain impact on human outdoor activities. In some embodiments of the present specification, by using environmental data as the input of the inference layer, the population distribution of the second region can be predicted in combination with environmental data, which helps to more reasonably determine the planning suggestions in combination with the simulated population distribution.

[0139] In some embodiments, the inference layer may be trained based on a large number of second training samples with the second label. The training of the inference layer is similar to that of the Figure 3 simulation model in Figure 3 the relevant description above.

[0140] In some embodiments, the second training sample may include a sample simulation map. For the acquisition of the sample simulation map, reference may be made to Figure 3 the relevant description above. When the input of the inference layer includes environmental data, the second training sample may include the historical environmental data corresponding to the sample simulation map. The historical environmental data may be obtained based on historical data.

[0141] The second label may be the actual population distribution of the second region corresponding to the second training sample. The second label may be obtained based on historical data.

[0142] In some embodiments, the correction layer may determine the simulated population distribution and the second flow data of the second region based on the second simulation map. The correction layer may be a machine learning model, such as a graph neural network model, etc.

[0143] In some embodiments, the input of the correction layer may include the second simulation map 420; the output may include the simulated population distribution of the first region, the simulated population distribution of the second region, and the second flow data. Among them, the simulated population distribution of the first region may be output through the first node, the simulated population distribution of the second region may be output through the second node, and the second flow data may be output through the edge.

[0144] The second simulation map refers to a map that includes the predicted population distribution of the second region on the basis of the first simulation map. In some embodiments, the processor may construct the second simulation map based on the first simulation map and the predicted population distribution of the second region.

[0145] Among them, the node attributes of the first node of the second simulation map include the identified population distribution and the confidence population distribution of the first region corresponding to the first node; the node attributes of the second node include the predicted population distribution of the second region corresponding to the second node. The edges of the second simulation map are similar to those of the first simulation map, and reference may be made to Figure 3 the relevant description above.

[0146] In some embodiments, the processor may be trained and obtained based on a large number of third training samples with the third label. The training of the correction layer is similar to that of Figure 3 the simulation model in Figure 3 the relevant description above.

[0147] In some embodiments, the third training sample may include a sample simulation map. For the acquisition of the sample simulation map, reference may be made to Figure 3 the relevant description above.

[0148] The third label may include the simulated population distribution of the first region, the simulated population distribution of the second region, and the second flow data corresponding to the third training sample. The acquisition of the third label is similar to that of the first label, and reference may be made to Figure 3The related description above.

[0149] In some embodiments of this specification, by dividing the simulation model into different layers, it is possible to predict the node attributes of the second nodes missing in the first simulation atlas, improving the data integrity of the input correction layer and facilitating the improvement of the accuracy of the simulated population distribution.

[0150] Figure 5 It is an exemplary schematic diagram for determining the target simulation result shown in some embodiments of this specification.

[0151] In some embodiments, for the candidate simulation result 510, the processor may determine the requirement compliance 531 and data accuracy 532 based on the data determination model 520; determine the simulation effect 540 based on the requirement compliance and data accuracy; and determine the target simulation result 550 based on the simulation effect. For more descriptions about the candidate simulation result and the target simulation result, reference can be made to Figure 2 The related description above.

[0152] The data determination model refers to a model used to determine the requirement compliance and data accuracy. In some embodiments, the data determination model may be a machine learning model, such as a neural network model, etc.

[0153] The requirement compliance is an index used to measure the degree of compliance between the candidate simulation result and the planned requirements.

[0154] The data accuracy is an index used to measure the accuracy of the candidate simulation result.

[0155] In some embodiments, the input of the data determination model may include the candidate simulation result, and the output may include the requirement compliance and data accuracy.

[0156] In some embodiments, the input of the data determination model further includes the environmental data 410. For more descriptions about the environmental data, reference can be made to Figure 2 And its related description.

[0157] In some embodiments of this specification, determining the requirement compliance and data accuracy based on the environmental data can make the determination of the requirement compliance and data accuracy conform to the actual activities of humans, contributing to improving the accuracy of the simulation effect determination.

