Smart parking space recommendation system and method based on big data

Abstract

The present invention relates to a smart parking space recommendation system and method based on big data, the system includes: a data acquisition module for acquiring multi-source data; a demand acquisition module for acquiring user demand and determining demand weights; an attribute calculation module for calculating the attribute value of each parking space according to the multi-source data and user demand; an initial recommendation module for determining the comprehensive score of each parking space according to the attribute value and the demand weight, and recommending an initial parking space to the user according to the comprehensive scores of multiple parking spaces; an adjustment factor module for determining a real-time adjustment factor according to dynamic change information of parking spaces, user demand and user positions; a target recommendation module for adjusting the comprehensive scores of each parking space according to the real-time adjustment factor to obtain an adjusted comprehensive score, and recommending a target parking space to the user based on the adjusted comprehensive score of each parking space. The system can improve the efficiency and accuracy of parking space recommendation.

Description

Technical Field

[0001] The present invention relates to the field of smart parking technology, and in particular to a smart parking space recommendation system and method based on big data. Background Art

[0002] Nowadays, smart parking systems are rapidly being used in major cities to alleviate the problem of parking difficulties in cities. These systems are designed to meet the parking needs of different groups of people in different scenarios, thereby improving people's parking efficiency.

[0003] However, most of the existing smart parking management platforms currently have a low level of intelligence and often fail to achieve the expected results. They are unable to quickly collect information on each parking lot and intelligently recommend the most reasonable parking spaces to users. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a smart parking space recommendation system and method based on big data, which can improve the accuracy and efficiency of parking space recommendation.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] A smart parking space recommendation system based on big data, the system comprising:

[0007] A data acquisition module, used to acquire multi-source data to be analyzed for recommending parking spaces to users;

[0008] A demand acquisition module, used to acquire the user demand of the user and determine the demand weight of each user demand;

[0009] An attribute calculation module, used to calculate the attribute value of each parking space according to the multi-source data and the user demand;

[0010] an initial recommendation module, configured to determine a comprehensive score of each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommend an initial parking space to the user according to the comprehensive scores of the plurality of parking spaces;

[0011] An adjustment factor module, used to determine a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0012] The target recommendation module is used to adjust the comprehensive score of each parking space according to the real-time adjustment factor to obtain an adjusted comprehensive score, and recommend a target parking space to the user based on the adjusted comprehensive score of each parking space.

[0013] Optionally, the initial recommendation module is specifically used for:

[0014] Obtaining a first adjustment parameter, a second adjustment parameter, and a third adjustment parameter for calculating a comprehensive score;

[0015] Calculate the score of the parking space for each user demand according to the attribute value corresponding to the parking space, the demand weight, and the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter;

[0016] The scores of the parking space for each user demand are summed to obtain a comprehensive score of the parking space.

[0017] Optionally, the initial recommendation module is further specifically used for:

[0018] Obtain a first product of each attribute value of the parking space and each demand weight;

[0019] constructing a first nonlinear function according to the attribute value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter;

[0020] A score of the parking space for each user demand is calculated according to the first product and the first nonlinear function.

[0021] Optionally, the comprehensive score of each parking space is expressed as:

[0022] ;

[0023] in, Represents the comprehensive score of each parking space, is the total number of user requirements, It is Parking space The attribute value of a user requirement, It is The weight of the user requirement, is the first adjustment parameter, is the second adjustment parameter, is the third adjustment parameter.

[0024] Optionally, the adjustment factor module is specifically used for:

[0025] Acquiring dynamic change information of the multi-source data;

[0026] Determine the impact of the dynamic change information on the user demand;

[0027] The real-time adjustment factor is determined according to the impact information.

[0028] Optionally, the target recommendation module is specifically used to:

[0029] Adjusting the attribute value of the parking space according to the real-time adjustment factor to obtain an adjusted attribute value;

[0030] Adjust the first product and the first nonlinear function respectively according to the adjusted attribute value to obtain a second product and a second nonlinear function;

[0031] The score of the parking space for each user demand is calculated according to the second product and the second nonlinear function.

[0032] Optionally, the adjusted comprehensive score of each parking space is expressed as:

[0033] ;

[0034] in, Represents the adjusted comprehensive score of each parking space, is the total number of user requirements, It is Parking space The attribute value of a user requirement, It is Parking space A real-time adjustment factor for each attribute value. It is The weight of the user requirement, is the first adjustment parameter, is the second adjustment parameter, is the third adjustment parameter.

