Site selection method and device for truck parking lot, computer equipment and storage medium

Abstract

The invention relates to a lorry parking lot site selection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the basic information of a lorry and the candidate parking lot information of a candidate parking lot; based on the basic information, determining the time cost of the truck to the candidate parking lot; determining a life convenience index and a resident disturbing index according to the basic information and the candidate parking lot information; constructing a weight vector of each candidate parking lot according to the acquired traffic index, time cost, life convenience index and resident disturbing index; according to the acquired traffic index, time cost, life convenience index and resident disturbing index, constructing a score vector of each candidate parking lot; and determining a target truck parking lot from the candidate parking lots according to the weight vector and the score vector. According to the invention, the problem of inaccurate site selection is solved, the accuracy of the site selection of the truck parking lot is improved, the truck parking is facilitated, the transport efficiency of trucks is improved, and the transport cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of logistics management and computer technology, and in particular to a method, apparatus, computer equipment, and storage medium for selecting a location for a truck parking lot. Background Art

[0002] The trucking industry occupies an important position in the modern logistics system. With the continuous development of economic globalization and e-commerce, the demand for cargo transportation continues to increase. Through effective path planning and optimization, the optimal transportation route can be selected to improve the efficiency and accuracy of cargo transportation.

[0003] In related technologies, the solution used is to determine the location of truck parking lots based on planning requirements. This solution does not consider the need to align parking spots with optimal routes, resulting in inaccurate location selection, reduced transportation efficiency, and increased transportation costs.

[0004] There is currently no effective solution to the problem of inaccurate site selection in related technologies, which leads to low transportation efficiency and high transportation costs. Summary of the Invention

[0005] In this embodiment, a method, apparatus, computer device, and storage medium for selecting a truck parking lot are provided to solve the problem in the related art of inaccurate site selection, resulting in low transportation efficiency and high transportation costs.

[0006] First, in this embodiment, a method for selecting a truck parking lot is provided, including: Obtaining basic information of the truck and candidate parking lot information of the candidate parking lot; matching the truck with the candidate parking lot; Determining the time cost of the truck to the candidate parking lot based on the basic information; Determining a life convenience index and a public nuisance index based on the basic information and the candidate parking lot information; Constructing a weight vector for each of the candidate parking lots based on the acquired traffic index, the time cost, the life convenience index, and the public nuisance index; and constructing a score vector for each of the candidate parking lots based on the acquired traffic index, the time cost, the life convenience index, and the public nuisance index; A target truck parking lot is determined from the candidate parking lots according to the weight vector and the score vector.

[0007] In some embodiments, determining the time cost of the truck to the candidate parking lot based on the basic information includes: The time cost of the truck reaching the candidate parking lot is determined based on the basic information and a preset time cost formula.

[0008] In some embodiments, determining a life convenience index and a public nuisance index based on the basic information and the candidate parking lot information includes: Determining a life convenience index based on the basic information, the candidate parking lot information, and a preset life convenience index formula; A nuisance index is determined according to the basic information, the candidate parking lot information, and a preset nuisance index formula.

[0009] In some embodiments, constructing a weight vector for each candidate parking lot based on the acquired traffic index, the time cost, the life convenience index, and the nuisance index includes: Constructing a matrix corresponding to each dimension based on the acquired traffic index, the time cost, the life convenience index, and the public nuisance index; the matrix includes a traffic index matrix, a time cost matrix, a life convenience index matrix, and a public nuisance index matrix; Normalizing the matrix to obtain a corresponding normalized matrix; A weight vector corresponding to the traffic index, the time cost, the life convenience index, and the public nuisance index is constructed using the average value of each column in the normalized matrix.

[0010] In some embodiments, constructing a scoring vector for each candidate parking lot based on the acquired traffic index, the time cost, the life convenience index, and the nuisance index includes: Selecting a corresponding maximum target and a minimum target according to the obtained traffic index, the time cost, the life convenience index, and the public nuisance index; The maximum target and the minimum target are input into a pre-examination mapping function to obtain a scoring vector corresponding to the traffic index, the time cost, the life convenience index, and the public nuisance index.

[0011] In some embodiments, determining the target parking lot for the truck according to the weight vector and the score vector includes: Determining a site selection score of each of the candidate parking lots relative to the truck based on the weight vector and the score vector; The candidate parking lot with the highest site selection score is selected as the target parking lot for the truck.

[0012] In some embodiments, determining the location score of each candidate parking lot relative to the truck based on the weight vector and the score vector includes: Determining a time cost score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the time cost; Determining a traffic index score corresponding to the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the traffic index; Determining a life convenience index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the life convenience index; Determining a nuisance index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the nuisance index; The time cost score, traffic index score, life convenience index score, and public nuisance index score are summarized to obtain a location score corresponding to the candidate parking lot relative to the truck.

