Intelligent traffic-based road condition detection and optimization method and device, and storage medium

Abstract

The embodiment of the specification provides a traffic condition detection and optimization method and device based on intelligent traffic and a storage medium, the method comprising the following steps: S1, acquiring a plurality of traffic condition detection areas of urban traffic, each traffic condition detection area being detected by a sensor node to collect traffic flow information; S2, setting the distance between two adjacent sensor nodes and the distance between two adjacent traffic condition detection areas; S3, setting the collection period of the sensor; S4, collecting the traffic flow information and sending the traffic flow information to a traffic condition data buffer; S5, when the traffic flow information is unstable, calculating the traffic flow information change coefficient of the two adjacent traffic condition detection areas respectively; S6, according to the change coefficient, whether the traffic flow information is suddenly changed and whether the sudden change is continuous; if yes, controlling the traffic signal lamp and changing the pedestrian passing direction. The technical scheme provided in the application solves the problem that the prior art does not consider the mutual influence of road conditions between road sections.

Description

TECHNICAL FIELD

[0001] The present document relates to the field of intelligent transportation technology, and in particular to a road condition detection and optimization method, device and storage medium based on intelligent transportation. BACKGROUND

[0002] Intelligent transportation is a technology that fully utilizes Internet of Things, cloud computing, Internet, artificial intelligence, automatic control, mobile Internet and other technologies in the field of transportation, collects traffic information through high-tech, and controls and supports traffic management, transportation, public travel and other aspects of transportation, as well as the whole process of traffic construction management, so that the transportation system has the ability of perception, interconnection, analysis, prediction and control in the regional, urban and even larger space-time range, so as to fully guarantee traffic safety, improve the efficiency of transportation infrastructure and the operation efficiency and management level of transportation system. In traffic management, monitoring road conditions is an essential process, which mainly relies on sensors installed on the road to collect data, so it is necessary to consider how to analyze the collected data to achieve better road condition monitoring efficiency.

[0003] Chinese Patent Publication No. CN115671577A discloses a road condition detection and optimization method based on intelligent transportation, comprising the following steps: step one, collecting road condition data of the area through sensors in the road condition detection area, and sending the collected road condition data to the road condition detection module; step two, after the road condition detection module receives the road condition data, analyzing the road condition data to determine whether the road condition of the area has been congested; if so, sending the road condition data of the area to the road condition optimization module, otherwise, returning to step one; step three, after the road condition optimization module receives the road condition data, filtering out the data that can be optimized in the road condition data through a pre-set road data model, optimizing the data that can be optimized, and sending the optimized data to the road condition feedback module; step four, after the road condition feedback module receives the optimized data, prompting the driver; the above technical solution utilizes real-time detection and optimization of road conditions, which can timely respond to road congestion problems and avoid congestion spread, but does not consider the complexity of actual driving process. Since the road conditions of each section are mutually influenced, optimization of a section may adversely affect another section.

[0004] In actual traffic, road condition detection areas and adjacent road condition detection areas are mutually influenced. If the results collected by the sensors are optimized, it may cause congestion in the adjacent road condition detection area, affecting the safety of actual road driving. SUMMARY

[0005] In order to solve at least one of the above technical problems, the present application aims to provide a road condition detection and optimization method, device and storage medium based on intelligent transportation.

[0006] In a first aspect, one or more embodiments of the present specification provide a traffic condition detection and optimization method based on intelligent transportation, comprising the following steps:

[0007] Step S1, obtaining a plurality of traffic condition detection areas of urban traffic, each traffic condition detection area being detected by a sensor node to collect traffic flow information;

[0008] Step S2, setting the distance between two adjacent sensor nodes and the distance between two adjacent traffic condition detection areas;

[0009] Step S3, setting the collection period of the sensor according to the distance between the two adjacent sensor nodes and the distance between the two adjacent traffic condition detection areas;

[0010] Step S4, collecting the traffic flow information in the traffic condition detection area, and sending the collected traffic flow information to a traffic condition data buffer;

