CN113744558A - Parking space-level parking inertial navigation system and navigation method based on MEMS sensor - Google Patents
Parking space-level parking inertial navigation system and navigation method based on MEMS sensor Download PDFInfo
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
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Abstract
The invention discloses a parking place level parking inertial navigation system and a navigation method based on an MEMS sensor, wherein an inertial navigation model is optimized by combining an inflection point correction algorithm, vehicle dynamics and BP model training, is applied to an underground parking lot, improves the underground navigation accuracy, gets rid of external information dependence and solves the problem of tail end navigation of the underground parking lot; the NB-IOT system is used for solving the information management problem of the parking lot, and an information management system constructed by equipment such as a computer technology and sensors is used for intelligent and orderly management, so that the safety and the economical efficiency of the parking lot are improved, and the parking efficiency of the parking lot is greatly improved. Big data analysis is fused on the basis of the parking place level navigation to realize parking place change prediction and preference analysis, so that the traveling efficiency of a user is improved, and traveling experience is improved. The regional resource allocation is optimized, urban trip and urban traffic are solved, the appearance of the city is improved, the urban traffic information development is assisted, and a huge economic effect can be generated.
Description
Technical Field
The invention belongs to the technical field of vehicle navigation, and relates to a parking space level parking inertial navigation system based on an MEMS sensor, and further relates to a parking space level parking inertial navigation method based on the MEMS sensor.
Background
By the end of 2020, the automobile holding capacity in China reaches 2.81 hundred million, and the number of parking spaces in China is only 1.19 hundred million. The latest statistical data of the traffic administration of the ministry of public security of 6 days at 4 months in 2021 show that the vacant rate of the parking lot at the wide and deep north parking lots reaches 44.6 percent, the utilization rate of the super-nine parking lots in cities in China is less than 50 percent, and a series of problems of difficult parking and difficult parking space finding, such as disordered parking, difficult parking and data isolation, cause wide social attention.
Through the on-the-spot investigation and the data analysis to domestic public parking area and private parking area, discover that many parking areas and parking stall overall arrangement are too complicated, lead to the parking stall to look for the difficulty, and the parking stall of parking area self providing is induced and reverse to look for car service intelligent inadequately, and the car owner is difficult to look for the parking stall. Meanwhile, for finding out that the management system is temporarily unavailable in APP investigation of intelligent parking, the functions of client real-time parking place intelligent induction, mobile reverse car finding and the like can be achieved, and therefore the current navigation cannot achieve complete travel chain accurate induction.
The problems that the existing underground parking lot is low in positioning and navigation precision and high in price of facility reconstruction of the parking lot are solved. The current underground navigation technology has poor positioning condition and high dependence on hardware facilities. The scheme provides two solutions: the dependence of underground navigation on hardware facilities is weakened, and data information provided by a mobile phone is fully utilized. And secondly, replacing the existing underground positioning systems such as Bluetooth and WIFI by using inertial navigation. And for the deficiency of the inertial navigation, iterative optimization is carried out by using constraint conditions and a mathematical model.
Disclosure of Invention
The invention aims to provide a parking place level parking inertial navigation system and a parking place level parking inertial navigation method based on an MEMS sensor, and solves the problems of low positioning navigation precision and high reconstruction price of facilities of a parking place in the prior underground parking place.
The invention adopts the technical scheme that a parking space level parking inertial navigation system based on an MEMS sensor comprises:
the physical layer provides data support for the operation of the navigation system, parking lot equipment provides parking lot parking space distribution maps and parking space states, and a mobile phone user side gyroscope is used for measuring angle change data and acceleration change data of a mobile phone;
the transfer layer comprises an information transmission layer, the information transmission layer comprises an NB-IOT base station and a parking lot host, the NB-IOT base station is responsible for forwarding data collected by the physical layer, the parking lot host forwards an instruction issued by the network layer, the transfer layer ensures the effectiveness, reliability and safety of data transmission, unifies development interfaces, and facilitates further expansion of equipment and future development;
the network layer manages equipment of each parking lot, processes data acquired by the physical layer, and presents the processed result to a user of the application layer, the network layer consists of a central host, transmits the data sent by the parking lot host to the central host for algorithm calculation, and the network layer is responsible for the bottom layer rule of the system function;
the application layer is mainly used for carrying out information interaction with a user and presenting the data of the network layer to the user in a map and character mode through the mobile phone user side;
and the user layer represents the vehicle owner, and replaces direct human-computer interaction with the voice assistant to ensure the safety of technical implementation, wherein the voice assistant firstly obtains the audio of the speaking of the person, performs operation and reply according to the content, and then plays the reply in a voice mode.
The invention adopts another technical scheme that a parking space level parking inertial navigation method based on an MEMS sensor is applied to the parking space level parking inertial navigation system based on the MEMS sensor, and the parking space level parking inertial navigation system based on the MEMS sensor comprises the following steps:
step 1, modeling is carried out according to the road structure and parking space distribution inside the parking lot to complete information conversion, a parking space detector in the parking lot detects the use condition of a parking space by using an image processing principle, and parking space information is sent to a parking space information processing end;
step 2, calling the parking lot standard drawing and the parking space number in the step 1, optimally distributing the parking spaces, receiving the parking space information fed back by all the parking space detectors in the parking lot by the parking space information processing terminal, summarizing the parking space information, and sending the information to the mobile phone user terminal;
and 3, calling information by using a mobile phone user side, judging the user state, positioning by using an inertial navigation algorithm, and correcting the positioning information by using an inflection point correction algorithm and a BP neural network algorithm. The mobile phone user terminal adopts shortest path planning to carry out path planning according to the parking space information and the positioning result obtained in the step 2, and finally obtains an optimal path from the current position of the user to the target idle parking space;
and 4, sending the optimal path obtained in the step 3 to a mobile phone user terminal, and displaying the result to a user.
