Multi-region transportation service mode matching method and system

Abstract

The invention discloses a multi-region transportation service mode matching method and system, and particularly relates to the technical field of transportation services, and the method comprises the following steps: dividing user identities into order-sending users and order-receiving users, and respectively obtaining an order-sending requirement set of a target order-sending user and a service set of all order-receiving users in a non-order-receiving state; hard condition screening is firstly carried out, order receiving users meeting hard conditions are reserved, and then elastic condition screening is carried out; if the order receiving users meeting all the elastic conditions exist, direct matching is carried out; otherwise, carrying out portrait analysis by using K-means clustering based on the historical record of the target order issuing user, and evaluating a matching failure rate; according to a failure rate result, negotiation communication channels are successively established until consistent matching is achieved; the method can deeply understand the demand characteristics and behavior patterns of the order issuing user, provides highly personalized matching services, improves the service quality of the platform, and further enhances the loyalty and satisfaction of the user.

Description

Technical Field

[0001] The present invention relates to the field of transportation service technology, and more specifically, to a multi-region transportation service mode matching method and system. Background Art

[0002] In existing service trading platforms, logistics scheduling systems, and e-commerce platforms, matching user needs with service providers is a key link. Traditional matching methods are difficult to cope with complex and changeable user needs, especially when users have different priorities or flexibility for multiple requirements. These traditional methods often cannot effectively solve the problem of needing to readjust or negotiate after a matching failure, resulting in a decline in user experience and waste of resources. After a matching failure, users can usually only manually adjust their requirements or wait for new service providers to appear. This process is time-consuming and inefficient, wasting users' time and platform resources, and also reducing users' experience and satisfaction. Therefore, a multi-regional transportation service mode matching method and system is proposed here to solve the above problems. Summary of the invention

[0003] To achieve the above object, the present invention provides the following technical solutions: A multi-regional transportation service mode matching method comprises the following steps: All user identities are divided into two roles: order-issuing users and order-receiving users. Then, the order-issuing requirement sets of the target order-issuing users are obtained respectively, and the order-receiving service sets of all order-receiving users who are not in the order-receiving state are obtained at the same time. Based on the order placing requirement set and order receiving service set, hard condition screening is performed to retain order receiving users that meet the hard conditions. Then, flexible condition screening is performed. If there is an order receiving user that meets all flexible conditions, the target order receiving user is obtained according to the preset satisfaction strategy and matched with the target order placing user. If there is no order-taking user that meets all elastic conditions, the historical order-issuing records of the target order-issuing user are obtained and the K-means clustering algorithm is used for portrait analysis, and the matching failure rate is evaluated based on the results of the portrait analysis; Based on the matching failure rate evaluation results, negotiation communication channels are successively established for the target order-issuing user until a consensus is reached, and the order-receiving user corresponding to the negotiation communication channel is matched with the target order-issuing user.

[0004] In a preferred embodiment, the order requirement set includes a hard requirement subset and a flexible requirement subset. The hard requirement subset refers to a set of all hard requirements of the target order user that cannot be changed, and the flexible requirement subset refers to a set of all flexible requirements of the target order user that can be changed. The order-taking service set includes a hard service subset and a flexible service subset. The basic service subset refers to the set of all hard services of the order-taking user that cannot be changed, and the flexible service subset refers to the set of all flexible services of the order-taking user that can be changed.

[0005] In a preferred embodiment, performing hard condition screening means that the order-taking service set satisfies all the hard requirements in the hard requirement subset that do not accept changes.

[0006] In a preferred embodiment, performing elastic condition screening refers to: Obtain a subset of elastic requirements in the order requirement set, and compare each elastic requirement in the elastic requirement subset with the order service set of the retained order-taking user one by one. If all elastic requirements are met, it is marked as an accepted matching object; if all elastic requirements are not met, it is marked as a rejected matching object.

[0007] In a preferred embodiment, obtaining the target order-taking user according to the preset satisfaction strategy means: The historical service scores of all order-taking users marked as accepting matching objects are obtained, and then sorted in descending order. The order-taking user ranked first is the target order-taking user.