[0011] Step S5, extracting the traffic flow information of the traffic condition data buffer, judging whether the traffic flow information of the two adjacent traffic condition detection areas is stable, if not, extracting the traffic flow information collected in the current period and the traffic flow information collected in the last period, and calculating the traffic flow information change coefficient of the two adjacent traffic condition detection areas respectively; and

[0012] Step S6, determining whether the traffic flow information is mutated according to the traffic flow information change coefficient of the two adjacent traffic condition detection areas; if the traffic flow information is mutated, calculating the traffic flow information increment of the two adjacent traffic condition detection areas, and judging whether the mutation is continuous; if yes, controlling the traffic signal lamp and changing the pedestrian passing direction.

[0013] Further, in step S1, the urban road is divided into a plurality of traffic condition detection areas; and a plurality of sensor nodes are evenly distributed on a plurality of road segments, and the traffic condition information is obtained by using the plurality of sensor nodes.

[0014] Further, in step S5, the traffic flow information of the traffic condition data buffer includes:

[0015] Step S401, determining the traffic flow information change trend reported by a plurality of time nodes in a collection period for the traffic condition detection area;

[0016] Step S402, determining whether the traffic flow reported by the sensor node in each time period is stable, if the traffic flow is not stable, extracting the average value of the traffic flow reported by the sensor node in each time period, if the traffic flow is stable, extracting the maximum value of the traffic flow reported by the sensor node.

[0017] Further, the traffic flow information change coefficient of the two adjacent road condition detection areas comprises a first change coefficient and a second change coefficient.

[0018] Step S6 comprises:

[0019] If the difference between the first change coefficient and the second change coefficient is greater than a set threshold, it is determined that the traffic flow information has a mutation.

[0020] Further, step S5 comprises:

[0021] For each of the two adjacent road condition detection areas:

[0022] determining the variance of the traffic flow information in a period;

[0023] if the variance is greater than a preset value, the traffic flow information of the corresponding road condition detection area is unstable; and

[0024] if the traffic flow information of any one of the two adjacent road condition detection areas is unstable, it is determined that the traffic flow information of the two adjacent road condition detection areas is unstable.

[0025] Further, step S6 comprises:

[0026] when the mutation occurs, the last period of the current period is the first period, the current period is the second period, and the next period of the current period is the third period;

[0027] determining a first traffic flow information increment according to the traffic flow information of the first period and the second period;

[0028] determining a second traffic flow information increment according to the traffic flow information of the first period and the third period;

[0029] determining whether the second traffic flow information increment is greater than a first preset value;

[0030] when the second traffic flow information increment is greater than the first preset value, calculating the difference between the second traffic flow information increment and the first traffic flow information increment;

[0031] determining whether the difference is less than a second preset value;

[0032] when the difference is not less than the second preset value, it is determined that the mutation is continuous.

[0033] Further, changing the pedestrian passing direction comprises:

[0034] guiding pedestrians to walk over a bridge or an underground passage.

[0035] In a second aspect, one or more embodiments of the present specification provide a road condition detection and optimization device based on intelligent traffic, comprising:

[0036] an acquisition module configured to acquire a plurality of traffic detection areas of urban traffic, each of the traffic detection areas being detected by a sensor node to collect traffic flow information;

[0037] a setting module configured to set a distance between two adjacent sensor nodes and a distance between two adjacent traffic detection areas, and set a collection period of the sensor according to the distance between the two adjacent sensor nodes and the distance between the two adjacent traffic detection areas;

[0038] a collection module configured to collect the traffic flow information in the traffic detection area and send the collected traffic flow information to a traffic data buffer;

[0039] a data processing module configured to extract the traffic flow information in the traffic data buffer, determine whether the traffic flow information of the two adjacent traffic detection areas is stable, if not, extract the traffic flow information collected in a current period and the traffic flow information collected in a previous period, and calculate a traffic flow information variation coefficient of each of the two adjacent traffic detection areas, respectively, according to the traffic flow information variation coefficients of the two adjacent traffic detection areas, determine whether the traffic flow information is suddenly changed, if the traffic flow information is suddenly changed, calculate a traffic flow information increment of the two adjacent traffic detection areas, and determine whether the sudden change is continuous, if yes, control the traffic signal lamp and change the pedestrian passing direction.