[0013] In a second aspect, a site selection device for a truck parking lot is provided in this embodiment, comprising: an acquisition module, a first processing module, a second processing module, a third processing module, and a site selection module; The acquisition module is used to acquire basic information of the truck and candidate parking lot information of the candidate parking lot; the truck is matched with the candidate parking lot; The first processing module is configured to determine a time cost for the truck to reach the candidate parking lot based on the basic information; The second processing module is configured to determine a life convenience index and a public nuisance index based on the basic information and the candidate parking lot information; The third processing module is configured to construct a weight vector for each candidate parking lot based on the acquired traffic index, the time cost, the life convenience index, and the public nuisance index; and to construct a score vector for each candidate parking lot based on the acquired traffic index, the time cost, the life convenience index, and the public nuisance index; The site selection module is configured to determine a target truck parking lot from the candidate parking lots based on the weight vector and the score vector.

[0014] In a third aspect, a computer device is provided in this embodiment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for selecting a truck parking lot as described in the first aspect above is implemented.

[0015] In a fourth aspect, a storage medium is provided in this embodiment, on which a computer program is stored. When the program is executed by a processor, the method for selecting a location for a truck parking lot described in the first aspect is implemented.

[0016] Compared with the related art, the site selection method, device, computer equipment and storage medium of the truck parking lot provided in this embodiment obtain basic information of the truck and candidate parking lot information of candidate parking lots; match the truck with the candidate parking lots; based on the basic information, determine the time cost of the truck to the candidate parking lot; determine the life convenience index and the nuisance index according to the basic information and the candidate parking lot information; construct a weight vector for each candidate parking lot according to the obtained traffic index, time cost, life convenience index and nuisance index; and, based on the obtained traffic index, time cost, life convenience index and nuisance index, construct a scoring vector for each candidate parking lot; determine the target truck parking lot from the candidate parking lots according to the weight vector and the scoring vector, which solves the problem of inaccurate site selection in the related art, resulting in low transportation efficiency and high transportation cost, improves the accuracy of truck parking lot site selection, facilitates truck parking, and improves truck transportation efficiency and reduces transportation cost.

[0017] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a hardware structure block diagram of a terminal device for selecting a location for a truck parking lot provided in one embodiment of the present application; Figure 2 This is a flowchart of the site selection for a truck parking lot provided in one embodiment of the present application; Figure 3 is a flowchart of step S230; Figure 4 is a flowchart of step S250; Figure 5 This is a structural block diagram of a color mapping device for digital images provided in one embodiment of the present application.

[0019] In the figure: 102, processor; 104, memory; 106, transmission device; 108, input and output device; 210, acquisition module; 220, first processing module; 230, second processing module; 240, third processing module; 250, site selection module. DETAILED DESCRIPTION

[0020] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0021] Unless otherwise defined, technical or scientific terms used in this application shall have the ordinary meanings as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these," and similar expressions in this application do not denote limitations on quantity and may be singular or plural. The terms "comprise," "include," "have," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include unlisted steps or modules (units) or other steps or modules (units) inherent to the process, method, product, or device. The terms "connected," "connected," "coupled," and similar expressions used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used in this application, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. Generally, the character " / " indicates that the objects in the preceding and following relationship are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0022] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 1 is a block diagram of the hardware structure of the terminal of the truck parking lot site selection method of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 The processor 102 (only one is shown) and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The terminal may also include a transmission device 106 for communication functions and an input / output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0023] Memory 104 can be used to store computer programs, such as software programs and modules for application software, such as the computer program corresponding to the truck parking lot site selection method in this embodiment. Processor 102 executes the computer program stored in memory 104 to execute various functional applications and data processing, thereby implementing the above-mentioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located relative to processor 102, and such remote memory may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0024] Transmission device 106 is used to receive or transmit data via a network. This network may include a wireless network provided by the terminal's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0025] In this embodiment, a method for selecting a truck parking lot is provided. Figure 2 FIG. 1 is a flow chart of the method for selecting a location for a truck parking lot according to the present embodiment. Figure 2 As shown, the process includes the following steps: Step S210, obtaining basic information of the truck and candidate parking lot information of the candidate parking lot; matching the truck with the candidate parking lot; Step S220, based on the basic information, determining the time cost of the truck to the candidate parking lot; Step S230: Determine a life convenience index and a public nuisance index based on the basic information and the candidate parking lot information; Step S240: constructing a weight vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index; and constructing a score vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index; Step S250 : determining a target truck parking lot from the candidate parking lots based on the weight vector and the score vector.

[0026] Specifically, candidate parking lots refer to multiple pre-selected truck parking lots; they can also be multiple truck parking lots that meet planning requirements. Candidate parking lot information is data related to the candidate parking lots, including but not limited to: the address information (also known as location information) of each candidate parking lot, a list of addresses summarizing each address information, civil buildings surrounding the candidate parking lot, and the distances between civil buildings and each candidate parking lot. Civil buildings include residential buildings and public buildings; residential buildings include residential buildings and dormitories; and public buildings include schools, hospitals, stores, restrooms, gas stations, etc. Trucks are pre-matched with candidate parking lots. Basic information includes but is not limited to truck traffic on hotspot routes, frequently used truck parking spots, and frequently used truck destinations and delivery points. Trucks that meet this basic information and are within a preset distance range of a candidate parking lot are considered matched.

