Foggy weather path decision method and device, storage medium and electronic device

Abstract

The present disclosure provides a method and device for making a path decision in foggy weather, a storage medium and an electronic device, and relates to the field of meteorological information technology. The method includes obtaining departure location information and destination location information; obtaining map information based on the departure location information and destination location information; obtaining fog data based on the map information; preprocessing the fog data based on the map information to obtain fitting data; generating a path to be selected based on the fitting data; and performing fuzzy decision making based on the path to be selected, and outputting a path decision result. By comprehensively considering weather conditions and path planning through comprehensive fog data and map data, the most suitable travel path is selected, thereby avoiding travel inconveniences caused by trip delays caused by foggy weather and ensuring travel safety. Furthermore, fuzzy decision making can cope with more complex environments, making the planned path more practical, facilitating user travel, and improving user experience.

Description

Background Art

[0002] Meteorological information is a service closely related to people's social production and life. Especially in the context of advanced transportation tools and developed transportation networks, meteorological conditions can have a great impact on people's long-distance cross-regional travel in a short period of time.

[0003] In certain weather conditions, especially foggy days, visibility has a great impact on people's travel. Existing path planning methods cannot solve the problem of navigation in foggy days, which causes travel delays and brings a lot of inconvenience to people's travel. What's more serious is that people's travel safety is also threatened.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide a foggy weather route decision method and device, a storage medium and an electronic device, which can at least to some extent overcome the problem that related technologies cannot solve the problem of foggy weather navigation, resulting in travel delays, which brings a lot of inconvenience to people's travel and threatens people's travel safety.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a method for path decision-making in foggy weather is provided, including: obtaining departure location information and destination location information; obtaining map information based on the departure location information and the destination location information; obtaining fog data based on the map information; preprocessing the fog data based on the map information to obtain fitting data; generating a path to be selected based on the fitting data; and performing fuzzy decision-making based on the path to be selected and outputting a path decision result.

[0008] In one embodiment of the present disclosure, preprocessing fog data according to map information to obtain fitting data includes: interpolating fog data to cover map information to obtain fitting data.

[0009] In one embodiment of the present disclosure, fog data is interpolated according to map information to obtain fitting data, including: performing two-dimensional cubic interpolation on the fog data to cover the map information to obtain first fitting data; obtaining missing points according to the map information; and improving the interpolation processing on the first fitting data according to the missing points to obtain second fitting data.

[0010] In one embodiment of the present disclosure, the first fitting data is improved and interpolated according to the missing points to obtain the second fitting data, including: determining whether the position of the missing point is within the convex hull of the first fitting data; if so, improving and interpolating the first fitting data based on the triangulation method; if not, improving and interpolating the first fitting data based on the circle averaging method.

[0011] In one embodiment of the present disclosure, a path to be selected is generated based on fitting data, including: discretizing the fitting data into multiple nodes; obtaining the shortest path based on a path algorithm; and obtaining a path whose mileage difference with the shortest path is within a first threshold as the path to be selected.

[0012] In one embodiment of the present disclosure, fuzzy decision making is performed based on the path to be selected and the path decision result is output, including: determining evaluation parameters of the path to be selected; determining the membership function of the evaluation parameters; determining the fuzzy relationship matrix based on the membership function; obtaining the decision weights of the evaluation parameters based on the fuzzy relationship matrix; and selecting a path based on the decision weights of the evaluation parameters, and outputting the path decision result.

[0013] In one embodiment of the present disclosure, the evaluation parameters include: the total mileage of the path to be selected, the fog concentration, the fog duration, and the fog coverage.

[0014] According to another aspect of the present disclosure, a foggy path decision device is provided, including: a first acquisition module, used to acquire departure location information and destination location information; a second acquisition module, used to acquire map information based on the departure location information and the destination location information; a third acquisition module, used to acquire fog data based on the map information; a preprocessing module, used to preprocess the fog data based on the map information to obtain fitting data; a path generation module, used to generate a path to be selected based on the fitting data; and an output module, used to perform fuzzy decision according to the path to be selected and output the path decision result.

[0015] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned foggy path decision method by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned foggy path decision method is implemented.

[0017] The foggy weather route decision method and device, storage medium and electronic device provided by the embodiments of the present disclosure comprehensively consider weather conditions and route planning by integrating fog data and map data, select the most suitable travel route, avoid travel inconvenience caused by trip delays due to foggy weather, and ensure travel safety.

