Offline shared bicycle searching method and system

Abstract

The invention discloses an offline shared bicycle searching method and system, and solves the problem that the existing shared bicycle cannot be searched offline and is inconvenient to search. The method comprises the steps that the intelligent lock scans peripheral equipment in real time to obtain vehicle data; filtering the vehicle data to obtain effective offline vehicle data; the effective offline vehicle data and the intelligent lock position information are integrated to form first data, and the first data are uploaded to a server; obtaining the position information of a physical station, and calculating the geographic distance between the offline vehicle and the physical station in combination with the first data; and determining the search position information of the offline vehicle based on the matching judgment of the geographic distance. According to the invention, the intelligent lock of the shared bicycle scans the peripheral equipment to obtain the offline bicycle data, the problem that the shared bicycle is not easy to search offline is effectively solved, the search position information is obtained by selectively combining with the physical station, the accuracy of the search position information is further improved, and the offline bicycle can be more conveniently searched.

Description

Technical Field

[0001] The present invention relates to the technical field of bicycle searching, and in particular to an offline shared bicycle searching method and system. Background Art

[0002] In recent years, as a new type of travel mode, shared bicycles have spread across major cities. Bicycle enterprises have put a lot of shared bicycles on the market. In order to balance the distribution of shared bicycles in various regions of the city or to maintain shared bicycles, operation and maintenance personnel need to recycle shared bicycles. However, there are currently many problems with the inability to locate and find shared bicycles. Generally, shared bicycles transmit information through their smart locks connected to the network. Some vehicles are used by users until the smart locks do not have enough power to go offline or the 4G signal is relatively weak, resulting in the smart locks going offline. This makes it difficult for operation and maintenance personnel to find these shared bicycles, leading to a waste of resources. In addition, the positioning accuracy of shared bicycles is not high, which also makes searching inconvenient. For example, in the invention application with the patent number 201810827138.7 and the name of a shared bicycle management system and method based on a cloud platform, it discloses a data interaction and synchronization module based on 3G or 4G wireless communication technology and Bluetooth technology, and a positioning and alarm module for sensing shared bicycle information. This invention also has the above-mentioned disadvantages. Once the power of the smart lock on the shared bicycle is insufficient or the signal is weak, the shared bicycle goes offline and is difficult to be found. Summary of the Invention

[0003] The present invention mainly solves the problems that current shared bicycles cannot be found when offline and are inconvenient to search, and provides an offline shared bicycle searching method and system.

[0004] The above technical problems of the present invention are mainly solved by the following technical solutions: An offline shared bicycle searching method includes:

[0005] The smart lock scans surrounding devices in real time to obtain vehicle data;

[0006] Filter the vehicle data to obtain effective offline vehicle data;

[0007] Integrate the effective offline vehicle data with the smart lock position information to form first data and upload it to the server;

[0008] Obtain the position information of the physical site, and combine the first data to calculate the geographical distance between the offline vehicle and the physical site;

[0009] Based on the matching judgment of the geographical distance, determine the searching position information of the offline vehicle.

[0010] The present invention obtains offline vehicle data by scanning surrounding devices with a shared bicycle intelligent lock, effectively solving the problem that it is not easy to find offline shared bicycles. And by selectively combining with physical stations to obtain location information for searching, the accuracy of the location information for searching is further improved, making it more convenient to find offline vehicles.

[0011] As a preferred solution, the filtering of vehicle data to obtain effective offline vehicle data includes:

[0012] The vehicle data includes the first quantity of all vehicles scanned within a period and the second quantity of vehicles in the offline state among them. Calculate the proportion of offline vehicles based on the first quantity and the second quantity;

[0013] Obtain the historical proportion of offline vehicles and establish a dynamic threshold function based on time and the historical distribution of offline vehicles;

[0014] Compare the proportion of offline vehicles with the dynamic threshold function. When the proportion of offline vehicles is greater than the value of the dynamic threshold function, obtain the offline vehicle data as effective offline vehicle data.

[0015] This solution filters vehicle data based on time and the historical distribution of offline vehicles, enabling more accurate judgment of effective offline vehicle data for better finding offline vehicles subsequently.

