Electric vehicle charging path planning method and system based on Internet of Things

Abstract

The invention relates to the technical field of path planning, in particular to an electric vehicle charging path planning method and system based on the Internet of Things, and the method comprises the steps: obtaining a plurality of electric vehicle charging areas in a preset range based on a started charging management system, judging whether there is an available charging station set, and if there is no available charging station set, executing the step 1; if yes, sending a charging station-free charging early warning to the target vehicle, otherwise, obtaining a recommended charging station set based on the available charging station set, obtaining a recommended path set by the recommended charging station set, receiving decision preference from the target vehicle, generating an identification navigation path by using the recommended path corresponding to the decision preference, and sending the identification navigation path to the target vehicle. A decision area map is generated according to the confirmation instruction and the identification navigation path, and then electric vehicle path planning based on the Internet of Things is completed. According to the invention, the path planning can be optimized according to the available state of the charging station and the user's willingness to ensure the availability of the charging station and reduce the time cost of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a method and system for planning a charging path for an electric vehicle based on the Internet of Things. Background Art

[0002] The rise and development of the Internet of Things has enabled ubiquitous connections between objects and between objects and people through network access, thereby achieving intelligent perception, identification and management of objects and processes. Electric vehicles refer to vehicles powered by onboard power supplies, which have the advantages of zero emissions, low noise and low cost. However, before the rapid development of battery technology, the power of electric vehicles was not enough to compete with traditional vehicles. Short battery life and high charging frequency have become the main problems of electric vehicles.

[0003] The traditional way of charging electric vehicles is to drive the electric vehicle to the charging station for charging. After the electric vehicle drives to the charging station, it connects to the charging station and finally completes the charging.

[0004] Although the above method can realize the charging of electric vehicles, with the increasing popularity of electric vehicles, the growth rate of the number of charging stations and the number of electric vehicles that can be served cannot keep up with the growth of the number of electric vehicles. It often happens that after the electric vehicle arrives at the charging station, it cannot be charged because the charging station is damaged or occupied. Therefore, an electric vehicle charging path planning method based on the Internet of Things is needed to optimize the path planning according to the available status of the charging station and the user's willingness, so as to ensure the availability of the charging station and reduce the user's time cost. Summary of the invention

[0005] The present invention provides an electric vehicle charging path planning method based on the Internet of Things. The computer can optimize the path planning according to the available status of the charging station and the user's willingness, so as to ensure the availability of the charging station and reduce the user's time cost.

[0006] To achieve the above purpose, the present invention provides an electric vehicle charging path planning based on the Internet of Things, and the specific steps are as follows:

[0007] Receiving a parking instruction from a target vehicle, starting a charging management system based on the parking instruction, and acquiring a plurality of electric vehicle charging areas within a preset range based on the started charging management system;

[0008] Determine whether there are available charging station sets in multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-built no-charging-station charging warning is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-built quantitative evaluation method, wherein each electric vehicle charging area includes zero or one available charging station set.

[0009] Perform identification visualization operations on the recommended charging station set on a pre-built real-time map to obtain a real-time map of charging recommendations, generate a set of recommended routes based on the real-time map of charging recommendations, receive decision preferences from the target vehicle based on the set of recommended routes, and generate an identification navigation route using the recommended route corresponding to the decision preference;

[0010] After confirming the receipt of the confirmation instruction from the target vehicle, generate a decision area map based on the confirmation instruction and the identification navigation route, and complete the path planning of the electric vehicle based on the Internet of Things based on the decision area map.

[0011] Optionally, determine whether there is a set of available charging stations in multiple electric vehicle charging areas. If it is confirmed that there is no set of available charging stations in each electric vehicle charging area among the multiple electric vehicle charging areas, issue a charging warning without a charging station pre-built to the target vehicle. Otherwise, obtain a set of recommended charging stations based on all the existing sets of available charging stations and a pre-built quantitative evaluation method, including:

[0012] Extract electric vehicle charging areas from multiple electric vehicle charging areas in sequence, and perform the following operations on each of the extracted electric vehicle charging areas:

[0013] Generate an initial access request based on the target vehicle and the electric vehicle charging area. After accessing the pre-built area charging system of the electric vehicle charging area using the pre-built security verification method and the initial access request, determine whether there is a set of available charging stations in the area charging system, where the set of available charging stations is a set composed of charging stations that can charge the target vehicle;

[0014] If there is no set of available charging stations in the area charging system, remove the electric vehicle charging area from the multiple electric vehicle charging areas. Otherwise, retain the electric vehicle charging area to obtain a target area;

[0015] Summarize the target areas to obtain a set of target areas;

[0016] If the set of target areas is an empty set, confirm that there is no set of available charging stations in each electric vehicle charging area among the multiple electric vehicle charging areas, and issue a charging warning without a charging station to the target vehicle;

[0017] If the set of target areas is not an empty set, perform a quantitative evaluation operation on the set of target areas, and screen the set of target areas after quantitative evaluation to obtain a set of recommended charging stations.

[0018] Optionally, accessing the pre-built area charging system of the electric vehicle charging area using the pre-built security verification method and the initial access request includes:

[0019] The area-based charging system receives an initial access request, parses the initial access request, and obtains access information. The access information includes: the target vehicle, the target vehicle type, the vehicle battery type, the target vehicle coordinates, the target vehicle license plate, the user identity identification code, the access time, and the target parking location.

[0020] Use a security verification method to determine whether the access information is preset trusted information. If it is confirmed that the access information is trusted information, identify the access user type based on the access information, retrieve the user access rights corresponding to the access user type from the pre-constructed permission decision tree, and generate a parsed access request for the initial access request to the area-based charging system according to the user access rights. Access the area-based charging system according to the parsed access request.

[0021] Optionally, use a security verification method to determine whether the access information is preset trusted information. If it is confirmed that the access information is trusted information, it includes:

[0022] Judge whether the target vehicle coordinates are within the pre-constructed jurisdiction area. If the target vehicle coordinates are not within the jurisdiction area, confirm the access information as preset untrusted information and send a pre-constructed non-service warning to the target vehicle.

[0023] If the target vehicle coordinates are within the jurisdiction area, obtain the rule credibility parameter based on the access information, and calculate the rule credibility of the access information based on the rule credibility parameter. The rule credibility parameter includes: an exact parameter set and a fuzzy parameter set.

[0024] Obtain the access sequence log of the target vehicle according to the access information, calculate the sequence credibility according to the access sequence log, and calculate the information credibility of the access information based on the preset rule credibility weight, preset sequence credibility weight, rule credibility, and sequence credibility.

[0025] Judge whether the information credibility is within the pre-constructed credibility interval. If the information credibility is not within the credibility interval, reject the initial access request. If the information credibility is within the credibility interval, confirm the access information as trusted information.

[0026] Optionally, identify the access user type based on the access information, and retrieve the user access rights corresponding to the access user type from the pre-constructed permission decision tree, including:

[0027] Obtain the user type set and the access right set. The user type set includes multiple user types, and the multiple user types are respectively: administrator, registered user, and non-registered user. The access right set includes multiple access rights. The access right includes: accessible resources and multiple access sub-rights. The access sub-right is the operation right for the access right.

[0028] Perform the following operations for each user type in the user type set:

[0029] Extract access permissions from the access permission set in sequence. If the user type authorizes the accessible resources corresponding to the extracted access permissions, construct a user-main permission path with the user type as the parent node and the extracted access permissions as the child nodes, and perform the following operations for each access sub-permission among the multiple access sub-permissions corresponding to the extracted access permissions:

[0030] If the user type authorizes the access sub-permission, construct a main permission-sub permission path with the access permission in the user-main permission path as the parent node and the access sub-permission as the child node; otherwise, skip the extracted access sub-permission;

[0031] If the user type is not authorized for the extracted access permission, skip the access permission;

[0032] After all the access permissions in the access permission set have been extracted, obtain the unit permission decision tree corresponding to the user type;

[0033] Summarize multiple unit permission decision trees to obtain the permission decision tree corresponding to the user type set;

[0034] Extract the access user type based on the access information, and extract the user type identical to the access user type from the user type set to obtain the target access user type. Retrieve the unit permission decision tree corresponding to the target access user type from the permission decision tree to obtain the user access permission.