[0027] The Traffic Index, short for Traffic Congestion Index or Traffic Performance Index (TPI), is a conceptual index value that comprehensively reflects the smoothness or congestion of the road network. The Traffic Index ranges from 0 to 10 and is divided into five levels. 0-2, 2-4, 4-6, 6-8, and 8-10 correspond to "smooth," "basically smooth," "mild congestion," "moderate congestion," and "severe congestion," respectively. Higher values ​​indicate more severe traffic congestion. The Traffic Index changes in real time and can be obtained through data published by third parties, with no restrictions. The Time Cost, Life Convenience Index, and Public Disturbance Index are calculated, including but not limited to using relevant formulas or pre-trained neural network models.

[0028] The obtained traffic index, time cost, convenience index, and nuisance index are then used to evaluate truck parking lot site selection. Based on these four dimensions, the site selection score of each candidate parking lot is evaluated to quickly select the desired target truck parking lot from the candidate parking lots. This balances the impact of each dimension on site selection, improves the accuracy of truck parking lot site selection, facilitates truck parking, and improves truck transportation efficiency and reduces transportation costs. Specifically, based on the obtained traffic index, time cost, convenience index, and nuisance index, a weight vector and a score vector are constructed for each candidate parking lot. Specifically, a corresponding weight vector and score vector are constructed for each candidate parking lot under each dimension. For each candidate parking lot, by combining the weight vectors and score vectors for each dimension, the desired target truck parking lot can be quickly selected from the candidate parking lots. In other embodiments, the final score of each candidate parking lot can be adjusted by adjusting the constructed weight vector to meet the user's site selection requirements based on different dimensions.

[0029] In the related art, the solution adopted is: determine the location of the truck parking lot according to the planning requirements. This solution does not take into account the need to fit the parking spot with the optimal operation route, resulting in inaccurate location selection, reduced transportation efficiency, and increased transportation costs. The present application obtains the basic information of the truck and the candidate parking lot information of the candidate parking lot; matches the truck with the candidate parking lot; based on the basic information, determines the time cost of the truck to the candidate parking lot; determines the life convenience index and the nuisance index based on the basic information and the candidate parking lot information; constructs a weight vector for each candidate parking lot based on the obtained traffic index, time cost, life convenience index, and nuisance index; and, constructs a scoring vector for each candidate parking lot based on the obtained traffic index, time cost, life convenience index, and nuisance index; determines the target truck parking lot from the candidate parking lots based on the weight vector and the scoring vector, which solves the problem of inaccurate location selection in the related art, resulting in low transportation efficiency and high transportation costs. It integrates four dimensions to construct a weight vector and a scoring vector for each candidate parking lot separately to balance the impact of each dimension on location selection, improve the accuracy of truck parking lot location selection, facilitate truck parking, and improve the transportation efficiency of trucks and reduce transportation costs.

[0030] In some embodiments, determining the time cost of the truck to the candidate parking lot based on the basic information in step S220 includes the following steps: Determine the time cost of the truck to the candidate parking lot based on the basic information and the preset time cost formula.

[0031] Specifically, the time cost formula is: ; Where: T is the time cost; i is the candidate parking lot address of the truck; j is the parking point of the truck's hot route; D ij N is the shortest distance from the hot spot route corresponding to parking point j to the candidate parking lot address; j is the number of trucks parking at the candidate parking lot per month on the hot route corresponding to parking point j; S ij is the average speed of trucks going to the candidate parking lot on the route corresponding to parking spot j in each month; k is the common parking spot for trucks; D ik is the distance from the common parking spot k to the candidate parking lot address; N k is the number of trucks at the common parking point k; P ik is the probability of parking at the candidate parking lot at the common parking spot k; F ik is the frequency of trucks from the common parking spot k parking at the candidate parking lot each month; S ik is the average speed of trucks from the common parking spot k to the candidate parking lot.

[0032] By substituting the basic information into the above time cost formula, the time cost of the truck reaching the candidate parking lot can be quickly calculated, thereby simplifying the calculation process and improving calculation efficiency and accuracy.

[0033] In some of these embodiments, Figure 3 As shown, step S230 of determining the life convenience index and the public nuisance index based on the basic information and the candidate parking lot information includes the following steps: Step S231, determining a life convenience index based on basic information, candidate parking lot information, and a preset life convenience index formula; Step S232: determining a nuisance index based on the basic information, the candidate parking lot information, and a preset nuisance index formula.

[0034] Specifically, both the convenience of living index and the nuisance index are calculated based on basic information and candidate parking lot information, but the formulas involved are different.

[0035] Among them, the expression of the life convenience index formula is: ; Where: L is the life convenience index; G i is the number of gas stations near parking point i; F i is the number of restaurants near parking spot i; T i is the number of toilets near parking spot i; R i is the number of rest areas or recreational facilities near parking point i; D ij is the distance from parking point i to the destination of truck j.