[0008] In a preferred embodiment, obtaining the historical order records of the target order issuing user and performing a portrait analysis using a K-means clustering algorithm refers to: Get the elastic requirement subset of the current order requirement set of the target order user, as well as the historical order records of the target order user, and extract information according to the elastic requirement items in the current elastic requirement subset: Mark the elastic requirement items in this elastic requirement subset as TXi, set the target elastic requirement item, obtain the transaction data of all successfully matched orders in the historical order records of the target order user, and the submission data when they were issued, and then calculate the modification amplitude value corresponding to the target elastic requirement item of each successfully matched order. The logic for obtaining the modification amplitude value is: First, calculate the difference between the transaction data at the time of transaction of the target elasticity requirement project and the submitted data at the time of issuance, then divide the difference by the submitted data at the time of issuance, and then perform absolute value processing on the obtained ratio to obtain the modified change ratio value; Then, the modification change ratio values ​​corresponding to the target elasticity requirement items of all successfully matched orders are averaged to obtain the modification amplitude value and marked as Mi; The modification amplitude value Mi of each elastic requirement item TXi is used as the feature input for clustering. The K-means clustering algorithm is used to perform cluster analysis on the modification amplitude values ​​of all elastic requirement items. The elbow method is used to determine the optimal number of clusters K. The elastic requirement items in each cluster form a level. Each elastic requirement item is matched with the classification level to obtain the user portrait of the target order-issuing user.

[0009] In a preferred embodiment, evaluating the matching failure rate based on the result of the portrait analysis refers to: Get the user profile of the target order-issuing user, and then set the weight coefficient for the elastic requirement items in this elastic requirement subset to meet the following requirements: ; Indicates the level of the elasticity requirement item, It represents the weight coefficient corresponding to the level of the elasticity requirement item, and K represents the total number of levels; Then use the Hamming distance to represent the matching results of each elastic requirement item: ; It represents the Hamming distance corresponding to the elastic requirement item. The difference amplitude value is obtained by first subtracting the data corresponding to the elastic requirement item in the order service set of the order-taking user from the corresponding requirement data in the elastic requirement subset, and then dividing the obtained difference by the requirement data in the elastic requirement subset. The obtained ratio is then processed into the absolute value to obtain the difference amplitude value; Calculate the overall difference value D: ; n represents the total number of elasticity requirement items; Then substitute it into the pre-trained comprehensive matching model formula: ; To adjust the sensitivity parameter, is the offset used to adjust the starting point of the matching degree. is the comprehensive matching degree; The matching failure rate is calculated by subtracting the comprehensive matching degree from the value one. get.

[0010] In a preferred embodiment, a multi-regional transportation service mode matching system includes: The identity information collection module divides all user identities and sets two roles: order-issuing users and order-receiving users. It then obtains the order-issuing requirement set of the target order-issuing users and the order-receiving service set of all order-receiving users who are not in the order-receiving state. The screening module performs hard condition screening based on the order placing requirement set and order receiving service set, retains the order receiving users who meet the hard conditions, and then performs flexible condition screening; The negotiation module, if there is no order-taking user that meets all elastic conditions, obtains the historical order-issuing records of the target order-issuing user and uses the K-means clustering algorithm to perform a portrait analysis, and evaluates the matching failure rate based on the results of the portrait analysis. Based on the matching failure rate evaluation results, negotiation communication channels are successively established for the target order-issuing users; The matching module obtains the target order-taking user and matches it with the target order-issuing user according to the preset satisfaction strategy if there is an order-taking user who meets all the elastic conditions, and when consensus is reached, the order-taking user corresponding to the negotiation communication channel is matched with the target order-issuing user.