[0035] Optionally, perform a quantitative evaluation operation on the target area set, and screen the quantitatively evaluated target area set to obtain a recommended charging station set, including:

[0036] Extract target areas from the target area set in sequence, and perform the following operations on the extracted target areas:

[0037] Obtain the target parking time based on the parking instruction, and confirm the set of available charging stations in the target area to obtain the unit charging station set. Perform the following operations for each unit charging station in the unit charging station set:

[0038] Calculate the estimated driving time of the target vehicle from the target vehicle coordinates to the unit charging station based on the pre-constructed driving time calculation model and the target parking time, and quantitatively evaluate the unit charging station according to the estimated driving time and the pre-constructed regional congestion parameter to obtain the feasibility evaluation value of the unit charging station;

[0039] Summarize the feasibility evaluation values in descending order based on preset values to obtain a feasibility evaluation sequence corresponding to the target area set. Extract the feasibility evaluation values of the recommended number from the feasibility evaluation sequence in turn using the preset recommended number, and obtain all unit charging stations corresponding to the feasibility evaluation values of the recommended number to obtain a recommended charging station set. Among them, the recommended charging station set includes multiple recommended charging stations, and the recommended charging stations correspond one by one to the feasibility evaluation values in the feasibility evaluation values of the recommended number.

[0040] Optionally, determining whether there is an available charging station set in the regional charging system includes:

[0041] Obtain the total charging station set based on the regional charging system, and filter the total charging station set using the preset usage status parameters to obtain an unused charging station set and a used charging station set;

[0042] Obtain the charging electrical performance parameters based on the target vehicle, and sequentially extract unused charging stations from the unused charging station set. If the extracted unused charging station meets the charging electrical performance parameters, the extracted unused charging station is confirmed as an available charging station;

[0043] Filter the used charging station set using the preset willingness time difference and charging electrical performance parameters to obtain an initial available charging station set;

[0044] Summarize the available charging stations and the initial available charging station set to obtain the available charging station set corresponding to the regional charging system.

[0045] Optionally, filtering the used charging station set using the preset willingness time difference and charging electrical performance parameters to obtain an initial available charging station set includes:

[0046] Based on the preset verification time interval, determine whether a secondary security query instruction from the target vehicle is received within the verification time interval, where the secondary security query instruction includes the user's willing waiting time;

[0047] If a secondary security query instruction is received within the verification time interval, the following operations are performed on all used charging stations in the used charging station set:

[0048] If it is confirmed that the used charging station meets the charging electrical performance parameters, obtain the remaining charging time of the used charging station, and calculate the charging station congestion waiting time and the queuing congestion probability based on the target parking time and the used charging station;

[0049] If the remaining charging time is greater than the estimated driving time, calculate the difference between the remaining charging time and the estimated driving time to obtain the initial waiting time, calculate the willingness time difference based on the user's willing waiting time and the charging station congestion waiting time, and if the willingness time difference is greater than the initial waiting time, confirm the used charging station as the initial charging station;

[0050] If the remaining charging time is less than or equal to the estimated driving time, the charging station used will be confirmed as the initial charging station;

[0051] Compare the queuing congestion probability with a preset congestion probability threshold. If it is confirmed that the queuing congestion probability is less than the preset congestion probability threshold, the initial charging station will be confirmed as the initial available charging station;

[0052] Summarize the initial available charging stations to obtain a set of initial available charging stations.

[0053] Optionally, the calculation formula for the information credibility is as follows:

[0054]

[0055] where T ust represents the information credibility, μ1 represents the rule credibility weight, represents the rule credibility, μ2 represents the sequence credibility weight, represents the sequence credibility, g1 represents the accuracy parameter credibility of the accurate parameter set, g2 represents the fuzzy parameter credibility of the fuzzy parameter set, n represents the total number of accurate parameters and fuzzy parameters in the rule credibility parameters, and ε represents the correction parameter of the rule credibility.

[0056] To achieve the above object, the present invention also provides an electric vehicle charging path planning system based on the Internet of Things, including:

[0057] A parking instruction acquisition module, configured to receive a parking instruction from a target vehicle, start a charging management system based on the parking instruction, and obtain a plurality of electric vehicle charging areas within a preset range based on the started charging management system;

[0058] An available charging station set acquisition module, configured to determine whether there is an available charging station set in a plurality of electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the plurality of electric vehicle charging areas, a pre-constructed no-charging-station charging warning will be issued to the target vehicle. Otherwise, a recommended charging station set will be obtained based on all the existing available charging station sets and a pre-constructed quantitative evaluation method, where each electric vehicle charging area includes zero or one available charging station set;

[0059] An identification navigation path generation module, configured to perform an identification visualization operation on the recommended charging station set on a pre-constructed real-time map to obtain a charging recommendation real-time map, generate a recommended path set according to the charging recommendation real-time map, receive the decision preference from the target vehicle based on the recommended path set, and generate an identification navigation path using the recommended path corresponding to the decision preference;

[0060] A confirmation module, configured to, after receiving a confirmation instruction from a target vehicle, generate a decision area map based on the confirmation instruction and an identified navigation path, and complete the path planning of an electric vehicle based on the Internet of Things based on the decision area map.

[0061] To solve the above problems, the present invention further provides an electronic device, which includes:

[0062] A memory, storing at least one instruction;

[0063] A processor, executing the instructions stored in the memory to implement the above-mentioned path planning method for an electric vehicle charging based on the Internet of Things.

[0064] To solve the above problems, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned path planning method for an electric vehicle charging based on the Internet of Things.

[0065] In order to solve the background technical problems, the present invention first receives a parking instruction from a target vehicle, starts a charging management system based on the parking instruction, and obtains multiple electric vehicle charging areas within a preset range based on the started charging management system. The present invention integrates multiple electric vehicle charging areas through the charging management system, so that multiple electric vehicle charging areas within the preset range can be queried through the network, and it is determined whether there are available charging station sets in the multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-constructed charging warning without a charging station is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-constructed quantitative evaluation method. The present invention determines the credibility of access information by using a method for calculating the credibility of information. After accessing the regional charging system, it clarifies the operations that the user of the target vehicle can perform and the content that can be accessed in the regional charging system, and allows the user of the target vehicle to access the regional charging system. In the present invention, in addition to being able to screen out unused charging stations that can charge the target vehicle from unused charging stations, it is also possible to screen out charging stations that can charge the target vehicle from the charging stations in use, and to eliminate damaged unused charging stations, thereby improving the screening efficiency and avoiding the situation where the target vehicle finds that the unused charging station is damaged and cannot be used after arriving at the unused charging station. The recommended charging station set is subjected to a label visualization operation on a pre-constructed real-time map to obtain a charging recommendation real-time map, a recommended path set is generated according to the charging recommendation real-time map, a decision preference from the target vehicle is received based on the recommended path set, a label navigation path is generated using the recommended path corresponding to the decision preference, and after confirming the receipt of the confirmation instruction from the target vehicle, a decision area map is generated based on the confirmation instruction and the label navigation path. The present invention displays the recommended path corresponding to the decision preference on the decision area map according to the user's decision preference, and does not display other unselected recommended paths, so that the final decision area map is concise and clear. Therefore, the present invention can optimize the path planning according to the available status of the charging station and the user's willingness to ensure the availability of the charging station and reduce the user's time cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of a flow chart of an electric vehicle charging path planning method based on the Internet of Things provided by an embodiment of the present invention;

[0067] Figure 2 A functional module diagram of an electric vehicle charging path planning system based on the Internet of Things provided by an embodiment of the present invention;

[0068] Figure 3 A schematic diagram of the structure of an electronic device for implementing an electric vehicle charging path planning method based on the Internet of Things provided by an embodiment of the present invention;

[0069] Description of reference numerals:

[0070] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0071] The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] The embodiments of the present application provide a method for planning an electric vehicle charging path based on the Internet of Things. The execution subject of the method for planning an electric vehicle charging path based on the Internet of Things includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for planning an electric vehicle charging path based on the Internet of Things can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0074] Referring to Figure 1 As shown, it is a flowchart of a method for planning an electric vehicle charging path based on the Internet of Things provided by an embodiment of the present invention. In this embodiment, the method for planning an electric vehicle charging path based on the Internet of Things is as follows:

[0075] S1. Receive a parking instruction from a target vehicle, start a charging management system based on the parking instruction, and obtain multiple electric vehicle charging areas within a preset range based on the started charging management system.