[0040] Further, the acquisition module is configured to divide the urban road into a plurality of traffic detection areas, and distribute a plurality of sensor nodes on a plurality of road segments, and acquire the traffic information by using the plurality of sensor nodes.

[0041] In a third aspect, one or more embodiments of the present specification provide a storage medium, comprising:

[0042] computer executable instructions for storing, the computer executable instructions being executed to implement the method of any one of the first aspect.

[0043] Compared with the prior art, the present application can at least achieve the following technical effects:

[0044] First, according to the distance between the two adjacent sensor nodes and the distance between the two adjacent traffic detection areas, the collection period is determined, the real-time of the traffic flow information collection is improved, and the foundation for subsequent data association is laid.

[0045] Second, by judging whether the traffic flow information of the two adjacent traffic detection areas is stable, whether it is suddenly changed, and whether the sudden change is continuous, the road segments are associated in real time to improve the accuracy of determining the congestion situation.

[0046] Third, by coordinating the traffic signal lights and changing the direction of the pedestrians, the vehicles and pedestrians on the traffic section are controlled, and the efficiency of dredging the congested section is improved.

[0047] Other features and advantages of the present application will be set forth in the following description, and in part will be apparent from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present specification or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0049] Figure 1 A flow chart of a road condition detection and optimization method based on intelligent traffic is provided for one or more embodiments of the present specification;

[0050] Figure 2 A structural schematic diagram of a road condition detection and optimization device based on intelligent traffic is provided for one or more embodiments of the present specification. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in the one or more embodiments of the present specification, the technical solutions in the one or more embodiments of the present specification will be described clearly and completely below with reference to the drawings in the one or more embodiments of the present specification. Obviously, the described embodiments are only some embodiments of the present specification, not all embodiments. Based on the one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present document.

[0052] The embodiments of the present application provide a road condition detection and optimization method based on intelligent traffic, as shown in Figure 1 The method comprises the following steps:

[0053] Step S1, acquiring a plurality of road condition detection areas of urban traffic, each road condition detection area being detected by a sensor node to collect traffic flow information.

[0054] In the embodiments of the present application, the traffic flow information is the vehicle flow in a preset period

[0055] Step S2, setting the distance between the two adjacent sensor nodes and the distance between the two adjacent road condition detection areas.

[0056] Step S3, setting the collection period of the sensor according to the distance between the two adjacent sensor nodes and the distance between the two adjacent road condition detection areas.

[0057] In the embodiment of the present application, the setting of the collection period of the sensor can associate the traffic flow information on each road section. For example, the collection period is determined according to the usual speed of the vehicle and the length of the road section. Then the collection period is divided into multiple time periods, so that the movement of the vehicle can be observed throughout the journey.

[0058] Step S4, collecting the traffic flow information in the road condition detection area and sending the collected traffic flow information to the road condition data buffer.

[0059] In the embodiment of the present application, in order to associate the data of each road section in time, the road condition data buffer is set.

[0060] Step S5, extracting the traffic flow information of the road condition data buffer, judging whether the traffic flow information of the two adjacent road condition detection areas is stable, if not, extracting the traffic flow information collected in the current period and the traffic flow information collected in the last period, and calculating the traffic flow information change coefficients of the two adjacent road condition detection areas respectively.

[0061] Step S6, determining whether the traffic flow information is suddenly changed according to the traffic flow information change coefficients of the two adjacent road condition detection areas; if the traffic flow information is suddenly changed, calculating the traffic flow information increment of the two adjacent road condition detection areas, and judging whether the sudden change is continuous; if yes, controlling the traffic signal lamp and changing the pedestrian passing direction.