[0035] Optionally, the demand weight is expressed as:

[0036] ;

[0037] in, is the demand weight, is the number value, is the total number of user requirements, that is, the total number of requirement weights;

[0038] Among them, the attribute value of the parking space is expressed as:

[0039] ;

[0040] in, is the attribute value of the parking space, and is the number value, Indicates User needs, Indicates parking spaces, Indicates Parking space The attribute value of a user requirement.

[0041] Optionally, the multi-source data includes parking space information data of the parking lot, traffic information, user information of the user, and parking lot charging data.

[0042] Based on the same inventive concept, the present invention also provides a smart parking space recommendation method based on big data, using the above-mentioned smart parking space recommendation system based on big data, including:

[0043] Obtaining multi-source data to be analyzed for recommending parking spaces to users;

[0044] Obtaining user requirements of the user, and determining a requirement weight for each of the user requirements;

[0045] Calculating the attribute value of each parking space according to the multi-source data and the user demand;

[0046] Determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces;

[0047] Determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0048] According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user.

[0049] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a smart parking space recommendation method based on big data as described above.

[0050] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements a smart parking space recommendation method based on big data as described above.

[0051] The beneficial effects of the present invention are:

[0052] (1) The present invention can combine user needs and real-time parking space information, accurately calculate the score of each parking space, intelligently recommend the best parking space, and improve the convenience and satisfaction of user parking;

[0053] (2) The present invention can reflect the dynamic changes of parking lots (such as parking space occupancy, traffic conditions, etc.) in real time and adjust the recommendation results in a timely manner to ensure that the recommended parking spaces always meet user needs;

[0054] (3) The present invention takes into account a variety of user needs (such as price, distance, safety, etc.) and performs weighted processing through demand weights to make the recommendation results more in line with the user's personalized preferences.

[0055] In summary, the present invention can realize an intelligent and dynamic parking space recommendation system by comprehensively considering user needs and real-time dynamic information and utilizing complex mathematical models and algorithms, which can significantly improve the efficiency of parking management and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A scene diagram of the smart parking space recommendation method based on big data provided by the present invention.

[0057] Figure 2 A flowchart of the big data-based smart parking space recommendation method provided by the present invention.

[0058] Figure 3 This is a schematic diagram of the structure of the smart parking space recommendation system based on big data provided by the present invention.

[0059] Figure 4 A schematic diagram of the hardware structure of the electronic device provided by the present invention.

[0060] Figure 5 A schematic diagram of the hardware structure of the computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0062] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0063] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0064] Embodiment 1

[0065] Figure 1 This is a scene diagram of the smart parking space recommendation method based on big data provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0066] It should be noted that Figure 1 The scenario diagram of a smart parking space recommendation method based on big data is only an example. The terminal, server and application scenario described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. A person of ordinary skill in the art can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0067] Among them, the terminal can be used for:

[0068] Obtaining multi-source data to be analyzed for recommending parking spaces to users;

[0069] Obtaining user requirements of the user, and determining a requirement weight for each of the user requirements;

[0070] Calculating the attribute value of each parking space according to the multi-source data and the user demand;

[0071] Determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces;

[0072] Determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0073] According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user.

[0074] Figure 2 A schematic diagram of the structure of a smart parking space recommendation system based on big data provided by the present invention.

[0075] The smart parking space recommendation system based on big data proposed in the embodiment of the present invention includes:

[0076] The data acquisition module 201 is used to acquire multi-source data to be analyzed for recommending parking spaces to users.

[0077] In some embodiments, the multi-source data may include parking space information data of a parking lot, traffic information, user information of the user, and charging data of the parking lot.

[0078] In some embodiments, the parking space information data of the parking lot may include: Parking space occupancy status: including which parking spaces are currently vacant and which are occupied. The real-time updated parking space occupancy status can ensure that the recommended parking spaces are available. Parking space location: the specific location of each parking space in the parking lot, including the parking space number and coordinates. This helps to calculate the distance between the parking space and the user's destination. Parking space type: including ordinary parking spaces, disabled parking spaces, electric vehicle charging spaces, etc., in order to meet the specific needs of different users. Additional facilities of the parking space: such as whether there is a sunshade, whether it is close to the entrance and exit, etc., this information can affect the user's choice.

