An intelligent management method for multi-channel vehicle service order integration

Abstract

The present invention relates to the field of data processing technology, and specifically to an intelligent management method for multi-channel vehicle service order integration, including: obtaining order information of all orders in an order information database, wherein all orders include target orders, calculating the estimated processing time of each target order, calculating the stability of each customer's selection of each service item based on all service items of all orders of each customer, analyzing each customer's preferred service items and attempted service items, and then obtaining each customer's habitual evaluation and customer level, obtaining the priority of each target order based on the estimated processing time of each target order and the customer's customer level, and obtaining sorted preferred orders and orders that can be delayed. The present invention evaluates the priority of all orders based on the customer's historical order status and the customer's relevant status, which can effectively solve the waste of customer time caused by failure to process orders in a timely manner and improve customer satisfaction.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent management method for integrating multi-channel vehicle service orders. Background Art

[0002] With the development of society, more and more devices can be connected to the Internet. Customers can book their own car time through many Internet channels and devices. For car service companies, how to efficiently and accurately handle multi-channel car service orders is crucial. Through order integration, companies can manage and process all orders on a unified platform, and can better grasp the status of all orders without having to process orders from different channels separately.

[0003] At present, the commonly used order integration methods usually include appointment systems, ordering platforms or programs, supply chain management systems, and customer relationship management systems, which are divided into three steps: accepting orders, integrating orders, and allocating orders. However, in the above process, the system cannot independently screen out orders that are difficult to complete, especially during the peak period of vehicle service. If the information from many channels cannot be unified in time, it will cause orders to not be completed in time, customer reputation will decline, and then the company's reputation will decline, which will reduce the intelligent management effect of vehicle service order integration. Summary of the invention

[0004] The present invention provides an intelligent management method for integrating multi-channel vehicle service orders to solve the existing problems.

[0005] The present invention provides an intelligent management method for multi-channel vehicle service order integration using the following technical solutions:

[0006] An embodiment of the present invention provides an intelligent management method for multi-channel vehicle service order integration, the method comprising the following steps:

[0007] Obtaining the customer, service item, vehicle information, scheduled location, start time, order consumption time, completion deadline and customer's personal location of all orders in the order information database; obtaining the distance between the scheduled location of each order and the customer's personal location and the distance between the scheduled location and each service point; all orders in the order information database include target orders to be completed;

[0008] According to the distance between the scheduled location of each target order and each service point, service items, vehicle information and order consumption time, the estimated processing time of each target order is obtained;

[0009] According to all the service items of all the orders of each customer in the order information database, the stability of each customer's selection of each service item is obtained; according to the stability of each customer's selection of each service item, the preferred service items and the tried service items of each customer are obtained;

[0010] According to the start time of the orders of all the preferred service items selected by each customer in the order information database and the stability of each service item selected by each customer, the habit evaluation of each customer is obtained; according to the distance between the scheduled locations of all the orders of each customer in the order information database and the personal location of the customer and the habit evaluation of each customer, the customer level of each customer is obtained;

[0011] The priority of each target order is obtained according to the customer level of the customer of each target order and the estimated processing time of each target order; the sorted preferred orders and postponed orders are obtained according to the priority, start time, estimated processing time and completion deadline of each target order.

[0012] Furthermore, the estimated processing time of each target order is obtained according to the distance between the scheduled location of each target order and each service point, the service items, the vehicle information and the order consumption time, and the specific steps include the following:

[0013] Select the service point with the shortest distance between the scheduled location of each target order and all service points as the nearest service point for each target order;

[0014] Obtaining semantic similarity between service items and vehicle information of each target order and service items and vehicle information of each order of the nearest service point of each target order in all orders in the order information database;

[0015] Each order of the nearest service point of each target order whose semantic similarity between the service items and vehicle information of each target order and the service items and vehicle information of each order of the nearest service point of each target order is greater than a preset judgment threshold is recorded as a reference order of each target order;

[0016] In the service point information database, the average of the order consumption time of all reference orders for each target order is recorded as the estimated processing time of each target order.

[0017] Furthermore, the method of obtaining the stability of each service item selected by each customer based on all service items of all orders of each customer in the order information database includes the following specific steps:

[0018] The ratio of the number of times each service item appears in all service items of all orders of each customer in the order information database to the number of all service items is recorded as the stability of each service item selected by each customer.