The invention is also characterized in that:
the step 1 comprises the following steps:
step 1.1, the parking space is coded, and the method comprises the following steps:
step 1.1.1, generating parking space codes according to a parking space basic coding rule;
step 1.1.2, the parking space coding is simplified, only a parking lot code, a parking space code and a 0-3 corner point position code are extracted, meanwhile, a parking space state coding is added, a one-bit coding is adopted, an empty parking space is 0, an occupied parking space is 1, and the other situations are 2, so that a simplified coding is obtained;
step 1.1.3, storing and calling a database of the simplified codes obtained in the step 1.1.2;
step 1.1.4, carrying out data monitoring on the parking space information;
step 1.2, converting the parking lot road information according to the following rules:
step 1.2.1, obtaining a parking lot road structure plan;
and step 1.2.2, because the internal road structure of the parking lot is simple, converting the structural plane graph by using the directed road network model to generate a parking lot standard graph, wherein the parking lot standard graph comprises all node sets and adjacent matrixes in the parking lot, each node corresponds to a unique ID number, and the node coordinates, the output degree and the input degree are obtained through the node IDs.
Step 1.1.3 comprises the following steps:
step 1.1.3.1, dividing the total database into a plurality of sub-databases according to the simplified codes, wherein the sub-databases formed by the parking space codes, the angular point position codes and the parking space state codes under the parking lots form the total database;
step 1.1.3.2, when the system records the parking space, the system acquires parking space information, namely an angular point position code and a parking space state code, according to the parking space code, and performs classified storage according to the simplified code;
step 1.1.3.3, based on the data storage and calling mechanism, when the parking space information in the total database is called, the sub-database is firstly positioned according to a search target and then specific bytes are positioned for traversing comparison, and the parking space information shows a default parking space state code at a mobile phone user side;
step 1.1.4 comprises the following steps:
step 1.1.4.1, achieving the purpose of real-time monitoring by receiving data collected by the parking space detector, and acquiring the data in a Web Service interface mode based on SOAP;
in step 1.1.4.2, the parking space data interface transmits the time, token and data. Time is for logging, token is for verifying the caller;
step 1.1.4.3, both parties agree a key in advance, after the interface receives the data, MD5 encryption is carried out on the time and the key, and the key is compared with token to verify whether the request is a legal request or not, so that the accuracy and the safety of the data are ensured, and the data adopt a self-defined data transmission protocol format;
1.1.4.4, through the existing image processing technology, the basic data collected by the parking space camera includes the parking space state, time, license plate number, vehicle type, brand and color information; in order to ensure that the common data can be quickly analyzed and improve the system efficiency, the system can pre-define the name of the common data so as to be directly analyzed.
And 1.1.4.5, changing the sub-database when the parking space information data changes, and synchronizing the sub-database information to the total database every 1 minute, so that the reliability of the information of the total database is ensured while the frequent change of the total database is avoided, and the unified management is facilitated.
In step 2, the optimal parking space allocation method is as follows:
step 2.1, acquiring a standard map of the parking lot, including a parking lot node set and an adjacent matrix, acquiring node coordinates of the parking lot, and acquiring the number and position coordinates of empty parking spaces in each area of the parking lot; if the parking lot is a multi-layer parking lot, the total number of layers of the parking lot is required to be obtained on the basis of the information;
2.2, on the premise that the number of vehicles in each area is approximately the same, selecting a certain area according to the priority, and traversing the empty parking spaces in the area: taking an entrance of the parking lot as an initial node, substituting the closest node of each parking space as a target node into a dijkstra algorithm to calculate an optimal path, and respectively storing the path corresponding to each parking space;
step 2.3, obtaining the stored path, and calculating the following information of the path: path length, number of inflection points, number of crossed layers and market entrance linear distance;
step 2.4, carrying out weighted average on the information obtained in the step 3, and recording as the path complexity, wherein the specific calculation formula is as follows:
in the formula (1), A is the complexity of the path, L is the sum of all side lengths of the standard diagram of the parking lot, L is the length of the path, N is the number of nodes of the standard diagram of the parking lot, and N is the number of inflection points. G is the total number of layers, and G is the number of spanning layers;
step 2.5, comparing the path complexity of all the paths obtained in the step 2.4, and screening out a path with the minimum complexity, wherein the parking space corresponding to the path is the optimal parking space;
and 2.6, sending the optimal parking space number to the mobile terminal, and releasing the program occupation of the host terminal and releasing the program cache after the successful sending is confirmed.
6. The parking space-level inertial navigation method based on MEMS sensor as claimed in claim 2, wherein step 3 comprises the following steps:
step 3.1, distinguishing the current state of the user side, comprising the following steps:
step 3.1.1, transferring the change data and the acceleration change data of the angle of the mobile phone measured by a gyroscope at the mobile phone end of the user;
step 3.1.2, removing abnormal values and deleting bad values, comprising the following steps:
step 3.1.2.1, deleting the bad value of incomplete data;
step 3.1.2.2, judging whether the data obey normal, if yes, deleting the abnormal value by a 3 sigma principle, and if not, deleting the abnormal value by a four-quadrant distance method;
3.1.3, detecting the shift degree of the gravity center of the human body through the angle change of the mobile phone measured by the gyroscope, judging whether the human body is walking, if so, determining that the human body is in a walking state, and if not, determining that the human body is in a driving state;
3.2, respectively calculating the current positions in a driving state and a walking state by using an inertial positioning algorithm;
step 3.3, carrying out inflection point correction on the position in the driving state;
step 3.4, correcting the position in the driving state and the walking state of each route section through a B-P neural network model;
step 3.5, after the user is determined to be positioned, performing first route planning according to the target parking space, and the method comprises the following steps:
and 3.5.1, acquiring a parking lot standard graph comprising a parking lot node set and an adjacent matrix. Searching the optimal parking space position coordinate in a database according to the optimal parking space number;
step 3.5.2, calculating the straight-line distance between two points according to the optimal parking space position coordinate and each node coordinate of the parking lot, comparing the distances, obtaining the nearest node label of the optimal parking space and storing the nearest node label;
step 3.5.3, taking the nearest node as a target node, taking the parking lot entrance as an initial node, substituting dijkstra algorithm for calculation, and obtaining a first planned path;
step 3.6, if the user deviates from the established route, performing secondary dynamic planning according to the latest positioning until the user reaches the target parking space, otherwise, repeating the step 3.6 until the user returns to the route of the secondary dynamic planning, and comprising the following steps:
step 3.6.1, determining the nearest node j which is planned and stored for the first time, wherein j is a label and an entrance node i, and acquiring a parking lot standard graph which comprises a parking lot node set and an adjacency matrix;
step 3.6.2, for each node k in the standard graph, determining whether the adjacent matrix has distance (i, j) < distance (i, k) + distance (k, j), if yes, making distance (i, j) ═ distance (i, k) + distance (k, j), constructing a matrix path, and storing the node k, specifically, path (i, j) ═ k. After traversing all the nodes, distance (i, j) is the shortest path distance; connecting inflection points arranged between i and j in all the inflection points passed by the optimal path according to the path matrix to obtain all the inflection points passed by the dynamic planning path;
step 3.6.3, judging the edges of the dynamic planning path passing through in sequence according to the parking lot standard diagram, and obtaining the dynamic planning path
And step 3.6.4, clearing the first planned path and displaying the dynamic planned path.