[0016] As a preferred solution,

[0017] Set an initial threshold, an adjustment coefficient, and the number of historical periods,

[0018] The dynamic threshold function is the product of the weighted sum of the proportion of offline vehicles in each historical period and the adjustment coefficient, plus the initial threshold.

[0019] The dynamic threshold function is dynamically adjusted based on time and the historical distribution of offline vehicles. The specific formula is expressed as:

[0020] θ(t)=θ0+α*∑ k i＝1 (w i *p t-i )

[0021] Where θ0 represents the initial threshold, α represents the adjustment coefficient, w i represents the weight coefficient of historical data, p t-i represents the proportion of offline vehicles at time t - i, and k is the number of historical time steps considered.

[0022] As a preferred solution, the integration of effective offline vehicle data and intelligent lock location information to form the first data includes:

[0023] Obtain the current location information of the intelligent lock as the location information of the offline vehicle;

[0024] Standardize the offline vehicle data and the offline vehicle location information to obtain a first standard value and a second standard value;

[0025] Perform a linear transformation based on the first standard value and the second standard value, and perform encoding settings to obtain the first data.

[0026] In this solution, after determining the valid offline vehicle data, the intelligent lock opening device is positioned to obtain the current location information, and the current location information is used as the location information of the scanned valid offline vehicle. Assume that the positioning accuracy of the intelligent lock is ε, and the error range of the obtained current location information is within ε. After obtaining the location information, it is combined with the offline vehicle data. In order to integrate the data more safely and efficiently, a data integration operation based on standardization processing and linear transformation is adopted. The standardization processing includes hash processing. The offline vehicle data and the location information are respectively subjected to hash processing. Let the offline vehicle data be denoted as D_offline, and the current location information be denoted as L_current. After hash processing, the first standard value H_offline = Hash(D_offline) and the second standard value H_current = Hash(L_current) are respectively obtained, where the hash function Hash can adopt SHA-256. Perform a linear transformation on the first standard value and the second standard value, which is specifically expressed as: A' = a * H_offline + b * H_current + c, where a, b, and c are respectively preset linear transformation coefficients. Encode the linearly transformed data through an encoding function. For example, adopt the encoding function Encode to obtain the first data, which is specifically expressed as: A = Encode(A').

[0027] As a preferred solution, the determination of the search location information of the offline vehicle based on the geographical distance includes:

[0028] Set the entity site location information, and calculate the geographical distance between the offline vehicle and each entity site;

[0029] By comparing the geographical distances, select the entity site location information or the offline vehicle location information as the search location information.

[0030] This solution combines entity sites to select and obtain the search location information. The matching of entity sites is based on the judgment of geographical distance. The geographical distance is the geographical distance between the offline vehicle and each entity site. By judging the geographical distance, select the entity site location linearly or the offline vehicle location information as the search location information. Due to the error in the intelligent lock position positioning, combined with the judgment of entity sites, if the vehicle is located in an entity site, directly search for the offline vehicle in the entity site, and the positioning is more accurate, and the offline vehicle can be found faster.

[0031] As a preferred solution, a distance threshold is set, and the geographical distance between the vehicle and the physical station is compared with the distance threshold.

[0032] If the geographical distance is less than the distance threshold, the offline vehicle matches the physical station, and the location information of the physical station is sent as the search location information.

[0033] If the geographical distance is not less than the distance threshold, the location information of the offline vehicle is sent as the search location information.

[0034] The shared bicycle of the present invention is equipped with physical stations, and the location information of the physical stations is more accurate. The information of each physical station is preset, including the number, station name and location information. The physical station information is saved on the server. The intelligent lock uploads the location information of the offline vehicle to the server. After obtaining the location information of the offline vehicle, the server calculates the geographical distance between the offline vehicle and each physical station, and determines the physical station with a geographical distance less than the distance threshold as the matching physical station by comparing the geographical distance with the distance threshold, that is, it is judged that the offline vehicle is in the physical station, and the location information of the physical station is used as the search location information. The server sends the location information of the physical station to the operation and maintenance APP. If the geographical distance is greater than the distance threshold, the offline vehicle does not match the physical station, and the offline vehicle is not in the physical station. The location information of the offline vehicle is used as the search location information, and the server sends the location information of the offline vehicle to the operation and maintenance APP. The operation and maintenance personnel go to the corresponding physical station or the corresponding location to search for the vehicle according to the received location information. Since the location information obtained by the intelligent lock positioning is not very accurate, there may be a location deviation when searching for the vehicle, which is not conducive to searching for the vehicle. If the vehicle is in the physical station, the location of the physical station is determined, and it is more convenient to directly search for the offline vehicle in the physical station. By analyzing the location of the offline vehicle, that is, by calculating and comparing the geographical distance, it is judged whether the offline vehicle is in the physical station. According to the judgment situation, the location information of the physical station or the offline vehicle is used as the search location information and sent to the operation and maintenance personnel to search for the offline vehicle.