[0011] Technical effects and advantages of the present invention: The present invention can intelligently sort all potential order-taking users based on detailed historical data and real-time matching failure rate assessment, and give priority to negotiating with order-taking users with the highest matching success rate. It can gradually eliminate factors that may lead to matching failures, continuously optimize matching conditions, and ultimately ensure that order-issuing users are matched with the most suitable order-taking users. Compared with traditional static matching methods, the present invention significantly improves the matching success rate, enables complex and changing needs to be met more efficiently, and greatly improves the matching quality of the platform.

[0012] Through intelligent sorting and negotiation mechanisms, the present invention reduces unnecessary communication and negotiation time between users. The system will automatically prioritize those order-receiving users who are most likely to reach an agreement with the order-issuing user for communication, which can avoid a large number of invalid attempts and repeated communications, greatly reducing the communication cost in the matching process. In this way, the system not only improves matching efficiency, but also optimizes the use of resources, allowing the platform to better handle a large number of matching requests while ensuring an improved user experience.

[0013] The present invention allows the order-issuing user and the order-receiving user to adjust their respective needs and service conditions in real time during the negotiation process, and the system can help users clearly understand the actual effect of each adjustment by dynamically adjusting the matching conditions and recalculating the matching failure rate. This flexible matching mechanism allows users to explore different matching solutions more freely, especially when faced with complex and changing needs, and can quickly find the most suitable solution through negotiation. This not only increases the matching success rate, but also enables the system to better adapt to the personalized needs of different users.

[0014] The present invention significantly reduces the trial-and-error cost of blind matching by giving priority to those order-taking users with a high matching success rate for negotiation. In traditional systems, users may need to make multiple attempts to find a suitable match, but the present invention significantly reduces the number of invalid attempts through intelligent sorting and multi-round negotiation mechanisms. Users can find suitable matches more quickly, avoiding unnecessary waste of time and resources, making the platform more efficient and significantly improving user satisfaction.

[0015] The present invention uses the K-means clustering algorithm to perform a portrait analysis on the historical data of order-issuing users, and combines the priority division of elastic requirements. The system can deeply understand the demand characteristics and behavior patterns of order-issuing users. This precise user portrait analysis enables the system to provide highly personalized matching services for each order-issuing user. According to the specific needs and priorities of the user, the system can intelligently adjust the matching strategy, thereby improving the accuracy and success rate of matching while meeting the user's needs. This personalized matching service not only improves the service quality of the platform, but also further enhances the loyalty and satisfaction of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 It is a schematic diagram of a multi-regional transportation service mode matching method in the present invention.

[0017] Figure 2 It is a schematic diagram of a multi-regional transportation service mode matching system in the present invention. DETAILED DESCRIPTION

[0018] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1 - Figure 2 The following examples are obtained: Embodiment 1: A multi-regional transportation service mode matching method comprises the following steps: All user identities are divided into two roles: order-issuing user and order-receiving user. Then, the order-issuing requirement set of the target order-issuing user is obtained respectively, and the order-receiving service set of all order-receiving users in the non-order-receiving state is obtained at the same time. The order-issuing requirement set includes a hard requirement subset and a flexible requirement subset. The hard requirement subset refers to the set of all hard requirements of the target order-issuing user that cannot be changed, and the flexible requirement subset refers to the set of all flexible requirements of the target order-issuing user that can be changed. The order-taking service set includes a hard service subset and a flexible service subset. The basic service subset refers to the set of all hard services of the order-taking user that cannot be changed, and the flexible service subset refers to the set of all flexible services of the order-taking user that can be changed.

[0020] Assume that there is a logistics platform where user A who issues an order needs to transport a batch of goods, and there are three order-receiving users B, C, and D on the platform who are not accepting orders. The following steps are used to explain in detail: User role division: Order-issuing user A: initiates transportation request. Order-receiving users B, C, D: provide transportation services.

[0021] Get the order requirement set and order service set: The requirement set of order issuing user A: Hard requirement subset: Transportation mode: Air transport; Departure place: Beijing; Destination: Shanghai; Flexible requirement subset: Transportation time: within 2 days, Price: within 2,000 yuan.