[0036] Then, by substituting the basic information and candidate parking lot information into the above-mentioned life convenience index formula, the life convenience index of each candidate parking lot can be quickly calculated, thereby simplifying the calculation process and improving calculation efficiency and accuracy.

[0037] The expression of the nuisance index formula is: ; Where: Z is the nuisance index; S i is the number of schools near parking spot i; H i is the number of hospitals near parking point i; D ir is the average distance from parking point i to the adjacent residential area; D is is the average distance from parking point i to the nearby hospital; D ih is the average distance from parking spot i to nearby schools.

[0038] By substituting the basic information and candidate parking lot information into the above nuisance index formula, the nuisance index of each candidate parking lot can be quickly calculated, thereby simplifying the calculation process and improving calculation efficiency and accuracy.

[0039] In some embodiments, step S240 constructs a weight vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index, including the following steps: According to the obtained traffic index, time cost, life convenience index and public nuisance index, a matrix corresponding to each dimension is constructed; the matrix includes a traffic index matrix, a time cost matrix, a life convenience index matrix and a public nuisance index matrix; Normalize the matrix to obtain the corresponding normalized matrix; The weight vectors corresponding to the traffic index, time cost, life convenience index and nuisance index are constructed using the average value of each column in the normalized matrix.

[0040] Specifically, corresponding weight vectors are calculated for each of the four dimensions: traffic index, time cost, convenience index, and nuisance index. The calculation process for each dimension can be considered as: first constructing the corresponding matrix, then normalizing the matrix to obtain the corresponding normalized matrix; then, the average value of each column in the normalized matrix is ​​used to construct the corresponding weight vector.

[0041] The implementation process of each dimension is described below: For the dimension of time cost: construct the matrix corresponding to the time cost to obtain the time cost matrix; the time cost matrix can be expressed as: ; Among them, i represents the i-th candidate parking lot, α 11 ~α ii Is a value from 1 to i. 11 Represents the importance of the time cost of the first candidate parking lot compared to the first candidate parking lot, and the value must be 1; α 12 The importance of the time cost of the second candidate parking lot is (α 11 Specific value of) multiple, α 21 The time cost importance of the first candidate parking lot is (α 21 Specific value of α) multiple. ii The time cost importance of the i-th candidate parking lot is (α ii The other items in the matrix can be obtained in the same way, so we will not give examples one by one.

[0042] For example, if there are four candidate parking lots, namely A, B, C, and D, then the constructed time cost matrix is: ; The first column and first row correspond to Parking Lot A; the second column and second row correspond to Parking Lot B; the third column and third row correspond to Parking Lot C; and the fourth column and fourth row correspond to Parking Lot D. The first value in the first row is 1, indicating that the time cost of Candidate Parking Lot A is equally important as that of Candidate Parking Lot A; the second value in the first row is 3, indicating that the time cost of Candidate Parking Lot B is three times as important as that of Candidate Parking Lot A (the specific values ​​can be adjusted based on actual conditions or based on user feedback). This matrix is ​​used to calculate the weight vector later. The specific values ​​of the matrix can be determined based on feedback collection and other methods. The larger the weight value, the greater the final score. If the time cost importance of each candidate parking lot is the same, then every specific value in the matrix is ​​1. This way, every value in the weight vector calculated later is also the same, and there is no impact on the final score. This allows for multiple step-by-step methods to precisely adjust the matrix to influence the final site selection score, improving the accuracy of the site selection score.

[0043] The time cost matrix is ​​then normalized to obtain the corresponding normalized time cost matrix. The normalization process is as follows: first, the values ​​of each row are added together to obtain the sum of each row, represented by Sum1, Sum2, ..., Sumi; then, each value in the i-th row is divided by Sumi in sequence to obtain the normalized matrix.

[0044] Take candidate parking lots A, B, C, and D as an example to illustrate the calculation process: First, calculate the sum of each row. After calculation, the sum of the first row is Sum1 = 1 + 3 + 2 + 4 = 10; the sum of the second row is Sum2 = 1 / 3 + 1 + 1 / 2 + 2 = 3.8333; the sum of the third row is Sum3 = 1 / 2 + 2 + 1 + 3 = 6.5; the sum of the fourth row is Sum4 = 1 / 4 + 1 / 2 + 1 / 3 + 1 = 2.0833. Then divide the elements of each row by the sum of the row; that is, the first row (1, 3, 2, 4) is divided by Sum1 = 10, and the result is (0.1, 0.3, 0.2, 0.4); the second row (1 / 3, 3, 1 / 2, 2) is divided by Sum2 = 3.8333, and the result is (0.08, 0.26, 0.13, 0.53). Then calculate the third and fourth rows to obtain the final time cost normalized matrix, which is expressed as: ; Finally, the average value of each column in the time cost normalization matrix is ​​used to construct the weight vector corresponding to the time cost, which can be expressed as V T =(V T1 ,V T2 ,…,V TiThe process of calculating the weight vector is as follows: first calculate the average value of each column, represented by Avg1, Avg2, ..., Avgi; the weight vector obtained is (Avg1, Avg2, ..., Avgi).