[0035] As a preferred solution, the geographical distance between the offline vehicle and each physical station is:

[0036] The location information of the offline vehicle includes the first longitude and the first latitude, and the location information of the physical station includes the second longitude and the second latitude.

[0037] The geographical distance is the product of the inverse cosine value of the sum of the product of the sine values of the first latitude and the second latitude and the product of the cosine values of the first latitude and the second latitude and the cosine value of the difference between the first longitude and the second longitude, and the product of the radius of the earth and the atmospheric refraction correction factor.

[0038] This solution adopts an improved geographical distance calculation method, introducing the Earth ellipsoid model and the atmospheric refraction correction factor. The location information is longitude and latitude. Let the location information of the offline vehicle be (Longitude_parsed, Latitude_parsed), and the location information of the entity site be (Longitude_i, Latitude_i). The average radius of the Earth is set as Re, and the atmospheric refraction correction factor is γ. The geographical distance d is expressed as follows:

[0039] d = γ * Re * arccos(sin(Latitude_parsed) * sin(Latitude_i) + cos(Latitude_parsed) * cos(Latitude_i) * cos(Longitude_parsed - Longitude_i)).

[0040] As a preferred solution,

[0041] The intelligent lock scans for surrounding vehicle data at an interval period T. The vehicle data includes vehicle number, offline status, and offline time.

[0042] The intelligent lock scans the surrounding devices at an interval period T to obtain the vehicle data scanned within the period T. The vehicle data contains the offline status, and based on the offline status, it can be known whether the vehicle is an offline vehicle.

[0043] As a preferred solution, the first data is uploaded to the server through an encryption method. The specific encryption method is:

[0044] The first data is encrypted using AES with a key in combination with an offset vector. The offset vector is an offset vector that changes with time.

[0045] This solution uses encrypted data transmission between the intelligent lock and the server to ensure the security of data transmission. The encryption method is to perform improved AES encryption on the first data using a key. Specifically, on the basis of the original AES encryption, an offset vector V(t) that changes with time is introduced. The encryption formula is:

[0046] B = AES_Encrypt(KEY, A ⊕ V(t))

[0047] V(t) is generated by a pseudo-random number generator in combination with the current timestamp, such as V(t) = PRNG(t, seed), where seed is a preset seed value.

[0048] An offline shared bicycle search system includes:

[0049] The intelligent lock scans the surrounding devices in real time to obtain vehicle data, filters to obtain valid offline vehicle data, and integrates the valid offline vehicle data with the intelligent lock location information to form the first data for uploading to the server.

[0050] The server calculates the geographical distance between the offline vehicle and the physical site, and determines the search location information of the offline vehicle based on the matching judgment of the geographical distance.

[0051] Therefore, the advantages of the present invention are: obtaining offline vehicle data by scanning the surrounding devices with the intelligent lock of the shared bicycle, effectively solving the problem that it is not easy to find the offline shared bicycle, and further improving the accuracy of the search location information by selectively combining with the physical site to obtain the search location information, making it more convenient to find the offline vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] The technical solutions of the present invention will be further specifically described below through embodiments and in conjunction with the drawings.

[0054] Embodiment 1:

[0055] A method for finding an offline shared bicycle in this embodiment mainly aims at the type of shared bicycle with a physical site. Due to the positioning error of the intelligent lock of the shared bicycle, the location information of the offline shared bicycle is determined by combining with the physical site, and the positioning is more accurate, making it more convenient for the operation and maintenance personnel to find the offline shared bicycle. As Figure 1 shown, the method of this embodiment includes the following steps:

[0056] S1. The intelligent lock scans the surrounding devices in real time to obtain vehicle data.