And step 3.6.5, when the user clicks to finish parking or leaves the parking lot, storing the parking space number and removing the program occupation of the mobile terminal. And when the user vehicle exits the parking lot, the parking space number is sent to the host side as a part of the historical parking record of the user, and the mobile side related storage is released.
Step 3.4 comprises the following steps:
step 3.4.1, correcting each road section through a B-P neural network model by taking the inflection point as a road section division basis;
step 3.4.2, calculating an output speed through a vehicle dynamics model;
step 3.4.3, recording the output speed of vehicle dynamics, starting timing from the entrance of the parking lot to obtain a timing variable t, and taking the timing variable t and the timing variable t as the input of the B-P neural network model;
step 3.4.4, confirming that the number of hidden layer layers is 2, and activating a function;
step 3.4.5, comparing the output quantity value which is the displacement which is traveled within the time t with theoretical displacement information which is obtained at the current road section in the parking lot plane graph under the speed v and the time t to obtain an error, performing reverse propagation, and correcting the weight between every two layers;
and 3.4.6, when an inflection point is met, according to the speed obtained by inflection point correction, resetting the time to be 0, inputting the B-P neural network model again, and repeating the 3.4.2 until the next inflection point is met or the navigation is finished.
Step 3.4.2 includes the following steps:
step 3.4.2.1, establishing a transverse, longitudinal and transverse three-degree-of-freedom nonlinear vehicle dynamics model, wherein the model can be equivalently simplified into a bicycle model formed by respectively concentrating front wheels and rear wheels at the midpoints of a front axle and a rear axle of the vehicle after the difference between the left wheel and the right wheel is ignored;
3.4.2.2, according to Newton mechanics, the dynamic model of the vehicle is
MVMbx＝MVMbyWMz-2Ftf sinδf-2Fsf cosδf-2Fsr (2)
MVMby＝-MVMbxWMz+2Ftf cosδf-2Fsf (3)
IzWMz＝2a Ftf sinδf+2a Fsf cosδf-2bFsr (4)
In formulae (2) to (4), VMbx、VMbyAnd WMzThe lateral speed, the longitudinal speed and the yaw rate of the vehicle are respectively; m, IzThe mass of the vehicle and the moment of inertia around the vertical axis are respectively; a. b is the distance from the wheel axle center of the front wheel and the rear wheel of the automobile to the center of mass respectively; δ f is the front wheel steering angle, determined by the steering wheel angle δ measured by the steering wheel angle sensor, divided by the steering gear ratio from the steering wheel to the front wheels; cdIs the air resistance coefficient; a. thefIs the vehicle forward area; rhoaIs the air density; ftf、FtrLongitudinal forces acting on the single front and rear wheels, respectively; fsf、FsrRespectively, lateral forces acting on the single front and rear wheels;
step 3.4.2.3, obtaining control input vector U ═ δ F, F of vehicle dynamics model using steering wheel angle sensor and wheel force sensor informationtf，Ftr]T；
Step 3.4.2.4, solving the differential equation through a fourth-order Runge Kutta method to obtain the transverse speed V of the vehicle in the carrier coordinate systemMbxLongitudinal speed VMbyAnd yaw rate WMzAnd obtaining the speed calculated by the dynamic model under the navigation coordinate system according to the attitude matrix calculated by the INS:
the activation function of the B-P neural network in step 3.4.2 is
In the formula (6), e is a natural constant.
The invention has the beneficial effects that: the integration application of the mathematical algorithm and the inertial navigation technology breaks away from the dependence of external information, solves the navigation problem of the underground parking lot and perfects the travel terminal navigation; the intelligent parking service of the whole travel chain is created, the transition problem from the tail end of dynamic traffic to static traffic is solved, the relation between the dynamic traffic and the static traffic is opened, the problems of vehicle owner induced parking, reverse vehicle searching, subsequent consumption service and the like in a parking lot are solved, and the travel service of a user is perfected. The orderly management of intelligence improves the security and the economic nature in parking area and promotes the parking efficiency in parking area by a wide margin. Big data analysis is fused on the basis of the parking place level navigation to realize parking place change prediction and preference analysis, so that the traveling efficiency of a user is improved, and traveling experience is improved. The regional resource allocation is optimized, urban trip and urban traffic are solved, the appearance of the city is improved, the urban traffic information development is assisted, and a huge economic effect can be generated.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic view of the parking space encoding of the present invention;
FIG. 3 is an inertial positioning operational schematic of the inertial navigation of the present invention;
FIG. 4 is a simplified model schematic of the vehicle dynamics of the present invention;
FIG. 5 is a schematic diagram of information propagation of each layer of the B-P neural network model of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
A parking space level inertial navigation system based on MEMS sensors, as shown in fig. 1, includes:
the physical layer provides data support for the operation of the navigation system, parking lot equipment provides parking lot parking space distribution maps and parking space states, and a mobile phone user side gyroscope is used for measuring angle change data and acceleration change data of a mobile phone;
the transfer layer comprises an information transmission layer, the information transmission layer comprises an NB-IOT base station and a parking lot host, the NB-IOT base station is responsible for forwarding data collected by the physical layer, the parking lot host forwards an instruction issued by the network layer, the transfer layer ensures the effectiveness, reliability and safety of data transmission, unifies development interfaces, and facilitates further expansion of equipment and future development;
the network layer manages equipment of each parking lot, processes data acquired by the physical layer, and presents the processed result to a user of the application layer, the network layer consists of a central host, transmits the data sent by the parking lot host to the central host for algorithm calculation, and the network layer is responsible for the bottom layer rule of the system function;
the application layer is mainly used for carrying out information interaction with a user and presenting the data of the network layer to the user in a map and character mode through the mobile phone user side;
and the user layer represents the vehicle owner, and replaces direct human-computer interaction with the voice assistant to ensure the safety of technical implementation, wherein the voice assistant firstly obtains the audio of the speaking of the person, performs operation and reply according to the content, and then plays the reply in a voice mode.
