CN111553517A - Road optimization method, system, terminal and computer readable storage medium - Google Patents
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Abstract
The invention relates to the field of data visualization, and discloses a road optimization method, a system, a terminal and a storage medium, wherein the method comprises the following steps: acquiring current spatial data of a road to be optimized, current road grade, historical flow of people and/or vehicles and spatial data corresponding to influence factors of the road to be optimized; obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors; judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized; if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized; and optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized. The method solves the problem that the congestion effect is poor by adopting a transformation scheme generated by a pure numerical analysis method for the road.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a road optimization method, a road optimization system, a road optimization terminal, and a computer-readable storage medium.
Background
Urban roads are one of important traffic infrastructures, and the development of the urban roads changes the travel mode of people to a great extent and shortens the space-time distance. Along with the rapid development of economy and the speed increase of urbanization process, the existing roads can not match with the rapidly increasing urban population and the demand of vehicles on traffic roads, so that the urban traffic jam problem is increasingly prominent. In order to solve the problem of urban traffic congestion, the existing road needs to be re-planned and reconstructed. The current road planning method is based on a pure numerical value, and carries out pure numerical value analysis on the congestion reasons of the current road to generate a transformation scheme, but the pure numerical value analysis cannot visually reflect the congestion reasons, so that the generated transformation scheme has poor effect of solving the road congestion.
Disclosure of Invention
The invention mainly aims to provide a road optimization method, a road optimization system, a road optimization terminal and a computer readable storage medium, and aims to solve the technical problem that the improvement scheme generated by a pure numerical analysis method for roads in the prior art is poor in congestion solving effect.
In order to achieve the above object, the present invention provides a road optimization method, comprising the steps of:
acquiring current spatial data of a road to be optimized, current road grade, historical flow of people and/or vehicles and spatial data corresponding to influence factors of the road to be optimized;
obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized;
and optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized.
Optionally, the step of obtaining the demand level of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influencing factors includes:
obtaining the distance between the road to be optimized and each corresponding influence factor according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
inputting the distance between the road to be optimized and each corresponding influence factor and the preset weight corresponding to each influence factor into the first road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xiFor the distance, f, between the road to be optimized and the corresponding i-th influencing factoriThe preset weight of the ith influence factor of the road to be optimized is N, and the N is the total number of the influence factors of the road to be optimized;
and inquiring the road grade corresponding to the grade score of the road to be optimized from a preset mapping relation between the grade score of the road and the road grade so as to determine the inquired road grade as the demand grade of the road to be optimized.
Optionally, the step of obtaining the demand level of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influencing factors includes:
dividing a road to be optimized into at least two sub-roads so as to obtain the spatial data of each sub-road according to the current spatial data of the road to be optimized;
acquiring the corresponding influence factors of each sub-road according to the spatial data of each sub-road and the spatial data corresponding to the influence factors;
obtaining the distance between each sub-road and the corresponding influence factor according to the spatial data of each sub-road and the spatial data of the corresponding influence factor;
inputting the distance between each sub-road and the corresponding influence factor and the preset weight of each influence factor into the second road scoring model
In obtaining a review of the road to be optimizedScore, wherein F is the score of the road to be optimized, xijIs the distance between the jth sub-road and the corresponding ith influence factor, fijIs a preset weight, N, of the jth sub-road and the corresponding ith influence factorjThe total number of the influence factors of the jth section of sub-road is, and M is the total number of the sub-roads of the road to be optimized;
and inquiring the road grade corresponding to the grade score of the road to be optimized from a preset mapping relation between the grade score of the road and the road grade so as to determine the inquired road grade as the demand grade of the road to be optimized.
Optionally, the step of generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized includes:
importing the current spatial data into GIS software to generate a two-dimensional model of a road to be optimized;
and carrying out three-dimension on the two-dimensional model according to the description information of each object in the current space data in the two-dimensional model so as to generate the current three-dimensional model of the road to be optimized.
Optionally, the step of optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized, the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized includes:
generating a corresponding people and/or vehicle flow distribution layer according to the historical people and/or vehicle flow;
carrying out superposition analysis on the correspondingly generated human and/or vehicle flow distribution layer and the current three-dimensional model to obtain congestion condition information of the road to be optimized;
optimizing the current spatial data of the road to be optimized according to the congestion condition information to obtain the optimized current spatial data, and taking the optimized current spatial data as candidate optimized spatial data of the road to be optimized;
performing road congestion verification on candidate optimization space data of the road to be optimized according to the correspondingly generated people and/or vehicle flow distribution layer;
and if the congestion verification is unqualified, returning and executing the congestion condition information according to the road to be optimized, optimizing the current spatial data of the road to be optimized until the congestion verification is qualified, and taking the candidate optimized spatial data qualified in the congestion verification as the optimized spatial data of the road to be optimized.
Optionally, the step of performing road congestion verification on the candidate optimization space data of the road to be optimized according to the correspondingly generated people and/or traffic flow distribution map layer includes:
generating a candidate optimization three-dimensional model of the road to be optimized according to the candidate optimization space data of the road to be optimized;
and performing superposition analysis on the correspondingly generated human and/or vehicle flow distribution image layer and the candidate optimization three-dimensional model, and judging whether congestion occurs, wherein the verification is qualified when the congestion does not occur.