[0018] Furthermore, fuzzy decision-making can cope with more complex environments, making the planned routes more practical, facilitating user travel and improving user experience.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0021] Figure 1 A schematic diagram showing the structure of a foggy weather path decision system according to an embodiment of the present disclosure;

[0022] Figure 2 A flow chart of a foggy weather path decision method according to an embodiment of the present disclosure is shown;

[0023] Figure 3 A flow chart of a method for preprocessing fog data according to map information to obtain fitting data in an embodiment of the present disclosure is shown;

[0024] Figure 4a A flow chart of a method for obtaining second fitting data by performing improved interpolation processing on first fitting data according to missing measurement points in an embodiment of the present disclosure is shown;

[0025] Figure 4b A schematic diagram showing a gradient-sub-gradient-inverse distance weighted interpolation method in an embodiment of the present disclosure is shown;

[0026] Figure 4c A schematic diagram showing a circular average interpolation method in an embodiment of the present disclosure;

[0027] Figure 5 A flow chart of a method for generating a path to be selected according to fitting data in an embodiment of the present disclosure is shown;

[0028] Figure 6a A flow chart of a method for executing fuzzy decision according to a path to be selected and outputting a path decision result in an embodiment of the present disclosure is shown;

[0029] Figure 6b A schematic diagram showing a statistical table for fuzzy analysis decision making in an embodiment of the present disclosure;

[0030] Figure 7a A module flow chart of a foggy path decision system based on vehicle control in an embodiment of the present disclosure is shown;

[0031] Figure 7b The MATLAB simulation two-dimensional data of a foggy path decision method according to an embodiment of the present disclosure is shown;

[0032] Figure 7c The MATLAB simulation three-dimensional interpolation simulation data of a foggy path decision method in an embodiment of the present disclosure is shown;

[0033] Figure 7d A schematic diagram showing a gradient-sub-gradient-inverse distance weighted interpolation method of a foggy path decision method in an embodiment of the present disclosure is shown;

[0034] Figure 7e A table showing a range of visibility levels of meteorological industry standards in an embodiment of the present disclosure;

[0035] Figure 7f A schematic diagram of foggy weather path planning of a foggy weather path decision method according to an embodiment of the present disclosure is shown;

[0036] Figure 8 A schematic diagram of a foggy path decision device according to an embodiment of the present disclosure is shown; and

[0037] Fig. 9 A structural block diagram of a foggy weather path decision-making computer device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0038] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0039] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0040] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0041] In view of the technical problems existing in the above-mentioned related technologies, the embodiments of the present disclosure provide a network congestion control method to solve at least one or all of the above-mentioned technical problems.

[0042] Figure 1 1 is a schematic diagram of a computer system provided by an exemplary embodiment of the present application. The system includes: a plurality of terminals 120 and a server cluster 140.

[0043] The terminal 120 may be a mobile terminal such as an in-vehicle electronic device, a mobile phone, a game console, a tablet computer, an e-book reader, smart glasses, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a smart home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, or the terminal 120 may also be a personal computer (PC), such as a laptop computer and a desktop computer.

[0044] The terminal 120 may be installed with an application for providing foggy weather path decision making.

[0045] The terminal 120 is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0046] The server cluster 140 is a server, or is composed of several servers, or is a virtualization platform, or is a cloud computing service center. The server cluster 140 is used to provide background services for applications that provide foggy weather path decision-making. Optionally, the server cluster 140 undertakes the main computing work, and the terminal 120 undertakes the secondary computing work; or, the server cluster 140 undertakes the secondary computing work, and the terminal 120 undertakes the main computing work; or, the terminal 120 and the server cluster 140 adopt a distributed computing architecture for collaborative computing.

[0047] In some optional embodiments, the server cluster 140 is used to store map data information and weather data information.

[0048] In the present application, the server cluster 140 is also connected to the blockchain system 160, and the server cluster 140 stores the map data information and / or the weather data information and / or the path data information in the blockchain system. In some optional embodiments, the server cluster 140 itself can also operate and store data as a node in the blockchain system.

[0049] Optionally, in the embodiment of the present application, the server cluster 140 includes a map data server 142 and a meteorological data server 144. The map data server 142 is used to implement the logic control of the application, such as obtaining map data, processing map data, etc. The meteorological data server 144 is part of the blockchain system 160 and is used to store meteorological data of various regions.

[0050] It should be noted that the map data server 142 and the weather data server 144 may belong to the same computer device, or the map data server 142 and the weather data server 144 may belong to different computer devices.

[0051] Optionally, the client of the application installed in different terminals 120 is the same, or the client of the application installed on the two terminals 120 is the client of the same type of application of different control system platforms. Based on the different terminal platforms, the specific form of the client of the application may also be different, for example, the application client may be a mobile client, a PC client or a World Wide Web (Web) client.

[0052] Those skilled in the art will appreciate that the number of the terminals 120 may be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or a greater number. The embodiment of the present application does not limit the number and device type of the terminals.

