Saas platform-based customized order receiving method and system for designated driver

Abstract

The invention discloses a customized order receiving method and system for a designated driver based on a saas platform. The invention relates to the technical field of designated driving services. Receiving demand data of a driver, wherein the demand data comprises order receiving preference, a service area and service time; receiving personal information input by a driver, wherein the personal information comprises basic information and order receiving preference setting; wherein the demand data comprises order receiving preferences, service areas and service time of drivers and users; wherein the basic information comprises a name, a driving age and a vehicle type; according to the invention, the driver is allowed to set personalized order receiving conditions according to own order receiving preference, service area and time, so that the working flexibility of the driver is greatly improved. The driver can autonomously select the order suitable for himself / herself, so that the rejection rate of improper orders is reduced, and the working efficiency and the satisfaction degree are improved. And through an intelligent order receiving matching algorithm, the system can quickly match the most appropriate order for the driver, so that the empty driving time and waiting time of the driver are reduced, and the working efficiency and income are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of designated driver services, and in particular to a customized order-taking mode for designated drivers on a SaaS (Software-as-a-Service) platform, and in particular to a customized order-taking method and system for designated drivers based on a SaaS platform. Background Art

[0002] As cloud computing technology continues to mature and become more popular, the SaaS (Software as a Service) platform, as an innovative software deployment model, has brought revolutionary changes to chauffeur-driven companies. The SaaS platform effectively solves many pain points in the traditional order-taking model by providing flexible and highly scalable software services. Specifically, the SaaS platform supports chauffeur-driven companies in achieving comprehensive automated management of a series of business processes such as driver order acceptance, dispatching, and settlement.

[0003] However, the traditional order-taking model, due to its rigidity and lack of flexibility, has increasingly shown its inability to respond to drivers' personalized service needs and users' diversified needs. This insufficiency is specifically manifested in low service efficiency and poor user experience, which has largely restricted the further development of the designated driver service industry and the improvement of its market competitiveness.

[0004] To this end, the present invention proposes a customized order-taking method and system for designated drivers based on a SaaS platform. Summary of the invention

[0005] In view of this, the present invention hopes to provide a customized order-taking method and system for designated drivers based on a SaaS platform to solve or alleviate the technical problems existing in the prior art, namely, how to support the driver's flexible order-taking needs to the greatest extent and at least provide a beneficial option for this; the technical solution of the present invention is implemented as follows:

[0006] First, the customized order-taking method for designated drivers based on the SaaS platform:

[0007] 1. Overview:

[0008] The present invention aims to achieve flexible satisfaction of drivers' order acceptance needs and a substantial improvement in the efficiency of designated driver services through highly refined and intelligent management methods. The solution first conducts in-depth research and collects the actual needs of drivers and users, and based on this, formulates personalized service plans and policy rules, fully considering the drivers' order acceptance preferences, service areas, and service times. Then, by building a dynamically updated capacity pool, managing the driver's available status in real time, and using advanced algorithms for intelligent order matching, ensure that each order can be quickly and accurately assigned to the most suitable driver. At the same time, the solution also focuses on the continuous optimization of service quality. By collecting user evaluations and driver performance data, it continuously adjusts and optimizes strategies to provide more accurate, efficient, and personalized designated driver services.

[0009] (II) Technical solution:

[0010] In order to achieve the above technical objectives, the present invention selects to execute the following operation steps.

[0011] 2.1 Step S1, data input and preprocessing:

[0012] Receive driver demand data A;

[0013] Receive basic information B and order preference settings C entered by the driver.

[0014] Among them, demand data A includes drivers and users’ order preferences, service areas and service times;

[0015] Among them, basic information B includes name, driving experience and car model;

[0016] Among them, the order preference setting C includes special order business, preferred order business and ordinary order business.

[0017] 2.2 Step S2, strategy formulation and storage:

[0018] Algorithms are used to generate personalized service plans and combined strategy rules that take into account drivers' order preferences, service capabilities and market demand, and are stored in a cloud database for subsequent order matching and strategy adjustments.

[0019] 2.2.1 Step S200, preference weight calculation:

[0020] Different weight values ​​are assigned to the preferential order business, the preferred order business and the common order business in the order preference setting C.