[0022] Service set of order-taking users: Order-taking user B: Hard service subset: air transport, Beijing to Shanghai; Flexible service subset: within 3 days, 1,900 yuan.

[0023] Order-taking user C: Hard service subset: air transport, Beijing to Shanghai; flexible service subset: within 2 days, 1,800 yuan.

[0024] Order-taking user D: Hard service subset: sea transportation, Tianjin to Shanghai; flexible service subset: within 5 days, 1,500 yuan.

[0025] Based on the order placing requirement set and the order receiving service set, hard condition screening is performed, and the order receiving users who meet the hard conditions are retained. Then, flexible condition screening is performed. If there is an order receiving user who meets all the flexible conditions, the target order receiving user is obtained according to the preset satisfaction strategy and matched with the target order placing user; hard condition screening means that the order receiving service set meets all the hard requirements in the hard requirement subset that cannot be changed, and compares the hard requirement subset of order placing user A with the hard service subset of each order receiving user.

[0026] Order-taking user B: Air transport (qualified), Beijing to Shanghai (qualified), passed the hard condition screening. Order-taking user C: Air transport (qualified), Beijing to Shanghai (qualified), passed the hard condition screening. Order-taking user D: Ocean transport (unqualified), Tianjin to Shanghai (unqualified), did not pass the hard condition screening. Screening results: Order-taking users B and C passed the hard condition screening, and order-taking user D was excluded.

[0027] Perform elastic condition screening on the order-taking users B and C who have passed the hard condition screening: Performing elastic condition screening means obtaining an elastic requirement subset in the order requirement set, and comparing each elastic requirement in the elastic requirement subset with the order service set of the retained order-taking users one by one. If all elastic requirements are met, it will be marked as an accepted matching object; if all elastic requirements are not met, it will be marked as a rejected matching object.

[0028] Order-taking user B: within 3 days (not fully compliant), 1900 yuan (compliant), meets all elastic conditions, and is marked as an accepted match object. Order-taking user C: within 2 days (compliant), 1800 yuan (compliant), fully meets all elastic conditions, and is marked as an accepted match object. Screening results: Order-taking user B and order-taking user C both fully meet the elastic conditions of order-issuing user A. Therefore, according to the preset strategy, the historical service scores of all order-taking users marked as accepted match objects are obtained, and then sorted in descending order. The order-taking user in the first place is the target order-taking user. The historical service score of order-taking user B is 97.65, and the historical service score of order-taking user C is 95.32. Therefore, order-taking user B is in the first place, and order-taking user B is successfully matched with order-issuing user A.

[0029] If there is no order-taking user who meets all elastic conditions, the historical ordering records of the target ordering user are obtained and the K-means clustering algorithm is used for portrait analysis. The matching failure rate is evaluated based on the results of the portrait analysis. The elastic requirement subset in the current order requirement set of the target ordering user and the historical ordering records of the target ordering user are obtained, and information is extracted according to the elastic requirement items in the current elastic requirement subset: Mark the elastic requirement items in this elastic requirement subset as TXi, set the target elastic requirement item, obtain the transaction data of all successfully matched orders in the historical order records of the target order user, and the submission data when they were issued, and then calculate the modification amplitude value corresponding to the target elastic requirement item of each successfully matched order. The logic for obtaining the modification amplitude value is: First, calculate the difference between the transaction data at the time of transaction of the target elasticity requirement project and the submitted data at the time of issuance, then divide the difference by the submitted data at the time of issuance, and then perform absolute value processing on the obtained ratio to obtain the modified change ratio value; Then, the modification change ratio values ​​corresponding to the target elasticity requirement items of all successfully matched orders are averaged to obtain the modification amplitude value and marked as Mi; The modification amplitude value Mi of each elastic requirement item TXi is used as the feature input for clustering. The K-means clustering algorithm is used to perform cluster analysis on the modification amplitude values ​​of all elastic requirement items. The elbow method is used to determine the optimal number of clusters K. The elastic requirement items in each cluster form a level. Each elastic requirement item is matched with the classification level to obtain the user portrait of the target order-issuing user.