[0158] In some embodiments, the processor may train the data determination model based on a large number of fourth training samples with the fourth label. The training process of the data determination model is similar to that of the simulation model, and reference can be made to Figure 3 The related description above.

[0159] In some embodiments, the fourth training sample may include the sample population simulation result. The fourth training sample can be obtained based on historical data.

[0160] The fourth label can be the demand fitting degree and data accuracy corresponding to the fourth training sample. The processor can determine the difficulty of determining the planning suggestion based on the sample population simulation result and performing the planning according to the planning suggestion, and determine the reciprocal of the difficulty of performing the planning as the demand fitting degree in the fourth label; and determine the final planning effect as the data accuracy in the fourth label.

[0161] The processor can determine the difficulty of performing the planning based on the consumption amount of computing resources and the amount of data to be collected during the planning. The greater the consumption amount of computing resources and the greater the amount of data to be collected, the greater the difficulty. The processor can determine the average value of the user's score for the final planning effect as the data accuracy.

[0162] In some embodiments, the processor can determine the simulation effect in various ways based on the demand fitting degree and data accuracy. For example, the higher the demand fitting degree and the higher the data accuracy, the better the corresponding simulation effect.

[0163] In some embodiments, the processor can determine the simulation effect by weighting based on the demand fitting degree and data accuracy, and the weights are related to the planning demand quantity and the planning demand quality.

[0164] The planning demand quantity refers to the quantity of indicators that need to be planned in the area to be simulated. The indicators for planning can include road planning indicators, parking lot planning indicators, rest area planning indicators, etc. The planning demand quantity can be determined based on the statistics of citizens, on-site surveys, etc.

[0165] The planning demand quality refers to the quality of planning for the area to be simulated. The planning demand quality can be preset by the system or manually according to the actual needs.

[0166] In some embodiments, the weight of the demand fitting degree is positively correlated with the planning demand quantity and negatively correlated with the planning demand quality; the weight of the data accuracy is negatively correlated with the planning demand quantity and positively correlated with the planning demand quality.

[0167] In some embodiments of this specification, based on the planning demand quantity and the planning demand quality, determining the weights corresponding to the demand fitting degree and data accuracy can combine the actual needs to determine the simulation effect after balancing the planning demand quantity and the planning demand quality.

[0168] In some embodiments, the processor can determine the candidate simulation result with the highest simulation effect as the target simulation result.

[0169] In some embodiments of this specification, determining the target simulation result based on the simulation effect can select the population simulation result with the best simulation effect as the planning basis before determining the planning suggestion, improving the rationality of urban planning.

[0170] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0171] Meanwhile, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0172] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and laminar flow hoods of this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0173] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0174] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of embodiments are, in some examples, modified by the modifiers "about", "approximately", or "substantially". Unless otherwise stated, "about", "approximately", or "substantially" indicate that the said numbers are allowed to have a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0175] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, as well as the documents that limit the broadest scope of the claims of this specification (currently or subsequently attached to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0176] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered to be consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for simulating urban crowd distribution, characterized in that: Executed by the processor, including: Determine a first region and a second region from the region to be simulated, wherein the first region is a sub-region of the region to be simulated whose data completeness satisfies a preset data condition, and the second region is a sub-region of the region to be simulated whose data completeness does not satisfy the preset data condition; Acquire the distribution of identified people in the first area and first flow data, wherein the distribution of identified people includes the number of identified people in the first area within at least one first time period; Determine a crowd simulation result based on the identified crowd distribution in the first area, wherein the crowd simulation result is a visualization result of the simulated crowd distribution in the area to be simulated; Determine a planning suggestion based on the crowd simulation result, and push the crowd simulation result and the planning suggestion to a user terminal; The simulated crowd distribution includes the simulated crowd distribution of the first area, the simulated crowd distribution of the second area, and second flow data, the second flow data includes the flow direction and flow volume of people between the first areas, between the first area and the second area, and between the second area in the at least one first time period, and the determination of the crowd simulation result based on the identified crowd distribution of the first area includes: Based on the identified population distribution and the first flow data, a first simulation map is constructed, wherein the first flow data includes the personnel flow direction and personnel flow volume between the first areas and between the first area and the second area in the at least one first time period, the first simulation map includes a first node and a second node and an edge, the first node corresponds to the first area, the second node corresponds to the second area, the edge reflects the adjacency relationship between a pair of adjacent nodes corresponding to the edge, and the node attribute of the first node includes the identified population distribution in the first area corresponding to the first node; Based on the first simulation map, determining the simulated population distribution in the first area, the simulated population distribution in the second area, and the second flow data through a simulation model, wherein the simulation model is a machine learning model; Determining the simulated crowd distribution in the area to be simulated based on the simulated crowd distribution in the first area, the simulated crowd distribution in the second area, and the second flow data; Based on the simulated crowd distribution, the crowd simulation result is generated.