[0019] Furthermore, the method of obtaining the preferred service items and the tried service items of each customer according to the stability of each customer selecting each service item includes the following specific steps:

[0020] Get the maximum and minimum values ​​of the stability of all service items selected by each customer;

[0021] The maximum and minimum values ​​of the stability of all service items selected by each customer are used as the initial cluster centers, and a clustering operation is performed on the stability of all service items selected by each customer to obtain the preferred service item clusters and the attempted service item clusters of each customer;

[0022] The service items corresponding to all sample points in each customer's preferred service item cluster and attempted service item cluster are recorded as each customer's preferred service item and attempted service item, respectively.

[0023] Furthermore, the specific calculation formula for obtaining the habit evaluation of each customer based on the start time of the order of all the favorite service items selected by each customer in the order information database and the stability of each service item selected by each customer is:

[0024]

[0025] In the formula, Indicates the customer Habit evaluation; Indicates the customer The number of preferred services; Represents the customer in the order information database Select Favorite service item The order is the same as the previous selected order The time interval between the start time of the order of the favorite service items; Represents the customer in the order information database All services in all orders The number of times a favorite service item appears; express All The standard deviation of Indicates the customer Select The stability of the service items that one likes; is an exponential function with a natural constant as its base.

[0026] Furthermore, the specific calculation formula for obtaining the customer level of each customer based on the distance between the scheduled location of all orders of each customer in the order information database and the customer's personal location and the habit evaluation of each customer is:

[0027]

[0028] In the formula, Indicates the customer Customer level; Represents the customer in the order information database The quantity of all orders; Represents the customer in the order information database No. The order location and customer distance between individual locations; Represents all service points and customers The shortest distance between individual locations; Indicates the customer Habit evaluation; is the hyperbolic tangent function.

[0029] Furthermore, the priority of each target order is obtained according to the customer level of the customer of each target order and the estimated processing time of each target order, and the specific steps include the following:

[0030] The normalized value of the ratio of the customer level of the customer of each target order to the estimated processing time of each target order is recorded as the priority of each target order.

[0031] Furthermore, the step of obtaining the sorted preferred orders and postponed orders according to the priority, start time, estimated processing time and completion deadline of each target order includes the following specific steps:

[0032] Based on the start time and estimated processing time of each target order, the estimated completion time of each target order is obtained;

[0033] According to the priority, estimated completion time and completion deadline of each target order, the sorted preferred orders and postponed orders are obtained.

[0034] Furthermore, the estimated completion time of each target order is obtained according to the start time and the estimated processing time of each target order, and the specific steps include the following:

[0035] The start time of each target order is added to the time node after the estimated processing time, and the estimated completion time of each target order is recorded.

[0036] Furthermore, the step of obtaining the sorted preferred orders and postponed orders according to the priority, estimated completion time and completion deadline of each target order includes the following specific steps:

[0037] When the time difference between the completion deadline of any target order and the estimated completion time is less than the preset time interval, the target order is recorded as a preferred order;

[0038] Among all target orders, all target orders except the preferred orders are recorded as postponeable orders;

[0039] According to the priority order of all target orders from large to small, all preferred orders and all postponed orders are sorted respectively to obtain sorted preferred orders and postponed orders.

[0040] The beneficial effects of the technical solution of the present invention are:

[0041] In an embodiment of the present invention, the order information of all orders in the order information library is obtained, wherein all orders include target orders, which are used to provide basic data for order management and optimization; the estimated processing time of each target order is calculated to ensure that the order can be completed in time; based on all service items of all orders of each customer, the stability of each service item selected by each customer is calculated, which helps to predict the customer's preferences and behavior patterns, analyze the preferred service items and attempted service items of each customer, and then obtain the habitual evaluation and customer level of each customer to ensure the accuracy of the sorting of the target orders; based on the estimated processing time of each target order and the customer's customer level, the priority of each target order is obtained, and the sorted preferred orders and delayed orders are obtained, thereby optimizing the overall service efficiency and quality. So far, the present invention evaluates the priority of all orders according to the customer's historical order situation and the customer's relevant situation, which can effectively solve the waste of customer time caused by failure to process orders in time and improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 The present invention is a flowchart of the steps of an intelligent management method for multi-channel vehicle service order integration. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of an intelligent management method for multi-channel vehicle service order integration proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0046] The specific scheme of an intelligent management method for multi-channel vehicle service order integration provided by the present invention is described in detail below with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for intelligent management of multi-channel vehicle service order integration provided by an embodiment of the present invention, the method comprising the following steps:

[0048] Step S001: Obtain the customer, service item, vehicle information, scheduled location, start time, order consumption time, completion deadline and customer's personal location of all orders in the order information database; obtain the distance between the scheduled location of each order and the customer's personal location and the distance between the scheduled location and each service point; all orders in the order information database include target orders to be completed.