[0030] The specific steps of cluster analysis are: Collect modification magnitude values: Extract the modification magnitude values ​​Mi of each elasticity requirement item TXi, which will be used as input features of the K-means algorithm. For example, if there are 10 different elasticity requirement items TX1, TX2, …, TX10, and the modification magnitude values ​​M1, M2, …, M10 of each item have been calculated, these values ​​will form a feature vector for clustering.

[0031] K-means is a commonly used unsupervised clustering algorithm that divides data into K clusters, each represented by a center (centroid). The algorithm clusters by minimizing the square distance between the data point and its corresponding cluster center, using the elbow method: The optimal number of clusters K is determined by the elbow method. The specific steps are as follows: Select multiple different K values ​​(such as from 1 to 10), perform K-means clustering for each K value, and calculate the sum of squared errors within the cluster. Plot the relationship between the sum of squared errors within the cluster and the K value, and find the "elbow" position, that is, the point where the curve begins to flatten out significantly, and use the corresponding K value as the optimal number of clusters.

[0032] Initialize cluster centers: randomly select K initial centroids. The initial centroids can be randomly selected from data points. Assign data points: assign the modification value Mi of each elastic requirement item to the cluster center (centroid) closest to it, calculated as Euclidean distance. Update cluster centers: calculate the average value of all data points in each cluster, and use this average value as the new cluster center. Repeat the iteration: continue the process of assigning data points and updating cluster centers until the cluster center no longer changes significantly (that is, the cluster assignment and the position of the centroid are stable). After the algorithm converges, all elastic requirement items TXi will be assigned to K different clusters, each cluster representing items with similar modification magnitudes.

[0033] Classification: Analyze based on the center value (centroid) of each cluster. Generally, clusters with smaller centroids may represent required items with smaller modifications (i.e., higher priority items), while clusters with larger centroids represent required items with larger modifications (lower priority items).

[0034] Sort the clusters by centroid size and divide them into different levels. For example: Level 1 (high priority): clusters with the smallest modification. Level 2 (medium priority): clusters with medium modification. Level 3 (low priority): clusters with the largest modification.

[0035] Get the user profile of the target order-issuing user, and then set the weight coefficient for the elastic requirement items in this elastic requirement subset to meet the following requirements: ; Indicates the level of the elasticity requirement item, Represents the weight coefficient corresponding to the level of the elastic requirement item, and K represents the total number of levels; through cluster analysis, the elastic requirement items of the target order-issuing user can be grouped according to their historical modification range. The items in each group have similar modification range characteristics. Optimize the matching process: The clustered elastic requirement items can better reflect the actual flexibility of the order-issuing user on these requirements. By dividing these items into different priorities, resources can be allocated and strategies adjusted more effectively during the matching process. Priority allocation: Through the levels divided after clustering, different weight coefficients can be set for each level. Different weight coefficients indicate that the order-issuing user has different degrees of acceptance of changes in different elastic requirements in the process of achieving a match.

[0036] Then use the Hamming distance to represent the matching results of each elastic requirement item: ; It represents the Hamming distance corresponding to the elastic requirement item. The difference amplitude value is obtained by first subtracting the data corresponding to the elastic requirement item in the order-taking service set of the order-taking user from the corresponding requirement data in the elastic requirement subset, and then dividing the difference by the requirement data in the elastic requirement subset. The obtained ratio is then processed by the absolute value to obtain the difference amplitude value; the modification amplitude value is calculated from the historical data in the previous article, so that it can be distinguished whether the order-issuing user is inclined to accept the difference in the elastic requirement items. For example, the elastic requirement items of the order-issuing user include: the delivery time is acceptable within 3 days, and the service item provided by the order-taking user is the delivery time within 4 days. The difference amplitude value of the elastic requirement item is calculated to be 0.33, and the modification amplitude value calculated from the historical data is 0.5. It is very likely that this elastic requirement will be accepted. Therefore, the Hamming distance calculation result is zero.