[0076] It can be understood that the target vehicle is the vehicle driven by the user, that is, the vehicle that needs to be charged. The parking instruction is an instruction issued by the driver of the target vehicle to park the target vehicle and charge it. The charging management system is a system for managing and planning the charging of the target vehicle based on the Internet of Things. Exemplarily, the charging management system can be a small program or an APP, etc., which can be used online on a terminal.

[0077] The specific preset range is a circle centered on the coordinates of the target parking position of the target vehicle with a preset radius. When a parking instruction of the target vehicle is received, multiple electric vehicle charging areas within the preset range are obtained, and this area can be an area where an electric vehicle can be charged.

[0078] It should be noted that when the user does not actively set the target parking position, the target parking position is defaulted to the target vehicle coordinates, which are the coordinates of the target vehicle obtained by using the pre-built positioning module in the charging management system. The target parking position is the position where the target vehicle expects to charge or park. For example, the target vehicle is at the position of longitude b and latitude d, but finally expects to charge the target vehicle at the position of longitude v and latitude m, and the position of longitude v and latitude m is set as the target parking position, then the target parking position is longitude v and latitude m.

[0079] Exemplarily, when Zhang drives the target vehicle and finds that the vehicle needs to be charged, he initiates a parking instruction. The charging management system built into Zhang's mobile phone receives the parking instruction and starts the charging management system. The started charging management system thus obtains multiple tram charging areas within the preset range. And the target vehicle can drive to the tram charging area.

[0080] S2. Determine whether there is an available charging station set in multiple tram charging areas. If it is confirmed that there is no available charging station set in each of the multiple tram charging areas, a pre-built no-charging-station charging warning is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all the existing available charging station sets and the pre-built quantitative evaluation method. Each tram charging area includes zero or one available charging station set.

[0081] It should be noted that the available charging station set is a set composed of charging stations that can charge the target vehicle in a tram charging area. If there is no available charging station in a tram charging area, there are zero available charging sets in the tram charging area. Otherwise, there is one available charging set in the tram charging area. In the embodiments of the present invention, a charging station is the smallest unit that can charge an electric vehicle, that is, a charging station can only charge one electric vehicle at the same time. There are multiple charging stations in a tram area, and among the multiple charging stations, there are charging stations that the target vehicle can use, damaged charging stations, or charging stations that are in use. Therefore, it is necessary to screen multiple tram charging areas. For the specific screening method, please refer to the subsequent embodiments.

[0082] Furthermore, the no-charging-station charging warning is a warning issued when it is reminded that there is no available charging station that can charge the target vehicle near the target parking position (i.e., multiple tram charging areas). Optionally, the no-charging-station charging warning is sent to the user of the target vehicle in text form, for example: "There is no available charging station in this area." The quantitative evaluation method is a method for scoring and screening different available charging stations by analyzing the status of different available charging stations. For the specific implementation method, please refer to the subsequent embodiments.

[0083] Further, determine whether there is a set of available charging stations in multiple electric vehicle charging areas. If it is confirmed that there is no set of available charging stations in each electric vehicle charging area among the multiple electric vehicle charging areas, issue a pre-constructed warning of no charging station for charging to the target vehicle. Otherwise, obtain a recommended set of charging stations based on all the existing sets of available charging stations and a pre-constructed quantitative evaluation method, including:

[0084] Extract electric vehicle charging areas from the multiple electric vehicle charging areas in sequence, and perform the following operations on each of the extracted electric vehicle charging areas:

[0085] Generate an initial access request based on the target vehicle and the electric vehicle charging area. After accessing the pre-constructed area charging system of the electric vehicle charging area using the pre-constructed security verification method and the initial access request, determine whether there is a set of available charging stations in the area charging system, where the set of available charging stations is a set composed of charging stations that can charge the target vehicle;

[0086] If there is no set of available charging stations in the area charging system, remove the electric vehicle charging area from the multiple electric vehicle charging areas. Otherwise, retain the electric vehicle charging area to obtain the target area;

[0087] Summarize the target areas to obtain a set of target areas;

[0088] If the set of target areas is an empty set, confirm that there is no set of available charging stations in each electric vehicle charging area among the multiple electric vehicle charging areas, and issue a warning of no charging station for charging to the target vehicle;

[0089] If the set of target areas is not an empty set, perform a quantitative evaluation operation on the set of target areas, and screen the set of target areas after quantitative evaluation to obtain a recommended set of charging stations.

[0090] It can be understood that the initial access request is a request initiated by the user of the target vehicle to access the area charging system, and the security verification method is a method for verifying the security of the user who initiates the initial access request. The area charging system is a management system for electric vehicle charging areas. For example, there are eight charging stations for electric vehicles at location A, and the system used to manage the eight charging stations for electric vehicles is the area charging system.

[0091] Further, accessing the pre-constructed area charging system of the electric vehicle charging area using the pre-constructed security verification method and the initial access request includes:

[0092] Receive the initial access request based on the area charging system, parse the initial access request to obtain access information, where the access information includes: the target vehicle, the type of the target vehicle, the type of the vehicle battery, the coordinates of the target vehicle, the license plate of the target vehicle, the user identification code, the access time, and the target parking position;

[0093] Use a security verification method to determine whether the access information is preset trusted information. If it is confirmed that the access information is trusted information, identify the access user type based on the access information, retrieve the user access permissions corresponding to the access user type from a pre-constructed permission decision tree, and generate an initial access request for the area charging system based on the user access permissions. Parse the access request according to the parsed access request and access the area charging system.

[0094] It should be noted that the target vehicle is the vehicle that the user drives and needs to be charged. The target vehicle type is the type of the target vehicle, including: pure electric vehicles, electric-gas hybrid electric vehicles, etc. The vehicle battery type is the type of the battery of the target vehicle, and the target vehicle license plate is the license plate number of the target vehicle. The user identity identification code is a unique identifier used to identify the user identity. For example, the user's ID number is used as the user identity identification code. The access time is the time when the target vehicle sends the initial access request.

[0095] It can be understood that the trusted information is preset information that is considered safe and trusted. When the access information is trusted information, it is considered that the initial access request of the target vehicle corresponding to the access information is not a malicious attack or virus, etc. The parsed access request is a request generated according to the user access permissions. Through the parsed access request, it can be clear what operations and content the user of the target vehicle can perform and access in the area charging system, and the user of the target vehicle is allowed to access the area charging system.

[0096] Furthermore, use a security verification method to determine whether the access information is preset trusted information. If it is confirmed that the access information is trusted information, it includes:

[0097] Judge whether the target vehicle coordinates are within the pre-constructed jurisdiction area. If the target vehicle coordinates are not within the jurisdiction area, the access information is confirmed as preset untrusted information, and a pre-constructed non-service warning is sent to the target vehicle;

[0098] If the target vehicle coordinates are within the jurisdiction area, obtain the rule credibility parameter based on the access information, and calculate the rule credibility of the access information based on the rule credibility parameter. The rule credibility parameter includes: an exact parameter set and a fuzzy parameter set;

[0099] Obtain the access sequence log of the target vehicle according to the access information, calculate the sequence credibility according to the access sequence log, and calculate the information credibility of the access information based on the preset rule credibility weight, preset sequence credibility weight, rule credibility, and sequence credibility;

[0100] Judge whether the information credibility is within the pre-constructed credibility interval. If the information credibility is not within the credibility interval, reject the initial access request. If the information credibility is within the credibility interval, confirm that the access information is trusted information.