Optionally, if there are at least two candidate optimized space data qualified for congestion verification, the step of using the candidate optimized space data qualified for congestion verification as the optimized space data of the road to be optimized includes:
calculating transformation cost corresponding to candidate optimized space data qualified by congestion verification according to current space data of a road to be optimized and the candidate optimized space data qualified by congestion verification;
and selecting candidate optimization space data which is qualified by congestion verification and has the lowest reconstruction cost as the optimization space data of the road to be optimized.
Further, to achieve the above object, the present invention provides a road optimizing system comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring current spatial data of a road to be optimized, a current road grade, historical flow of people and/or vehicles and spatial data corresponding to influence factors of the road to be optimized;
the obtaining module is used for obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
the judging module is used for judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
the generation model is used for generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized if the current three-dimensional model is not the same as the model;
and the optimization module is used for optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized.
Furthermore, to achieve the above object, the present invention also provides a road optimization terminal comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the computer program, when executed by the processor, implementing the steps of the road optimization method as described above.
Furthermore, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the road optimization method as described above.
According to the road optimization method, the road optimization system, the road optimization terminal and the computer readable storage medium, the current spatial data of the road to be optimized, the current road grade, the historical flow of people and/or vehicles and the spatial data corresponding to the influence factors of the road to be optimized are obtained; obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors; judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized; if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized; and optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized. Therefore, roads which are determined to be optimized are screened out according to the grading model, then three-dimensional modeling is carried out on the roads based on the spatial data of the roads, and the congestion condition of the roads is analyzed on the three-dimensional modeling based on the historical pedestrian flow and the historical vehicle flow, so that more details are provided, a road designer can integrally preview the congestion condition of the whole traffic road from macro to micro, the road designer can design an optimization scheme for pertinently solving congestion, and the solution effect on the congestion problem is improved.
Drawings
FIG. 1 is a schematic diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a road optimization method according to the present invention;
FIG. 3 is a detailed flowchart of step S20 in the first embodiment of the road optimization method according to the present invention;
FIG. 4 is a detailed flowchart of step S20 in the first embodiment of the road optimization method according to the present invention;
FIG. 5 is a detailed flowchart of step S50 in the second embodiment of the road optimization method according to the present invention;
FIG. 6 is a schematic flow chart of a road optimization method according to a third embodiment of the present invention;
FIG. 7 is a functional block diagram of the road optimization system of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of a road optimization terminal provided in various embodiments of the present invention. The road optimization terminal comprises components such as a communication module 100, a memory 200 and a processor 300. Those skilled in the art will appreciate that the road optimization terminal shown in fig. 1 may also include more or fewer components than shown, or combine certain components, or a different arrangement of components. Wherein, the processor 300 is connected to the memory 200 and the communication module 100, respectively, and the memory 200 stores thereon a computer program, which is executed by the processor 300 at the same time.
The communication module 100 may be connected to an external device through a network. The communication module 100 may receive data sent by an external device, and may also send data, instructions, and information to the external device, where the external device may be an electronic device such as a mobile phone, a tablet computer, a notebook computer, and a desktop computer.
The memory 200 may be used to store software programs and various data. The memory 200 may mainly 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 (acquiring current spatial data of a road to be optimized), and the like; the storage data area may store data or information created according to the use of the road optimization terminal, or the like. Further, the memory 200 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 300, which is a control center of the road optimization terminal, connects various parts of the entire road optimization terminal using various interfaces and lines, and performs various functions of the road optimization terminal and processes data by operating or executing software programs and/or modules stored in the memory 200 and calling data stored in the memory 200, thereby performing overall monitoring of the road optimization terminal. Processor 300 may include one or more processing units; preferably, the processor 300 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 300.
Although not shown in fig. 1, the road optimization terminal may further include a circuit control module, where the circuit control module is used for being connected to a mains supply to implement power control and ensure normal operation of other components.
Those skilled in the art will appreciate that the configuration of the road optimization termination shown in FIG. 1 is not intended to be limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
Various embodiments of the method of the present invention are presented in terms of the above-described hardware architecture.
Referring to fig. 2, in a first embodiment of the road optimization method of the present invention, the road optimization method includes the steps of:
step S10, acquiring current spatial data of the road to be optimized, the current road grade, the historical flow of people and/or vehicles and spatial data corresponding to the influence factors of the road to be optimized;
in this embodiment, when the terminal may derive the current spatial data of the road to be optimized and the spatial data of the influencing factors of the road to be optimized based on the dimensions of the point, the line and the plane by using the preset GIS software, the current map coordinate system in the GIS software may be used as a reference, or a new spatial reference system may be reassigned. The current spatial data of the road to be optimized comprises total length data and total width data of the road to be optimized, the length, width, curve radius, gradient, walking direction, lane number, lane width, sidewalk number, pedestrian path width, entrance position and width of the road to be optimized, intersection position and width with other roads, overpass position, width and number on the road to be optimized, underground passage position, width and number in the road to be optimized and the like of each section. The influence factors of the road to be optimized include schools, hospitals, markets, residential quarters and the like, and the spatial data of the influence factors mainly include position data, which can be longitude and latitude, and also can be relative positions taking the road to be optimized as a reference system.