[0045] Taking candidate parking lots A, B, C, and D as examples to illustrate the calculation process: the average value of the first column, Avg1, is (0.1+0.08+0.08+0.12) / 4=0.095; the average value of the second column, Avg2, is (0.3+0.26+0.31+0.24) / 4=0.2775; the average value of the third column, Avg3, is (0.2+0.13+0.15+0.16) / 4=0.16; the average value of the fourth column, Avg4, is (0.4+0.53+0.43+0.48) / 4=0.4675; so the weight vector corresponding to the time cost is (0.095, 02775, 0.16, 0.4675).

[0046] The calculation process for the three dimensions of traffic index, living convenience index and public nuisance index is similar and will not be repeated here.

[0047] The traffic index matrix corresponding to the traffic index is expressed as: ; The weight vector corresponding to the traffic index is expressed as: V G =(V G1 ,V G2 ,…,V Gi ); The life convenience index matrix corresponding to the life convenience index is expressed as: ; The weight vector corresponding to the life convenience index is expressed as: V L =(V L1 ,V L2 ,…,V Li ); The nuisance index matrix corresponding to the nuisance index is expressed as: ; The weight vector corresponding to the nuisance index is expressed as: V D =(V D1 ,V D2 ,…,V Di ).

[0048] Through this embodiment, a corresponding weight vector is constructed for each dimension, thereby realizing a variety of step-by-step methods to accurately adjust the matrix to influence the final site selection score and improve the accuracy of the site selection score. In other embodiments, the weight vector can also be implemented through related algorithms, or by setting fixed values ​​or other methods.

[0049] In some embodiments, step S230 constructs a scoring vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index, including the following steps: Select the corresponding maximum and minimum targets based on the traffic index, time cost, life convenience index, and public nuisance index; The maximum target and the minimum target are input into the pre-examination mapping function to obtain the score vector corresponding to the traffic index, time cost, life convenience index and nuisance index.

[0050] Specifically, corresponding scoring vectors are calculated for each of the four dimensions: traffic index, time cost, convenience index, and nuisance index. The calculation process for each dimension can be considered as follows: first, the corresponding maximum and minimum targets are selected based on the obtained traffic index, time cost, convenience index, and nuisance index; then, the maximum and minimum targets are input into the pre-qualified mapping function to obtain the scoring vectors corresponding to the traffic index, time cost, convenience index, and nuisance index. This constructs a corresponding scoring vector for each dimension, providing a basis for accurately evaluating truck parking site selection.

[0051] The implementation process of each dimension is described below: Regarding the dimension of time cost: The time cost T for each candidate parking lot is obtained. i Based on T i Take the reciprocal (because the lower the time cost, the better, and the final calculated score is the larger the better, so we need to take the reciprocal), and then T i The inverse of is mapped to the interval [1-10] to obtain the specific time cost score. The specific process is as follows: The time cost formula is used to obtain the time cost of each candidate parking lot: (S T1 , S T2 ,…,S Ti ); Take the reciprocal and get (1 / S T1 , 1 / S T2 ,…,1 / S Ti ); Compare the size of each value in turn and record the maximum value Max(S T ) and the minimum value Min(S T ).

[0052] The mapping function is expressed as: MF(i)=((1 / S Ti -Min(S T )) / (Max(S T )-Min(S T )))×9+1; where Min(ST ) is the minimum value recorded, Max(S T ) is the maximum value recorded. The function is to map the value to the interval [1-10]. The time cost score after taking the inverse is calculated through the mapping function MF(i) to obtain the time cost score of each candidate parking lot in the interval [1-10]. These values ​​​​compose the time cost scoring vector S T =(S T1 ,S T2 ,…,S Ti ).

[0053] Take candidate parking lots A, B, C, and D as an example to illustrate the calculation process: Assume that the time cost score of each candidate parking lot is (30, 50, 213, 5); Taking the reciprocal, we get (1 / 30, 1 / 50, 1 / 213, 1 / 5); That is, (0.03333333333333333, 0.02, 0.004694835680751174, 0.2).

[0054] Record the maximum value Max(S T ): 0.2, minimum value Min(S T ): 0.004694835680751174; Mapping function MF(A)=((0.03333333333333333-0.004694835680751174) / (0.2-0.004694835680751174))×9+1=2.32; similarly, we get MF(B)=1.71; MF(C)=1; and MF(D)=10.

[0055] Then the final time cost scoring vector is: (2.32, 1.71, 1, 10).