[0079] In some embodiments, traffic information may include: Road traffic conditions: including real-time traffic flow, congestion and driving time on roads around the parking lot. This information can help users choose a parking lot with more convenient transportation. Navigation information: providing the best route to the parking lot, taking into account the current traffic conditions, to ensure that users can quickly reach the recommended parking space. Public transportation information: including information such as nearby bus stops and subway stations, providing reference for users who use public transportation.

[0080] In some embodiments, user information may include user preferences: including user preferences for parking space price, safety, distance, etc. Historical parking data: user's parking history, including frequently parked areas, parking space type selection, etc., which can be used for personalized recommendations. User vehicle information: including vehicle type, size, whether charging is required, etc., to ensure that the recommended parking space can meet the vehicle's parking needs.

[0081] In some embodiments, parking lot charging data may include:

[0082] Charging standard: The charging standard for each parking space, including hourly, daily or other billing methods, to help users choose a parking space that fits their budget. Discount information: Including discounts, coupons and other information provided by the parking lot, to provide users with affordable parking options. Payment method: Supported payment methods, such as cash, credit card, mobile payment, etc., to facilitate users to settle accounts.

[0083] By way of example only, multi-source data may include the following:

[0084] Parking lot information data:

[0085] Parking space number: such as parking space A1, A2. Real-time status: parking space A1 is vacant, parking space A2 is occupied. Location coordinates: parking space A1 (10, 20), parking space A2 (15, 25). Parking space type: ordinary parking space, electric parking space. Facility information: parking space A1 is close to the elevator, and parking space A2 has a sunshade.

[0086] Traffic Information:

[0087] Real-time traffic volume on surrounding roads: The traffic volume on road A around the parking lot is moderate, and the estimated driving time is 15 minutes. Best route: The best route from the user's current location to the parking lot is via road B, and the estimated driving time is 10 minutes. Public transportation stations: There are bus stations C and subway stations D near the parking lot, which are 200 meters and 500 meters away from the parking lot respectively.

[0088] User information includes:

[0089] User preference: Users prefer parking spaces that are close (weight 0.5), cheap (weight 0.3), and safe (weight 0.2).

[0090] Historical parking records: The user often parks in area E in the past and prefers underground parking lots.

[0091] Vehicle information: The user's vehicle is an electric vehicle and requires charging facilities.

[0092] Parking lot charges:

[0093] Charging standard: Parking space A1 charges 5 yuan per hour, and parking space A2 charges 6 yuan per hour.

[0094] Special offer: Parking space A1 has a 20% discount on weekends.

[0095] Payment methods: Cash, credit card and WeChat payment are supported.

[0096] Through comprehensive analysis of these multi-source data, this solution can provide users with intelligent and personalized parking space recommendations, significantly improving users' parking experience and management efficiency.

[0097] The demand acquisition module 202 is used to acquire the user demand of the user and determine the demand weight of each user demand.

[0098] In some embodiments, the demand weight may be expressed as: ;in, is the demand weight, is the number value, is the total number of user demands, that is, the total number of demand weights.

[0099] In the specific implementation, it is assumed that there are several user requirements: price, distance and security, and the demand weight vector It can be expressed as: ;in, They are the weight of price, the weight of distance and the weight of security respectively.

[0100] In some embodiments, the weights of the requirements can be determined by the user's preferences. These weights can be obtained by a variety of methods, including: User survey: directly asking users how much they value different requirements. Historical data analysis: inferring the user's demand weights based on the user's past parking selection behavior. User interaction: allowing users to set the weights of each requirement through the APP or system interface. Just as an example, It can be 0.3, 0.5 and 0.2 respectively.

[0101] The attribute calculation module 203 is used to calculate the attribute value of each parking space according to the multi-source data and the user demand.

[0102] In some embodiments, the attribute value of a parking space may be expressed as:

[0103] ;

[0104] in, is the attribute value of the parking space, and is the number value, Indicates User needs, Indicates parking spaces, Indicates Parking space The attribute value of a user requirement.

[0105] Just as an example, assuming that the user's requirements are still price, distance, and safety, the attribute value vector of the parking space can be expressed as:

[0106] ;

[0107] in, The parking spaces are rated for price, distance and safety respectively.

[0108] In some embodiments, the price of a parking space may be provided by a parking lot management system, which generally includes: Base price: parking fee per hour. Additional charges: such as holidays, nighttime charges, etc. The parking system automatically calculates the current parking fee based on the time and date. For example, the price of a parking space may be 10 yuan per hour.