The historical pedestrian volume of the road to be optimized comprises historical total pedestrian volume of the road to be optimized and historical pedestrian volume of each of two sides of the road to be optimized, and the historical pedestrian volume can be a pedestrian volume peak value or a pedestrian volume average value counted in a preset time (such as the last year or the last half year) or a pedestrian volume peak value or a pedestrian volume average value counted in a preset time (such as the last year or the last half year) in different time periods.
The historical traffic flow of the road to be optimized comprises historical total traffic flow of the road to be optimized and historical traffic flow of two different trends of the road to be optimized, and the historical traffic flow can be a traffic flow peak value or a traffic flow mean value counted in a preset time (for example, in the last year or last half year) or a traffic flow peak value or a traffic flow mean value counted in the preset time (for example, in the last year or last half year) in different time periods.
Step S20, obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
the grade of the road and the design of the road can accommodate the maximum flow of people and the flow of vehicles, and the maximum flow of people and the flow of vehicles which can be accommodated by the road in different grades are different. Because the influencing factors within a certain preset range from the road to be optimized comprise schools, hospitals, markets, shopping malls, residential quarters and the like, the amount, the occupied area, the scale and the distance from the road influence the pedestrian flow and the traffic flow on the road to be optimized. In the initial design stage of the road, the design grade of the road is determined according to the influence factors within the preset range during design, namely the design grade and the requirement grade of the road at the time are the same. With the development of the periphery of the road, the influence factors of the road are changed, and the demand level of the road is also changed. The terminal can determine the current demand level of the road to be optimized according to the acquired current spatial data of the road to be optimized and the influence factors of the road to be optimized.
Specifically, referring to fig. 3, fig. 3 is a detailed schematic view of a flow of step S20 in the implementation of the present application, and based on the foregoing embodiment, the step S20 includes:
step S21, obtaining the distance between the road to be optimized and each corresponding influence factor according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
according to the current spatial data of the road to be optimized and the spatial data of the influence factors of the road to be optimized, the distance between the road to be optimized and the corresponding influence factors can be calculated, wherein the distance can be the shortest distance between the influence factors and the road to be optimized or the straight-line distance between the influence factors and the entrance, closest to the influence factors, in the road to be optimized.
Step S22, the road to be optimized and the corresponding influences are processedInputting the distance between the factors and the preset weight corresponding to each influence factor into the first road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xiFor the distance, f, between the road to be optimized and the corresponding i-th influencing factoriThe preset weight of the ith influence factor of the road to be optimized is N, and the N is the total number of the influence factors of the road to be optimized;
the terminal can prestore preset weights corresponding to the influence factors, different weights can be preset for the preset weights of the influence factors according to the types of the influence factors, the preset weight of a school is a, the preset weight of a hospital is b, the weight of a market or a market is c, different weights can be set for the influence factors which belong to the same type and have different scales, for example, the weight of the school with the number of students exceeding 1000 is a1, the weight of the school with the number of students 500 plus 1000 is a2, and a1 is greater than a 2. The terminal obtains the distance between each influence factor and the road to be optimized and the preset weight corresponding to each influence factor, and inputs the distance to the first road scoring model
The scoring model can be preset in the terminal, and the first road scoring model is used for carrying out weighted average calculation on the distance between each influence factor and the road to be optimized and the preset weight corresponding to each influence factor to obtain the scoring score of the road to be optimized, wherein F is the scoring score of the road to be optimized, and x is the scoring score of the road to be optimizediFor the distance, f, between the road to be optimized and the corresponding i-th influencing factoriThe preset weight of the ith influence factor of the road to be optimized is N, and the total number of the influence factors of the road to be optimized is N.
Step S23, the road grade corresponding to the grade score of the road to be optimized is inquired from the preset mapping relation between the grade score of the road and the road grade, and the inquired road grade is determined as the requirement grade of the road to be optimized.
The terminal inquires the road grade corresponding to the grade score of the road to be optimized from the preset mapping relation between the grade score of the road and the road grade, and determines the inquired road grade as the requirement grade of the road to be optimized, wherein the preset mapping relation between the grade score of the road and the road grade can be stored in the terminal in advance.
Referring to fig. 4, fig. 4 is a detailed schematic view of a flow of step S20 in the present application, and based on the foregoing embodiment, the step S20 includes:
step S24, dividing the road to be optimized into at least two sub-roads so as to obtain the space data of each sub-road according to the current space data of the road to be optimized;
possibly, under the condition that some roads to be optimized are long and influence factors of the roads are many, the terminal segments the roads to be optimized, divides the roads to be optimized into at least two sub-roads, and correspondingly divides the current spatial data of the roads to be optimized to obtain the spatial data of each sub-road.