[0049] Obtain an order information database, where each order contains the customer, service item, vehicle information, reservation location, start time, order consumption time, and the customer's personal location; obtain the distance between the reservation location and the customer's personal location for each order;

[0050] Get all target orders, each of which contains customers, service items, vehicle information, scheduled locations, and completion deadlines; get the distance between the scheduled location of each target order and all service points.

[0051] It should be noted that the service items of each order may include multiple service items, and the reserved location is one of all service points; all orders in the order information database are completed orders, and the target order is the order to be completed, where the completion deadline is the completion deadline set for the order.

[0052] Step S002: Obtain the estimated processing time of each target order based on the distance between the scheduled location of each target order and each service point, service items, vehicle information, and order consumption time.

[0053] Customers who place orders for vehicle services tend to pay more attention to the principle of proximity, that is, to complete vehicle inspection and cleaning services near their home or company. Based on the estimated location filled in the order, they select several nearby service points and estimate the processing status of the target order based on the processing of similar orders by these service points and the inventory status of the required spare parts.

[0054] Select the service point with the minimum distance between the target order's scheduled location and all service points as the nearest service point for the target order; thereby obtaining the nearest service points for all target orders;

[0055] Among all orders of the nearest service point of each target order in the order information database, the Jaccard technology is used to obtain the semantic similarity between the service items and vehicle information of each target order and the service items and vehicle information of each order of the nearest service point of each target order, wherein the Jaccard technology is a method for comparing the similarity between sample sets, which is a well-known technology;

[0056] The judgment threshold value of 0.6 preset in this embodiment is described as an example, and other values ​​may be set in other implementation modes, which are not limited in this embodiment;

[0057] Among all orders of the nearest service point of each target order in the order information database, each order of the nearest service point of each target order whose semantic similarity between the service items and vehicle information of each target order and the service items and vehicle information of each order of the nearest service point of each target order is greater than a preset judgment threshold is recorded as a reference order of each target order;

[0058] In the service point information database, the average of the order consumption time of all reference orders for each target order is recorded as the estimated processing time of each target order;

[0059] It should be noted that the estimated processing time for each target order is the time from the vehicle being delivered to the service point until the service is completed.

[0060] Step S003: Based on all service items of all orders of each customer in the order information database, obtain the stability of each service item selected by each customer; based on the stability of each service item selected by each customer, obtain each customer's favorite service items and tried service items.

[0061] Since different customers have different usage habits, they will place orders from different channels, but the customer is always the same. The order priority is assigned based on the customer's past orders and the time required to process the corresponding order.

[0062] The better the customer's consumption habits, the more important the customer is, and the more attention should be paid to such a customer; and the customer's consumption habits are mainly an assessment of the service items and service times they have booked; the more fixed the service items the customer consumes, the more the customer likes the corresponding service items, that is, for the company, the project has captured the customer's preferences, and customers with favorite projects can be better maintained, and their corresponding customer level will be higher; the more stable the customer's consumption time, that is, he comes to consume once every certain period of time, his consumption is predictable to the company, and such a customer is a stable customer for the company.

[0063] According to all the service items of all the orders of each customer in the order information database, the corresponding calculation formula for the stability of each service item selected by each customer is:

[0064]

[0065] In the formula, Indicates the customer Select The stability of each service project; Represents the customer in the order information database All services in all orders The number of times a service item appears; Represents the customer in the order information database The quantity of all service items for all orders.

[0066] It should be noted that the customer in the order information database All services in all orders The number of service items and customers The larger the ratio of the quantity of all service items in all orders, the Select The higher the stability of a service project.

[0067] Since customers may try new service items every time, some service items may have poor stability and are therefore not useful for reference.