The parking place level parking inertial navigation method based on the MEMS sensor applies the parking place level parking inertial navigation system based on the MEMS sensor, and comprises the following steps:
step 1, modeling is carried out according to the road structure and parking space distribution inside the parking lot to complete information conversion, a parking space detector in the parking lot detects the use condition of a parking space by using an image processing principle, and parking space information is sent to a parking space information processing end;
step 2, calling the parking lot standard drawing and the parking space number in the step 1, optimally distributing the parking spaces, receiving the parking space information fed back by all the parking space detectors in the parking lot by the parking space information processing terminal, summarizing the parking space information, and sending the information to the mobile phone user terminal;
and 3, calling information by using a mobile phone user side, judging the user state, positioning by using an inertial navigation algorithm, and correcting the positioning information by using an inflection point correction algorithm and a BP neural network algorithm. The mobile phone user terminal adopts shortest path planning to carry out path planning according to the parking space information and the positioning result obtained in the step 2, and finally obtains an optimal path from the current position of the user to the target idle parking space;
and 4, sending the optimal path obtained in the step 3 to a mobile phone user terminal, and displaying the result to a user.
The step 1 comprises the following steps:
step 1.1, the parking space is coded, and the method comprises the following steps:
step 1.1.1, as shown in fig. 2, according to the group standard "parking space basic coding rule" issued by the China transportation Association, the parking space number in the parking lot is confirmed, the parking space code includes a unified address code section, a space zone bit code section, a management attribute code section and a check code section 4, the unified address code includes 7 fields of city district code, street code, community code, basic grid code, building code, parking lot number and parking space number, and totally 27 bits, the space zone bit code section includes 0 corner point space position code, 1-3 corner point space position codes, and totally 3 fields of height code, totally total 41 bits, the management attribute code section includes acquisition time code, position attribute classification code, proper parking vehicle classification code, business property classification code, form classification code, building property classification code, property right classification code, The use classification code has 8 fields, and the total number is 15 bits.
Step 1.1.2, because the parking space code structure is too long, in order to facilitate manual marking and identification, the parking space code is simplified, only the parking lot code, the parking space code and the 0-3 corner point position code are extracted, meanwhile, the parking space state code is added, a one-bit code is adopted, the empty parking space is 0, the occupied parking space is 1, and the other situations are 2, so that the simplified code is obtained;
step 1.1.3, storing and calling a database of the simplified codes obtained in the step 1.1.2;
step 1.1.4, carrying out data monitoring on the parking space information;
step 1.2, converting the parking lot road information according to the following rules:
step 1.2.1, obtaining a parking lot road structure plan;
and step 1.2.2, because the internal road structure of the parking lot is simple, converting the structural plane graph by using the directed road network model to generate a parking lot standard graph, wherein the parking lot standard graph comprises all node sets and adjacent matrixes in the parking lot, each node corresponds to a unique ID number, and node coordinates, the output and the input can be obtained through the node IDs.
Step 1.1.3 comprises the following steps:
1.1.3.1, in order to avoid searching a huge database, dividing the total database into a plurality of sub-databases according to simplified codes, and forming the total database by the sub-databases formed by parking space codes, angular point position codes and parking space state codes under each parking lot;
step 1.1.3.2, when the system records the parking space, the system acquires parking space information, namely an angular point position code and a parking space state code, according to the parking space code, and performs classified storage according to the simplified code;
step 1.1.3.3, based on the data storage and calling mechanism, when the parking space information in the total database is called, the sub-database is firstly positioned according to a search target and then specific bytes are positioned for traversing comparison, and the parking space information shows a default parking space state code at a mobile phone user side;
step 1.1.4 comprises the following steps:
step 1.1.4.1, achieving the purpose of real-time monitoring by receiving data collected by the parking space detector, and acquiring the data in a Web Service interface mode based on SOAP;
in step 1.1.4.2, the parking space data interface transmits the time, token and data. Time is for logging, token is for verifying the caller;
step 1.1.4.3, both parties agree a key in advance, after the interface receives the data, MD5 encryption is carried out on the time and the key, and the key is compared with token to verify whether the request is a legal request or not, so that the accuracy and the safety of the data are ensured, and the data adopt a self-defined data transmission protocol format;
1.1.4.4, through the existing image processing technology, the basic data collected by the parking space camera includes information such as parking space state, time, license plate number, vehicle type, brand, color, etc.; in order to ensure that the common data can be quickly analyzed and improve the system efficiency, the system can pre-define the names of the common data, such as license plate numbers and parking space states, so as to directly analyze the common data.
And 1.1.4.5, changing the sub-database when the parking space information data changes, and synchronizing the sub-database information to the total database every 1 minute, so that the reliability of the information of the total database is ensured while the frequent change of the total database is avoided, and the unified management is facilitated.