[0021] Assume that the weight of the preferential single service is w1, the weight of the preferred single service is w2, the weight of the ordinary single service is w3, and w1+w2+w3=1.

[0022] Among them, the entropy weight method is a method for determining the weight of indicators based on the principle of information entropy. It reflects the inverse relationship between the information entropy of indicators and their weights, that is, the smaller the information entropy, the greater the amount of information provided by the indicator, and the greater the corresponding weight. In the order preference setting, the entropy weight method is applied to determine the weights of preferential orders, preferred orders, and ordinary orders based on their historical data or importance.

[0023] S2000, first, the preferential single service weight w1, the preferred single service weight w2 and the common single service weight w3 need to be standardized to eliminate the impact of the dimension; let x i is the original data of the i-th business type, i=1,2,3, then the standardized data is:

[0024]

[0025] Among them, p ij Represents the standardized value of the i-th business type for the j-th driver or time period, etc., and satisfies

[0026] S2001, information entropy i calculate:

[0027]

[0028] Where n is the number of drivers or time periods, etc., depending on the dimension of the data; ln3 is a normalization factor because there are three types of business here.

[0029] S2002, calculate the weight of each service type according to the information entropy:

[0030]

[0031] And it satisfies w1+w2+w3=1.

[0032] 2.2.2 Step S201, service plan generation:

[0033] Generate a preliminary set of service plans based on the driver's service area and service time, combined with current market demand and order distribution. For each service plan, calculate its matching degree with the driver's order acceptance preference. The matching degree calculation method is:

[0034] Matching degree = w1×preferential order matching degree + w2×preferred order matching degree + w3×ordinary order matching degree;

[0035] Among them, the special order matching degree, preferred order matching degree and ordinary order matching degree respectively indicate the degree of matching between the service plan and the driver's preferences for special order business, preferred order business and ordinary order business.

[0036] 2.2.3 Step S202, strategy rule combination:

[0037] The service plans are sorted according to their matching degree, and the service plan with the highest matching degree is selected as the candidate plan.

[0038] 2.3 Step S3, driver dispatches and matches orders:

[0039] After the driver sets out, the system marks him as effective capacity and adds him to the capacity pool.

[0040] Matching orders based on real-time monitoring data and stored policy rules, so that drivers can receive orders that best suit their preferences and capabilities;

[0041] Similarly, after receiving the dispatch instruction, the driver goes to the user's location according to the route planned by the system to complete the designated driving service.

[0042] 2.3.1 Step S300, driver departure status mark:

[0043] Send the vehicle dispatch status information to the system through the mobile phone APP or vehicle-mounted equipment; mark the driver as effective capacity and add him to the current capacity pool;

[0044] 2.3.2 Step S301, real-time monitoring and data acquisition:

[0045] Obtain the driver's location information, speed and direction dynamic data in real time through GPS positioning or network requests, as well as specific information of the order, such as the starting point, end point and user preferences.

[0046] 2.3.3 Step S302, policy rule matching:

[0047] When a new order is generated, the drivers in the capacity pool are screened and sorted according to the strategy rules to find the driver who best meets the order requirements and driver preferences. During the matching process, the driver's order priority is adjusted according to the real-time situation.

[0048] For example, higher priority may be given to drivers who have not accepted orders for a long time, or higher priority may be given to specific drivers for orders in specific areas.

[0049] 2.3.4 Step S303: Sending order instructions:

[0050] Instruction issuance: The dispatch instruction is issued to the selected driver through APP push, SMS or vehicle-mounted equipment, including the specific information of the order, user location and recommended route. After receiving the dispatch instruction, the driver can confirm the order or reject it on the APP. If rejected, the system will re-match according to the policy rules. If the order is confirmed, the system will update the driver's status and order status, and start tracking the service process.

[0051] 2.4 Step S4, service evaluation and strategy adjustment:

[0052] Collect user evaluation data and analyze it in combination with driver performance data and market demand changes; based on the analysis results, automatically adjust strategy parameters to optimize order matching and service quality; the optimized strategy is updated and stored in the cloud database to provide services for the next order matching.