[0068] Get the maximum and minimum values ​​of the stability of all service items selected by each customer;

[0069] use The clustering algorithm takes the maximum and minimum values ​​of the stability of all service items selected by each customer as the initial cluster center, performs clustering operations on the stability of all service items selected by each customer, and obtains the clustering clusters of the preferred service items and the tried service items of each customer, where Clustering is a well-known technique;

[0070] The service items corresponding to all sample points in each customer's preferred service item cluster and attempted service item cluster are recorded as each customer's preferred service item and attempted service item respectively;

[0071] It should be noted that the cluster where the maximum value of the stability of all service items selected by each customer is located is the cluster of service items preferred by each customer, and the cluster where the minimum value of the stability of all service items selected by each customer is located is the cluster of service items tried by each customer.

[0072] Step S004: Obtain each customer's habit evaluation based on the start time of all orders of preferred service items selected by each customer in the order information database and the stability of each customer's selection of each service item; obtain each customer's customer level based on the distance between the scheduled locations of all orders of each customer in the order information database and the customer's personal location and each customer's habit evaluation.

[0073] According to the start time of the order of all the favorite service items selected by each customer in the order information database and the stability of each service item selected by each customer, the corresponding calculation formula for each customer's habit evaluation is obtained as follows:

[0074]

[0075] In the formula, Indicates the customer Habit evaluation; Indicates the customer The number of preferred services; Represents the customer in the order information database Select Favorite service item The order is the same as the previous selected order The time interval between the start time of the order of the favorite service items; Represents the customer in the order information database All services in all orders The number of times a favorite service item appears; express All The standard deviation of Indicates the customer Select The stability of the service items that one likes; is an exponential function with a natural constant as the base. To present the inverse proportional relationship and normalization processing, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0076] It should be noted that Represents the customer in the order information database Two consecutive selections The smaller the standard deviation of the time interval between the start times of the preferred service items, the more likely the customer Two consecutive selections The more similar the time intervals between the start times of the preferred service items are, the more likely the customer Regularly select the preferred service items, customers The higher the habit evaluation; Indicates the customer Select The stability of the preferred service items, the larger the value, the more customers The higher the habit evaluation.

[0077] Since there are multiple vehicle service companies around customers, customers have multiple choices, and they will always go to the company that suits their preferences better, that is, customers have a preference for vehicle service companies. There are many reasons for this preference, but the most obvious one is that the closer the distance, the stronger the customer's preference; the more times a customer chooses a single company in multiple choices, the stronger the customer's preference for the company; in addition, if two companies have opened multiple stores at different distances from customers, if the customer would rather take a long detour to go to the store of that company, it means that the customer prefers this company more. In addition, the better the customer's consumption habits in this company and the more he prefers this company, the higher his corresponding customer level will be.

[0078] There are many vehicle service companies around customers. Customers are faced with multiple choices and usually choose the service point they think is more suitable. Customer preferences are affected by many factors, one of the most significant factors is distance. Service points that are closer are often more favored by customers because this saves time and transportation costs. After choosing service points many times, customers will gradually form a preference for a certain service point. If customers frequently choose the same service point, their preference for this service point will gradually strengthen. In addition, if a company opens multiple service points in the customer's location and the customer is willing to choose a service point that is farther away, this indicates that the customer has a higher preference for this service point. In addition, the better the customer's consumption habits, the stronger the customer's preference. These factors combined will affect the customer's customer level, that is, the customer's status and importance.

[0079] According to the distance between the scheduled location of all orders of each customer in the order information database and the customer's personal location and the habit evaluation of each customer, the corresponding calculation formula for each customer's customer level is obtained as follows:

[0080]

[0081] In the formula, Indicates the customer Customer level; Represents the customer in the order information database The quantity of all orders; Represents the customer in the order information database No. The order location and customer distance between individual locations; Represents all service points and customers The shortest distance between individual locations; Indicates the customer Habit evaluation; is the hyperbolic tangent function, normalizing the data values ​​to Within the range.

[0082] It should be noted that Represents the customer in the order information database All orders from selected booking locations and distances to customers The degree of the nearest service point between personal locations, the larger the value, the more willing the customer is to choose a service point that is farther away, and the higher the customer's preference for this service point. The higher the customer level, the higher the customer The higher the habit evaluation, the more likely the customer The higher the customer level.