[0053] Optionally, the system may also include a management device ( Figure 1 (not shown), the management device is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0054] Optionally, the above-mentioned wireless network or wired network uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or any combination of a virtual private network). In some embodiments, the data exchanged through the network is represented by technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0055] The following will describe in more detail the various steps of the foggy weather path decision method in this example implementation with reference to the accompanying drawings and embodiments.

[0056] Figure 2 The method provided by the embodiment of the present disclosure can be executed by any electronic device with computing and processing capabilities, such as Figure 1 The terminal 120 and / or the server cluster 140 in the embodiment. In the following example description, the server cluster 140 is used as the execution subject for example description.

[0057] like Figure 2 As shown, the foggy weather path decision method provided by the embodiment of the present disclosure may include the following steps:

[0058] Step S210, obtaining departure location information and destination location information.

[0059] In some embodiments of the present disclosure, the location information of the departure place and the destination may be obtained by using the communication function of the terminal. In other embodiments of the present disclosure, the location information of the departure place may be obtained manually or automatically according to a positioning system. The departure place and the destination may be two regions in one city, or different regions in two cities.

[0060] Step S220, obtaining map information according to the departure location information and the destination location information.

[0061] In some embodiments of the present disclosure, map information of the corresponding area is obtained based on the departure location information and the destination location information, wherein the map information includes the areas of the departure and destination and road data of the relevant areas, and the road data includes: intercity roads and urban roads, wherein intercity roads include: expressways, national roads, provincial roads, county roads and township roads; urban roads include: urban expressways, main roads, secondary roads and branch roads.

[0062] In some embodiments of the present disclosure, the roads can be classified according to their actual conditions. The present application does not limit the road classification method. Then, weights are further assigned according to the road types to facilitate path decision making.

[0063] Step S230, obtaining fog data according to the map information.

[0064] In some embodiments of the present disclosure, fog data may be defined as including multiple scales, for example, large-scale fog data is a heavy fog warning in the area; medium-scale fog data is a nowcast data from a nearby meteorological station; and small-scale fog data is traffic visibility meter data.

[0065] In some embodiments of the present disclosure, fog data of observation stations at various scales corresponding to the map information are obtained, and corresponding information such as location coordinates, fog concentration, fog duration, and fog coverage is written into a database.

[0066] Step S240: pre-process the fog data according to the map information to obtain fitting data.

[0067] In some embodiments of the present disclosure, the map information is fitted with the fog data stored in the aforementioned step through position coordinates to obtain high-precision grid fitting data.

[0068] In some other embodiments of the present disclosure, fog data is interpolated to cover map information to obtain fitting data. Through interpolation processing, high-precision information is obtained, thereby reducing errors and improving the accuracy of path planning.

[0069] Step S250, generating a path to be selected according to the fitting data.

[0070] In some embodiments of the present disclosure, the fitting data can be discretized into multiple nodes, and the fitting data can be converted into grid data composed of multiple nodes. The weights of the paths in the grid can be set based on the road fog concentration data in the grid data, and then the shortest path can be found using a path algorithm. For example, the A* algorithm, Dijkstra algorithm, and other methods can be used, and the path to be selected can be obtained from the shortest path.

[0071] Step S260, performing fuzzy decision according to the path to be selected, and outputting the path decision result.

[0072] In some embodiments of the present disclosure, a factor set for comprehensive path evaluation can be established in advance. For example, the factor set may include path mileage, path time, fog concentration (visibility), fog duration, fog coverage, path congestion, road type, etc.

[0073] In some embodiments of the present disclosure, after determining the factors of the above-mentioned comprehensive evaluation of the path, it is necessary to normalize the data to generate an evaluation set from the factor set. In some embodiments, it includes normalizing all data to a fixed range, such as [0,1]. In other embodiments, it also includes quantifying the comments, such as converting "poor visibility" to "fog concentration (visibility) <100 meters", that is, fog concentration (visibility) ∈ (0,100), and then normalizing from "(0,100)" to the range of [0,1].

[0074] In some embodiments of the present disclosure, the data is hierarchically analyzed according to the evaluation set to assign weights to each factor. In some embodiments, an entropy weight method may be used to assign weights to each factor in the evaluation set.

[0075] In some embodiments of the present disclosure, the decision-making process may also include calculating priorities, such as selecting safety priority / speed priority / energy saving priority.

[0076] In some embodiments of the present disclosure, a module for triggering execution based on an output decision path is also included, especially a method applied to automatic control.

[0077] By using the foggy day route decision method of the present application, weather conditions and route planning are comprehensively considered by integrating fog data and map data, and the most suitable travel route is selected, thereby avoiding travel inconveniences caused by trip delays caused by foggy days and ensuring travel safety.