[0057] This method utilizes the intelligent lock of a normally operating shared bicycle to scan the surrounding shared bicycles during the riding or stopping of the shared bicycle. The intelligent lock of the shared bicycle uses a low-power signal transmitting chip to broadcast in real time to the surrounding. Even when the battery of the intelligent lock is insufficient, the chip can still keep broadcasting to the surrounding, making it possible to be discovered by the intelligent locks of other shared bicycles.

[0058] The vehicle intelligent lock is set to turn on Bluetooth scanning, continuously scan the surrounding devices, and at the same time set a scanning period T, and scan and obtain vehicle data at intervals of period T. The vehicle data includes vehicle number, offline status, and offline time information. The length of this period T is set according to requirements, and is preferably set to 10 seconds. After obtaining the vehicle data, it is possible to judge whether the vehicle is offline according to the included offline status information.

[0059] S2. Filter the vehicle data to obtain valid offline vehicle data.

[0060] Calculate the proportion of offline vehicles.

[0061] Based on the vehicle data scanned in a cycle, the first quantity of all vehicles can be obtained. According to the offline status information included in the vehicle data, the second quantity of offline vehicles can be obtained. Calculate the proportion of offline vehicles based on the first quantity and the second quantity. The specific formula is expressed as:

[0062] p = m / n,

[0063] where m represents the first quantity of all vehicles obtained, n represents the second quantity of offline vehicles obtained, and m ≤ n.

[0064] Set a dynamic threshold function.

[0065] Obtain the historical proportion of offline vehicles and establish a dynamic threshold function based on time and the historical distribution of offline vehicles.

[0066] Specifically, set an initial threshold, a regulation coefficient, and the number of historical cycles. The dynamic threshold function is the product of the weighted sum of the proportions of offline vehicles in each historical cycle and the regulation coefficient, plus the initial threshold. The specific formula is expressed as follows:

[0067] θ(t) = θ0 + α * ∑ k i＝1 (w i *p t-i )

[0068] where θ0 represents the initial threshold, α represents the regulation coefficient, w i represents the weight coefficient of historical data, p t-i represents the proportion of offline vehicles at time t - i, and k is the number of historical time steps considered.

[0069] Filter to obtain valid offline vehicle data from the vehicle data.

[0070] Compare the proportion of offline vehicles with the dynamic threshold function. When the proportion of offline vehicles is greater than the value of the dynamic threshold function, i.e., p > θ(t), obtain the offline vehicle data as valid offline vehicle data.

[0071] This step filters the vehicle data based on time and the historical distribution of offline vehicles, and can more accurately judge the valid offline vehicle data, so as to better locate the offline vehicles subsequently.

[0072] S3. Integrate the valid offline vehicle data with the intelligent lock position information to form the first data and upload it to the server.

[0073] After obtaining valid offline vehicle data, the intelligent lock activates the device positioning function to obtain the current position information of the intelligent lock as the offline vehicle position information. Assume the positioning accuracy of the intelligent lock is ε, and the error range of the obtained current position information is within ε.

[0074] Standardize the offline vehicle data and the offline vehicle position information to obtain a first standard value and a second standard value.

[0075] After obtaining the position information, combine it with the offline vehicle data. For safer and more efficient data integration, use data integration operations based on standardization processing and linear transformation. The standardization processing includes hashing. Respectively perform hashing on the offline vehicle data and the position information. Denote the offline vehicle data as D_offline and the current position information as L_current. After hashing, obtain the first standard value H_offline = Hash(D_offline) and the second standard value H_current = Hash(L_current), where the hash function Hash can use SHA - 256.

[0076] Perform a linear transformation based on the first standard value and the second standard value, and perform encoding settings to obtain the first data.

[0077] Perform a linear transformation on the first standard value and the second standard value, specifically expressed as:

[0078] A' = a * H_offline + b * H_current + c,

[0079] where a, b, and c are respectively preset linear transformation coefficients.

[0080] Encode the data after the linear transformation through an encoding function. For example, use the encoding function Encode to obtain the first data A, specifically expressed as: A = Encode(A').