[0056] For the traffic index dimension: The traffic index G of each candidate parking lot is obtained i . i Take the reciprocal (because the lower the traffic index, the better, and the final calculated score is the bigger the better, so we need to take the reciprocal), and then G i The inverse of is mapped to the interval [1-10] to obtain the specific traffic index score. The specific process is as follows: Collect the traffic index of each candidate parking lot: (S G1 , S G2 ,…,S Gi ); Take the reciprocal and get (1 / S G1, 1 / S G2 ,…,1 / S Gi ). Compare the size of each value in turn and record the maximum value Max(S G ) and the minimum value Min(S G ); Mapping function MF(i)=((1 / S Gi -Min(S G )) / (Max(S G )-Min(S G )))×9+1; where Min(S G ) is the minimum value recorded, Max(S G ) is the maximum value recorded. This function is used to map the value to the interval [1-10]. The inverse of the traffic index score is calculated through the mapping function MF(i) to obtain the traffic index score of each candidate parking lot in the interval [1-10]. These values ​​​​compose the traffic index scoring vector S G =(S G1 ,S G2 ,…,S Gi ).

[0057] Regarding the dimension of living index: After obtaining the life index L of each candidate parking lot i Based on the life index L i The mapping is carried out in the range of [1-10] to obtain the specific life index score. The specific process is as follows: According to the life index formula, the life index score of each candidate parking lot is: (S L1 , S L2 ,…,S Li ).

[0058] Compare the size of each value in turn and record the maximum value Max(S L ) and the minimum value Min(S L ).

[0059] Mapping function MF(i)=((S Li -Min(S L )) / (Max(S L )-Min(S L )))×9+1,Min(S T ) is the minimum value recorded, Max(S T ) is the maximum value recorded. The function is to map the value to the interval [1-10]. The life index score is calculated by the mapping function MF(i) to obtain the life index score of each candidate parking lot in the interval [1-10]. These values ​​compose the life index scoring vector SL =(S L1 ,S L2 ,…,S Li ).

[0060] Regarding the dimension of nuisance index: The nuisance index Z at each candidate parking lot i Based on the nuisance index Z i Take the inverse (because the lower the nuisance index, the better, and the final calculated score is the larger the better, so we need to take the inverse), and then use the nuisance index Z i The inverse of is mapped to the interval [1-10] to obtain the specific nuisance index score. The specific process is as follows: The nuisance index of each candidate parking lot is obtained by the nuisance index formula: (S Z1 , S Z2 ,…,S Zi ); Take the reciprocal and get (1 / S Z1 , 1 / S Z2 ,…,1 / S Zi ). Compare the size of each value in turn and record the maximum value Max(S Z ) and the minimum value Min(S Z ).

[0061] Mapping function MF(i)=((1 / S Zi -Min(S Z )) / (Max(S Z )-Min(S Z )))×9+1,Min(S Z ) is the minimum value recorded, Max(S Z ) is the maximum value recorded. The function is to map the value to the interval [1-10]. The inverse of the nuisance index score is calculated through the mapping function MF(i) to obtain the nuisance index score of each candidate parking lot in the interval [1-10]. These values ​​compose the nuisance index scoring vector S Z =(S Z1 ,S Z2 ,…,S Zi ).

[0062] Through this embodiment, a corresponding scoring vector is constructed for each dimension, providing a basis for accurately evaluating the location of truck parking lots. In other embodiments, the scoring vector can also be implemented through other methods such as relevant algorithms.

[0063] In some of these embodiments, Figure 4 As shown, determining the target parking lot for the truck according to the weight vector and the score vector in step S250 includes the following steps: Step S251, determining the location score of each candidate parking lot relative to the truck based on the weight vector and the score vector; Step S252: Select the candidate parking lot with the highest site selection score as the target parking lot for the truck.

[0064] Specifically, the weight vector and score vector of each dimension are directly multiplied or the coefficient of each dimension is added to determine the location score of each candidate parking lot relative to the truck; then the location score of each candidate parking lot relative to the truck is ranked, and the candidate parking lot with the highest location score is selected as the target parking lot for the truck.

[0065] Through this embodiment, the analytic hierarchy process is used to comprehensively consider the impact of multiple dimensions on the location of the parking lot, making the final location selection more accurate.

[0066] In some embodiments, step S251, determining the location score of each candidate parking lot relative to the truck based on the weight vector and the score vector, includes the following steps: Determine the time cost score of the candidate parking lot relative to the truck based on the weight vector and score vector corresponding to the time cost; Determine the traffic index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the traffic index; Determine the life convenience index score of the corresponding candidate parking lot relative to the truck based on the weight vector and score vector corresponding to the life convenience index; Determine the nuisance index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the nuisance index; The time cost score, traffic index score, life convenience index score, and public nuisance index score are summarized to obtain the location score of the corresponding candidate parking lot relative to the truck.

[0067] Specifically, the above process uses the Analytic Hierarchy Process (AHP) to accurately determine the site selection score of each candidate parking lot relative to trucks, thereby selecting a parking lot that provides truck owners with a better experience. Based on the above embodiment, a weight vector and a score vector corresponding to each dimension are obtained. The weight vector and score vector corresponding to each dimension are multiplied together to obtain a score for the corresponding dimension. The scores are then aggregated to obtain a final site selection score.