[0037] Calculate the overall difference value D: ; n represents the total number of elastic requirement items; the overall difference value D is a comprehensive indicator calculated based on the difference result di of each elastic requirement item and the corresponding weight wi. D represents the sum of the weighted difference values ​​between all elastic requirement items, reflecting the overall mismatch between the requirements of the order-issuing user and the services of the order-receiving user. The larger the value, the greater the difference between the two users and the higher the mismatch.

[0038] Then substitute it into the pre-trained comprehensive matching model formula: ; To adjust the sensitivity parameter, is the offset used to adjust the starting point of the matching degree. The comprehensive matching degree S is a normalized value, usually between 0 and 1, used to quantify the matching degree between the order-issuing user and the order-receiving user. The closer the S value is to 1, the higher the possibility of successful matching; the closer it is to 0, the lower the possibility of successful matching. Reflecting the matching quality: Through the calculation of S, the matching quality can be quantitatively expressed, providing users with an intuitive matching score, thereby helping users make decisions and adjusting the matching sensitivity parameters. Control the degree of influence of D on S, The larger the value, the more significant the impact of D on S and the more sensitive the matching degree. is the offset used to adjust the starting point of the matching calculation, affecting the baseline level of S, adjusting the starting position of the matching, and ensuring that the comprehensive matching model achieves the expected accuracy during training. The matching failure rate is calculated by subtracting the comprehensive matching from the value one. The matching failure rate is used to quantify the risk of matching failure. If the failure rate is close to 1, it means that the probability of successful matching is very low; Decision support: By calculating the matching failure rate, suggestions can be provided to users during the matching process to help them evaluate the feasibility of the current matching and whether negotiation or readjustment of matching conditions is needed in the future.

[0039] Based on the evaluation results of the matching failure rate, negotiation communication channels are successively established for the target order-issuing user until consensus is reached and the order-receiving user corresponding to this negotiation communication channel is matched with the target order-issuing user. Successively means sorting based on the matching failure rate and selecting the order-receiving user with the highest success rate each time to establish a negotiation communication channel between the target order-issuing user and the order-receiving user. This can be achieved through the chat function, telephone, email or other real-time communication tools within the platform. The two parties can discuss possible adjustments through the negotiation communication channel. For example, the order-issuing user may be willing to relax the time requirements, and the order-receiving user may be willing to make certain concessions on the price. Moreover, during the negotiation process, the matching conditions can be updated in real time and the matching failure rate can be recalculated to help both parties understand the effect of the current adjustment. If no consensus is reached in this negotiation, the next order-receiving user will be selected for negotiation until consensus is reached and the order-receiving user corresponding to this negotiation communication channel is matched with the target order-issuing user.

[0040] By sorting based on the matching failure rate, the order-taking user with the highest matching success rate is selected to negotiate with the order-issuing user each time. This "sequential" process means that the system will prioritize the matches that are most likely to succeed, gradually eliminating the possibility of failure until the best matching solution is found. Reduce invalid communication: Prioritizing the order-taking user with a higher matching success rate for negotiation can reduce unnecessary communication and negotiation time and improve efficiency. During the negotiation process, both parties can adjust their respective requirements and service conditions, or update the matching conditions in real time and recalculate the matching failure rate. Users can clearly understand the effect of each adjustment, making it easier for both parties to reach an agreement. If a negotiation fails to reach an agreement, the system can quickly select the next best order-taking user for negotiation until the match is successful. This multi-round negotiation mechanism ensures that even if the initial match fails, a most suitable matching solution can be found through continuous adjustments. By prioritizing the order-taking user with the highest success rate for negotiation, the trial and error cost of blind matching is reduced. Users can find suitable matches more quickly and avoid unnecessary waste of time and resources.