Step S25, obtaining the corresponding influence factors of each sub-road according to the space data of each sub-road and the space data corresponding to the influence factors;
the terminal divides the corresponding affected range for each sub-road according to the space data of each sub-road, the affected ranges of each sub-road are not overlapped, and the affected ranges of the sub-roads at two adjacent ends have the same boundary. The terminal divides each influence factor into influence factors of different sub-roads according to the spatial data of each influence factor on the road to be optimized and the influenced range of each sub-road, for example, the influence factor A can be determined to fall within the influenced range of the sub-road B according to the spatial data of the influence factor A, and then the influence factor A is divided into the influence factors of the sub-road B.
Step S26, obtaining the distance between each sub-road and the corresponding influence factor according to the space data of each sub-road and the space data of the corresponding influence factor;
after determining the influence factors of each sub-road, the terminal may calculate a distance between each sub-road and each corresponding influence factor according to the spatial data of each sub-road and the corresponding spatial data of each influence factor, where the distance may be a shortest distance between the influence factor and the corresponding sub-road, or a straight-line distance between the influence factor and an entry in the sub-road closest to the influence factor.
Step S27, inputting the distance between each sub-road and the corresponding influence factor and the preset weight of each influence factor into the second road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xijIs the distance between the jth sub-road and the corresponding ith influence factor, fijIs a preset weight, N, of the jth sub-road and the corresponding ith influence factorjThe total number of the influence factors of the jth section of sub-road is, and M is the total number of the sub-roads of the road to be optimized;
the terminal can prestore preset weights corresponding to the influence factors, different weights can be preset for the preset weights of the influence factors according to the types of the influence factors, the preset weight of a school is a, the preset weight of a hospital is b, the weight of a market or a market is c, different weights can be set for the influence factors which belong to the same type and have different scales, for example, the weight of the school with the number of students exceeding 1000 is a1, the weight of the school with the number of students 500 plus 1000 is a2, and a1 is greater than a 2.
The terminal obtains the distance between each influence factor and the corresponding sub-road and the preset weight corresponding to each influence factor, and inputs the distance to the second road scoring model
The grading model can be preset in the terminal, weighted average calculation is carried out on the distance between each influence factor and the corresponding sub-road and the preset weight corresponding to each influence factor through the grading model to obtain the grading score of the sub-road, the grading scores of the sub-roads are summed to obtain the grading score of the road to be optimized, wherein F is the grading score of the road to be optimized, x is the grading score of the road to be optimized, and the weighting weight is preset according to the grading modelijFor the jth sub-road and the corresponding ith influence factorDistance of (f)ijIs a preset weight, N, of the jth sub-road and the corresponding ith influence factorjThe total number of the influence factors of the jth section of sub-road is, and M is the total number of the sub-roads of the road to be optimized.
Step S28, the road grade corresponding to the grade score of the road to be optimized is inquired from the preset mapping relation between the grade score of the road and the road grade, and the inquired road grade is determined as the requirement grade of the road to be optimized.
The terminal inquires the road grade corresponding to the grade score of the road to be optimized from the preset mapping relation between the grade score of the road and the road grade, and determines the inquired road grade as the requirement grade of the road to be optimized, wherein the preset mapping relation between the grade score of the road and the road grade can be stored in the terminal in advance.
Step S30, judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
after the terminal obtains the demand grade of the road to be optimized, the current road grade of the road to be optimized is compared with the demand grade of the road to be optimized, whether the current road grade of the road to be optimized meets the demand grade of the road to be optimized is judged, if yes, the road to be optimized does not need to be optimized, and if not, the road to be optimized needs to be subsequently optimized.
Step S40, if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized;
when the terminal determines that the current road grade of the road to be optimized does not meet the requirement grade of the road to be optimized, the current spatial data of the road to be optimized is input into software with a three-dimensional modeling function, and a three-dimensional model of the road to be optimized is generated, such as GIS software and BIM software.
Specifically, based on the above embodiment, the step S40 includes:
step S41, importing the current spatial data into GIS software to generate a two-dimensional model of the road to be optimized;
and the terminal imports data such as point dimension, line dimension, surface dimension and the like in the current spatial data of the road to be optimized into GIS software to generate a two-dimensional model of the road to be optimized.
And step S42, performing three-dimensionality on the two-dimensional model according to the description information of each object in the two-dimensional model in the current spatial data to generate a current three-dimensional model of the road to be optimized.
The terminal inputs description information of each object in the two-dimensional model in the current space data into GIS software to carry out three-dimensionality on the two-dimensional model, wherein the description information comprises height data and a scale. The vertical height of each object in the two-dimensional model in the three-dimensional model is obtained according to the height data and the scale of each object, and then the corresponding objects are lifted and combined to form the three-dimensional model according to the vertical height corresponding to the objects.
And step S50, optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized.