[0062] The above scheme has the following technical effects:

[0063] First, according to the distance between the two adjacent sensor nodes and the distance between the two adjacent road condition detection areas, the collection period is determined, the real-time performance of the traffic flow information collection is improved, and the foundation for subsequent data association is laid.

[0064] Second, by judging whether the traffic flow information of the two adjacent road condition detection areas is stable, whether it is suddenly changed, and whether the sudden change is continuous, the road sections of each road section are associated in real time to improve the accuracy of determining the congestion situation.

[0065] Third, by coordinating the control of the traffic signal lamp and the change of the pedestrian passing direction, the vehicle and the pedestrian on the traffic section are controlled, and the efficiency of dredging the congested road section is improved.

[0066] In the embodiment of the present application, the urban road is divided into a plurality of road condition detection areas; and a plurality of sensor nodes are evenly distributed on a plurality of road segments, and the road condition information is obtained by using the plurality of sensor nodes. In some road segments, the vehicles are unevenly distributed, so as to ensure that the sensor nodes are evenly arranged on the corresponding road segments, so as to ensure that the subsequent calculation is not affected.

[0067] In the embodiment of the present application, the traffic flow inevitably fluctuates in various degrees, and such fluctuation will affect the subsequent determination of whether the traffic flow is stable or whether there is a mutation. In order to avoid the influence of the fluctuation of the traffic flow on the subsequent calculation result, the following method is adopted:

[0068] Step S401, for the road condition detection area, determining the traffic flow information change trend reported by the plurality of time nodes in a collection period;

[0069] Step S402, determining whether the traffic flow reported by the sensor node in each time period is stable, if the traffic flow is not stable, extracting the average value of the traffic flow reported by the sensor node in each time period, if the traffic flow is stable, extracting the maximum value of the traffic flow reported by the sensor node.

[0070] In the embodiment of the present application, the traffic flow reported by the sensor node is unstable, which indicates that the vehicles are unevenly distributed on the road surface and are not prone to congestion. At this time, the average value of the traffic flow reported by the sensor node in each time period of the road segment is taken as the traffic flow information. The traffic flow information is stable, which indicates that the vehicles are evenly distributed on the road surface and there is a risk of congestion. In order to avoid congestion as soon as possible, the maximum value of the traffic flow reported by the sensor node is extracted as the traffic flow information.

[0071] In the embodiment of the present application, the traffic flow information mutation refers to an uncaused mutation. For example, the increase of the vehicle flow on the upstream road segment will lead to the increase of the vehicle flow on the downstream road segment, and such mutation belongs to a caused mutation. The vehicle flow on the upstream road segment does not increase or even decreases, but the vehicle flow on the downstream road segment still increases, which belongs to an uncaused increase. Therefore, the vehicle flow in a unit period is taken as a traffic flow information change coefficient, and whether the traffic flow information mutation occurs is determined according to the traffic flow information change coefficient.

[0072] Specifically, the traffic flow information change coefficients of the adjacent two road condition detection areas include a first change coefficient and a second change coefficient; if the difference between the first change coefficient and the second change coefficient is greater than a set threshold, it is determined that the traffic flow information mutation occurs.

[0073] In the embodiment of the present application, the traffic flow information instability refers to that the traffic flow fluctuates greatly in a time period, but does not reach the mutation degree. And such instability is prone to become a precursor of road congestion, so the traffic flow information instability is monitored first, and the specific method is as follows:

[0074] For each of the two adjacent road condition detection areas:

[0075] determine the variance of the traffic flow information in a period; if the variance is greater than a preset value, the traffic flow information of the corresponding road condition detection area is unstable; when the traffic flow information of any one of the two adjacent road condition detection areas is unstable, it is determined that the traffic flow information of the two adjacent road condition detection areas is unstable.

[0076] It should be noted that since the roads are interconnected, the traffic flow information of a certain road is unstable, which will probably cause the traffic flow information of the adjacent road to be unstable. Therefore, when monitoring whether the traffic flow information is stable, the present application is directed to the two adjacent road condition detection areas.