[0109] In some embodiments, the distance of a parking space refers to the straight-line distance or walking distance from the user's current location to the parking space, which can be determined by the following steps:

[0110] User location acquisition: Get the user's current location through GPS and other positioning technologies.

[0111] Parking space location acquisition: Parking lot systems usually record the specific location coordinates of parking spaces.

[0112] Distance calculation: Use the geographic coordinate system to calculate the distance between the user's current location and the parking space. The commonly used formula is the Haversine formula. For example, the distance between the user's current location and the parking space is calculated to be 200 meters.

[0113] In some embodiments, the safety score of a parking space can be evaluated and comprehensively scored through the following aspects: Historical safety data: historical data in the parking lot management system, such as records of vehicle damage and theft. Monitoring facilities: the number and coverage of monitoring cameras around the parking space. Security personnel: the patrol frequency and number of security personnel in the parking lot. Ambient lighting: the lighting conditions around the parking space. Places with sufficient light are generally safer. Assume that each factor is given a score, and then the weighted average is used to obtain the total safety score. For example, assume that the calculated safety score is 82 points.

[0114] Just as an example, suppose for parking spaces , price: 10 yuan, distance: 200 meters, safety: 80 points, then the attribute value vector is: .

[0115] The initial recommendation module 204 is used to determine the comprehensive score of each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommend an initial parking space to the user according to the comprehensive scores of multiple parking spaces.

[0116] In some embodiments, the initial recommendation module 204 is specifically used to:

[0117] Obtaining a first adjustment parameter, a second adjustment parameter, and a third adjustment parameter for calculating a comprehensive score;

[0118] Calculate the score of the parking space for each user demand according to the attribute value corresponding to the parking space, the demand weight, and the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter;

[0119] The scores of the parking space for each user demand are summed to obtain a comprehensive score of the parking space.

[0120] In some embodiments, the initial recommendation module 204 is further specifically configured to:

[0121] Obtain a first product of each attribute value of the parking space and each demand weight;

[0122] constructing a first nonlinear function according to the attribute value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter;

[0123] A score of the parking space for each user demand is calculated according to the first product and the first nonlinear function.

[0124] In some embodiments, the comprehensive score of each parking space is expressed as:

[0125] ;

[0126] in, Represents the comprehensive score of each parking space, is the total number of user requirements, It is Parking space The attribute value of a user requirement, It is The weight of the user requirement, is the first adjustment parameter, is the second adjustment parameter, is the third adjustment parameter.

[0127] in, is the first product, is the first nonlinear function, It is Parking spaces in Specific attribute values ​​of items based on user needs, such as price, distance, safety, etc.

[0128] Indicates the user's The importance of a demand is the demand weight. The higher the weight, the more users value the demand.

[0129] , , Used for The attribute values ​​of the user requirements are adjusted nonlinearly to reflect the impact of the attribute values ​​on the comprehensive score. The choice of adjustment parameters will affect the calculation results and can be determined through data analysis and tuning.

[0130] Reflects the contribution of the attribute value in the comprehensive score. is the first nonlinear function, used to adjust the influence of the attribute value. Increase, the exponential term in the denominator This adjustment can prevent an attribute value that is too high from having an excessive impact on the overall score.

[0131] In some embodiments, it is assumed that the user demand weight vector =[0.3,0.5,0.2]. , , You can set the same value for price, distance, and safety, or you can set different values ​​for each. , we can calculate the attribute value of each part of the parking space:

[0132] The price part is: ;

[0133] The distance part is: ;

[0134] The security section is: .

[0135] Finally calculated =2.75+99.63+15.68=118.06.

[0136] The adjustment factor module 205 is used to determine a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position.

[0137] In some embodiments, the adjustment factor module 205 is specifically used to:

[0138] Acquiring dynamic change information of the multi-source data;

[0139] Determine the impact of the dynamic change information on the user demand;

[0140] The real-time adjustment factor is determined according to the impact information.

[0141] As an example, assume that the parking space information of the parking lot changes in real time, specifically, the parking space Due to changes in the road conditions or location of the user, the user The distance between the parking spaces increases significantly. The comprehensive score is reduced, and a specific adjustment factor is introduced according to the degree of distance increase. Similarly, if the price of parking spaces is increased during holidays or the security measures for parking spaces are increased, the corresponding r value can be obtained.