[0081] S4. Determine the search location information of the offline vehicle based on the matching judgment of the geographical distance.

[0082] Calculate the geographical distance.

[0083] Set the entity site position information and calculate the geographical distance between the offline vehicle and each entity site.

[0084] The shared bicycles involved in the present invention are equipped with physical stations, and the location information of the physical stations is more accurate. The information of each physical station is preset, including the number, the station name and the location information. The location information specifically includes longitude and latitude. The information of the physical stations is stored on the server. Combining the physical stations to select and obtain the location information, the matching of the physical stations is based on the judgment of the geographical distance. The geographical distance is the geographical distance between the offline vehicle and each physical station. The calculation process includes:

[0085] The offline vehicle location information includes the first longitude and the first latitude, and the physical station location information includes the second longitude and the second latitude.

[0086] The geographical distance is the product of the sine values of the first latitude and the second latitude, and the product of the cosine values of the first latitude and the second latitude and the cosine value of the difference between the first longitude and the second longitude, and the product of the inverse cosine value and the earth radius and the atmospheric refraction correction factor.

[0087] In this embodiment, an improved geographical distance calculation method is adopted, introducing the earth ellipsoid model and the atmospheric refraction correction factor. The location information is longitude and latitude. Specifically, let the offline vehicle location information be (Longitude_parsed, Latitude_parsed), and the physical station location information be (Longitude_i, Latitude_i). Set the average earth radius Re and the atmospheric refraction correction factor γ. The formula for the geographical distance d is expressed as follows:

[0088] d = γ * Re * arccos(sin(Latitude_parsed) * sin(Latitude_i) + cos(Latitude_parsed) * cos(Latitude_i) * cos(Longitude_parsed - Longitude_i)).

[0089] By comparing the geographical distances, select the physical station location information or the offline vehicle location information as the location information to be searched.

[0090] Through the judgment of the geographical distance, select the physical station location linearly or the offline vehicle location information as the location information to be searched. Due to the error in the location positioning of the intelligent lock, combined with the judgment of the physical station, if the vehicle is located in the physical station, directly search for the offline vehicle in the physical station, and the positioning is more accurate, and the offline vehicle can be found faster.

[0091] Specifically, it includes the following process:

[0092] Set a distance threshold δ, and compare the geographical distance between the vehicle and the physical station with the distance threshold.

[0093] If the geographical distance is less than the distance threshold, the offline vehicle is matched with the physical site, and the location information of the physical site is sent as the search location information;

[0094] If the geographical distance is not less than the distance threshold, the location information of the offline vehicle is sent as the search location information.

[0095] The intelligent lock uploads the location information of the offline vehicle to the server. After obtaining the location information of the offline vehicle, the server calculates the geographical distance between the offline vehicle and each physical site, and compares the geographical distance d with the distance threshold δ respectively. It judges whether the geographical distance d is less than the distance threshold δ. If so, the physical site with the geographical distance less than the distance threshold is obtained as the matching physical site, that is, it is judged that the offline vehicle is in the physical site, and the location information of the physical site is used as the search location information. The server sends the location information of the physical site to the operation and maintenance APP, and the operation and maintenance personnel go to the specified physical site to search for the offline vehicle. If not, the offline vehicle does not match the physical site, and the offline vehicle is not in the physical site. The location information of the offline vehicle is used as the search location information. The server sends the location information of the offline vehicle to the operation and maintenance APP, and the operation and maintenance personnel go to the location where the offline vehicle is located to search for the offline vehicle.

[0096] Since the location information located by the intelligent lock is not very accurate, there may be a location deviation when searching for the vehicle, which is not conducive to searching for the vehicle. If the vehicle is in the physical site, the location of the physical site is determined, and it is more convenient and accurate to directly search for the offline vehicle in the physical site. By analyzing the location of the offline vehicle, that is, through the calculation and comparison of the geographical distance, it is judged whether the offline vehicle is in the physical site. According to the judgment situation, the location information of the physical site or the offline vehicle is used as the search location information and sent to the operation and maintenance personnel to search for the offline vehicle.