[0053] (III) Mechanism for solving technical problems:

[0054] 3.1 Personalized order preference settings:

[0055] Drivers are allowed to set their own order preferences in the system, including service type (such as long-distance, short-distance, night service, etc.), service area, minimum order amount, service time period, etc. These preferences are recorded by the system and used as an important basis for order matching, ensuring that drivers only receive orders that meet their personal wishes and capabilities, thereby improving order satisfaction and efficiency.

[0056] The system updates and manages a dynamic capacity pool in real time, and dynamically adjusts capacity resources based on the driver's online status, location, order preferences and other information. When a new order is generated, the system quickly selects the driver who best meets the order requirements from the capacity pool to achieve efficient and accurate order matching. At the same time, the driver can also adjust the online status or location information at any time according to his own situation, so that the system can update the capacity pool status in real time.

[0057] 3.2 Intelligent order matching algorithm:

[0058] The system has a built-in intelligent order matching algorithm, which comprehensively considers multiple factors such as order demand, driver order preferences, location, service time, etc. to recommend the most suitable orders to drivers. Through complex calculations and logical judgments, the algorithm can filter out the orders that best meet the driver's order preferences from a large number of orders in a short period of time and recommend them to the driver first. This intelligent matching method greatly improves order acceptance efficiency and matching accuracy.

[0059] Secondly, the customized order-taking system for designated drivers based on the SaaS platform:

[0060] like Figure 4 As shown, the system is used to implement the customized order-taking method and system for designated drivers based on the SaaS platform as described above, which includes:

[0061] (1) The data layer is responsible for storing and managing all data required by the system: including driver information, user information, order information, service information, policy rules, etc. The data layer is usually connected to the cloud database to ensure data security and accessibility. It includes:

[0062] Data input and preprocessing module: This module is responsible for receiving input data from users (including drivers and users), such as driver information, order preferences, service areas, etc. The received data is cleaned, sorted and formatted to ensure data consistency and accuracy. The processed data will be sent to the service layer for further processing and storage.

[0063] (2) The service layer that provides basic services: such as data preprocessing, algorithm calculation, strategy generation and adjustment, etc. These services support the operation of upper-layer applications and process data requests from the user interaction layer. They include:

[0064] Strategy formulation and storage module: This module uses algorithms to analyze pre-processed data to generate personalized service inventions and combined strategy rules. The strategy rules take into account multiple factors such as the driver's order preferences, service capabilities, and market demand. The generated strategy rules are stored in the cloud database of the data layer for subsequent order matching and strategy adjustment.

[0065] (3) The application layer contains applications that implement specific business logic: Applications call the basic services provided by the service layer to complete corresponding tasks based on user needs and business rules. This includes:

[0066] Driver dispatch and order matching module: This module is responsible for monitoring the driver's dispatch status and marking it as effective capacity to add it to the capacity pool. Based on real-time monitoring data and stored policy rules, it performs order matching operations and recommends the most suitable orders to the driver. After the driver receives the order instruction, the module is also responsible for planning the route and providing navigation services to help the driver successfully complete the designated driver service.

[0067] (4) The user interaction layer is responsible for interacting with users: receiving user input data and displaying system processing results. The user interaction layer usually includes the driver-side APP, the user-side APP, and the administrator backend. It includes:

[0068] Service evaluation and strategy adjustment module: This module is responsible for collecting user evaluation data and driver performance data. This data is analyzed in combination with changes in market demand to evaluate the effectiveness of the current strategy. Based on the analysis results, the strategy parameters are automatically adjusted to optimize order matching and service quality. The updated strategy rules will be re-stored in the cloud database of the data layer for subsequent use.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. Improve driver work flexibility and satisfaction: The present invention allows drivers to set personalized order conditions according to their order preferences, service areas and time, which greatly improves the driver's work flexibility. Drivers can independently choose orders that suit them, reducing the rejection rate of inappropriate orders, thereby improving work efficiency and satisfaction. Through the intelligent order matching algorithm, the system can quickly match the most suitable order for the driver, reducing the driver's idle time and waiting time, and further improving work efficiency and income.

[0071] 2. Improve service quality and user satisfaction: The present invention uses big data and artificial intelligence technology to accurately match the needs of drivers and users, ensuring that drivers can receive orders that match their capabilities and preferences, while users can also obtain a better and more personalized service experience.