[0142] The target recommendation module 206 is used to adjust the comprehensive score of each parking space according to the real-time adjustment factor to obtain an adjusted comprehensive score, and recommend a target parking space to the user based on the adjusted comprehensive score of each parking space.

[0143] In some embodiments, the target recommendation module 206 is specifically used to:

[0144] Adjusting the attribute value of the parking space according to the real-time adjustment factor to obtain an adjusted attribute value;

[0145] Adjust the first product and the first nonlinear function respectively according to the adjusted attribute value to obtain a second product and a second nonlinear function;

[0146] The score of the parking space for each user demand is calculated according to the second product and the second nonlinear function.

[0147] In some embodiments, the adjusted comprehensive score of each parking space is expressed as:

[0148] ;

[0149] in, Represents the adjusted comprehensive score of each parking space, is the total number of user requirements, It is Parking space The attribute value of a user requirement, It is Parking space A real-time adjustment factor for each attribute value. It is The weight of the user requirement, is the first adjustment parameter, is the second adjustment parameter, is the third adjustment parameter.

[0150] In a specific embodiment, in a specific implementation, It is a real-time adjustment factor used to reflect the dynamic changes in various attribute values ​​of parking spaces, such as changes caused by real-time parking space occupancy, traffic conditions, etc.

[0151] Just as an example, Reflects the dynamic contribution of the attribute value in the comprehensive score. is the second nonlinear function, used to adjust the dynamic influence of the attribute value. and Increase, the exponential term in the denominator This adjustment can prevent an excessively high attribute value from having an excessive impact on the overall score.

[0152] Adjusted composite score Calculation method and comprehensive score The same, this embodiment will not be repeated. , the comprehensive score for the above parking spaces is =118.06, after the real-time adjustment factor is adjusted, the adjusted comprehensive score of the parking space .

[0153] See also Figure 3 , provides a flow chart of a smart parking space recommendation method based on big data of the present invention, comprising the following steps:

[0154] Step 301: Acquire multi-source data to be analyzed for recommending parking spaces to users;

[0155] Step 302: Obtain user requirements of the user, and determine the requirement weight of each user requirement;

[0156] Step 303: Calculate the attribute value of each parking space according to the multi-source data and the user demand;

[0157] Step 304: determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces;

[0158] Step 305: determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0159] Step 306: According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user.

[0160] It should be noted that the relevant contents of the above steps have been described in the aforementioned part about the smart parking space recommendation system based on big data, and will not be repeated here.

[0161] Embodiment 2

[0162] See also Figure 4 , Figure 4 A schematic diagram of an electronic device according to an embodiment of the present invention.

[0163] The embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0164] Obtaining multi-source data to be analyzed for recommending parking spaces to users;

[0165] Obtaining user requirements of the user, and determining a requirement weight for each of the user requirements;

[0166] Calculating the attribute value of each parking space according to the multi-source data and the user demand;

[0167] Determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces;

[0168] Determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0169] According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user.

[0170] Embodiment 3

[0171] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0172] This embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0173] Obtaining multi-source data to be analyzed for recommending parking spaces to users;

[0174] Obtaining user requirements of the user, and determining a requirement weight for each of the user requirements;

[0175] Calculating the attribute value of each parking space according to the multi-source data and the user demand;

[0176] Determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces;

[0177] Determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position;

[0178] According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user.

[0179] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as systems, methods, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0182] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0185] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The smart parking space recommendation system based on big data is characterized by: The system comprises: A data acquisition module, used to acquire multi-source data to be analyzed for recommending parking spaces to users; A demand acquisition module, used to acquire the user demand of the user and determine the demand weight of each user demand; An attribute calculation module, used to calculate the attribute value of each parking space according to the multi-source data and the user demand; an initial recommendation module, configured to determine a comprehensive score of each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommend an initial parking space to the user according to the comprehensive scores of the plurality of parking spaces; An adjustment factor module, used to determine a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position; a target recommendation module, configured to adjust the comprehensive score of each parking space according to the real-time adjustment factor to obtain an adjusted comprehensive score, and recommend a target parking space to the user based on the adjusted comprehensive score of each parking space; The initial recommendation module is specifically used for: Obtaining a first adjustment parameter, a second adjustment parameter, and a third adjustment parameter for calculating a comprehensive score; Calculate the score of the parking space for each user demand according to the attribute value corresponding to the parking space, the demand weight, and the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter; Sum the scores of the parking space for each user demand to obtain a comprehensive score of the parking space; The initial recommendation module is also specifically used for: Obtain a first product of each attribute value of the parking space and each demand weight; constructing a first nonlinear function according to the attribute value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter; Calculating a score of the parking space for each user demand according to the first product and the first nonlinear function; The comprehensive score of each parking space is expressed as: Among them, P j represents the comprehensive score of each parking space, n is the total number of user demands, s ji is the attribute value of the i-th user’s demand for the j-th parking space, u i is the weight of the i-th user demand, α i is the first adjustment parameter, β i is the second tuning parameter, γ i is the third adjustment parameter.