2. The method according to claim 1, characterized in that The method comprises: Based on the identified population distribution and the trusted population distribution in the first area, the first simulation map is constructed, the trusted population distribution includes the trusted population interval of the first area in at least one second time period, and the trusted population distribution in the first area is related to the area type of the first area; the node attributes of the first node in the first simulation map also include the trusted population distribution of the first area corresponding to the first node.

3. The method according to claim 1, characterized in that The method further comprises: Before determining the planning suggestion, performing at least one round of simulation model prediction to obtain at least one candidate simulation result; Based on the at least one candidate simulation result, a target simulation result is determined.

4. The method according to claim 3, characterized in that The determining of the target simulation result based on the at least one candidate simulation result comprises: For the candidate simulation results, determining the degree of fit to requirements and the data accuracy based on a data determination model, wherein the data determination model is a machine learning model; Determining a simulation effect based on the demand fit and the data accuracy; Based on the simulation effect, the target simulation result is determined.

5. A city crowd distribution simulation system, characterized in that: include: A first determination module is configured to determine a first area and a second area from the area to be simulated, wherein the first area is a sub-area of ​​the area to be simulated whose data completeness satisfies a preset data condition, and the second area is a sub-area of ​​the area to be simulated whose data completeness does not satisfy the preset data condition; An acquisition module is configured to acquire the distribution of identified people in the first area and first flow data, wherein the distribution of identified people includes the number of identified people in the first area within at least one first time period; A second determination module is configured to determine a crowd simulation result based on the identified crowd distribution in the first area, wherein the crowd simulation result is a visualization result of the simulated crowd distribution in the area to be simulated; A third determination module is configured to determine a planning suggestion based on the crowd simulation result, and push the crowd simulation result and the planning suggestion to a user terminal; The simulated crowd distribution includes the simulated crowd distribution of the first area, the simulated crowd distribution of the second area, and second flow data, the second flow data includes the flow direction and flow volume of people between the first areas, between the first area and the second area, and between the second areas in the at least one first time period, and the second determination module is further configured as follows: Based on the identified population distribution and the first flow data, a first simulation map is constructed, wherein the first flow data includes the personnel flow direction and personnel flow volume between the first areas and between the first area and the second area in the at least one first time period, the first simulation map includes a first node and a second node and an edge, the first node corresponds to the first area, the second node corresponds to the second area, the edge reflects the adjacency relationship between a pair of adjacent nodes corresponding to the edge, and the node attribute of the first node includes the identified population distribution in the first area corresponding to the first node; Based on the first simulation map, determining the simulated population distribution in the first area, the simulated population distribution in the second area, and the second flow data through a simulation model, wherein the simulation model is a machine learning model; Determining the simulated crowd distribution in the area to be simulated based on the simulated crowd distribution in the first area, the simulated crowd distribution in the second area, and the second flow data; Based on the simulated crowd distribution, the crowd simulation result is generated.

6. The system according to claim 5, characterized in that The second determining module is further configured to: Based on the identified population distribution and the trusted population distribution of the first area, the first simulation map is constructed, the trusted population distribution includes the trusted population interval of at least one second time period of the corresponding first area, and the trusted population distribution of the first area is related to the area type of the first area; the node attributes of the first node in the first simulation map also include the trusted population distribution of the first area corresponding to the first node.

7. A device for simulating the distribution of urban crowds, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the urban crowd distribution simulation method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the urban crowd distribution simulation method as described in any one of claims 1 to 4.

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