[0097] This embodiment also discloses an offline shared bicycle search system for implementing the above method. The system includes:

[0098] An intelligent lock that scans surrounding devices in real time to obtain vehicle data, filters to obtain valid offline vehicle data, and integrates the valid offline vehicle data with the intelligent lock location information to form the first data and upload it to the server;

[0099] A server that calculates the geographical distance between the offline vehicle and the physical site, compares the geographical distance between the vehicle and the physical site with the distance threshold. If the geographical distance is less than the distance threshold, the offline vehicle is matched with the physical site, and the location information of the physical site is sent as the search location information; if the geographical distance is not less than the distance threshold, the location information of the offline vehicle is sent as the search location information.

[0100] Embodiment 2:

[0101] Another implementation of the offline shared bicycle search method is disclosed in this embodiment, which specifically includes the following steps:

[0102] S1. The intelligent lock scans the surrounding devices in real time to obtain vehicle data.

[0103] This method utilizes the intelligent lock of a normally operating shared bicycle. During the process of riding or stopping the shared bicycle, it scans the surrounding shared bicycles. The intelligent lock of the shared bicycle uses a low-power signal transmission chip to send broadcasts to the surrounding in real time. Even when the battery of the intelligent lock is low, the chip can still keep sending broadcasts to the surrounding, enabling it to be discovered by other intelligent locks of shared bicycles.

[0104] The vehicle intelligent lock is set to turn on Bluetooth scanning and continuously scan the surrounding devices. At the same time, a scanning period T is set, and vehicle data is obtained by scanning at intervals of period T. The vehicle data includes vehicle number, offline status, and offline time information. The length of this period T is set according to requirements, and is preferably set to 10 seconds. After obtaining the vehicle data, it is possible to determine whether the vehicle is offline based on the included offline status information.

[0105] S2. Filter the vehicle data to obtain valid offline vehicle data.

[0106] Calculate the proportion of offline vehicles.

[0107] Based on the vehicle data scanned periodically, the first quantity of all vehicles can be obtained. Based on the offline status information included in the vehicle data, the second quantity of offline vehicles can be obtained. Calculate the proportion of offline vehicles according to the first quantity and the second quantity. The specific formula is expressed as:

[0108] p = m / n,

[0109] where m represents the first quantity of all vehicles obtained, n represents the second quantity of offline vehicles obtained, and m ≤ n.

[0110] Set a dynamic threshold function.

[0111] Obtain the historical proportion of offline vehicles and establish a dynamic threshold function based on time and the historical distribution of offline vehicles.

[0112] Specifically, set an initial threshold, an adjustment coefficient, and the number of historical periods. The dynamic threshold function is the weighted sum of the proportions of offline vehicles in each historical period, multiplied by the adjustment coefficient and then added to the initial threshold. The specific formula is expressed as follows:

[0113] θ(t) = θ0 + α * ∑ k i＝1 (w i *p t-i )

[0114] where θ0 represents the initial threshold, α represents the adjustment coefficient, wi represents the weight coefficient of historical data, p t-i represents the proportion of offline vehicles at time t-i, and k is the number of historical time steps considered.

[0115] Filter and obtain valid offline vehicle data from vehicle data.

[0116] Compare the proportion of offline vehicles with the dynamic threshold function. When the proportion of offline vehicles is greater than the value of the dynamic threshold function, i.e., p > θ(t), obtain the offline vehicle data as valid offline vehicle data.

[0117] This step filters vehicle data based on time and historical offline vehicle distribution, enabling more accurate judgment of valid offline vehicle data and facilitating better search for offline vehicles subsequently.

[0118] S3. Integrate the valid offline vehicle data with the intelligent lock location information to form the first data.

[0119] S4. Encrypt the first data and transmit it to the server.

[0120] The encryption method is to perform AES encryption on the first data combined with an offset vector using a key. The offset vector is an offset vector that changes with time.

[0121] This solution uses encrypted data transmission between the intelligent lock and the server to ensure the security of data transmission. The encryption method is to perform improved AES encryption on the first data using a key. Specifically, a time-varying offset vector V(t) is introduced on the basis of the original AES encryption. The encryption formula is:

[0122] B = AES_Encrypt(KEY, A ⊕ V(t))

[0123] B represents the ciphertext, and V(t) is generated by a pseudo-random number generator combined with the current timestamp, e.g., V(t) = PRNG(t, seed), where seed is a preset seed value.