After the terminal obtains the current three-dimensional model of the road to be optimized, road congestion conditions can be simulated on the three-dimensional model according to historical pedestrian flow and historical vehicle flow, and the position and the reason of a congestion point on the road to be optimized are obtained. Due to the fact that the congestion places and reasons of the roads at different moments are different, for example, during school-time and school-time periods, the congestion places on the roads to be optimized are road sections close to the vicinity of the school, and the terminal can conduct road condition simulation according to historical pedestrian flow and historical vehicle flow at different moments, so that the congestion point positions and reasons of the roads to be optimized are obtained in more detail. And the terminal performs integral optimization on the current spatial data of the road to be optimized according to the obtained position and reason of each congestion point on the road to be optimized, so as to obtain the optimized spatial data of the road to be optimized.
Specifically, referring to fig. 5, fig. 5 is a flowchart illustrating a second embodiment of the road optimization method according to the present invention, based on the first embodiment, step S50 includes:
step S51, generating a corresponding people and/or vehicle flow distribution layer according to the historical people and/or vehicle flow;
the terminal generates a corresponding people flow distribution layer according to the obtained historical people flow, the distribution layer reflects the people flow density of different places on the road, the people flow density can be represented by numerical values, and the people flow density can also be represented by colors, for example, the red people flow density is larger than the blue people flow density.
Similarly, the terminal generates a corresponding traffic distribution layer according to the obtained historical traffic, wherein the distribution layer reflects the traffic density of different places on the road, and the traffic density can be represented by numerical values or colors, for example, the red represents the density of people flow is greater than the blue represents the density of people flow.
Step S52, carrying out superposition analysis on the correspondingly generated people and/or traffic flow distribution layer and the current three-dimensional model to obtain the congestion condition information of the road to be optimized;
the terminal maps the pedestrian flow distribution layer and/or the vehicle flow distribution layer in a current three-dimensional model of the road to be optimized to form a three-dimensional pedestrian flow graph and/or a three-dimensional vehicle flow graph, the condition of the road to be optimized can be more specifically and visually checked based on the three-dimensional pedestrian flow graph and/or the three-dimensional vehicle flow graph, and the congestion condition information of the road to be optimized is obtained, wherein the congestion condition information comprises congestion positions, congestion degrees, congestion reasons and the like.
It should be noted that, in order to better analyze the congestion status of the road to be optimized, people flow distribution layers and/or vehicle flow distribution layers at multiple continuous moments within a certain time period may be obtained, and the people flow distribution layers and/or vehicle flow distribution layers at multiple continuous moments are superimposed on the current three-dimensional model to obtain a three-dimensional dynamic people flow graph and/or a three-dimensional dynamic vehicle flow graph of the road to be optimized, so that the conditions before and after the congestion status occurs may be known in more detail, and further, the detailed reason for the congestion status may be obtained based on two time and space layers.
Step S53, according to the congestion condition information, optimizing the current spatial data of the road to be optimized to obtain the optimized current spatial data, and taking the optimized current spatial data as candidate optimized spatial data of the road to be optimized;
and the terminal optimizes the current spatial data of the road to be optimized according to the congestion condition information, for example, the width of the road to be optimized in the current spatial data is increased or decreased, or the width of a certain section of the road to be optimized is increased or decreased, or the number of entrances of the road to be optimized is increased or decreased, and the like, obtains the optimized current spatial data after the optimization is completed, and takes the optimized current spatial data as candidate optimized spatial data of the road to be optimized.
Step S54, performing road congestion verification on candidate optimization space data of the road to be optimized according to the correspondingly generated people and/or traffic flow distribution image layer; if the congestion verification is not qualified, returning to execute the step S53; executing step S55 until the congestion is qualified;
the terminal can construct a candidate optimization two-dimensional layer or a three-dimensional model of the road to be optimized based on the candidate optimization spatial data, the people flow distribution layer and/or the vehicle flow distribution layer are/is subjected to superposition analysis with the candidate optimization two-dimensional layer or the three-dimensional model to obtain an optimized condition, the congestion solving effect is evaluated according to the optimized road condition, if congestion exists, the road congestion verification is determined to be unqualified, and if congestion does not exist, the road congestion verification is determined to be qualified.
The terminal can also input the pedestrian flow distribution layer, the vehicle flow distribution layer and the candidate optimization space data into a road congestion verification model based on the neural network for road congestion verification, output a result of whether congestion occurs or not, and determine whether the road congestion verification of the candidate optimization space data is qualified or not according to the output result.
If the terminal carries out congestion verification on the candidate optimized space data of the road to be optimized and congestion conditions still exist, the congestion verification of the candidate optimized space data is determined to be unqualified. And the terminal can continue to optimize the current spatial data of the road to be optimized according to the congestion condition information of the road to be optimized until the congestion verification of the candidate optimized spatial data is qualified.
Specifically, based on the above embodiment, the step S54 includes:
step S541, generating a candidate optimization three-dimensional model of the road to be optimized according to the candidate optimization space data of the road to be optimized;
and the terminal imports data such as point dimension, line dimension, surface dimension and the like in the candidate optimization space data of the road to be optimized into GIS software to generate a candidate optimization two-dimensional model of the road to be optimized.