[0077] In the embodiment of the present application, whether the road has a congestion risk is determined based on whether the traffic flow information mutation is continuous. Specifically, when the mutation occurs, the last period of the current period is the first period, the current period is the second period, and the next period of the current period is the third period.

[0078] According to the traffic flow information of the first period and the second period, a first traffic flow information increment is determined.

[0079] According to the traffic flow information of the first period and the third period, a second traffic flow information increment is determined.

[0080] It is determined whether the second traffic flow information increment is greater than a first preset value.

[0081] When the second traffic flow information increment is less than the preset value, it indicates that the traffic flow is rapidly decreasing with the increase of time, and at this time the mutation cannot be continuous.

[0082] When the second traffic flow information increment is greater than the first preset value, it indicates that the traffic flow may increase or decrease slowly with the increase of time, and at this time there is still a congestion risk. At this time, the difference between the second traffic flow information increment and the first traffic flow information increment is calculated in order to further determine whether the mutation is continuous.

[0083] It is determined whether the difference is less than a second preset value.

[0084] When the difference is less than the second preset value, it indicates that the traffic flow is decreasing slowly with the increase of time, but the traffic pressure can be relieved without intervention, and at this time the mutation cannot be continuous.

[0085] When the difference is not less than the second preset value, it indicates that the traffic flow is increasing or decreasing slowly with the increase of time, which will probably cause congestion, and at this time it is determined that the mutation is continuous.

[0086] In the embodiments of the present application, in the road congestion section, people and vehicles affect each other. For example, when there are more pedestrians, the parking time of vehicles is increased, thereby causing congestion. When there are more vehicles, the vehicle flow continuously turns right, thereby hindering pedestrians from crossing the red light, and thereby accumulating a large amount of people flow. Once the vehicle flow stops turning right, the accumulated people flow is likely to cause new road congestion. Therefore, it is necessary to guide the pedestrians to cross the road from a bridge or an underground passage, so as to break this vicious cycle. In addition, the pedestrians who have not reached the destination can also be guided to cross the road at other intersections, so as to further alleviate the road congestion.

[0087] The embodiments of the present application provide a road condition detection and optimization device based on intelligent traffic, as shown in Figure 2 The device comprises:

[0088] The acquisition module 201 is configured to acquire a plurality of road condition detection areas of urban traffic, each road condition detection area being detected by a sensor node to collect traffic flow information.

[0089] The setting module 202 is configured to set the distance between two adjacent sensor nodes and the distance between two adjacent road condition detection areas, and set the collection period of the sensor according to the distance between the two adjacent sensor nodes and the distance between the two adjacent road condition detection areas.

[0090] The collection module 203 is configured to collect the traffic flow information in the road condition detection area, and send the collected traffic flow information to a road condition data buffer.

[0091] The data processing module 204 is configured to extract the traffic flow information of the road condition data buffer, judge whether the traffic flow information of the two adjacent road condition detection areas is stable, if not, extract the traffic flow information collected in the current period and the traffic flow information collected in the last period, and calculate the traffic flow information change coefficients of the two adjacent road condition detection areas respectively; according to the traffic flow information change coefficients of the two adjacent road condition detection areas, determine whether the traffic flow information is mutated; if the traffic flow information is mutated, calculate the traffic flow information increment of the two adjacent road condition detection areas, and judge whether the mutation is continuous; if yes, control the traffic signal lamp and change the pedestrian passing direction.

[0092] In the embodiments of the present application, the acquisition module is configured to divide the urban road into a plurality of road condition detection areas, and distribute a plurality of sensor nodes on a plurality of road sections, and acquire road condition information by using the plurality of sensor nodes.

[0093] The embodiments of the present application provide a storage medium, comprising:

[0094] The computer executable instructions are used to store computer executable instructions, and the computer executable instructions are used to implement the method of any one of the above embodiments when executed.