[0101] Furthermore, the calculation formula for information credibility is as follows:

[0102]

[0103] Among them, T ust represents information credibility, μ1 represents the rule credibility weight, represents rule credibility, μ2 represents the sequence credibility weight, represents sequence credibility, g1 represents the credibility of the precise parameters in the precise parameter set, g2 represents the credibility of the fuzzy parameters in the fuzzy parameter set, n represents the total number of precise parameters and fuzzy parameters in the rule credibility parameters, and ε represents the correction parameter of rule credibility.

[0104] It can be understood that the jurisdiction area is the geographical area range for pre - constructing the credibility corresponding to the coordinates of the target vehicle. For example, if the coordinates of the target vehicle are in City A, while the regional charging system selected by the target vehicle is in City B, the access information will be confirmed as untrusted information at this time. Untrusted information is the opposite of trusted information. When the access information is untrusted information, it is considered that the initial access request of the target vehicle corresponding to the access information is a malicious attack or virus, etc. Subsequently, the regional charging system sends a pre - constructed non - service warning to the target vehicle. For example, the non - service warning is in text: "You are not in the server", and rejects the initial access request of the target vehicle, making the user of the target vehicle unable to access the regional charging system.

[0105] Furthermore, the precise parameter set is a set composed of multiple precise parameters. A precise parameter is a parameter that can be precisely obtained and used to judge whether the access information is credible, such as the access time in the access information. The fuzzy parameter set is a set composed of multiple fuzzy parameters. A fuzzy parameter is a parameter that cannot directly obtain a numerical value to judge whether the access information is credible, such as: "The user logs in to the system at night". It should be noted that the access information can also include other information other than the target vehicle, target vehicle type, vehicle battery type, target vehicle coordinates, target vehicle license plate, user identity identification code, access time, and target parking location, such as user IP, user access stay time, etc. Rule credibility is a value obtained by calculating the credibility degree of rule credibility parameters.

[0106] Further, the access sequence log is a log used to record the access behaviors and operation records of users, and the sequence credibility is a value obtained by judging whether the user of the target vehicle can be trusted through the access sequence log. The rule credibility weight is the weight of the rule credibility preset by humans, and the sequence credibility weight is the weight of the sequence credibility preset by humans. The information credibility is the degree of trustworthiness of the initial access request of the target vehicle in terms of access information, source, transmission process, etc. The credibility interval is an interval set by humans. When the information credibility is within the credibility interval, it is considered that the initial access request of the target vehicle corresponding to the information credibility is not a malicious attack or virus. When the information credibility is not within the credibility interval, it is considered that the user of the target vehicle may maliciously attack the regional charging system, so the initial access request is rejected.

[0107] It can be understood that the fuzzy parameter credibility is the credibility of each fuzzy parameter in the fuzzy parameter set. For example, when quantifying the credibility of the fuzzy parameter "the user logs in to the system at night", the obtained value is the fuzzy parameter credibility. The technology for calculating the fuzzy parameter credibility of fuzzy parameters is an existing technology and will not be elaborated here. The precise parameter credibility is the credibility of each precise parameter in the precise parameter set. For example, the value obtained when quantifying whether the target vehicle type in the access information is a credible parameter. This technology is an existing technology and will not be elaborated here.

[0108] Further, identify the access user type based on the access information, and retrieve the user access permissions corresponding to the access user type in the pre-constructed permission decision tree, including:

[0109] Obtain the user type set and the access permission set. Among them, the user type set includes multiple user types, and the multiple user types are respectively: administrator, registered user, and unregistered user. The access permission set includes multiple access permissions. The access permissions include: accessible resources and multiple access sub-permissions. The access sub-permission is the operation permission for the access permission.

[0110] Perform the following operations on each user type in the user type set:

[0111] Extract the access permissions from the access permission set in sequence. If the user type authorizes the accessible resources corresponding to the extracted access permissions, construct a user-main permission path with the user type as the parent node and the extracted access permissions as the child nodes, and perform the following operations on each access sub-permission among the multiple access sub-permissions corresponding to the extracted access permissions:

[0112] If the user type authorizes the access sub-permission, construct a main permission-sub-permission path with the access permission in the user-main permission path as the parent node and the access sub-permission as the child node; otherwise, skip the extracted access sub-permission.

[0113] If the user type is not authorized for the extracted access rights, skip the access rights.

[0114] After all the access rights in the access right set are extracted, a unit permission decision tree corresponding to the user type is obtained.

[0115] Summarize multiple unit permission decision trees to obtain a permission decision tree corresponding to the user type set.

[0116] Extract the access user type based on the access information, extract the user type identical to the access user type from the user type set to obtain the target access user type, and retrieve the unit permission decision tree corresponding to the target access user type from the permission decision tree to obtain the user access rights.

[0117] It can be understood that a registered user is a user who has registered in the charging management system in the embodiments of the present invention. A non-registered user is a user who has not registered in the charging management system.

[0118] Exemplarily, the accessible resource included in the access right is the remaining power consumption of a certain charging station, and the corresponding multiple access sub-rights are: read right, write right, and audit right. Then the read right, write right, and audit right are respectively three operation rights executed on the accessible resource of the remaining power consumption of a certain charging station.

[0119] Furthermore, if the user type authorizes the meaning of the accessible resource corresponding to the extracted access right: for the user type, having any one of the multiple access sub-rights corresponding to the accessible resource. For example, for the accessible resource of the access right being the remaining power consumption of a certain charging station, registered users and non-registered users have the read right, then the registered users and non-registered users authorize the accessible resource corresponding to the extracted access right.

[0120] Specifically, the user-main permission path is a branch constructed with the user type as the parent node and the extracted access right as the child node. The branch is a basic component of the structure tree. Therefore, there are multiple existing technologies to implement the construction of the user-main permission path, which will not be elaborated here. The purpose of constructing the user-main permission path is to connect the user type and the accessible resource of the user. When the user corresponding to the user type can access the accessible resource (i.e., authorizes the extracted access right), a branch is constructed between the user type and the access right corresponding to the accessible resource.

[0121] It is understandable that if the user type authorizes the access sub - permission, it means that for the user type, the permission for the accessible resources is this access sub - permission. The method of constructing the main - permission - sub - permission path is similar to that of constructing the user - main - permission path and can achieve the same effect, which will not be elaborated here. However, the parent node in the main - permission - sub - permission path is the access permission in the user - main - permission path. Its purpose is to connect the main - permission - sub - permission path with the constructed user - main - permission path and form a path.

[0122] Exemplarily, the user type is an administrator, whose access permissions are A and B, and the access sub - permissions for A are a, b, and c, and the access sub - permissions for B are a, b, and c. The user type is a registered user, and its access permission is A, and the access sub - permissions for A are a and b. Therefore, at this time, for the administrator, when constructing the unit permission decision tree, first extract the access permission A. Since the administrator authorizes the access permission A, the constructed user - main - permission path is [Administrator - A]. First extract the access sub - permission a of the administrator in A. Taking A in the user - main - permission path as the parent node and the access sub - permission a as the child node, construct the main - permission - sub - permission path, and then [Administrator - A - a] can be obtained. Then extract the access sub - permission b of the administrator in A in turn. Taking A in the user - main - permission path as the parent node and the access sub - permission b as the child node, construct the main - permission - sub - permission path, and so on. The brief schematic of the unit permission decision tree corresponding to the administrator is as follows:

[0123]

[0124] The brief schematic of the unit permission decision tree of the registered user is as follows:

[0125]

[0126] It is understandable that the target access user type is the user type in the user type set that is the same as the access user type, and the user access permission is all the accessible resources and their corresponding access sub - permissions owned by the target access user recorded in the unit permission decision tree.