The terminal inputs description information of each object in the candidate optimization two-dimensional model in the candidate optimization space data into GIS software, and three-dimensionality is carried out on the candidate optimization two-dimensional model, wherein the description information comprises height data, a scale and the like. According to the height data and the scale of each object, the vertical height of each object in the candidate optimized two-dimensional model in the three-dimensional model is obtained, and then the corresponding objects are lifted and combined to form the candidate optimized three-dimensional model according to the vertical height corresponding to the objects.
And S542, performing superposition analysis on the correspondingly generated human and/or vehicle flow distribution image layer and the candidate optimization three-dimensional model, and judging whether congestion occurs or not, wherein the verification is qualified when congestion does not occur.
The terminal maps the pedestrian flow distribution layer and/or the vehicle flow distribution layer in a candidate optimization three-dimensional model of the road to be optimized to form an optimized three-dimensional pedestrian flow graph and an optimized three-dimensional vehicle flow graph, the condition of the road to be optimized can be more specifically and visually checked based on the three-dimensional pedestrian flow graph and/or the three-dimensional vehicle flow graph, whether congestion occurs or not is judged, if not, the congestion is determined to be qualified, and if so, the congestion is determined to be unqualified.
It should be noted that, in order to better verify the prediction effect of the candidate optimization space data of the road to be optimized on solving the congestion condition of the road to be optimized, the pedestrian flow distribution layers and/or the vehicle flow distribution layers at multiple continuous moments within a certain time period can be obtained, the pedestrian flow distribution layers and/or the vehicle flow distribution layers at multiple continuous moments are overlapped with the candidate optimization three-dimensional model, and the optimized three-dimensional dynamic pedestrian flow graph and the optimized three-dimensional dynamic vehicle flow graph of the road to be optimized are obtained, so that the prediction effect of solving the congestion condition of the road to be optimized is evaluated on the basis of two layers of time and space.
And step S55, taking the candidate optimized space data qualified by congestion verification as the optimized space data of the road to be optimized.
And if the terminal carries out congestion verification on the candidate optimized space data of the road to be optimized, and the congestion verification of the candidate optimized space data is determined to be qualified if no congestion condition is found, taking the candidate optimized space data qualified in congestion verification as the final optimized space data of the road to be optimized. And optimizing and transforming the road to be optimized according to the final optimized spatial data.
Specifically, referring to fig. 6, fig. 6 is a flowchart illustrating a road optimization method according to a third embodiment of the present invention, based on the second embodiment, and based on the above embodiment, if there are at least two candidate optimized spatial data qualified by congestion verification, the step S55 includes:
step S551, calculating transformation cost corresponding to candidate optimized space data qualified by congestion verification according to current space data of a road to be optimized and the candidate optimized space data qualified by congestion verification;
when the number of candidate optimized space data of the road to be optimized, which is determined by the terminal to be qualified through congestion verification, is at least two, before the earliest optimized space data is determined, comparative analysis and calculation are carried out on the current space data of the road to be optimized and the candidate optimized space data which is determined to be qualified through congestion verification, and the reconstruction cost of the candidate optimized space data which is determined to be qualified through congestion verification is determined, wherein the reconstruction cost comprises material cost and labor cost.
Step S552, selecting candidate optimized space data qualified by congestion verification with the lowest reconstruction cost as optimized space data of the road to be optimized.
And the terminal selects the candidate optimized space data with the lowest transformation cost from the candidate optimized space data qualified by the congestion verification as the optimized space data of the road to be optimized.
It should be noted that when the number of candidate optimized space data of the road to be optimized, which is determined by the terminal to pass the congestion verification, is at least two, the reconstruction cost of the candidate optimized space data passing the congestion verification is calculated, the reconstruction time, the influence degree on the surrounding environment and the safety of the candidate optimized space data passing the congestion verification can be obtained, the factors are scored and corresponding weights are set, the candidate optimized space data passing the congestion verification are scored by adopting a weight method, the scores of the candidate optimized space data passing the congestion verification are compared, and the candidate optimized space data passing the congestion verification and corresponding to the highest or lowest score is selected as the optimized space data of the road to be optimized.
According to the technical scheme, the current spatial data of the road to be optimized, the current road grade, the historical flow of people and/or vehicles and the spatial data corresponding to the influence factors of the road to be optimized are obtained; obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors; judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized; if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized; and optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized. Therefore, roads which are determined to be optimized are screened out according to the grading model, then three-dimensional modeling is carried out on the roads based on the spatial data of the roads, and the congestion condition of the roads is analyzed on the three-dimensional modeling based on the historical pedestrian flow and the historical vehicle flow, so that more details are provided, a road designer can integrally preview the congestion condition of the whole traffic road from macro to micro, the road designer can design an optimization scheme for pertinently solving congestion, and the solution effect on the congestion problem is improved.
Referring to fig. 7, the present invention also provides a road optimization system, the system comprising:
the acquisition module 10 is configured to acquire current spatial data of a road to be optimized, a current road grade, historical human and/or vehicle flow, and spatial data corresponding to an influence factor of the road to be optimized;
the obtaining module 20 is configured to obtain a demand level of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factor;
the judging module 30 is used for judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
the generation model 40 is used for generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized if the current spatial data of the road to be optimized is not the same as the spatial data of the road to be optimized;
and the optimization module 50 is configured to optimize the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles, so as to obtain the optimized spatial data of the road to be optimized.