[0068] Alternatively, the candidate parking lot scoring formula may be used to calculate the candidate parking lot by combining the weight vectors and the scoring vectors of each dimension. The candidate parking lot scoring formula is expressed as follows: ; Among them, S i Represents the final site selection score of the i-th candidate parking lot, V TiThe weight vector representing the time cost of the i-th candidate parking lot, S Ti The scoring vector representing the time cost of the i-th candidate parking lot, V Gi The weight vector representing the traffic index of the i-th candidate parking lot, S Gi The score vector representing the traffic index of the i-th candidate parking lot, V Zi The weight vector representing the nuisance index of the i-th candidate parking lot, S Zi The scoring vector representing the nuisance index of the i-th candidate parking lot, V Li The weight vector representing the life index of the i-th candidate parking lot, S Li A rating vector representing the living index of the i-th candidate parking lot.

[0069] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0070] This embodiment also provides a truck parking lot site selection device, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. The terms "module," "unit," "subunit," etc. used below may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0071] Figure 5 This is a structural block diagram of the site selection device for the truck parking lot of this embodiment. Figure 5 As shown, the device includes: an acquisition module 210, a first processing module 220, a second processing module 230, a third processing module 240 and a site selection module 250; The acquisition module 210 is used to obtain basic information of the truck and candidate parking lot information of the candidate parking lot; the truck is matched with the candidate parking lot; The first processing module 220 is used to determine the time cost of the truck to the candidate parking lot based on the basic information; The second processing module 230 is used to determine a life convenience index and a public nuisance index based on the basic information and the candidate parking lot information; The third processing module 240 is configured to construct a weight vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index; and to construct a score vector for each candidate parking lot based on the acquired traffic index, time cost, life convenience index, and public nuisance index; The site selection module 250 is configured to determine a target truck parking lot from the candidate parking lots based on the weight vector and the score vector.

[0072] The above device solves the problem of inaccurate site selection in related technologies, which leads to low transportation efficiency and high transportation costs. It improves the accuracy of site selection for truck parking lots, facilitates truck parking, and improves the transportation efficiency and reduces transportation costs of trucks.

[0073] In some embodiments, the first processing module 220 is further configured to determine the time cost of the truck reaching the candidate parking lot based on the basic information and a preset time cost formula.

[0074] In some embodiments, the second processing module 230 is further configured to determine a life convenience index based on the basic information, the candidate parking lot information, and a preset life convenience index formula; The nuisance index is determined based on the basic information, the candidate parking lot information and the preset nuisance index formula.

[0075] In some embodiments, the third processing module 240 is further configured to construct a matrix corresponding to each dimension based on the acquired traffic index, time cost, life convenience index, and public nuisance index; the matrix includes a traffic index matrix, a time cost matrix, a life convenience index matrix, and a public nuisance index matrix; Normalize the matrix to obtain the corresponding normalized matrix; The weight vectors corresponding to the traffic index, time cost, life convenience index and nuisance index are constructed using the average value of each column in the normalized matrix.

[0076] In some embodiments, the third processing module 240 is further configured to select a corresponding maximum target and a minimum target based on the acquired traffic index, time cost, life convenience index, and public nuisance index; The maximum target and the minimum target are input into the pre-examination mapping function to obtain the score vector corresponding to the traffic index, time cost, life convenience index and nuisance index.

[0077] In some embodiments, the site selection module 250 is further configured to determine a site selection score of each candidate parking lot relative to the truck based on the weight vector and the score vector; The candidate parking lot with the highest site selection score is selected as the target parking lot for the truck.

[0078] In some embodiments, the site selection module 250 is further configured to determine a time cost score of the corresponding candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the time cost; Determine the traffic index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the traffic index; Determine the life convenience index score of the corresponding candidate parking lot relative to the truck based on the weight vector and score vector corresponding to the life convenience index; Determine the nuisance index score of the candidate parking lot relative to the truck based on the weight vector and the score vector corresponding to the nuisance index; The time cost score, traffic index score, life convenience index score, and public nuisance index score are summarized to obtain the location score of the corresponding candidate parking lot relative to the truck.

[0079] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0080] This embodiment further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0081] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0082] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program: S1, obtaining basic information of the truck and candidate parking lot information of the candidate parking lot; matching the truck with the candidate parking lot; S2, based on the basic information, determines the time cost of the truck to the candidate parking lot; S3, determining the life convenience index and the public nuisance index based on the basic information and the candidate parking lot information; S4, constructing a weight vector for each candidate parking lot based on the obtained traffic index, time cost, life convenience index, and public nuisance index; and constructing a score vector for each candidate parking lot based on the obtained traffic index, time cost, life convenience index, and public nuisance index; S5, determining a target truck parking lot from the candidate parking lots based on the weight vector and the score vector.

[0083] It should be noted that, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0084] In addition, in conjunction with the truck parking lot site selection method provided in the above embodiments, a storage medium may also be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the truck parking lot site selection methods in the above embodiments.

[0085] It should be noted that the information and data involved in this application are all authorized by the user or fully authorized by all parties and will be used legally.