[0041] Embodiment 2: A multi-regional transportation service mode matching system, comprising: The identity information collection module divides all user identities and sets two roles: order-issuing users and order-receiving users. It then obtains the order-issuing requirement set of the target order-issuing users and the order-receiving service set of all order-receiving users who are not in the order-receiving state. The screening module performs hard condition screening based on the order placing requirement set and order receiving service set, retains the order receiving users who meet the hard conditions, and then performs flexible condition screening; The negotiation module, if there is no order-taking user that meets all elastic conditions, obtains the historical order-issuing records of the target order-issuing user and uses the K-means clustering algorithm to perform a portrait analysis, and evaluates the matching failure rate based on the results of the portrait analysis. Based on the matching failure rate evaluation results, negotiation communication channels are successively established for the target order-issuing users; The matching module obtains the target order-taking user and matches it with the target order-issuing user according to the preset satisfaction strategy if there is an order-taking user who meets all the elastic conditions, and when the negotiation is in agreement, the order-taking user corresponding to the negotiation communication channel is matched with the target order-issuing user; Service feedback module: after the service is completed, the target order-issuing user will give a service rating to the order-receiving user who has been matched with the service.

[0042] Multi-region may refer to multiple geographical regions, multiple market environments, and diverse user groups. The order-taking users include but are not limited to the following groups: taxis (or other urban transportation tools, such as tricycles, motorcycles, vans, buses, etc.); food delivery personnel (the transportation tools they use are not limited); truck drivers, logistics companies, air transport companies, shipping companies, and other people or companies that provide land, water, and air transportation services.

[0043] Order-issuing users include but are not limited to the following groups: passengers taking taxis (or other urban transportation such as tricycles, motorcycles, vans, buses, etc.); merchants or consumers using food delivery services; and people or companies using water, land, and air transportation services.

[0044] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by software simulation of a large amount of data. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0045] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0046] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0047] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-regional transportation service mode matching method, characterized in that: The following steps are involved: All user identities are divided into two roles: order-issuing users and order-receiving users. Then, the order-issuing requirement sets of the target order-issuing users are obtained respectively, and the order-receiving service sets of all order-receiving users who are not in the order-receiving state are obtained at the same time. Based on the order placing requirement set and order receiving service set, hard condition screening is performed to retain order receiving users that meet the hard conditions. Then, flexible condition screening is performed. If there is an order receiving user that meets all flexible conditions, the target order receiving user is obtained according to the preset satisfaction strategy and matched with the target order placing user. If there is no order-taking user that meets all elastic conditions, the historical order-issuing records of the target order-issuing user are obtained and the K-means clustering algorithm is used for portrait analysis, and the matching failure rate is evaluated based on the results of the portrait analysis; Based on the matching failure rate evaluation results, negotiation communication channels are successively established for the target order-issuing user until a consensus is reached, and the order-receiving user corresponding to the negotiation communication channel is matched with the target order-issuing user.

2. A multi-regional transportation service mode matching method according to claim 1, characterized in that: The order requirement set includes a hard requirement subset and a flexible requirement subset. The hard requirement subset refers to the set of all hard requirements of the target order user that cannot be changed, and the flexible requirement subset refers to the set of all flexible requirements of the target order user that can be changed. The order-taking service set includes a hard service subset and a flexible service subset. The basic service subset refers to the set of all hard services of the order-taking user that cannot be changed, and the flexible service subset refers to the set of all flexible services of the order-taking user that can be changed.

3. A multi-regional transportation service mode matching method according to claim 2, characterized in that: Hard condition screening means that the order-accepting service set satisfies all the hard requirements in the hard requirement subset that cannot be changed.

4. A multi-regional transportation service mode matching method according to claim 2, characterized in that: Flexible condition screening means: Obtain a subset of elastic requirements in the order requirement set, and compare each elastic requirement in the elastic requirement subset with the order service set of the retained order-taking user one by one. If all elastic requirements are met, it is marked as an accepted matching object; if all elastic requirements are not met, it is marked as a rejected matching object.

5. A multi-regional transportation service mode matching method according to claim 4, characterized in that: The target order-taking users obtained according to the preset satisfaction strategy are: The historical service scores of all order-taking users marked as accepting matching objects are obtained, and then sorted in descending order. The order-taking user ranked first is the target order-taking user.