[0095] The above described embodiments of the present description have been described. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0096] In the 1930s, it was clear to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structure of diodes, transistors, switches, etc.) or in software (e.g., improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by a hardware entity module. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital method is "integrated" on a PLD by the designer programming it himself, without having to ask a chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually manufacturing an integrated circuit chip, this programming is now mostly implemented using "logic compiler" software, which is similar to the software compiler used when developing a program, and the original code before compilation must also be written in a specific programming language, called a hardware description language (HDL), and there are many such languages, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in one of the above hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0097] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, e.g. software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to being implemented in pure computer readable program code, the controller can equally well be implemented to perform the same functions using logic gates, switches, an application specific integrated circuit, a programmable logic controller and an embedded microcontroller, etc. by means of a logical programming of the method steps. The controller can thus be considered as a hardware component, and the means comprised therein for performing the various functions can be considered as structures within the hardware component. Alternatively, the means for performing the various functions can even be considered as both a software module implementing the method and a structure within the hardware component.

[0098] The methods, devices, modules or units illustrated by the above embodiments can be implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0099] For the sake of convenience, the above devices are described by dividing them into various units according to their functions in the description. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware when implementing the embodiments of the present specification.

[0100] Those skilled in the art will appreciate that one or more embodiments of the specification can be provided as a method, a method or a computer program product. Therefore, one or more embodiments of the specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The specification is presented with reference to flow diagrams and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 The flow diagram and / or block diagram in the flow diagrams and / or block diagrams illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present specification. In this regard, each flow and / or block in the flow diagrams and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specified logic. The flow diagrams and / or block diagrams also can represent a flow of data between the modules, segments, or portions of code. Figure 1 The flow diagram and / or block diagram in the flow diagrams and / or block diagrams illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present specification. In this regard, each flow and / or block in the flow diagrams and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specified logic. The flow diagrams and / or block diagrams also can represent a flow of data between the modules, segments, or portions of code.

[0102] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 The flow diagram and / or block diagram in the flow diagrams and / or block diagrams illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present specification. In this regard, each flow and / or block in the flow diagrams and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specified logic. The flow diagrams and / or block diagrams also can represent a flow of data between the modules, segments, or portions of code. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks.

[0103] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 The flow diagram and / or block diagram in the flow diagrams and / or block diagrams illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments of the present specification. In this regard, each flow and / or block in the flow diagrams and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specified logic. The flow diagrams and / or block diagrams also can represent a flow of data between the modules, segments, or portions of code. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks.

[0104] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0105] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. A

[0106] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0107] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0108] One or more embodiments of the present specification can be described in the general context of computer-executable instructions being executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0109] Each embodiment in the present specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for method embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0110] The above merely provides the example of the present document and is not intended to limit the present document. For those skilled in the art, the present document can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present document shall be included in the scope of claims of the present document.