In step 2, the optimal parking space allocation method is as follows:
step 2.1, acquiring a parking lot standard graph which comprises a parking lot node set and an adjacent matrix, acquiring parking lot node coordinates, and acquiring the number and position coordinates of empty parking spaces in each area (A, B, C, D area) of a parking lot; if the parking lot is a multi-layer parking lot, the total number of layers of the parking lot is required to be obtained on the basis of the information;
2.2, on the premise that the number of vehicles in each area is approximately the same, selecting a certain area according to the priority (area A > area B > area C > area D), and traversing the empty parking spaces in the area: taking an entrance of the parking lot as an initial node, substituting the closest node of each parking space as a target node into a dijkstra algorithm to calculate an optimal path, and respectively storing the path corresponding to each parking space;
step 2.3, obtaining the stored path, and calculating the following information of the path: path length, number of inflection points (number of nodes passed), number of crossed layers and market entrance linear distance;
step 2.4, carrying out weighted average on the information obtained in the step 3, and recording as the path complexity, wherein the specific calculation formula is as follows:
in the formula (1), A is the complexity of the path, L is the sum of all side lengths of the standard diagram of the parking lot, L is the length of the path, N is the number of nodes of the standard diagram of the parking lot, and N is the number of inflection points. G is the total number of layers (layers), and G is the number of spanning layers;
step 2.5, comparing the path complexity of all the paths obtained in the step 2.4, and screening out a path with the minimum complexity, wherein the parking space corresponding to the path is the optimal parking space;
and 2.6, sending the optimal parking space number to the mobile terminal, and releasing the program occupation of the host terminal and releasing the program cache after the successful sending is confirmed.
As shown in fig. 3, step 3 includes the following steps:
step 3.1, distinguishing the current state of the user side, comprising the following steps:
step 3.1.1, transferring the change data and the acceleration change data of the angle of the mobile phone measured by a gyroscope at the mobile phone end of the user;
step 3.1.2, removing abnormal values and deleting bad values, comprising the following steps:
step 3.1.2.1, deleting the bad value of incomplete data;
step 3.1.2.2, judging whether the data obey normal, if yes, deleting the abnormal value by a 3 sigma principle, and if not, deleting the abnormal value by a four-quadrant distance method;
3.1.3, detecting the shift degree of the gravity center of the human body through the angle change of the mobile phone measured by the gyroscope, judging whether the human body is walking, if so, determining that the human body is in a walking state, and if not, determining that the human body is in a driving state; (or by drawing an image of the change in acceleration, it can be judged that the vehicle is walking if the image is a cos image, or driving if the image is not a cos image.)
3.2, respectively calculating the current positions in a driving state and a walking state by using an inertial positioning algorithm;
step 3.3, carrying out inflection point correction on the position in the driving state;
step 3.4, correcting the position in the driving state and the walking state of each route section through a B-P neural network model;
step 3.5, after the user is determined to be positioned, performing first route planning according to the target parking space, and the method comprises the following steps:
and 3.5.1, acquiring a parking lot standard graph comprising a parking lot node set and an adjacent matrix. Searching the optimal parking space position coordinate in a database according to the optimal parking space number;
step 3.5.2, calculating the straight-line distance between two points according to the optimal parking space position coordinate and each node coordinate of the parking lot, comparing the distances, obtaining the nearest node label of the optimal parking space and storing the nearest node label;
step 3.5.3, taking the nearest node as a target node, taking a parking lot entrance (or a market entrance, hereinafter referred to as an entrance) as an initial node, substituting dijkstra algorithm for calculation, and obtaining a first planning path;
step 3.6, if the user deviates from the established route, performing secondary dynamic planning according to the latest positioning until the user reaches the target parking space, otherwise, repeating the step 3.6 until the user returns to the route of the secondary dynamic planning, and comprising the following steps:
step 3.6.1, determining the nearest node j which is planned and stored for the first time, wherein j is a label and an entrance node i, and acquiring a parking lot standard graph which comprises a parking lot node set and an adjacency matrix;
step 3.6.2, for each node k in the standard graph, determining whether the adjacent matrix has distance (i, j) < distance (i, k) + distance (k, j), if yes, making distance (i, j) ═ distance (i, k) + distance (k, j), constructing a matrix path, and storing the node k, specifically, path (i, j) ═ k. After traversing all the nodes, distance (i, j) is the shortest path distance; connecting inflection points arranged between i and j in all the inflection points passed by the optimal path according to the path matrix to obtain all the inflection points passed by the dynamic planning path;
step 3.6.3, judging the edges of the dynamic planning path passing through in sequence according to the parking lot standard diagram, and obtaining the dynamic planning path
And step 3.6.4, clearing the first planned path and displaying the dynamic planned path.
And step 3.6.5, when the user clicks to finish parking or only leaves the parking lot, storing the parking space number and removing the program occupation of the mobile terminal. And when the user vehicle exits the parking lot, the parking space number is sent to the host side as a part of the historical parking record of the user, and the mobile side related storage is released.
Step 3.4 comprises the following steps:
step 3.4.1, correcting each road section through a B-P neural network model by taking the inflection point as a road section division basis;
step 3.4.2, calculating an output speed through a vehicle dynamics model;
step 3.4.3, recording the output speed of vehicle dynamics, starting timing from the entrance of the parking lot to obtain a timing variable t, and taking the timing variable t and the timing variable t as the input of the B-P neural network model;
step 3.4.4, confirming that the number of hidden layer layers is 2, and activating a function;
step 3.4.5, comparing the output quantity value which is the displacement which is traveled within the time t with theoretical displacement information which is obtained at the current road section in the parking lot plane graph under the speed v and the time t to obtain an error, performing reverse propagation, and correcting the weight between every two layers;
and 3.4.6, when an inflection point is met, according to the speed obtained by inflection point correction, resetting the time to be 0, inputting the B-P neural network model again, and repeating the 3.4.2 until the next inflection point is met or the navigation is finished.
Step 3.4.2 includes the following steps:
step 3.4.2.1, establishing a transverse, longitudinal and transverse three-degree-of-freedom nonlinear vehicle dynamics model, wherein the model can be equivalently simplified into a bicycle model formed by respectively concentrating front wheels and rear wheels at the midpoints of a front axle and a rear axle of the vehicle after the difference between the left wheel and the right wheel is ignored;
as can be seen in fig. 4: x is the number ofnoynTo navigate the coordinate system, xnAxial east, ynThe axis is north; x is the number ofboybFor a carrier coordinate system fixed at the centre of mass of the vehicle, xbThe axis coincides with the transverse axis of the carrier and is positive to the right, ybThe axis coincides with the longitudinal axis of the carrier and is positive going forward.