[0083] Step S005: Obtain the priority of each target order based on the customer level of the customer of each target order and the estimated processing time of each target order; obtain the sorted preferred orders and deferred orders based on the priority, start time, estimated processing time and completion deadline of each target order.

[0084] For the company, it needs to handle the orders from new customers better so as to attract more new customers, improve the reputation of new customers, and entice them to come back next time; at the same time, it needs to pay attention to the orders from important customers so as to maintain the good impression of important customers towards the company; for an order, the higher the level of the customer placing the order, the higher the priority or importance of its processing; for orders from the same customer level, the shorter the processing time and the earlier the completion deadline, the higher the priority.

[0085] According to the customer level of each target order's customer and the estimated processing time of each target order, the corresponding calculation formula for the priority of each target order is obtained as follows:

[0086]

[0087] In the formula, Indicates The priority of the target orders; Indicates The customer level of the customer of the target order; Indicates Estimated processing time for target orders; is the normalization function, which normalizes the data value to Within the range.

[0088] It should be noted that The higher the customer level of the target order, the The higher the priority of the target order, the higher the priority of the The shorter the estimated processing time of the target order, the The higher the priority of a target order.

[0089] The preset time interval in this embodiment is 2 days, which is described as an example. Other implementations may set it to other values, which are not limited in this embodiment.

[0090] The start time of each target order is added to the time node after the estimated processing time, and the estimated completion time of each target order is recorded;

[0091] When the time difference between the completion deadline of any target order and the estimated completion time is less than the preset time interval, the target order is recorded as a preferred order;

[0092] Among all target orders, all target orders except the preferred orders are recorded as postponeable orders;

[0093] According to the priority order of all target orders from large to small, all preferred orders and all postponed orders are sorted respectively to obtain sorted preferred orders and postponed orders.

[0094] It should be noted that when the target orders have the same priority, they are sorted in descending order according to the customer levels of the customers of the target orders. The staff can process all target orders based on the completion deadline and estimated completion time of each target order, so as to optimize the intelligent management method of vehicle service order integration.

[0095] So far, the present invention is completed.

[0096] In summary, in an embodiment of the present invention, the order information of all orders in the order information library is obtained, wherein all orders include target orders, the estimated processing time of each target order is calculated, the stability of each customer's selection of each service item is calculated based on all service items of all orders of each customer, the preferred service items and attempted service items of each customer are analyzed, and then the habitual evaluation and customer level of each customer are obtained, and the priority of each target order is obtained based on the estimated processing time of each target order and the customer's customer level, and the sorted preferred orders and deferred orders are obtained. The present invention evaluates the priority of all orders based on the customer's historical order status and the customer's relevant status, which can effectively solve the waste of customer time caused by failure to process orders in a timely manner and improve customer satisfaction.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent management method for multi-channel vehicle service order integration, characterized in that: The method comprises the following steps: Obtaining the customer, service item, vehicle information, scheduled location, start time, order consumption time, completion deadline and customer's personal location of all orders in the order information database; obtaining the distance between the scheduled location of each order and the customer's personal location and the distance between the scheduled location and each service point; all orders in the order information database include target orders to be completed; According to the distance between the scheduled location of each target order and each service point, service items, vehicle information and order consumption time, the estimated processing time of each target order is obtained; According to all the service items of all the orders of each customer in the order information database, the stability of each customer's selection of each service item is obtained; according to the stability of each customer's selection of each service item, the preferred service items and the tried service items of each customer are obtained; The specific calculation formula for obtaining each customer's habit evaluation based on the start time of the order of all the favorite service items selected by each customer in the order information database and the stability of each service item selected by each customer is: In the formula, Indicates the customer Habit evaluation; Indicates the customer The number of preferred services; Represents the customer in the order information database Select Favorite service item The order is the same as the previous selected order The time interval between the start time of the order of the favorite service items; Represents the customer in the order information database All services in all orders The number of times a favorite service item appears; express All The standard deviation of Indicates the customer Select The stability of the service items that one likes; is an exponential function with a natural constant as base; According to the start time of the orders of all the preferred service items selected by each customer in the order information database and the stability of each service item selected by each customer, the habit evaluation of each customer is obtained; according to the distance between the scheduled locations of all the orders of each customer in the order information database and the personal location of the customer and the habit evaluation of each customer, the customer level of each customer is obtained; The priority of each target order is obtained according to the customer level of the customer of each target order and the estimated processing time of each target order; the sorted preferred orders and postponed orders are obtained according to the priority, start time, estimated processing time and completion deadline of each target order.