[0072] 3. Optimize resource allocation and operational efficiency: The present invention dynamically manages the capacity pool and updates the driver's online status, location and other information in real time to ensure that the system can accurately grasp the available capacity resources. This helps the system to allocate orders more reasonably and improve overall operational efficiency. The present invention collects and analyzes a large amount of driver order data, user evaluation data, etc. to provide data support for strategy formulation and optimization. Based on the analysis results of these data, the system can automatically adjust the strategy parameters to cope with market changes and the diversity of user needs, and achieve optimal resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0075] Figure 2 It is a schematic diagram of the strategy synchronization and change of the present invention;

[0076] Figure 3 It is a schematic diagram of the strategy combination of the present invention;

[0077] Figure 4 It is a schematic diagram of the system composition of the present invention. DETAILED DESCRIPTION

[0078] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below;

[0079] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0080] Explanation of relevant terms:

[0081] (1) Order acceptance preferences: refers to the preferences for the type, location, time, etc. of orders that drivers set based on their personal wishes and service capabilities.

[0082] (2) Service area: The geographical area covered by the services provided by the driver, which is determined based on the driver’s wishes and market demand.

[0083] (3) Service time: The time period during which the driver is willing to provide the designated driver service, which can be all day, a specific period of time, or set on demand.

[0084] (4) Capacity pool: The collection of all available driver resources managed by the system, which is dynamically updated based on the driver’s status and location.

[0085] (5) Order matching: The system automatically assigns the most suitable order to the driver based on user needs and driver conditions.

[0086] (6) Strategy: In order to optimize order matching and service quality, the system formulates rules and algorithms based on data analysis to guide drivers in accepting orders and providing user services.

[0087] (7) Order matching: Same as above, refers to the process by which the system intelligently matches the user's order with the most suitable driver.

[0088] (8) Special offer business: Based on the service provider's standard billing, discounts are calculated for the estimated prices of the starting and ending location information. During off-peak periods, more orders are placed through price leverage.

[0089] (9) Preferred single business: Providing high-quality services at a higher price to meet the demands of some users who have high requirements for service quality but are not sensitive to price, thereby enhancing the brand image.

[0090] (10) Ordinary single business: pricing and service are carried out in accordance with the price rules set by the service provider.

[0091] Embodiment 1: Figures 1 to 3 As shown, this embodiment discloses a customized order-taking method for a designated driver based on a SaaS platform; a designated driver SaaS platform that provides customized order-taking services for registered drivers. The platform collects the driver's demand data, basic information, and order-taking preference settings, generates personalized service plans and combination strategy rules, so as to maximize support for the driver's flexible order-taking needs.

[0092] In this embodiment, regarding step S1: data input and preprocessing: receiving the driver's demand data A: the driver enters his order preferences through the APP or web page, such as the ratio of special orders, preferred orders or ordinary orders, as well as the service area and service time. The system receives and stores this data for subsequent analysis. Receive the basic information B and order preference settings C entered by the driver: the driver provides basic information such as name, driving experience, and vehicle model. The driver sets the order preferences, including the preferred ratio of special orders, preferred orders, and ordinary orders. The system preprocesses the received information to ensure the accuracy and completeness of the data.

[0093] In this embodiment, regarding step S2: policy formulation and storage:

[0094] Preference weight calculation (S200): The weights of the preferential single service, the preferred single service and the ordinary single service are calculated using the entropy weight method. The calculation process includes data standardization, information entropy calculation and weight allocation to ensure that w1+w2+w3=1.

[0095] Specifically, different weight values ​​are respectively assigned to the preferential order business, the preferred order business and the common order business in the order preference setting C.

[0096] Assume that the weight of the preferential single service is w1, the weight of the preferred single service is w2, the weight of the ordinary single service is w3, and w1+w2+w3=1.

[0097] Among them, the entropy weight method is a method for determining the weight of indicators based on the principle of information entropy. It reflects the inverse relationship between the information entropy of indicators and their weights, that is, the smaller the information entropy, the greater the amount of information provided by the indicator, and the greater the corresponding weight. In the order preference setting, the entropy weight method is applied to determine the weights of preferential orders, preferred orders, and ordinary orders based on their historical data or importance.