[0127] Furthermore, perform a quantitative evaluation operation on the target area set, and screen the quantitatively evaluated target area set to obtain a recommended charging station set, including:

[0128] Extract the target area from the target area set in turn, and perform the following operations on the extracted target area:

[0129] Based on the parking instruction, obtain the target parking moment, and confirm the available charging station set of the target area to obtain the unit charging station set. Perform the following operations on each unit charging station in the unit charging station set:

[0130] Based on using a pre - constructed travel time calculation model and the target parking time to calculate the estimated travel time of the target vehicle from the target vehicle coordinates to the unit charging station, and quantitatively evaluating the unit charging station according to the estimated travel time and the pre - constructed regional congestion parameter to obtain the feasibility evaluation value of the unit charging station;

[0131] Based on the preset order of numerical descending order, summarize the feasibility evaluation values to obtain the feasibility evaluation sequence corresponding to the target area set. Use the preset number of recommendations to sequentially extract the feasibility evaluation values of the number of recommendations from the feasibility evaluation sequence, and obtain all the unit charging stations corresponding to the feasibility evaluation values of the number of recommendations to obtain the recommended charging station set.

[0132] It can be understood that the target parking time is the time when the target vehicle sends a parking instruction. The travel time calculation model is a model used to calculate the travel time of the target vehicle from the target vehicle coordinates to the unit charging station. There are various technologies to implement this travel time calculation model, including but not limited to existing map apps on the market. Therefore, it will not be elaborated here. The estimated travel time is the travel time of the target vehicle predicted by the travel time calculation model from the target vehicle coordinates to the unit charging station at the target parking time. The regional congestion parameter is a parameter used to describe the vehicle congestion degree in the target area at the target parking time. The regional congestion parameter can be evaluated by calculating the average vehicle speed of the main roads, expressways, secondary roads, and branch roads within the target area. For example, assign corresponding coefficients to the average vehicle speeds of the main roads, expressways, secondary roads, and branch roads respectively, and then perform weighted calculation according to the average vehicle speeds of the main roads, expressways, secondary roads, and branch roads and the corresponding coefficients to obtain an evaluation score, and confirm the evaluation score as the regional congestion parameter. When setting the coefficients, it should be such that the higher the regional congestion index, the more congested the target area is. For example, the average vehicle speeds of the main roads, expressways, secondary roads, and branch roads and the corresponding coefficients are real numbers within the interval [-1, 0].

[0133] Furthermore, the method for quantitatively evaluating the unit charging station according to the estimated travel time and the regional congestion parameter is to assign a time weight coefficient and a congestion weight coefficient to the estimated travel time and the regional congestion parameter respectively. The time weight coefficient and the congestion weight coefficient are the coefficients of the weights of the estimated travel time and the regional congestion parameter in the process of calculating the feasibility evaluation value.

[0134] Specifically, after setting the time weight coefficient and the congestion weight coefficient, perform weighted calculation on the estimated travel time and the regional congestion parameter. The obtained value is the feasibility evaluation value. When the estimated travel time is longer and the regional congestion index is higher, it means that the time required for the target vehicle to reach the unit charging station is longer, and the degree of congestion on the way is high, the average vehicle speed is low, and the feasibility evaluation value is lower.

[0135] Specifically, the order of numerical value descending is the order from the largest value to the smallest value. The feasibility evaluation sequence is the sequence obtained by sorting multiple feasibility evaluation values in descending order. The recommended number is the number set by humans for extracting feasibility evaluation values from the feasibility evaluation sequence. Since the feasibility evaluation sequence is sorted in descending order of the feasibility evaluation values, when extracting the feasibility evaluation values from the feasibility evaluation sequence in turn, the first extracted feasibility evaluation value is the largest feasibility evaluation value in the feasibility evaluation sequence, that is, the shorter the time required for the target vehicle to reach the unit charging station, the lower the degree of congestion on the way, and the higher the average vehicle speed. Exemplarily, the feasibility evaluation sequence is: [85, 74, 73, 65], and the recommended number is 2, then the feasibility evaluation values extracted from the feasibility evaluation sequence in turn are [85, 74]. At this time, the recommended charging station set is the two unit charging stations corresponding to the feasibility evaluation values [85, 74].

[0136] Further, determining whether there is an available charging station set in the regional charging system includes:

[0137] Obtaining the total charging station set based on the regional charging system, and screening the total charging station set by using the preset usage status parameters to obtain the unused charging station set and the used charging station set;

[0138] Obtaining the charging electrical performance parameters based on the target vehicle, and sequentially extracting the unused charging stations from the unused charging station set. If the extracted unused charging station meets the charging electrical performance parameters, the extracted unused charging station is confirmed as an available charging station;

[0139] Screening the used charging station set by using the preset willing time difference and the charging electrical performance parameters to obtain the initial available charging station set;

[0140] Summarizing the available charging stations and the initial available charging station set to obtain the available charging station set corresponding to the regional charging system.

[0141] Specifically, the total charging station set is the set composed of all charging stations in the regional charging system. The usage status parameter is a parameter used to describe the usage status of the charging station, including idle and charging. When the usage status parameter of the charging station in the total charging station set is idle, the charging station is confirmed as an unused charging station, otherwise, the charging station is confirmed as a used charging station. The unused charging stations and the used charging stations are summarized respectively to obtain the unused charging station set and the used charging station set.

[0142] Further, the charging electrical performance parameters are parameters composed of the electrical conditions required for the target vehicle to charge. For example: frequency change range, AC input voltage fluctuation range, AC input voltage asymmetry, input power range, etc. The embodiments of the present invention do not limit this here. If the unused charging station extracted meets the charging electrical performance parameters, it means that the extracted unused charging station can charge the target vehicle, and it also means that the extracted unused charging station is not damaged. Therefore, the extracted unused charging station is confirmed as an available charging station.

[0143] It should be noted that the fact that the unused charging station meets the charging electrical performance parameters indicates that the extracted unused charging station is not damaged because: when the unused charging station is damaged, it cannot charge the target vehicle. Therefore, the embodiments of the present invention also exclude the damaged unused charging stations, thereby improving the screening efficiency and avoiding the situation where the target vehicle arrives at the unused charging station and finds that the unused charging station is damaged and cannot be used.

[0144] It can be understood that in the embodiments of the present invention, in addition to being able to screen out the unused charging stations that can charge the target vehicle from the unused charging stations, it is also possible to screen out the charging stations that can charge the target vehicle from the charging stations that are in use. For the specific screening method, please refer to the subsequent embodiments.

[0145] Further, use the preset willing time difference and charging electrical performance parameters to screen the set of used charging stations to obtain the initial set of available charging stations, including:

[0146] Based on the preset verification time interval, determine whether a secondary security query instruction from the target vehicle is received within the verification time interval, where the secondary security query instruction includes the user's willing waiting time;

[0147] If a secondary security query instruction is received within the verification time interval, then perform the following operations on all the used charging stations in the set of used charging stations:

[0148] If it is confirmed that the used charging station meets the charging electrical performance parameters, then obtain the remaining charging time of the used charging station, and calculate the charging station congestion waiting time and the queuing congestion probability based on the target parking time and the used charging station;

[0149] If the remaining charging time is greater than the estimated driving time, then calculate the difference between the remaining charging time and the estimated driving time to obtain the initial waiting time, calculate the willing time difference based on the user's willing waiting time and the charging station congestion waiting time, and if the willing time difference is greater than the initial waiting time, then confirm the used charging station as the initial charging station;

[0150] If the remaining charging time is less than or equal to the estimated driving time, then confirm the used charging station as the initial charging station;

[0151] Compare the queuing congestion probability with a preset congestion probability threshold. If it is confirmed that the queuing congestion probability is less than the preset congestion probability threshold, then the initial charging station is confirmed as an initially available charging station;

[0152] Summarize the initially available charging stations to obtain a set of initially available charging stations.

[0153] It should be noted that the verification time interval is the time interval for receiving a secondary security query instruction from the target vehicle. When a secondary security query instruction from the target vehicle is received within the verification time interval, it is possible to determine whether there is an initially available charging station in the set of charging stations used according to the secondary security query instruction. An initially available charging station is similar to an available charging station, which refers to a charging station that can charge the target vehicle extracted from the charging stations in use. When no secondary security query instruction from the target vehicle is received within the verification time interval, there is no set of initially available charging stations screened from the charging stations in use in the final set of available charging stations. Therefore, the secondary security query instruction is an instruction initiated by the user after passing the secondary security verification of the user of the target vehicle to query the charging stations in use that can be used within the user's willing waiting time in the set of charging stations in use. The remaining charging time is the time that the charging station in use still needs to charge the vehicle that is being charged.