[0124] After the server receives the device ciphertext B sent by the intelligent lock, perform improved AES decryption using the same key KEY and the corresponding offset vector V(t). The decryption formula is:

[0125] A = AES_Decrypt(KEY, B) ⊕ V(t)

[0126] After decryption, the server obtains the first data A and obtains the offline vehicle location information in the first data through reverse analysis.

[0127] S5. Determine the search location information of the offline vehicle based on the matching judgment of geographical distance.

[0128] Calculate the geographical distance.

[0129] Set the location information of the physical sites, and calculate the geographical distances between the offline vehicles and each physical site.

[0130] The shared bicycles involved in the present invention are equipped with physical sites, and the location information of the physical sites is more accurate. The information of each physical site is preset, including the number, site name, and location information. The location information specifically includes longitude and latitude. The physical site information is saved on the server. Select and obtain the location information by combining the physical sites. The matching of the physical sites is based on the judgment of the geographical distance, which is the geographical distance between the offline vehicle and each physical site. The calculation process includes:

[0131] The offline vehicle location information includes the first longitude and the first latitude, and the physical site location information includes the second longitude and the second latitude.

[0132] The geographical distance is the product of the anti-cosine value of the sum of the product of the sine values of the first latitude and the second latitude and the product of the cosine values of the first latitude and the second latitude and the cosine value of the difference between the first longitude and the second longitude, multiplied by the radius of the earth and the atmospheric refraction correction factor.

[0133] This embodiment adopts an improved geographical distance calculation method, introducing the earth ellipsoid model and the atmospheric refraction correction factor. The location information is longitude and latitude. Specifically, let the offline vehicle location information be (Longitude_parsed, Latitude_parsed), and the physical site location information be (Longitude_i, Latitude_i). Set the average radius of the earth Re and the atmospheric refraction correction factor γ. The formula for the geographical distance d is as follows:

[0134] d = γ * Re * arccos(sin(Latitude_parsed) * sin(Latitude_i) + cos(Latitude_parsed) * cos(Latitude_i) * cos(Longitude_parsed - Longitude_i)).

[0135] By comparing the geographical distances, select the physical site location information or the offline vehicle location information as the search location information.

[0136] Through the judgment of the geographical distance, select the physical site location linearly or the offline vehicle location information as the search location information. Due to the error in the location positioning of the smart lock, combined with the judgment of the physical site, if the vehicle is located in the physical site, directly search for the offline vehicle in the physical site, and the positioning is more accurate, and the offline vehicle can be found faster.

[0137] Specifically, it includes the following process:

[0138] Set a distance threshold δ, and compare the geographical distance between the vehicle and the physical site with the distance threshold.

[0139] If the geographical distance is less than the distance threshold, the offline vehicle matches the physical site, and the location information of the physical site is sent as the search location information.

[0140] If the geographical distance is not less than the distance threshold, the location information of the offline vehicle is sent as the search location information.

[0141] The intelligent lock uploads the location information of the offline vehicle to the server. After obtaining the location information of the offline vehicle, the server calculates the geographical distance between the offline vehicle and each physical site, and compares the geographical distance d with the distance threshold δ respectively to determine whether the geographical distance d is less than the distance threshold δ. If so, the physical site with the geographical distance less than the distance threshold is obtained as the matching physical site, that is, it is determined that the offline vehicle is in the physical site, and the location information of the physical site is used as the search location information. The server sends the location information of the physical site to the operation and maintenance APP, and the operation and maintenance personnel go to the specified physical site to search for the offline vehicle. If not, the offline vehicle does not match the physical site, and the offline vehicle is not in the physical site. The location information of the offline vehicle is used as the search location information. The server sends the location information of the offline vehicle to the operation and maintenance APP, and the operation and maintenance personnel go to the location where the offline vehicle is located to search for the offline vehicle.

[0142] The present invention obtains offline vehicle data by scanning surrounding devices with a shared bicycle intelligent lock, effectively solves the problem that it is not easy to find offline shared bicycles, and further improves the accuracy of the search location information by selectively combining with physical sites, making it more convenient to find offline vehicles.