6. A multi-regional transportation service mode matching method according to claim 5, characterized in that: Obtaining the historical order records of the target order-issuing user and using the K-means clustering algorithm for portrait analysis means: Get the elastic requirement subset of the current order requirement set of the target order user, as well as the historical order records of the target order user, and extract information according to the elastic requirement items in the current elastic requirement subset: Mark the elastic requirement items in this elastic requirement subset as TXi, set the target elastic requirement item, obtain the transaction data of all successfully matched orders in the historical order records of the target order user, and the submission data when they were issued, and then calculate the modification amplitude value corresponding to the target elastic requirement item of each successfully matched order. The logic for obtaining the modification amplitude value is: First, calculate the difference between the transaction data at the time of transaction of the target elasticity requirement project and the submitted data at the time of issuance, then divide the difference by the submitted data at the time of issuance, and then perform absolute value processing on the obtained ratio to obtain the modified change ratio value; Then, the modification change ratio values ​​corresponding to the target elasticity requirement items of all successfully matched orders are averaged to obtain the modification amplitude value and marked as Mi; The modification amplitude value Mi of each elastic requirement item TXi is used as the feature input for clustering. The K-means clustering algorithm is used to perform cluster analysis on the modification amplitude values ​​of all elastic requirement items. The elbow method is used to determine the optimal number of clusters K. The elastic requirement items in each cluster form a level. Each elastic requirement item is matched with the classification level to obtain the user portrait of the target order-issuing user.

7. A multi-regional transportation service mode matching method according to claim 6, characterized in that: Evaluating the matching failure rate based on the results of the profiling analysis refers to: Get the user profile of the target order-issuing user, and then set the weight coefficient for the elastic requirement items in this elastic requirement subset to meet the following requirements: ; Indicates the level of the elasticity requirement item, It represents the weight coefficient corresponding to the level of the elasticity requirement item, and K represents the total number of levels; Then use the Hamming distance to represent the matching results of each elastic requirement item: ; It represents the Hamming distance corresponding to the elastic requirement item. The difference amplitude value is obtained by first subtracting the data corresponding to the elastic requirement item in the order service set of the order-taking user from the corresponding requirement data in the elastic requirement subset, and then dividing the obtained difference by the requirement data in the elastic requirement subset. The obtained ratio is then processed into the absolute value to obtain the difference amplitude value; Calculate the overall difference value D: ; n represents the total number of elasticity requirement items; Then substitute it into the pre-trained comprehensive matching model formula: ; To adjust the sensitivity parameter, is the offset used to adjust the starting point of the matching degree. is the comprehensive matching degree; The matching failure rate is calculated by subtracting the comprehensive matching degree from the value one. get.

8. A multi-regional transportation service mode matching system, based on a multi-regional transportation service mode matching method according to any one of claims 1 to 7, characterized in that: include: The identity information collection module divides all user identities and sets two roles: order-issuing users and order-receiving users. It then obtains the order-issuing requirement set of the target order-issuing users and the order-receiving service set of all order-receiving users who are not in the order-receiving state. The screening module performs hard condition screening based on the order placing requirement set and order receiving service set, retains the order receiving users who meet the hard conditions, and then performs flexible condition screening; The negotiation module, if there is no order-taking user that meets all elastic conditions, obtains the historical order-issuing records of the target order-issuing user and uses the K-means clustering algorithm to perform a portrait analysis, and evaluates the matching failure rate based on the results of the portrait analysis. Based on the matching failure rate evaluation results, negotiation communication channels are successively established for the target order-issuing users; The matching module obtains the target order-taking user and matches it with the target order-issuing user according to the preset satisfaction strategy if there is an order-taking user who meets all the elastic conditions, and when the negotiation is in agreement, the order-taking user corresponding to the negotiation communication channel is matched with the target order-issuing user; Service feedback module: after the service is completed, the target order-issuing user will give a service rating to the order-receiving user who has been matched with the service.

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