Further, the obtaining module 20 includes:
the first obtaining unit 21 is configured to obtain, according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors, distances between the road to be optimized and the corresponding influence factors;
a first scoring unit 22, configured to input the distance between the road to be optimized and each corresponding influence factor and the preset weight corresponding to each influence factor into the first road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xiFor the distance, f, between the road to be optimized and the corresponding i-th influencing factoriThe preset weight of the ith influence factor of the road to be optimized is N, and the N is the total number of the influence factors of the road to be optimized;
the first determining unit 23 is configured to query a road grade corresponding to the score of the road to be optimized from a preset mapping relationship between the score of the road and the road grade, so as to determine the queried road grade as the requirement grade of the road to be optimized.
Further, the obtaining module 20 includes:
the dividing unit 24 is configured to divide the road to be optimized into at least two sub-roads, so as to obtain spatial data of each sub-road according to the current spatial data of the road to be optimized;
a second obtaining unit 25, configured to obtain the corresponding influence factor of each sub-road according to the spatial data of each sub-road and the spatial data corresponding to the influence factor;
a third obtaining unit 26, configured to obtain a distance between each sub-road and a corresponding influence factor according to the spatial data of each sub-road and the spatial data of the corresponding influence factor;
a second scoring unit 27 for inputting the distance between each sub-road and the corresponding influence factor and the preset weight of each influence factor into a second road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xijIs the distance between the jth sub-road and the corresponding ith influence factor, fijIs a preset weight, N, of the jth sub-road and the corresponding ith influence factorjThe total number of the influence factors of the jth section of sub-road is, and M is the total number of the sub-roads of the road to be optimized;
the second determining unit 28 is configured to query a road grade corresponding to the score of the road to be optimized from a preset mapping relationship between the score of the road and the road grade, so as to determine the queried road grade as the demand grade of the road to be optimized.
Further, the generating module 40 includes:
the first generating unit 41 is configured to import the current spatial data into GIS software, and generate a two-dimensional model of a road to be optimized;
and a three-dimensionalizing unit 42, configured to perform three-dimensionalization on the two-dimensional model according to description information of each object in the two-dimensional model in the current spatial data, so as to generate a current three-dimensional model of the road to be optimized.
Further, the optimization module 50 includes:
the second generating unit 51 is configured to generate a corresponding people and/or vehicle flow distribution map layer according to the historical people and/or vehicle flow;
the first superimposing unit 52 is configured to perform superimposing analysis on the correspondingly generated people and/or traffic flow distribution map layer and the current three-dimensional model, so as to obtain congestion condition information of the road to be optimized;
the optimizing unit 53 is configured to optimize current spatial data of a road to be optimized according to the congestion status information, obtain optimized current spatial data, and use the optimized current spatial data as candidate optimized spatial data of the road to be optimized;
the verification unit 54 is configured to perform road congestion verification on candidate optimization space data of a road to be optimized according to the correspondingly generated people and/or traffic flow distribution map layer;
and the third determining unit 55 is configured to, if the congestion verification is not qualified, invoke the optimizing unit 53 to perform corresponding operations, and when the congestion verification is qualified, use the candidate optimized spatial data qualified in the congestion verification as the optimized spatial data of the road to be optimized.
Further, the verification unit 54 includes:
a generating subunit 541, configured to generate a candidate optimized three-dimensional model of the road to be optimized according to the candidate optimized spatial data of the road to be optimized;
and the analysis subunit 542 is configured to perform superposition analysis on the correspondingly generated people and/or traffic flow distribution map layer and the candidate optimized three-dimensional model, and determine whether congestion occurs, where the verification is qualified when congestion does not occur.
Further, if there are at least two candidate optimized spatial data qualified by congestion verification, the third determining unit 55 includes:
the calculation subunit 551 is configured to calculate, according to the current spatial data of the road to be optimized and the candidate optimized spatial data qualified in congestion verification, a reconstruction cost corresponding to the candidate optimized spatial data qualified in congestion verification;
and the selecting subunit 552 is configured to select candidate optimized space data that is qualified by congestion verification and has the lowest reconstruction cost as optimized space data of a road to be optimized.
The invention also proposes a computer-readable storage medium on which a computer program is stored. The computer-readable storage medium may be the Memory 200 in the road optimization terminal of fig. 1, and may also be at least one of a ROM (Read-only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, and the computer-readable storage medium includes several pieces of information for enabling the road optimization terminal to perform the methods according to the embodiments of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of road optimization, the method comprising the steps of:
acquiring current spatial data of a road to be optimized, current road grade, historical flow of people and/or vehicles and spatial data corresponding to influence factors of the road to be optimized;
obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
if not, generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized;
and optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized.