[0078] Furthermore, fuzzy decision-making can cope with more complex environments, making the planned routes more practical, facilitating user travel and improving user experience.

[0079] Figure 3A flow chart of a method for preprocessing fog data according to map information to obtain fitting data in an embodiment of the present disclosure is shown. Figure 3 As shown, step S240 may include:

[0080] S310, performing two-dimensional cubic interpolation on fog data to cover map information, and obtaining first fitting data.

[0081] In some embodiments of the present disclosure, two-dimensional cubic interpolation may be performed according to formula (1):

[0082] a*X 3 +b*X 2 +c*X+d=concentration formula (1)

[0083] Where X = [x, y], the two-dimensional coordinates of the interpolation point; a, b, c, d are coefficients. However, in some cases where the amount of data is small, using only the cubic function interpolation method is prone to large errors. Therefore, it is necessary to perform improved interpolation processing on the first fitting data to obtain the second fitting data.

[0084] S320, obtaining missing points according to the map information.

[0085] In some embodiments of the present disclosure, road nodes not covered by fog data are obtained according to map information as missing points. In some embodiments, there may be one or more missing points.

[0086] S330: performing improved interpolation processing on the first fitting data according to the missing measurement points to obtain second fitting data.

[0087] In some embodiments of the present disclosure, the missing points are improved and interpolated based on the first fitting data to generate the second fitting data. In some embodiments, the improved interpolation is based on the triangulation (Delaunay) method, and the circle average method or other methods may also be used, which is not limited in this embodiment. Further, in some embodiments, the interpolation algorithm may be selected based on the positional relationship between the missing points and the adjacent triangles. In other embodiments, the interpolation algorithm may also be selected based on the positional relationship between the missing points and the convex hull of the first fitting data. After the interpolation of all the missing points is completed, the second fitting data is obtained.

[0088] Through the method in this application, the fitting data with a small amount of data is improved and interpolated to obtain high-precision information, thereby meeting the requirements of fast calculation speed, small amount of information required for restoring three-dimensional data, high drawing accuracy, etc. in actual scenes. Using less information to generate high-quality three-dimensional graphics, the calculation process is simple and the calculation efficiency is high, thereby further reducing errors and improving the accuracy of path planning.

[0089] Figure 4a A flow chart of a method for obtaining second fitting data by performing improved interpolation processing on first fitting data according to missing measurement points in an embodiment of the present disclosure is shown. Figure 4a As shown, including:

[0090] Step S410, obtaining missing points according to map information.

[0091] Step S410 and Figure 3 The process is similar to step S320 in , and will not be repeated here.

[0092] Step S420, determining whether the position of the missing point is within the convex hull range of the first fitting data.

[0093] In an embodiment of the present disclosure, the convex hull of the first fitting data may be constructed using a convex combination of all points in the first fitting data.

[0094] In some embodiments of the present disclosure, the improved interpolation may include a gradient-subgradient-inverse distance weighted method based on triangulation, and a circular average method. In some embodiments, an interpolation method may be selected based on the position of the missing point and the convex hull position of the first fitting data.

[0095] Step S430: if yes, then performing improved interpolation on the first fitting data based on the triangulation method.

[0096] In some embodiments of the present disclosure, if the missing point is within the convex hull of the first fitting data, an improved interpolation is performed on the first fitting data based on a triangulation method. Figure 4b A schematic diagram of a gradient-subgradient-inverse distance weighted interpolation method in an embodiment of the present disclosure is shown. Figure 4b As shown, including:

[0097] Step S432 (not shown in the figure) determines the gradient direction and sub-gradient direction formed by the three points, that is, the direction with the fastest descent speed (3→0.1) and the second fastest descent speed (2→0.1). It also includes constructing a parallel gradient line L1 and a parallel sub-gradient line L2 with the missing point ① as the end point, and determining the starting point values ​​of L1 and L2 in proportion.

[0098] Step S434 (not shown in the figure), according to the starting point value and gradient and sub-gradient descent speed of L1 and L2, the distance L3 and L4 from the missing point ① to the data endpoint are calculated, and the weights of L3 and L4 are determined respectively using inverse distance weighting (the farther the distance, the smaller the weight, because the influence is smaller);

[0099] Step S436 (not shown in the figure), use the direction and descent speed of the gradient and sub-gradient on the edge of the triangle, assign the weights of L3 and L4, and determine the value of the missing point ①.

[0100] Step S440: if not, then performing improved interpolation on the first fitting data based on the circle averaging method.