[0154] Furthermore, the charging station congestion waiting time is the average duration for the electric vehicle to leave after the charging station in use finishes charging the electric vehicle, calculated based on the target parking moment. The user willing waiting time is the waiting time preset by the target vehicle user who is willing to pay extra for charging the target vehicle. For example, at the target parking moment, the vehicle being charged at this charging station in use leaves on average ten minutes after finishing charging, that is, the charging station congestion waiting time is ten minutes, and the user willing waiting time is thirty minutes. This means that when the remaining charging time of the charging station in use is about twenty minutes, it can make the vehicle that was previously charged at the charging station in use leave, and the target charging vehicle arrives at the charging station in use just in time and can use the charging station. At this time, the willing time difference = user willing waiting time - charging station congestion waiting time.

[0155] Furthermore, when the remaining charging time is greater than the estimated driving time, it means that when the target vehicle selects this charging station in use as the target and drives to this location, this charging station in use is still charging the previous vehicle. If the user of the target vehicle wants to use this charging station in use to charge the target vehicle, waiting is required. The initial waiting time is the time that the user of the target vehicle needs to wait at this time if they want to use this charging station in use to charge the target vehicle.

[0156] It can be understood that when the willing time difference is greater than the initial waiting time, there is the following relational expression:

[0157] T willing -T s-wait >T left -T pre-drive

[0158] That is:

[0159] T willing >T left +T s-wait -T pre-drive

[0160] Wherein, T willing represents the waiting time desired by the user, T s-wait represents the waiting time due to charging station congestion, T left represents the remaining charging time, T pre-drive represents the estimated driving time;

[0161] Exemplarily, if the waiting time desired by the user is 30 min, the waiting time due to charging station congestion is 10 min, the remaining charging time is 15 min, and the estimated driving time is 10 min, then the difference in desired time is 20 min. That is, the charging station still needs to charge the previous vehicle for 15 min, and it is assumed that the previous vehicle using the charging station leaves 10 min after finishing charging. Therefore, starting from the target parking time, the time required for the previous vehicle using the charging station to completely leave is 15 min + 10 min, that is, 25 min. When the target vehicle arrives at the charging station and uses it for 10 min, that is, the estimated driving time is 10 min. Therefore, when the target vehicle arrives at the charging station, the previous vehicle using the charging station still needs 15 min to completely leave. Therefore, it is preliminarily determined that the user of the target vehicle can wait for the previous vehicle using the charging station to completely leave and charge at this charging station. At this time, this charging station is confirmed as the initial charging station.

[0162] Furthermore, if the remaining charging time is less than or equal to the estimated driving time, and the estimated driving time is used as the time for the target user to drive to the charging station, it means that the time required for the charging station to charge the previous vehicle is less than the time for the target user to drive to the charging station. That is, the charging station is in an idle state before the target vehicle arrives at the charging station. Therefore, this charging station is confirmed as the initial charging station.

[0163] It should be noted that in the embodiments of the present invention, the situation where the previous vehicle does not leave the charging station after charging is not considered. Only when the vehicle using the charging station finishes charging, it will leave within the preset leaving time. For example, the preset leaving time is ten minutes. When the user of the target vehicle initiates a secondary safety query instruction, it already includes the user's willing waiting time, indicating that the target vehicle is willing to pay an additional waiting time in order to use the charging station for the target charging station. When setting the user's willing waiting time, the user's willing waiting time should be greater than the leaving time of the previous vehicle, and the leaving time can be limited in various ways, such as overtime charging, etc. The purpose is to avoid the situation where the vehicle does not leave after being fully charged at the charging station and occupies public resources.

[0164] It can be understood that the queuing congestion probability is the probability that the initial charging station is in a congested state at the target parking moment. For example, at the target parking moment, every time a vehicle leaves the initial charging station within one minute, the probability that another vehicle uses the initial charging station for charging is the queuing congestion probability. The higher the queuing congestion probability, the higher the probability that another vehicle will preemptively use the initial charging station at the target parking moment of the initial charging station. As a result, during the process of the target vehicle driving to the initial charging station, it is more likely to be preemptively charged by other electric vehicles, making it impossible to charge after the target charging station arrives at the initial charging station. Therefore, in the embodiments of the present invention, the queuing congestion probability is compared with the preset congestion probability threshold, and the initial charging station with the queuing congestion probability less than the congestion probability threshold is identified as the initially available charging station. The congestion probability threshold is the maximum value of the queuing congestion probability set by humans. The initially available charging station set is a set obtained by summarizing all the initially available charging stations in the charging station set in use. If there is no initially available charging station in the charging station in use, the initially available charging station set is an empty set.

[0165] It can be understood that in summary, the conditions for screening out the initially available charging stations from the charging station set in use are as follows: First, it meets the charging electrical performance parameters; Second, if the remaining charging time is greater than the estimated driving time, then the willing time difference needs to be greater than the initial waiting time, or the remaining charging time is less than or equal to the estimated driving time; Third, the queuing congestion probability is less than the preset congestion probability threshold.

[0166] S3. Perform an identification visualization operation on the recommended charging station set on the pre-constructed real-time map to obtain a real-time charging recommendation map, and generate a recommended path set according to the real-time charging recommendation map.

[0167] Further, the real-time map is a map capable of real-time updating and displaying geographical information, and there are various existing technologies to achieve this, such as AutoNavi Map, etc. The identification visualization operation includes: sequentially extracting recommended charging stations from the set of recommended charging stations, and identifying the extracted charging stations on the real-time map. After confirming that all recommended charging stations are identified on the real-time map, a real-time charging recommendation map is obtained. Among them, the identification operation on the real-time map can use icons to identify on the real-time map. The set of recommended paths is a set composed of multiple recommended paths, and the recommended paths correspond one-to-one with the recommended charging stations. The recommended path is a path available for the target vehicle to travel from the target vehicle coordinates to the recommended charging station.

[0168] S4. Receive the decision preference from the target vehicle based on the set of recommended paths, and generate an identified navigation path using the recommended path corresponding to the decision preference.

[0169] It can be understood that the decision preference is the only recommended path selected by the user of the target vehicle from the set of recommended paths. The identified navigation path is the recommended path corresponding to the decision preference displayed on the decision area map, and the other unselected recommended paths in the set of recommended paths are not displayed on the decision area map. The decision area map is a real-time map that only displays the identified navigation path.

[0170] S5. After confirming to receive the confirmation instruction from the target vehicle, generate a decision area map based on the confirmation instruction and the identified navigation path, and complete the path planning of the electric vehicle based on the Internet of Things based on the decision area map.

[0171] It can be understood that the confirmation instruction is an instruction from the user of the target vehicle to confirm the use of the identified navigation path. After receiving the confirmation instruction, the decision area map will navigate for the user of the target vehicle according to the identified navigation path. In the embodiment of the present invention, the recommended path corresponding to the decision preference displayed on the decision area map according to the user's decision preference, and the other unselected recommended paths are not displayed, making the final decision area map simple, clear and understandable.

[0172] In order to solve the background technical problems, the present invention first receives a parking instruction from a target vehicle, starts a charging management system based on the parking instruction, and obtains multiple electric vehicle charging areas within a preset range based on the started charging management system. The present invention integrates multiple electric vehicle charging areas through the charging management system, so that multiple electric vehicle charging areas within the preset range can be queried through the network, and it is determined whether there are available charging station sets in the multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-constructed charging warning without a charging station is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-constructed quantitative evaluation method. The present invention determines the credibility of access information by using a method for calculating the credibility of information. After accessing the regional charging system, it clarifies the operations that the user of the target vehicle can perform and the content that can be accessed in the regional charging system, and allows the user of the target vehicle to access the regional charging system. In the present invention, in addition to being able to screen out unused charging stations that can charge the target vehicle from unused charging stations, it is also possible to screen out charging stations that can charge the target vehicle from the charging stations in use, and to eliminate damaged unused charging stations, thereby improving the screening efficiency and avoiding the situation where the target vehicle finds that the unused charging station is damaged and cannot be used after arriving at the unused charging station. The recommended charging station set is subjected to a label visualization operation on a pre-constructed real-time map to obtain a charging recommendation real-time map, a recommended path set is generated according to the charging recommendation real-time map, a decision preference from the target vehicle is received based on the recommended path set, a label navigation path is generated using the recommended path corresponding to the decision preference, and after confirming the receipt of the confirmation instruction from the target vehicle, a decision area map is generated based on the confirmation instruction and the label navigation path. The present invention displays the recommended path corresponding to the decision preference on the decision area map according to the user's decision preference, and does not display other unselected recommended paths, so that the final decision area map is concise and clear. Therefore, the present invention can optimize the path planning according to the available status of the charging station and the user's willingness to ensure the availability of the charging station and reduce the user's time cost.