2. The road optimization method according to claim 1, wherein the step of obtaining the demand level of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influencing factors comprises:
obtaining the distance between the road to be optimized and each corresponding influence factor according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
inputting the distance between the road to be optimized and each corresponding influence factor and the preset weight corresponding to each influence factor into the first road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xiFor the distance, f, between the road to be optimized and the corresponding i-th influencing factoriThe preset weight of the ith influence factor of the road to be optimized is N, and the N is the total number of the influence factors of the road to be optimized;
and inquiring the road grade corresponding to the grade score of the road to be optimized from a preset mapping relation between the grade score of the road and the road grade so as to determine the inquired road grade as the demand grade of the road to be optimized.
3. The road optimization method according to claim 1, wherein the step of obtaining the demand level of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influencing factors comprises:
dividing a road to be optimized into at least two sub-roads so as to obtain the spatial data of each sub-road according to the current spatial data of the road to be optimized;
acquiring the corresponding influence factors of each sub-road according to the spatial data of each sub-road and the spatial data corresponding to the influence factors;
obtaining the distance between each sub-road and the corresponding influence factor according to the spatial data of each sub-road and the spatial data of the corresponding influence factor;
inputting the distance between each sub-road and the corresponding influence factor and the preset weight of each influence factor into the second road scoring model
Obtaining the score of the road to be optimized, wherein F is the score of the road to be optimized, and xijIs the distance between the jth sub-road and the corresponding ith influence factor, fijIs a preset weight, N, of the jth sub-road and the corresponding ith influence factorjThe total number of the influence factors of the jth section of sub-road is, and M is the total number of the sub-roads of the road to be optimized;
and inquiring the road grade corresponding to the grade score of the road to be optimized from a preset mapping relation between the grade score of the road and the road grade so as to determine the inquired road grade as the demand grade of the road to be optimized.
4. The road optimization method according to claim 1, wherein the step of generating a current three-dimensional model of the road to be optimized from the current spatial data of the road to be optimized comprises:
importing the current spatial data into GIS software to generate a two-dimensional model of a road to be optimized;
and carrying out three-dimension on the two-dimensional model according to the description information of each object in the current space data in the two-dimensional model so as to generate the current three-dimensional model of the road to be optimized.
5. The road optimization method according to any one of claims 1 to 4, wherein the step of optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized, the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized comprises the following steps:
generating a corresponding people and/or vehicle flow distribution layer according to the historical people and/or vehicle flow;
carrying out superposition analysis on the correspondingly generated human and/or vehicle flow distribution layer and the current three-dimensional model to obtain congestion condition information of the road to be optimized;
optimizing the current spatial data of the road to be optimized according to the congestion condition information to obtain the optimized current spatial data, and taking the optimized current spatial data as candidate optimized spatial data of the road to be optimized;
performing road congestion verification on candidate optimization space data of the road to be optimized according to the correspondingly generated people and/or vehicle flow distribution layer;
and if the congestion verification is unqualified, returning and executing the congestion condition information according to the road to be optimized, optimizing the current spatial data of the road to be optimized until the congestion verification is qualified, and taking the candidate optimized spatial data qualified in the congestion verification as the optimized spatial data of the road to be optimized.
6. The road optimization method according to claim 5, wherein the step of performing road congestion verification on the candidate optimization space data of the road to be optimized according to the correspondingly generated people and/or traffic flow distribution map layer comprises:
generating a candidate optimization three-dimensional model of the road to be optimized according to the candidate optimization space data of the road to be optimized;
and performing superposition analysis on the correspondingly generated human and/or vehicle flow distribution image layer and the candidate optimization three-dimensional model, and judging whether congestion occurs, wherein the verification is qualified when the congestion does not occur.
7. The road optimization method according to claim 6, wherein if there are at least two candidate optimized spatial data qualified for congestion verification, the step of using the candidate optimized spatial data qualified for congestion verification as the optimized spatial data of the road to be optimized includes:
calculating transformation cost corresponding to candidate optimized space data qualified by congestion verification according to current space data of a road to be optimized and the candidate optimized space data qualified by congestion verification;
and selecting candidate optimization space data which is qualified by congestion verification and has the lowest reconstruction cost as the optimization space data of the road to be optimized.
8. A road optimization system, comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring current spatial data of a road to be optimized, a current road grade, historical flow of people and/or vehicles and spatial data corresponding to influence factors of the road to be optimized;
the obtaining module is used for obtaining the demand grade of the road to be optimized according to the current spatial data of the road to be optimized and the spatial data corresponding to the influence factors;
the judging module is used for judging whether the current road grade of the road to be optimized meets the requirement grade of the road to be optimized;
the generation model is used for generating a current three-dimensional model of the road to be optimized according to the current spatial data of the road to be optimized if the current three-dimensional model is not the same as the model;
and the optimization module is used for optimizing the current spatial data of the road to be optimized according to the current three-dimensional model of the road to be optimized and the historical flow of people and/or vehicles to obtain the optimized spatial data of the road to be optimized.
9. A road optimization terminal, characterized in that it comprises a memory, a processor and a computer program stored on said memory and executable on said processor, said computer program, when executed by said processor, implementing the steps of the road optimization method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the road optimization method according to one of the claims 1 to 7.
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