[0101] In some embodiments of the present disclosure, if the missing point is outside the convex hull range of the first fitting data, an improved interpolation is performed on the first fitting data based on the circle averaging method. Figure 4c FIG. 2 is a schematic diagram showing a circular average interpolation method in an embodiment of the present disclosure. Figure 4c As shown, including:

[0102] Step S442 (not shown in the figure), a triangle abc adjacent to the missing point ① forms a circle, and all data in the circle are averaged with the data of the triangle endpoints. When the point to be measured is covered by the circle, the assignment is successful, otherwise, step S444 is executed.

[0103] In step S444 (not shown in the figure), the missing point ② and points a and b are connected to form a triangle, which is further used to form a circle. This circle is averaged again with the values ​​within the circle averaged in step S442 (i.e., the average value of a, b, c is averaged with a and b) to obtain the data of the point to be measured ①.

[0104] Step S450: generating second fitting data.

[0105] Repeat the above steps until all missing points are interpolated successfully to obtain the second fitting data.

[0106] The method of this application can effectively solve the problems of large errors and insufficient algorithm robustness caused by too little data in ordinary two-dimensional cubic interpolation algorithms, as well as the abnormal calculation results of single triangulation interpolation methods for extrapolation situations, improve interpolation accuracy, and ultimately achieve high-precision path decision-making.

[0107] Figure 5 A flow chart of a method for generating a path to be selected according to fitting data in an embodiment of the present disclosure is shown. Figure 5 As shown, step S250 includes:

[0108] S510, discretizing the fitting data into a plurality of nodes.

[0109] In some embodiments of the present disclosure, the fitting data may be discretized into a plurality of nodes, and then the adjacent nodes may be connected in sequence. In some embodiments, the fitting data may be divided according to hierarchical logic, for example, it may be set to a four-level structure of "layer 1 - layer 2 - layer 3 - layer 4". The road data in "layer 1" is managed by "layer 2", "layer 2" may be further divided into different "layers 3", and "layer 3" includes layer 4 which actually has map data. The specific logic may be determined according to the actual amount of data to be processed.

[0110] S520, obtaining the shortest path based on a path algorithm.

[0111] In some embodiments of the present disclosure, weights are assigned to paths between adjacent nodes according to mileage, and then the shortest path or multiple paths to be selected are found according to a path algorithm, wherein the path algorithm includes but is not limited to the A* algorithm, the Dijkstra algorithm, etc., and the present application does not limit this.

[0112] S530, obtaining a path whose mileage difference with the shortest path is within a first threshold as a path to be selected.

[0113] In some embodiments of the present disclosure, the path to be selected can be determined based on the mileage min_mile of the shortest path found by the aforementioned method. Specifically, a first threshold mileage range can be set. For example, the first threshold can be within 1.10 times of the shortest mileage min_mile; it can also be a specific number, which is not limited in the present application.

[0114] The above method combines map data and location information received by the system in real time, and uses the shortest path algorithm to find the optimal path to the current destination for the user, so that the timeliness of the path decision results and the obstacle avoidance performance are better.

[0115] Figure 6a A flow chart of a method for executing fuzzy decision according to a path to be selected and outputting a path decision result in an embodiment of the present disclosure is shown. Figure 6a As shown, step S260 includes:

[0116] Step S610, determining the evaluation parameters of the path to be selected.

[0117] In some embodiments of the present disclosure, evaluation parameters of the path to be selected are determined, and a factor set for comprehensive evaluation of the path is pre-established. Figure 6b FIG. 1 is a schematic diagram showing a statistical table for fuzzy analysis decision making in an embodiment of the present disclosure. Figure 6b As shown, three paths to be selected are determined, and the evaluation parameters corresponding to the three paths are collected to establish a factor set, where the factor set may include but is not limited to: distance mileage, journey time, fog concentration (visibility), fog duration, fog coverage, energy consumption and traffic volume.

[0118] Step S620, determining the membership function of the evaluation parameter.

[0119] In some embodiments of the present disclosure, membership functions may be set for various parameters in the factor set to fuzzily express the factor data.

[0120] Step S630, determining the fuzzy relationship matrix according to the membership function.

[0121] In some embodiments of the present disclosure, the factor set data of each path to be selected is preprocessed according to the membership function. Specifically, the membership degree of each factor of each path to be selected is obtained according to the membership function, and then normalized to the range of [0, 1]. Further, the fuzzy relationship matrix is ​​determined according to the calculation results.

[0122] Step S640, obtaining the decision weights of the evaluation parameters according to the fuzzy relationship matrix.

[0123] In some embodiments of the present disclosure, the weights of various items in the decision can be obtained by using an objective entropy weight method, or other methods can be used. In this embodiment, taking the entropy weight method as an example, the decision weight method for obtaining the evaluation parameter can be as follows:

[0124] Step S642 (not shown): Standardize the four factor indicators according to formula (2):

[0125]

[0126] where y ij is the jth index value of the i-th path after standardization, x ij is the j-th index value of the i-th path.