Step 3.4.2.2, as shown in FIG. 4, the dynamic model of the vehicle is based on Newton mechanics
MVMbx＝MVMbyWMz-2Ftf sinδf-2Fsf cosδf-2Fsr (2)
MVMby＝-MVMbxWMz+2Ftf cosδf-2Fsf (3)
IzWMz＝2a Ftf sinδf+2a Fsf cosδf-2bFsr (4)
In formulae (2) to (4), VMbx、VMbyAnd WMzThe lateral speed, the longitudinal speed and the yaw rate of the vehicle are respectively; m, IzThe mass of the vehicle and the moment of inertia around the vertical axis are respectively; a. b is the distance from the wheel axle center of the front wheel and the rear wheel of the automobile to the center of mass respectively; δ f is the front wheel steering angle, determined by the steering wheel angle δ measured by the steering wheel angle sensor, divided by the steering gear ratio from the steering wheel to the front wheels; cdIs the air resistance coefficient; a. thefIs the vehicle forward area; rhoaIs the air density; ftf、FtrLongitudinal forces acting on the single front and rear wheels, respectively; fsf、FsrRespectively, lateral forces acting on the single front and rear wheels;
step 3.4.2.3, obtaining control input vector U ═ δ F, F of vehicle dynamics model using steering wheel angle sensor and wheel force sensor informationtf，Ftr]T；
Step 3.4.2.4, solving the differential equation through a fourth-order Runge Kutta method to obtain the transverse speed V of the vehicle in the carrier coordinate systemMbxLongitudinal speed VMbyAnd yaw rate WMzAnd obtaining the speed calculated by the dynamic model under the navigation coordinate system according to the attitude matrix calculated by the INS:
as shown in FIG. 5, the activation function of the B-P neural network in step 3.4.2 is
In the formula (6), e is a natural constant.
Claims (9)
1. The utility model provides a parking stall level inertial navigation system that parks based on MEMS sensor which characterized in that includes:
the physical layer provides data support for the operation of the navigation system, parking lot equipment provides parking lot parking space distribution maps and parking space states, and a mobile phone user side gyroscope is used for measuring angle change data and acceleration change data of a mobile phone;
the transfer layer comprises an information transmission layer, the information transmission layer comprises an NB-IOT base station and a parking lot host, the NB-IOT base station is responsible for forwarding data collected by the physical layer, the parking lot host forwards an instruction issued by the network layer, the transfer layer ensures the effectiveness, reliability and safety of data transmission, unifies a development interface, and facilitates the further expansion of equipment and the future development;
the network layer manages equipment of each parking lot, processes data acquired by the physical layer, and presents the processed result to a user of the application layer, the network layer consists of a central host, transmits the data sent by the parking lot host to the central host for algorithm calculation, and the network layer is responsible for the bottom layer rule of the system function;
the application layer is mainly used for carrying out information interaction with a user and presenting the data of the network layer to the user in a map and character mode through the mobile phone user side;
and the user layer represents the vehicle owner, and replaces direct human-computer interaction with the voice assistant to ensure the safety of technical implementation, wherein the voice assistant firstly obtains the audio of the speaking of the person, performs operation and reply according to the content, and then plays the reply in a voice mode.
2. The parking space level parking inertial navigation method based on the MEMS sensor is characterized in that the parking space level parking inertial navigation system based on the MEMS sensor according to claim 1 is applied, and comprises the following steps:
step 1, modeling is carried out according to the road structure and parking space distribution inside the parking lot to complete information conversion, a parking space detector in the parking lot detects the use condition of a parking space by using an image processing principle, and parking space information is sent to a parking space information processing end;
step 2, calling the parking lot standard drawing and the parking space number in the step 1, optimally distributing the parking spaces, receiving the parking space information fed back by all the parking space detectors in the parking lot by the parking space information processing terminal, summarizing the parking space information, and sending the information to the mobile phone user terminal;
step 3, calling information by using a mobile phone user side, judging the user state, positioning through an inertial navigation algorithm, and correcting the positioning information by using an inflection point correction algorithm and a BP neural network algorithm; the mobile phone user terminal adopts shortest path planning to carry out path planning according to the parking space information and the positioning result obtained in the step 2, and finally obtains an optimal path from the current position of the user to the target idle parking space;
and 4, sending the optimal path obtained in the step 3 to a mobile phone user terminal, and displaying the result to a user.
3. The method for parking space-level parking inertial navigation based on MEMS sensor as claimed in claim 2, wherein said step 1 comprises the following steps:
step 1.1, the parking space is coded, and the method comprises the following steps:
step 1.1.1, generating parking space codes according to a parking space basic coding rule;
step 1.1.2, simplifying the parking space codes, only extracting parking lot codes, parking space codes and 0-3 corner point position codes, and simultaneously adding parking space state codes, wherein one-bit codes are adopted, the empty parking space is 0, the occupied parking space is 1, and the other situations are 2, so that simplified codes are obtained;
step 1.1.3, storing and calling a database of the simplified codes obtained in the step 1.1.2;
step 1.1.4, carrying out data monitoring on the parking space information;
step 1.2, converting the parking lot road information according to the following rules:
step 1.2.1, obtaining a parking lot road structure plan;
and step 1.2.2, because the internal road structure of the parking lot is simple, converting the structure plane graph by using a directed road network model to generate a parking lot standard graph, wherein the parking lot standard graph comprises all node sets and adjacent matrixes in the parking lot, each node corresponds to a unique ID number, and the node coordinates, the output degree and the input degree are obtained through the node IDs.