[0098] S2000, first, the preferential single service weight w1, the preferred single service weight w2 and the common single service weight w3 need to be standardized to eliminate the impact of the dimension; let x iis the original data of the i-th business type, i=1,2,3, then the standardized data is:

[0099]

[0100] Among them, p ij Represents the standardized value of the i-th business type for the j-th driver or time period, etc., and satisfies

[0101] S2001, information entropy i calculate:

[0102]

[0103] Where n is the number of drivers or time periods, etc., depending on the dimension of the data; ln3 is a normalization factor because there are three types of business here.

[0104] S2002, calculate the weight of each service type according to the information entropy:

[0105]

[0106] And it satisfies w1+w2+w3=1.

[0107] Step S201, service plan generation: Generate a preliminary set of service plans based on the driver's service area and service time, combined with current market demand and order distribution. For each service plan, calculate its matching degree with the driver's order acceptance preference. The matching degree calculation method is:

[0108] Matching degree = w1×preferential order matching degree + w2×preferred order matching degree + w3×ordinary order matching degree;

[0109] Among them, the special order matching degree, preferred order matching degree and ordinary order matching degree respectively indicate the degree of matching between the service plan and the driver's preferences for special order business, preferred order business and ordinary order business.

[0110] Service plan generation (S201): The system generates a preliminary set of service plans based on the driver's service area, service time, current market demand and order distribution. For each service plan, the system calculates its matching degree with the driver's order acceptance preference. The higher the matching degree, the more the plan meets the driver's preference.

[0111] Strategy rule combination (S202): The system sorts the service plans according to their matching degree and selects the service plan with the highest matching degree as the candidate plan. At the same time, the system considers real-time market changes and driver status and dynamically adjusts the strategy rules to ensure that drivers can receive orders that best suit their preferences and capabilities.

[0112] In this embodiment, regarding step S3: matching the driver's dispatching and receiving orders:

[0113] Driver dispatch status mark (S300): The driver sends dispatch status information to the system through the APP or vehicle-mounted device. After receiving the information, the system immediately marks the driver as effective capacity and adds him to the current capacity pool.

[0114] Real-time monitoring and data acquisition (S301): The system obtains the driver's location information, speed, direction and other dynamic data in real time through GPS positioning and network requests. At the same time, the system obtains specific information of the order, such as the starting point, end point and user preferences.

[0115] Strategy rule matching (S302): When a new order is generated, the system screens and sorts the drivers in the capacity pool according to the stored strategy rules. The system considers multiple factors such as the driver's order acceptance preference, ability, current location, etc. to find the driver who best meets the order requirements and driver preferences. During the matching process, the system adjusts the driver's order acceptance priority according to real-time conditions, such as giving higher priority to drivers who have not accepted orders for a long time.

[0116] Sending order instructions (S303): Sending order instructions to the selected driver through APP push, SMS or vehicle-mounted equipment. After receiving the order instruction, the driver can confirm or reject the order on the APP. If rejected, the system will re-match according to the policy rules. If the order is confirmed, the system will update the driver's status and order status, and start tracking the service process.

[0117] In this embodiment, regarding step S4, service evaluation and strategy adjustment: collect user evaluation data, and analyze it in combination with the driver's performance data and market demand changes; based on the analysis results, automatically adjust strategy parameters to optimize order matching and service quality; the optimized strategy is updated and stored in the cloud database to provide services for the next order matching.

[0118] Embodiment 2: Based on Embodiment 1, this embodiment further provides a specific Python execution program as follows:

[0119] import numpy as np

[0120] import pandas as pd

[0121] #Step S1: Data input and preprocessing

[0122] class Driver:

[0123] def__init__(self,name,driving_years,car_model,preference):

[0124] self.name = name

[0125] self.driving_years=driving_years

[0126] self.car_model = car_model

[0127] self.preference=preference#Preference is set to [special offer ratio, preferred ratio, normal ratio]

[0128] drivers=[

[0129] Driver("Zhang San",5,"Car Type A",[0.5,0.3,0.2]),

[0130] Driver("Li Si",3,"Car Type B",[0.2,0.5,0.3]),

[0131]

[0132]

[0133] pass

[0134] #Feedback data

[0135] feedback_data=[...]#

[0136] adjust_strategy_based_on_feedback(feedback_data)

[0137] In the above program, a Driver class is defined to store the driver's information, including name, driving experience, vehicle type, and order preferences. Several Driver instances are created and their data is converted into DataFrame for subsequent processing.