[0127] Step S644 (not shown): define and standardize according to formula (3):

[0128]

[0129] Step S646 (not shown): Calculate the information entropy value e of the indicator according to formula (4): j :

[0130]

[0131] According to formula (5), the information utility value d of the indicator is calculated j :

[0132] d j =1-e j Formula (5)

[0133] Step S648 (not shown): Determine the weight of each evaluation index according to formula (6). The greater the information utility value, the greater the importance of the index to the evaluation. The weight of each factor evaluation index is finally obtained as:

[0134]

[0135] Taking the result in the form of A=(0.1 0.2 0.3 0.4) as an example, it represents the weights of the four factors.

[0136] Step S650, select a path according to the decision weight of the evaluation parameter, and output the path decision result. In some embodiments of the present disclosure, the decision weight is multiplied by the fuzzy relationship matrix to obtain a comprehensive evaluation result, and then a relatively optimal path is selected according to the result.

[0137] Through the fuzzy decision-making method of the present application, factors such as fog concentration, fog duration, fog coverage, etc. can be comprehensively considered and combined with path mileage to achieve multi-objective optimization, so as to improve the overall performance of path decision-making and better adapt to decision-making needs.

[0138] Figure 7a FIG. 1 shows a flow chart of a foggy path decision system module based on vehicle control in an embodiment of the present disclosure. Figure 7a As shown, it includes: a meteorological data receiving / decoding module 710, a fuzzy analysis decision module 720, a fog avoidance trigger module 730, and an intelligent control system 740. Specifically, an implementation of the foggy path decision system based on the above vehicle control includes but is not limited to the following process:

[0139] The meteorological data receiving / decoding module 710 is used to execute step 712 (not shown in the figure) after the system obtains the location information of the departure point A and the destination B when starting to drive, read the fog forecast information of various scale stations in the area, calibrate the coordinates (x, y), fog concentration (visibility data), fog duration, and fog coverage, and write them into the database.

[0140] In some embodiments of the present disclosure, the meteorological data receiving / decoding module 710 is also used to execute step 714 (not shown in the figure), perform data preprocessing, use a two-dimensional cubic interpolation method, interpolate and fit the fog information covering the entire area, locate the coordinates to the base map, and project the base map to the map of the area to obtain high-precision information. In some embodiments, the interpolation accuracy can also be adjusted according to the map accuracy. In an embodiment of the present disclosure, a small amount of fog data is simulated, and the simulation demonstration using matlab coding is as follows:

[0141] In some embodiments of the present disclosure, taking a simulated fog condition as an example, the sample program is:

[0142] clear clc

[0143] x=[129 140 103.5 88 185.5 195 105 157.5 107.5 77 81 162 162 117.5];

[0144] y=[7.5 141.5 23 147 22.5 137.5 85.5 -6.5 -81 3 56.5 -66.5 84 -33.5]; %xy coordinates, the measuring station is a discrete node

[0145] z=[4 4 6 8 6 4 8 4 9 8 8 4 4 2]; %Simulated fog density [0,10].

[0146] xi=75:1:200;

[0147] yi=-50:1:150;

[0148] zi=griddata(x,y,z,xi,yi','cubic')

[0149] subplot(1,2,1),plot(x,y,'*') % output coordinate position

[0150] subplot(1,2,2),mesh(xi,yi,zi) % output three-dimensional fog data

[0151] The above method can be used to obtain the two-dimensional coordinate position map of fog data, such as Figure 7b As shown. The three-dimensional fog data is obtained, such as Figure 7c shown.

[0152] Furthermore, the meteorological data receiving / decoding module 710 receives the following Figure 7c The first fitting data shown is subjected to improved interpolation processing, including gradient-subgradient-inverse distance weighted interpolation (such as Figure 7d As shown) and the circular average method interpolation, Figure 7d Take the following as an example to calculate the fog density of the middle missing point:

[0153] First, determine the gradient direction and sub-gradient direction, then construct parallel gradient line ① and parallel sub-gradient line ② with the missing point as the end point, determine the starting values ​​of the gradient line 2.8 and 2.4 in proportion, the gradient descent rate is 2, and the value after descent is 2.8-2=0.8, the sub-gradient descent rate is 1, and the value after descent is 2.4-1=1.4. Connect the missing point and the data endpoint to form ①' and ②', and determine the weights to be 0.66 and 0.34 respectively according to the inverse distance weighting, and then calculate the concentration of the missing point according to formula (7):

[0154] Gradient descent value*gradient weight+sub-gradient descent value*sub-gradient weight=concentration (Formula (7)), so the value of the missing point is 0.8*0.66+1.4*0.34≈1.