4. The method according to claim 3, wherein the step 1.1.3 comprises the following steps:
step 1.1.3.1, dividing the total database into a plurality of sub-databases according to the simplified codes, wherein the sub-databases formed by the parking space codes, the angular point position codes and the parking space state codes under the parking lots form the total database;
step 1.1.3.2, when the system records the parking space, the system acquires parking space information, namely an angular point position code and a parking space state code, according to the parking space code, and performs classified storage according to the simplified code;
step 1.1.3.3, based on the data storage and calling mechanism, when the parking space information in the total database is called, the sub-database is firstly positioned according to a search target and then specific bytes are positioned for traversing comparison, and the parking space information shows a default parking space state code at a mobile phone user side;
the 1.1.4 comprises the following steps:
step 1.1.4.1, achieving the purpose of real-time monitoring by receiving data collected by the parking space detector, and acquiring the data in a Web Service interface mode based on SOAP;
step 1.1.4.2, the parking space data interface transmits time, token and data, the time is for recording, the token is for verifying the caller;
step 1.1.4.3, both parties agree a key in advance, after the interface receives the data, MD5 encryption is carried out on the time and the key, and the key is compared with token to verify whether the request is a legal request or not, so that the accuracy and the safety of the data are ensured, and the data adopt a self-defined data transmission protocol format;
1.1.4.4, through the existing image processing technology, the basic data collected by the parking space camera includes the parking space state, time, license plate number, vehicle type, brand and color information; in order to ensure that the common data is quickly analyzed and improve the system efficiency, the system can pre-define the name of the common data so as to be directly analyzed,
and 1.1.4.5, changing the sub-database when the parking space information data changes, and synchronizing the sub-database information to the total database every 1 minute, so that the reliability of the information of the total database is ensured while the frequent change of the total database is avoided, and the unified management is facilitated.
5. The inertial navigation method for parking at a parking space level based on MEMS sensors as claimed in claim 2, wherein in step 2, the optimal parking space is assigned as follows:
step 2.1, acquiring a standard map of the parking lot, including a parking lot node set and an adjacent matrix, acquiring node coordinates of the parking lot, and acquiring the number and position coordinates of empty parking spaces in each area of the parking lot; if the parking lot is a multi-layer parking lot, the total number of layers of the parking lot is required to be obtained on the basis of the information;
2.2, on the premise that the number of vehicles in each area is approximately the same, selecting a certain area according to the priority, and traversing the empty parking spaces in the area: taking an entrance of the parking lot as an initial node, substituting the closest node of each parking space as a target node into a dijkstra algorithm to calculate an optimal path, and respectively storing the path corresponding to each parking space;
step 2.3, obtaining the stored path, and calculating the following information of the path: path length, number of inflection points, number of crossed layers and market entrance linear distance;
step 2.4, carrying out weighted average on the information obtained in the step 3, and recording as the path complexity, wherein the specific calculation formula is as follows:
in the formula (1), A is the complexity of a path, L is the sum of all side lengths of a standard diagram of a parking lot, L is the length of the path, N is the number of nodes of the standard diagram of the parking lot, N is the number of inflection points, G is the total number of layers, and G is the number of spanning layers;
step 2.5, comparing the path complexity of all the paths obtained in the step 2.4, and screening out a path with the minimum complexity, wherein the parking space corresponding to the path is the optimal parking space;
and 2.6, sending the optimal parking space number to the mobile terminal, and releasing the program occupation of the host terminal and releasing the program cache after the successful sending is confirmed.
6. The method for parking space-level parking inertial navigation based on MEMS sensor as claimed in claim 2, wherein said step 3 comprises the following steps:
step 3.1, distinguishing the current state of the user side, comprising the following steps:
step 3.1.1, transferring the change data and the acceleration change data of the angle of the mobile phone measured by a gyroscope at the mobile phone end of the user;
step 3.1.2, removing abnormal values and deleting bad values, comprising the following steps:
step 3.1.2.1, deleting the bad value of incomplete data;
step 3.1.2.2, judging whether the data obey normal, if yes, deleting the abnormal value by a 3 sigma principle, and if not, deleting the abnormal value by a four-quadrant distance method;
3.1.3, detecting the shift degree of the gravity center of the human body through the angle change of the mobile phone measured by the gyroscope, judging whether the human body is walking, if so, determining that the human body is in a walking state, and if not, determining that the human body is in a driving state;
3.2, respectively calculating the current positions in a driving state and a walking state by using an inertial positioning algorithm;
step 3.3, carrying out inflection point correction on the position in the driving state;
step 3.4, correcting the position in the driving state and the walking state of each route section through a B-P neural network model;
step 3.5, after the user is determined to be positioned, performing first route planning according to the target parking space, and the method comprises the following steps:
step 3.5.1, obtaining a parking lot standard graph which comprises a parking lot node set and an adjacent matrix; searching the optimal parking space position coordinate in a database according to the optimal parking space number;
step 3.5.2, calculating the straight-line distance between two points according to the optimal parking space position coordinate and each node coordinate of the parking lot, comparing the distances, obtaining the nearest node label of the optimal parking space and storing the nearest node label;
step 3.5.3, taking the nearest node as a target node, taking the parking lot entrance as an initial node, substituting dijkstra algorithm for calculation, and obtaining a first planned path;
step 3.6, if the user deviates from the established route, performing secondary dynamic planning according to the latest positioning until the user reaches the target parking space, otherwise, repeating the step 3.6 until the user returns to the route of the secondary dynamic planning, and comprising the following steps:
step 3.6.1, determining the nearest node j which is planned and stored for the first time, wherein j is a label and an entrance node i, and acquiring a parking lot standard graph which comprises a parking lot node set and an adjacency matrix;
step 3.6.2, judging whether the adjacent matrix has distance (i, j) < distance (i, k) + distance (k, j) for each node k in the standard graph, if yes, making distance (i, j) ═ distance (i, k) + distance (k, j), constructing a matrix path and storing the node k, specifically, path (i, j) ═ k; after traversing all the nodes, distance (i, j) is the shortest path distance; connecting inflection points arranged between i and j in all the inflection points passed by the optimal path according to the path matrix to obtain all the inflection points passed by the dynamic planning path;
step 3.6.3, judging the edges of the dynamically planned path in sequence according to the standard drawing of the parking lot, namely obtaining the dynamically planned path
Step 3.6.4, clearing the first planned path and displaying the dynamic planned path;
step 3.6.5, when the user clicks to finish parking or only the user leaves the parking lot, storing the serial number of the parking space and removing the program occupation of the mobile terminal; and when the user vehicle exits the parking lot, the parking space number is sent to the host side as a part of the historical parking record of the user, and the mobile side related storage is released.