[0173] like Figure 2 , which is a functional module diagram of an electric vehicle charging path planning system based on the Internet of Things provided by an embodiment of the present invention.

[0174] The electric vehicle charging path planning system 100 based on the Internet of Things of the present invention can be installed in an electronic device. According to the functions implemented, the electric vehicle charging path planning system 100 based on the Internet of Things can include a parking instruction acquisition module 101, an available charging station set acquisition module 102, an identification navigation path generation module 103 and a confirmation module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0175] A parking instruction acquisition module 101 is configured to receive a parking instruction from a target vehicle, start a charging management system based on the parking instruction, and obtain a plurality of electric vehicle charging areas within a preset range based on the started charging management system;

[0176] An available charging station set acquisition module 102 is configured to determine whether there is an available charging station set in the plurality of electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the plurality of electric vehicle charging areas, a pre-constructed charging warning without a charging station is sent to the target vehicle. Otherwise, a recommended charging station set is obtained based on all the existing available charging station sets and a pre-constructed quantitative evaluation method. Each of the electric vehicle charging areas includes zero or one available charging station set;

[0177] An identification navigation path generation module 103 is configured to perform an identification visualization operation on the recommended charging station set on a pre-constructed real-time map to obtain a charging recommendation real-time map, generate a set of recommended paths based on the charging recommendation real-time map, receive a decision preference from the target vehicle based on the set of recommended paths, and generate an identification navigation path using the recommended path corresponding to the decision preference;

[0178] A confirmation module 104 is configured to, after receiving a confirmation instruction from the target vehicle, generate a decision area map based on the confirmation instruction and the identification navigation path, and complete the path planning of the electric vehicle based on the Internet of Things based on the decision area map.

[0179] Specifically, each module in the Internet of Things-based electric vehicle charging path planning system 100 in the embodiments of the present invention uses the same technical means as the above-mentioned Figure 1 Internet of Things-based electric vehicle charging path planning method, and can produce the same technical effects, which will not be elaborated here.

[0180] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing an Internet of Things-based electric vehicle charging path planning method provided by an embodiment of the present invention.

[0181] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as an Internet of Things-based electric vehicle charging path planning method program.

[0182] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and also includes an external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the electric vehicle charging path planning method program based on the Internet of Things, etc., but also can be used to temporarily store the data that has been output or will be output.

[0183] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions packaged, including the combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing the programs or modules stored in the memory 11 (such as the electric vehicle charging path planning method program based on the Internet of Things, etc.), and calling the data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0184] The bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is set to achieve the connection and communication between the memory 11 and at least one processor 10, etc.

[0185] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 3 The shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine some components, or have different component arrangements.

[0186] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering each component. Preferably, the power source may be logically connected to at least one processor 10 through a power management system, so as to implement functions such as charging management, discharging management, and power consumption management through the power management system. The power source may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0187] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0188] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0189] The program of the electric vehicle charging path planning method based on the Internet of Things stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can:

[0190] Receive a parking instruction from a target vehicle, start a charging management system based on the parking instruction, and obtain a plurality of electric vehicle charging areas within a preset range based on the started charging management system;

[0191] Determine whether there is a set of available charging stations in the multiple electric vehicle charging areas. If it is confirmed that there is no set of available charging stations in each electric vehicle charging area among the multiple electric vehicle charging areas, a pre-constructed warning of no charging station for the target vehicle will be issued. Otherwise, based on all the existing sets of available charging stations and a pre-constructed quantitative evaluation method, a recommended set of charging stations will be obtained, where each electric vehicle charging area includes zero or one set of available charging stations;

[0192] Perform identity visualization operations on the recommended charging station set on a pre-built real-time map to obtain a real-time charging recommendation map, generate a recommended path set based on the real-time charging recommendation map, receive decision preferences from the target vehicle based on the recommended path set, and generate an identity navigation path using the recommended path corresponding to the decision preference;

[0193] After confirming the receipt of the confirmation instruction from the target vehicle, generate a decision area map based on the confirmation instruction and the identity navigation path, and complete the path planning of the electric vehicle based on the Internet of Things based on the decision area map.

[0194] Specifically, the specific implementation method of the above instructions by the processor 10 can refer to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0195] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0196] The present invention also provides a computer-readable storage medium, and the readable storage medium stores a computer program. When the computer program is executed by the processor of the electronic device, it can implement:

[0197] Receive a parking instruction from the target vehicle, start the charging management system based on the parking instruction, and obtain multiple electric vehicle charging areas within a preset range based on the started charging management system;

[0198] Determine whether there is a set of available charging stations in the multiple electric vehicle charging areas. If it is confirmed that there is no set of available charging stations in each of the multiple electric vehicle charging areas, issue a pre-built warning of no charging station for the target vehicle. Otherwise, obtain a recommended charging station set based on all the existing available charging station sets and a pre-built quantitative evaluation method, where each electric vehicle charging area includes zero or one set of available charging stations;

[0199] Perform identity visualization operations on the recommended charging station set on a pre-built real-time map to obtain a real-time charging recommendation map, generate a recommended path set based on the real-time charging recommendation map, receive decision preferences from the target vehicle based on the recommended path set, and generate an identity navigation path using the recommended path corresponding to the decision preference;

[0200] After receiving the confirmation instruction from the target vehicle, generate a decision area map based on the confirmation instruction and the marked navigation path, and complete the path planning of the electric vehicle based on the Internet of Things based on the decision area map.

[0201] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and there may be other partitioning methods in actual implementation.

[0202] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, each functional module in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

[0204] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for planning a charging path for an electric vehicle based on the Internet of Things, characterized in that: The specific steps are as follows: Receiving a parking instruction from a target vehicle, and starting a charging management system to obtain a plurality of electric vehicle charging areas within a preset range based on the parking instruction; Determine whether there are available charging station sets in multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-built no-charging-station charging warning is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-built quantitative evaluation method, wherein each electric vehicle charging area includes zero or one available charging station set. Performing a label visualization operation on the recommended charging station set on a pre-built real-time map to obtain a charging recommendation real-time map, generating a recommended path set based on the charging recommendation real-time map, receiving a decision preference from a target vehicle based on the recommended path set, and generating a label navigation path using the recommended path corresponding to the decision preference; After confirming receipt of the confirmation instruction from the target vehicle, a decision area map is generated based on the confirmation instruction and the marked navigation path.

2. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 1, characterized in that: Determine whether there is an available charging station set in multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-built no charging station charging warning is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-built quantitative evaluation method, including: The electric vehicle charging areas are extracted from the multiple electric vehicle charging areas in sequence, and the following operations are performed on the extracted electric vehicle charging areas: Generate an initial access request based on the target vehicle and the electric vehicle charging area, use the pre-built security verification method and the initial access request to access the regional charging system pre-built in the electric vehicle charging area, and then determine whether there is an available charging station set in the regional charging system, wherein the available charging station set is a set of charging stations that can charge the target vehicle; If there is no available charging station set in the regional charging system, the electric vehicle charging area is removed from the multiple electric vehicle charging areas, otherwise, the electric vehicle charging area is retained to obtain the target area; Summarize the target areas to obtain a target area set; If the target area set is an empty set, it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, and a charging warning without a charging station is issued to the target vehicle; If the target area set is not an empty set, a quantitative evaluation operation is performed on the target area set, and the target area set after the quantitative evaluation is screened to obtain a recommended charging station set.

3. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 2, characterized in that: Use pre-built security authentication methods and initial access requests to access the pre-built regional charging system of the electric vehicle charging area, including: Receiving an initial access request based on the regional charging system, parsing the initial access request, and obtaining access information, wherein the access information includes: a target vehicle, a target vehicle type, a vehicle battery type, a target vehicle coordinate, a target vehicle license plate, a user identification code, an access time, and a target parking location; A security verification method is used to determine whether the access information is the preset trusted information. If the access information is confirmed to be trusted information, the access user type is identified based on the access information, and the user access rights corresponding to the access user type are retrieved in the pre-built permission decision tree according to the access user type. An initial access request is generated based on the user access rights, and a parsed access request for the regional charging system is generated, and access to the regional charging system is performed based on the parsed access request.

4. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 3, characterized in that: Use the security verification method to determine whether the access information is the preset trusted information. If the access information is confirmed to be trusted information, it includes: Determine whether the target vehicle coordinates are within the pre-established jurisdiction area. If the target vehicle coordinates are not within the jurisdiction area, confirm the access information as preset untrustworthy information, and send a pre-established unserviceable warning to the target vehicle; If the target vehicle coordinates are within the jurisdiction, a rule credibility parameter is obtained based on the access information, and the rule credibility of the access information is calculated based on the rule credibility parameter, wherein the rule credibility parameter includes: a precise parameter set and a fuzzy parameter set; Obtaining an access sequence log of the target vehicle according to the access information, calculating the sequence credibility according to the access sequence log, and calculating the information credibility of the access information based on a preset rule credibility weight, a preset sequence credibility weight, the rule credibility, and the sequence credibility; Determine whether the information credibility is within the pre-built credibility interval. If the information credibility is not within the credibility interval, the initial access request is rejected. If the information credibility is within the credibility interval, the access information is confirmed to be credible information.

5. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 4, characterized in that: Identify the access user type based on the access information, and retrieve the user access rights corresponding to the access user type in the pre-built permission decision tree, including: Obtain a user type set and an access permission set, wherein the user type set includes multiple user types, and the multiple user types are: administrator, registered user and unregistered user, the access permission set includes multiple access permissions, and the access permissions include: accessible resources and multiple access sub-permissions, and the access sub-permissions are operation permissions performed on the access permissions; Perform the following operations on all user types in the user type set: Access rights are extracted from the access right set in sequence. If the user type authorizes the accessible resources corresponding to the extracted access rights, a user-primary permission path is constructed with the user type as the parent node and the extracted access rights as the child node. The following operations are performed on the access sub-rights in the multiple access sub-rights corresponding to the extracted access rights: If the user type authorizes access sub-permissions, the main permission-sub-permission path is constructed with the access permission in the user-main permission path as the parent node and the access sub-permission as the child node. Otherwise, the extracted access sub-permissions are skipped. If the user type is not authorized for the access rights being extracted, the access rights are skipped; After all access rights in the access right set are extracted, a unit permission decision tree corresponding to the user type is obtained; Aggregate multiple unit permission decision trees to obtain the permission decision tree corresponding to the user type set; The access user type is extracted based on the access information, and the user type that is the same as the access user type is extracted from the user type set to obtain the target access user type. The unit permission decision tree corresponding to the target access user type is retrieved from the permission decision tree to obtain the user access rights.

6. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 5, characterized in that: A quantitative evaluation operation is performed on the target area set, and the target area set after the quantitative evaluation is screened to obtain a recommended charging station set, including: The target regions are extracted from the target region set in sequence, and the following operations are performed on the extracted target regions: The target parking time is obtained based on the parking instruction, and the available charging station set in the target area is confirmed to obtain the unit charging station set. The following operations are performed for each unit charging station in the unit charging station set: Based on the pre-built travel time calculation model and the target parking time, the estimated travel time of the target vehicle from the target vehicle coordinates to the unit charging station is calculated, and the unit charging station is quantitatively evaluated according to the estimated travel time and the pre-built regional congestion parameters to obtain the feasibility evaluation value of the unit charging station; The feasibility assessment values ​​are summarized in descending order based on preset numerical values ​​to obtain a feasibility assessment sequence corresponding to the target area set, and the feasibility assessment values ​​of the recommended number are extracted from the feasibility assessment sequence in sequence using the preset recommended number, and all unit charging stations corresponding to the feasibility assessment values ​​of the recommended number are obtained to obtain a recommended charging station set.

7. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 6, characterized in that: Determine whether there is an available charging station set in the regional charging system, including: A total charging station set is obtained based on the regional charging system, and the total charging station set is filtered using a preset usage status parameter to obtain an unused charging station set and a used charging station set; Acquiring charging electrical performance parameters based on the target vehicle, extracting unused charging stations from the unused charging station set in sequence, and if the extracted unused charging stations meet the charging electrical performance parameters, confirming the extracted unused charging stations as available charging stations; Using the preset desired time difference and charging electrical performance parameters, a set of charging stations is screened to obtain an initial set of available charging stations; The available charging stations and the initial available charging station set are summarized to obtain the available charging station set corresponding to the regional charging system.

8. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 7, characterized in that: The preset desired time difference and charging electrical performance parameters are used to screen the charging station set to obtain the initial available charging station set, including: Determine whether a secondary safety query instruction is received from the target vehicle within a preset verification time interval, the instruction including a user-intentioned waiting time; If a secondary safety query instruction is received within the verification time interval, the following operations are performed on all charging stations in the charging station concentration: If it is confirmed that the charging station meets the charging electrical performance parameters, the remaining charging time of the charging station is obtained, and the congestion waiting time and queue congestion probability of the charging station are calculated based on the target parking time and the charging station; If the remaining charging time is greater than the estimated driving time, the difference between the remaining charging time and the estimated driving time is calculated to obtain the initial waiting time. The desired time difference is calculated based on the user's desired waiting time and the congested waiting time of the charging station. If the desired time difference is greater than the initial waiting time, the charging station to be used is confirmed as the initial charging station. If the remaining charging time is less than or equal to the estimated driving time, the charging station to be used is confirmed as the initial charging station; Comparing the queue congestion probability with a preset congestion probability threshold, if it is determined that the queue congestion probability is less than the preset congestion probability threshold, confirming the initial charging station as an initial available charging station; The initial available charging stations are summarized to obtain an initial available charging station set.

9. The method for planning an electric vehicle charging path based on the Internet of Things as claimed in claim 8, characterized in that: The calculation formula for information credibility is as follows: Among them, T ust represents information credibility, μ1 represents rule credibility weight, represents the credibility of the rule, μ2 represents the sequence credibility weight, represents the sequence credibility, g1 represents the precise parameter credibility of the precise parameter set, g2 represents the fuzzy parameter credibility of the fuzzy parameter set, n represents the total number of precise parameters and fuzzy parameters in the rule credibility parameter, and ε represents the correction parameter of the rule credibility.

10. An electric vehicle charging path planning system based on the Internet of Things, characterized in that: include: A parking instruction acquisition module, used to receive a parking instruction from a target vehicle, start a charging management system based on the parking instruction, and acquire multiple electric vehicle charging areas within a preset range based on the started charging management system; An available charging station set acquisition module is used to determine whether there is an available charging station set in multiple electric vehicle charging areas. If it is confirmed that there is no available charging station set in each of the multiple electric vehicle charging areas, a pre-built no charging station charging warning is issued to the target vehicle. Otherwise, a recommended charging station set is obtained based on all existing available charging station sets and a pre-built quantitative evaluation method, wherein each electric vehicle charging area includes zero or one available charging station set; The identification navigation path generation module is used to perform identification visualization operations on the recommended charging station set on a pre-built real-time map to obtain a charging recommendation real-time map, generate a recommended path set based on the charging recommendation real-time map, receive a decision preference from a target vehicle based on the recommended path set, and generate an identification navigation path using the recommended path corresponding to the decision preference; The confirmation module is used to confirm the receipt of the confirmation instruction from the target vehicle and generate a decision area map based on the confirmation instruction and the marked navigation path.

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