2. The smart parking space recommendation system based on big data according to claim 1 is characterized in that: The regulating factor module is specifically used for: Acquiring dynamic change information of the multi-source data; Determine the impact of the dynamic change information on the user demand; The real-time adjustment factor is determined according to the impact information.

3. The smart parking space recommendation system based on big data according to claim 2 is characterized in that: The target recommendation module is specifically used for: Adjusting the attribute value of the parking space according to the real-time adjustment factor to obtain an adjusted attribute value; Adjust the first product and the first nonlinear function respectively according to the adjusted attribute value to obtain a second product and a second nonlinear function; The score of the parking space for each user demand is calculated according to the second product and the second nonlinear function.

4. The smart parking space recommendation system based on big data according to claim 3 is characterized in that: The adjusted comprehensive score of each parking space is expressed as: Among them, P′ j represents the adjusted comprehensive score of each parking space, n is the total number of user demands, s ji is the attribute value of the i-th user's demand for the j-th parking space, r ji is the real-time adjustment factor of the i-th attribute value of the j-th parking space, u i is the weight of the i-th user demand, α i is the first adjustment parameter, β i is the second tuning parameter, γ i is the third adjustment parameter.

5. The smart parking space recommendation system based on big data according to claim 4 is characterized in that: The demand weight is expressed as: U I =[u1,u2,…,u n ]; Among them, U I is the demand weight, I is the number value, and n is the total number of user demands, that is, the total number of demand weights; Among them, the attribute value of the parking space is expressed as: S ji =[s j1 ,s j2 ,…,s jn ]; Among them, S ji is the attribute value of the parking space, i and j are the number values, j represents the jth parking space, s jn Represents the attribute value of the nth user demand for the jth parking space.

6. The smart parking space recommendation system based on big data according to any one of claims 1 to 5, characterized in that: The multi-source data includes parking space information data of the parking lot, traffic information, user information of the user, and charging data of the parking lot.

7. A smart parking space recommendation method based on big data, using the smart parking space recommendation system based on big data as described in claim 6, characterized in that: include: Obtaining multi-source data to be analyzed for recommending parking spaces to users; Obtaining user requirements of the user, and determining a requirement weight for each of the user requirements; Calculating the attribute value of each parking space according to the multi-source data and the user demand; Determining a comprehensive score for each parking space according to the attribute value of each parking space and the corresponding demand weight, and recommending an initial parking space for the user according to the comprehensive scores of the plurality of parking spaces; Determining a real-time adjustment factor according to the parking space, the user demand and the dynamic change information of the user's position; According to the real-time adjustment factor, the comprehensive score of each parking space is adjusted to obtain an adjusted comprehensive score, and based on the adjusted comprehensive score of each parking space, a target parking space is recommended to the user; Determining the comprehensive score of each parking space according to the attribute value of each parking space and the corresponding demand weight includes: Obtaining a first adjustment parameter, a second adjustment parameter, and a third adjustment parameter for calculating a comprehensive score; Calculate the score of the parking space for each user demand according to the attribute value corresponding to the parking space, the demand weight, and the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter; Sum the scores of the parking space for each user demand to obtain a comprehensive score of the parking space; The calculating, according to the attribute value corresponding to the parking space, the demand weight, and the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter, a score of the parking space for each user demand includes: Obtain a first product of each attribute value of the parking space and each demand weight; constructing a first nonlinear function according to the attribute value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter; Calculating a score of the parking space for each user demand according to the first product and the first nonlinear function; The comprehensive score of each parking space is expressed as: Among them, P j represents the comprehensive score of each parking space, n is the total number of user demands, s ji is the attribute value of the i-th user’s demand for the j-th parking space, u i is the weight of the i-th user demand, α i is the first adjustment parameter, β i is the second tuning parameter, γ i is the third adjustment parameter.

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