2. According to claim 1, an intelligent management method for multi-channel vehicle service order integration is characterized in that: The estimated processing time of each target order is obtained according to the distance between the scheduled location of each target order and each service point, the service items, the vehicle information and the order consumption time, and the specific steps include the following: Select the service point with the shortest distance between the scheduled location of each target order and all service points as the nearest service point for each target order; Obtaining semantic similarity between service items and vehicle information of each target order and service items and vehicle information of each order of the nearest service point of each target order in all orders in the order information database; Each order of the nearest service point of each target order whose semantic similarity between the service items and vehicle information of each target order and the service items and vehicle information of each order of the nearest service point of each target order is greater than a preset judgment threshold is recorded as a reference order of each target order; In the service point information database, the average of the order consumption time of all reference orders for each target order is recorded as the estimated processing time of each target order.

3. The intelligent management method for multi-channel vehicle service order integration according to claim 1 is characterized in that: The specific steps of obtaining the stability of each service item selected by each customer based on all service items of all orders of each customer in the order information database are as follows: The ratio of the number of times each service item appears in all service items of all orders of each customer in the order information database to the number of all service items is recorded as the stability of each service item selected by each customer.

4. The intelligent management method for multi-channel vehicle service order integration according to claim 1 is characterized in that: The steps of obtaining the preferred service items and the tried service items of each customer according to the stability of each customer selecting each service item include the following: Get the maximum and minimum values ​​of the stability of all service items selected by each customer; The maximum and minimum values ​​of the stability of all service items selected by each customer are used as the initial cluster centers, and a clustering operation is performed on the stability of all service items selected by each customer to obtain the preferred service item clusters and the attempted service item clusters of each customer; The service items corresponding to all sample points in each customer's preferred service item cluster and attempted service item cluster are recorded as each customer's preferred service item and attempted service item, respectively.

5. The intelligent management method for multi-channel vehicle service order integration according to claim 1 is characterized in that: The specific calculation formula for obtaining the customer level of each customer based on the distance between the scheduled location of all orders of each customer in the order information database and the customer's personal location and the habit evaluation of each customer is: In the formula, Indicates the customer Customer level; Represents the customer in the order information database The quantity of all orders; Represents the customer in the order information database No. The order location and customer distance between individual locations; Represents all service points and customers The shortest distance between individual locations; Indicates the customer Habit evaluation; is the hyperbolic tangent function.

6. The intelligent management method for multi-channel vehicle service order integration according to claim 1 is characterized in that: The step of obtaining the priority of each target order according to the customer level of the customer of each target order and the estimated processing time of each target order includes the following specific steps: The normalized value of the ratio of the customer level of the customer of each target order to the estimated processing time of each target order is recorded as the priority of each target order.

7. The intelligent management method for multi-channel vehicle service order integration according to claim 1 is characterized in that: The specific steps of obtaining the sorted preferred orders and postponed orders according to the priority, start time, estimated processing time and completion deadline of each target order are as follows: Based on the start time and estimated processing time of each target order, the estimated completion time of each target order is obtained; According to the priority, estimated completion time and completion deadline of each target order, the sorted preferred orders and postponed orders are obtained.

8. The intelligent management method for multi-channel vehicle service order integration according to claim 7 is characterized in that: The method of obtaining the estimated completion time of each target order according to the start time and the estimated processing time of each target order includes the following specific steps: The start time of each target order is added to the time node after the estimated processing time, and the estimated completion time of each target order is recorded.

9. The intelligent management method for multi-channel vehicle service order integration according to claim 7 is characterized in that: The specific steps of obtaining the sorted preferred orders and postponed orders according to the priority, estimated completion time and completion deadline of each target order are as follows: When the time difference between the completion deadline of any target order and the estimated completion time is less than the preset time interval, the target order is recorded as a preferred order; Among all target orders, all target orders except the preferred orders are recorded as postponeable orders; According to the priority order of all target orders from large to small, all preferred orders and all postponed orders are sorted respectively to obtain sorted preferred orders and postponed orders.

Images