Claims

1. A traffic condition detection and optimization method based on intelligent transportation, characterized in that The method comprises the following steps: Step S1, obtaining a plurality of traffic condition detection areas of urban traffic, each of which is detected by a sensor node to collect traffic flow information; Step S2, setting the distance between two adjacent sensor nodes and the distance between two adjacent traffic condition detection areas; Step S3, setting the collection period of the sensor according to the distance between the two adjacent sensor nodes and the distance between the two adjacent traffic condition detection areas; Step S4, collecting the traffic flow information in the traffic condition detection area and sending the collected traffic flow information to a traffic condition data buffer; Step S5, extracting the traffic flow information in the traffic condition data buffer, judging whether the traffic flow information of the two adjacent traffic condition detection areas is stable, if not, extracting the traffic flow information collected in the current period and the traffic flow information collected in the last period, and calculating the traffic flow information change coefficients of the two adjacent traffic condition detection areas respectively; And Step S6, determining whether the traffic flow information is mutated according to the traffic flow information change coefficients of the two adjacent traffic condition detection areas; if the traffic flow information is mutated, calculating the traffic flow information increment of the two adjacent traffic condition detection areas, and judging whether the mutation is continuous; if yes, controlling the traffic signal lamp and changing the pedestrian passing direction; In step S5, the traffic flow information in the traffic condition data buffer is extracted Comprising: Step S401, determining the traffic flow information change trend reported by the sensor node at a plurality of time nodes in a collection period for the traffic condition detection area; Step S402, determining whether the traffic flow reported by the sensor node in each time period is stable, if the traffic flow is not stable, extracting the average traffic flow reported by the sensor node in each time period, if the traffic flow is stable, extracting the maximum value of the traffic flow reported by the sensor node; Step S5 comprises: For each of the two adjacent traffic condition detection areas: Determine the variance of the traffic flow information in a period; If the variance is greater than a preset value, the traffic flow information of the corresponding traffic condition detection area is unstable; and When the traffic flow information of any one of the two adjacent traffic condition detection areas is unstable, it is determined that the traffic flow information of the two adjacent traffic condition detection areas is unstable; Step S6 comprises: When the mutation occurs, the last period of the current period is the first period, the current period is the second period, and the next period of the current period is the third period; According to the traffic flow information of the first period and the second period, the first traffic flow information increment is determined; According to the traffic flow information of the first period and the third period, the second traffic flow information increment is determined; Determine whether the second traffic flow information increment is greater than the first preset value; When the second traffic flow information increment is greater than the first preset value, the difference between the second traffic flow information increment and the first traffic flow information increment is calculated; Determine whether the difference is less than the second preset value; When the difference is not less than the second preset value, it is determined that the mutation is continuous.

2. The method according to claim 1, wherein In step S1, the urban road is divided into a plurality of traffic condition detection areas; and a plurality of sensor nodes are uniformly distributed on a plurality of road sections, and the traffic condition information is obtained by the plurality of sensor nodes.

3. The method of claim 1, wherein the traffic flow information change coefficient of the two adjacent traffic detection areas comprises a first change coefficient and a second change coefficient. The traffic flow information change coefficient of the two adjacent traffic detection areas comprises a first change coefficient and a second change coefficient. The step S6 comprises: If the difference between the first change coefficient and the second change coefficient is greater than a set threshold, it is determined that the traffic flow information has changed abruptly.

4. The method of claim 1, wherein the changing the pedestrian traffic direction comprises: Guiding the pedestrians to walk on a bridge or an underground passage. The method comprises:

5. An optimization device based on the intelligent transportation-based road condition detection and optimization method according to any one of claims 1-4, characterized in that, An acquisition module is configured to acquire a plurality of traffic detection areas of urban traffic, each of which is detected by a sensor node to collect traffic flow information. A setting module is configured to set the distance between two adjacent sensor nodes and the distance between two adjacent traffic detection areas. According to the distance between the two adjacent sensor nodes and the distance between the two adjacent traffic detection areas, the acquisition period of the sensor is set. An acquisition module is configured to collect the traffic flow information in the traffic detection area and send the collected traffic flow information to a traffic data buffer. A data processing module is configured to extract the traffic flow information from the traffic data buffer, determine whether the traffic flow information of the two adjacent traffic detection areas is stable, if not, extract the traffic flow information collected in the current period and the traffic flow information collected in the last period, and calculate the traffic flow information change coefficient of the two adjacent traffic detection areas respectively. If the traffic flow information changes abruptly, the traffic flow information increment of the two adjacent traffic detection areas is calculated to determine whether the abrupt change is continuous. If so, the traffic signal is controlled and the pedestrian traffic direction is changed.

6. The device of claim 5, wherein the acquisition module is configured to divide the urban road into a plurality of traffic detection areas, and distribute a plurality of sensor nodes on the road segments evenly, and use the plurality of sensor nodes to acquire the traffic information.

7. A storage medium characterized by comprising: The device comprises: A computer executable instruction storage module is configured to store computer executable instructions, which, when executed, implement the method of any one of claims 1-4.

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