[0138] The calculate_weights function uses the entropy weight method to calculate the weights of preferential orders, preferred orders, and ordinary orders. First, the preference data is standardized to eliminate the impact of the dimension. Then the information entropy is calculated, and the weight of each business type is calculated based on the information entropy to ensure that the sum of the weights is 1.

[0139] The match_order_to_driver function matches the best driver based on the calculated weights and the driver's preferences. The adjust_strategy_based_on_feedback function is a framework for adjusting strategy parameters based on user feedback and market demand changes.

[0140] All the above embodiments only express the implementation methods of the relevant practical applications of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

[0141] For those skilled in the art, it can be further appreciated that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. 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 the present invention.

[0142] At the same time, those skilled in the art can understand that all or part of the processes in all the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Claims

1. A customized order-taking method for designated drivers based on the SaaS platform, characterized in that: The following steps are included: S1, receiving the driver’s demand data A, basic information B and order preference settings C; S2, generate personalized service solutions and combination strategy rules; S3: After the driver leaves the vehicle, the system marks him as effective capacity and adds him to the capacity pool; Order matching is performed based on real-time monitoring data and stored policy rules.

2. The customized order-taking method according to claim 1, characterized in that: In S1, the demand data includes the driver's and user's order preferences, service areas and service times; the basic information includes name, driving experience and vehicle model; and the order preference settings include special order services, preferred order services and ordinary order services.

3. The customized order-taking method according to claim 2, characterized in that: In S2, the generating method is: Assume that the weight of the preferential single service is w1, the weight of the preferred single service is w2, and the weight of the ordinary single service is w3, and that w1+w2+w3=1; For each service plan, calculate its matching degree with the driver's order preference. The matching degree calculation method is: matching degree = w1×special order matching degree + w2×preferred order matching degree + w3×ordinary order matching degree; Among them, the special order matching degree, preferred order matching degree and ordinary order matching degree respectively indicate the degree of matching between the service plan and the driver's preferences for special order business, preferred order business and ordinary order business.

4. The customized order-taking method according to claim 3, characterized in that: In S2, the three weights are assigned as follows: S2000, to eliminate the influence of dimension; let x i is the original data of the i-th business type, i=1,2,3, then the standardized data is: Among them, p ij Represents the standardized value of the i-th business type for the j-th driver or time period, etc., and satisfies S2001, information entropy i calculate: Where n is the number of drivers or time periods, etc., depending on the dimension of the data; ln3 is the normalization factor; S2002, calculate the weight of each service type according to the information entropy: And it satisfies w1+w2+w3=1.

5. The customized order-taking method according to claim 3, characterized in that: In S2, the service solutions are sorted according to their matching degrees, and the service solution with the highest matching degree is selected as a candidate solution.

6. The customized order-taking method according to claim 1, characterized in that: The execution method of S3 includes: S300, sending the vehicle dispatch status information to the system through the mobile phone APP or the vehicle-mounted device; marking the driver as effective transport capacity and adding him to the current transport capacity pool; S301, obtaining the driver's location information, speed and direction dynamic data in real time through GPS positioning or network request; S302, when a new order is generated, the drivers in the capacity pool are screened and sorted according to the strategy rules to find the driver that best meets the order requirements and driver preferences; S303, sending order instructions to the selected driver through APP push, SMS or vehicle-mounted equipment, including specific information of the order, user location and recommended route.

7. The customized order-taking method according to claim 1, characterized in that: It also includes S4, which collects user evaluation data and analyzes it in combination with driver performance data and changes in market demand; Based on the analysis results, the strategy parameters are automatically adjusted.

8. A system for implementing the customized order-taking method according to any one of claims 1 to 7, characterized in that: The system comprises: The data layer is responsible for storing and managing all the data needed by the system; The service layer that provides basic services; The application layer contains applications that implement specific business logic; The user interaction layer is responsible for interacting with the user.

9. The system according to claim 8, characterized in that: The service layer includes a module for generating personalized services and a combination strategy rule policy formulation and storage module, and the strategy rules are stored in a cloud database of the data layer.

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