[0086] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0087] Obviously, the accompanying drawings are merely examples or embodiments of the present application. A person skilled in the art can also apply the present application to other similar situations based on these drawings without inventive effort. Furthermore, it is understandable that, although the work involved in this development process may be complex and lengthy, certain design, manufacturing, or production changes based on the technical content disclosed in this application are merely routine technical means for a person skilled in the art and should not be considered to constitute a deficiency in the disclosure of the present application.

[0088] The term "embodiment" as used in this application refers to specific features, structures, or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily mean that the embodiment is the same, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is understood, either explicitly or implicitly, by those skilled in the art that the embodiments described in this application can be combined with other embodiments when there is no conflict.

[0089] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for selecting a location for a truck parking lot, characterized in that: include: Obtain basic information of the truck and candidate parking lot information of the candidate parking lot; The truck is matched with a candidate parking lot; Based on the basic information, determining the time cost of the truck to the candidate parking lot; Determine a life convenience index and a public nuisance index according to the basic information and the candidate parking lot information; According to the obtained traffic index, the time cost, the life convenience index and the nuisance index, a weight vector of each candidate parking lot is constructed; and, according to the obtained traffic index, the time cost, the life convenience index and the nuisance index, a score vector of each candidate parking lot is constructed; A target truck parking lot is determined from the candidate parking lots according to the weight vector and the score vector.

2. The method for selecting a truck parking lot according to claim 1, characterized in that: Determining the time cost of the truck to the candidate parking lot based on the basic information includes: The time cost of the truck to the candidate parking lot is determined according to the basic information and a preset time cost formula.

3. The method for selecting a truck parking lot according to claim 1, characterized in that: Determining a life convenience index and a public nuisance index according to the basic information and the candidate parking lot information includes: Determine a life convenience index according to the basic information, the candidate parking lot information and a preset life convenience index formula; A nuisance index is determined according to the basic information, the candidate parking lot information and a preset nuisance index formula.

4. The method for selecting a truck parking lot according to claim 1, characterized in that: According to the acquired traffic index, the time cost, the life convenience index and the nuisance index, a weight vector of each candidate parking lot is constructed, including: According to the acquired traffic index, the time cost, the life convenience index and the nuisance index, a matrix corresponding to each dimension is constructed; the matrix includes a traffic index matrix, a time cost matrix, a life convenience index matrix and a nuisance index matrix; Normalizing the matrix to obtain a corresponding normalized matrix; A weight vector corresponding to the traffic index, the time cost, the life convenience index and the nuisance index is constructed with the average value of each column in the normalized matrix.

5. The method for selecting a truck parking lot according to claim 1, characterized in that: According to the acquired traffic index, the time cost, the life convenience index and the nuisance index, a scoring vector of each candidate parking lot is constructed, including: Selecting the corresponding maximum target and minimum target according to the acquired traffic index, the time cost, the life convenience index, and the nuisance index; The maximum target and the minimum target are input into the preliminary mapping function to obtain a scoring vector corresponding to the traffic index, the time cost, the life convenience index and the nuisance index.

6. The method for selecting a location of a truck parking lot according to any one of claims 1 to 5, characterized in that: Determining a target parking lot for the truck according to the weight vector and the score vector includes: Determining a site selection score of each of the candidate parking lots relative to the truck according to the weight vector and the score vector; The candidate parking lot with the highest site selection score is selected as the target parking lot for the truck.

7. The method for selecting a truck parking lot according to claim 6, characterized in that: Determining the site selection score of each of the candidate parking lots relative to the truck according to the weight vector and the score vector includes: Determine a time cost score corresponding to the candidate parking lot relative to the truck according to the weight vector and the score vector corresponding to the time cost; Determining a traffic index score corresponding to the candidate parking lot relative to the truck according to the weight vector and the score vector corresponding to the traffic index; Determine, according to the weight vector and the score vector corresponding to the life convenience index, a life convenience index score corresponding to the candidate parking lot relative to the truck; Determining a nuisance index score of the candidate parking lot relative to the truck according to the weight vector and the score vector corresponding to the nuisance index; The time cost score, traffic index score, life convenience index score and nuisance index score are summarized to obtain a location score corresponding to the candidate parking lot relative to the truck.

8. A site selection device for a truck parking lot, characterized in that: include: An acquisition module, a first processing module, a second processing module, a third processing module and a site selection module; The acquisition module is used to acquire basic information of the truck and candidate parking lot information of the candidate parking lot; the truck is matched with the candidate parking lot; The first processing module is used to determine the time cost of the truck to the candidate parking lot based on the basic information; The second processing module is used to determine a life convenience index and a nuisance index according to the basic information and the candidate parking lot information; The third processing module is used to construct a weight vector for each of the candidate parking lots according to the acquired traffic index, the time cost, the life convenience index and the nuisance index; and to construct a score vector for each of the candidate parking lots according to the acquired traffic index, the time cost, the life convenience index and the nuisance index; The site selection module is used to determine a target truck parking lot from the candidate parking lots according to the weight vector and the score vector.

9. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps of the method for selecting a location of a truck parking lot according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for selecting a location of a truck parking lot according to any one of claims 1 to 7 are implemented.

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