7. The method according to claim 6, wherein said step 3.4 comprises the following steps:
step 3.4.1, correcting each road section through a B-P neural network model by taking the inflection point as a road section division basis;
step 3.4.2, calculating an output speed through a vehicle dynamics model;
step 3.4.3, recording the output speed of vehicle dynamics, starting timing from the entrance of the parking lot to obtain a timing variable t, and taking the timing variable t and the timing variable t as the input of the B-P neural network model;
step 3.4.4, confirming that the number of hidden layer layers is 2, and activating a function;
step 3.4.5, comparing the output quantity value which is the displacement which is traveled within the time t with theoretical displacement information which is obtained at the current road section in the parking lot plane graph under the speed v and the time t to obtain an error, performing reverse propagation, and correcting the weight between every two layers;
and 3.4.6, when an inflection point is met, according to the speed obtained by inflection point correction, resetting the time to be 0, inputting the B-P neural network model again, and repeating the 3.4.2 until the next inflection point is met or the navigation is finished.
8. The method according to claim 7, wherein said step 3.4.2 comprises the following steps:
step 3.4.2.1, establishing a transverse, longitudinal and transverse three-degree-of-freedom nonlinear vehicle dynamics model, wherein after the difference between the left wheel and the right wheel is ignored, the model is equivalent and simplified into a bicycle model formed by respectively concentrating the front wheel and the rear wheel at the midpoints of the front axle and the rear axle of the vehicle;
xnoynto navigate the coordinate system, xnAxial east, ynThe axis is north; x is the number ofboybFor a carrier coordinate system fixed at the centre of mass of the vehicle, xbThe axis coincides with the transverse axis of the carrier and is positive to the right, ybThe axis is coincident with the longitudinal axis of the carrier and is positive forwards;
3.4.2.2, according to Newton mechanics, the dynamic model of the vehicle is
MVMbx＝MVMbyWMz-2Ftf sinδf-2Fsf cosδf-2Fsr (2)
MVMby＝-MVMbxWMz+2Ftf cosδf-2Fsf (3)
IzWMz＝2a Ftf sinδf+2a Fsf cosδf-2bFsr (4)
In formulae (2) to (4), VMbx、VMbyAnd WMzThe lateral speed, the longitudinal speed and the yaw rate of the vehicle are respectively; m, IzThe mass of the vehicle and the moment of inertia around the vertical axis are respectively; a. b is the distance from the wheel axle center of the front wheel and the rear wheel of the automobile to the center of mass respectively; δ f is the front wheel steering angle, determined by the steering wheel angle δ measured by the steering wheel angle sensor, divided by the steering gear ratio from the steering wheel to the front wheels; cdIs the air resistance coefficient; a. thefIs the vehicle forward area; rhoaIs the air density; ftf、FtrLongitudinal forces acting on the single front and rear wheels, respectively; fsf、FsrActing on a single front wheel and rear wheel respectivelyLateral forces on the wheel;
step 3.4.2.3, obtaining control input vector U ═ δ F, F of vehicle dynamics model using steering wheel angle sensor and wheel force sensor informationtf，Ftr]T；
3.4.2.4, solving a differential equation through a fourth-order Runge Kutta method to obtain the transverse speed V of the vehicle in a carrier coordinate systemMbxLongitudinal speed VMbyAnd yaw rate WMzAnd obtaining the speed calculated by the dynamic model under the navigation coordinate system according to the attitude matrix calculated by the INS:
9. the method as claimed in claim 8, wherein the activation function of the B-P neural network in step 3.4.2 is as follows
In the formula (6), e is a natural constant.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114724405A (en) * | 2022-04-27 | 2022-07-08 | 南通智慧交通科技有限公司 | Parking guidance method and system based on big data |
| CN115035742A (en) * | 2022-05-24 | 2022-09-09 | 智慧互通科技股份有限公司 | Urban public area vacant parking space configuration method and system based on dynamic allocation algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139644A (en) * | 2015-05-27 | 2015-12-09 | 重庆大学 | Indoor parking space positioning method based on APP and GPS inertial guidance |
| CN107144284A (en) * | 2017-04-18 | 2017-09-08 | 东南大学 | Inertial navigation combination navigation method is aided in based on the vehicle dynamic model that CKF is filtered |
| CN108091170A (en) * | 2018-01-08 | 2018-05-29 | 西安电子科技大学 | Parking stall inducible system based on intelligent terminal |
| CN108399789A (en) * | 2018-03-28 | 2018-08-14 | 成都天合世纪科技有限责任公司 | A kind of intelligent parking management system and method |
| WO2019084936A1 (en) * | 2017-11-03 | 2019-05-09 | 深圳市沃特沃德股份有限公司 | Parking lot navigation method, apparatus and system |
-
2021
- 2021-09-06 CN CN202111038523.1A patent/CN113744558B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139644A (en) * | 2015-05-27 | 2015-12-09 | 重庆大学 | Indoor parking space positioning method based on APP and GPS inertial guidance |
| CN107144284A (en) * | 2017-04-18 | 2017-09-08 | 东南大学 | Inertial navigation combination navigation method is aided in based on the vehicle dynamic model that CKF is filtered |
| WO2019084936A1 (en) * | 2017-11-03 | 2019-05-09 | 深圳市沃特沃德股份有限公司 | Parking lot navigation method, apparatus and system |
| CN108091170A (en) * | 2018-01-08 | 2018-05-29 | 西安电子科技大学 | Parking stall inducible system based on intelligent terminal |
| CN108399789A (en) * | 2018-03-28 | 2018-08-14 | 成都天合世纪科技有限责任公司 | A kind of intelligent parking management system and method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114724405A (en) * | 2022-04-27 | 2022-07-08 | 南通智慧交通科技有限公司 | Parking guidance method and system based on big data |
| CN114724405B (en) * | 2022-04-27 | 2022-11-18 | 南通智慧交通科技有限公司 | Parking guidance method and system based on big data |
| CN115035742A (en) * | 2022-05-24 | 2022-09-09 | 智慧互通科技股份有限公司 | Urban public area vacant parking space configuration method and system based on dynamic allocation algorithm |
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