[0155] In some embodiments of the present disclosure, the meteorological data receiving / decoding module 710 is further used to calculate the three-dimensional fog fitting data at different times according to the duration of the fog, and write the data into the database.

[0156] In some embodiments of the present disclosure, a path decision module (not shown in the figure) is further included to determine the path to be selected between the departure point A and the destination B. Specifically, it includes but is not limited to: the continuous road between A and B is discretized into multiple nodes, the adjacent nodes on the path are connected into edges, and the A* algorithm is used to find the shortest path according to the known mileage weighting, mark the shortest path found, record the mileage min_mile, continue to find paths within the mileage range less than min_mile*1.10, and mark all paths as paths to be selected.

[0157] The fuzzy analysis decision module 720 is used to unify the accuracy and resolution of the location coordinates of the path passing through the area and the coordinates of the fog database. For example, they can be divided into the same node for path decision.

[0158] In some embodiments of the present disclosure, the fuzzy analysis decision module 720 is also used to package all coordinates of the pending path (marked 1, 2, 3, 4 in the example) for calling out the fog information of each package coordinate. For example, including but not limited to the implementation of the stored procedure in SQL language:

[0159] CREATE PROC fog_info

[0160] @position_x int,@position_y int

[0161] AS

[0162] SELECT fog concentration, fog generation and elimination FROM db_fogingotable

[0163] Where x=@position_x and y=@position_y

[0164] EXEC fog_info@position_x=100,@position_y=100

[0165] Take the results of four paths and four factors as an example:

[0166]

[0167] In some embodiments of the present disclosure, the fuzzy analysis decision module 720 is further used to determine corresponding membership functions according to the above four factors, including but not limited to the following situations:

[0168] The mileage linear membership function can be set as shown in formula (8):

[0169]

[0170] The membership of the four paths to be selected with respect to mileage is obtained from formula (8): μ A =[1 0 0.66 0.33].

[0171] The membership function of fog concentration is based on the meteorological industry standard as follows: Figure 7e As shown in the figure, corresponding to the visibility level, and then sorted to [0,1], the membership of the four paths to be selected with respect to fog concentration (visibility) is obtained: μ B =[0.2 0.8 0.6 0.6].

[0172] The linear membership function of fog duration can be set as shown in formula (9):

[0173]

[0174] The membership of the four paths to be selected with respect to the fog duration is obtained: μ C =[0 1 0.55 0.55].

[0175] The percentage of fog range is directly sorted to [0,1], and the membership of the four paths to be selected with respect to the fog range is obtained: μ D =[0.5 0.7 0.5 0.5].

[0176] In some embodiments of the present disclosure, the fuzzy analysis decision module 720 determines the fuzzy relationship matrix according to the degree of membership of each factor corresponding to each candidate path:

[0177]

[0178] Furthermore, according to the entropy weight method, the weight A = [0.4 0.4 0.1 0.1] is obtained, and the comprehensive evaluation score B is obtained according to the weight A and the fuzzy relationship matrix R:

[0179] B=A*R=[0.53 0.49 0.609 0.477]

[0180] The score of the path III to be selected is the largest, which means the result is optimal, and the fuzzy analysis decision module 720 selects the path III. Figure 7f As shown, the number next to the station is the fog density of the station (0-10). It is a missing point. Plan the optimal path from point A to point B. Route A→3→9→B is the existing shortest path, which does not take into account the regional fog concentration. Route A→3→1→B is the optimal path planned considering the existing sites (without considering the missing points). It can be seen that the point with a fog concentration of 9 is avoided. Through the improved interpolation method of the present application, a triangulation is constructed near the missing points, and according to the previous steps, the concentration of the points to be measured is interpolated, and the path is replanned. The route A→1→1→B is obtained. On the basis of the shortest path, fog avoidance is comprehensively considered to find the optimal route.

[0181] In some embodiments of the present disclosure, Figure 7a The figure also includes a fog avoidance trigger module 730, which is used to trigger the intelligent control system 740 to execute instructions according to the decision, wherein the intelligent control system 740 is used to perform actions according to the instructions of the trigger.

[0182] In some embodiments of the present disclosure, other actions are also triggered, for example: if fog is encountered (missing weather information or wrong forecast), the trigger module 730 instructs the control system 740 to start the foggy road condition system, automatically remove fog droplets from the surface of the image collector lens, automatically decelerate, automatically turn on the fog lights, automatically maintain the distance between vehicles, automatically change lanes, automatically (timely) honk the horn, etc. In some embodiments, if heavy fog is encountered, the trigger module 730 instructs the control system 740 to stay, automatically select a parking position, and wait for the road conditions to improve.