[0143] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art of the present invention can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0144] Although terms such as intelligent lock, server, effective offline vehicle data, first data, first quantity, second quantity, first standard value, and second standard value are used more frequently in this article, the possibility of using other terms is not excluded. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional limitation is contrary to the spirit of the present invention.

Claims

1. An offline method for finding shared bicycles, characterized in that, Including: The intelligent lock scans the surrounding devices in real time to obtain vehicle data; Filter the vehicle data to obtain valid offline vehicle data; Integrate the valid offline vehicle data with the intelligent lock location information to form the first data and upload it to the server; Obtain the physical site location information, and calculate the geographical distance between the offline vehicle and the physical site in combination with the first data; Determine the search location information of the offline vehicle based on the matching judgment of the geographical distance.

2. The offline shared bicycle search method according to claim 1, characterized in that, The filtering of the vehicle data to obtain valid offline vehicle data includes: The vehicle data includes the first quantity of all vehicles scanned within a period and the second quantity of vehicles in the offline state. Calculate the proportion of offline vehicles according to the first quantity and the second quantity; Obtain the historical proportion of offline vehicles, and establish a dynamic threshold function based on time and the distribution of historical offline vehicles; Compare the proportion of offline vehicles with the dynamic threshold function. When the proportion of offline vehicles is greater than the value of the dynamic threshold function, obtain the offline vehicle data as valid offline vehicle data.

3. The method for finding an offline shared bicycle according to claim 2, characterized in that: Set an initial threshold, an adjustment coefficient, and the number of historical periods, The dynamic threshold function is the weighted sum of the proportions of offline vehicles in each historical period, multiplied by the adjustment coefficient and then added to the initial threshold.

4. An offline shared bicycle search method according to claim 1 or 2 or 3, characterized in that, The integration of the valid offline vehicle data with the intelligent lock location information to form the first data includes: Obtain the current location information of the intelligent lock as the offline vehicle location information; Perform standardization processing on the offline vehicle data and the offline vehicle location information to obtain the first standard value and the second standard value; Perform linear transformation according to the first standard value and the second standard value, and perform coding settings to obtain the first data.

5. The method for finding an offline shared bicycle according to claim 4, characterized in that, The determination of the search location information of the offline vehicle based on the matching judgment of the geographical distance includes: Set the physical site location information, and calculate the geographical distance between the offline vehicle and each physical site; By comparing the geographical distances, select the physical site location information or the offline vehicle location information as the search location information.

6. The method for finding an offline shared bicycle according to claim 5, characterized in that: Set a distance threshold, compare the geographical distance between the vehicle and the physical site with the distance threshold, If the geographical distance is less than the distance threshold, the offline vehicle matches the physical site, and send the physical site location information as the search location information; If the geographical distance is not less than the distance threshold, send the offline vehicle location information as the search location information.

7. A method for finding offline shared bicycles according to claim 5, characterized in that The geographical distance between the offline vehicle and each physical site is: The offline vehicle location information includes the first longitude and the first latitude, and the physical site location information includes the second longitude and the second latitude, The geographical distance is the product of the arcsine of the first latitude and the arcsine of the second latitude, and the product of the cosine of the first latitude and the cosine of the second latitude and the cosine of the difference between the first longitude and the second longitude, and the product of the radius of the earth and the atmospheric refraction correction factor.

8. The method for finding an offline shared bicycle according to claim 1 or 2 or 3, characterized in that: The intelligent lock scans to obtain the surrounding vehicle data at an interval period T. The vehicle data includes the vehicle number, the offline state, and the offline time.

9. A method for finding offline shared bicycles according to claim 1 or 2 or 3, characterized in that: The first data is uploaded to the server by an encryption method. The specific encryption method is: The first data combination offset vector is encrypted using AES with a key, and the offset vector is an offset vector that changes over time.

10. An offline shared bicycle search system according to claim 1, implementing the method according to any one of claims 1-9, characterized in that, It includes: An intelligent lock that scans surrounding devices in real time to obtain vehicle data, filters to obtain valid offline vehicle data, and integrates the valid offline vehicle data with the intelligent lock location information to form first data and upload it to the server; A server that calculates the geographical distance between the offline vehicle and the physical site, and determines the search location information of the offline vehicle based on the matching judgment of the geographical distance.

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