[0183] By using the vehicle fog path decision method of the present application, weather conditions and path planning are comprehensively considered by integrating fog data and map data, and the most suitable driving path is selected, thereby avoiding travel inconveniences caused by driving delays caused by foggy days and ensuring driving safety.

[0184] Furthermore, fuzzy decision-making can be used to comprehensively and quantitatively consider a variety of complex traffic factors, making the planned driving route more practical, facilitating user driving, and improving user experience.

[0185] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0186] Figure 8 FIG. 2 shows a schematic diagram of a foggy weather path decision device according to an embodiment of the present disclosure. Figure 8 As shown, including:

[0187] The first acquisition module 810 is used to acquire departure location information and destination location information.

[0188] The second acquisition module 820 acquires map information according to the departure location information and the destination location information.

[0189] The third acquisition module 830 is used to obtain fog data according to the map information.

[0190] The preprocessing module 840 is used to preprocess the fog data according to the map information to obtain fitting data.

[0191] The path generation module 850 is used to generate a path to be selected according to the fitting data.

[0192] The output module 860 is used to perform fuzzy decision according to the path to be selected and output the path decision result.

[0193] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0194] Refer to the following Fig. 9 An electronic device 900 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 900 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0195] like Fig. 9 As shown, the electronic device 900 is in the form of a general computing device. The components of the electronic device 900 may include but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0196] The storage unit stores program codes, which can be executed by the processing unit 910, so that the processing unit 910 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 910 can perform the following steps: Figure 2 As shown in S210, the departure location information and the destination location information are obtained; S220, the map information is obtained according to the departure location information and the destination location information; S230, the fog data is obtained according to the map information; S240, the fog data is preprocessed according to the map information to obtain fitting data; S250, the path to be selected is generated according to the fitting data; S260, the fuzzy decision is performed according to the path to be selected, and the path decision result is output.

[0197] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 9201 and / or a cache storage unit 9202 , and may further include a read-only storage unit (ROM) 9203 .

[0198] The storage unit 920 may also include a program / utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0199] Bus 930 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0200] The electronic device 900 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or may communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 950. Furthermore, the electronic device 900 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 via a bus 930. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0201] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0202] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0203] The program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0204] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0205] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0206] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0207] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0208] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0209] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0210] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0211] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A foggy path decision method, characterized in that: include: Obtain departure location information and destination location information; Acquiring map information according to the departure location information and the destination location information; Acquire fog data according to the map information; Preprocessing the fog data according to the map information to obtain fitting data includes: performing two-dimensional cubic interpolation on the fog data to cover the map information to obtain first fitting data; obtaining missing points according to the map information; and determining whether the positions of the missing points are within the convex hull of the first fitting data; if yes, performing improved interpolation on the first fitting data based on a triangulation method; if not, performing improved interpolation on the first fitting data based on a circle averaging method; generating a path to be selected according to the fitting data; and Fuzzy decision making is performed according to the path to be selected, and a path decision result is output.

2. The foggy weather path decision method according to claim 1, characterized in that: Generating a path to be selected according to the fitting data comprises: discretize the fitting data into a plurality of nodes; Obtaining the shortest path based on a path algorithm; and A path whose mileage difference with the shortest path is within a first threshold is obtained as the path to be selected.

3. The foggy weather path decision method according to any one of claims 1 to 2, characterized in that: Performing fuzzy decision making according to the path to be selected and outputting a path decision result includes: Determining the evaluation parameters of the path to be selected; Determining the membership function of the evaluation parameter; Determine a fuzzy relationship matrix according to the membership function; Obtaining decision weights of the evaluation parameters according to the fuzzy relationship matrix; and A path is selected according to the decision weight of the evaluation parameter, and a path decision result is output.

4. The foggy weather path decision method according to claim 3, wherein: The evaluation parameters include: The total mileage, fog concentration, fog duration and fog coverage of the path to be selected.

5. A foggy path decision device, characterized in that: include: A first acquisition module is used to acquire departure location information and destination location information; A second acquisition module acquires map information according to the departure location information and the destination location information; A third acquisition module is used to acquire fog data according to the map information; A preprocessing module is used to preprocess the fog data according to the map information to obtain fitting data, which includes: performing two-dimensional cubic interpolation on the fog data to cover the map information to obtain first fitting data; obtaining missing points according to the map information; and judging whether the position of the missing point is within the convex hull range of the first fitting data; if yes, performing improved interpolation on the first fitting data based on the triangulation method; if not, performing improved interpolation on the first fitting data based on the circle average method; A path generation module, used for generating a path to be selected according to the fitting data; and The output module is used to perform fuzzy decision according to the path to be selected and output the path decision result.

6. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the foggy weather path decision method according to any one of claims 1 to 4 by executing the executable instructions.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the foggy weather path decision method according to any one of claims 1 to 4 is implemented.

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